Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical Folding Codes for Proteins.

Exploring and understanding the protein-folding problem has been a long-standing challenge in molecular biology. Here, using molecular dynamics simulation, we reveal how parallel distributed adjacent planar peptide groups of unfolded proteins fold reproducibly following explicit physical folding codes in aqueous environments due to electrostatic attractions. Superfast folding of protein is found to be powered by the contribution of the formation of hydrogen bonds. Temperature-induced torsional waves propagating along unfolded proteins break the parallel distributed state of specific amino acids, inferred as the beginning of folding. Electric charge and rotational resistance differences among neighboring side-chains are used to decipher the physical folding codes by means of which precise secondary structures develop. We present a powerful method of decoding amino acid sequences to predict native structures of proteins. The method is verified by comparing the results available from experiments in the literature.
Transformed $\ell_1$ Regularization for Learning Sparse Deep Neural Networks

Deep neural networks (DNNs) have achieved extraordinary success in numerous areas. However, to attain this success, DNNs often carry a large number of weight parameters, leading to heavy costs of memory and computation resources. Overfitting is also likely to happen in such network when the training data are insufficient. These shortcomings severely hinder the application of DNNs in resource-constrained platforms. In fact, many network weights are known to be redundant and can be removed from the network without much loss of performance. To this end, we introduce a new non-convex integrated transformed $\ell_1$ regularizer to promote sparsity for DNNs, which removes both redundant connections and unnecessary neurons simultaneously. To be specific, we apply the transformed $\ell_1$ to the matrix space of network weights and utilize it to remove redundant connections. Besides, group sparsity is also employed as an auxiliary to remove unnecessary neurons. An efficient stochastic proximal gradient algorithm is presented to solve the new model at the same time. To the best of our knowledge, this is the first work to utilize a non-convex regularizer in sparse optimization based method to promote sparsity for DNNs. Experiments on several public datasets demonstrate the effectiveness of the proposed method.
Fast Multi-Class Probabilistic Classifier by Sparse Non-parametric Density Estimation

The model interpretation is essential in many application scenarios and to build a classification model with a ease of model interpretation may provide useful information for further studies and improvement. It is common to encounter with a lengthy set of variables in modern data analysis, especially when data are collected in some automatic ways. This kinds of datasets may not collected with a specific analysis target and usually contains redundant features, which have no contribution to a the current analysis task of interest. Variable selection is a common way to increase the ability of model interpretation and is popularly used with some parametric classification models. There is a lack of studies about variable selection in nonparametric classification models such as the density estimation-based methods and this is especially the case for multiple-class classification situations. In this study we study multiple-class classification problems using the thought of sparse non-parametric density estimation and propose a method for identifying high impacts variables for each class. We present the asymptotic properties and the computation procedure for the proposed method together with some suggested sample size. We also repost the numerical results using both synthesized and some real data sets.
PHYSIM - A Physical Layer Simulation Software

Modern communication systems use different physical layer (PHY) techniques with diverse performance characteristics. Each PHY configuration option introduces trade-offs for spectral efficiency, robustness, out-of-band emissions, peak-to-average power ratio, and implementation complexity. This paper introduces the cross-platform PHYSIM software framework. PHYSIM can compare established modulations like orthogonal frequency-division multiplexing (OFDM) with modern alternatives, e.g., filter bank multi-carrier, universal filtered multi-carrier, and filtered-OFDM. Additionally, PHYSIM can draw performance comparisons between new coding schemes like polar codes and more established methods like low-density parity-check codes and turbo codes. The PHYSIM graphical user interface (GUI) assists in the design of physical layer parameters. The GUI offers plots to compare the performance of PHY configurations in transmission channels. The plots display the transmitted spectra, the received constellation diagrams, as well as the modulation, bit, and frame error ratios. Hence, the PHYSIM simulation framework assists the designer of a physical layer system and helps to find PHY settings for a target application.
Adaptive AUTOSAR for vision based algorithms

Significance of algorithms which are part of Advanced Driving Assistance Systems is in the growing focus of the automotive industry. We in the automotive industry strive to improve the consumers driving experience and safety. Often times when developing any algorithm on an embedded platform, the challenges range from low performance execution to troubles in receiving inputs, and displaying the processing result. Recently, the automotive industry elements have shifted toward software oriented solutions, such as for example, software stacks or computing platforms for vehicles. It has become common that multiple algorithms run under the same System on Chip, which increases the need for computational power. Each new platform that can support the computational costs, or enable better optimization, flexibility, and scalability, such as adaptive AUTOSAR requires platform specific redesign of existing methods during the platform initialization in the industry. In this paper we present developing vision based algorithms on novel Adaptive AUTOSAR platform.
Studying the Effects of Feature Extraction Settings on the Accuracy and Memory Requirements of Neural Networks for Keyword Spotting

Due to the always-on nature of keyword spotting (KWS) systems, low power consumption micro-controller units (MCU) are the best choices as deployment devices. However, small computation power and memory budget of MCUs can harm the accuracy requirements. Although, many studies have been conducted to design small memory footprint neural networks to address this problem, the effects of different feature extraction settings are rarely studied. This work addresses this important question by first, comparing six of the most popular and state of the art neural network architectures for KWS on the Google Speech-Commands dataset. Then, keeping the network architectures unchanged it performs comprehensive investigations on the effects of different frequency transformation settings, such as number of used mel-frequency cepstrum coefficients (MFCCs) and length of the stride window, on the accuracy and memory footprint (RAM/ROM) of the models. The results show different preprocessing settings can change the accuracy and RAM/ROM requirements significantly of the models. Furthermore, it is shown that DS-CNN outperforms the other architectures in terms of accuracy with a value of 93.47% with least amount of ROM requirements, while the GRU outperforms all other networks with an accuracy of 91.02% with smallest RAM requirements.
Designing Subspecies of Hardware Trojans and Their Detection Using Neural Network Approach

Due to the recent technological development, home appliances and electric devices are equipped with high-performance hardware device. Since demand of hardware devices is increased, production base become internationalized to mass-produce hardware devices with low cost and hardware vendors outsource their products to third-party vendors. Accordingly, malicious third-party vendors can easily insert malfunctions (also known as “hardware Trojans”) into their products. In this paper, we design six kinds of hardware Trojans at a gate-level netlist, and apply a neural-network (NN) based hardware-Trojan detection method to them. The designed hardware Trojans are different in trigger circuits. In addition, we insert them to normal circuits, and detect hardware Trojans using a machine-learning-based hardware-Trojan detection method with neural networks. In our experiment, we learned Trojan-infected benchmarks using NN, and performed cross validation to evaluate the learned NN. The experimental results demonstrate that the average TPR (True Positive Rate) becomes 72.9%, the average TNR (True Negative Rate) becomes 90.0%.
Hypervisor based approach for integrated cockpit solutions

Rapid advances in SoC architectures have resulted in a shift in the automotive industry to integrate in-vehicle entertainment (infotainment) and selected ADAS functionality onto a single system, reducing system cost and payload. Standalone electronic control units (ECU) for surround-view (SRV),rear-view camera (RVC), heads-up display (HUD) and digitally instrument cluster are being substituted by integrated cockpit systems. Integrating the standalone ECUs into a single system represents a dichotomy of safety versus generality. Advanced driver assistance systems (ADAS) are expected to be fault tolerant and are traditionally managed by a safety-certified RTOS. Infotainment applications have fewer constraints on safety but stress on interoperability. Thus, infotainment applications are driven by open-source libraries and are managed by a high-level operating systems (HLOS). To achieve an integrated cockpit system while adhering to each systems constraints, a hypervisor based solution is proposed. The hypervisor facilitates running multiple operating systems simultaneously, partitioning of system resources and providing virtualization of shared peripherals. The result of the proposed architecture is an integrated cockpit system with ADAS and infotainment components isolated from each other, and the ability to run multiple HLOS sharing the peripherals.
Combining heart and breathing rate for car driver stress recognition

This paper investigates the use of heart rate (HR) and breathing rate (BR) signals for the automatic recognition of car driver stress. The problem is defined as a binary classification problem, that is stress vs no-stress, and it is tackled by using the Support Vector Machines classification strategy. We propose the use of a combination of traditional state of the art features and raw values of the acquired signals as data representation. Experimentation is carried out on a subset of data belonging to a publicly available dataset of simulated driving. The HR and BR signals are acquired with an adrenergic sensor connected to a chest strap that is worn underneath the driver’s clothing. The experiments are made considering the leave-one-subject-out configuration, that is the subject under test is not included in the training set. The goodness of HR and BR is tested separately and then a combination of them. The results obtained are very encouraging, especially considering that the leave-one-subject-out configuration is adopted. The results reach a mean average accuracy of about 70% in the case where the combination of HR and BR is adopted.
Deep Tracking Using Convolutional Features and Adaptive Frame Update

This paper presents a robust tracking algorithm using convolutional features. The proposed tracking algorithm consists of three steps: i) training correlation filters using features extracted by a convolutional neural network, ii) localizing the target position in the next frame by multiplying weight and correlation response of each layer, and iii) re-training correlation filters every 10-frame using the proposed position error score. Experimental results show that the proposed algorithm outperforms an existing deep learning-based tracker.
Effect of Increasing Age on Brain Dysfunction in Cirrhosis

Patients with cirrhosis are growing older, which could have an impact on brain dysfunction beyond hepatic encephalopathy. Our aim was to study the effect of concomitant aging and cirrhosis on brain inflammation and degeneration using human and animal experiments. For the human study, age‐matched patients with cirrhosis and controls between 65 and 85 years underwent cognitive testing, quality of life (QOL) assessment, and brain magnetic resonance (MR) spectroscopy and resting state functional MR imaging (rs‐fMRI) analysis. Data were compared between groups. For the animal study, young (10‐12 weeks) and old (1.5 years) C57BL/6 mice were given either CCl4 gavage to develop cirrhosis or a vehicle control and were followed for 12 weeks. Cortical messenger RNA (mRNA) expression of inflammatory mediators (interleukin [IL]‐6, IL‐1β, transforming growth factor β [TGF‐β], and monocyte chemoattractant protein 1), sirtuin‐1, and gamma‐aminobutyric acid (GABA)‐ergic synaptic plasticity (neuroligin‐2 [NLG2], discs large homolog 4 [DLG4], GABA receptor, subunit gamma 1/subunit B1 [GABRG1/B1]) were analyzed and compared between younger/older control and cirrhotic mice. The human study included 46 subjects (23/group). Patients with cirrhosis had worse QOL and cognition. On MR spectroscopy, patients with cirrhosis had worse changes related to ammonia and lower N‐acetyl aspartate, whereas rs‐fMRI analysis revealed that these patients demonstrated functional connectivity changes in the frontoparietal cortical region compared to controls. Results of the animal study showed that older mice required lower CCl4 to reach cirrhosis. Older mice, especially with cirrhosis, demonstrated higher cortical inflammatory mRNA expression of IL‐6, IL‐1β, and TGF‐β; higher glial and microglial activation; and lower sirtuin‐1 expression compared to younger mice. Older mice also had lower expression of DLG4, an excitatory synaptic organizer, and higher NLG2 and GABRG1/B1 receptor expression, indicating a predominantly inhibitory synaptic organization. Conclusion: Aging modulates brain changes in cirrhosis; this can affect QOL, cognition, and brain connectivity. Cortical inflammation, microglial activation, and altered GABA‐ergic synaptic plasticity could be contributory.
Phylogeographic analyses of a widely distributed Populus davidiana: Further evidence for the existence of glacial refugia of cool‐temperate deciduous trees in northern East Asia

Abstract Despite several phylogeographic studies had provided evidence to support the existence of glacial refugia of cool‐temperate deciduous trees in northeast China, the species used in these studies were limited by the species ranges, which could not exclude the possibility that northern populations were the colonists from southern refugial populations during the last glacial maximum (LGM). Here, we estimated the nucleotide variation in Populus davidiana, a widespread species distributed in Eurasia. Three groups in northeast, central, and southwest China were constructed according to the simulation results from SAMOVA, composition of chloroplast haplotypes and structure results. We revealed that the northeast China had endemic haplotypes, the haplotypes and nucleotide diversity in northern regions were not lower than that in southern China, and this species has not experienced population expansion base on the estimation of Bayesian skyline plots. Ecological niche modeling (ENM) indicated that the northeast China had a high suitability score during the last glacial maximum. The combined evidence clearly demonstrated that northeastern and southwestern refugia were maintained across the current distributional range of P. davidiana during the LGM. The genetic differentiation between these two refugia might be mainly caused by differences of climate among these areas. The phylogeographic analyses of a widely distributed P. davidiana provided robust evidence to clarify the issue of refugia in northeast China, and these results are of great importance for understanding the influence of Quaternary glaciations on the distribution and evolution of species in East Asia.
Moving From Heart Failure Guidelines to Clinical Practice: Gaps Contributing to Readmissions in Patients With Multiple Comorbidities and Older Age

This feature article for the thematic series on congestive heart failure (CHF) readmissions aims to outline important gaps in guidelines for patients with multiple comorbidities and the elderly. Congestive heart failure diagnosis manifests as a 3-phase journey between the hospital and community, during acute, chronic stable, and end-of-life (palliative) phases. This journey requires in variable intensities a combination of multidisciplinary care within tertiary hospital or ambulatory care from hospital outpatients or primary health services, within the general community. Management goals are uniform, ie, to achieve the lowest New York Heart Association class possible, with improvement in ejection fraction, by delivering gold standard therapies within a CHF program. Comorbidities are an important common denominator that influences outcomes. Comorbidities include diabetes mellitus, chronic obstructive airways disease, chronic renal impairment, hypertension, obesity, sleep apnea, and advancing age. Geriatric care includes the latter as well as syndromes such as frailty, falls, incontinence, and confusion. Many systems still fail to comprehensively achieve all aspects of such programs. This review explores these factors.
Genomic divergence in allopatric Northern Cardinals of the North American warm deserts is linked to behavioral differentiation

Abstract Biogeographic barriers are considered important in initiating speciation through geographic isolation, but they rarely indiscriminately and completely reduce gene flow across entire communities. Explicitly demonstrating which factors are associated with gene‐flow levels across barriers would help elucidate how speciation is initiated and isolation maintained. Here, we investigated the association of behavioral isolation on population differentiation in Northern Cardinals (Cardinalis cardinalis) distributed across the Cochise Filter Barrier, a region of transitional habitat which separates the Sonoran and Chihuahuan deserts of North America. Using genomewide markers, we modeled demographic history by fitting the data to isolation and isolation‐with‐migration models. The best‐fit model indicated that desert populations diverged in the Pleistocene with low, historic, and asymmetric gene flow across the barrier. We then tested behavioral isolation using reciprocal call‐broadcast experiments to compare song recognition between deserts, controlling for song dialect changes within deserts. We found that male Northern Cardinals in both deserts were most aggressive to local songs and failed to recognize across‐barrier songs. A correlation of genomic differentiation and strong song discrimination is consistent with a model where speciation is initiated across a barrier and maintained by behavioral isolation.
Extracellular Vesicles of Neutrophils

Extracellular vesicles (EVs) are membrane-derived vesicles that mediate intercellular communications. As professional phagocytes, neutrophils also produce EVs in response to various inflammatory stimuli during inflammatory processes. Neutrophil-derived EVs can be categorized into 2 subtypes according to the mechanism of generation. Neutrophil-derived trails (NDTRs) are generated from migrating neutrophils. The uropods of neutrophils are elongated by adhesion to endothelial cells, and small parts of the uropods are detached, leaving submicrometer-sized NDTRs. Neutrophil-derived microvesicles (NDMVs) are generated from neutrophils which arrived at the inflammatory foci. Membrane blebbing occurs in response to various stimuli at the inflammatory foci, and small parts of the blebs are detached from the neutrophils, leaving NDMVs. These 2 subtypes of neutrophil-derived EVs share common features such as membrane components, receptors, and ligands. However, there are substantial differences between these 2 neutrophil-derived EVs. NDTRs exert pro-inflammatory functions by guiding subsequent immune cells through the inflammatory foci. On the other hand, NDMVs exert anti-inflammatory functions by limiting the excessive immune responses of nearby cells. This review outlines the current understanding of the different subtypes of neutrophil-derived EVs and provides insights into the clinical relevance of neutrophil-derived EVs.
Commentary and Reflection Related to the Perspectives of Nurses Toward Telehealth Efficacy and Quality of Health Care

Telehealth nursing enabling communication with chronic disease patients has shown to improve medical outcomes for patients suffering from diabetes mellitus, chronic obstructive pulmonary disease (COPD), and chronic heart disease. Studies have been conducted to examine whether telehealth technology impacts the perceived level of internal service quality (SQ) delivered by nurses within a telehealth organization. Overall, the perspectives toward telehealth SQ researched through case studies showed positive results. The innovative role of telehealth nursing and related technology toward patient care was highlighted. Literature establishes how telehealth provides daily monitoring of patient health, which has the benefits of peace of mind, immediate feedback for patients, family, and caregivers, as well as the convenience of scheduling. This commentary not only reflects but also aims to analyze the effectiveness of the telehealth nursing interventions and reasons for the delayed implementation across United States. Improving and implementing telehealth methods in the case of chronic patients suffering from diabetes, COPD, and heart disease can lead to reductions in health-care errors and readmissions. Telehealth nursing has the potential of decreasing the length of hospital stay, at the same time improving satisfaction of care teams, while the patient management continues at home.
PI3Kinases in Diabetes Mellitus and Its Related Complications

Recent studies have shown that phosphoinositide 3-kinases (PI3Ks) have become the target of many pharmacological treatments, both in clinical trials and in clinical practice. PI3Ks play an important role in glucose regulation, and this suggests their possible involvement in the onset of diabetes mellitus. In this review, we gather our knowledge regarding the effects of PI3K isoforms on glucose regulation in several organs and on the most clinically-relevant complications of diabetes mellitus, such as cardiomyopathy, vasculopathy, nephropathy, and neurological disease. For instance, PI3K α has been proven to be protective against diabetes-induced heart failure, while PI3K γ inhibition is protective against the disease onset. In vessels, PI3K γ can generate oxidative stress, while PI3K β inhibition is anti-thrombotic. Finally, we describe the role of PI3Ks in Alzheimer’s disease and ADHD, discussing the relevance for diabetic patients. Given the high prevalence of diabetes mellitus, the multiple effects here described should be taken into account for the development and validation of drugs acting on PI3Ks.