Miracle-dz/UniFAR-FTUs huggingface dataset:
Multifactorial processes underlie parallel opsin loss in neotropical bats

The loss of previously adaptive traits is typically linked to relaxation in selection, yet the molecular steps leading to such repeated losses are rarely known. Molecular studies of loss have tended to focus on gene sequences alone, but overlooking other aspects of protein expression might underestimate phenotypic diversity. Insights based almost solely on opsin gene evolution, for instance, have made mammalian color vision a textbook example of phenotypic loss. We address this gap by investigating retention and loss of opsin genes, transcripts, and proteins across ecologically diverse noctilionoid bats. We find multiple, independent losses of short-wave-sensitive opsins. Mismatches between putatively functional DNA sequences, mRNA transcripts, and proteins implicate transcriptional and post-transcriptional processes in the ongoing loss of S-opsins in some noctilionoid bats. Our results provide a snapshot of evolution in progress during phenotypic trait loss, and suggest vertebrate visual phenotypes cannot always be predicted from genotypes alone.
Shadow Block: Accelerating ORAM Accesses with Data Duplication

Oblivious RAM (ORAM) is a cryptographic primitive designed to hide memory access patterns. To achieve this objective, the intended data block is loaded and evicted back together with other data blocks and dummy blocks in each ORAM access. To further protect the timing pattern, extra dummy ORAM accesses are triggered periodically. Such designs lead to huge memory access overheads. Many techniques have been proposed to mitigate this problem by reducing the total number of ORAM accesses and the number of blocks per access. However, the impact of the access order of intended data block in an ORAM access is not addressed yet. In this work, we argue that higher performance can be achieved by advancing the access to the intended data block in ORAM accesses. However, changing the access order of blocks directly compromises the ORAM security. To solve this problem, we propose a duplication method to advance the access to the intended data blocks without compromising the ORAM security. The method leverages dummy blocks to store extra copies of data blocks, to facilitate early access of intended data blocks. These dummy blocks with valid data duplications are called Shadow blocks in this work. We further introduce two data duplication techniques, called RD-Dup and HD-Dup, to reorder the data block access for different purposes. In addition, we propose ORAM space partitioning to make RD-Dup and HD-Dup cooperate with each other efficiently. Compared with state-of-the-art ORAMs, our design can achieve a 32% reduction in system execution time on average, with negligible hardware overheads.
Clustering of Usage Profiles for Electric Vehicle Behaviour Analysis

Accurately predicting the behaviour of electric vehicles is going to be imperative for network operators. In order for vehicles to participate in either smart charging schemes or providing grid services, their availability and charge requirements must be forecasted. Their relative novelty means that data concerning electric vehicles is scarce and biased, however we have been collecting data on conventional vehicles for many years. This paper uses cluster analysis of travel survey data from the UK to identify typical conventional vehicle usage profiles. To this end, we determine the feature vector, introduce an appropriate distance metric, and choose a number of clusters. Five clusters are identified, and their suitability for electrification is discussed. A smaller data set of electric vehicles is then used to compare the current electric fleet behaviour with the conventional one.
Bioarchaeological evidence for adaptive plasticity and constraint: Exploring life‐history trade‐offs in the human past

The Developmental Origins of Health and Disease paradigm evaluates the consequences of early life stress on health at later stages of life. Interacting with this paradigm represents a profound opportunity to leverage the lifespan and contextual approaches to human skeletal remains adopted by bioarchaeological research. Teeth and bone provide evidence for stressors experienced early in life. These events represent evidence for adaptive plasticity as Individuals survive the events through reallocation of energy to essential physiological functions, which inhibits enamel and skeletal growth. Age‐at‐death, adult body size, chronic infection, or childhood mortality may be used as covariates to better understand the physiological constraints operating on individual bodies following survival of early life stress. Contextual evidence from cemeteries provides clues to the ecological and cultural contingencies that exacerbate or mitigate the expression of these trade‐offs. Future studies should incorporate newly derived methods that provide reproducible and precise ways to evaluate early life stress, while incorporating populations that are often neglected.
Power Quality Compensation In Microgrid Using MFGTI

Microgrid otherwise distributed electrical power plant, whichconsists of different energy inputs, integrated using renewable energy source (RES)based distribution generators (DG), which is on power electronics basedprototypes, so power quality issues in distribution sector are seriously increasing. If it is controlled properly, DG inverters used for mitigating power quality problems like harmonics, compensation for reactive power and unbalancing of the power distribution system. This type of inverter is called as multifunctional grid tied inverter (MFGTI). With thelatest developments in this micro grid technology, power quality can be enhancement using flexiblecontrol of MFGTI and also it can eliminates the usage of additional compensating devicesand results in a cost effective system. The Simulation performances done using MATLAB.
Physical Activity Increases White Matter Microstructure in Children

Children are becoming increasingly inactive, unfit, and overweight, yet there is relatively little causal evidence regarding the effects of physical activity on brain health during childhood. The present study examined the effects of an after-school physical activity program (FITKids2) on the microstructure of white matter tracts in 7- to 9-year-old children. We measured the microstructural properties of white matter via diffusion tensor imaging in 143 children before and after random assignment to either a 9-month after-school physical activity program (N = 76, mean age = 8.7 years) or a wait list control group (N = 67, mean age = 8.7 years). Our results demonstrate that children who participated in the physical activity program showed increased white matter microstructure in the genu of the corpus callosum, with no changes in white matter microstructure in the wait list control group which reflects typical development. Specifically, children in the physical activity program showed increases in fractional anisotropy (FA) and decreases in radial diffusivity (RD) in the genu from pre- to post-test, thereby suggesting more tightly bundled and structurally compact fibers (FA) and increased myelination (RD), with no changes in estimates of axonal fiber diameter (axial diffusivity, AD). The corpus callosum integrates cognitive, motor, and sensory information between the left and right hemispheres of the brain, and the white matter tract plays a role in cognition and behavior. Our findings reinforce the importance of physical activity for brain health during child development.
Dash-Based Device-to-Device Video Streaming for Cellular Networks with High User Density

Supporting video streaming services and providing high Quality of Experience (QoE) to end users have become main concerns for cellular network operators. In this work, we present an architecture for improving the QoE of video streaming in cellular networks with high user density. The architecture employs progressive caching of video contents, Dynamic Adaptive Streaming over HTTP (DASH), and Device-to-Device (D2D) communication. The Base-Station (BS) controls the progressive caching process of video contents and the Peer-to-Peer (P2P) transmission of video segments among User Equipments (UEs). We present two different implementations of the architecture. The implementations differ in the employed approach for video contents caching and distribution over the UEs. We use the Discrete EVent System Specification (DEVS) formalism to build a model for the proposed architecture in an LTE-A network and use the model to study the performance achieved by the proposed architecture in terms of many video streaming QoE metrics.
Low Admission Plasma Gelsolin Concentrations Identify Community-acquired Pneumonia Patients at High Risk for Severe Outcomes.

Background Plasma gelsolin (pGSN) is an abundant circulating protein that neutralizes actin exposed by damaged cells, modulates inflammatory responses, and enhances alveolar macrophage antimicrobial activity. We investigated whether adults with low pGSN at hospital admission for community-acquired pneumonia (CAP) were at high risk for severe outcomes. Methods Admission pGSN concentrations were measured by enzyme-linked immunosorbent assay in 455 adults hospitalized with CAP. Patients were grouped into four hierarchical, mutually-exclusive categories based on maximum clinical severity experienced during their hospitalization: (1) general floor care without intensive care (ICU) unit admission, invasive respiratory or vasopressor support (IRVS), or death; (2) ICU care without IRVS or death; (3) IRVS without death; or (4) death. Admission pGSN concentrations were compared across these discrete outcome categories. Additionally, outcomes among patients in the lowest quartile of pGSN concentration were compared with those in the upper three quartiles. Results Overall, median (interquartile range) pGSN concentration was 38.1 (32.1, 45.7) μg/mL. Patients with more severe outcomes had lower pGSN concentrations (p=0.0001); median values were: 40.3 μg/ml for floor patients; 36.7 μg/ml for ICU patients; 36.5 μg/ml for patients receiving IRVS; and 25.7 μg/ml for patients who died. Compared with patients with higher pGSN concentrations, patients in the lowest quartile (pGSN ≤32.1 μg/ml) more often required IRVS (21.2% vs 11.7%, p=0.0114) and died (8.8% vs 0.9%, p<0.0001). Conclusions Among adults hospitalized with CAP, lower pGSN concentrations were associated with more severe clinical outcomes. Future studies are planned to investigate possible therapeutic benefits of recombinant human pGSN in this population.
The Cambridge introduction to theatre studies

Preface Introduction: theatre and theatre studies Part I. Elements of Theatre: 1. Performers and actors 2. Spectators and audiences 3. Spaces and places Part II. Subjects and Methods: 4. Theories of theatre 1: Historical paradigms 5 Theories of theatre 2: Systematic and critical approaches 6. Theatre historiography 7. Text and performance 8. Performance analysis 9. Music theatre 10. Dance theatre Part III. Theatre Studies between Disciplines: 11. Applied theatre 12. Theatre and media.
People to People Recommendation using Coupled Nonnegative Boolean Matrix Factorization

In the era of Web 3.0, people to people recommendation is important to identify and suggest the potential person who one might be interested to connect with. In most of the cases, the interaction between people generated is very sparse as people usually connect within a fewer circle. Matrix factorization has been successfully employed in generating recommendations of items to users under sparse condition and usually user to user friendship is used as an additional trust information in generating more accurate item recommendations. In this paper we develop a coupled matrix factorization model to accurately generate people to people recommendation by utilizing users' interaction behaviour with items. Our empirical results shows that the proposed model achieves higher quality than the state of the art methods.
Inducible down-regulation of MHC class I results in natural killer cell tolerance

&NA; Natural killer (NK) cells are innate lymphocytes that are thought to kill cells that down‐regulate MHC class I (MHC‐I) through “missing‐self” recognition. NK cells from B2m−/− mice that lack surface MHC‐I, however, are not autoreactive as predicted by the missing‐self hypothesis. As a result, it is unclear if MHC‐I down‐regulation in vivo induces NK cell reactivity or tolerance to missing‐self. Here, we generated a floxed B2m mouse to acutely down‐regulate MHC‐I in vivo in a host that normally expresses MHC‐I. Global down‐regulation of MHC‐I induced NK cell hyporesponsiveness and tolerance to missing‐self without overt missing‐self reactivity. In contrast, down‐regulation of MHC‐I on a small fraction of hematopoietic cells triggered missing‐self reactivity. Surprisingly, down‐regulation of MHC‐I only on CD4+ T cells predominately induced tolerance to missing‐self without resetting NK cell responsiveness. In this setting, inflammation triggered substantial missing‐self reactivity. These results show that MHC‐I down‐regulation can induce either NK cell tolerance or killing in vivo and that inflammation promotes missing‐self reactivity. &NA; Bern et al. demonstrate that acute down‐regulation of self‐MHC‐I in vivo can induce NK cell tolerance to missing‐self, depending on the extent of MHC‐I deficiency. Additionally, inflammation promotes missing‐self reactivity in response to MHC‐I down‐regulation.
A Data Assimilation Enabled Model for Coupling Dual Porosity Flow with Free Flow

Coupling of dual porosity flow and free flow arises in many important applications, e.g., groundwater system and industrial filtrations. Existing Stokes-Darcy types of models cannot accurately describe this type of coupled problem since they only consider single porosity media. With the support of lab experiment data we are developing a new coupled multi-physics, multiscale model and an efficient numerical method to solve it. Furthermore, both the lab and field data provide the possibility to improve the accuracy of the model prediction through data assimilation.
Automated Quantification of PV Hosting Capacity In Distribution Networks Under User-Defined Control and Optimisation Procedures

With the rising popularity of photovoltaic (PV) power, utility companies are experiencing a growing number of interconnection requests on their networks. Combined with power quality issues, the distribution system operator (DSO) must go through strenuous impact studies before proceeding to the installation of PV systems. To address this issue, this work proposes a tool to rapidly and accurately compute the total PV hosting capacity (PVHC) of their network. Moreover, the proposed tool allows the implementation of user-defined control strategies in order to assess their beneficial effects with respect to the PVHC. In this prospect, we investigated the impact of smart PV inverters. We implemented this tool as a plugin for PowerFactory and tested it on the CIGRE benchmark distribution grid. First, we determined the PVHC with inverters injecting at unity power factor, then we estimated the gains brought by smart inverter control (SIC). The latter increased the hosting capacity by up to 19.7 %.
Toll-like Receptor 2 Prevents Neutrophil-Driven Immunopathology during Infection with Mycobacterium tuberculosis by Curtailing CXCL5 Production

The W-Beijing strain family is globally distributed and is associated with multidrug-resistant tuberculosis (TB) and treatment failure. Therefore, in this study, we examined the contribution of Toll-like receptor 2 (TLR2) to host resistance against Mycobacterium tuberculosis HN878, a clinical isolate belonging to the W-Beijing family. ABSTRACT The W-Beijing strain family is globally distributed and is associated with multidrug-resistant tuberculosis (TB) and treatment failure. Therefore, in this study, we examined the contribution of Toll-like receptor 2 (TLR2) to host resistance against Mycobacterium tuberculosis HN878, a clinical isolate belonging to the W-Beijing family. We show that TLR2 knockout (TLR2KO) mice infected with M. tuberculosis HN878 exhibit increased bacterial burden and are unable to control tissue-damaging, pulmonary neutrophilic inflammation. Consistent with a critical role for CXCL5 in regulating neutrophil influx, expression of epithelial cell-derived CXCL5 is significantly enhanced in TLR2KO mice prior to their divergence from wild-type (WT) mice in M. tuberculosis replication and neutrophilic inflammation. Depletion of neutrophils in TLR2KO mice by targeting Ly6G reverts lung inflammation and bacterial burden to levels comparable to those of WT mice. Together, the results establish that TLR2 controls neutrophil-driven immunopathology during infection with M. tuberculosis HN878 infection, likely by curtailing CXCL5 production.
Fuzzy Logic and Deep Steering Control based Recommendation System for Self-Driving Car

Automatic cruise control system such as steering and velocity control are complex tasks that work as fundamental to all automatic systems in self-driving cars. The challenging task is to deal with steering signals at high speeds and to manage the vehicle’s velocity with significant changes in steering signals. In order to reach a destination with optimized time and stable trajectory, an autonomous vehicle system should utilize steering control and velocity signal in constraints. In this article, we propose a recommendation system based on Fuzzy logic and Deep Steering Neural Networks. Convolution neural networks(CNNs) works as a front-end stage for predicting steering control, and fuzzy logic in a back-end stage works as natural inferences for recommending velocity and adapting new steering control. The front-end stage uses CNNs with the input from raw sensory data, and the output extracts steering control prediction that feeds forward to the back-end stage. Key functions of back-end stage combine a number of dynamic vehicle information including, steering prediction to extract more effective steering control, and velocity for the autonomous car. The convolution neural network was trained on the Udacity driving datasheet with a number of hours of training and testing data, and the all system is built on MATLAB with GPU coder.
A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart disease is important for heart failure prevention and treatment. The diagnosis of heart disease through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart disease, noninvasive-based methods such as machine learning are reliable and efficient. In the proposed study, we developed a machine-learning-based diagnosis system for heart disease prediction by using heart disease dataset. We used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and seven classifiers performance evaluation metrics such as classification accuracy, specificity, sensitivity, Matthews’ correlation coefficient, and execution time. The proposed system can easily identify and classify people with heart disease from healthy people. Additionally, receiver optimistic curves and area under the curves for each classifier was computed. We have discussed all of the classifiers, feature selection algorithms, preprocessing methods, validation method, and classifiers performance evaluation metrics used in this paper. The performance of the proposed system has been validated on full features and on a reduced set of features. The features reduction has an impact on classifiers performance in terms of accuracy and execution time of classifiers. The proposed machine-learning-based decision support system will assist the doctors to diagnosis heart patients efficiently.
Compressing Chinese Dark Chess Endgame Databases by Deep Learning

Endgame databases can be difficult to use in tree search when database sizes remain large even after compression. Given the same endgame, we discover that the compression ratios vary significantly when using different encoding schemes. The intuition is that when a set of positions mapped into a continuous chunk of segments have similar values, block-based compression libraries such as zlib can yield a better compression ratio than cases where segments contain diversified values. However, finding the optimal encoding scheme by exhaustive enumeration is time-infeasible for endgame databases with a large number of pieces. We propose a novel approach using deep learning to obtain an encoding scheme so that the compression ratio is suitable for practical purposes. Our approach can be applied to chess-like games.
Coronary Artery Disease Associated Transcription Factor TCF21 Regulates Smooth Muscle Precursor Cells That Contribute to the Fibrous Cap

Recent genome wide association studies have identified a number of genes that contribute to the risk for coronary heart disease. One such gene, TCF21, encodes a basic-helix-loop-helix transcription factor believed to serve a critical role in the development of epicardial progenitor cells that give rise to coronary artery smooth muscle cells (SMC) and cardiac fibroblasts. Using reporter gene and immunolocalization studies with mouse and human tissues we have found that vascular TCF21 expression in the adult is restricted primarily to adventitial cells associated with coronary arteries and also medial SMC in the proximal aorta of mouse. Genome wide RNA-Seq studies in human coronary artery SMC (HCASMC) with siRNA knockdown found a number of putative TCF21 downstream pathways identified by enrichment of terms related to CAD, including “vascular disease,” “disorder of artery,” and “occlusion of artery,” as well as disease-related cellular functions including “cellular movement” and “cellular growth and proliferation. ” In vitro studies in HCASMC demonstrated that TCF21 expression promotes proliferation and migration and inhibits SMC lineage marker expression. Detailed in situ expression studies with reporter gene and lineage tracing revealed that vascular wall cells expressing Tcf21 before disease initiation migrate into vascular lesions of ApoE-/- and Ldlr-/- mice. While Tcf21 lineage traced cells are distributed throughout the early lesions, in mature lesions they contribute to the formation of a subcapsular layer of cells, and others become associated with the fibrous cap. The lineage traced fibrous cap cells activate expression of SMC markers and growth factor receptor genes. Taken together, these data suggest that TCF21 may have a role regulating the differentiation state of SMC precursor cells that migrate into vascular lesions and contribute to the fibrous cap and more broadly, in view of the association of this gene with human CAD, provide evidence that these processes may be a mechanism for CAD risk attributable to the vascular wall.