Miracle-dz/UniFAR-FTUs huggingface dataset:
Lipoprotein Lipase Expression in Hypothalamus Is Involved in the Central Regulation of Thermogenesis and the Response to Cold Exposure

Lipoprotein lipase (LPL) is expressed in different areas of the brain, including the hypothalamus and plays an important role in neural control of the energy balance, including feeding behavior and metabolic fluxes. This study tested the hypothesis that hypothalamic LPL participates in the control of body temperature. We first showed that cold exposure induces decreased activity and expression of LPL in the mouse hypothalamus. We then selectively deleted LPL in the mediobasal hypothalamus (MBH) through an adeno-associated virus approach in LPL-floxed mice and generated MBHΔLpl mice with 30–35% decrease in hypothalamic LPL activity. Results showed a decrease in body temperature in MBHΔLpl mice when compared with controls at 22°C. Exposure to cold (4°C for 4 h) decreased the body temperature of the control mice while that of the MBHΔLpl mice remained similar to that observed at 22°C. MBHΔLpl mice also showed increased energy expenditure during cold exposure, when compared to controls. Finally, the selective MBH deletion of LPL also increased the expression of the thermogenic PRMD16 and Dio2 in subcutaneous and perigonadal adipose tissues. Thus, the MBH LPL deletion seems to favor thermogenesis. These data demonstrate that for the first time hypothalamic LPL appears to function as a regulator of body temperature and cold-induced thermogenesis.
A structured light based measuring system used for an autonomous interior finishing robot

The processings involved in interior finishing work are unhealthy and labor intensive. Thus it is expected that the introduction of autonomous robots can change the situation. This paper describes a robot developed for this purpose. It is designed to implement wall polishing work which belongs to the fundamental wall finishing works and is harmful for workers due to the accompanied dust. The proposed robot system consists of a structured light based 3D scanner and a robot arm (UR5) fixed on an mobile platform. The 3D scanner is adopted for recognizing the defects on the wall surface and measuring its roughness. With these information, it is expected that a robot arm equipped with a specially designed grinder can finish the polishing work. The paper addresses the implementation of the structured light based 3D scanner and the fundamental algorithms for wall surface measurement and the related polishing experiments.
Efficient estimation of personalized biventricular mechanical function employing gradient‐based optimization

Individually personalized computational models of heart mechanics can be used to estimate important physiological and clinically‐relevant quantities that are difficult, if not impossible, to directly measure in the beating heart. Here, we present a novel and efficient framework for creating patient‐specific biventricular models using a gradient‐based data assimilation method for evaluating regional myocardial contractility and estimating myofiber stress. These simulations can be performed on a regular laptop in less than 2 h and produce excellent fit between measured and simulated volume and strain data through the entire cardiac cycle. By applying the framework using data obtained from 3 healthy human biventricles, we extracted clinically important quantities as well as explored the role of fiber angles on heart function. Our results show that steep fiber angles at the endocardium and epicardium are required to produce simulated motion compatible with measured strain and volume data. We also find that the contraction and subsequent systolic stresses in the right ventricle are significantly lower than that in the left ventricle. Variability of the estimated quantities with respect to both patient data and modeling choices are also found to be low. Because of its high efficiency, this framework may be applicable to modeling of patient specific cardiac mechanics for diagnostic purposes.
Heterogeneity in Genetic Diversity among Non-Coding Loci Fails to Fit Neutral Coalescent Models of Population History

Inferring aspects of the population histories of species using coalescent analyses of non-coding nuclear DNA has grown in popularity. These inferences, such as divergence, gene flow, and changes in population size, assume that genetic data reflect simple population histories and neutral evolutionary processes. However, violating model assumptions can result in a poor fit between empirical data and the models. We sampled 22 nuclear intron sequences from at least 19 different chromosomes (a genomic transect) to test for deviations from selective neutrality in the gadwall (Anas strepera), a Holarctic duck. Nucleotide diversity among these loci varied by nearly two orders of magnitude (from 0.0004 to 0.029), and this heterogeneity could not be explained by differences in substitution rates alone. Using two different coalescent methods to infer models of population history and then simulating neutral genetic diversity under these models, we found that the observed among-locus heterogeneity in nucleotide diversity was significantly higher than expected for these simple models. Defining more complex models of population history demonstrated that a pre-divergence bottleneck was also unlikely to explain this heterogeneity. However, both selection and interspecific hybridization could account for the heterogeneity observed among loci. Regardless of the cause of the deviation, our results illustrate that violating key assumptions of coalescent models can mislead inferences of population history.
A fully integrated scalable W-band phased-array module with integrated antennas, self-alignment and self-test

Advanced SiGe BiCMOS and CMOS processes continue to push the frontier on millimeter-wave (mm-wave) and highly integrated phased-array systems for a variety of communication applications [1,3]. Furthermore, next-generation mobile technology (5G) demands ultra-low latency and high data-rates with ubiquitous deployment supporting multi-users through the use of pico-cells. These cells may require up to hundreds of active elements capable of producing thousands of beam patterns. In order to make wide adoption of such mm-wave systems a reality, the overall cost of the system must be significantly reduced. This can be accomplished through several means. First, producing highly-integrated phased arrays eliminates the need for additional external components (such as expensive mm-wave synthesizers, amplifiers and switches), which reduces the overall system costs. Second, eliminating exotic packaging processes and materials would allow low-cost traditional manufacturing techniques to be applied to mm-wave systems. Lastly, incorporating self-test, fault-detection, health-monitoring and self-calibration into the RFIC significantly reduces the costs of factory testing (by eliminating the need for any mm-wave verifications) and enables remote-maintenance and system-reconfiguration in case of failures.
Dissecting the Structural Elements for the Activation of β-Ketoacyl-(Acyl Carrier Protein) Reductase from Vibrio cholerae

ABSTRACT β-Ketoacyl-(acyl carrier protein) reductase (FabG) catalyzes the key reductive reaction in the elongation cycle of fatty acid synthesis (FAS), which is a vital metabolic pathway in bacteria and a promising target for new antibiotic development. The activation of the enzyme is usually linked to the formation of a catalytic triad and cofactor binding, and crystal structures of FabG from different organisms have been captured in either the active or inactive conformation. However, the structural elements which enable activation of FabG require further exploration. Here we report the findings of structural, enzymatic, and binding studies of the FabG protein found in the causative agent of cholera, Vibrio cholerae (vcFabG). vcFabG exists predominantly as a dimer in solution and is able to self-associate to form tetramers, which is the state seen in the crystal structure. The formation of the tetramer may be promoted by the presence of the cofactor NADP(H). The transition between the dimeric and tetrameric states of vcFabG is related to changes in the conformations of the α4/α5 helices on the dimer-dimer interface. Two glycine residues adjacent to the dimer interface (G92 and G141) are identified to be the hinge for the conformational changes, while the catalytic tyrosine (Y155) and a glutamine residue that forms hydrogen bonds to both loop β4-α4 and loop β5-α5 (Q152) stabilize the active conformation. The functions of the aforementioned residues were confirmed by binding and enzymatic assays for the corresponding mutants. IMPORTANCE This paper describes the results of structural, enzymatic, and binding studies of FabG from Vibrio cholerae (vcFabG). In this work, we dissected the structural elements responsible for the activation of vcFabG. The structural information provided here is essential for the development of antibiotics specifically targeting bacterial FabG, especially for the multidrug-resistant strains of V. cholerae.
Modeling Singing F0 With Neural Network Driven Transition-Sustain Models

This study focuses on generating fundamental frequency (F0) curves of singing voice from musical scores stored in a midi-like notation. Current statistical parametric approaches to singing F0 modeling meet difficulties in reproducing vibratos and the temporal details at note boundaries due to the oversmoothing tendency of statistical models. This paper presents a neural network based solution that models a pair of neighboring notes at a time (the transition model) and uses a separate network for generating vibratos (the sustain model). Predictions from the two models are combined by summation after proper enveloping to enforce continuity. In the training phase, mild misalignment between the scores and the target F0 is addressed by back-propagating the gradients to the networks' inputs. Subjective listening tests on the NITech singing database show that transition-sustain models are able to generate F0 trajectories close to the original performance.
Impact Evaluation Methods in Public Economics

Recent years have seen a large expansion in the use of rigorous impact evaluation techniques. Increasingly, public administrations are collaborating with academic economists and other quantitative social scientists to apply such rigorous methods to the study of public finance. These developments allow for more reliable measurements of the effects of different policy options on the behavioral responses of citizens, firm owners, or public officials. They can help decision makers in tax administrations, public procurement offices, and other public agencies design programs informed by well-founded evidence. This article provides an introductory overview of the most frequently used impact evaluation methods. It is aimed at facilitating communication and collaboration between practitioners and academics by introducing key vocabulary and concepts used in rigorous impact evaluation methods, starting with randomized controlled trials and comparing them with other methods ranging from simple pre–post analysis to difference-in-differences, matching estimations, and regression discontinuity designs.
A Content-Adaptive Multi-View Video Color Correction Algorithm

A content-adaptive color correction algorithm for multi-view video is proposed due to variation in lighting or camera parameters. We first establish color correction property between the target image and source image. Then color correction matrix can be obtained by global correction or preferred region matching correction. Finally, video tracking technique is used to correct multi-view video sequences. Experimental results show the proposed algorithm has better correction effect for different multi-view video sequences.
Deep reinforcement learning for time series: playing idealized trading games

Deep Q-learning is investigated as an end-to-end solution to estimate the optimal strategies for acting on time series input. Experiments are conducted on two idealized trading games. 1) Univariate: the only input is a wave-like price time series, and 2) Bivariate: the input includes a random stepwise price time series and a noisy signal time series, which is positively correlated with future price changes. The Univariate game tests whether the agent can capture the underlying dynamics, and the Bivariate game tests whether the agent can utilize the hidden relation among the inputs. Stacked Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM) units, Convolutional Neural Network (CNN), and multi-layer perceptron (MLP) are used to model Q values. For both games, all agents successfully find a profitable strategy. The GRU-based agents show best overall performance in the Univariate game, while the MLP-based agents outperform others in the Bivariate game.
Fasting triglycerides as a predictor of incident diabetes, insulin resistance and β-cell function in a Canadian First Nation

ABSTRACT Background: Diabetes prevalence is substantially higher among Canadian First Nations populations than the non-First Nation population. Fasting serum triglycerides have been found to be an important predictor of incident diabetes among non-indigenous populations. However, there is a great need to understand diabetes progression within specific ethnic groups, particularly First Nations populations. Objective: The purpose of this study was to test for an association between fasting serum triglycerides and incident diabetes, changes in insulin resistance and changes in β-cell function in a Manitoba First Nation cohort. Methods: Study data were from two diabetes screening studies in Sandy Bay First Nation in Manitoba, Canada, collected in 2002/2003 and 2011/2012. The cohort was composed of respondents to both screening studies (n=171). Fasting blood samples and anthropometric, health and demographic data were collected. A generalised linear model with Poisson distribution was used to test for an association between fasting triglycerides and incident diabetes. Results: There were 35 incident cases of diabetes among 128 persons without diabetes at baseline. Participants who developed incident type 2 diabetes were significantly older and had significantly higher body mass index (BMI; p=0.012), total cholesterol (p=0.007), fasting triglycerides (p<0.001), and Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) (p<0.001). Fasting triglyceride level was found to be a statistically significant positive predictor of incident diabetes independent of age, sex and waist circumference at baseline. Participants with triglycerides in the highest tertile (≥2.11 mmol/l) had a 4.0-times higher risk of developing incident diabetes compared to those in the lowest tertile (p=0.03). Notably, neither waist circumference nor BMI were significant predictors of incident diabetes independent of age, sex and triglycerides. Conclusion: Fasting triglycerides may be useful as a clinical predictor of insulin resistance and diabetes development among First Nations populations. Unlike other ethnic groups, BMI and waist circumference may be less important factors in diabetes development.
Brain Subtyping Enhances The Neuroanatomical Discrimination of Schizophrenia

Identifying distinctive subtypes of schizophrenia could ultimately enhance diagnostic and prognostic accuracy. We aimed to uncover neuroanatomical subtypes of chronic schizophrenia patients to test whether stratification can enhance computer-aided discrimination of patients from control subjects. Unsupervised, data-driven clustering of structural MRI (sMRI) data was used to identify 2 subtypes of schizophrenia patients drawn from a US-based open science repository (n = 71) and we quantified classification improvements compared to controls (n = 74) using supervised machine learning. We externally validated the unsupervised and supervised learning models in a heterogeneous German validation sample (n = 316), and characterized symptom, cognition, and longitudinal symptom change signatures. Stratification improved classification accuracies from 68.5% to 73% (subgroup 1) and 78.8% (subgroup 2), respectively. Increased accuracy was also found when models were externally validated, and an average gain of 9% was found in supplementary analyses. The first subgroup was associated with cortical and subcortical volume reductions coupled with substantially longer illness duration, whereas the second subgroup was mainly characterized by cortical reductions, reduced illness duration, and comparatively less negative symptoms. Individuals within each subgroup could be identified using just 10 clinical questions at an accuracy of 81.2%, and differential cognitive and symptom course signatures were suggested in multivariate analyses. Our findings suggest that sMRI-based subtyping enhances the neuroanatomical discrimination of schizophrenia by identifying generalizable brain patterns that align with a clinical staging model of the disorder. These findings could be used to improve illness stratification for biomarker-based computer-aided diagnoses.
Reduced Cardiac Vagal Modulation Impacts on Cognitive Performance in Chronic Fatigue Syndrome

Background Cognitive difficulties and autonomic dysfunction have been reported separately in patients with chronic fatigue syndrome (CFS). A role for heart rate variability (HRV) in cognitive flexibility has been demonstrated in healthy individuals, but this relationship has not as yet been examined in CFS. The objective of this study was to examine the relationship between HRV and cognitive performance in patients with CFS. Methods Participants were 30 patients with CFS and 40 healthy controls; the groups were matched for age, sex, education, body mass index, and hours of moderate exercise/week. Questionnaires were used to obtain relevant medical and demographic information, and assess current symptoms and functional impairment. Electrocardiograms, perceived fatigue/effort and performance data were recorded during cognitive tasks. Between–group differences in autonomic reactivity and associations with cognitive performance were analysed. Results Patients with CFS showed no deficits in performance accuracy, but were significantly slower than healthy controls. CFS was further characterized by low and unresponsive HRV; greater heart rate (HR) reactivity and prolonged HR-recovery after cognitive challenge. Fatigue levels, perceived effort and distress did not affect cognitive performance. HRV was consistently associated with performance indices and significantly predicted variance in cognitive outcomes. Conclusions These findings reveal for the first time an association between reduced cardiac vagal tone and cognitive impairment in CFS and confirm previous reports of diminished vagal activity.
population biology : differentiation of hematopoietic multipotent progenitors to common lymphoid and myeloid progenitors Permalink

The hematopoietic stem cell (HSC) system is a demand control system, with the demand coming from the organism, since the products of the common myeloid and lymphoid progenitor (CMP, CLP respectively) cells are essential for activity and defense against disease. We show how ideas from population biology (combining population dynamics and evolutionary considerations) can illuminate the feedback control of the HSC system by the fully differentiated products, which has recently been verified experimentally. We develop models for the penultimate differentiation of HSC Multipotent Progenitors (MPPs) into CLP and CMP and introduce two concepts from population biology into stem cell biology. The first concept is the Multipotent Progenitor Commitment Response (MPCR) which is the probability that a multipotent progenitor cell follows a CLP route rather than a CMP route. The second concept is the link between the MPCR and a measure of Darwinian fitness associated with organismal performance and the levels of differentiated lymphoid and myeloid cells. We show that many MPCRs are consistent with homeostasis, but that they will lead to different dynamics of cells and signals following a wound or injury and thus have different consequences for Darwinian fitness. We show how coupling considerations of life history to dynamics of the HSC system and its products allows one to compute the selective pressures on cellular processes. We discuss ways that this framework can be used and extended.
A Context-Aware Budget-Constrained Targeted Advertising System for Vehicular Networks

The emergence of self-driving automobiles has drawn great attention to VANETs, where vehicles can interact with each other through wireless communications. A variety of interesting applications thus have been developed to enable vehicles to monitor traffic/congestion, and share information/files real-time. One of most promising services over Vehicular ad hoc networks (VANETs) is the advertisements dissemination that provides users (drivers and passengers) with commercial ads, such as tourism/shopping/restaurant promotions. Owing to the mobility of vehicles, advertisements can spread to anywhere as the vehicles move through vehicle-to-vehicle communications. In this paper, we address the problem of advertisements (ads) dissemination in VANETs with a budget constraint, where ads are first sent from road side units to a selected set of vehicles (seed vehicles), then forwarded to nearby vehicles as seed vehicles moving. We aim to maximize the number of vehicles that receive ads during the dissemination process and prove that this optimization problem is NP-hard. We then propose a heuristic algorithm based on genetic methods to solve the problem. In particular, we consider the user preferences when advertising making sure that a perfect message reaches the perfect audience at the perfect time. Simulation results demonstrate that the proposed algorithm outperforms existing methods by delivering ads to more vehicles under different traffic scenarios.
Enhancing the Azimuthal Mode Couplings in a Helical Fiber Grating by Using Phase Sampling

In a single-helix helical long-period fiber grating (SHLPG), due to the inherent one-fold azimuthal symmetric structure of the fiber core, it is very hard to obtain an efficient high-order azimuthal mode coupling. In this letter, we have proposed and demonstrated theoretically a phase-sampling approach to overcome this obstacle, which is realized by periodically inserting phase shifts in the azimuthal direction of the SHLPG. As results of the proposed method, high-efficient couplings between the fundamental mode and the second- and third-order azimuthal modes in a SHLPG have been successfully demonstrated. The proposed method can be applied to generate and convert high-order orbital angular momentum modes even with the SHLPG.
A Hybrid Model for Multiple Object Category Detection and Localization

This paper presents a new hybrid approach to simultaneous detection and localization of multiple object categories using both generative and discriminative models. Our approach consists of first learning the generative model (pLSA) and discriminative model (SVM) using bag of visual words and merging features, respectively. Our merging feature combines spatial shape and appearance of an object. At the same time context graphs are generated from the labeled training datasets. Then, given a new unlabeled test image, a set of promising hypotheses are generated for each object category using pLSA model and bag of visual words representing each object. The discriminative part verifies each hypothesis using SVM classifier with merging features. In the post-processing stage, context information along with the probabilistic output of the SVM classifier is used to improve the overall performance of the system. A combination of features and context information are used to investigate the accuracy of the system. The performance of the proposed framework is evaluated on the various standards (MIT-CSAIL, UIUC, TUD etc.) and the authors’ own datasets. In experiments we achieved superior results to some state of the art methods over a number of standard datasets.