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An Attention-Based Predictive Agent for Static and Dynamic Environments

Real-world applications of intelligent agents demand accuracy and efficiency, and seldom provide reinforcement signals. Currently, most agent models are reinforcement-based and concentrate exclusively on accuracy. We propose a general-purpose agent model consisting of proprioceptive and perceptual pathways. The agent actively samples its environment via a sequence of glimpses. It completes the partial propriocept and percept sequences observed till each sampling instant, and learns where and what to sample by minimizing prediction error, without reinforcement or supervision (class labels). The model is evaluated by exposing it to two kinds of stimuli: images of fully-formed handwritten numerals and alphabets, and videos of gradual formation of numerals. It yields state-of-the-art prediction accuracy upon sampling only 22.6% of the scene on average. The model saccades when exposed to images and tracks when exposed to videos. This is the first known attention-based agent to generate realistic handwriting with state-of-the-art accuracy and efficiency by interacting with and learning end-to-end from static and dynamic environments.

Towards Automatic and Fast Annotation of Seismocardiogram Signals Using Machine Learning

The automatic annotation of Seismocardiogram (SCG) potentially aid to estimate various cardiac health parameters continuously. However, the inter-subject variability of SCG poses great difficulties to automate its accurate annotation. The objective of the research is to design SCG peak retrieval methods on the top of the ensemble features extracted from the SCG morphology for the automatic annotation of SCG signals. The annotation scheme is formulated as a binary classification problem. Three binary classifiers such as Naive Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) are employed for the annotation and the results are compared with the recent state-of-the-art schemes. The performance evaluation is carried out using 9000 SCG signals of 20 presumably healthy volunteers with no known serious cardiac abnormalities. The SCG signals are acquired from the Physionet public repository "cebsdb". The models are rigorously validated using metrics "Precision", "Recall", and "F-measure" followed by 5-fold cross-validation. The experimental validation with recent state-of-the-art solutions establishes the robustness of the proposed NB, SVM and LRwith average annotation accuracy of 0.86, 0.925 and 0.935, respectively. The mean response time of proposed models is in the fraction of 1/10 sec, which establishes its application for the real-time annotation.

Strengthening Sensory Sustainability ScienceTheoretical and Methodological Considerations

Sustainability science is marked by a quarter century of conceptual and methodological development. Based on innovative approaches, such as transformative transdisciplinarity, sustainability science makes the claim to contribute solution-oriented knowledge to sustainable development. Despite successful expansion and promising experiences, there are limitations to be considered. This article argues that the multisensorial reality of human life in socio-material practices has not been adequately captured in sustainability science. Theoretical approaches addressing the sensoriality and corporality of human existence as well as methodological approaches of ethnography and arts-based research to access relevant human dimensions beyond the cognitive are discussed, and the perspective of sensory sustainability science is sketched.

Adaptive emotional memory: the key hippocampal-amygdalar interaction

For centuries philosophical and clinical studies have emphasized a fundamental dichotomy between emotion and cognition, as, for instance, between behavioral/emotional memory and explicit/representative memory. However, the last few decades cognitive neuroscience have highlighted data indicating that emotion and cognition, as well as their underlying neural networks, are in fact in close interaction. First, it turns out that emotion can serve cognition, as exemplified by its critical contribution to decision-making or to the enhancement of episodic memory. Second, it is also observed that reciprocally cognitive processes as reasoning, conscious appraisal or explicit representation of events can modulate emotional responses, like promoting or reducing fear. Third, neurobiological data indicate that reciprocal amygdalar-hippocampal influences underlie such mutual regulation of emotion and cognition. While supporting this view, the present review discusses experimental data, obtained in rodents, indicating that the hippocampal and amygdalar systems not only regulate each other and their functional outcomes, but also qualify specific emotional memory representations through specific activations and interactions. Specifically, we review consistent behavioral, electrophysiological, pharmacological, biochemical and imaging data unveiling a direct contribution of both the amygdala and hippocampal-septal system to the identification of the predictor of a threat in different situations of fear conditioning. Our suggestion is that these two brain systems and their interplay determine the selection of relevant emotional stimuli, thereby contributing to the adaptive value of emotional memory. Hence, beyond the mutual quantitative regulation of these two brain systems described so far, we develop the idea that different activations of the hippocampus and amygdala, leading to specific configurations of neural activity, qualitatively impact the formation of emotional memory representations, thereby producing either adaptive or maladaptive fear memories.

Improving mental and physical health outcomes in general healthcare settings: a Gedenkschrift in honor of Wayne Katon, MD (1950-2015)

This special article pays tribute to Wayne Katon, MD (1950-2015) with a Gedenkschrift, or review, of his prolific academic career. Abstracts of all of Dr. Katon's Medline citations were reviewed to develop a narrative of his seminal epidemiological and interventional research findings. Specifically, we describe: (a) how Dr. Katon's clinical work and observational epidemiology and health services research informed and guided interventional studies; (b) the evolution of multidisciplinary interventional trials from primary care-based psychiatric consultation to primary care-based collaborative care for depression to multicondition collaborative care; and (c) how Dr. Katon's research has informed the work of other leading researchers in the field of psychosomatic medicine and helped develop a new generation of researchers at the interface of psychiatry and primary care. For more than three decades, Dr. Katon led a multidisciplinary research team that conducted seminal epidemiological studies and randomized trials and that influenced the thinking and research in the field of psychiatry in a number of areas: (a) the importance and impact of mental disorders presenting in primary care settings and (b) the organization of effective multidisciplinary care for primary care patients with common mental disorders and comorbid medical conditions. Dr. Katon's work revolutionized the care of psychiatric illnesses in primary care and other medical care settings to the benefit of countless patients worldwide.

Identification and differential usage of a host metalloproteinase entry pathway by SARS-CoV-2 Delta and Omicron

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) spike glycoprotein (S) binds to angiotensin-converting enzyme 2 (ACE2) to mediate membrane fusion via two distinct pathways: 1) a surface, serine protease-dependent or 2) an endosomal, cysteine protease-dependent pathway. In this study, we found that SARS-CoV-2 S has a wider protease usage and can also be activated by TMPRSS13 and matrix metalloproteinases (MMPs). We found that MMP-2 and MMP-9 played roles in SARS-CoV-2 S cell-cell fusion and TMPRSS2- and cathepsin-independent viral entry in cells expressing high MMP levels. MMP-dependent viral entry required cleavage at the S1/S2 junction in viral producer cells, and differential processing of variants of concern S dictated its usage; the efficiently processed Delta S preferred metalloproteinase-dependent entry when available, and less processed Omicron S was unable to us metalloproteinases for entry. As MMP-2/9 are released during inflammation, they may play roles in S-mediated cytopathic effects, tropism, and disease outcome.

Albendazole exerts an anti-hepatocellular carcinoma effect through a WWOX-dependent pathway

Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third most common cause of cancer-related deaths. The WW-domain containing oxidoreductase (WWOX) protein suppresses carcinogenesis and its absence is closely related to aggressive HCC phenotypes. In this study, by using SPR analysis, cell viability assay and xenograft mice models, we found that albendazole (ABZ), a safe and effective anthelmintic drug, exhibited the binding affinity with WWOX protein and potential inhibition effect on HCC cells in vitro and in vivo. Overexpression and knockdown of WWOX confirmed that the suppression of HCC by ABZ. Flow cytometric analysis, western blotting analysis and Co-IP were conducted to study the mechanism of ABZ. Our data showed that ABZ regulated the interaction between WWOX and its binding proteins including p53 and C-MYC. Furthermore, ABZ triggered p53-induced intrinsic apoptosis and suppressed EMT-mediated migration by C-MYC/Fibronectin axis. In addition, ∆NP73 expression was significantly inhibited by ABZ, which further sensitized p53-induced intrinsic apoptosis and cell cycle arrest. In summary, ABZ could suppress the proliferation and migration of HCC cells by regulating WWOX-dependent signaling pathway.

Spectral Response and Energy Output of Concentrator Multijunction Solar Cells

The spectral response of concentrator multijunction solar cells has been measured over a temperature range of 25-75 degrees C These data are combined with reference spectra representing the AMI.5 standard as well as annual spectral irradiance at representative geographical locations. The results suggest that higher performance in the field may be obtained if multijunction cells art, designed for an effective air mass higher than AMI.5. Copyright (C) 2008 John Wiley & Sons, Ltd.

Telomerase activity promotes osteoblast differentiation by modulating IGF-signaling pathway

The contribution of deficient telomerase activity to age-related decline in osteoblast functions and bone formation is poorly studied. We have previously demonstrated that telomerase over-expression led to enhanced osteoblast differentiation of human bone marrow skeletal (stromal) stem cells (hMSC) in vitro and in vivo. Here, we investigated the signaling pathways underlying the regulatory functions of telomerase in osteoblastic cells. Comparative microarray analysis and Western blot analysis of telomerase-over expressing hMSC (hMSC-TERT) versus primary hMSC revealed significant up-regulation of several components of insulin-like growth factor (IGF) signaling. Specifically, a significant increase in IGF-induced AKT phosphorylation and alkaline phosphatase (ALP) activity were observed in hMSC-TERT. Enhanced ALP activity was reduced in presence of IGF1 receptor inhibitor: picropodophyllin. In addition, telomerase deficiency caused significant reduction in IGF signaling proteins in osteoblastic cells cultured from telomerase deficient mice (Terc(-/-)). The low bone mass exhibited by Terc(-/-) mice was associated with significant reduction in serum levels of IGF1 and IGFBP3 as well as reduced skeletal mRNA expression of Igf1, Igf2, Igf2r, Igfbp5 and Igfbp6. IGF1-induced osteoblast differentiation was also impaired in Terc(-/-) MSC. In conclusion, our data demonstrate that impaired IGF/AKT signaling contributes to the observed decreased bone mass and bone formation exhibited by telomerase deficient osteoblastic cells.

Optimal Synthesis of Shaped Beams Through Concentric Ring Isophoric Sparse Arrays

An innovative deterministic approach to the optimal power synthesis of mask-constrained shaped beams through the concentric-ring isophoric sparse arrays is presented. The design procedure exploits at best the state-of-the-art techniques, respectively, available in the cases of circular-ring isophoric arrays radiating pencil beams and of linear isophoric arrays generating shaped beams. The technique avoids the exploitation of global-optimization algorithms and allows us to significantly outperform all the (few) available procedures.

Subcategory Classifiers for Multiple-Instance Learning and Its Application to Retinal Nerve Fiber Layer Visibility Classification

We propose a novel multiple-instance learning (MIL) method to assess the visibility (visible/not visible) of the retinal nerve fiber layer (RNFL) in fundus camera images. Using only image-level labels, our approach learns to classify the images as well as to localize the RNFL visible regions. We transform the original feature space into a discriminative subspace, and learn a region-level classifier in that subspace. We propose a margin-based loss function to jointly learn this subspace and the region-level classifier. Experiments with an RNFL data set containing 884 images annotated by two ophthalmologists give a system-annotator agreement (kappa values) of 0.73 and 0.72, respectively, with an interannotator agreement of 0.73. Our system agrees better with the more experienced annotator. Comparative tests with three public data sets (MESSIDOR and DR for diabetic retinopathy, and UCSB for breast cancer) show that our novel MIL approach improves performance over the state of the art. Our MATLAB code is publicly available at https://github.com/ManiShiyam/Sub-categoryclassifiers-for-Multiple-Instance-Learning/wiki.

Semantic-integrated software watermarking with tamper-proofing

The existing works of software watermarking have the intrinsic defects: watermarking is independent of program semantics and have weak strength and resilience to state-of-the-art reverse engineering such as symbolic execution, dynamic taint analysis and theorem proving. In this paper, we propose a semantic-integrated watermarking with tamper-proofing to mitigate such problems. This work chooses neural network as the "integrator" and skillfully integrates the watermarking and tamper-proofing module into program semantics. The difficult of reverse engineering or tampering with watermarked program is equal to extracting the rules from neural networks, which had be proven as a NP-hard problem. We have deployed our work in SPECint-2006 benchmarks to evaluate the overhead, strength and resilience. Experiment results show that our watermarking could effectively resist the state-of-the-art reverse engineering, and the introduced overhead is acceptable.

In vitro particle-associated uridyltransferase activity of the rotavirus VP1 polymerase

Rotaviruses are 11-segmented, double-stranded RNA (dsRNA) viruses with a unique intra-particle RNA synthesis mechanism. During genome replication, the RNA-dependent RNA polymerase (VP1) performs minus-strand RNA (-ssRNA) synthesis on positive-strand RNA (+ssRNA) templates to create dsRNA segments. Recombinant VP1 catalyzes -ssRNA synthesis using substrate NTPs in vitro, but only when the VP2 core shell protein or virus-like particles made of VP2 and VP6 (2/6-VLPs) are included in the reaction. The dsRNA product can be labeled using [α32P]-UTP and separated from the input +ssRNA template by polyacrylamide gel electrophoresis. Here, we report the generation of [α32P]-labeled rotavirus +ssRNA templates in reactions that lacked non-radiolabeled NTPs but contained catalytically-active VP1, 2/6-VLPs, and [α32P]-UTP. Non-radiolabeled UTP competed with [α32P]-UTP to decrease product levels, whereas CTP and GTP had little effect. Interesting, ATP stimulated [α32P]-labeled product production. These results suggest that rotavirus VP1 transferred [α32P]-UMP onto viral + ssRNA in vitro via a particle-associated uridyltransferase activity.

Pulmonary drug delivery: an effective and convenient delivery route to combat COVID-19

The recent outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China has spread rapidly around the world, leading to a widespread and urgent effort to develop and use comprehensive approaches in the treatment of COVID-19. While oral therapy is accepted as an effective and simple method, since the primary site of infection and disease progression of COVID-19 is mainly through the lungs, inhaled drug delivery directly to the lungs may be the most appropriate route of administration. To prevent or treat primary SARS-CoV-2 infections, it is essential to target the virus port of entry in the respiratory tract and airway epithelium, which requires rapid and high-intensity inhibition or control of viral entry or replication. To achieve success in this field, inhalation therapy is the most attractive treatment approach due to efficacy/safety profiles. In this review article, pulmonary drug delivery as a unique treatment option in lung diseases will be briefly reviewed. Then, possible inhalation therapies for the treatment of symptoms of COVID-19 will be discussed and the results of clinical trials will be presented. By pulmonary delivery of the currently approved drugs for COVID-19, efficacy of the treatment would be improved along with reducing systemic side effects.

Cell population dynamics in the course of adult hippocampal neurogenesis: Remaining unknowns

Neural stem cells (NSCs) generate new neurons throughout life in the mammalian hippocampus. The distinct developmental steps in the course of adult neurogenesis, including NSC activation, expansion, and neuronal integration, are increasingly well characterized down to the molecular level. However, substantial gaps remain in our knowledge about regulators and mechanisms involved in this biological process. This review highlights three long-standing unknowns. First, we discuss potency and identity of NSCs and the quest for a unifying model of short- and long-term self-renewal dynamics. Next, we examine cell death, specifically focusing on the early demise of newborn cells. Then, we outline the current knowledge on cell integration dynamics, discussing which (if any) neurons are replaced by newly added neurons in the hippocampal circuits. For each of these unknowns, we summarize the trajectory of studies leading to the current state of knowledge. Finally, we offer suggestions on how to fill the remaining gaps by taking advantage of novel technology to reveal currently hidden secrets in the course of adult hippocampal neurogenesis.

Adult acne versus adolescent acne: a narrative review with a focus on epidemiology to treatment

Acne vulgaris is one of the most common chronic inflammatory diseases and is characterized by papules, pustules, comedones, and nodules. Although adolescence is the preferential age group, acne may affect various age groups. Acne shares different properties in adults and adolescents. These differences extend from epidemiology to treatments. Increased awareness of these two subtypes will allow for better management of the disease. In this review, the authors examined all aspects of acne in adults and adolescents under the light of current literature.

Sustainability of Artists in Precarious Times; How Arts Producers and Individual Artists Have Adapted during a Pandemic

Making a living as an artist, whatever the discipline, is challenging. In addition to skills and talents, artists need resilience, adaptability, creativity, and the ability to withstand endless setbacks and rejections. Most critically, they need an on-going, stable income. Several studies have demonstrated that the income of most artists is usually very low. To survive, artists often find other sources of income aside from their creative work. Ideally, they also need a place to work, the capacity to do their work and a sense of validation from others of their work. When your livelihood disappears over night because of a pandemic, how do you then sustain that creative work? Using multiple sources of data and a qualitative methodology, including case studies and interviews, this paper addresses the ways that artists and producers from different art forms have addressed these challenges in Australia. It is concluded that while the impact of the pandemic on artists' lives has been considerable, some artists have been able to survive, adapt, and move forward.

Creating morphological diversity in reptilian temporal skull region: A review of potential developmental mechanisms

Reptilian skull morphology is highly diverse and broadly categorized into three categories based on the number and position of the temporal fenestrations: anapsid, synapsid, and diapsid. According to recent phylogenetic analysis, temporal fenestrations evolved twice independently in amniotes, once in Synapsida and once in Diapsida. Although functional aspects underlying the evolution of tetrapod temporal fenestrations have been well investigated, few studies have investigated the developmental mechanisms responsible for differences in the pattern of temporal skull region. To determine what these mechanisms might be, we first examined how the five temporal bones develop by comparing embryonic cranial osteogenesis between representative extant reptilian species. The pattern of temporal skull region may depend on differences in temporal bone growth rate and growth direction during ontogeny. Next, we compared the histogenesis patterns and the expression of two key osteogenic genes, Runx2 and Msx2, in the temporal region of the representative reptilian embryos. Our comparative analyses suggest that the embryonic histological condition of the domain where temporal fenestrations would form predicts temporal skull morphology in adults and regulatory modifications of Runx2 and Msx2 expression in osteogenic mesenchymal precursor cells are likely involved in generating morphological diversity in the temporal skull region of reptiles.

Anchoring and Sleep Inertia

Many occupational settings require individuals to make important decisions immediately after awakening. Although a plethora of psychological research has separately examined both sleep and anchoring effects on decision-making, little is known about their interaction. In the present study, we seek to shed light on the link between sleep inertia, the performance impairment immediately after awakening, and individuals' susceptibility to the anchoring bias. We proposed that sleep inertia would moderate participants' adjustment from anchors because sleep inertia leads to less cognitive effort invested, resulting in a stronger anchoring effect. One hundred four subjects were randomly assigned to an experimental group that answered anchoring tasks immediately after being awakened at nighttime or a control group that answered anchoring tasks at daytime. Our findings replicated the well-established anchoring effect in that higher anchors led participants to higher estimates than lower anchors. We did not find significant effects of sleep inertia. While the sleep inertia group reported greater sleepiness and having invested less cognitive effort compared to the control group, no systematic anchoring differences emerged, and cognitive effort did not qualify as a mediator of the anchoring effect. Bayesian analyses provide empirical evidence for these null findings. Implications for the anchoring literature and future research are discussed.

Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks

Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, the scan time limitations may prohibit the acquisition of certain contrasts, and some contrasts may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts can improve diagnostic utility. For multi-contrast synthesis, the current methods learn a nonlinear intensity transformation between the source and target images, either via nonlinear regression or deterministic neural networks. These methods can, in turn, suffer from the loss of structural details in synthesized images. Here, in this paper, we propose a new approach for multi-contrast MRI synthesis based on conditional generative adversarial networks. The proposed approach preserves intermediate-to-high frequency details via an adversarial loss, and it offers enhanced synthesis performance via pixel-wise and perceptual losses for registered multi-contrast images and a cycle-consistency loss for unregistered images. Information from neighboring cross-sections are utilized to further improve synthesis quality. Demonstrations on T-1 - and T-2 - weighted images from healthy subjects and patients clearly indicate the superior performance of the proposed approach compared to the previous state-of-the-art methods. Our synthesis approach can help improve the quality and versatility of the multi-contrast MRI exams without the need for prolonged or repeated examinations.

A Flexible and Energy-Efficient Convolutional Neural Network Acceleration With Dedicated ISA and Accelerator

State-of-the-art convolutional neural networks (CNNs) usually have a large number of layers and filter weights which bring huge computation and communication overheads. A general purpose instruction set architecture (ISA) is flexible but has low code density and high power consumption. The existing CNN-specific accelerators are much more efficient but usually are inflexible or require a complex controller to handle the computation and data transfer of different CNNs. In this brief, we propose a new CNN-specific ISA which embeds the parallel computation and data reuse parameters in the instructions. An instruction generator deploys the instruction parameters according to the feature of CNNs and hardware's computation and storage resources. In addition, a reconfigurable accelerator with 225 multipliers and 24 adder trees is realized to obtain efficient parallel computation and data transfer. Compared with x86 processors, our design has 392 times better energy efficiency and 16 times higher code density. Compared with other state-of-the-art accelerators, our solution has a higher flexibility to support all popular CNNs and a higher energy efficiency.

GPU-Accelerated Adaptive PCBSO Mode-Based Hybrid RLA for Sparse LU Factorization in Circuit Simulation

LU factorization is extensively used in engineering and scientific computations for solution of large set of linear equations. Particularly, circuit simulators rely heavily on sparse version of LU factorization for solution involving circuit matrices. One of the recent advances in this field is exploiting the emerging computing platform of graphics processing units (GPUs) for parallel and sparse LU factorization. In this article, following contributions are made to advance the state of the art in hybrid right-looking algorithm (RLA): 1) a novel GPU kernel based on parallel column and block size optimization (PCBSO) is developed for adaptively allocating the block size while optimizing the number of columns for parallel execution based on the size of their associated submatrices at every level. The proposed approach helps to minimize the resource contention and to improve the computational performance and 2) an algorithm is developed to enable the execution of the new adaptive mode with dynamic parallelism. Also, a comprehensive performance comparison using a set of benchmark circuit examples is presented. The results indicate that, the proposed advancements can improve the results of state-of-the-art right looking sparse LU factorization in GPU by 1.54x (Arithmetic Mean).

WiFi Fingerprinting Indoor Localization Using Local Feature-Based Deep LSTM

Indoor localization has attracted more and more attention because of its importance in many applications. One of the most popular techniques for indoor localization is the received signal strength indicator (RSSI) based fingerprinting approach. Since RSSI values are very complicated and noisy, conventional machine learning algorithms often suffer from limited performance. Recently developed deep learning algorithms have been shown to be powerful for the analysis of complex data. In this paper, we propose a local feature-based deep long short-term memory (LF-DLSTM) approach for WiFi fingerprinting indoor localization. The local feature extractor attempts to reduce the noise effect and extract robust local features. The DLSTM network is able to encode temporal dependencies and learn high-level representations for the extracted sequential local features. Real experiments have been conducted in two different environments, i.e., a research lab and an office. We also compare the proposed approach with some state-of-the-art methods for indoor localization. The results show that the proposed approach achieves the best localization performance with mean localization errors of 1.48 and 1.75 m under the research lab and office environments, respectively. The improvements of our proposed approach over the state-of-the-art methods range from18.98% to 53.46%.

Robustly Tracking People with LIDARs in a Crowded Museum for Behavioral Analysis

This introduces a method which uses LIDAR to identify humans and track their positions, body orientation, and movement trajectories in any public space to read their various types of behavioral responses to surroundings. We use a network of LIDAR poles, installed at the shoulder level of typical adults to reduce potential occlusion between persons and/or objects even in large-scale social environments. With this arrangement, a simple but effective human tracking method is proposed that works by combining multiple sensors' data so that large-scale areas can be covered. The effectiveness of this method is evaluated in an art gallery of a real museum. The result revealed good tracking performance and provided valuable behavioral information related to the art gallery.

Real-Time GPU-Based Ultrasound Simulation Using Deformable Mesh Models

This paper presents a real-time capable graphics processing unit (GPU)-based ultrasound simulator suitable for medical education. The main focus of the simulator is to synthesize realistic looking ultrasound images in real-time including artifacts, which are essential for the interpretation of this data. The simulation is based on a convolution-enhanced ray-tracing approach and uses a deformable mesh model. Deformations of the mesh model are calculated using the PhysX engine. Our method advances the state of the art for real-time capable ultrasound simulators by following the path of the ultrasound pulse, which enables better simulation of ultrasound-specific artifacts. An evaluation of our proposed method in comparison with recent generative slicing-based strategies as well as real ultrasound images is performed. Hereby, a gelatin ultrasound phantom containing syringes filled with different media is scanned with a real transducer. The obtained images are then compared to images which are simulated using a slicing-based technique and our proposed method. The particular benefit of our method is the accurate simulation of ultrasound-specific artifacts, like range distortion, refraction and acoustic shadowing. Several test scenarios are evaluated regarding simulation time, to show the performance and the bottleneck of our method. While being computationally more intensive than slicing techniques, our simulator is able to produce high-quality images in real-time, tracing over 5000 rays through mesh models with more than 2 000 000 triangles of which up to 200 000 may be deformed each frame.

Promoting a Pro-Ecological View: The Effects of Art on Engineering Students' Perceptions of the Environment

In an effort to suggest an extended role that art could play in promoting a pro-ecological worldview, this study reviews a two-week artist-led workshop, organized as part of an undergraduate art course offered by a university specializing in engineering and the natural sciences. To explore the potential impact of studio work on engineering student perceptions, we collected data from multiple sources, including field notes, participant observations, outcomes of the group projects, and participants' responses to studio work during the workshop. In particular, to provide educational implications, our review focused on the findings from post-project surveys collected through online questionnaires and in-person interviews. In order to make suggestions on art courses that are specifically designed to cultivate engineering students' perceptions of the environment, we carried out online surveys based on the New Ecological Paradigm (NEP) scale. The results of the NEP-based surveys indicated that engineering students' anti-anthropocentrism, rejection of human exceptionalism, and acknowledgement of the possibility of an eco-crisis were significantly correlated with a belief in public welfare. By comparison, respondents' stronger public welfare beliefs were not associated with beliefs in limits to growth and the fragility of nature's balance. This study responds to today's complex socio-environmental issues by contributing to the discussion about the need to integrate interdisciplinary approaches into engineering education on environmental sustainability.

Maple Leaf Inspired Conductive Fiber with Hierarchical Wrinkles for Highly Stretchable and Integratable Electronics

Stretchable and durable conductors are significant to the development of wearable devices, robots, human-machine interfaces, and other artificial intelligence products. However, the desirable strain-insensitive conductivity and low hysteresis are restricted by the failure of stretchable structures and mismatch of mechanical properties (rigid conductive layer and elastic core substrate) under large deformation. Here, based on the principles of fractal geometry, a stretchable conductive fiber with hierarchical wrinkles inspired by the unique shape of the maple leaf was fabricated by combining surface modification, interfacial polymerization, and improved prestrain finishing methods to break through this dilemma. The shape and size of wrinkles predicted by buckling analysis via the finite element method fit well with that of actual wrinkles (30-80 μm of macro wrinkles and 4-6 μm of micro wrinkles) on the fabricated fiber. Such hierarchically wrinkled conductive fiber (HWCF) exhibited not only excellent strain-insensitive conductivity denoted by the relative resistance change ΔR/R0 = 0.66 with R0 the original resistance and ΔR the change of resistance after the concrete strain reaching up to 600%, but also low hysteresis (0.04) calculated by the difference in area between stretching and releasing curve of the ΔR/R0 strain under 300% strain and long-term durability (>1000 stretching-releasing cycles). Furthermore, the elastic conductive fiber with such a bionic structure design can be applied as highly stretchable electrical circuits for illumination and monitors for the human motion under large strains through tiny and rapid resistance changes as well. Such a smart biomimetic material holds great prospects in the field of stretchable electronics.

Spatio Temporal Sparsity in Homicide Prediction Models

Homicide prediction is a challenging task due to the spatio-temporal sparsity of these crime events. In this paper we report the results of using several approaches to mitigate this sparsity condition in machine learning models specially tailored towards modeling homicides events. Since spatial resolution is a direct determinant of sparsity, we focus on the performance of these models across different resolutions of interest to police authorities. We use a simple count model as benchmark and propose some enhancements of it directed towards improving prediction performance. We then compare the results to more complex models motivated by manifold learning and graph signal processing methods. We found that the simple benchmark models are as good as state of the art models for low resolution, but, as resolution increases, the performance of machine learning models outperform the benchmark. These results provide a rationality for the use of state of the art machine learning models for homicide prediction at the high resolution of interest for the deployment of police resources.

Simultaneous Neural and Movement Recording in Large-Scale Immersive Virtual Environments

Virtual reality (VR) allows precise control and manipulation of rich, dynamic stimuli that, when coupled with on-line motion capture and neural monitoring, can provide a powerful means both of understanding brain behavioral relations in the high dimensional world and of assessing and treating a variety of neural disorders. Here we present a system that combines state-of-the-art, fully immersive, 3D, multi-modal VR with temporally aligned electroencephalographic (EEG) recordings. The VR system is dynamic and interactive across visual, auditory, and haptic interactions, providing sight, sound, touch, and force. Crucially, it does so with simultaneous EEG recordings while subjects actively move about a space. The overall end-to-end latency between real movement and its simulated movement in the VR is approximately 40 ms. Spatial precision of the various devices is on the order of millimeters. The temporal alignment with the neural recordings is accurate to within approximately 1 ms. This powerful combination of systems opens up a new window into brain-behavioral relations and a new means of assessment and rehabilitation of individuals with motor and other disorders.

Lightweight residual densely connected convolutional neural network

Extremely efficient convolutional neural network architectures are one of the most important requirements for limited-resource devices (such as embedded and mobile devices). The computing power and memory size are two important constraints of these devices. Recently, some architectures have been proposed to overcome these limitations by considering specific hardware-software equipment. In this paper, the lightweight residual densely connected blocks are proposed to guaranty the deep supervision, efficient gradient flow, and feature reuse abilities of convolutional neural network. The proposed method decreases the cost of training and inference processes without using any special hardware-software equipment by just reducing the number of parameters and computational operations while achieving a feasible accuracy. Extensive experimental results demonstrate that the proposed architecture is more efficient than the AlexNet and VGGNet in terms of model size, required parameters, and even accuracy. The proposed model has been evaluated on the ImageNet, MNIST, Fashion MNIST, SVHN, CIFAR-10, and CIFAR-100. It achieves state-of-the-art results on Fashion MNIST dataset and reasonable results on the others. The obtained results show the superiority of the proposed method to efficient models such as the SqueezNet. It is also comparable with state-of-the-art efficient models such as CondenseNet and ShuffleNet.

SCG: Saliency and Contour Guided Salient Instance Segmentation

Different from conventional instance segmentation, salient instance segmentation (SIS) faces two difficulties. The first is that it involves segmenting salient instances only while ignoring background, and the second is that it targets generic object instances without pre-defined object categories. In this paper, based on the state-of-the-art Mask R-CNN model, we propose to leverage complementary saliency and contour information to handle these two challenges. We first improve Mask R-CNN by introducing an interleaved execution strategy and proposing a novel mask head network to incorporate global context within each RoI. Then we add two branches to Mask R-CNN for saliency and contour detection, respectively. We fuse the Mask R-CNN features with the saliency and contour features, where the former supply pixel-wise saliency information to help with identifying salient regions and the latter provide a generic object contour prior to help detect and segment generic objects. We also propose a novel multiscale global attention model to generate attentive global features from multiscale representative features for feature fusion. Experimental results demonstrate that all our proposed model components can improve SIS performance. Finally, our overall model outperforms state-of-the-art SIS methods and Mask R-CNN by more than 6% and 3%, respectively. By using additional multitask training data, we can further improve the model performance on the ILSO dataset.

Fast and efficient contrast-enhanced super-resolution without real-world data using concatenated recursive compressor-decompressor network

The authors propose a novel model called concatenated recursive compressor-decompressor network (CRCDNet) for contrast-enhanced super-resolution. The characteristics of authors' model can be summarised as follows. First, a compression-decompression process reduces the computational complexity compared with the general fully convolutional model. Second, an internal/external skip-connection is used to preserve information of the preceding layers. Finally, by employing a recursive module, authors' model has a small number of parameters, yet is a deep and robust network. The authors apply authors' proposed network to license plate images. As a real application, license plates can provide important evidence for investigation of crimes and for security, but it is very difficult to collect the vast amounts of license plates required for analysis based on a data-driven approach. To solve this problem, the authors generated virtual datasets to train authors' model, while analysing the performance with real license plate datasets. Authors' method achieves better performance than the state-of-the-art models on license plate images.

Video Extrapolation Method Based on Time-Varying Energy Optimization and CIP

Video extrapolation/prediction methods are often used to synthesize new videos from images. For fluid-like images and dynamic textures as well as moving rigid objects, most state-of-the-art video extrapolation methods use non-physics-based models that learn orthogonal bases from a number of images but at high computation cost. Unfortunately, data truncation can cause image degradation, i.e., blur, artifact, and insufficient motion changes. To extrapolate videos that more strictly follow physical rules, this paper proposes a physics-based method that needs only a few images and is truncation-free. We utilize physics-based equations with image intensity and velocity: optical flow, Navier-Stokes, continuity, and advection equations. These allow us to use partial difference equations to deal with the local image feature changes. Image degradation during extrapolation is minimized by updating model parameters, where a novel time-varying energy balancer model that uses energy based image features, i.e., texture, velocity, and edge. Moreover, the advection equation is discretized by high-order constrained interpolation profile for lower quantization error than can be achieved by the previous finite difference method in long-term videos. Experiments show that the proposed energy based video extrapolation method outperforms the state-of-the-art video extrapolation methods in terms of image quality and computation cost.

Traffic surveillance camera calibration by 3D model bounding box alignment for accurate vehicle speed measurement

In this paper, we focus on fully automatic traffic surveillance camera calibration, which we use for speed measurement of passing vehicles. We improve over a recent state-of-the-art camera calibration method for traffic surveillance based on two detected vanishing points. More importantly, we propose a novel automatic scene scale inference method. The method is based on matching bounding boxes of rendered 3D models of vehicles with detected bounding boxes in the image. The proposed method can be used from arbitrary viewpoints, since it has no constraints on camera placement. We evaluate our method on the recent comprehensive dataset for speed measurement BrnoCompSpeed. Experiments show that our automatic camera calibration method by detection of two vanishing points reduces error by 50% (mean distance ratio error reduced from 0.18 to 0.09) compared to the previous state-of-the-art method. We also show that our scene scale inference method is more precise, outperforming both state-of-the-art automatic calibration method for speed measurement (error reduction by 86 % - 7.98 km/h to 1.10 km/h) and manual calibration (error reduction by 19 % - 1.35 km/h to 1.10 km/h). We also present qualitative results of the proposed automatic camera calibration method on video sequences obtained from real surveillance cameras in various places, and under different lighting conditions (night, dawn, day). (C) 2017 Elsevier Inc. All rights reserved.

Numerical Methods With Engineering Applications and Their Visual Analysis via Polynomiography

Polynomiography is a fusion of Mathematics and Art, which as a software results in a new form of abstract art. Rendered images are through algorithmic visualization of solving a polynomial equation via iteration schemes. Images are beautiful and diverse, yet unique. In short, polynomiography allows us to draw unique and complex-patterned images of polynomials which be re-colored in different ways through different iteration schemes. In the modern age, polynomiography covers a variety of applications in different fields of art and science. The aim of this paper is to present polynomiography using newly constructed root-finding algorithms for the solution of non-linear equations. The constructed algorithms are two-step predictor-corrector methods. For reducing computational cost and making the algorithm more effective, we approximate the second derivative via interpolation technique. These methods have been derived by employing Householder's method, interpolation technique and Taylor's series expansion. The convergence criterion of the newly developed algorithms has been discussed and proved their sixth-order convergence which is higher than many existing algorithms. To analyze the accuracy, validity and applicability of the proposed methods, several arbitrary and engineering problems have been tested and the obtained numerical results certify the better efficiency of the suggested methods against the other well-known iteration schemes given in the literature. Finally, we present polynomiography through the constructed iteration schemes and give a detailed comparison with the other iteration schemes which reflects the convergence properties and graphical aspects of the constructed algorithms.

Semi-Supervised Nonlinear Distance Metric Learning via Forests of Max-Margin Cluster Hierarchies

Metric learning is a key problem for many data mining and machine learning applications, and has long been dominated by Mahalanobis methods. Recent advances in nonlinear metric learning have demonstrated the potential power of non-Mahalanobis distance functions, particularly tree-based functions. We propose a novel nonlinear metric learning method that uses an iterative, hierarchical variant of semi-supervised max-margin clustering to construct a forest of cluster hierarchies, where each individual hierarchy can be interpreted as a weak metric over the data. By introducing randomness during hierarchy training and combining the output of many of the resulting semi-random weak hierarchy metrics, we can obtain a powerful and robust nonlinear metric model. This method has two primary contributions: first, it is semi-supervised, incorporating information from both constrained and unconstrained points. Second, we take a relaxed approach to constraint satisfaction, allowing the method to satisfy different subsets of the constraints at different levels of the hierarchy rather than attempting to simultaneously satisfy all of them. This leads to a more robust learning algorithm. We compare our method to a number of state-of-the-art benchmarks on k-nearest neighbor classification, large-scale image retrieval and semi-supervised clustering problems, and find that our algorithm yields results comparable or superior to the state-of-the-art.

Enhanced access and isolation by simple modifications of dental armamentarium

A good armamentarium facilitates the efficient working of the dentist which in turn improves the quality of treatment rendered to the patient. The present invention of the unit consisting of the flexible mirror attached to the suction and the dual suction tip aims at improving the clinical efficiency of dental treatments provided. This compact unit is designed to improve the visualization and isolation of the operating field. It is also easy to fabricate and alleviates the very relevant shortcomings of clinical work.

On Deep Reinforcement Learning for Static Routing and Wavelength Assignment

Deep Reinforcement Learning (DRL) is rising as a promising tool for solving optimization problems in optical networks. Though studies employing DRL for solving static optimization problems in optical networks are appearing, assessing strengths and weaknesses of DRL with respect to state-of-the-art solution methods is still an open research question. In this work, we focus on Routing and Wavelength Assignment (RWA), a well-studied problem for which fast and scalable algorithms leading to better optimality gaps are always sought for. We develop two different DRL-based methods to assess the impact of different design choices on DRL performance. In addition, we propose a Multi-Start approach that can improve the average DRL performance, and we engineer a shaped reward that allows efficient learning in networks with high link capacities. With Multi-Start, DRL gets competitive results with respect to a state-of-the-art Genetic Algorithm with significant savings in computational times. Moreover, we assess the generalization capabilities of DRL to traffic matrices unseen during training, in terms of total connection requests and traffic distribution, showing that DRL can generalize on small to moderate deviations with respect to the training traffic matrices. Finally, we assess DRL scalability with respect to topology size and link capacity.

Moving Urban Sculptures towards Sustainability: The Urban Sculpture Planning System in China

Following the continuous development characterized by large-scale constructions, Chinese urban development has shifted to the promotion of refined urban space quality. Urban sculpture, an important part of public arts, has been receiving increased attention in China as an important carrier for highlighting urban characteristics, culture, and history within cultural policies. As a type of cultural capital, it offers innovative methods to address the issues of economic, social, and environmental sustainability, in particular cultural sustainability. Interdisciplinary theories of urban planning are creatively applied to guide, coordinate, and improve the sustainable production of urban sculptures in China. This research was initiated to: (1) Illustrate how urban sculptures are produced through an urban planning system in the context of China; (2) explain what kind of influencing factors in relation to sustainability exist, mainly within the framework of planning strategies and cultural policies; and (3) put forward sustainable planning strategies to produce urban sculptures. To answer the above inquiries, we reviewed more than 100 articles, plans, and government documents, and we conducted several semi-structured interviews. The article argues that urban planning strategies and policies have been conceived as strategic instruments by the Chinese municipal governments to realize sustainable development of urban sculptures. Our findings would enrich knowledge on geographic studies of public art planning through the contextualized analysis of a Chinese urban sculpture planning system. It also fills the gap in the literature on the sustainability of urban sculptures by approaching the perspectives of planning strategies and cultural policies.

Ultralight, Elastic, Hybrid Aerogel for Flexible/Wearable Piezoresistive Sensor and Solid-Solid/Gas-Solid Coupled Triboelectric Nanogenerator

Aerogels have been attracting wide attentions in flexible/wearable electronics because of their light weight, excellent flexibility, and electrical conductivity. However, multifunctional aerogel-based flexible/wearable electronics for human physiological/motion monitoring, and energy harvest/supply for mobile electronics, have been seldom reported yet. In this study, a kind of hybrid aerogel (GO/CNT HA) based on graphene oxide (GO) and carboxylated multiwalled carbon nanotubes (CMWCNTs) is prepared which can not only used as piezoresistive sensors for human motion and physiological signal detections, but also as high performance triboelectric nanogenerator (TENG) coupled with both solid-solid and gas-solid contact electrifications (CE). The repeatedly loading-unloading tests with 20 000 cycles exhibit its high and ultrastable piezoresistive sensor performances. Moreover, when the obtained aerogel is used as the electrode of a TENG, high electric output performance is produced due to the synergistic effect of solid-solid, and gas-solid interface CEs (3D electrification: solid-solid interface CE between the two solid electrification layers; gas-solid interface CE between the inner surface of GO/CNT HA and the air filled in the aerogel pores). This kind of aerogel promises good applications for human physiological/motion monitoring and energy harvest/supply in flexible/wearable electronics such as piezoresistive sensors and flexible TENG.

Quantifying the state of the art of electric powertrains in battery electric vehicles: Range, efficiency, and lifetime from component to system level of the Volkswagen ID.3

With the rise of battery electric vehicles to mass production, many technical improvements have been realized to drastically increase the electric range, efficiency, and sustainability. However, insights into those valuable state-of-the-art solutions are usually not shared with researchers due to the strict nondisclosure policies of electric vehicle manufacturers. Many studies, therefore, rely on assumptions, best-guess estimates, or insider knowledge. This article presents an in-depth multi-scale analysis of the electric powertrain characteristics of a Volkswagen ID.3 Pro Performance. The focus is set on the range, power, and lifetime perceivable by the user. Thorough experimental tests are performed from the battery cell to vehicle level, following the energy conversion from source to sink. Energy densities are determined at all levels and the absolute electric range is quantified under varying operating conditions. Power capability and efficiency is evaluated at cell level by quantifying the battery cell and pack performance with current rate tests in charge/discharge scenarios and impedance determination, as well as by determining powertrain energy conversion efficiency with in-vehicle measurements. Moreover, accelerated aging tests of the lithium-ion battery cells are performed with close to real-world conditions and projected to vehicle level, demonstrating that the lithium-ion battery pack achieves mileages outperforming the warranty information of the manufacturer under real-world operation. Overall, the results provide valuable insights into the current state of the art and can serve as a reference for automotive engineering in academia. Over 10 GB of lithium-ion battery cell, pack, and overall powertrain measurement data from the lab and real-world environment is available as open source alongside the article. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Effect of ammonia stress on AMPK regulating-carbohydrate and lipid metabolism in Chinese striped-neck turtle (Mauremys sinensis)

In aquatic organisms, ammonia is one of the major factors that affect energy levels when it exceeds its optimal concentration. Numerous studies have examined the effects of ammonia on aquatic animals, but its effect on metabolism is still unknown. The effect of ammonia on carbohydrates and lipid metabolism in the Chinese striped neck turtle (Mauremys sinensis) was investigated in this study by exposing the turtle to two different ammonia concentrations (A100: 1.53 mg L-1) and (A200: 2.98 mg L-1) for 24 and 48 h, respectively. Our results showed that the mRNA expression of adenosine monophosphate-activated protein kinase α1 (AMPKα1) significantly increased only in A100 at 24 h, whereas its activity increased in both ammonia-exposed groups. The two AMPK-regulated transcription factors responsible for carbohydrate metabolism also exhibited changes in ammonia-treated groups, as hepatocyte nuclear factor-4-alpha (HNF4α) increased and forkhead box protein O1 (FoxO1) decreased. The expression of phosphofructokinase (PFK) and glucose-6-phosphatase (G-6-PAS) was subsequently downregulated. In addition, transcription factors, carbohydrate-responsive element-binding protein (ChREBP), and sterol regulatory element-binding protein 1c (SREBP-1c), which are known to be involved in lipogenesis, were suppressed. These downstream genes include fatty acid synthase, stearoyl CoA desaturase, and acetyl-CoA carboxylase (FAS, SCD-1 and ACC). Moreover, the glucose content decreased, whereas the triglyceride content increased significantly in A200 at 24 h. We concluded that AMPK signaling inhibits gluconeogenesis and lipogenesis, and promotes glycolysis to meet energy demand under stressful conditions in M. sinensis.

Recent advances and challenges in the early diagnosis and management of invasive fungal infections in Africa

Invasive fungal infections are a serious concern globally, especially in African settings which are typified by poorly funded and fragile healthcare systems. Low performance diagnostics, limited therapeutics and poor societal awareness of invasive fungal infections are some of the perennial challenges which have contributed to the unacceptably high death rates from these serious infections. However, recent advances have been recorded in fungal diagnostics and therapeutics development. Research into the development of vaccines to prevent fungal disease is beginning to yield promising results. Here we highlight key successes recorded and gaps in this journey and argue that national governments and relevant stakeholders need to do more to prioritise invasive fungal infections. Pragmatic and context-specific measures are proposed to mitigate the peculiar challenges Africa faces in tackling invasive fungal infections.

Growth of Firms in a Fragmented Cultural Industry: Italian Commercial Art Galleries' Competitive Strategies

The growth of cultural firms is important in developing local economies, enhancing employment, and improving urban sustainability, but it is difficult to achieve in fragmented industries that are populated by the smallest firms and offer a particularly unfavorable context for growth. The study takes a contingency perspective and contributes to both the literature on business strategy in fragmented industries and that on the growth of small and medium-sized enterprises (SMEs) by identifying the strategies implemented in a fragmented cultural industry, determining which of them are associated with a firm's expected growth, and finding the few firm-specific factors that are associated with growth. It also complements the extant literature on art galleries by looking at them from the understudied strategic perspective. Suggestions for practitioners and policy makers are included.

Barriers to and Facilitators of Antiretroviral Therapy Adherence in Nepal: A Qualitative Study

Patient's adherence is crucial to get the best out of antiretroviral therapy (ART). This study explores in-depth the barriers to and facilitators of ART adherence among Nepalese patients and service providers prescribing ART. Face-to-face semi-structured interviews were conducted with 34 participants. Interviews were audio-taped, transcribed, and translated into English before being analyzed thematically. ART-prescribed patients described a range of barriers for failing to adhere to ART. Financial difficulties, access to healthcare services, frequent transport blockades, religious/ritual obstacles, stigma and discrimination, and side-effects were the most-frequently discussed barriers whereas trustworthy health workers, perceived health benefits, and family support were the most-reported facilitators. Understanding barriers and facilitators can help in the design of an appropriate and targeted intervention. Healthcare providers should address some of the practical and cultural issues around ART whilst policy-makers should develop appropriate social policy to promote adherence among ART-prescribed patients.

Nanocarrier-Assisted Delivery of Metformin Boosts Remodeling of Diabetic Periodontal Tissue via Cellular Exocytosis-Mediated Regulation of Endoplasmic Reticulum Homeostasis

Endoplasmic reticulum (ER) dysfunction is a potential contributor to the impaired repair capacity of periodontal tissue in diabetes mellitus (DM) patients. Restoring ER homeostasis is thus critical for successful regenerative therapy of diabetic periodontal tissue. Recent studies have shown that metformin can modulate DM-induced ER dysfunction, yet its mechanism remains unclear. Herein, we show that high glucose elevates the intracellular miR-129-3p level due to exocytosis-mediated release failure and subsequently perturbs ER calcium homeostasis via downregulating transmembrane and coiled-coil domain 1 (TMCO1), an ER Ca2+ leak channel, in periodontal ligament stem cells (PDLSCs). This results in the degradation of RUNX2 via the ubiquitination-dependent pathway, in turn leading to impaired PDLSCs osteogenesis. Interestingly, metformin could upregulate P2X7R-mediated exosome release and decrease intracellular miR-129-3p accumulation, which restores ER homeostasis and thereby rescues the impaired PDLSCs. To further demonstrate the in vivo effect of metformin, a nanocarrier for sustained local delivery of metformin (Met@HALL) in periodontal tissue is developed. Our results demonstrate that compared to controls, Met@HALL with enhanced cytocompatibility and pro-osteogenic activity could boost the remodeling of diabetic periodontal tissue in rats. Collectively, our findings unravel a mechanism of metformin in restoring cellular ER homeostasis, enabling the development of a nanocarrier-mediated ER targeting strategy for remodeling diabetic periodontal tissue.

Hybrid energy coordination control strategy on associated gas power grid

This paper reviews the state-of-the-art in offshore oil and energy system design, develops a prototype hybrid marine renewable energy system and discusses an artificial intelligence algorithm to optimize the structure. A control strategy for the coordination of hybrid energy in an interconnected gas grid is developed. Numerical example results show that the optimization of the coordinated control strategy can provide a better balance between economy and reliability. (C) 2021 Published by Elsevier Ltd.

A noise resistant image matching method using angular radial transform

In this paper, we extend the concept of the optimal similarity measure, originally developed for Zernike moments (ZMs) which belong to a class of orthogonal rotation invariant moments (ORIMs), to angular radial transform (ART) which is non-orthogonal. The proposed distance measure not only uses the magnitude of the ART coefficients but also incorporates phase component unlike the existing L-1-distance and L-2-distance measures which use only the magnitude of ART in image matching problems. Experimental results show that the new distance measure outperforms L-2-distance measure. The performance of the proposed method is highly robust to Gaussian noise and salt-and-pepper noise even at very high level of noise. The results are compared with the ZMs-based optimal similarity measure. It is shown that the recognition rate of the proposed distance measure is comparable to that of the ZMs, however, at very low computational complexity. (C) 2014 Elsevier Inc. All rights reserved.

Anomaly Matters: An Anomaly-Oriented Model for Medical Visual Question Answering

Medical images contain various abnormal regions, most of which are closely related to the lesions or diseases. The abnormality or lesion is one of the major concerns during clinical practice and therefore becomes the key in answering questions about medical images. However, the recent efforts still focus on constructing a generic Visual Question Answering framework for medical-domain tasks, which is not adequate for practical medical requirements and applications. In this paper, we present two novel medical-specific modules named multiplication anomaly sensitive module and residual anomaly sensitive module to utilize weakly supervised anomaly localization information in medical Visual Question Answering. Firstly, the proposed multiplication anomaly sensitive module designed for anomaly-related questions can mask the feature of the whole image according to the anomaly location map. Secondly, the residual anomaly sensitive module could learn a flexible anomaly feature while preserving the information of the original questioned image, which is more helpful in answering anomaly-unrelated questions. Thirdly, the transformer decoder and multi-task learning strategy are combined to further enhance the question-reasoning ability and the model generalization performance. Finally, qualitative and quantitative experiments on a variety of medical datasets exhibit the superiority of the proposed approaches compared to the state-of-the-art methods.

Brain O-GlcNAcylation: From Molecular Mechanisms to Clinical Phenotype

O-GlcNAc is the attachment of β-N-acetylglucosamine to the hydroxyl group of serine and threonine in nuclear and cytoplasmic proteins. It is generally not further elongated but exists as a monosaccharide that can be rapidly added or removed. Thousands of proteins involved in gene transcription, protein translation and degradation as well as the regulation of signal transduction contain O-GlcNAc. Brain is one of the tissues where O-GlcNAc is the most highly expressed and deletion of neuronal O-GlcNAc leads to death early in development. O-GlcNAc is also important for normal adult brain function, where dynamic processes like learning and memory at least in part depend on the modification of specific proteins by O-GlcNAc. Conversely, too much or too little O-GlcNAc in the brain contributes to several disorders including obesity, intellectual disability and Alzheimer's disease. In this chapter, we describe the expression and regulation of O-GlcNAc in the nervous system.

Dose finding studies for therapies with late-onset toxicities: A comparison study of designs

An objective of phase I dose-finding trials is to find the maximum tolerated dose; the dose with a particular risk of toxicity. Frequently, this risk is assessed across the first cycle of therapy. However, in oncology, a course of treatment frequently consists of multiple cycles of therapy. In many cases, the overall risk of toxicity for a given treatment is not fully encapsulated by observations from the first cycle, and hence it is advantageous to include toxicity outcomes from later cycles in phase I trials. Extending the follow up period in a trial naturally extends the total length of the trial which is undesirable. We present a comparison of eight methods that incorporate late onset toxicities while not extensively extending the trial length. We conduct simulation studies over a number of scenarios and in two settings; the first setting with minimal stopping rules and the second setting with a full set of standard stopping rules expected in such a dose finding study. We find that the model-based approaches in general outperform the model-assisted approaches, with an interval censored approach and a modified version of the time-to-event continual reassessment method giving the most promising overall performance in terms of correct selections and trial length. Further recommendations are made for the implementation of such methods.

Measurements for indoor air quality assessment at the Capodimonte Museum in Naples (Italy)

The state of works of art exhibited inside museums can be strongly influenced by indoor air quality, due to chemical activity of gaseous pollutants and particulate matter. For that reason it is important to carry out periodic air quality controls to check if the concentration levels of air pollutants comply with the limits specified by the national laws. In this work we show results obtained in a monitoring campaign carried out at the Museum of Capodimonte in Naples, one of the most important museums in Southern Italy. Results concern indoor monitoring of inorganic and organic gaseous pollutants and PM10 and PM2.5 fractions; moreover we also present the comparison between indoor and outdoor particulate matter concentration. From this analysis emerged a satisfying indoor condition with respect to gaseous pollutants, without any limit exceedance; in contrast particulate matter exhibits high concentration levels with frequent exceedances. Comparison with outdoor concentrations demonstrates the influence of dust and organic matter transported from the park surrounding the museum due to wind and visitors stamping.

Efficient Epileptic Seizure Prediction Based on Deep Learning

Epilepsy is one of the worlds most common neurological diseases. Early prediction of the incoming seizures has a great influence on epileptic patients' life. In this paper, a novel patient-specific seizure prediction technique based on deep learning and applied to long-term scalp electroencephalogram (EEG) recordings is proposed. The goal is to accurately detect the preictal brain state and differentiate it from the prevailing interictal state as early as possible and make it suitable for real time. The features extraction and classification processes are combined into a single automated system. Raw EEG signal without any preprocessing is considered as the input to the system which further reduces the computations. Four deep learning models are proposed to extract the most discriminative features which enhance the classification accuracy and prediction time. The proposed approach takes advantage of the convolutional neural network in extracting the significant spatial features from different scalp positions and the recurrent neural network in expecting the incidence of seizures earlier than the current methods. A semi-supervised approach based on transfer learning technique is introduced to improve the optimization problem. A channel selection algorithm is proposed to select the most relevant EEG channels which makes the proposed system good candidate for real-time usage. An effective test method is utilized to ensure robustness. The achieved highest accuracy of 99.6 and lowest false alarm rate of 0.004 ${{{\bf h}}<^>{ - 1}}$ along with very early seizure prediction time of 1 h make the proposed method the most efficient among the state of the art.

Low resolution face recognition using a two-branch deep convolutional neural network architecture

We propose a novel coupled mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution images consists of 14 layers and the other branch which maps the low resolution face images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution images are backpropagated to train the networks. Our proposed method is evaluated on FERET, LFW, and MBGC datasets and compared with state-of-the-art competing methods. Our extensive experimental evaluations show that the proposed method significantly improves the recognition performance especially for very low resolution probe face images (5% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution image from its corresponding low resolution probe image which is comparable with the state-of-the-art super-resolution methods in terms of visual quality. (C) 2019 Published by Elsevier Ltd.

Molecular 'email': Electrochemical aptasensing of fish pathogens, molecular information encoding, encryption and hiding applications

DNA with data encoding and molecular recognition is rarely used in combination with electrochemistry for multipurpose integrated applications (especially in sensing, information communication and security). Herein, we demonstrated an electrochemical aptasensing, information communication and safety system for detection of fish pathogens (Aeromonas hydrophila or Edwardsiella tarda) and molecular information encryption and hiding. Two fish pathogens can be easily and quickly detected by electrochemistry, respectively, with high selectivity and sensitivity (detection limit lower than 1 cfu/mL) without the need for traditional time-consuming biochemical culturing process. The specific interaction of the probe (DNA aptamer) with targets (pathogens) on the tiny and imperceptible electrochemical platform provides protection for hiding DNA aptamers containing the encoded message, but also offers a foundation for developing of molecular cryptography and steganography. This electrochemical system, which is similar to mail communication, does not record information on paper, but a molecular mail that records information through DNA and reads information using electrochemical sensing, or more precisely, molecular electrochemical mail (namely molecular 'email'). Our study proved that the combination of the recognition and encoding capabilities of DNA aptamers with electrochemistry can open a new door for molecular-level digitization technology. In the future, large-capacity, easy-to-operate, resettable, and flexible molecular crypto-steganography will be developed for molecular cascade communication and control.

TFIIH stabilization recovers the DNA repair and transcription dysfunctions in thermo-sensitive trichothiodystrophy

Trichothiodystrophy (TTD) is a rare hereditary disease whose prominent feature is brittle hair. Additional clinical signs are physical and neurodevelopmental abnormalities and in about half of the cases hypersensitivity to UV radiation. The photosensitive form of TTD (PS-TTD) is most commonly caused by mutations in the ERCC2/XPD gene encoding a subunit of the transcription/DNA repair complex TFIIH. Here we report novel ERCC2/XPD mutations affecting proper protein folding, which generate thermo-labile forms of XPD associated with thermo-sensitive phenotypes characterized by reversible aggravation of TTD clinical signs during episodes of fever. In patient cells, the newly identified XPD variants result in thermo-instability of the whole TFIIH complex and consequent temperature-dependent defects in DNA repair and transcription. Improving the protein folding process by exposing patient cells to low temperature or to the chemical chaperone glycerol allowed rescue of TFIIH thermo-instability and a concomitant recovery of the complex activities. Besides providing a rationale for the peculiar thermo-sensitive clinical features of these new cases, the present findings demonstrate how variations in the cellular concentration of mutated TFIIH impact the cellular functions of the complex and underlie how both quantitative and qualitative TFIIH alterations contribute to TTD clinical features.

The effect of bergamot (KoksalGarry) supplementation on lipid profiles: A systematic review and meta-analysis of randomized controlled trials

This systematic review and meta-analysis were conducted to evaluate the impact of bergamot (KoksalGarry) and its nutraceutical compounds on lipid profiles. PubMed, Web of Knowledge, Scopus, and Google Scholar searched for relevant articles. Trials investigating the effect of oral bergamot supplementation on serum levels of total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in adults were included. The mean differences and standard deviations were pooled using a random-effects model. Fourteen trials were included in this systematic review and meta-analysis. Bergamot supplementation significantly decreased serum levels of TC (weighted mean difference (WMD): -63.60 mg/dL; 95% CI: -78.03 to -49.18; p < .001), TG (WMD: -74.72 mg/dL; 95% CI: -83.58 to -65.87; p < .001), LDL-C (WMD: -55.43 mg/dL; 95% CI: -67.26 to -43.60; p < .001), and increased HDL-C (WMD: 5.78 mg/dL; 95% CI: 3.27 to 8.28; p < .001), respectively. Our systematic review of the effects of nutraceuticals containing bergamot on lipid markers showed inconsistent results. The results showed that bergamot supplementation might improve lipid profiles. The findings for nutraceutical compounds containing bergamot were inconsistent. However, the clinical efficacy of bergamot on lipid profiles needs to be further established through higher-quality studies.

[Toward a better understanding of fatigue in schizophrenia]

Despite being one of the most common complaints of people with schizophrenia, fatigue remains largely unexplored in this population. The lack of knowledge regarding this complex symptom makes it often underdiagnosed and undertreated in schizophrenia. The aim of this brief perspective review is to outline the potential origins (distinguishing primary and secondary fatigue) and consequences of fatigue and to explore some potential treatments in this population. The current literature in schizophrenia has mainly investigated fatigue as a trait, using a self-administered questionnaire. Beyond this observational approach, which does not allow to capture the symptom in real life situations where high levels of fatigue can emerge rapidly, we propose to consider the state level of fatigue, for instance occurring after a prolonged period of cognitive activity (i.e. mental fatigue). We elaborate on the potential relationships between mental fatigue and negative symptoms of schizophrenia and propose some research avenues to test the effects of acute fatigue on effort intentions and behaviours. The consideration of the multidimensional aspects of fatigue will allow to move beyond the sole pharmacological approach to treat fatigue in schizophrenia. Targeting the cognitive as well as the performance components of fatigue through interventions such as concomitant aerobic exercise - mental training offers attractive prospects to reduce fatigue in this population and minimize its functional negative impact.

A Bayesian Based Deep Unrolling Algorithm for Single-Photon Lidar Systems

Deploying 3D single-photon Lidar imaging in real world applications presents multiple challenges including imaging in high noise environments. Several algorithms have been proposed to address these issues based on statistical or learning-based frameworks. Statistical methods provide rich information about the inferred parameters but are limited by the assumed model correlation structures, while deep learning methods show state-of-the-art performance but limited inference guarantees, preventing their extended use in critical applications. This paper unrolls a statistical Bayesian algorithm into a new deep learning architecture for robust image reconstruction from single-photon Lidar data, i.e. the algorithm's iterative steps are converted into neural network layers. The resulting algorithm benefits from the advantages of both statistical and learning based frameworks, providing best estimates with improved network interpretability. Compared to existing learning-based solutions, the proposed architecture requires a reduced number of trainable parameters, is more robust to noise and mismodelling of the system impulse response function, and provides richer information about the estimates including uncertainty measures. Results on synthetic and real data show competitive results regarding the quality of the inference and computational complexity when compared to state-of-the-art algorithms.

A novel pixel range calculation technique for texture classification

In this article, a fractal base method has been implemented as a texture descriptor. As, fractal deals with self-similarity, the proposed method is able to find the similarity of local patterns of various images of similar group and discriminate the images of dissimilar patterns. Images are converted to binary images by using the concept of local binary pattern. In this paper we have proposed a new approach for texture classification called Pixel Range Calculation (PRC) method, which has been implemented by converting the color images to gray images. Four benchmarking datasets, such as, ALOT (Amsterdam Library Of Textures), UMD (University of MarylanD), KTH-TIPS2b (Kungliga Tekniska Hogskolan-Textures under varying Illumination, Pose and Scale) and UIUC (University of Illinois at Urbana-Champaign) has been used for our experimental purpose. The proposed method along with two state of art method namely, Gliding Box Method (GBM) and Multi Fractal Spectrum (MFS) has been implemented and tested on the above-mentioned datasets. The experimental results based on the classification accuracy show the PRC technique of fractal dimension estimation method out performs the GBM and MFS. It has been observed that the computational complexity of PRC method is much less than other two state of art methods.

Directions-of-Arrival Estimation Through Bayesian Compressive Sensing Strategies

The estimation of the directions of arrival (DoAs) of narrow-band signals impinging on a linear antenna array is addressed within the Bayesian compressive sensing (BCS) framework. Unlike several state-of-the-art approaches, the voltages at the output of the receiving sensors are directly used to determine the DoAs of the signals thus avoiding the computation of the correlation matrix. Towards this end, the estimation problem is properly formulated to enforce the sparsity of the solution in the linear relationships between output voltages (i.e., the problem data) and the unknown DoAs. Customized implementations exploiting the measurements collected at a unique time instant (single-snapshot) and multiple time instants (multiple-snapshots) are presented and discussed. The effectiveness of the proposed approaches is assessed through an extensive numerical analysis addressing different scenarios, signal configurations, and noise conditions. Comparisons with state-of-the-art methods are reported, as well.

Ratiometric ECL sensor based on Apt-AuNS@Lu nanoprobe for analyzing cell swelling-induced ATP release

A novel ratiometric electrochemiluminescence (ECL) system based on gold nanostars (AuNSs) support was constructed for the determination of hypotonicity-induced ATP release from HepG2 cells. AuNS@Lu nanoprobe was used as anodic luminophore and K2S2O8 as cathodic luminophore as well as anodic co-reactant. AuNS with the large specific surface was adopted to adsorb plentiful luminol to form solid-state probe and as affinity support to immobilize ATP aptamer (Apt). The obtained nanocomposite (Apt-AuNS@Lu) generated a strong ECL signal at + 0.4 V (vs. Ag/AgCl) with co-reactant K2S2O8, because of excellent conductivity and catalytic activity of AuNS. Furthermore, graphene oxide was reduced onto indium tin oxide (ITO) electrodes to facilitate the electron transfer. Following, polydopamine (PDA) film was formed via self-polymerization, improving stability and adhesion of the electrode surface. To immobilize ATP capture aptamer (AptC), abounding AuNSs were attached to RGO/PDA surface. When the sensor was incubated in the mixture solution of Apt-AuNS@Lu and target ATP, the ECL signal of Apt-AuNS@Lu increased with the increase of ATP concentration, meanwhile, the signal of K2S2O8 declined. The ratio of the two luminophores was used for the quantitative determination of ATP. The linear range was 5 to 250 nM, and the limit of detection was 1.4 nM at (3σ)/S. The method was successfully applied to analyze ATP release from HepG2 cells stimulated by 0.45% NaCl hypotonic solution. The results showed that the release kinetics profile of ATP had a sigmoidal shape with rapid release within 10 min and then slowed. Compared to the isotonic groups, the intracellular ATP concentration was 3.7 ± 0.3 µM (n = 3) decreasing by 40.3% and the extracellular was 23.4 ± 1.2 nM (n = 3) increasing by 9.2 times in the hypotonicity for 10 min, which showed ATP release from cells and good agreement with commercial ELISA test. The proposed strategy would be beneficial to broadening application of ECL technology in studying cell biological functions.

Exposing collaborative spammer groups through the review-response graph

Deceptive opinions of online merchandises, also known as review spams, cause great loss for consumers, manufacturers and even business-to-customer platforms. However, due to the weak supervision problem, especially the lack of ground-truth labels, identifying these untruthful reviews is challenging. What's even worse is that crowdsourcing workers out of manipulation campaigns always collaborate to distort an item's reputation, rendering the product together with its brand difficult to be rehabilitated. State-of-the-art solutions on spammer group recognition highlight co-reviewing behaviours or sentiment similarity to cluster reviews, which can only yield loosely-coupled candidates of reviewer sets. In this paper, we highlight the commenting interaction between reviews and model it as a bipartite graph and discover a new low-budget spam, i.e., responsive spam. Furthermore, we recognize strong-correlated groups of spam through a propagation technique upon two widely adopted spam indicators, i.e., text duplication and posting burstiness. Comparative results show that our approach is effective and outperforms state-of-the-art solutions with great significance.

Segmentation and Track-Analysis in Time-Lapse Imaging of Bacteria

In this paper, we have developed tools to analyze prokaryotic cells growing in monolayers in a microfluidic device. Individual bacterial cells are identified using a novel curvature based approach and tracked over time for several generations. The resulting tracks are thereafter assessed and filtered based on track quality for subsequent analysis of bacterial growth rates. The proposed method performs comparable to the state-of-the-art methods for segmenting phase contrast and fluorescent images, and we show a 10-fold increase in analysis speed.

Identification of Painted Rock-Shelter Sites Using GIS Integrated with a Decision Support System and Fuzzy Logic

The conservation and protection of painted rock shelters is an important issue. Throughout the world, if unprotected, they are vulnerable to vandalism or to industrial activities such as quarrying. This research explores the integrated use of a Geographic Information System (GIS) with a multi-criteria decision support system and fuzzy logic to identify possible rock art sites over the Vindhyan Plateau in the district of Mirzapur, Central India. The methodology proposed compares results obtained by spatial modelling with validation data derived from recent exhaustive field surveys of more than forty newly discovered rock-shelters in the Vindhyan region. The zones obtained by predictive modelling are in agreement with validation datasets and show that the method can be used for new site prospection. This method represents a potential tool for landscape planners and policy makers to employ when seeking protection from anthropogenic activities of potential areas of painted rock-shelter sites and archaeological deposits.

Viral spillover risk increases with climate change in High Arctic lake sediments

The host spectrum of viruses is quite diverse, as they can sustainedly infect a few species to several phyla. When confronted with a new host, a virus may even infect it and transmit sustainably in this new host, a process called 'viral spillover'. However, the risk of such events is difficult to quantify. As climate change is rapidly transforming environments, it is becoming critical to quantify the potential for spillovers. To address this issue, we resorted to a metagenomics approach and focused on two environments, soil and lake sediments from Lake Hazen, the largest High Arctic freshwater lake in the world. We used DNA and RNA sequencing to reconstruct the lake's virosphere in both its sediments and soils, as well as its range of eukaryotic hosts. We then estimated the spillover risk by measuring the congruence between the viral and the eukaryotic host phylogenetic trees, and show that spillover risk increases with runoff from glacier melt, a proxy for climate change. Should climate change also shift species range of potential viral vectors and reservoirs northwards, the High Arctic could become fertile ground for emerging pandemics.

Embedding Based on Function Approximation for Large Scale Image Search

The objective of this paper is to design an embedding method that maps local features describing an image (e.g., SIFT) to a higher dimensional representation useful for the image retrieval problem. First, motivated by the relationship between the linear approximation of a nonlinear function in high dimensional space and the state-of-the-art feature representation used in image retrieval, i.e., VLAD, we propose a new approach for the approximation. The embedded vectors resulted by the function approximation process are then aggregated to form a single representation for image retrieval. Second, in order to make the proposed embedding method applicable to large scale problem, we further derive its fast version in which the embedded vectors can be efficiently computed, i.e., in the closed-form. We compare the proposed embedding methods with the state of the art in the context of image search under various settings: when the images are represented by medium length vectors, short vectors, or binary vectors. The experimental results show that the proposed embedding methods outperform existing the state of the art on the standard public image retrieval benchmarks.

Risk Model Development and Validation in Clinical Oncology: Lessons Learned

Reliable risk models can greatly facilitate patient-centered inferences and decisions. Herein we summarize key considerations related to risk modeling in clinical oncology. Often overlooked challenges include data quality, missing data, effective sample size estimation, and selecting the variables to be included in the risk model. The stability and quality of the model should be carefully interrogated with particular emphasis on rigorous internal validation.

Weakly Supervised Neuron Reconstruction From Optical Microscopy Images With Morphological Priors

Manually labeling neurons from high-resolution but noisy and low-contrast optical microscopy (OM) images is tedious. As a result, the lack of annotated data poses a key challenge when applying deep learning techniques for reconstructing neurons from noisy and low-contrast OM images. While traditional tracing methods provide a possible way to efficiently generate labels for supervised network training, the generated pseudo-labels contain many noisy and incorrect labels, which lead to severe performance degradation. On the other hand, the publicly available dataset, BigNeuron, provides a large number of single 3D neurons that are reconstructed using various imaging paradigms and tracing methods. Though the raw OM images are not fully available for these neurons, they convey essential morphological priors for complex 3D neuron structures. In this paper, we propose a new approach to exploit morphological priors from neurons that have been reconstructed for training a deep neural network to extract neuron signals from OM images. We integrate a deep segmentation network in a generative adversarial network (GAN), expecting the segmentation network to be weakly supervised by pseudo-labels at the pixel level while utilizing the supervision of previously reconstructed neurons at the morphology level. In our morphological-prior-guided neuron reconstruction GAN, named MP-NRGAN, the segmentation network extracts neuron signals from raw images, and the discriminator network encourages the extracted neurons to follow the morphology distribution of reconstructed neurons. Comprehensive experiments on the public VISoR-40 dataset and BigNeuron dataset demonstrate that our proposed MP-NRGAN outperforms state-of-the-art approaches with less training effort.

Near-infrared fluorescent probe with a large Stokes shift for bioimaging of β-galactosidase in living cells and zebrafish develop at different period

Overexpression of β-galactosidase (β-gal) in tumor cells may serve as a valuable biomarker for the early diagnosis of some cancers (such as ovarian cancer). In addition, abnormal accumulation of β-gal is also considered an essential marker of cell senescence. Therefore, it is important to construct fluorescent probes with excellent fluorescence properties to visualize β-gal in biological systems. Here, we designed and screened a novel fluorescent probe XM for the detection of β-gal. Spectral data show that the probe has a good affinity (Km = 2.6 μM) for β-gal, large stokes shift (190 nm), fast response speed (stable within 20 min), and low detection limit (6.7 × 10-3 U/mL). Based on the above advantages, XM can not only detect β-gal content in cancer cells but also track the changes of β-gal content in zebrafish at different developmental period. We believe XM will become a powerful tool for the early cancer diagnosis and cellular senescence.

Naturalness- and information-preserving image recoloring for red-green dichromats

More than 100 million individuals around the world suffer from color vision deficiency (CVD). Image recoloring algorithms have been proposed to compensate for CVD. This study has proposed a new recoloring algorithm to make up shortages of contrast enhancement and naturalness preservation of the state-of-the-art methods. The recoloring task is formulated as an optimization problem that is solved by using the colors in a simulated CVD color space to maximize contrast and to preserve the original color as much as possible. In addition, the dominant colors are extracted for recoloring. They are then propagated to the whole image so that the optimization problem could be solved at a reasonable cost independent of the image size. In the quantitative evaluation, the results of the proposed method are competitive with those of the best existing method. The evaluation involving subjects with CVD demonstrates that the proposed method outperforms the state-of-the-art method in preserving both the information and the naturalness of images.

Art Inheritance: An Education Course on Traditional Pattern Morphological Generation in Architecture Design Based on Digital Sculpturism

Cultural communication and art heritage represent a repository of highly condensed information of past time that depends not only on the expansion of emerging digital media but also on the transformation of language and knowledge. In this course, students try to extract single elements from the traditional Chinese cultural patterns and redesign them, then combine them into existing buildings, attempting to balance the coherence, heterogeneity of space, and the uniqueness of projects. It provides favorable conditions in the new era for the traditional patterns to be re-activated. It is a combination of technology and art. The purpose of this course is not to discover new theories or new technologies but to provide morphological possibilities under the existing digital techniques in cultural symbols. We try to build new ideas and innovative digital expressions for traditional Chinese patterns, realize the collision of culture and technology from experimental trials on the existing architectures in other unimagined forms. It is not only the research reproduction of traditional patterns but also brings new vitality and fresh ideas for architecture design.

Dual Learning-Based Graph Neural Network for Remote Sensing Image Super-Resolution

High-resolution (HR) remote sensing imagery plays a critical role in remote sensing image interpretation, and single image super-resolution (SISR) reconstruction technology is becoming increasingly valuable and significant. The state-of-the-art deep-learning-based SISR methods have demonstrated remarkable advantages while reconstructing complex texture details still remains a big challenge. Besides, as a typical ill-posed inverse problem, how to determine the optimal solution is another important topic. To address these problems, in this work, a dual learning-based graph neural network (DLGNN) is proposed, in which the graph neural network (GNN) is utilized to consider the self-similarity patches in remote sensing imagery by aggregating cross-scale neighboring feature patches, and dual learning strategy is adopted to refine the reconstruction results by constraining the mapping process in terms of the loss function, transferring the typical ill-posed problem to a well-posed one. Abundant experiments on 3K VEHICLE_SR datasets and Massachusetts Roads demonstrate the validity and outstanding performance for remote sensing image super-resolution (SR) tasks compared with other state-of-the-art SR construction methods. Code is available at https://github.com/CUG-RS/DLGNN

Association of Probiotic Treatment With Antibiotics in Male Accessory Gland Infections

Male accessory gland infection (MAGI) represents a frequent disease, commonly treated with antibiotics alone. However, in approximately 40% to 50% of patients, persistent infection is detected. Intestinal dysbiosis is involved in the pathogenesis of prostatitis. We aimed to evaluate the efficacy of antibiotic treatment in association with a specific probiotic supplementation. A total of 104 infertile patients, with microbiological analysis on semen and/or prostatic secretions positive for Gram-negative bacteria, have been enrolled. All patients received antibiotic treatment with fluoroquinolones. In total, 84 patients received a commercial association of Enterococcus faecium and Saccharomyces boulardii during antibiotic treatment, followed by treatment with Lactobacilli. After the treatment, a complete microbiological analysis was repeated. Polymicrobial infections have been observed in 11% of patients, while infections due to a single germ were reported in 89% of the patients. After the treatment was performed, a complete eradication with negative semen culture and microbiological analysis on prostatic secretion was observed in 64 of 84 patients (76.2%), while only 10 of 20 patients receiving antibiotics alone (50%; p < .05) reported negative microbiological analysis. Persistent infections have been observed only in patients with infections due to Enterococcus faecalis and Escherichia coli. This study represents the first approach demonstrating the efficacy of a specific probiotic treatment in reducing the rate of persistent infections in patients with MAGI.

Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models

In the last three decades, a wide range of protein features have been discovered to annotate a protein. Numerous attempts have been made to integrate these features in a software package/platform so that the user may compute a wide range of features from a single source. To complement the existing methods, we developed a method, Pfeature, for computing a wide range of protein features. Pfeature allows to compute more than 200,000 features required for predicting the overall function of a protein, residue-level annotation of a protein, and function of chemically modified peptides. It has six major modules, namely, composition, binary profiles, evolutionary information, structural features, patterns, and model building. Composition module facilitates to compute most of the existing compositional features, plus novel features. The binary profile of amino acid sequences allows to compute the fraction of each type of residue as well as its position. The evolutionary information module allows to compute evolutionary information of a protein in the form of a position-specific scoring matrix profile generated using Position-Specific Iterative Basic Local Alignment Search Tool (PSI-BLAST); fit for annotation of a protein and its residues. A structural module was developed for computing of structural features/descriptors from a tertiary structure of a protein. These features are suitable to predict the therapeutic potential of a protein containing non-natural or chemically modified residues. The model-building module allows to implement various machine learning techniques for developing classification and regression models as well as feature selection. Pfeature also allows the generation of overlapping patterns and features from a protein. A user-friendly Pfeature is available as a web server python library and stand-alone package.

Sustainability in the Opera Sector: Main Drivers and Limitations to Improve the Environmental Performance of Scenography

Private and public organizations are becoming increasingly involved in achieving the Sustainable Development Goals. This includes organizations within the cultural sector, with a central role in the progress of society. This study presents a state-of-the-art analysis of actions towards sustainability of the opera sector with a life cycle perspective and focusing on the impact of opera sets' scenery. Our research is based on a review of literature and experiences, the results of interviews, a survey, and an experts' forum to analyze the related systems, standards, and practices. The study contributes with novel research that provides an understanding of the factors that determine the environmental performance which are synthesized with a sustainability SWOT analysis. Findings are relevant for academic researchers analyzing the potential conflicts among organizational strategic goals and sustainability and for scenic arts' practitioners and managers who aim to develop a roadmap towards improving the sustainability of their sector.

Effects of increasing levels of whole Black Soldier Fly (Hermetia illucens) larvae in broiler rations on acceptance, nutrient and energy intakes and utilization, and growth performance of broilers

Meal of black soldier fly larvae (BSFL), which requires extraction of protein and fat, is a novel protein source for poultry, while unprocessed whole BSFL could even directly be fed to chickens. Newly hatched Ross-308 chicks (n = 252) received whole BSFL at 10% (L10), 20% (L20), or 30% (L30) of voluntary feed intake (FI) of control chickens (CON) that received no BSFL but only age-specific diets (n = 63 birds / group) for 42 days (d). Acceptance and nutrient and energy intake of birds by BSFL and FI were calculated. Plasma metabolites were measured using an automatic enzymatic analyzer and immunoglobulins with ELISA. Depending on the variable, data were analyzed using ANOVA or repeated measures ANOVA to address treatment, time and interaction effects. Birds consumed all offered larvae. With the exception of d1, time spent by birds eating their daily portion of larvae (TSL, min/pen) did not differ among the larvae supply groups (P = 0.982). The L10 had a higher larvae eating rate (LER) that is, speed of larvae intake than did L20 and L30 (P < 0.05), implying increased competition for less available BSFL. The ratio of LER to feed eating rate (FER) was greater than 50 fold change difference (FCD), indicating a strong interest of chickens in BSFL over regular feed. Whole BSFL intake up to 30% of voluntary FI did not adversely affect broiler growth (P > 0.05). The L30 had lower total dry matter and metabolizable energy intakes (P < 0.05), although total fat intake was higher in L30 than in CON (P < 0.05). Compared with CON, 30% whole BSFL increased dietary protein-to-energy ratios, plasma uric acid and serum alkaline phosphatase concentrations (P < 0.05). We conclude that whole BSFL can be included in broiler rations up to 20% without negatively affecting growth performance and nutrient conversion efficiency, whereas a higher proportion is associated with lower protein utilization efficiency, possibly due to lower total energy intake.

Endoscopic Findings of Enteropathy-Associated T-Cell Lymphoma Type II: A Case Series

Enteropathy-associated T-cell lymphoma (EATL) is a rare extranodal T-cell lymphoma arising from the intestine. Two types of EATL have been reported. In contrast to the classic EATL type I, EATL type II occurs sporadically, is unrelated to celiac disease, and comprises 10% to 20% of all EATL cases. A total of five cases of EATL type II were diagnosed at our clinic from January 2009 to September 2012. Four of the five patients were diagnosed with the help of endoscopy. Among the four patients, two of the cases involved both the small and large intestines, whereas in the other two patients, EATL was limited to the small intestine. Common endoscopic findings included innumerable fine granularities (also called mosaic mucosal patterns) and diffuse thickening of the mucosa with a semicircular shallow ulceration in the lesions of the small bowel. In contrast, the endoscopic findings of the colon were nonspecific and could not distinguish EATL type II from other diseases. There are only few published reports regarding the representative endoscopic findings of EATL. Here, we present the clinical and endoscopic findings of four cases of EATL type II diagnosed by endoscopy.

Street vending, vulnerability and exclusion during the COVID-19 pandemic: the case of Cali, Colombia

The COVID-19 pandemic has had a profound effect on livelihoods everywhere, but especially in the informal economy where crucial forms of protection and security are often absent. A detailed understanding of the impacts for informal workers, the public policy approaches that could most effectively respond to their needs, and the barriers to such policy, is urgently needed. This paper discusses the results of a 2021 street vendor survey in Cali, Colombia, focusing on (1) vendors' socioeconomic circumstances and (2) their political engagement and attitudes on key policy and governance issues. It argues that while the pandemic and the government responses to it negatively impacted street vendors, there are steps that government could have taken, and can still take, to address vendors' needs and priorities. To ensure a just, equitable, sustainable recovery, and to protect economically marginalized groups from future crises, informal workers must be more meaningfully included in decision-making processes.

The Effects of a Professor's Professionalism and Diversity on the Perception and Satisfaction of Education in the Liberal Arts Curriculum

The purpose of this study was to investigate the awareness of the necessity and importance of liberal arts education and to examine the satisfaction of college students with their liberal arts courses. This study was conducted from June 1-15, 2018, for college students who are taking liberal arts courses. The collected data were analyzed using the SPSS 24.0 and AMOS 24.0 statistical package programs. To understand the general characteristics of the survey subjects, a frequency analysis, exploratory factor analysis, correlation analysis, and reliability analysis were performed to measure the reliability and validity of the measurement tools, and a structural model analysis was conducted to verify the proposed research model. The result shows that a professor's professionalism has a positive influence on the perception of a subject's importance and necessity after the course, diversity has a positive influence on satisfaction in liberal arts education. Favorable changes in the perception of importance and necessity have a positive effect on satisfaction level. Our findings imply that colleges should operate an integrated student-selective education course that allows all students to select and take liberal arts courses. It should be organized to secure full-time professors who will be exclusively responsible for liberal arts curriculums.

A qualitative meta-synthesis of the caregiving experiences of adult children providing care for cancer patients in China: Implications for multidisciplinary healthcare teams

Qualitative meta-synthesis is a coherent approach to answering an overarching research question by synthesising past qualitative studies so as to create new meanings from their results. We conducted a qualitative meta-synthesis to systematically evaluate and integrate the caregiving experiences of adult children providing care for an elderly parent with cancer. The search was conducted in the databases Web of Science, PubMed, Embase, MEDLINE, Cochrane Library, Grew Literature in the Health Sciences, CNKI, WanFang Data, VIP, SINOMED and China Academic Journals as well as Chinese grey literature databases (China Academic Conference Literature Database/, National Science and Technology Library) from inception to June 9, 2021. Thirteen studies were included in the final synthesis. The caregiver experiences they describe are synthesised into three primary themes: care needs, care burden and care gains, with numerous secondary themes. Besides our findings that seem to align with those from studies focused on other cultures, we have highlighted three main discoveries from the synthesis that stand out in the Chinese context: (1) many sub-themes related to specific caregiving skills; (2) a strong expectation for health professionals to improve their communication skills with family caregivers; (3) the negative and positive influences of filial piety in caregiving experiences. Our findings can help multidisciplinary healthcare teams in China support adult children as caregivers in their emphasis on improving caregiver education and training, ways of making the most of potential care gains, and ways of easing care burdens.

Wettability-based ultrasensitive detection of amphiphiles through directed concentration at disordered regions in self-assembled monolayers

Various forms of ecological monitoring and disease diagnosis rely upon the detection of amphiphiles, including lipids, lipopolysaccharides, and lipoproteins, at ultralow concentrations in small droplets. Although assays based on droplets' wettability provide promising options in some cases, their reliance on the measurements of surface and bulk properties of whole droplets (e.g., contact angles, surface tensions) makes it difficult to monitor trace amounts of these amphiphiles within small-volume samples. Here, we report a design principle in which self-assembled monolayer-functionalized microstructured surfaces coated with silicone oil create locally disordered regions within a droplet's contact lines to effectively concentrate amphiphiles within the areas that dominate the droplet static friction. Remarkably, such surfaces enable the ultrasensitive, naked-eye detection of amphiphiles through changes in the droplets' sliding angles, even when the concentration is four to five orders of magnitude below their critical micelle concentration. We develop a thermodynamic model to explain the partitioning of amphiphiles at the contact line by their cooperative association within the disordered, loosely packed regions of the self-assembled monolayer. Based on this local analyte concentrating effect, we showcase laboratory-on-a-chip surfaces with positionally dependent pinning forces capable of both detecting industrially and biologically relevant amphiphiles (e.g., bacterial endotoxins), as well as sorting aqueous droplets into discrete groups based on their amphiphile concentrations. Furthermore, we demonstrate that the sliding behavior of amphiphile-laden aqueous droplets provides insight into the amphiphile's effective length, thereby allowing these surfaces to discriminate between analytes with highly disparate molecular sizes.

Prediction of Human Eye Fixation by a Single Filter

Saliency modeling has played an important part in computer vision studies over the past 30 years. Many state-of-the-art models adopted complex mathematical and machine learning theories. In this paper, a simple and effective visual attention model is proposed. We find that a single fixed template is enough for saliency map generation; this idea is inspired by the receptive field of the human visual system. All that is needed is to convolve the input image with this template with additional post-processing. Experiments show that our model is extremely fast and performs better than state-of-the-art models in human eye fixation prediction.

Co-amorphous Systems of Sinomenine with Platensimycin or Sulfasalazine: Physical Stability and Excipient-Adjusted Release Behavior

There is strong interest to develop affordable treatments for the infection-associated rheumatoid arthritis (RA). Here, we present a drug-drug co-amorphous strategy against RA and the associated bacterial infection by the preparation and characterization of two co-amorphous systems of sinomenine (SIN) with platensimycin (PTM) or sulfasalazine (SULF), two potent antibiotics. Both of them were comprehensively characterized using powder X-ray diffraction, temperature-modulated differential scanning calorimetry, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy. The co-amorphous forms of SIN-PTM and SIN-SULF exhibited high Tgs at 139.10 ± 1.0 and 153.3 ± 0.2 °C, respectively. After 6 months of accelerated tests and 1 month of drug-excipient compatibility experiments, two co-amorphous systems displayed satisfactory physical stability. The formation of salt and strong intermolecular interactions between SIN and PTM or SULF, as well as the decreased molecular mobility in co-amorphous systems, may be the intrinsic mechanisms underlying the excellent physical stability of both co-amorphous systems. In dissolution tests, two co-amorphous systems displayed distinct reduced SIN-accumulative releases (below 20% after 6 h of release experiments), which may lead to its poor therapeutic effect. Hence, we demonstrated a controlled release strategy for SIN by the addition of a small percentage of polymers and a small-molecule surfactant to these two co-amorphous samples as convenient drug excipients, which may also be used to improve the unsatisfactory dissolution behaviors of the previously reported SIN co-amorphous systems. Several hydrogen bonding interactions between SIN and PTM or SULF could be identified in NMR experiments in DMSO-d6, which may be underlying reasons of decreased dissolution behaviors of both co-amorphous forms. These drug-drug co-amorphous systems could be a potential strategy for the treatment of infection-associated RA.

Thyroid disorders and gastrointestinal and liver dysfunction: A state of the art review

Thyroid disorders commonly impact on the gastrointestinal system and may even present with gastrointestinal symptoms in isolation; for example, metastatic medullary thyroid carcinoma typically presents with diarrhoea. Delays in identifying and treating the underlying thyroid dysfunction may lead to unnecessary investigations and treatment, with ongoing morbidity, and can potentially be life-threatening. Similarly, gastrointestinal diseases can impact on thyroid function tests, and an awareness of the concept and management of non-thyroidal illness is necessary to avoid giving unnecessary thyroid therapies that could potentially exacerbate the underlying gastrointestinal disease. Dual thyroid and gastrointestinal pathologies are also common, with presentations occurring concurrently or sequentially, the latter after a variable time lag that can even extend over decades. Such an association aetiologically relates to the autoimmune background of many thyroid disorders (e.g. Graves' disease and Hashimoto's thyroiditis) and gastrointestinal disorders (e.g. coeliac disease and inflammatory bowel disease); such autoimmune conditions can sometimes occur in the context of autoimmune polyglandular syndrome. Emphasis should also be given to the gastrointestinal side effects of some of the medications used for thyroid disease (e.g. anti-thyroid drugs causing hepatotoxicity) and vice versa (e.g. interferon therapy causing autoimmune thyroid dysfunction). In this review, we discuss disorders of the thyroid-gut axis and identify the evidence base behind the management of such disorders.

Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features

It is becoming increasingly clear that mitochondria play an important role in neural function. Recent studies show mitochondrial morphology to be crucial to cellular physiology and synaptic function and a link between mitochondrial defects and neuro-degenerative diseases is strongly suspected. Electron microscopy (EM), with its very high resolution in all three directions, is one of the key tools to look more closely into these issues but the huge amounts of data it produces make automated analysis necessary. State-of-the-art computer vision algorithms designed to operate on natural 2-D images tend to perform poorly when applied to EM data for a number of reasons. First, the sheer size of a typical EM volume renders most modern segmentation schemes intractable. Furthermore, most approaches ignore important shape cues, relying only on local statistics that easily become confused when confronted with noise and textures inherent in the data. Finally, the conventional assumption that strong image gradients always correspond to object boundaries is violated by the clutter of distracting membranes. In this work, we propose an automated graph partitioning scheme that addresses these issues. It reduces the computational complexity by operating on supervoxels instead of voxels, incorporates shape features capable of describing the 3-D shape of the target objects, and learns to recognize the distinctive appearance of true boundaries. Our experiments demonstrate that our approach is able to segment mitochondria at a performance level close to that of a human annotator, and outperforms a state-of-the-art 3-D segmentation technique.

Class sparsity signature based Restricted Boltzmann Machine

Restricted Boltzmann Machines (RBMs) have been extensively utilized in machine learning as core units in constructing deep learning architectures such as Deep Boltzmann Machines (DBMs) and Deep Belief Networks (DBNs). However, they are prone to overfitting and several regularization techniques have been proposed to mitigate this effect. In this paper, we propose the semi-supervised class sparsity signature based RBM formulation by combining unsupervised generative training of the RBM with a supervised sparsity regularizer. The proposed approach, termed as cssRBM, enforces sparsity at the class level to ensure that coherent and discriminative representations are learnt during training. Combining unsupervised learning with supervised learning allows the model to utilize external training data to learn better generative features while the supervised learning enables fine-tuning for discrimination using the learned features. We construct both DBMs and DBN5 with cssRBM units and evaluate the performance on multiple publicly available benchmark datasets. Experiments on the MNIST and CIFAR-10 databases demonstrate that the proposed approaches are comparable with state-of-the-art deep learning architectures in the literature. We also evaluate the performance on one of the most challenging face databases, i.e., the Point and Shoot Challenge dataset. The results show that the proposed approaches improve state-of-the-art results by 15% on the PaSC database. (C) 2016 Elsevier Ltd. All rights reserved.

Recalibrating Fully Convolutional Networks With Spatial and Channel "Squeeze and Excitation" Blocks

In a wide range of semantic segmentation tasks, fully convolutional neural networks (F-CNNs) have been successfully leveraged to achieve the state-of-the-art performance. Architectural innovations of F-CNNs have mainly been on improving spatial encoding or network connectivity to aid gradient flow. In this paper, we aim toward an alternate direction of recalibrating the learned feature maps adaptively, boosting meaningful features while suppressing weak ones. The recalibration is achieved by simple computational blocks that can be easily integrated in F-CNNs architectures. We draw our inspiration from the recently proposed "squeeze and excitation" (SE) modules for channel recalibration for image classification. Toward this end, we introduce three variants of SE modules for segmentation: 1) squeezing spatially and exciting channel wise; 2) squeezing channel wise and exciting spatially; and 3) joint spatial and channel SE. We effectively incorporate the proposed SE blocks in three state-of-the-art F-CNNs and demonstrate a consistent improvement of segmentation accuracy on three challenging benchmark datasets. Importantly, SE blocks only lead to a minimal increase in model complexity of about 1.5%, while the Dice score increases by 4%-9% in the case of U-Net. Hence, we believe that SE blocks can be an integral part of future F-CNN architectures.

An overlapping semantic community detection algorithm base on the ARTs multiple sampling models

Since the semantic Social Network (SSN) is a new kind of complex networks, the traditional community detection algorithms require giving the number of the communities and could not detect the overlapping communities. To solve this problem, we propose improving multiple sampling models ARTs, consisting of ART, LART, ARTF and LARTF, sampling the textual information specific to node, link, node field, and link field correspondingly. The proposed ARTs models separate the semantic community detection into context sampling and communities detecting stage. After the context sampling, the quantized semantic coordinate is allocated to each sampling element, by which the cohesion for each sampling field can be established, avoiding the presetting of the number of communities. As the ARTs models are not easy to convergence, we explore the multiple sampling to accelerate the convergence, and the parameters of ARTs are analyzed by experimental analysis. In evaluation aspect, some traditional evaluation models are extended for semantic community measurement. Finally, efficiency of ARTs is verified by experiment. (C) 2014 Elsevier Ltd. All rights reserved.

Lysobacter selenitireducens sp. nov., isolated from river sediment

A Gram-stain-negative, yellow-pigmented, motile, flagellated and rod-shaped bacterium, designated as 13AT, was isolated from a river sediment sample of Fuyang River in Hengshui City, Hebei Province, PR China. Strain 13AT grew at 10-37 °C (optimum, 30 °C), at pH 5.0-11.0 (optimum, pH 7.0) and at 0-7 % (w/v) NaCl concentration (optimum, 0 %). Phylogenetic analysis based on the 16S rRNA gene sequence showed that strain 13AT belongs to the genus Lysobacter, and was most closely related to Lysobacter spongiicola DSM 21749T (97.8 %), Lysobacter concretionis DSM 16239T (97.5 %), Lysobacter daejeonensis GIM 1.690T (97.3 %) and Lysobacter arseniciresistens CGMCC 1.10752T (96.9 %). Meanwhile, the type species Lysobacter enzymogenes ATCC 29487T was selected as a reference strain (95.2 %). The genomic size of strain 13AT was 3.0 Mb and the DNA G+C content was 69.0 %. The average nucleotide identity values between strain 13AT and each of the reference type strains L. spongiicola DSM 21749T, L. concretionis DSM 16239T, L. daejeonensis GIM 1.690T, L. arseniciresistens CGMCC 1.10752T and L. enzymogenes ATCC 29487T were 75.9, 76.1, 77.7, 78.0 and 73.2 %, respectively. The digital DNA-DNA hybridization values between strain 13AT and each of the reference type strains were 21.7, 22.2, 21.9, 22.7 and 23.2 %, respectively. The average amino acid identity values between strain 13AT and each of the reference type strains were 72.5, 72.9, 72.3, 75.0 and 69.2 %, respectively. The major fatty acids were iso-C15 : 0, iso-C16 : 0 and summed feature 9 (iso-C17 : 1 ω9c and/or C16 : 0 10-methyl). The sole respiratory quinone was identified as ubiquinone-8. The polar lipid profile contained phosphatidylglycerol, diphosphatidylglycerol, phosphatidylethanolamine, an unidentified aminolipid, an unidentified lipid, four unidentified phospholipids and two unidentified glycolipids. Based on the phenotypic, physiological, phylogenetic and chemotaxonomic data, strain 13AT represents a novel species of the genus Lysobacter, for which the name Lysobacter selenitireducens sp. nov. is proposed. The type strain is 13AT (=JCM 34786T=GDMCC 1.2722T).

Measurement approaches for Thermal Impedance Spectroscopy of Li-ion batteries

This work introduces two novel measurement methods for capturing the thermal behaviour of Li-ion cells in the frequency domain. For the validation, we compare them to the state of the art measurement method, in which the battery is thermally stimulated by a set of discrete frequencies, each tested individually in a sequential manner. All experiments are conducted with a cylindrical 18650 LFP-Graphite cell and lead to similar thermal impedance spectra. We validate a 0-D thermal model fitted to the respective measurement results with a thermal step response in the time domain. The results show, that all three models accurately represent the thermal behaviour of a Li-ion cell, with a mean absolute error below 0.3 K on the validation test. A spider-web diagram classifies and compares the given methods based on five values showing, that the state of art thermal measurement time can be reduced by 62% while also increasing the impedance measurement resolution by measuring multiple frequencies simultaneously.

What is the interference in "verbal interference"?

Research on the interrelation between language and other components of cognition makes frequent use of verbal interference paradigms. In this, participants are engaged in a primary nonverbal task, while simultaneously repeating non-sense syllables from memory or playback to occupy their articulatory buffer, which is assumed to block internal language use. However, language production involves different subprocesses and levels of representation, and no previous study has explicitly investigated which of these are affected by an occupied articulatory buffer. Thus, the current study addresses the question whether an occupied articulatory buffer significantly interferes with conceptualization. In Experiment 1, speakers name simple objects as fast and as accurately as they can under three conditions. In an interference condition, the verbalization task runs in parallel to a secondary, syllable memorization/recall task, which was expected to induce a situation in which the articulatory buffer temporarily holds phonological information while speakers engage in conceptualization. The articulatory buffer was not occupied in two control conditions. In Experiment 2, speakers performed a similar but more complex task. They verbally responded to visual depictions of actions, again under an interference condition and two control conditions. Results obtained in both experiments suggested no interference. Taken together, the findings suggest that an occupied articulatory buffer does not significantly affect conceptualization.

Antibodies to Glycolipids in Guillain-Barré Syndrome, Miller Fisher Syndrome and Related Autoimmune Neurological Diseases

Guillain-Barré syndrome (GBS) and Miller Fisher syndrome (MFS) are acute immune-mediated neuropathies, often preceded by an infection. Anti-glycolipid antibodies are frequently detected in patients' sera in the acute-phase. In particular, IgG anti-GQ1b antibodies are positive in as high as 90% of MFS cases. Anti-glycolipid antibodies are useful for the diagnosis of GBS and MFS. In addition, those antibodies may be directly involved in the pathogenetic mechanisms by binding specifically to the regions where the target glycolipid antigen is densely localized. This was proven by the development of animal models of anti-glycolipid antibody-mediated neuropathies. The presence of antibodies that specifically recognize a new conformational epitope formed by two gangliosides (ganglioside complex) in the acute-phase sera of some GBS patients suggested existence of a carbohydrate-carbohydrate interaction between glycolipids. Further intensive research is needed to clarify this point.

A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction

Deep learning-based techniques are the state of the art in road traffic prediction or forecasting. Several deep neural networks have been proposed to predict the traffic but they have not been evaluated under common datasets. Current studies analyze their capacity to predict road traffic in general but do not focus on their capacity to predict the formation of congestions. This is critical for avoiding congestions or mitigate their negative impact. This paper progresses the current state of the art by presenting a comprehensive comparison of the state-of-the-art deep neural networks for road traffic prediction. The comparison is conducted using the same real traffic datasets, and under normal and congested traffic conditions. The evaluation includes new deep neural networks and error recurrent models. Our study first demonstrates that accurately predicting the traffic overall does not imply that a deep neural network can accurately predict the traffic when congestions are being formed. This reinforces the idea that prediction techniques must also be evaluated under congestion conditions. Our analysis also shows that exploiting the spatiotemporal evolution of the traffic (and not just the temporal one) provides better prediction accuracy overall and in particular under congestion conditions. The study also demonstrates that error recurrent models outperform deep neural networks that do not utilize an error feedback both under normal and congested traffic conditions. In particular, our study shows that the error recurrent model eRCNN is the deep learning technique that achieves to date the best traffic prediction accuracy. It is also important emphasizing that error recurrent models achieve better prediction accuracy with shallower neural networks and therefore lower computational cost.

Computing a maximum clique in geometric superclasses of disk graphs

In the 90's Clark, Colbourn and Johnson wrote a seminal paper where they proved that maximum clique can be solved in polynomial time in unit disk graphs. Since then, the complexity of maximum clique in intersection graphs of d-dimensional (unit) balls has been investigated. For ball graphs, the problem is NP-hard, as shown by Bonamy et al. (FOCS '18). They also gave an efficient polynomial time approximation scheme (EPTAS) for disk graphs. However, the complexity of maximum clique in this setting remains unknown. In this paper, we show the existence of a polynomial time algorithm for a geometric superclass of unit disk graphs. Moreover, we give partial results toward obtaining an EPTAS for intersection graphs of convex pseudo-disks.

4D Golden-Angle Radial MRI at Subsecond Temporal Resolution

Intraframe motion blurring, as a major challenge in free-breathing dynamic MRI, can be reduced if high temporal resolution can be achieved. To address this challenge, this work proposes a highly accelerated 4D (3D + time) dynamic MRI framework with subsecond temporal resolution that does not require explicit motion compensation. The method combines standard stack-of-stars golden-angle radial sampling and tailored GRASP-Pro (Golden-angle RAdial Sparse Parallel imaging with imProved performance) reconstruction. Specifically, 4D dynamic MRI acquisition is performed continuously without motion gating or sorting. The k-space centers in stack-of-stars radial data are organized to guide estimation of a temporal basis, with which GRASP-Pro reconstruction is employed to enforce joint low-rank subspace and sparsity constraints. This new basis estimation strategy is the new feature proposed for subspace-based reconstruction in this work to achieve high temporal resolution (e.g., subsecond/3D volume). It does not require sequence modification to acquire additional navigation data, it is compatible with commercially available stack-of-stars sequences, and it does not need an intermediate reconstruction step. The proposed 4D dynamic MRI approach was tested in abdominal motion phantom, free-breathing abdominal MRI, and dynamic contrast-enhanced MRI (DCE-MRI). Our results have shown that GRASP-Pro reconstruction with the new basis estimation strategy enables highly-accelerated 4D dynamic imaging at subsecond temporal resolution (with five spokes or less for each dynamic frame per image slice) for both free-breathing non-DCE-MRI and DCE-MRI. In the abdominal phantom, better image quality with lower root mean square error and higher structural similarity index was achieved using GRASP-Pro compared with standard GRASP. With the ability to acquire each 3D image in less than 1 s, intraframe respiratory blurring can be intrinsically reduced for body applications with our approach, which eliminates the need for explicit motion detection and motion compensation.

From pixels to sentiment: Fine-tuning CNNs for visual sentiment prediction

Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich feelings and latent dispositions embedded in these media. In this work, we explore how Convolutional Neural Networks (CNNs), a now de facto computational machine learning tool particularly in the area of Computer Vision, can be specifically applied to the task of visual sentiment prediction. We accomplish this through fine-tuning experiments using a state-of-the-art CNN and via rigorous architecture analysis, we present several modifications that lead to accuracy improvements over prior art on a dataset of images from a popular social media platform. We additionally present visualizations of local patterns that the network learned to associate with image sentiment for insight into how visual positivity (or negativity) is perceived by the model. (C) 2017 Elsevier B.V. All rights reserved.

Towards a Quantum-Inspired Binary Classifier

Machine Learning classification models learn the relation between input as features and output as a class in order to predict the class for the new given input. Several research works have demonstrated the effectiveness of machine learning algorithms but the state-of-the-art algorithms are based on the classical theories of probability and logic. Quantum Mechanics (QM) has already shown its effectiveness in many fields and researchers have proposed several interesting results which cannot be obtained through classical theory. In recent years, researchers have been trying to investigate whether the QM can help to improve the classical machine learning algorithms. It is believed that the theory of QM may also inspire an effective algorithm if it is implemented properly. From this inspiration, we propose the quantum-inspired binary classifier, which is based on quantum detection theory. We used text corpora and image corpora to explore the effect of our proposed model. Our proposed model outperforms the state-of-the-art models in terms of precision, recall, and F-measure for several topics (categories) in the 20 newsgroup text corpora. Our proposed model outperformed all the baselines in terms of recall when the MNIST handwritten image dataset was used; F-measure is also higher for most of the categories and precision is also higher for some categories. Our proposed model suggests that binary classification effectiveness can be achieved by using quantum detection theory. In particular, we found that our Quantum-Inspired Binary Classifier can increase the precision, recall, and F-measure of classification where the state-of-the-art methods cannot.

Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification

Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools.

Enhancement of Sensitivity in AlGaN/GaN HEMT Based Sensor Using Back-Barrier Technique

In the state of the art, most of the Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT) based sensors provide current readout rather than voltage readout. Further, to improve the sensitivity, surface side optimization has been done rather than epi-layer optimization. In this paper, a back-barrier (epi-layer) technique is proposed to improve the sensitivity of the GaN based sensor. To measure the sensitivity, voltage readout is used as it provides better sensitivity than that of a current readout. SILVACO ATLAS Technology Computer Aided Design (TCAD) is used to analyze the performance of the proposed technique. In order to perform a realistic simulation, the TCAD simulation is validated with measured data. The proposed back-barrier technique in the GaN HEMT is shown to improve the sensitivity by 14% as compared to the state of the art. Therefore, the use of the proposed back-barrier technique is considered promising for the GaN HEMT based sensors.