In response to the pandemic, 17.9% of hospitals reported reduced in-person lactation support, and 72.9% reported discharging mothers and their newborns less then 48 hours after birth. Some of the infection prevention and control (IPC) practices that hospitals were implementing conflicted with evidence-based care to support breastfeeding. Mothers who are separated from their newborn or not feeding directly at the breast might need additional postdischarge breastfeeding support. In addition, the American Academy of Pediatrics (AAP) recommends that newborns discharged before 48 hours receive prompt follow-up with a pediatric health care provider.During August 7-16, 2020, a motorcycle rally was held in western South Dakota that attracted approximately 460,000 persons from across the United States to numerous indoor and outdoor events over a 10-day period. During August-September 2020, the Minnesota Department of Health (MDH) investigated a coronavirus disease 2019 (COVID-19) outbreak associated with the rally in Minnesota residents. Fifty-one primary event-associated cases were identified, and 35 secondary or tertiary cases occurred among household, social, and workplace contacts, for a total of 86 cases; four patients were hospitalized, and one died. Approximately one third (34%) of 87 counties in Minnesota had at least one primary, secondary, or tertiary case associated with this rally. Genomic sequencing supported the associations with the motorcycle rally. These findings support current recommendations for mask use, physical distancing, reducing the number of attendees at gatherings, isolation for patients with COVID-19, and quarantine for close contacts to slow the spread of SARS-CoV-2 (1). Furthermore, although these findings did not capture the impact of the motorcycle rally on residents of other states, they demonstrate the rationale for consistent mitigation measures across states.Sexual violence is prevalent and, for many victims, begins early in life (1). In the United States, one in five women and one in 38 men report completed or attempted rape victimization during their lifetime, with 43.2% of female and 51.3% of male victims reporting that their first rape victimization occurred before age 18 years (1). Media have been shown to act as a socializing agent for a range of health and social behaviors (2). Media portrayals might influence, reinforce, or modify how the public responds to incidents of sexual violence and their support for prevention efforts and media might construct a lens through which the public can understand who is affected by sexual violence, what forms it takes, why it happens, and who is responsible for addressing it (3). Media portrayals of sexual violence were assessed using a systematic random sample of newspaper articles from 48 of the top 50 distributed traditional print media outlets that were examined for sexual violence content and potential differences by geographic region and year of publication. Differences by year and region in type of sexual violence covered, media language used, and outcomes reported were identified, highlighting an opportunity for public health officials, practitioners, and journalists to frame sexual violence as a preventable public health issue and to incorporate best practices from CDC and the National Sexual Violence Resource Center’s Sexual Violence Media Guide (4).Wearing masks is a CDC-recommended* approach to reduce the spread of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), by reducing the spread of respiratory droplets into the air when a person coughs, sneezes, or talks and by reducing the inhalation of these droplets by the wearer. On July 2, 2020, the governor of Kansas issued an executive order† (state mandate), effective July 3, requiring masks or other face coverings in public spaces. CDC and the Kansas Department of Health and Environment analyzed trends in county-level COVID-19 incidence before (June 1-July 2) and after (July 3-August 23) the governor’s executive order among counties that ultimately had a mask mandate in place and those that did not. As of August 11, 24 of Kansas’s 105 counties did not opt out of the state mandate§ or adopted their own mask mandate shortly before or after the state mandate was issued; 81 counties opted out of the state mandate, as permitted by state law, and did not adopt their own mask mandate. After the governor’s executive order, COVID-19 incidence (calculated as the 7-day rolling average number of new daily cases per 100,000 population) decreased (mean decrease of 0.08 cases per 100,000 per day; net decrease of 6%) among counties with a mask mandate (mandated counties) but continued to increase (mean increase of 0.11 cases per 100,000 per day; net increase of 100%) among counties without a mask mandate (nonmandated counties). The decrease in cases among mandated counties and the continued increase in cases in nonmandated counties adds to the evidence supporting the importance of wearing masks and implementing policies requiring their use to mitigate the spread of SARS-CoV-2 (1-6). Community-level mitigation strategies emphasizing wearing masks, maintaining physical distance, staying at home when ill, and enhancing hygiene practices can help reduce transmission of SARS-CoV-2.BACKGROUND Autism spectrum disorder (ASD) is a complicated neuropsychiatric disease that displays significant heterogeneity. The diagnosis of ASD is currently primarily dependent upon descriptions of clinical symptoms, and it remains urgent to find biological markers for the detection and diagnosis of autism. The current study applied the urinary metabolic profiling approach to characterize metabolic phenotypes in ASD. MATERIAL AND METHODS Urine was obtained from children with ASD and their matched healthy siblings. Samples were analyzed using 1H NMR-based methods designed to measure a broad range of metabolites. Partial least-square-discriminant analysis (PLS-DA) was used to develop models to identify metabonomic variations that can be used to distinguish between individuals with ASD and their unaffected siblings. RESULTS A significant difference was observed between the metabolomic profiles of children with ASD and that of their healthy siblings. An increase in the levels of tryptophan, hippurate, glycine, and creatine, and a decrease in trigonelline, melatonin, pantothenate, serotonin, and taurine were observed compared to the control group. We conclude that several metabolic pathways are affected by autism, which suggests that a gut-brain link may be important in the pathophysiology of ASD. CONCLUSIONS 1H NMR-based metabonomic analysis of the urine can determine perturbations of specific metabolic pathways related to ASD and help identify a characteristic metabolic fingerprint to better understand the disease and its causes.BACKGROUND The pathogenic mitochondrial DNA variant m.3243A>G is associated with a wide range of clinical features, making disease course and prognosis extremely difficult to predict. We aimed to understand the cause of this broad intra-familial phenotypic heterogeneity in a large family carrying the variant m.3243A>G. CASE REPORT Thirteen family members were clinically affected. Clinical manifestations occurred in the brain, eyes, ears, endocrine organs, myocardium, intestines, kidneys, muscle, and nerves. Five family members carried the m.3243A>G variant. The 2 most severely affected patients were the index patient, a 60-year-old woman, and her sister, who was deceased. The phenotypic features most frequently found were hypoacusis and cerebellar atrophy. Hypertrophic cardiomyopathy was diagnosed in 3 family members. Short PQ syndrome and gestosis had not been reported to date. The broad phenotypic heterogeneity was attributed to variable heteroplasmy rates and variable mtDNA copy numbers. All affected patients benefited from symptomatic treatment. CONCLUSIONS The mitochondrial DNA variant m.3243A>G can manifest phenotypically with a non-syndromic, multisystem phenotype with wide intra-familial heterogeneity. Rare manifestations of the m.3243A>G variant are gestosis and short PQ syndrome. The broad intra-familial phenotypic heterogeneity may be related to fluctuating heteroplasmy rates or mitochondrial DNA copy numbers and may lead to misdiagnosis for years.Nonlinear optical and thermo-optical properties of two pure ionic liquids, BMIOMe.NTf2 and BMIOMe.N(CN)2, were examined in this study. This was the first nonlinear refractive index determination of a pristine ionic liquid by a standard self-refraction experiment. The nonlinear optical characterisations were performed using Z-scan and EZ-scan techniques in the thermally managed approach, with a mode-locked femtosecond laser source. Thermal properties were analysed concomitantly, and the thermo-optical coefficient, thermal characteristic time, and lens strength were characterised. These results define the parameters to be adopted in the method of nanoparticles formation by laser ablation in an ionic liquid solution and indicate that BMIOMe.NTf2 is a prominent material to be engineered for photonics applications.

The prevalence of acrophobia is high, especially with the rise of many high-rise buildings. In the recent few years, researchers have begun to analyse acrophobia from the neuroscience perspective, especially to improve the virtual reality exposure therapy (VRET). Electroencephalographic (EEG) is an informative neuroimaging technique, but it is rarely used for acrophobia. The purpose of this study is to evaluate the effectiveness of using EEGs to identify the degree of acrophobia objectively.

EEG data were collected by virtual reality (VR) exposure experiments. We classified all subjects’ degrees of acrophobia into three categories, where their questionnaire scores and behavior data showed significant differences. Using synchronization likelihood, we computed the functional connectivity between each pair of channels and then obtained complex networks named functional brain networks (FBNs). Basic topological features and community structure features were extracted from the FBNs. Statistical results demonstr of acrophobia. The proposed CNN framework can provide objective feedback, which could help build closed-loop VRET portable systems.Diffraction and imaging using x-rays and neutrons are widely utilized in different fields of engineering, biology, chemistry and/or materials science. The additional information gained from the diffraction signal by x-ray diffraction and computed tomography (XRD-CT) can give this method a distinct advantage in materials science applications compared to classical tomography. Its active development over the last decade revealed structural details in a non-destructive way with unprecedented sensitivity. In the current contribution an attempt to adopt the well-established XRD-CT technique for neutron diffraction computed tomography (ND-CT) is reported. A specially designed 'phantom’, an object displaying adaptable contrast sufficient for both XRD-CT and ND-CT, was used for method validation. The feasibility of ND-CT is demonstrated, and it is also shown that the ND-CT technique is capable to provide a non-destructive view into the interior of the 'phantom’ delivering structural information consistent with a reference XRD-CT experiment.The local structure of La(Fe1-xMnx)AsO has been investigated using temperature dependent Fe K-edge extended x-ray absorption fine structure (EXAFS) measurements. The EXAFS data reveal distinct behavior of Fe-As and Fe-Fe atomic displacements with a clear boundary between x≦0.02 and x>0.02. The Fe-As bondlength shows a gradual thermal expansion while the Fe-Fe bond manifests a temperature dependent anomaly at ∼180 K for x>0.02. It is interesting to find characteristically different nature of Fe-As and Fe-Fe bondlengths shown by the temperature dependent mean square relative displacements. Indeed, the Fe-As bond, stiffer than that of the Fe-Fe, gets softer for x$\le$0.02 and hardly shows any change for x>0.02. On the other hand, Fe-Fe bond tends to be stiffer for x≦0.02 followed by a substantial softening for x>0.02. Such a distinction has been seen also in the As K-edge x-ray absorption near edge structure (XANES), probing local geometry around As atom together with the valence electronic structure. The results suggest that local atomic displacements by Mn substitution inducing increased iron local magnetic moment that should be the main reason for its dramatic effect in iron-based superconductors.Germanene, though with Dirac valleys, is not deemed as a good valleytronic material due to its minute band gap, negligible spin-orbit coupling and spatial inversion symmetry. In comparison of interfacing germanene with MoS2, we proposed that forming heterostructure with Tl2S, an anti-MoS2 material with two outer heavy metal layers, could be more effective in raising spin-orbit coupling and band gap in germanene due to the direct Ge-metal contact. By carrying out first-principles calculations, we studied the valleytronic properties of germanene enhanced by monolayer Tl2S. It is found that the Ge-Tl direct interaction is strong to a proper extent so that the valleys of germanene still persist and simultaneously the valley gap is drastically increased from 23 to 370 meV. The valley spin splitting, being zero in pristine germanene, become 45 meV, which is opposite at inequivalent valleys owing to the time reversal symmetry. The inversion symmetry of germanene is broken by Tl2S, resulting in large Berry curvature near the valleys and hence laying the ground for Berry phase physics in germanene, e.g., valley spin Hall effect and valley-spin locking, as revealed in our study. The calculations found a perfect valley-selective circular dichroism, by which the valley and spin degrees of freedom can be manipulated selectively and correlatively.A comparative study has been carried out on the magnetocaloric properties of as-cast and annealed Tb2Ni0.90Si2.94 intermetallic compound. While the as-cast material exhibits ferromagnetic cluster-glass behaviour below 9.9 K coexisting with antiferromagnetic (AFM) interaction, the annealed system shows AFM ordering below 13.5 K and spin freezing occurs below 4 K. The compound exhibits moderate magnetocaloric performance with maximum isothermal entropy changes (-ΔS M) 8.8 and 10.9 J kg-1 K-1, relative cooling power (RCP) 306 and 365 J kg-1, along with adiabatic temperature change (ΔT ad) 5.5 and 8.15 K for 70 kOe magnetic field change in as-cast and annealed forms, respectively. The estimated magnetic entropy change is found to be larger for annealed sample in comparison to that of as-cast analogue. However, the full width at half maxima (FWHM) of -ΔS M(T) behaviour is larger in as-cast compound due to the presence of inherent structural disorder which reduces with thermal annealing. A positive isothermal entropy change (-ΔS M) and adiabatic temperature change (ΔT ad) is observed for the as-cast compound in the measured field and temperature region. In contrast, the annealed system exhibits inverse magnetocaloric effect in the low field and temperature region where AFM interactions dominate. Magnetocaloric effect (MCE) is used as a tool to establish a subtle correlation between the observed magnetocaloric effect and the reported magnetic properties of the system.Edge states of various two-dimensional materials such as graphene are intrinsically spin-polarized. In other materials, electric field and charge doping are required for introducing magnetism to their edges. In this work, by using first-principles calculations, we studied the effects of transverse electric field on the edge states of the armchair blue phosphorene nanoribbon (ABPNR), and found that a transverse electric field drives the edge electronic state occupied and at the same time spin-polarized. We also doped electrons to the ABPNR and found that these additional electrons occupy and spin-polarize the electronic states of both edges of the nanoribbon.Effective human motor augmentation should rely on biological signals that can be volitionally modulated without compromising natural motor control. We provided human subjects with real-time information on the power of two separate spectral bands of the spiking activity of motor neurons innervating the tibialis anterior muscle the low-frequency band ( less then 7Hz), which is directly translated into natural force control, and the beta band (13-30Hz), which is outside the dynamics of the neuromuscular system. Subjects could gain control over the powers in these two bands to navigate a cursor towards specific targets in a 2-D space (experiment 1) and to up- and down-modulate beta activity while keeping steady force contractions (experiment 2). Results indicate that beta projections to the spinal motor neuron pool can be voluntarily controlled partially decoupled from natural muscle contractions and, therefore, they could be valid control signals for implementing effective human motor augmentation platforms.

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has instigated immediate and massive worldwide research efforts. Rapid publication of research data may be desirable but also carries the risk of quality loss.

This analysis aimed to correlate the severity of the COVID-19 outbreak with its related scientific output per country.

All articles related to the COVID-19 pandemic were retrieved from Web of Science and analyzed using the web application SciPE (science performance evaluation), allowing for large data scientometric analyses of the global geographical distribution of scientific output.

A total of 7185 publications, including 2592 articles, 2091 editorial materials, 2528 early access papers, 1479 letters, 633 reviews, and other contributions were extracted. The top 3 countries involved in COVID-19 research were the United States, China, and Italy. The confirmed COVID-19 cases or deaths per region correlated with scientific research output. The United States was most active in terms of collaborative efforts, sharing a significant amount of manuscript authorships with the United Kingdom, China, and Italy. The United States was China’s most frequent collaborative partner, followed by the United Kingdom.

The COVID-19 research landscape is rapidly developing and is driven by countries with a generally strong prepandemic research output but is also significantly affected by countries with a high prevalence of COVID-19 cases. Our findings indicate that the United States is leading international collaborative efforts.

The COVID-19 research landscape is rapidly developing and is driven by countries with a generally strong prepandemic research output but is also significantly affected by countries with a high prevalence of COVID-19 cases. Our findings indicate that the United States is leading international collaborative efforts.

During the COVID-19 lockdown period in the United Kingdom that began on March 23, 2020, more than a quarter of a million people with cancer reported worsening mental health. Help to Overcome Problems Effectively (Hope) is a self-management program for people with cancer, designed to provide support for distress, unmet needs, and poor psychological health. In light of social distancing during the COVID-19 pandemic, digital delivery of the Hope Programme has become ever more vital for people with cancer. Previous pre-post studies of the digital Hope Programme have found reduced anxiety and depression and improved well-being for people with cancer. However, evaluation of this evidence has been limited by the lack of a control group in these previous studies.

We now present a protocol for a feasibility randomized controlled trial of the digital Hope Programme for people with cancer during the COVID-19 pandemic. Primary outcomes will be recruitment, dropout, and adherence rates, and estimations of sample and end of August 2020.

This feasibility study will provide data to inform the design of a future definitive trial. Wider-scale provision of the digital Hope Programme has potential to improve the lives of thousands of people with cancer and reduce the burden on health care providers during these unprecedented times.

ISRCTN Registry ISRCTN79623250; http//www.isrctn.com/ISRCTN79623250.

DERR1-10.2196/24264.

DERR1-10.2196/24264.Unsupervised feature selection (UFS) aims to remove the redundant information and select the most representative feature subset from the original data, so it occupies a core position for high-dimensional data preprocessing. Many proposed approaches use self-expression to explore the correlation between the data samples or use pseudolabel matrix learning to learn the mapping between the data and labels. Furthermore, the existing methods have tried to add constraints to either of these two modules to reduce the redundancy, but no prior literature embeds them into a joint model to select the most representative features by the computed top ranking scores. To address the aforementioned issue, this article presents a novel UFS method via a convex non-negative matrix factorization with an adaptive graph constraint (CNAFS). Through convex matrix factorization with adaptive graph constraint, it can dig up the correlation between the data and keep the local manifold structure of the data. To our knowledge, it is the first work that integrates pseudo label matrix learning into the self-expression module and optimizes them simultaneously for the UFS solution. Besides, two different manifold regularizations are constructed for the pseudolabel matrix and the encoding matrix to keep the local geometrical structure. Eventually, extensive experiments on the benchmark datasets are conducted to prove the effectiveness of our method. The source code is available at https//github.com/misteru/CNAFS.This article investigates the problem of fixed-time event-triggered output consensus tracking for high-order multiagent systems (MASs) under directed interaction graphs. First, a fixed-time event-triggered distributed observer and triggering functions are proposed. Next, fixed-time convergence of the presented distributed observer is proved by the Lyapunov function approach, and an analysis is conducted to show the proposed distributed observer excludes zeno behavior. Then, an event-triggered adaptive dynamic surface fixed-time controller is designed to stabilize the tracking error system. Finally, simulation results are given to show the effectiveness and superiority of the consensus scheme developed. The contribution of this article is to present a novel event-triggered fixed-time distributed observer and a novel fixed-time controller, which can reduce frequency of communication and control update, avoid continuous monitor, exclude zeno behavior, eliminate the effect of mismatched disturbance caused by observation error, and achieve practical fixed-time output consensus tracking of high-order MAS under directed interaction graphs.Feature extraction is an essential process in the intelligent fault diagnosis of rotating machinery. Although existing feature extraction methods can obtain representative features from the original signal, domain knowledge and expert experience are often required. In this article, a novel diagnosis approach based on evolutionary learning, namely, automatic feature extraction and construction using genetic programming (AFECGP), is proposed to automatically generate informative and discriminative features from original vibration signals for identifying different fault types of rotating machinery. To achieve this, a new program structure, a new function set, and a new terminal set are developed in AFECGP to allow it to detect important subband signals and extract and construct informative features, automatically and simultaneously. More important, AFECGP can produce a flexible number of features for classification. Having the generated features, k-Nearest Neighbors is employed to perform fault diagnosis. The performance of the AFECGP-based fault diagnosis approach is evaluated on four fault diagnosis datasets of varying difficulty and compared with 14 baseline methods. The results show that the proposed approach achieves better fault diagnosis accuracy on all the datasets than the competitive methods and can effectively identify different fault conditions of rolling bearing, gear, and rotor.This article pays close attention to a distributed optimization problem for multiagent systems subject to exogenous disturbances. A novel distributed model reference adaptive control (D-MRAC) scheme is proposed that no explicit disturbance observer or internal model unit is involved, which not only enhances robustness but also improves transient performance. In contrast to the continuous communication that is often assumed in the existing distributed optimization works, the new method allows for more realistic scenarios in which the agents communicate with each other at discrete-time instants. It is shown by Lyapunov analysis that the concerned distributed optimization problem can be solved by the proposed D-MRAC scheme as long as the communication interval is smaller than a given threshold, which can be calculated by following the steps given in this article. Numerical simulations have shown the effectiveness of the presented method.In this article, the free-will arbitrary time consensus is formulated for multiagent systems. This consensus protocol is independent of initial conditions and any other system parameters. With such a protocol, the multiagent system is shown to attain consensus as well as average consensus within the prespecified arbitrary time. Agents rendezvous can also be accomplished with the given protocol. Communication imperfections are easily handled with the designed protocol. Robust free-will arbitrary time consensus protocol is also designed. The stability of such nonlinear nonautonomous protocols is established using suitable Lyapunov functions. Simulation examples confirm the theoretical findings.A new type of asymptotic stability for nonlinear hybrid neutral stochastic systems with constant delays was investigated recently, where the criteria depended on the delays’ sizes. Unfortunately, developed theory so far is not sufficient to deal with challenging problems of the decay rate, time-varying delays, and nonautonomous issues. These problems have not been tackled in the existing literature. Consequently, under the weak constraints, this article focuses on the general decay, including the exponential stability and the polynomial stability, for nonlinear nonautonomous hybrid neutral stochastic systems with time-varying delays by the approach of the multiple degenerate functionals. Moreover, this article derives the interesting assertions related to the general H∞ stability and the polynomial growth at most.This article explores the exponential stabilization issue of a class of state-based switched inertial complex-valued neural networks with multiple delays via event-triggered control. First, the state-based switched inertial complex-valued neural networks with multiple delays are modeled. Second, by separating the real and imaginary parts of complex values, the state-based switched inertial complex-valued neural networks are transformed into two state-based switched inertial real-valued neural networks. Through the variable substitution method, the model of the second-order inertial neural networks is transformed into a model of the first-order neural networks. Third, an event-triggered controller with the transmission sequence is designed to study the exponential stabilization issue of neural networks constructed above. Then, by constructing the Lyapunov functions and based on some inequalities, we obtain sufficient conditions for exponential stabilization of the proposed neural networks. Furthermore, it is proved that the Zeno phenomenon cannot happen under the designed event-triggered controller. Finally, a simulation example is given to illustrate the correctness of the results.In the wake of Big Data, traditional Machine Learn-ing techniques are now often integrated in the clinical workflow. Despite more capable, Deep Learning methods are not equally accepted given their unsatiated need for great amounts of training data and transversal use of the same architectures in fundamentally different areas with weakly-substantiated adaptations. To address the former, a cardiorespiratory signal synthesizer was designed by conditional sampling from a multimodally trained stochastic system of Gaussian copulas integrated in a Markov chain. With respect to the latter, a multi-branch convolutional neural network architecture was conceived to learn the best cardiac sensor-fusion strategy at every abstraction layer. The network was tailored to the tasks of cycle detection and classification for different cardiac modality combinations by a synthesizer-based data augmentation training framework and Bayesian hyperparameter optimization. The synthesizer yielded highly realistic signals in the time, frequency and phase domains for both healthy and pathological heart cycles as well as artifacts of different modalities. Benchmarking suggested that the network is able to surpass previous architectures and data augmentation provided a performance boost in realistic data availability scenarios. These included insufficient training data volume, as low as 150 cycles long, artifact contamination and absence of a classification data type in training.Cardiovascular disease is the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as measurements can be taken by patients at home. While the oscillometric technique is most common, some automated NIBP measurement methods have been developed based on the auscultatory technique. By utilizing (relatively) large BP data annotated by experts, models can be trained using machine learning and statistical concepts to develop novel NIBP estimation algorithms. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification and feature extraction problems. This paper reviews AI-based BP estimation methods with a focus on recent advances in deep learning-based approaches within the field. Various architectures and methodologies proposed todate are discussed to clarify their strengths and weaknesses. Based on the literature reviewed, deep learning brings plausible benefits to the field of BP estimation. We also discuss some limitations which can hinder the widespread adoption of deep learning in the field and suggest frameworks to overcome these challenges.It remains challenging how to find existing but undiscovered genome sequence mutations or predict potential genome sequence mutations based on real sequence data. Motivated by this, we develop approaches to detect new, undiscovered genome sequences. Because discovering new genome sequences through biological experiments is resource-intensive, we want to achieve the new genome sequence detection task mathematically. However, little literature tells us how to detect new, undiscovered genome sequence mutations mathematically. We form a new framework based on natural vector convex hull method that conducts alignment-free sequence analysis. Our newly developed two approaches, Random-permutation Algorithm with Penalty (RAP) and Random-permutation Algorithm with Penalty and COstrained Search (RAPCOS), use the geometry properties captured by natural vectors. In our experiment, we discover a mathematically new human immunodeficiency virus (HIV) genome sequence using some real HIV genome sequences. Significantly, the proposed methods are applicable to solve the new genome sequence detection challenge and have many good properties, such as robustness, rapid convergence, and fast computation.SEDA (SEquence DAtaset builder) is a multiplatform desktop application for the manipulation of FASTA files containing DNA or protein sequences. The convenient graphical user interface gives access to a collection of simple (filtering, sorting, or file reformatting, among others) and advanced (BLAST searching, protein domain annotation, gene annotation, and sequence alignment) utilities not present in similar applications, which eases the work of life science researchers working with DNA and/or protein sequences, especially those who have no programming skills. This paper presents general guidelines on how to build efficient data handling protocols using SEDA, as well as practical examples on how to prepare high-quality datasets for single gene phylogenetic studies, the characterization of protein families, or phylogenomic studies. The user-friendliness of SEDA also relies on two important features (i) the availability of easy-to-install distributable versions and installers of SEDA, including a Docker image for Linux, and (ii) the facility with which users can manage large datasets. SEDA is open-source, with GNU General Public License v3.0 license, and publicly available at GitHub (https//github.com/sing-group/seda). SEDA installers and documentation are available at https//www.sing-group.org/seda/.Since the brain lesion detection and classification is a vital diagnosis task, in this paper, the problem of brain magnetic resonance imaging (MRI) classification is investigated. Recent advantages in machine learning and deep learning allows the researchers to develop the robust computer-aided diagnosis (CAD) tools for classification of brain lesions. Feature extraction is an essential step in any machine learning scheme. Time-frequency analysis methods provide localized information that makes them more attractive for image classification applications. Owing to the advantages of two-dimensional discrete orthonormal Stockwell transform (2D DOST), we propose to use it to extract the efficient features from brain MRIs and obtain the feature matrix. Since there are some irrelevant features, two-directional two-dimensional principal component analysis ((2D)2PCA) is used to reduce the dimension of the feature matrix. Finally, convolution neural networks (CNNs) are designed and trained for MRI classification. Simulation results indicate that the proposed CAD tool outperforms the recently introduced ones and can efficiently diagnose the MRI scans.This paper is the first in a two-part series analyzing human arm and hand motion during a wide range of unstructured tasks. The wide variety of motions performed by the human arm during daily tasks makes it desirable to find representative subsets to reduce the dimensionality of these movements for a variety of applications, including the design and control of robotic and prosthetic devices. This paper presents a novel method and the results of an extensive human subjects study to obtain representative arm joint angle trajectories that span naturalistic motions during Activities of Daily Living (ADLs). In particular, we seek to identify sets of useful motion trajectories of the upper limb that are functions of a single variable, allowing, for instance, an entire prosthetic or robotic arm to be controlled with a single input from a user, along with a means to select between motions for different tasks. Data driven approaches are used to discover clusters and representative motion averages for the wrist 3 degree of freedom (DOF), elbow-wrist 4 DOF, and full-arm 7 DOF motions. The proposed method makes use of well-known techniques such as dynamic time warping (DTW) to obtain a divergence measure between motion segments, Ward’s distance criterion to build hierarchical trees, and functional principal component analysis (fPCA) to evaluate cluster variability. The emerging clusters associate various recorded motions into primarily hand start and end location for the full-arm system, motion direction for the wrist-only system, and an intermediate between the two qualities for the elbow-wrist system.Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine learning algorithms offer a solution by enabling the training of different models to represent the gait patterns of different subjects. Here our aim is twofold to remove the need for training stages using unsupervised learning, and to modify the parameters according to the changes within a walking trial using adaptive procedures. We developed two adaptive unsupervised algorithms for real-time detection of four gait events, using only signals from two single-IMU foot-mounted wearable devices. We evaluated the algorithms using data collected from five healthy adults and seven subjects with Parkinson’s disease (PD) walking overground and on a treadmill. Both algorithms obtained high performance in terms of accuracy ( F1 -score ≥ 0.95 for both groups), and timing agreement using a force-sensitive resistors as reference (mean absolute differences of 66 ± 53 msec for the healthy group, and 58 ± 63 msec for the PD group). The proposed algorithms demonstrated the potential to learn optimal parameters for a particular participant and for detecting gait events without additional sensors, external labeling, or long training stages.A better understanding of neural pain processing and of the development of pain over time, is critical to identify objective measures of pain and to evaluate the effect of pain alleviation therapies. One issue is, that the brain areas known to be related to pain processing are not exclusively responding to painful stimuli, and the neuronal activity is also influenced by other brain areas. Functional connectivity reflects synchrony or covariation of activation between groups of neurons. Previous studies found changes in connectivity days or weeks after pain induction. However, less in known on the temporal development of pain. Our objective was therefore to investigate the interaction between the anterior cingulate cortex (ACC) and primary somatosensory cortex (SI) in the hyperacute (minute) and sustained (hours) response in an animal model of neuropathic pain. Intra-cortical local field potentials (LFP) were recorded in 18 rats. In 10 rats the spared nerve injury model was used as an intervention. The intra-cortical activity was recorded before, immediately after, and three hours after the intervention. The interaction was quantified as the calculated correlation and coherence. The results from the intervention group showed a decrease in correlation between ACC and SI activity, which was most pronounced in the hyperacute phase but a longer time frame may be required for plastic changes to occur. This indicated that both SI and ACC are involved in hyperacute pain processing.Many objective tracking methods are based on the framework of correlation filtering (CF) due to its high efficiency. In this paper, we propose a l2 -norm based sparse response regularization term to restrain unexpected crests in response for CF framework. CF trackers learn online to regress the region of interest into a Gaussian response. However, due to the uncertain transformations of tracked object, there are many unexpected crests in the response map. When the response of tracked object is corrupted by other crests, the tracker will lost the object. Therefore, the sparse response is used to increase the robustness to transformations of tracked object. Since the novel term is directly incorporated into the objective function of the CF framework, it can be used to improve the performance of many methods which are based on this framework. Moreover, from the solutions we derive, the new method will not increase the computational complexity. Through the experiments on benchmarks of OTB-100, TempleColor, VOT2016 and VOT2017, the proposed regularization term can improve the tracking performance of various CF trackers, including those based on standard discriminative CF framework and those based on context-aware CF framework. We also embed the sparse response regularization term in the state-of-the-art integrated tracker MCCT to test its generalization performance. Although MCCT is an expert integrated tracker and owns an exquisite algorithm for selecting experts, the experimental results show that our method can still improve its long-term tracking performance without increasing computational complexity.In this paper, we develop new techniques for monitoring image processes under a fairly general setting with spatially correlated pixels in the image. Monitoring and handling the pixels directly is infeasible due to an extremely high image resolution. To overcome this problem, we suggest control charts that are based on regions of interest. The regions of interest cover the original image which leads to a dimension reduction. Nevertheless, the data are still high-dimensional. We consider residual charts based on the generalized likelihood ratio approach. Existing control statistics typically depend on the inverse of the covariance matrix of the process, involving high computing times and frequently generating instable results in a high-dimensional setting. As a solution of this issue, we suggest two further control charts that can be regarded as modifications of the generalized likelihood ratio statistic. Within an extensive simulation study, we compare the newly proposed control charts using the median run length as a performance criterion.3D object recognition is one of the most important tasks in 3D data processing, and has been extensively studied recently. Researchers have proposed various 3D recognition methods based on deep learning, among which a class of view-based approaches is a typical one. However, in the view-based methods, the commonly used view pooling layer to fuse multi-view features causes a loss of visual information. To alleviate this problem, in this paper, we construct a novel layer called Dynamic Routing Layer (DRL) by modifying the dynamic routing algorithm of capsule network, to more effectively fuse the features of each view. Concretely, in DRL, we use rearrangement and affine transformation to convert features, then leverage the modified dynamic routing algorithm to adaptively choose the converted features, instead of ignoring all but the most active feature in view pooling layer. We also illustrate that the view pooling layer is a special case of our DRL. In addition, based on DRL, we further present a Dynamic Routing Convolutional Neural Network (DRCNN) for multi-view 3D object recognition. Our experiments on three 3D benchmark datasets show that our proposed DRCNN outperforms many state-of-the-arts, which demonstrates the efficacy of our method.Classifying multi-temporal scene land-use categories and detecting their semantic scene-level changes for remote sensing imagery covering urban regions could straightly reflect the land-use transitions. Existing methods for scene change detection rarely focus on the temporal correlation of bi-temporal features, and are mainly evaluated on small scale scene change detection datasets. In this work, we proposed a CorrFusion module that fuses the highly correlated components in bi-temporal feature embeddings. We first extract the deep representations of the bi-temporal inputs with deep convolutional networks. Then the extracted features will be projected into a lower-dimensional space to extract the most correlated components and compute the instance-level correlation. The cross-temporal fusion will be performed based on the computed correlation in CorrFusion module. The final scene classification results are obtained with softmax layers. In the objective function, we introduced a new formulation to calculate the temporal correlation more efficiently and stably. The detailed derivation of backpropagation gradients for the proposed module is also given. Besides, we presented a much larger scale scene change detection dataset with more semantic categories and conducted extensive experiments on this dataset. The experimental results demonstrated that our proposed CorrFusion module could remarkably improve the multi-temporal scene classification and scene change detection results.Adaptive stochastic gradient descent, which uses unbiased samples of the gradient with stepsizes chosen from the historical information, has been widely used to train neural networks for computer vision and pattern recognition tasks. This paper revisits the theoretical aspects of two classes of adaptive stochastic gradient descent methods, which contain several existing state-of-the-art schemes. We focus on the presentation of novel findings In the general smooth case, the nonergodic convergence results are given, that is, the expectation of the gradients’ norm rather than the minimum of past iterates is proved to converge; We also studied their performances under Polyak-Łojasiewicz property on the objective function. In this case, the nonergodic convergence rates are given for the expectation of the function values. Our findings show that more substantial restrictions on the steps are needed to guarantee the nonergodic function values’ convergence (rates).Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, detecting ismall-scaled pedestrians and occluded pedestrians remains a challenging problem. In this paper, we propose a pedestrian detection method with a couple-network to simultaneously address these two issues. One of the sub-networks, the gated multi-layer feature extraction sub-network, aims to adaptively generate discriminative features for pedestrian candidates in order to robustly detect pedestrians with large variations on scale. The second sub-network targets on handling the occlusion problem of pedestrian detection by using deformable regional region of interest (RoI)-pooling. We investigate two different gate units for the gated sub-network, namely, the channel-wise gate unit and the spatio-wise gate unit, which can enhance the representation ability of the regional convolutional features among the channel dimensions or across the spatial domain, repetitively. Ablation studies have validated the effectiveness of both the proposed gated multi-layer feature extraction sub-network and the deformable occlusion handling sub-network. With the coupled framework, our proposed pedestrian detector achieves promising results on both two pedestrian datasets, especially on detecting small or occluded pedestrians. On the CityPersons dataset, the proposed detector achieves the lowest missing rates (i.e. 40.78% and 34.60%) on detecting small and occluded pedestrians, surpassing the second best comparison method by 6.0% and 5.87%, respectively.High intensity focused ultrasound (HIFU) is a widely used technique capable of providing non-invasive heating and ablation for a wide range of applications. However, the major challenges lie on the determination of the position and the amount of heat deposition over a target area. In order to assure that the thermal area is confined to tumor locations, an optimization method should be employed. Sequential quadratic programming and steepest gradient method with closed-form solution have been previously used to solve this kind of problem. However, these methods are complex and computationally inefficient. The goal of this paper is to solve and control the solution of inverse problems with Partial Differential Equation (PDE) constrains. Therefore, a distinguishing challenge of this technique is the handling of large numbers of optimization variables in combination with the complexities of discretized PDEs. In our method, the objective function is formulated as the square difference of the actual thermal dose and the desired one. At each iteration of the optimization procedure, we need to develop and solve the variation problem, adjoint problem and the gradient of the objective function. The analytical formula for the gradient is derived and calculated based on the solution of the adjoint problem. Several factors have been taken into consideration to demonstrate the robustness and efficiency of the proposed algorithm. The simulations results for all cases indicate the robustness and the computational efficiency of our proposed method compared to the steepest gradient descent method with the closed-form solution.This survey article summarizes research trends on the topic of anomaly detection in video feeds of a single scene. We discuss the various problem formulations, publicly available datasets and evaluation criteria. We categorize and situate past research into an intuitive taxonomy and provide a comprehensive comparison of the accuracy of many algorithms on standard test sets. Finally, we also provide best practices and suggest some possible directions for future research.The Zebrafish Posterior Lateral Line primordium migrates in a channel between the skin and somites. Its migration depends on the coordinated movement of its mesenchymal-like leading cells and trailing cells, which form epithelial rosettes, or protoneuromasts. We describe a superficial population of flat primordium cells that wrap around deeper epithelialized cells and extend polarized lamellipodia to migrate apposed to the overlying skin. Polarization of lamellipodia extended by both superficial and deeper protoneuromast-forming cells depends on Fgf signaling. Removal of the overlying skin has similar effects on superficial and deep cells lamellipodia are lost, blebs appear instead, and collective migration fails. When skinned embryos are embedded in Matrigel, basal and superficial lamellipodia are recovered; however, only the directionality of basal protrusions is recovered, and migration is not rescued. These observations support a key role played by superficial primordium cells and the skin in directed migration of the Posterior Lateral Line primordium.Fluorescent d-amino acids (FDAAs) are molecular probes that are widely used for labelling the peptidoglycan layer of bacteria. When added to growing cells they are incorporated into the stem peptide by a transpeptidase reaction, allowing the timing and localization of peptidoglycan synthesis to be determined by fluorescence microscopy. Herein we describe the chemical synthesis of an OregonGreen488-labelled FDAA (OGDA). We also demonstrate that OGDA can be efficiently incorporated into the PG of Gram-positive and some Gram-negative bacteria, and imaged by super-resolution stimulated emission depletion (STED) nanoscopy at a resolution well below 100 nm.Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) altered the delivery of outpatient care and expanded the use of telehealth solutions. This research underscores the importance of providing options for accessing health care services and diagnostic testing in a clinically rigorous manner. Providing options for patients will be essential in curtailing the spread of COVID-19, and any concomitant confusion caused by the overlapping symptomology of the flu and other upper respiratory viruses. Methods A survey was sent to patients to collect information related to their experience with testing, guidance, and consults in a telehealth model for SARS-CoV-2. Specifically, patients were asked where they would have sought testing and care had this model not been available, and their satisfaction level with the service itself. Results More than 1,400 patients responded to the survey for a response rate of 15%. Results demonstrate that patients who underwent testing and received guidance/consults through this model would have visited other in-person clinical environments such as emergency rooms or urgent care centers.