Miracle-dz/UniFAR-FTUs huggingface dataset:
L-90283-2008R3 UROKINASE RECEPTOR MEDIATES LUNG FIBROBLAST ATTACHMENT AND MIGRATION TOWARDS PROVISIONAL MATRIX PROTEINS THROUGH INTERACTION WITH MULTIPLE INTEGRINS

Sha Zhu, C. L . Gladson, K.E. White, Qiang Ding, J. Stewart, Jr., T. Jin, H. A. Chapman, Jr, and M. A. Olman From the Departments of Medicine and Pathology, University of Alabama at Birmingham, Birmingham AL, the Cardiovascular Research Institute, University of California at San Francisco, San Francisco, CA and the Departments of Pathobiology and Cancer Biology, Lerner Research Institute of the Cleveland Clinic, Cleveland OH
Low-cost coarse airborne particulate matter sensing for indoor occupancy detection

In the energy-efficient smart building, occupancy detection and localization is an area of increasing interest, as services, such as lighting and ventilation, could be targeted towards individual occupants instead of an entire room or floor. Also, an increasing quantity and diversity of environmental sensors are being added to smart buildings to ensure the quality of services provided by the building. The need for particulate matter (PM) sensors in consumer devices such as air purifiers, is an example where manufacturing advances have made the sensors much less expensive than laboratory equipment. Beyond their original intended use, air quality, they can also be used for occupancy monitoring. The work presented in this article proposes to use a low-cost (<; 8 USD) PM sensor to infer the local movement of occupants in a corridor by sensing the resuspension of coarse (≥ 2.5 μm) particles. To obtain meaningful values from the inexpensive sensors, we have calibrated them against a laboratory-grade instrument. After calibration, we conducted a 7.8 hour experiment measuring coarse PM within a pedestrian corridor of a heavily-used office area. Comparing against ground truth data obtained by a camera, we show that the PM sensor readings are correlated with human activity, thus enabling statistical methods to infer one from the other.
A Non-invasive Framework for Detecting Hypercholesterolemia using Computer Vision

- Hypercholesterolemia or high cholesterol levels in the blood is quite commonly observed in the adults today. This condition is generally caused due to unhealthy diets or lack of exercise among other reasons and could lead to stroke or atherosclerosis. The total cholesterol in the blood can be calculated using invasive methods, i
The connectome from the cerebral cortex to the viscera using viral transneuronal tracers

: As an emerging framework in neuroscience, brain connectomics is well suited for investigating key ques-tions on brain complexity by combining viral transneuronal tracing and whole brain graphic methodologies using analytical tools of network science. Transsynaptic viral tract-tracing in the toolbox of neural labeling methods has been a significant development in the connectomics field to decipher the circuit-level architecture of the cerebral cortex. In the present work, we reviewed the current methods enabling structural connectivity from the viscera to the cerebral cortex mapping with viral transneuronal tracers and showed how such neuroanatomic connectomic data could be used to infer new structural and functional information in viscera-cerebral cortex circuits.
Challenges and Opportunities of Rooftop Solar PV Penetration to National Grid: Bangladesh Perspective

The electricity consumption in Bangladesh is higher than ever. A large number of people are still deprived of electricity and demand is rapidly increasing. In this scenario self-powering, our houses will be a great idea which will meet our daily demand. Here, we have discussed the challenges and opportunities of a bi-directional grid-connected rooftop solar power generation system which utilizes the enormous natural resources of solar. We have considered the free rooftop of our housing for power generation. Solar photovoltaicmodules areplaces upon the rooftop free space where sunlight is converted to electricitywhenmade fall on solar PV moduled. This generated electricity is used for self-powering houses and surpassed power will be delivered to the grid company. This system comes with numerous challenges, for example, designing an MPPT for maximizing the solar panel output, grid-tied inverter, investment security and many more. A Transformerless grid tie inverteris proposed which employs a new techniquecalled Dual-Stage Switch for Buck-Boost conversion. It swells the efficiency of the inverter up to 95%. A constant voltage based algorithm is designed to minimize conversion losses. The simulation results are analyzed through PSIM, PROTEUS, MATLAB software. The simulation results show that the rooftop solar energy system has the potential to meet the challenges of the power crises in Bangladesh where land and resources are limited.
Read/write Performance Optimization based on Ceph Heterogeneous Storage

: The arrival of the era of big data has brought great challenges to the storage and management of massive data. Distributed storage is an extremely important solution to storage capacity pressure and cost in the era of big data. As an emerging open source distributed system, Ceph can provide three storage functions: object storage, block storage and file storage. The pseudo-random data mapping function CRUSH is used to complete the mapping of data to storage nodes, eliminating the traditional centralized metadata nodes, while system scalability has no theoretical upper limit. However, Ceph adopts a strong consistency strategy and only reads the primary node when reading objects, which results in low read-write efficiency. At the same time, Ceph does not perceive heterogeneous storage and does not make full use of SSD storage characteristics. Therefore, a combined storage strategy based on weak consistency model and multi-node read optimization is proposed to optimize Ceph storage. The system can effectively reduce the read-write delay of Ceph and improve its storage efficiency. Experiments show that compared with the original mechanism of Ceph, the proposed optimization mechanism can improve the writing efficiency by more than ten times and the reading efficiency by more than six times.
Investigation of Neurodegeneration Investigation of Neurodegeneration

Alzheimer’s, Parkinson's, and multiple sclerosis are all major complex neurodegenerative disorders. Neurodegeneration results from nervous system cell deterioration thereby yielding severe cognitive, behavioral, and motor impairments which greatly diminish quality of life. This paper investigates the neurodegeneration of such diseases while highlighting current and novel therapeutic approaches, relevant molecular mechanisms, and risk factors to identify future directions. To treat and one day overcome these diseases, more must be understood.
Identiﬁcation of Virulence Genes in a Pathogenic Strain of Pseudomonas aeruginosa by Representational Difference Analysis

Pseudomonas aeruginosa is an opportunistic pathogen that may cause severe infections in humans and other vertebrates. In addition, a human clinical isolate of P. aeruginosa , strain PA14, also causes disease in a variety of nonvertebrate hosts, including plants, Caenorhabditis elegans , and the greater wax moth, Galleria mellonella . This has led to the development of a multihost pathogenesis system in which plants, nematodes, and insects have been used as adjuncts to animal models for the identiﬁcation of P. aeruginosa virulence factors. Another approach to identifying virulence genes in bacteria is to take advantage of the natural differences in patho- genicity between isolates of the same species and to use a subtractive hybridization technique to recover relevant genomic differences. The sequenced strain of P. aeruginosa , strain PAO1, has substantial differences in virulence from strain PA14 in several of the multihost models of pathogenicity, and we have utilized the technique of representational difference analysis (RDA) to directly identify genomic differences between P. aeruginosa strains PA14 and PAO1. We have found that the pilC , pilA , and uvrD genes in strain PA14 differ substantially from their counterparts in strain PAO1. In addition, we have recovered a gene homologous to the ybtQ gene from Yersinia , which is speciﬁcally present in strain PA14 but absent in strain PAO1. Mutation of the ybtQ homolog in P. aeruginosa strain PA14 signiﬁcantly attenuates the virulence of this strain in both G. mellonella and a burned mouse model of sepsis to levels comparable to those seen with PAO1. This suggests that the increased virulence of P. aeruginosa strain PA14 compared to PAO1 may relate to speciﬁc genomic differences identiﬁable by RDA. mo-bilized plasmid integrated homologous recombination. sertional mutation was conﬁrmed by Southern hybridization of outstretched
Can Brief Interventions Improve Functioning in Engineering Student Dyads?

Poor interpersonal dynamics can hinder collaboration, but engineering educators have failed to address this problem. Short interactive exercises may ameliorate such problems. We introduced an introductory lecture and interactive exercises into engineering classes to evaluate their effects on interpersonal outcomes in student dyads. Two large-sample quantitative studies and one small qualitative study were conducted ( N = 227) to evaluate the exercises. Although the qualitative results (Study 2) indicated mixed effects, we found no evidence in the large-sample studies that the intervention improved any outcomes. The results suggest that cohesion and similar factors are enhanced through collaboration, and short exercises do not cause any further enhancement. Intensive long-term interventions may be necessary to produce stronger effects than acquaintance.
Algorithm Theoretical Basis Document for GEDI Footprint Aboveground Biomass Density

The Global Ecosystem Dynamics Investigation (GEDI) lidar is a multibeam laser altimeter on the International Space Station (ISS). GEDI is the first spaceborne instrument designed to measure vegetation height and to quantify aboveground carbon stocks in temperate and tropical forests and woodlands. This document describes the algorithm theoretical basis underpinning the development of the GEDI Level‐4A (GEDI04_A) footprint aboveground biomass density (AGBD) data product. The GEDI04_A data product contains estimates of AGBD for individual GEDI footprints and associated prediction intervals. The algorithm uses GEDI02_A relative height metrics and 13 linear models to predict AGBD in 32 combinations of plant functional type and world region within the observation limits of the ISS. GEDI04_A models for the release 1 and release 2 data products were developed using 8,587 quality‐filtered simulated GEDI waveforms associated with field estimates of AGBD in 21 countries. Although this is the most geographically comprehensive data available for the development of AGBD models using lidar remote sensing, important regions are underrepresented, including the forests of continental Asia, deciduous broadleaf forests and savannas of the dry tropics, and evergreen broadleaf forests north of Australia. We describe the scientific and statistical assumptions required to develop globally representative estimates of AGBD using GEDI lidar, including generalization beyond training data, and exclusion of GEDI02_A observations that do not meet requirements of the GEDI04_A algorithm. The footprint‐level predictions generated by this process provide globally comprehensive estimates of AGBD. These footprint‐level predictions are a prerequisite for the GEDI04_B gridded AGBD data product.
Model development for the design of an anaerobic upflow filter separated in two and three phases

In this study, the development of models for the design of an anaerobic upflow filters separated into two and three phases are presented, DI-FAFS and TRI-FAFS. Both reactors have been evaluated in the COD removal performance on a total of 54 tests. The experimentalfactors are the volumetric organic load, the temperature and the depth relationship between two consective phases. The conceptual modelis based on equations derived from a mass balance under stationary conditions dS/dt = 0 and advectives dS/dZ ≠ 0; eight equationsapplicable to the DI-FAFS and TRI-FAFS reactors; four equations for each reactor.The equations to obtain the parameters of degradationof organic matter were compared with bibliographic references. Two models were selected and proposed, after adjusted to observationswith R2 adjusted greater than 0.7 and with standard errors of estimation and the absolute average error in the minimum values.
Hybrid artificial neural network and locally weighted regression models for lane-based short-term urban traffic flow forecasting

ABSTRACT In recent years, there has been considerable research interest in short-term traffic flow forecasting. However, forecasting models offering a high accuracy at a fine temporal resolution (e.g. 1 or 5 min) and lane level are still rare. In this study, a combination of genetic algorithm, neural network and locally weighted regression is used to achieve optimal prediction under various input and traffic settings. The genetically optimized artificial neural network (GA-ANN) and locally weighted regression (GA-LWR) models are developed and tested, with the former forecasting traffic flow every 5-min within a 30-min period and the latter for forecasting traffic flow of a particular 5-min period of each for four lanes of an urban arterial road in Beijing, China. In particular, for morning peak and off-peak traffic flow prediction, the GA-ANN 5-min traffic flow model results in average errors of 3–5% and most 95th percentile errors of 7–14% for each of the four lanes; for the peak and off-peak time traffic flow predictions, the GA-LWR 5-min traffic flow model results in average errors of 2–4% and most 95th percentile errors are lower than 10% for each of the four lanes. When compared to previous models that usually offer average errors greater than 6–15%, such empirical findings should be of interest to and instrumental for transportation authorities to incorporate in their city- or state-wide Advanced Traveller Information Systems (ATIS).
Debugging by Design: Students’ Reflections on Designing Buggy E-Textile Projects

(style: Abstract title) Much attention has focused on designing tools and activities that support learners in designing fully finished and functional applications such as games, robots, or e-textiles to be shared with others. But helping students learn to debug their applications often takes on a surprisingly more instructionist stance by giving them checklists, teaching them strategies or providing them with test programs. The idea of designing bugs for learning—or debugging by design —makes learners again agents of their own learning and, more importantly, of making and solving mistakes. In this paper, we report on our first implementation of “debugging by design” activities in a classroom of 25 high school students over a period of eight hours as part of a longer e-textiles unit. Here students were asked to craft buggy circuits and code for their peers to solve. In this paper we introduce the design of the debugging by design unit and, drawing on observations and interviews with students and the teacher, address the following research questions: (1) What did students gain from designing and solving bugs for others? (2) How did this experience shape students’ completion of the e-textiles unit? In the discussion, we address how debugging by design contributes to students’ learning of debugging skills.
Influence of Antenna Beamwidth on the Observed Characteristics of an Outdoor Vehicular Channel

—Autonomous vehicles will require vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, which require very reliable, high-speed and low latency communications. Fifth-generation (5G) and beyond wireless networks become a solid base to build such infrastructure, using millimetre wave (mmWave) frequencies and beamforming techniques. The novelty of this paper is to investigate the influence of Doppler shift, Doppler spread and signal attenuation on a V2I communication channel employing a 26GHz phased array antenna from Anokiwave. This antenna facet acts as a spatial filter, eliminating unwanted multipath components (MPCs) and allowing the main beam to be electronically steered. The results show line-of-sight (LoS) communications between a base station (BS) and User Equipment (UE) for up to 150m distance for a transmitted power of 22dBm. A stable channel gain was observed during the measurements and related to the actual direction of the main beam. Further, the results indicated an attenuation between 78.2dB and 78.8dB when a vehicle obscures the LoS connection between the transmitter and the receiver. However, the appearance probability shows less attenuation experienced by the received signal when the phased array antenna employed a uniform beamwidth. Moreover, indirect communication, such as the non-line-of-sight scenario NLoS, is observed due to the reflections and scattering. Further, by driving the test vehicle in a car park with almost full of parked cars and at a speed of 5 mph, a very small Doppler shift was observed because of the suppression through spatial filtering from the array of the rich scattering and reflections within the environment.
NDT_A_300719 1907..1915

Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, People’s Republic of China; Department of Psychiatry, Shenzhen Kangning Hospital, Shenzhen, People’s Republic of China; Department of Psychiatry, Shenzhen Health Development Research Center, Shenzhen, People’s Republic of China; Clinical Psychology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, People’s Republic of China; Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology (scUT), Guangzhou, People’s Republic of China; Department of Psychiatry, Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, People’s Republic of China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China; CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, People’s Republic of China
Exploring the role of the potential surface in the behaviour of early warning signals

The theory of critical slowing down states that a system displays increasing relaxation times as it approaches a critical transition. These changes can be seen in statistics generated from timeseries data, which can be used as early warning signals of a transition. Such early warning signals would be of value for emerging infectious diseases or to understand when an endemic disease is close to elimination. However, in applications to a variety of epidemiological models there is frequent disagreement with the general theory of critical slowing down, with some indicators performing well on prevalence data but not when applied to incidence data. Furthermore, the alternative theory of critical speeding up predicts contradictory behaviour of early warning signals prior to some stochastic transitions. To investigate the possibility of observing critical speeding up in epidemiological models we characterise the behaviour of common early warning signals in terms of a system's potential surface and noise around a quasi-steady state. We then describe a method to obtain these key features from timeseries data, taking as a case study a version of the SIS model, adapted to demonstrate either critical slowing down or critical speeding up. We show this method accurately reproduces the analytic potential surface and diffusion function, and that these results can be used to determine the behaviour of early warning signals and correctly identify signs of both critical slowing down and critical speeding up.
Neighbourhood deprivation across eight decades and late-life cognitive function in the Lothian Birth Cohort 1936: a life-course study

Introduction Although neighbourhood may predict late-life cognitive function, studies mostly relies on measurements at a single time point, with few investigations applying a life-course approach. Further, it is unclear whether the associations between neighbourhood and cognitive tests scores relate to specific cognitive domains or general ability. This study explored how neighbourhood deprivation across eight decades contributes to late-life cognitive function. Methods Data were drawn from the Lothian Birth Cohort 1936 (n=1091) with cognitive function measured through 10 tests at ages 70, 73, 76, 79 and 82. Participants residential history was gathered with lifegrid questionnaires and linked to neighbourhood deprivation in childhood, young adulthood, and mid-to-late adulthood. Associations were tested with latent growth curve models for levels and slopes of general (g) and domain-specific abilities (visuospatial ability, memory, and processing speed), life-course associations were explored with path analysis. Results Higher mid-to-late adulthood neighbourhood deprivation was associated with lower age 70 levels (beta=-0.113, 95%CI: -0.205, -0.021) and faster decline of g over 12 years (beta=-0.160, 95%CI: -0.290, -0.031). Initially-apparent findings with domain-specific cognitive functions (e.g. processing speed) were due to their shared variance with g. Path analyses suggested that childhood neighbourhood disadvantage is indirectly linked to late-life cognitive function through education and selective residential mobility. Conclusions To our knowledge, we provide the most comprehensive assessment of the relationship between life-course neighbourhood deprivation and cognitive ageing. Living in advantaged areas in mid-to-late adulthood may contribute to better cognitive function and slower decline, whereas advantaged childhood neighbourhood environment likely affects functioning through cognitive reserves.