Miracle-dz/UniFAR-FTUs huggingface dataset:
IFNAR2 Is Required for Anti-influenza Immunity and Alters Susceptibility to Post-influenza Bacterial Superinfections

Influenza virus infections particularly when followed by bacterial superinfections (BSI) result in significant morbidities and mortalities especially during influenza pandemics. Type I interferons (IFNs) regulate both anti-influenza immunity and host susceptibility to subsequent BSIs. These type I IFNs consisting of, among others, 14 IFN-α's and a single IFN-β, are recognized by and signal through the heterodimeric type I IFN receptor (IFNAR) comprised of IFNAR1 and IFNAR2. However, the individual receptor subunits can bind IFN-β or IFN-α's independently of each other and induce distinct signaling. The role of type I IFN signaling in regulating host susceptibility to both viral infections and BSI has been only examined with respect to IFNAR1 deficiency. Here, we demonstrate that despite some redundancies, IFNAR1 and IFNAR2 have distinct roles in regulating both anti-influenza A virus (IAV) immunity and in shaping host susceptibility to subsequent BSI caused by S. aureus. We found IFNAR2 to be critical for anti-viral immunity. In contrast to Ifnar1−/− mice, IAV-infected Ifnar2−/− mice displayed both increased and accelerated morbidity and mortality compared to WT mice. Furthermore, unlike IFNAR1, IFNAR2 was sufficient to generate protection from lethal IAV infection when stimulated with IFN-β. With regards to BSI, unlike what we found previously in Ifnar1−/− mice, Ifnar2−/− mice were not susceptible to BSI induced on day 3 post-IAV, even though absence of IFNAR2 resulted in increased viral burden and an increased inflammatory environment. The Ifnar2−/− mice similar to what we previously found in Ifnar1−/− mice were less susceptible than WT mice to BSI induced on day 7 post-IAV, indicating that signaling through a complete receptor increases BSI susceptibility late during clinical IAV infection. Thus, our results support a role for IFNAR2 in induction of anti-IAV immune responses that are involved in altering host susceptibility to BSI and are essential for decreasing the morbidity and mortality associated with IAV infection. These results begin to elucidate some of the mechanisms involved in how the individual IFNAR subunits shape the anti-viral immune response. Moreover, our results highlight the importance of examining the contributions of entire receptors, as individual subunits can induce distinct outcomes as shown here.
An Efficient Multi-Source DOA Estimation Algorithm for Underdetermined System

We propose a two-stage algorithm for the estimation of direction-of-arrival (DOA) of speech signals in a multi-source environment under the assumption that the source signals are sparse in the time-frequency (TF) domain. The signals are first separated, in each frequency bin, from their mixtures via time-frequency masking. The generalized cross-correlation method is subsequently used in the second stage to simultaneously estimate the source directions. The proposed algorithm does not require one to solve the permutation problem and is suitable even for under-determined cases where the number of source signals is greater than the number of sensors.
Comparative Harmonic Analysis of Diode Clamped Multi-Level Inverter

Power quality has been main issue among the consumers nowadays. The consumer devices are based on power electronics devices for better efficiency which tend to introduce various current harmonics in our power system at the point of common coupling. With the accession of renewable energy sources as the better and environment friendly alternative to meet the increasing energy demand, the inverters find inevitable application in the integration of renewable energy plants with the grid, the quality of power has emerged as a future complication as these also tend to generate current harmonics in the line. It has been always difficult to maintain the quality of electric power so as to keep it within the acceptable limits. Diode clamped multilevel inverter is a matured topology used in several industries and also being used in renewable energy applications due to its several advantages over traditional two-level inverter. This paper is focused on the study of five-level and fifteen-level cascaded Hbridge inverter and total harmonic distortion in output voltage waveforms is compared. In lieu of this a diode clamped multilevel inverter is simulated in MATLAB/Simulink environment which reduces the current THD of the distorted signal below the limit defined as per the IEEE 519 Std.
3-D Image-Based Dosimetry in Radionuclide Therapy

Radionuclide therapy is the use of radioactive drugs for internal radiotherapy, mainly for the treatment of metastatic disease. As opposed to systemic cancer therapies in general, the use of radioactively labeled drugs results not only in a targeted therapy but also the possibility of imaging the distribution of the drug during therapy. From such images, the absorbed doses delivered to tumors and organs at risk can be calculated. Calculation of the absorbed dose from 3-D images such as single-photon emission computed tomography (SPECT)/CT, and in some cases positron emission tomography (PET)/CT, relies on image-based activity quantification. Quantification is accomplished by modeling the physics involved in the image-formation process, and applying image-processing methods. From a time-sequence of such quantitative images, the absorbed doses are then calculated. Although individual-patient dosimetry is a standard component of other forms of radiotherapy, it is still overlooked in the majority of radionuclide therapies. In this review, we summarize the physical and technical problems that need to be addressed in image-based dosimetry. The focus is on SPECT, since most of the radionuclides used are single-photon emitters, although the use of PET is also discussed. Practical issues of relevance for the practical implementation of personalized dosimetry in radionuclide therapy are also highlighted.
Learning Dense Stereo Matching for Digital Surface Models from Satellite Imagery

Digital Surface Model generation from satellite imagery is a difficult task that has been largely overlooked by the deep learning community. Stereo reconstruction techniques developed for terrestrial systems including self driving cars do not translate well to satellite imagery where image pairs vary considerably. In this work we present neural network tailored for Digital Surface Model generation, a ground truthing and training scheme which maximizes available hardware, and we present a comparison to existing methods. The resulting models are smooth, preserve boundaries, and enable further processing. This represents one of the first attempts at leveraging deep learning in this domain.
A multi-objective workflow scheduling algorithm for cloud environment

The workflow scheduling problems involve the task-resource mapping satisfying some functional and nonfunctionalquality of service. Workflow applications require high computational power and often involve a large amount of data transfer from one place to another. Furthermore, due to dependencies existed among tasks; schedules must be brought forth according to given precedence constraints. Cloud computing is a new business-oriented platform service that facilitates an infinite number of services by providing heterogeneous, virtualized resources to users based on a pay-as-you-go model with the distinctive quality of service (QoS) Constraints. Due to its market-oriented approach, conventional workflow scheduling strategies are facing new challengeslike on-demand payment,unprecendented openness and autonomy. This paperpresents anadaptive privileged multi-objective workflow scheduling algorithm (APMWSA) which optimally run the workflow execution process for minimization of total cost and makespan. This algorithm uses the concept of novel adaptive elite-based particle swarm optimization (NAEB-PSO) for taskresource mapping.A comparative study of presented algorithm is also made with some existing algorithms.
Temporal effects of cytokine treatment on lubricant synthesis and matrix metalloproteinase activity of fibroblast‐like synoviocytes

Fibroblast‐like synoviocytes (FLS) are major contributors to the composition and function of synovial fluid (SF). In disease, changes to important SF molecules such as hyaluronic acid (HA), lubricin, and numerous inflammatory markers contribute to a loss of SF functional properties. Previous studies characterized the ability of FLS to produce SF molecules in short‐term cultures using continuous cytokine supplementation. This study assessed the HA, lubricin, and matrix metalloproteinase‐2 (MMP‐2) secretion profile of FLS over 12 days of culture. FLS were subjected to continuous, intermittent, and sequential cytokine treatments of interleukin‐1 beta (IL‐1β), tumour necrosis factor‐alpha (TNF‐α), and transforming growth factor‐beta 1 (TGF‐β1). HA was assessed by an enzyme‐linked immunosorbent assay (ELISA) for content and agarose gel electrophoresis for molecular weight distribution. Relative lubricin content was determined by western blot. Pro MMP‐2 and active MMP‐2 were quantified by gelatin zymography. All intermittent and sequential treatments significantly increased secretion of high‐molecular‐weight (>3 MDa) HA for the duration of the culture. Sequentially treated groups elevated lubricin synthesis, whereas only groups receiving IL‐1β and TNF‐α for 2 days followed by TGF‐β1 for 1 day reduced active MMP‐2 to unstimulated control levels. These data provide important information on the long‐term functional potential of cytokine‐stimulated FLS and suggest that temporal regulation of cytokine exposure can be a powerful tool to guide healthy synovial secretions.
Privacy Preserving k-Nearest Neighbor for Medical Diagnosis in e-Health Cloud

Cloud computing is highly suitable for medical diagnosis in e-health services where strong computing ability is required. However, in spite of the huge benefits of adopting the cloud computing, the medical diagnosis field is not yet ready to adopt the cloud computing because it contains sensitive data and hence using the cloud computing might cause a great concern in privacy infringement. For instance, a compromised e-health cloud server might expose the medical dataset outsourced from multiple medical data owners or infringe on the privacy of a patient inquirer by leaking his/her symptom or diagnosis result. In this paper, we propose a medical diagnosis system using e-health cloud servers in a privacy preserving manner when medical datasets are owned by multiple data owners. The proposed system is the first one that achieves the privacy of medical dataset, symptoms, and diagnosis results and hides the data access pattern even from e-health cloud servers performing computations using the data while it is still robust against collusion of the entities. As a building block of the proposed diagnosis system, we design a novel privacy preserving protocol for finding the k data with the highest similarity (PE-FTK) to a given symptom. The protocol reduces the average running time by 35% compared to that of a previous work in the literature. Moreover, the result of the previous work is probabilistic, i.e., the result can contain some error, while the result of our PE-FTK is deterministic, i.e., the result is correct without any error probability.
Molecular Mechanism of ATP Hydrolysis in an ABC Transporter

Hydrolysis of nucleoside triphosphate (NTP) plays a key role for the function of many biomolecular systems. However, the chemistry of the catalytic reaction in terms of an atomic-level understanding of the structural, dynamic, and free energy changes associated with it often remains unknown. Here, we report the molecular mechanism of adenosine triphosphate (ATP) hydrolysis in the ATP-binding cassette (ABC) transporter BtuCD-F. Free energy profiles obtained from hybrid quantum mechanical/molecular mechanical (QM/MM) molecular dynamics (MD) simulations show that the hydrolysis reaction proceeds in a stepwise manner. First, nucleophilic attack of an activated lytic water molecule at the ATP γ-phosphate yields ADP + HPO42– as intermediate product. A conserved glutamate that is located very close to the γ-phosphate transiently accepts a proton and thus acts as catalytic base. In the second step, the proton is transferred back from the catalytic base to the γ-phosphate, yielding ADP + H2PO4–. These two chemical reaction steps are followed by rearrangements of the hydrogen bond network and the coordination of the Mg2+ ion. The rate constant estimated from the computed free energy barriers is in very good agreement with experiments. The overall free energy change of the reaction is close to zero, suggesting that phosphate bond cleavage itself does not provide a power stroke for conformational changes. Instead, ATP binding is essential for tight dimerization of the nucleotide-binding domains and the transition of the transmembrane domains from inward- to outward-facing, whereas ATP hydrolysis resets the conformational cycle. The mechanism is likely relevant for all ABC transporters and might have implications also for other NTPases, as many residues involved in nucleotide binding and hydrolysis are strictly conserved.
Effect of Out-of-Band Blockers on the Required Linearity, Phase Noise, and Harmonic Rejection of SDR Receivers Without Input SAW Filter

The required linearity of wideband receivers for applications, such as software-defined radio in the absence of input surface-acoustic wave (SAW) filters, is studied. It is shown that the required linearity of wideband SAW-less receivers is surprisingly higher than what has been commonly assumed. For these receivers, although the out-of-band blockers can reduce the compression point of the receiver, they can also potentially generate interferers that appear in the desired frequency band, resulting in higher noise floor and signal corruption. The required out-of-band IIP2 and IIP3 of low-noise amplifier (LNA) and mixer for single and concurrent applications are analytically derived. The required wideband SAW-less receiver phase noise is also studied, and it is shown that blockers located at the frequencies of the local oscillator harmonics can significantly increase the noise floor of the receiver. The effects of input and intermediate filters on the above applications along with the conditions under which they help to alleviate the limitations of LNA and mixer are defined. The validity of the models and subsequent derivations is verified by actual measurements using a wideband SAW-less receiver.
Spectral-Spatial Graph Convolutional Networks for Semel-Supervised Hyperspectral Image Classification

Collecting label samples is quite costly and time consuming for hyperspectral image (HSI) classification tasks. Semi-supervised learning framework, which combines the intrinsic information of labeled and unlabeled samples can alleviate the deficient labeled samples and increase the accuracy of HSI classification. In this paper, we propose a novel framework for semi-supervised learning on multiple spectral-spatial graphs that is based on graph convolutional networks (SGCN). In the filtering operation on graphs we consider the spatial information and spectral signatures of HSI simultaneously. The experimental results on three real-life HSI data sets, i.e. Botswana Hyperion, Kennedy Space Center, and Indian Pines, show that the proposed SGCN can significantly improve the classification accuracy. For instance, the over accuracy on Indian Pine data is increased from 78 % to 93 %.
PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools

Abstract PANTHER (Protein Analysis Through Evolutionary Relationships, http://pantherdb.org) is a resource for the evolutionary and functional classification of genes from organisms across the tree of life. We report the improvements we have made to the resource during the past two years. For evolutionary classifications, we have added more prokaryotic and plant genomes to the phylogenetic gene trees, expanding the representation of gene evolution in these lineages. We have refined many protein family boundaries, and have aligned PANTHER with the MEROPS resource for protease and protease inhibitor families. For functional classifications, we have developed an entirely new PANTHER GO-slim, containing over four times as many Gene Ontology terms as our previous GO-slim, as well as curated associations of genes to these terms. Lastly, we have made substantial improvements to the enrichment analysis tools available on the PANTHER website: users can now analyze over 900 different genomes, using updated statistical tests with false discovery rate corrections for multiple testing. The overrepresentation test is also available as a web service, for easy addition to third-party sites.
An Introduction to 3D Bioprinting: Possibilities, Challenges and Future Aspects

Bioprinting is an emerging field in regenerative medicine. Producing cell-laden, three-dimensional structures to mimic bodily tissues has an important role not only in tissue engineering, but also in drug delivery and cancer studies. Bioprinting can provide patient-specific spatial geometry, controlled microstructures and the positioning of different cell types for the fabrication of tissue engineering scaffolds. In this brief review, the different fabrication techniques: laser-based, extrusion-based and inkjet-based bioprinting, are defined, elaborated and compared. Advantages and challenges of each technique are addressed as well as the current research status of each technique towards various tissue types. Nozzle-based techniques, like inkjet and extrusion printing, and laser-based techniques, like stereolithography and laser-assisted bioprinting, are all capable of producing successful bioprinted scaffolds. These four techniques were found to have diverse effects on cell viability, resolution and print fidelity. Additionally, the choice of materials and their concentrations were also found to impact the printing characteristics. Each technique has demonstrated individual advantages and disadvantages with more recent research conduct involving multiple techniques to combine the advantages of each technique.
Social Connectedness Moderates the Relationship Between Warfare Exposure, PTSD Symptoms, and Health Among Older Adults

Objective: The goal of the current study is to examine whether social connectedness among older adults mitigates the risk of poor health due to exposure to warfare and related symptoms of posttraumatic stress disorder (PTSD). Prior research has indicated a protective role for social connectedness in the face of trauma in general. However, this same association has not been examined among older adults exposed to warfare, even though they are potentially a more vulnerable population with unique social needs. Method: The study used cross-sectional data from the Israeli component of the Survey of Health, Ageing, and Retirement in Europe (SHARE-Israel; N = 1,557, mean age = 69.03, age range = 50 to 105). It employed a composite scale of social connectedness, data on exposure to war-related events during the 2014 Israel–Gaza conflict, and ensuing symptoms of PTSD. Social connectedness, warfare exposure, and PTSD symptoms were used to predict physical and mental health, as well as engagement in health behaviors. Results: Warfare exposure and PTSD symptoms were related to worse health. Moreover, social connectedness moderated this association, such that persons suffering from PTSD symptoms suffered less from worse health when they were highly connected. This trend was found for physical and mental health and in relation to health behaviors. Conclusions: While war-related PTSD is indeed related to poorer health, social connectedness moderates the strength of the association. Practitioners should be aware of the protective role of social connectedness in the context of warfare exposure in old age, and intervention programs with this population should strive toward bolstering social connections.
Attachment style and changes in systemic inflammation following migration to a new country among international students

ABSTRACT Attachment style may influence the acculturation process by shaping the types of social relationships that individuals form and maintain. Social relationship quantity and quality have been linked to chronic low-grade systemic inflammation, an immune process associated with risk for age-related diseases. The goal of this study was to investigate whether attachment style moderates the change in systemic inflammation during the early phases of the acculturation process, a period associated with rapid changes in social functioning. In this 5-month longitudinal study, 58 new international students completed the Adult Attachment Scale and provided blood samples for C-reactive protein (CRP) analysis shortly after arrival in the host country as well as 2 and 5 months later. Results indicated that individuals with higher attachment anxiety experienced larger increases in CRP over time. During a period of rapid changes in social functioning, attachment anxiety promotes immune changes that may increase health risk over time.
Cystic Fibrosis Gene Therapy: Looking Back, Looking Forward

Cystic fibrosis (CF) is an autosomal recessive disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene that encodes a cAMP-regulated anion channel. Although CF is a multi-organ system disease, most people with CF die of progressive lung disease that begins early in childhood and is characterized by chronic bacterial infection and inflammation. Nearly 90% of people with CF have at least one copy of the ΔF508 mutation, but there are hundreds of CFTR mutations that result in a range of disease severities. A CFTR gene replacement approach would be efficacious regardless of the disease-causing mutation. After the discovery of the CFTR gene in 1989, the in vitro proof-of-concept for gene therapy for CF was quickly established in 1990. In 1993, the first of many gene therapy clinical trials attempted to rescue the CF defect in airway epithelia. Despite the initial enthusiasm, there is still no FDA-approved gene therapy for CF. Here we discuss the history of CF gene therapy, from the discovery of the CFTR gene to current state-of-the-art gene delivery vector designs. While implementation of CF gene therapy has proven more challenging than initially envisioned; thanks to continued innovation, it may yet become a reality.
A Video Based Fire Smoke Detection Using Robust AdaBoost

This work considers using camera sensors to detect fire smoke. Static features including texture, wavelet, color, edge orientation histogram, irregularity, and dynamic features including motion direction, change of motion direction and motion speed, are extracted from fire smoke to train and test with different combinations. A robust AdaBoost (RAB) classifier is proposed to improve training and classification accuracy. Extensive experiments on well known challenging datasets and application for fire smoke detection demonstrate that the proposed fire smoke detector leads to a satisfactory performance.