Miracle-dz/UniFAR-FTUs huggingface dataset:
A General Efficient and Flexible Approach for Genome‐Wide Association Analyses of Imputed Genotypes in Family‐Based Designs

Genotype imputation is a critical technique for following up genome‐wide association studies. Efficient methods are available for dealing with the probabilistic nature of imputed single nucleotide polymorphisms (SNPs) in population‐based designs, but not for family‐based studies. We have developed a new analytical approach (FBATdosage), using imputed allele dosage in the general framework of family‐based association tests to bridge this gap. Simulation studies showed that FBATdosage yielded highly consistent type I error rates, whatever the level of genotype uncertainty, and a much higher power than the best‐guess genotype approach. FBATdosage allows fast linkage and association testing of several million of imputed variants with binary or quantitative phenotypes in nuclear families of arbitrary size with arbitrary missing data for the parents. The application of this approach to a family‐based association study of leprosy susceptibility successfully refined the association signal at two candidate loci, C1orf141‐IL23R on chromosome 1 and RAB32‐C6orf103 on chromosome 6.
Separating reflections in human iris images for illumination estimation

A method is presented for separating corneal reflections in an image of human irises to estimate illumination from the surrounding scene. Previous techniques for reflection separation have demonstrated success in only limited cases, such as for uniform colored lighting and simple object textures, so they are not applicable to irises which exhibit intricate textures and complicated reflections of the environment. To make this problem feasible, we present a method that capitalizes on physical characteristics of human irises to obtain an illumination estimate that encompasses the prominent light contributors in the scene. Results of this algorithm are presented for eyes of different colors, including light colored eyes for which reflection separation is necessary to determine a valid illumination estimate
Effects of Ncl. Basalis Meynert volume on the Trail‐Making‐Test are restricted to the left hemisphere

Cortical acetylcholine released from cells in the basal forebrain facilitates cue detection and improves attentional performance. Cholinergic fibres to the cortex originate from the CH4 cell group, sometimes referred to as the Nucleus basalis of Meynert and the Nucleus subputaminalis of Ayala. The aim of this work was to investigate the effects of volumes of cholinergic nuclei on attention and executive function.
Polymers for Improving the In Vivo Transduction Efficiency of AAV2 Vectors

Background Adeno-associated virus has attracted great attention as vehicle for body-wide gene delivery. However, for the successful treatment of a disease such as Duchenne muscular dystrophy infusion of very large amounts of vectors is required. This not only raises questions about the technical feasibility of the large scale production but also about the overall safety of the approach. One way to overcome these problems would be to find strategies able to increase the in vivo efficiency. Methodology Here, we investigated whether polymers can act as adjuvants to increase the in vivo efficiency of AAV2. Our strategy consisted in the pre-injection of polymers before intravenous administration of mice with AAV2 encoding a murine secreted alkaline phosphatase (mSeAP). The transgene expression, vector biodistribution and tissue transduction were studied by quantification of the mSeAP protein and real time PCR. The injection of polyinosinic acid and polylysine resulted in an increase of plasmatic mSeAP of 2- and 12-fold, respectively. Interestingly, polyinosinic acid pre-injection significantly reduced the neutralizing antibody titer raised against AAV2. Conclusions Our results show that the pre-injection of polymers can improve the overall transduction efficiency of systemically administered AAV2 and reduce the humoral response against the capsid proteins.
Concealed knowledge identification using facial thermal imaging

In this paper, we present a non-intrusive lie detection system based on thermal imaging technologies. The system consists of the following modules: thermal camera, face detection and tracking, face landmark detection, feature extraction, and pattern recognition for concealed knowledge inference. We have discovered the most sensitive areas on the human face to monitor facial temperature changes. Detection algorithms are then developed to identify concealed knowledge from thermal imaging automatically. Face landmark tracking is used directly on the thermal video images to detect regions of interest (ROI) and extract features for concealed knowledge inference. We achieved an equal error rate (EER) of 16.5% in concealed knowledge recognition for 16 subjects on test data. Our non-contact method of concealed knowledge detection using thermal data achieves similar or better recognition accuracy as traditional intrusive methods, such as polygraph or EEG.
Object Detection from Video Tubelets with Convolutional Neural Networks

Deep Convolution Neural Networks (CNNs) have shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation. For object detection, particularly in still images, the performance has been significantly increased last year thanks to powerful deep networks (e.g. GoogleNet) and detection frameworks (e.g. Regions with CNN features (RCNN)). The lately introduced ImageNet [6] task on object detection from video (VID) brings the object detection task into the video domain, in which objects' locations at each frame are required to be annotated with bounding boxes. In this work, we introduce a complete framework for the VID task based on still-image object detection and general object tracking. Their relations and contributions in the VID task are thoroughly studied and evaluated. In addition, a temporal convolution network is proposed to incorporate temporal information to regularize the detection results and shows its effectiveness for the task. Code is available at https://github.com/ myfavouritekk/vdetlib.
Reconnaissance and latent learning in ants

We show that ants can reconnoitre their surroundings and in effect plan for the future. Temnothorax albipennis colonies use a sophisticated strategy to select a new nest when the need arises. Initially, we presented colonies with a new nest of lower quality than their current one that they could explore for one week without a need to emigrate. We then introduced a second identical low quality new nest and destroyed their old nest so that they had to emigrate. Colonies showed a highly significant preference for the (low quality) novel new nest over the identical but familiar one. In otherwise identical experiments, colonies showed no such discrimination when the choice was between a familiar and an unfamiliar high-quality nest. When, however, either all possible pheromone marks were removed, or landmarks were re-orientated, just before the emigration, the ants chose between identical low-quality new nests at random. These results demonstrate for the first time that ants are capable of assessing and retaining information about the quality of potential new nest sites, probably by using both pheromones and landmark cues, even though this information may only be of strategic value to the colony in the future. They seem capable, therefore, of latent learning and, more explicitly, learning what not to do.
Therapeutic Potential of Nrf2 Activators in Streptozotocin-Induced Diabetic Nephropathy

OBJECTIVE To determine whether dietary compounds targeting NFE2-related factor 2 (Nrf2) activation can be used to attenuate renal damage and preserve renal function during the course of streptozotocin (STZ)-induced diabetic nephropathy. RESEARCH DESIGN AND METHODS Diabetes was induced in Nrf2+/+ and Nrf2−/− mice by STZ injection. Sulforaphane (SF) or cinnamic aldehyde (CA) was administered 2 weeks after STZ injection and metabolic indices and renal structure and function were assessed (18 weeks). Markers of diabetes including blood glucose, insulin, polydipsia, polyuria, and weight loss were measured. Pathological alterations and oxidative damage in glomeruli were also determined. Changes in protein expression of the Nrf2 pathway, as well as transforming growth factor-β1 (TGF-β1), fibronectin (FN), collagen IV, and p21/WAF1Cip1 (p21) were analyzed. The molecular mechanisms of Nrf2-mediated protection were investigated in an in vitro model using human renal mesangial cells (HRMCs). RESULTS SF or CA significantly attenuated common metabolic disorder symptoms associated with diabetes in Nrf2+/+ but not in Nrf2−/− mice, indicating SF and CA function through specific activation of the Nrf2 pathway. Furthermore, SF or CA improved renal performance and minimized pathological alterations in the glomerulus of STZ-Nrf2+/+ mice. Nrf2 activation reduced oxidative damage and suppressed the expression of TGF-β1, extracellular matrix proteins and p21 both in vivo and in HRMCs. In addition, Nrf2 activation reverted p21-mediated growth inhibition and hypertrophy of HRMCs under hyperglycemic conditions. CONCLUSIONS We provide experimental evidence indicating that dietary compounds targeting Nrf2 activation can be used therapeutically to improve metabolic disorder and relieve renal damage induced by diabetes.
OSMAN ― A Novel Arabic Readability Metric

We present OSMAN (Open Source Metric for Measuring Arabic Narratives) - a novel open source Arabic readability metric and tool. It allows researchers to calculate readability for Arabic text with and without diacritics. OSMAN is a modified version of the conventional readability formulas such as Flesch and Fog. In our work we introduce a novel approach towards counting short, long and stress syllables in Arabic which is essential for judging readability of Arabic narratives. We also introduce an additional factor called “Faseeh” which considers aspects of script usually dropped in informal Arabic writing. To evaluate our methods we used Spearman’s correlation metric to compare text readability for 73,000 parallel sentences from English and Arabic UN documents. The Arabic sentences were written with the absence of diacritics and in order to count the number of syllables we added the diacritics in using an open source tool called Mishkal. The results show that OSMAN readability formula correlates well with the English ones making it a useful tool for researchers and educators working with Arabic text.
Transistor delay analysis and effective channel velocity extraction in AlGaN/GaN HFETs

Performed a thorough transistor delay analysis on 0.2 /spl mu/m AlGaN/GaN HFETs implemented on sapphire substrates to identify the various contributions to the total transistor delay 1/2/spl pi/f/sub T/ = /spl tau//sub T/ as a function of gate-drain separation L/sub GD/. We found that the main delay component depends linearly upon the total access resistance of the source and drain regions determined from 'COLDFET' S-parameter measurements, indicating the contribution of extrinsic regions to the transistor delay cannot be neglected for AlGaN/GaN HFETs. Stripping the masking effects of the R/sub S/ and R/sub D/ series resistances reveals an effective channel velocity of /spl sim/3.3 /spl times/ 10/sup 7/ cm/s which is much higher than the values of 1.2-1.3 /spl times/ 10/sup 7/ cm/s generally inferred from f/sub T/ data, but in excellent agreement with predictions from Monte Carlo transport simulations. We also show that process-specific details for devices fabricated on the same epitaxial layers affect the f/sub T/(L/sub GD/) dependence.
Learning Algorithms for Automata with Observations

We consider the problem of learning the behavior of a POMDP (Partially Observable Markov Decision Process) with deterministic actions and observations. This is a challenging problem due to the fact that the observations can only partially identify the states. Recent work by Holmes and Isbell offers an approach for inferring the hidden states from experience in deterministic POMDP environments. We propose an alternative algorithm that ensures more accurate predictions, and we show that in fact it produces the minimal predicting machine.
Virus-Like Particle Vaccine Protects against 2009 H1N1 Pandemic Influenza Virus in Mice

Background The 2009 influenza pandemic and shortages in vaccine supplies worldwide underscore the need for new approaches to develop more effective vaccines. Methodology/Principal Findings We generated influenza virus-like particles (VLPs) containing proteins derived from the A/California/04/2009 virus, and tested their efficacy as a vaccine in mice. A single intramuscular vaccination with VLPs provided complete protection against lethal challenge with the A/California/04/2009 virus and partial protection against A/PR/8/1934 virus, an antigenically distant human isolate. VLP vaccination induced predominant IgG2a antibody responses, high hemagglutination inhibition (HAI) titers, and recall IgG and IgA antibody responses. HAI titers after VLP vaccination were equivalent to those observed after live virus infection. VLP immune sera also showed HAI responses against diverse geographic pandemic isolates. Notably, a low dose of VLPs could provide protection against lethal infection. Conclusion/Significance This study demonstrates that VLP vaccination provides highly effective protection against the 2009 pandemic influenza virus. The results indicate that VLPs can be developed into an effective vaccine, which can be rapidly produced and avoid the need to isolate high growth reassortants for egg-based production.
Comparative studies on Indian traditional nanomedicine Yashadha Bhasma and zinc oxide nanoparticles for anti-diabetic activity

Diabetes mellitus is the most common endocrine disorder due to carbohydrate metabolism. Also, zinc and its supplements have been used in Indian traditional medicines for treating urinary tract infections. In this work, an attempt has been made to compare the properties of ‘Yashadha Bhasma’ a traditional ayurvedic ZnO supplement for diabetic treatment with the laboratory-synthesized ZnO nanoparticles. The nano-sized ZnO particles are synthesized using co-precipitation method and calcined at 400 °C for further purification. Confirmation of ZnO and presence of Ca and K elements additional to Zn in Yashadha Bhasma is confirmed from XPS. The morphology of ZnO is found to be spherical with average diameter of 15 nm. TEM results show that ZnO rods of Yashadha Bhasma are porous and non-uniform. Glucose degradation studies revealed good performance with ZnO nanoparticles with 80% degradation occurring within 15 min itself. Antibacterial studies also performed well establishing efficacy of ZnO nanoparticles against both gram-positive and gram-negative bacterial strains, thereby establishing suitable material for treating diabetes mellitus and also curing bacterial wound infections arising due to diabetes mellitus.
Analysis of Periodic and Random Capacitively-Loaded Loop (CLL) Metamaterial Structures for Antenna Enhancement Applications

After being theorized by Veselago in 1967, recent developments in metamaterials over the last two decades have allowed scientists and researchers to physically demonstrate that artificial composite media can be engineered to exhibit exotic material properties, such as negative refractive index, by exploiting features in arrays of sub-wavelength unit inclusions. These unconventional electromagnetic properties are realized through the coupling of the microscopic unit inclusions, which govern the macroscopic properties of the structure. After demonstrating that a periodic array of capacitively-loaded loop (CLL) inclusions paired with continuous wire results in negative refraction, this study performs numerical simulations to characterize random metamaterial structures. These structures consist of CLLs that are randomized in both position and orientation. In addition, this thesis introduces an innovative antenna enhancing structure consisting of capacitively-loaded loop (CLL) metamaterial elements loaded radially around a standard dipole antenna at an electrically small distance. As a result of this innovative arrangement, the dipole antenna is easily transformed into a directive mechanically scanned antenna with high realized gain. The desired directivity and gain can be tuned based on the number of radial CLL fins placed around the dipole. Interactions between the antenna and metamaterial elements result in significant enhancement of the maximum radiated field amplitude and front-to-back ratio. This innovative CLL-loaded dipole antenna is compared to the conventional Yagi-Uda antenna. The structures presented in this thesis are modeled using fullwave simulation, and one antenna structure is experimentally verified as a proof-of-concept.
Impact of scaling channel length on the performances of nanoscale FETs

The investigation of short-channel effects (SCE) due to channel length reduction for four different types of n-channel FETs: Bulk MOSFET, SOI MOSFET, DG MOSFET and CNTFET are carried out in this work. Simulators are used to investigate SCEs like threshold voltage (V<inf>th</inf>) roll-off, subthreshold Swing (SS) and I<inf>on</inf>/I<inf>off</inf> ratio. Our study shows that DG MOSFET, SOI MOSFET and Bulk MOSFET reach their scalable limit at 30 nm, 50 nm and 100 nm channel length respectively due to elevation of leakage power consumption as they exhibit rapid degradation of V<inf>th</inf>, SS beyond 100mV/decade and less I<inf>on</inf>/I<inf>off</inf> ratio. On the contrary, CNTFET can be scaled down below 10 nm as it shows negligible SCEs with stable V<inf>th</inf>, ideal SS (60mV/decade) and high I<inf>on</inf>/I<inf>off</inf> ratio as channel length decreases. Our numerical analysis shows CNTFET creates only 1.11% V<inf>th</inf> variation whereas DG MOSFET creates 39.33% V<inf>th</inf> variation. CNTFETs advantages over MOSFET make it viable for faster and enhanced applications in nanoelectronics.
Methane-Carbon Flow into the Benthic Food Web at Cold Seeps – A Case Study from the Costa Rica Subduction Zone

Cold seep ecosystems can support enormous biomasses of free-living and symbiotic chemoautotrophic organisms that get their energy from the oxidation of methane or sulfide. Most of this biomass derives from animals that are associated with bacterial symbionts, which are able to metabolize the chemical resources provided by the seeping fluids. Often these systems also harbor dense accumulations of non-symbiotic megafauna, which can be relevant in exporting chemosynthetically fixed carbon from seeps to the surrounding deep sea. Here we investigated the carbon sources of lithodid crabs (Paralomis sp.) feeding on thiotrophic bacterial mats at an active mud volcano at the Costa Rica subduction zone. To evaluate the dietary carbon source of the crabs, we compared the microbial community in stomach contents with surface sediments covered by microbial mats. The stomach content analyses revealed a dominance of epsilonproteobacterial 16S rRNA gene sequences related to the free-living and epibiotic sulfur oxidiser Sulfurovum sp. We also found Sulfurovum sp. as well as members of the genera Arcobacter and Sulfurimonas in mat-covered surface sediments where Epsilonproteobacteria were highly abundant constituting 10% of total cells. Furthermore, we detected substantial amounts of bacterial fatty acids such as i-C15∶0 and C17∶1ω6c with stable carbon isotope compositions as low as −53‰ in the stomach and muscle tissue. These results indicate that the white microbial mats at Mound 12 are comprised of Epsilonproteobacteria and that microbial mat-derived carbon provides an important contribution to the crab's nutrition. In addition, our lipid analyses also suggest that the crabs feed on other 13C-depleted organic matter sources, possibly symbiotic megafauna as well as on photosynthetic carbon sources such as sedimentary detritus.
Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing

The replica method is a non-rigorous but widely-accepted technique from statistical physics used in the asymptotic analysis of large, random, nonlinear problems. This paper applies the replica method to non-Gaussian maximum a posteriori (MAP) estimation. It is shown that with random linear measurements and Gaussian noise, the asymptotic behavior of the MAP estimate of an n-dimensional vector "decouples" as n scalar MAP estimators. The result is a counterpart to Guo and Verdu's replica analysis of minimum mean-squared error estimation. The replica MAP analysis can be readily applied to many estimators used in compressed sensing, including basis pursuit, lasso, linear estimation with thresholding, and zero norm-regularized estimation. In the case of lasso estimation the scalar estimator reduces to a soft-thresholding operator, and for zero norm-regularized estimation it reduces to a hard-threshold. Among other benefits, the replica method provides a computationally-tractable method for exactly computing various performance metrics including mean-squared error and sparsity pattern recovery probability.
Lessons Learned in Part-of-Speech Tagging of Conversational Speech

This paper examines tagging models for spontaneous English speech transcripts. We analyze the performance of state-of-the-art tagging models, either generative or discriminative, left-to-right or bidirectional, with or without latent annotations, together with the use of ToBI break indexes and several methods for segmenting the speech transcripts (i.e., conversation side, speaker turn, or human-annotated sentence). Based on these studies, we observe that: (1) bidirectional models tend to achieve better accuracy levels than left-to-right models, (2) generative models seem to perform somewhat better than discriminative models on this task, and (3) prosody improves tagging performance of models on conversation sides, but has much less impact on smaller segments. We conclude that, although the use of break indexes can indeed significantly improve performance over baseline models without them on conversation sides, tagging accuracy improves more by using smaller segments, for which the impact of the break indexes is marginal.
Automatic Quality Assessment of Peptide Tandem Mass Spectra

MOTIVATION A powerful proteomics methodology couples high-performance liquid chromatography (HPLC) with tandem mass spectrometry and database-search software, such as SEQUEST. Such a set-up, however, produces a large number of spectra, many of which are of too poor quality to be useful. Hence a filter that eliminates poor spectra before the database search can significantly improve throughput and robustness. Moreover, spectra judged to be of high quality, but that cannot be identified by database search, are prime candidates for still more computationally intensive methods, such as de novo sequencing or wider database searches including post-translational modifications. RESULTS We report on two different approaches to assessing spectral quality prior to identification: binary classification, which predicts whether or not SEQUEST will be able to make an identification, and statistical regression, which predicts a more universal quality metric involving the number of b- and y-ion peaks. The best of our binary classifiers can eliminate over 75% of the unidentifiable spectra while losing only 10% of the identifiable spectra. Statistical regression can pick out spectra of modified peptides that can be identified by a de novo program but not by SEQUEST. In a section of independent interest, we discuss intensity normalization of mass spectra.