Miracle-dz/UniFAR-FTUs huggingface dataset:
5-HT2A Receptor-Induced Morphological Reorganization of PKCγ-Expressing Interneurons Gates Inflammatory Mechanical Allodynia in Rat

Mechanical allodynia, a widespread pain symptom that still lacks effective therapy, is associated with the activation of a dorsally directed polysynaptic circuit within the spinal dorsal horn (SDH) or medullary dorsal horn (MDH), whereby tactile inputs into deep SDH/MDH can gain access to superficial SDH/MDH, eliciting pain. Inner lamina II (IIi) interneurons expressing the γ isoform of protein kinase C (PKCγ+) are key elements for allodynia circuits, but how they operate is still unclear. Combining behavioral, ex vivo electrophysiological, and morphological approaches in an adult rat model of facial inflammatory pain (complete Freund's adjuvant, CFA), we show that the mechanical allodynia observed 1 h after CFA injection is associated with the following (1) sensitization (using ERK1/2 phosphorylation as a marker) and (2) reduced dendritic arborizations and enhanced spine density in exclusively PKCγ+ interneurons, but (3) depolarized resting membrane potential (RMP) in all lamina IIi PKCγ+/PKCγ− interneurons. Blocking MDH 5HT2A receptors (5-HT2AR) prevents facial mechanical allodynia and associated changes in the morphology of PKCγ+ interneurons, but not depolarized RMP in lamina IIi interneurons. Finally, activation of MDH 5-HT2AR in naive animals is enough to reproduce the behavioral allodynia and morphological changes in PKCγ+ interneurons, but not the electrophysiological changes in lamina IIi interneurons, induced by facial inflammation. This suggests that inflammation-induced mechanical allodynia involves strong morphological reorganization of PKCγ+ interneurons via 5-HT2AR activation that contributes to open the gate for transmission of innocuous mechanical inputs to superficial SDH/MDH pain circuitry. Preventing 5-HT2AR-induced structural plasticity in PKCγ+ interneurons might represent new avenues for the specific treatment of inflammation-induced mechanical hypersensitivity. SIGNIFICANCE STATEMENT Inflammatory or neuropathic pain syndromes are characterized by pain hypersensitivity such as mechanical allodynia (pain induced by innocuous mechanical stimuli). It is generally assumed that mechanisms underlying mechanical allodynia, because they are rapid, must operate at only the level of functional reorganization of spinal or medullary dorsal horn (MDH) circuits. We discovered that facial inflammation-induced mechanical allodynia is associated with rapid and strong structural remodeling of specifically interneurons expressing the γ isoform of protein kinase C (PKCγ) within MDH inner lamina II. Moreover, we elucidated a 5-HT2A receptor to PKCγ/ERK1/2 pathway leading to the behavioral allodynia and correlated morphological changes in PKCγ interneurons. Therefore, descending 5-HT sensitize PKCγ interneurons, a putative “gate” in allodynia circuits, via 5-HT2A receptor-induced structural reorganization.
Naturalistic Lane Change Analysis for Human-Like Trajectory Generation

Human-like driving is of great significance for safety and comfort of autonomous vehicles, but existing trajectory planning methods for on-road vehicles rarely take the similarity with human behavior into consideration. From a representative trajectory-generation-based planning algorithm, this paper analyzes the systematic deviation of the generated trajectories from human trajectories, and proposes a new scheme of trajectory generation by compensating the deviation using a deviation profile learned from data. Experimental results show that the proposed trajectory generator is able to fit the human trajectories considerably better than the original one with only one additional degree of freedom. When used for online trajectory planning, with the same level of computational complexity, the proposed generator is able to generate trajectories that are more human-like than original generator does, which provides basis for autonomous vehicle to perform human-like trajectory planning.
An Adversarial Learning Approach to Medical Image Synthesis for Lesion Removal

The analysis of lesion within medical image data is desirable for efficient disease diagnosis, treatment and prognosis. The common lesion analysis tasks like segmentation and classification are mainly based on supervised learning with well-paired image-level or voxel-level labels. However, labeling the lesion in medical images is laborious requiring highly specialized knowledge. Inspired by the fact that radiologists make diagnoses based on expert knowledge on “healthiness” and “unhealthiness” developed from extensive experience, we propose an medical image synthesis model named abnormal-to-normal translation generative adversarial network (ANT-GAN) to predict a normallooking medical image based on its abnormal-looking counterpart without the need of paired data for training. Unlike typical GANs, whose aim is to generate realistic samples with variations, our more restrictive model aims at producing the underlying normal-looking image corresponding to an image containing lesions, and thus requires a specialized design. With an ability to segment normal from abnormal tissue, our model is able to generate a highly realistic lesion-free medical image based on its true lesioncontaining counterpart. Being able to provide a “normal” version of a medical image (possibly the same image if there is no illness) is not only an intriguing topic, but also can serve as a pre-processing and provide useful side information for medical imaging tasks like lesion segmentation or classification validated by our experiments.
Trends in the socioeconomic patterning of overweight/obesity in India: a repeated cross-sectional study using nationally representative data

Objectives We aimed to examine trends in prevalence of overweight/obesity among adults in India by socioeconomic position (SEP) between 1998 and 2016. Design Repeated cross-sectional study using nationally representative data from India collected in 1998/1999, 2005/2006 and 2015/2016. Multilevel regressions were used to assess trends in prevalence of overweight/obesity by SEP. Setting 26, 29 and 36 Indian states or union territories, in 1998/99, 2005/2006 and 2015/2016, respectively. Participants 628 795 ever-married women aged 15–49 years and 93 618 men aged 15–54 years. Primary outcome measure Overweight/obesity defined by body mass index >24.99 kg/m2. Results Between 1998 and 2016, overweight/obesity prevalence increased among men and women in both urban and rural areas. In all periods, overweight/obesity prevalence was consistently highest among higher SEP individuals. In urban areas, overweight/obesity prevalence increased considerably over the study period among lower SEP adults. For instance, between 1998 and 2016, overweight/obesity prevalence increased from approximately 15%–32% among urban women with no education. Whereas the prevalence among urban men with higher education increased from 26% to 34% between 2005 and 2016, we did not observe any notable changes among high SEP urban women between 1998 and 2016. In rural areas, more similar increases in overweight/obesity prevalence were found among all individuals across the study period, irrespective of SEP. Among rural women with higher education, overweight/obesity increased from 16% to 25% between 1998 and 2016, while the prevalence among rural women with no education increased from 4% to 14%. Conclusions We identified some convergence of overweight/obesity prevalence across SEP in urban areas among both men and women, with fewer signs of convergence across SEP groups in rural areas. Efforts are therefore needed to slow the increasing trend of overweight/obesity among all Indians, as we found evidence suggesting it may no longer be considered a ‘diseases of affluence’.
Key Enabling Technologies for Point-of-Care Diagnostics

A major trend in biomedical engineering is the development of reliable, self-contained point-of-care (POC) devices for diagnostics and in-field assays. The new generation of such platforms increasingly addresses the clinical and environmental needs. Moreover, they are becoming more and more integrated with everyday objects, such as smartphones, and their spread among unskilled common people, has the power to improve the quality of life, both in the developed world and in low-resource settings. The future success of these tools will depend on the integration of the relevant key enabling technologies on an industrial scale (microfluidics with microelectronics, highly sensitive detection methods and low-cost materials for easy-to-use tools). Here, recent advances and perspectives will be reviewed across the large spectrum of their applications.
Training Generative Adversarial Networks Via Turing Test

In this article, we introduce a new mode for training Generative Adversarial Networks (GANs). Rather than minimizing the distance of evidence distribution $\tilde{p}(x)$ and the generative distribution $q(x)$, we minimize the distance of $\tilde{p}(x_r)q(x_f)$ and $\tilde{p}(x_f)q(x_r)$. This adversarial pattern can be interpreted as a Turing test in GANs. It allows us to use information of real samples during training generator and accelerates the whole training procedure. We even find that just proportionally increasing the size of discriminator and generator, it succeeds on 256x256 resolution without adjusting hyperparameters carefully.
Trajectory optimization for Car-Like Vehicles in Structured and Semi-Structured Environments*

In this paper we propose a local trajectory planner for front steered car-like vehicles based on a combined direct optimization of the lateral and longitudinal vehicle guidance. The planner is designed for continuously optimizing a local trajectory based on a provided reference path in a structured or semi-structured driving environment. The planner respects constraints of the driving dynamics as well as actuator lim- itations and avoids static and dynamic obstacles. It is not restricted to a limited set of maneuvers. The implementation of the planner allows an online adaptation of the resulting driving behavior to satisfy different comfort or driving style demands. After Software-in-the-Loop simulations the algorithm was tested in two different real-world driving scenarios in ika’s automated vehicle which provides interfaces for full lateral and longitudinal control.
Evaluating Location Compliance Approaches for Automated Road Vehicles

This work presents techniques for efficiently checking location compliance of automated road vehicles. We refer to location compliance as an allowed translational and rotational positioning of a vehicle on a road network, i.e., the vehicle does not enter forbidden lanes or regions reserved for other traffic participants, such as bike lanes or dedicated bus routes. Previous work has mostly focused on efficient collision detection between traffic participants and static obstacles represented as bounded sets. We formulate location compliance as a set enclosure problem, which cannot be solved directly with collision detection; thus, different algorithms from computational geometry have to be applied. We present polygon enclosure and boundary mesh generation approaches and evaluate them using existing road geometries from the CommonRoad database. For a fair comparison, we generate thousands of random instances which are evaluated statistically.
Clonality and geographic structure of host-specialized populations of Corynespora cassiicola causing emerging target spot epidemics in the southeastern United States

Corynespora cassiicola is a destructive plant-pathogenic fungus causing widespread target spot epidemics, including outbreaks on cotton, soybean, and tomato in the southeastern United States. Previous studies revealed that populations from the three hosts are genetically distinct and host specialized. Although variation in aggressiveness to cotton and tomato were observed, no genetic diversity was detected within populations sampled from each of these hosts. We aimed to gain a better understanding of the emerging target spot epidemics by developing microsatellite markers for C. cassiicola to assess genetic variation, population structure, and to infer modes of reproduction and mechanisms of dispersal. Two hundred sixty-five isolates from cotton, soybean, tomato, and other host plants were genotyped with 13 microsatellite markers. Genotypic diversity revealed genetic variation within each of the populations collected from different hosts, with the population from cotton dominated by clonal genotypes and showing the least genetic diversity. In addition, C. cassiicola populations on different host species were genetically distinct and structured based on host species. No association between genetic and geographic distances was identified in the tomato populations, and the association in cotton populations was low. However, significant regional geographic structure was detected in the soybean populations of C. cassiicola. These results further support previous findings of introduced host specialized isolates or the evolution of more aggressive strains on each host. The lack of geographic structure suggests that the clones on cotton and tomato spread rapidly, or similar founder populations were established by human-mediated dispersal, and that dispersal is not limited. However, regional geographic structure of populations on soybean suggests limited dispersal among more established populations of C. cassiicola, or genetic differences in founder populations that colonized different geographic areas.
Crowd-Informed Goal Models

A topic of recent interest is how to apply crowdsourced information toward producing better software requirements. A research question that has received little attention so far is how to leverage crowdsourced information toward creating better-informed models of requirements. In this paper, we contribute a method following which information in online discussions may be leveraged toward constructing goal models. A salient feature of our method is that it applies high-level queries to draw out potentially relevant information from discussions. We also give a subjective logic-based method for deriving an ordering of the goals based on the amount of supporting and rebutting evidence in the discussions. Such an ordering can potentially be applied toward prioritizing goals for implementation.
Effect of a four-week virtual reality-based training versus conventional therapy on upper limb motor function after stroke: A multicenter parallel group randomized trial

Background Virtual reality-based training has found increasing use in neurorehabilitation to improve upper limb training and facilitate motor recovery. Objective The aim of this study was to directly compare virtual reality-based training with conventional therapy. Methods In a multi-center, parallel-group randomized controlled trial, patients at least 6 months after stroke onset were allocated either to an experimental group (virtual reality-based training) or a control group receiving conventional therapy (16x45 minutes within 4 weeks). The virtual reality-based training system replicated patients´ upper limb movements in real-time to manipulate virtual objects. Blinded assessors tested patients twice before, once during, and twice after the intervention up to 2-month follow-up for dexterity (primary outcome: Box and Block Test), bimanual upper limb function (Chedoke-McMaster Arm and Hand Activity Inventory), and subjective perceived changes (Stroke Impact Scale). Results 54 eligible patients (70 screened) participated (15 females, mean age 61.3 years, range 20–81 years, time since stroke 3.0±SD 3 years). 22 patients were allocated to the experimental group and 32 to the control group (3 drop-outs). Patients in the experimental and control group improved: Box and Block Test mean 21.5±SD 16 baseline to mean 24.1±SD 17 follow-up; Chedoke-McMaster Arm and Hand Activity Inventory mean 66.0±SD 21 baseline to mean 70.2±SD 19 follow-up. An intention-to-treat analysis found no between-group differences. Conclusions Patients in the experimental and control group showed similar effects, with most improvements occurring in the first two weeks and persisting until the end of the two-month follow-up period. The study population had moderate to severely impaired motor function at entry (Box and Block Test mean 21.5±SD 16). Patients, who were less impaired (Box and Block Test range 18 to 72) showed higher improvements in favor of the experimental group. This result could suggest that virtual reality-based training might be more applicable for such patients than for more severely impaired patients. Trial registration ClinicalTrials.gov NCT01774669.
Human monocytes and macrophages regulate immune tolerance via integrin αvβ8–mediated TGFβ activation

Monocytes are crucial immune cells involved in regulation of inflammation either directly or via differentiation into macrophages in tissues. However, many aspects of how their function is controlled in health and disease are not understood. Here we show that human blood monocytes activate high levels of the cytokine TGF&bgr;, a pathway that is not evident in mouse monocytes. Human CD14+, but not CD16+, monocytes activate TGF&bgr; via expression of the integrin &agr;v&bgr;8 and matrix metalloproteinase 14, which dampens their production of TNF&agr; in response to LPS. Additionally, when monocytes differentiate into macrophages, integrin expression and TGF&bgr;-activating ability are maintained in anti-inflammatory macrophages but down-regulated in pro-inflammatory macrophages. In the healthy human intestine, integrin &agr;v&bgr;8 is highly expressed on mature tissue macrophages, with these cells and their integrin expression being significantly reduced in active inflammatory bowel disease. Thus, our data suggest that integrin &agr;v&bgr;8–mediated TGF&bgr; activation plays a key role in regulation of monocyte inflammatory responses and intestinal macrophage homeostasis.
Differential Gene Expression in Macrophages From Human Atherosclerotic Plaques Shows Convergence on Pathways Implicated by Genome-Wide Association Study Risk Variants

Objective— Plaque macrophages are intricately involved in atherogenesis and plaque destabilization. We sought to identify functional pathways in human plaque macrophages that are differentially regulated in respect of (1) plaque stability and (2) lipid content. We hypothesized that differentially regulated macrophage gene sets would relate to genome-wide association study variants associated with risk of acute complications of atherosclerosis. Approach and Results— Forty patients underwent carotid magnetic resonance imaging for lipid quantification before endarterectomy. Carotid plaque macrophages were procured by laser capture microdissection from (1) lipid core and (2) cap region, in 12 recently symptomatic and 12 asymptomatic carotid plaques. Applying gene set enrichment analysis, a number of gene sets were found to selectively upregulate in symptomatic plaque macrophages, which corresponded to 7 functional pathways: inflammation, lipid metabolism, hypoxic response, cell proliferation, apoptosis, antigen presentation, and cellular energetics. Predicted upstream regulators included IL-1&bgr;, TNF-&agr;, and NF-&kgr;B. In vivo lipid quantification by magnetic resonance imaging correlated most strongly with the upregulation of genes of the IFN/STAT1 pathways. Cross-interrogation of gene set enrichment analysis and meta-analysis gene set enrichment of variant associations showed lipid metabolism pathways, driven by genes coding for APOE and ABCA1/G1 coincided with known risk-associated SNPs (single nucleotide polymorphisms) from genome-wide association studies. Conclusions— Macrophages from recently symptomatic carotid plaques show differential regulation of functional gene pathways. There were additional quantitative relationships between plaque lipid content and key gene sets. The data show a plausible mechanism by which known genome-wide association study risk variants for atherosclerotic complications could be linked to (1) a relevant cellular process, in (2) the key cell type of atherosclerosis, in (3) a human disease-relevant setting.
ESTUDO DE CASO DO CORREDOR ECOLÓGICO E URBANO DO MINDU: MAPEAMENTO DE PASSIVOS AMBIENTAIS DO PARQUE MUNICIPAL DO MINDU, MANAUS-AM

Nas primeiras incursões operacionais e mesmo administrativas no parque municipal do Mindu, percebeu-se que muitos dados eram coletados e arquivados dispersadamente sem que houvesse algum tratamento ou análise criteriosa sobre os mesmos. Com muitos dados dispersos estabeleceu-se um procedimento de lançamento dos mesmos em um sistema único, escolhendo um que desse a oportunidade de gerar e disponibilizar informações geográficas sobre a área, utilizando, portanto, um SIG livre, o SPRING, por possuir um amplo espectro de recepção para o tratamento dos dados para obtenção de informações precisas sobre a unidade de conservação, a custo zero. O presente trabalho objetivou de forma simples e barata, aperfeiçoar os recursos humanos e materiais, para obter um mapeamento preciso dos passivos ambientais que afetavam o parque municipal do Mindu, direcionando as ações das equipes que compõem a gestão desta unidade de conservação, para concentrar os esforços no que é de maior problemática no presente para garantir a perpetuidade do patrimônio natural existente nesta área protegida a longo prazo.
Towards Context-Aware Process Guidance in Cyber-Physical Systems with Augmented Reality

Assembly, configuration, maintenance, and repair processes in cyber-physical systems (e.g., a press line in a plant) comprise a multitude of complex tasks, whose execution needs to be controlled, coordinated and monitored. Amongst others, a process-centric guidance of users (e.g. service operators) is required, taking the high variability in the assembly of cyber-physical systems (e.g. press line variability) into account. Moreover, the tasks to be performed along these processes may be related to physical components, sensors and actuators, which need to be properly recognized, integrated and operated. In order to digitize cyber-physical processes as well as to guide users in a process-centric way, therefore, we suggest integrating process management technology, sensor/actuator interfaces, and augmented reality techniques. The paper discusses fundamental requirements for such an integration and presents an approach for process-centric user guidance that combines context and process management with augmented reality enhanced tasks. For evaluation purposes, we analyzed the cyber-physical processes of pharmaceutical packaging machines and implemented selected ones based on the approach. Overall, we are able to demonstrate the usefulness of context-aware process management for the flexible support of cyber-physical processes in the Industrial Internet of Things.
Context Impacts in Accelerometer-Based Walk Detection and Step Counting

Walk detection (WD) and step counting (SC) have become popular applications in the recent emergence of wearable devices. These devices monitor user states and process data from MEMS-based accelerometers and optional gyroscope sensors. Various algorithms have been proposed for WD and SC, which are generally sensitive to the contexts of applications, i.e., (1) the locations of sensor placement; (2) the sensor orientations; (3) the user’s walking patterns; (4) the preprocessing window sizes; and (5) the sensor sampling rates. A thorough understanding of how these dynamic factors affect the algorithms’ performances is investigated and compared in this paper. In particular, representative WD and SC algorithms are introduced according to their design methodologies. A series of experiments is designed in consideration of different application contexts to form an experimental dataset. Different algorithms are then implemented and evaluated on the dataset. The evaluation results provide a quantitative performance comparison indicating the advantages and weaknesses of different algorithms under different application scenarios, giving valuable guidance for algorithm selection in practical applications.