Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-apoptotic role of Sonic hedgehog protein at the early stages of nervous system organogenesis.

In vertebrates the neural tube, like most of the embryonic organs, shows discreet areas of programmed cell death at several stages during development. In the chick embryo, cell death is dramatically increased in the developing nervous system and other tissues when the midline cells, notochord and floor plate, are prevented from forming by excision of the axial-paraxial hinge (APH), i.e. caudal Hensen's node and rostral primitive streak, at the 6-somite stage ( Charrier, J. B., Teillet, M.-A., Lapointe, F. and Le Douarin, N. M. (1999). Development 126, 4771-4783). In this paper we demonstrate that one day after APH excision, when dramatic apoptosis is already present in the neural tube, the latter can be rescued from death by grafting a notochord or a floor plate fragment in its vicinity. The neural tube can also be recovered by transplanting it into a stage-matched chick embryo having one of these structures. In addition, cells engineered to produce Sonic hedgehog protein (SHH) can mimic the effect of the notochord and floor plate cells in in situ grafts and transplantation experiments. SHH can thus counteract a built-in cell death program and thereby contribute to organ morphogenesis, in particular in the central nervous system.
A SURVEY ON VIDEO-BASED FACE RECOGNITION

: Humans make use of face as an important cue for identifying people. This makes automatic face recognition very crucial from the point of view of a wide range of commercial and law enforcement applications. Although significant work has been done, the current systems are still not close to the human perceptual system. Traditionally, face recognition research has been limited to recognizing faces from still images. Most of these approaches discount the inherent 3-D structure of the face and therefore are very susceptible to pose changes. Face recognition is a kind of automated biometric identification technique that recognizes an individual based on their facial features as essential elements of distinction. This paper provides an up-to-date survey of video-based face recognition research.
Remission and recovery associated with lurasidone in the treatment of major depressive disorder with subthreshold hypomanic symptoms (mixed features): post-hoc analysis of a randomized, placebo-controlled study with longer-term extension

Objective This post-hoc analysis assessed rates of symptomatic and functional remission, as well as recovery (combination of symptomatic and functional remission), in patients treated with lurasidone for major depressive disorder (MDD) associated with subthreshold hypomanic symptoms (mixed features). Method Patients with MDD plus two or three manic symptoms (defined as per the DSM–5 mixed-features specifier) were randomly assigned to flexible-dose lurasidone 20–60 mg/day (n=109) or placebo (n=100) for 6 weeks, followed by a 3-month open-label, flexible-dose extension study for U.S. sites only (n=48). Cross-sectional recovery was defined as the presence of both symptomatic remission (Montgomery–Åsberg Depression Rating Scale score ≤ 12) and functional remission (all Sheehan Disability Scale [SDS] domain scores ≤3) at week 6, and at both months 1 and 3 of the extension study (“sustained recovery”). Results A significantly higher proportion of lurasidone-treated patients (31.3%) achieved recovery (assessed cross-sectionally) compared to placebo (12.2%, p=0.002) at week 6. The number of manic symptoms at baseline moderated the effect size for attaining cross-sectional recovery for lurasidone treatment (vs. placebo) (p=0.028). Sustained recovery rates were higher in patients initially treated with lurasidone (20.8%) versus placebo (12.5%). Conclusions In this post-hoc analysis of a placebo-controlled study with open-label extension that involved patients with MDD and mixed features, lurasidone was found to significantly improve the rate of recovery at 6 weeks (vs. placebo) that was sustained at month 3 of the extension study. The presence of two (as opposed to three) manic symptoms moderated recovery at the acute study endpoint.
Size Matters: Space/Time Tradeoffs to Improve GPGPU Applications Performance

GPUs offer drastically different performance characteristics compared to traditional multicore architectures. To explore the tradeoffs exposed by this difference, we refactor MUMmer, a widely-used, highly-engineered bioinformatics application which has both CPU- and GPU-based implementations. We synthesize our experience as three high-level guidelines to design efficient GPU-based applications. First, minimizing the communication overheads is as important as optimizing the computation. Second, trading-off higher computational complexity for a more compact in-memory representation is a valuable technique to increase overall performance (by enabling higher parallelism levels and reducing transfer overheads). Finally, ensuring that the chosen solution entails low pre- and post-processing overheads is essential to maximize the overall performance gains. Based on these insights, MUMmerGPU++, our GPU-based design of the MUMmer sequence alignment tool, achieves, on realistic workloads, up to 4x speedup compared to a previous, highly optimized GPU port.
Optimal DoS attacks on Bayesian quickest change detection

In this paper, the optimal denial-of-service (DoS) attack strategies on Bayesian quickest change detection are developed. Specifically, a sensor monitoring an environment that may change randomly sends its observation at each time via a wireless channel to a remote center. Upon receiving the data from the sensor, the remote center decides sequentially whether a change of the environment takes place or not and the remote center aims to detect such a change as soon as possible subject to a certain false alarm rate. An attacker eavesdropping the wireless channel can launch jamming attacks (e.g., block the wireless channel) between the sensor and the remote center for at most N times. To make the detection cost (a linear combination of detection delay and the probability of false alarm) for the center as large as possible, the attacker needs to decide when to implement such DoS attacks. We solve this problem by formulating it as an infinite horizon MDP problem. The asymptotic lower and upper bound of the expected detection delay at the center, when the probability of false alarm goes to zero, of such an attack is also investigated. A numerical example is shown to illustrate the main results.
Using the distance distribution for approximate similarity queries in high-dimensional metric spaces

We investigate the problem of approximate similarity (nearest neighbor) search in high-dimensional metric spaces, and describe how the distance distribution of the query object can be exploited so as to provide probabilistic guarantees on the quality of the result. This leads to a new paradigm for similarity search, called PAC-NN (probably approximately correct nearest neighbor) queries, aiming to break the "dimensionality curse". PAC-NN queries return, with probability at least 1-/spl delta/, a (1+/spl epsiv/)-approximate NN-an object whose distance from the query q is less than (1+/spl epsiv/) times the distance between q and its NN. Analytical and experimental results obtained for sequential and index-based algorithms show that PAC-NN queries can be efficiently processed even on very high-dimensional spaces and that control can be exerted in order to tradeoff the accuracy of the result and the cost.
On Human-Machine Interaction during Online Image Classifier Training

This paper considers a number of issues that arise when a trainable machine vision system learns directly from humans, rather than from a "cleaned" data set, i.e. data items which are perfectly labelled with complete accuracy. This is done within the context of a generic system for the visual surface inspection of manufactured parts. The issues treated are relevant not only to wider computer vision applications, but also to classification more generally. Some of these issues arise from the nature of humans themselves: they will be not only internally inconsistent, but will often not be completely confident about their decisions, especially if they are making decisions rapidly. People will also often differ systematically from each other in the decisions they make. Other issues may arise from the nature of the process, which may require the machine learning to have the capacity for real-time, online adaptation in response to users' input. It may be that the users cannot always provide input to a consistent level of detail. We describe how all of these issues may be tackled within a coherent methodology. Using a range of classifiers trained on real data sets from a CD imprint production process, we will present results which show that properly addressing most of these issues may actually lead to improved performance.
SUPPORTING COOPERATION THROUGH CLANS

Cooperation is the foundation of multi-agent systems, and allows agents to interact to achieve complex goals. In dynamic environments cooperation gives rise to inherent uncertainty since we cannot predict how agents will respond to environmental change. Consequently, agents must have some mechanism for coping with this uncertainty. In particular, agents must manage the risk resulting from interacting with others who have different objectives, or who may fail to fulfil their commitments. In this paper, we introduce the concept of a clan: a group of agents who trust each other, have similar objectives, and treat each other favourably with respect to cooperation.
Calibrated Landsat TM LAI retrieval for monitoring vegetation cover change after ecological releases to the lower Tarim river

This study used high resolution images as a ground measurement tool for a region, where mapping of spatial and temporal vegetation cover variation by ground survey is impossible. Leaf Area Index (LAI) maps were derived from DigitalGlobe or GeoEye images and resampled to 30m resolution. Landsat TM imagery was used to produce NDVI maps. The NDVI from TM and LAI from high resolution images show good correlation for the three test sites chosen in the lower Tarim river basin. The correlations were then used to produce LAI maps on river basin scale from Landsat TM imagery. The results showed that this method provides good local calibration for converting NDVI maps from Landsat TM imagery to LAI maps to be used in hydrological modeling.
Scaling-Up Quadratic Programming Feature Selection

Domains such as vision, bioinformatics, web search and web rankings involve datasets where number of features is very large. Feature selection is commonly employed to deal with high dimensional data. Recently, Quadratic Programming Feature Selection (QPFS) has been shown to outperform many of the existing feature selection methods for a variety of datasets. In this paper, we propose a Sequential Minimal Optimization (SMO) based framework for QPFS. This helps in reducing the cubic computational time (in terms of data dimension) of the standard QPFS to quadratic time in practice. Further, our approach has significantly less memory requirement than QPFS. This memory saving can be critical for doing feature selection in high dimensions. The performance of our approach is demonstrated using three publicly available benchmark datasets from bioinformatics domain.
Serum lipid profile changes predict neurodegeneration in interferon-β1a-treated multiple sclerosis patients[S]

The purpose of this work was to determine whether changes in cholesterol profiles after interferon-β (IFN-β)1a treatment initiation following the first demyelinating event suggestive of multiple sclerosis are associated with clinical and MRI outcomes over 4 years. A group of 131 patients (age: 27.9 ± 7.8 years, 63% female) with serial 3-monthly clinical and 12-monthly MRI follow-ups over 4 years were investigated. Serum cholesterol profiles, including total cholesterol (TC), HDL cholesterol (HDL-C), and LDL cholesterol (LDL-C) were obtained at baseline, 1 month, 3 months, and every 6 months thereafter. IFN-β1a initiation caused rapid decreases in serum HDL-C, LDL-C, and TC within 1 month of IFN-β1a initiation (all P < 0.001) that returned slowly toward baseline. In predictive mixed model analyses, greater percent decreases in HDL-C after 3 months of IFN-β1a treatment initiation were associated with less brain atrophy over the 4 year time course, as assessed by percent brain volume change (P < 0.001), percent gray matter volume change (P < 0.001), and percent lateral ventricle volume change (P = 0.005). Decreases in cholesterol biomarkers following IFN-β1a treatment are associated with brain atrophy outcomes over 4 years. Pharmacological interventions targeting lipid homeostasis may be clinically beneficial for disrupting neurodegenerative processes.
Targeted delivery of transferrin and TAT co-modified liposomes encapsulating both paclitaxel and doxorubicin for melanoma

Abstract The purpose of this study was to develop an efficient dual-ligand based liposomal drug delivery system with targeting specificity as well as properties that would kill melanoma cells. Liposomes modified with transferrin (Tf) and cell-penetrating peptide TAT was prepared, which encapsulated two kinds of chemotherapy drugs, paclitaxel and doxorubicin (Tf/TAT-PTX/DOX-LP). The Tf ligands specifically bind to the overexpressed Tf receptors on the surface of melanoma cells, while the TAT ligands functioned as a classical cell penetrating peptide, helping dual-ligand liposomes be internalized by melanoma cells. The effect of dual-targeting system and “double-drug” combination therapy were evaluated both in vitro and in vivo. In vitro, cellular uptake, intracellular distribution and tumor spheroids penetration studies demonstrated that the system could not only be selectively and efficiently penetrate melanoma cells. Besides, apoptosis staining assay and cytotoxicity showed effective anti-tumor capability and obvious synergistic effect of combination therapy of PTX and DOX. In vivo imaging and fluorescent images of tumor section further demonstrated that Tf/TAT-PTX/DOX-LP had the highest tumor distribution. The results of these experiments demonstrated that double-drug liposomal drug delivery systems (DDS) had both enhanced targeting efficiency and increased therapeutic efficacy.
Bcl-2–Modifying Factor Induces Renal Proximal Tubular Cell Apoptosis in Diabetic Mice

This study investigated the mechanisms underlying tubular apoptosis in diabetes by identifying proapoptotic genes that are differentially upregulated by reactive oxygen species in renal proximal tubular cells (RPTCs) in models of diabetes. Total RNAs isolated from renal proximal tubules (RPTs) of 20-week-old heterozygous db/m+, db/db, and db/db catalase (CAT)-transgenic (Tg) mice were used for DNA chip microarray analysis. Real-time quantitative PCR assays, immunohistochemistry, and mice rendered diabetic with streptozotocin were used to validate the proapoptotic gene expression in RPTs. Cultured rat RPTCs were used to confirm the apoptotic activity and regulation of proapoptotic gene expression. Additionally, studies in kidney tissues from patients with and without diabetes were used to confirm enhanced proapoptotic gene expression in RPTs. Bcl-2–modifying factor (Bmf) was differentially upregulated (P < 0.01) in RPTs of db/db mice compared with db/m+ and db/db CAT-Tg mice and in RPTs of streptozotocin-induced diabetic mice in which insulin reversed this finding. In vitro, Bmf cDNA overexpression in rat RPTCs coimmunoprecipated with Bcl-2, enhanced caspase-3 activity, and promoted apoptosis. High glucose (25 mmol/L) induced Bmf mRNA expression in RPTCs, whereas rotenone, catalase, diphenylene iodinium, and apocynin decreased it. Knockdown of Bmf with small interfering RNA reduced high glucose–induced apoptosis in RPTCs. More important, enhanced Bmf expression was detected in RPTs of kidneys from patients with diabetes. These data demonstrate differential upregulation of Bmf in diabetic RPTs and suggest a potential role for Bmf in regulating RPTC apoptosis and tubular atrophy in diabetes.
Location-aware object detection via coherent region grouping

We present a scene adaptation algorithm for object detection. Our method discovers scene-dependent features discriminative to classifying foreground objects into different categories. Unlike previous works suffering from insufficient training data collected online, our approach incorporated with a similarity grouping procedure can automatically gather more consistent training examples from a neighbour area. Experimental results show that the proposed method outperforms several related works with higher detection accuracies.
Novel approach to textile design for barriers against electromagnetic radiation

Human-friendly barriers against microwave radiation are designed using non-woven multiple layers of natural (flax) fibres, as well as flax and polypropylene fibre blends, with the split-ring resonators implanted so as to allocate the barrier properties in the pre-determined frequency band (X-band is selected for its applications in traffic radars and telecommunication). Oscillator model is proposed for determining the ring resonance frequency. The band of non-transmission up to ∼40 dB is observed. The band is found to shift depending on the flax and polypropylene contents in the blend, the distance between the ring centres, and on the number of textile layers.
Genome-wide Single Nucleotide Polymorphism Analyses Reveal Genetic Diversity and Structure of Wild and Domestic Cattle in Bangladesh

In spite of variation in coat color, size, and production traits among indigenous Bangladeshi cattle populations, genetic differences among most of the populations have not been investigated or exploited. In this study, we used a high-density bovine single nucleotide polymorphism (SNP) 80K Bead Chip derived from Bos indicus breeds to assess genetic diversity and population structure of 2 Bangladeshi zebu cattle populations (red Chittagong, n = 28 and non-descript deshi, n = 28) and a semi-domesticated population (gayal, n = 17). Overall, 95% and 58% of the total SNPs (69,804) showed polymorphisms in the zebu and gayal populations, respectively. Similarly, the average minor allele frequency value was as high 0.29 in zebu and as low as 0.09 in gayal. The mean expected heterozygosity varied from 0.42±0.14 in zebu to 0.148±0.14 in gayal with significant heterozygosity deficiency of 0.06 (FIS) in the latter. Coancestry estimations revealed that the two zebu populations are weakly differentiated, with over 99% of the total genetic variation retained within populations and less than 1% accounted for between populations. Conversely, strong genetic differentiation (FST = 0.33) was observed between zebu and gayal populations. Results of population structure and principal component analyses suggest that gayal is distinct from Bos indicus and that the two zebu populations were weakly structured. This study provides basic information about the genetic diversity and structure of Bangladeshi cattle and the semi-domesticated gayal population that can be used for future appraisal of breed utilization and management strategies.
#ChennaiFloods: Leveraging Human and Machine Learning for Crisis Mapping during Disasters Using Social Media

The recent emergence of ubiquitous smart communication devices accelerate people to post the current trending topics in real time as micro blogs, tweets, posts and multimedia content on social media sites along with geographical location tags (geo-tags). Specifically, during recent floods in Tamilnadu 2015, the early warnings about flooded areas emerged to get posted in popular social media with geo-parsed hash tags continuously. In the humanitarian view, the real-time crisis sparked great interest in designing an innovative methodology using big social media data analysis along with supervised machine learning techniques to actuate immediate disaster response and rescue efforts in near future. The proposed system performs disaster tweet collection based on trending disaster hash tags. Our system performs Naive-Bayesian (multinomial) and SSVM classification on collected tweets to identify the severity of the disaster. Based on location-to-interpolation cluster proximity, disaster geographic map is generated for the affected area. Our approach detects the tweets fitted into correct classifier label, and generate an output with detection rate of 79% to 91% of the time. The predicted disaster mapping results are highly accurate up to 89% for real time geo-parsed tweets that matched with actual location at-risk during the flood.
YME1L degradation reduces mitochondrial proteolytic capacity during oxidative stress

Mitochondrial proteostasis is maintained by a network of ATP‐dependent quality control proteases including the inner membrane protease YME1L. Here, we show that YME1L is a stress‐sensitive mitochondrial protease that is rapidly degraded in response to acute oxidative stress. This degradation requires reductions in cellular ATP and involves the activity of the ATP‐independent protease OMA1. Oxidative stress‐dependent reductions in YME1L inhibit protective YME1L‐dependent functions and increase cellular sensitivity to oxidative insult. Collectively, our results identify stress‐induced YME1L degradation as a biologic process that attenuates protective regulation of mitochondrial proteostasis and promotes cellular death in response to oxidative stress.
Activation of Type 5 Metabotropic Glutamate Receptors and Diacylglycerol Lipase-α Initiates 2-Arachidonoylglycerol Formation and Endocannabinoid-Mediated Analgesia

Acute stress reduces pain sensitivity by engaging an endocannabinoid signaling circuit in the midbrain. The neural mechanisms governing this process and molecular identity of the endocannabinoid substance(s) involved are unknown. We combined behavior, pharmacology, immunohistochemistry, RNA interference, quantitative RT-PCR, enzyme assays, and lipidomic analyses of endocannabinoid content to uncover the role of the endocannabinoid 2-arachidonoyl-sn-glycerol (2-AG) in controlling pain sensitivity in vivo. Here, we show that footshock stress produces antinociception in rats by activating type 5 metabotropic glutamate receptors (mGlu5) in the dorsolateral periaqueductal gray (dlPAG) and mobilizing 2-AG. Stimulation of mGlu5 in the dlPAG with DHPG [(S)-3,5-dihydroxyphenylglycine] triggered 2-AG formation and enhanced stress-dependent antinociception through a mechanism dependent upon both postsynaptic diacylglycerol lipase (DGL) activity, which releases 2-AG, and presynaptic CB1 cannabinoid receptors. Pharmacological blockade of DGL activity in the dlPAG with RHC80267 [1,6-bis(cyclohexyloximinocarbonylamino)hexane] and (−)-tetrahydrolipstatin (THL), which inhibit activity of DGL-α and DGL-β isoforms, suppressed stress-induced antinociception. Inhibition of DGL activity in the dlPAG with THL selectively decreased accumulation of 2-AG without altering levels of anandamide. The putative 2-AG-synthesizing enzyme DGL-α colocalized with mGlu5 at postsynaptic sites of the dlPAG, whereas CB1 was confined to presynaptic terminals, consistent with a role for 2-AG as a retrograde signaling messenger. Finally, virally mediated silencing of DGL-α, but not DGL-β, transcription in the dlPAG mimicked effects of DGL inhibition in suppressing both endocannabinoid-mediated stress antinociception and 2-AG formation. The results indicate that activation of the postsynaptic mGlu5–DGL-α cascade triggers retrograde 2-AG signaling in vivo. This pathway is required for endocannabinoid-mediated stress-induced analgesia.