Miracle-dz/UniFAR-FTUs huggingface dataset:
STING Contributes to Antiglioma Immunity via Triggering Type I IFN Signals in the Tumor Microenvironment

Ohkuri, Ghosh, Kosaka, and colleagues show that a STING-mediated DNA-sensor signaling is involved in IFN induction in the sterile microenvironment of brain tumor that enhances antitumor immunity, providing a proof-of-concept for developing STING agonists for cancer immunotherapy. Although type I IFNs play critical roles in antiviral and antitumor activity, it remains to be elucidated how type I IFNs are produced in sterile conditions of the tumor microenvironment and directly affect tumor-infiltrating immune cells. Mouse de novo gliomas show increased expression of type I IFN messages, and in mice, CD11b+ brain-infiltrating leukocytes (BIL) are the main source of type I IFNs that are induced partially in a STING (stimulator of IFN genes)-dependent manner. Consequently, glioma-bearing StingGt/Gt mice showed shorter survival and lower expression levels of Ifns compared with wild-type mice. Furthermore, BILs of StingGt/Gt mice showed increased CD11b+ Gr-1+ immature myeloid suppressor and CD25+ Foxp3+ regulatory T cells (Treg) and decreased IFNγ-producing CD8+ T cells. CD4+ and CD8+ T cells that received direct type I IFN signals showed lesser degrees of regulatory activity and increased levels of antitumor activity, respectively. Finally, intratumoral administration of a STING agonist (cyclic diguanylate monophosphate; c-di-GMP) improved the survival of glioma-bearing mice associated with enhanced type I IFN signaling, Cxcl10 and Ccl5, and T-cell migration into the brain. In combination with subcutaneous OVA peptide vaccination, c-di-GMP increased OVA-specific cytotoxicity of BILs and prolonged their survival. These data demonstrate significant contributions of STING to antitumor immunity via enhancement of type I IFN signaling in the tumor microenvironment and suggest a potential use of STING agonists for the development of effective immunotherapy, such as the combination with antigen-specific vaccinations. Cancer Immunol Res; 2(12); 1199–208. ©2014 AACR.
A comparative analysis of anxiety in a precompetitive state between badminton and Tennis University levelled players

The primary purpose was to compare anxiety in a pre-competitive state of male badminton and tennis players. A total of twenty (N=20) male racket sports players with their age ranging between 17 to 23 years were selected. Further the total subjects were divided into 10 subjects each from badminton and tennis sports practices. The subjects were selected from the study centre of Lakshmibai National Institute of Physical Education both Gwalior and Guwahati center. In order to measure the pre competitive anxiety level, questionnaires, namely, Illiniois self-evaluation Inventory and Sports competition anxiety test (SCAT) were implemented on racket sports players and used as the tool for data collection. In order to analyze the data collected descriptive statistics such as mean, standard deviation and comparative statistics of multivariate analysis of variance was employed and was tested at 0.05 level of significance. The multivariate analysis was found to be insignificant in the sub factors of pre-competitive anxiety betwee n badminton and tennis players as the value of Wilks’ lambda (0.818) was found to be insignificant at 0.05 level of significance (p= 0.686). The insignificant value of MANOVA depicted that the sub factors of pre-competitive anxiety was at similar levels when compared between the male badminton and tennis sports players.
Smart Prediction of Water Quality System for Aquaculture Using Machine Learning Algorithms

Intelligent and Smart aquaculture is nowadays one of the most important latest development trends in the field of the aquaculture industry to overcome the problems of the farmers due to water quality monitoring, food feeding, temperature imbalance, and recycling of water. Smart water quality prediction in the real-time environment using intelligent machine learning models and IoT sensors establishes the basis for the evaluation, planning, and intelligent regulation of the aquaculture environment. The purpose of this study is to propose an intelligent Machine learning and IoT-based Biofloc system that enhances the efficiency, production, water recycling system, and automatic food feeding system. This paper proposed a system that collects process data from sensors, stores data in the cloud, and analyses it using different latest machine learning models such as a Decision tree classification model, and Random Forest Model to predict the water quality and provides real-time monitoring through an android app. The article presented a system that collects data using sensors, analyzes them using a machine learning model, generates decisions with the help of Artificial Intelligence (AI), and sends notifications to the user. The proposed linear regression, Random Forest, and other ML models have been implemented and tested to validate and achieve a satisfactory result. A performance comparative analysis of the different ML algorithms has been conducted based on a few metrics such as accuracy, recall, precision, and F1-score. Random Forest Classifier has surpassed all the other models in terms of accuracy, recall, precision, and F1 score. Random Forest Classifier has exhibited an accuracy of 73.76%, recall, precision, and F1-score of 90%, 75%, and 82% respectively.
The Road To Excellence: The Acquisition of Expert Performance in the Arts and Sciences, Sports, and Games

Contents: Preface. K.A. Ericsson, The Acquisition of Expert Performance: An Introduction to Some of the Issues. N. Charness, R. Krampe, U. Mayr, The Role of Practice and Coaching in Entrepreneurial Skill Domains: An International Comparison of Life-Span Chess Skill Acquisition. J.L. Starkes, J.M. Deakin, F. Allard, N.J. Hodges, A. Hayes, Deliberate Practice in Sports: What Is It Anyway? J.A. Sloboda, The Acquisition of Musical Performance Expertise: Deconstructing the "Talent" Account of Individual Differences in Musical Expressivity. V.L. Patel, D.R. Kaufman, S.A. Magder, The Acquisition of Medical Expertise in Complex Dynamic Environments. H.B. Richman, F. Gobet, J.J. Staszewski, H.A. Simon, Perceptual and Memory Processes in the Acquisition of Expert Performance: The EPAM Model. R.K. Wagner, K.E. Stanovich, Expertise in Reading. D.K. Simonton, Creative Expertise: A Life-Span Developmental Perspective. M.J.A. Howe, The Childhoods and Early Lives of Geniuses: Combining Psychological and Biographical Evidence. E. Winner, The Rage to Master: The Decisive Role of Talent in the Visual Arts. R. Glaser, Changing the Agency for Learning: Acquiring Expert Performance. F.L. Holmes, Expert Performance and the History of Science. J.B. Shea, G. Paull, Capturing Expertise in Sports. R.M. Shiffrin, Laboratory Experimentation on the Genesis of Expertise. R.J. Sternberg, Costs of Expertise.
Phenotyping agronomic and physiological traits in peanut under mid‐season drought stress using UAV‐based hyperspectral imaging and machine learning

Agronomic and physiological traits in peanut (Arachis hypogaea) are important to breeders for selecting high‐yielding and resilient genotypes. However, direct measurement of these traits is labor‐intensive and time‐consuming. This study assessed the feasibility of using unmanned aerial vehicles (UAV)‐based hyperspectral imaging and machine learning (ML) techniques to predict three agronomic traits (biomass, pod count, and yield) and two physiological traits (photosynthesis and stomatal conductance) in peanut under drought stress. Two different approaches were evaluated. The first approach employed eighty narrowband vegetation indices as input features for an ensemble model that included K‐nearest neighbors, support vector regression, random forest, and multi‐layer perceptron (MLP). The second approach utilized mean and standard deviation of canopy spectral reflectance per band. The resultant 400 features were used to train a deep learning (DL) model consisting of one‐dimensional convolutional layers followed by an MLP regressor. Predictions of the agronomic traits obtained using feature learning and DL (R2 = 0.45–0.73; symmetric mean absolute percentage error [sMAPE] = 24%–51%) outperformed those obtained using feature engineering and conventional ML models (R2 = 0.44–0.61, sMAPE = 27%–59%). In contrast, the ensemble model had a slightly better performance in predicting physiological traits (R2 = 0.35–0.57; sMAPE = 37%–70%) compared to the results obtained from the DL model (R2 = 0.36–0.52; sMAPE = 47%–64%). The results showed that the combination of UAV‐based hyperspectral imaging and ML techniques have the potential to assist breeders in rapid screening of genotypes for improved yield and drought tolerance in peanut.
The personal and social experiences of community-dwelling younger adults after stroke in Australia: a qualitative interview study

Objectives To examine the personal and social experiences of younger adults after stroke. Design Qualitative study design involving in-depth semi-structured interviews and rigorous qualitative descriptive analysis informed by social constructionism. Participants Nineteen younger stroke survivors aged 18 to 55 years at the time of their first-ever stroke. Setting Participants were recruited from urban and rural settings across Australia. Interviews took place in a clinic room of the Florey Institute of Neuroscience and Mental Health (Melbourne, Australia), over an online conference platform or by telephone. Results Four main themes emerged from the discourses: (1) psycho-emotional experiences after young stroke; (2) losing pre-stroke life construct and relationships; (3) recovering and adapting after young stroke; and (4) invalidated by the old-age, physical concept of stroke. While these themes ran through the narratives of all participants, data analysis also drew out interesting variation between individual experiences. Conclusions For many younger adults, stroke is an unexpected and devastating life event that profoundly diverts their biography and presents complex and continued challenges to fulfilling age-normative roles. While adaptation, resilience and post-traumatic growth are common, this study suggests that more bespoke support is needed for younger adults after stroke. Increasing public awareness of young stroke is also important, as is increased research attention to this problem.
Volatile organic compound emissions from solvent- and water-borne coatings – compositional differences and tracer compound identifications

Abstract. The emissions of volatile organic compounds (VOCs) from volatile chemical products (VCPs) – specifically personal care products, cleaning agents, coatings, adhesives, and pesticides – are emerging as the largest source of petroleum-derived organic carbon in US cities. Previous work has shown that the ambient concentration of markers for most VCP categories correlates strongly with population density, except for VOCs predominantly originating from solvent- and water-borne coatings (e.g., parachlorobenzotrifluoride (PCBTF) and Texanol®, respectively). Instead, these enhancements were dominated by distinct emission events likely driven by industrial usage patterns, such as construction activity. In this work, the headspace of a variety of coating products was analyzed using a proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) and a gas chromatography (GC) preseparation front end to identify composition differences for various coating types (e.g., paints, primers, sealers, and stains). Evaporation experiments of several products showed high initial VOC emission rates, and for the length of these experiments, the majority of the VOC mass was emitted during the first few hours following application. The percentage of mass emitted as measured VOCs (<1 % to 83 %) mirrored the VOC content reported by the manufacturer (<5 to 550 g L−1). Ambient and laboratory measurements, usage trends, and ingredients compiled from architectural coatings surveys show that both PCBTF and Texanol account for ∼10 % of the total VOC ingredient sales and, therefore, can be useful tracers for solvent- and water-borne coatings.
Simulation results , Post-processing experimentations and Comparisons results for navigation , homing and multiple vehicles operations with a new Positioning method using one transponder

(AUV) applications. This paper presents the both problems with respect to a single beacon and an extension of the method for multiple AUV operation using relative location. The difficulties of this approach are due to the fact that a single range measurement does not completely constrain the beacon's position in the vehicle frame. In order to triangulate his position, the AUV need to maneuver while measuring its displacements between ranges. In addition, range measurements are noisy and sometimes spurious, speed bias and underwater currents affect dead-reckoning measurements. All operation need to have the same beginning. In a first time, we have an initialization phase which is necessary to obtain an initial estimate of the vehicle's location with respect to a fixed or moving beacon. These initials estimates are refined during the actual displacement towards the beacon by means of a Kalman filter. Two kinds of navigation are used so as to maximize the information matrix and to maintain an accurate absolute position. We present also post-processing results in comparison with LBL navigation and mission experimentation post-processing treatment. And then we applied our method for two vehicles operations. I INTRODUCTION Actually, there is a real challenge on application of Autonomous Underwater Vehicle, particularly for offshore industry, marine laboratory or military operation in bathymetry exploration, pipeline verification, environmental measurements or minefield detection. Instead of realizing many missions with one AUV, the future is to plan one mission with a flotilla. In some cases, more sophisticated capabilities such as homing and docking are required. For the long term deployment of AUVs (Autonomous Ocean Sampling Network concept), the vehicles must be capable of returning to their moorings for recharging battery, data downloading and mission programming. Many different approaches have then been implemented and tested based on acoustic (Ultra Short Base Line, Short Base Line and Long Base Line system), electromagnetic or optical sensors. Long baseline navigation systems have generally been used for positioning of underwater equipment and manned submersibles, and more recently for navigation of Autonomous underwater vehicles [1][2][3][4]. Although, these systems can offer a good positioning accuracy, provided the array is correctly calibrated, they also have several drawbacks. The deployment and the recovery of the transponders as well as the calibration of the array are ship-time consuming and therefore expensive. Furthermore, the whole process has to be repeated each time the array is moved to a different place. The overall cost of the …
The buffered optimization methods for online transfer function identification employed on DEAP actuator

Identiﬁcationplaysanimportantroleinrelationtocontrolobjectsandprocessesasitenables the control system to be properly tuned. The identiﬁcation methods described in this paper use the Stochastic Gradient Descent algorithms, which have so far been successfully presented in machine learning. The article presents the results of the Adam and AMSGrad algorithms for online estimation of the Dielectric Electroactive Polymer actuator (DEAP) parameters. This work also aims to validate the learning by batch methodology, which allows to obtain faster convergence and more reliable parameter estimation. This approach is innovative in the ﬁeld of identiﬁcation of control systems. The research was supplemented with the analysis of the variable amplitude of the input signal. The dynamics of the DEAP parameter convergence depending on the normalization process was presented. Our research has shown how to eﬀectively identify parameters with the use of innovative optimization methods. The results presented graphically conﬁrm that this approach can be successfully applied in the ﬁeld of control systems.
Retinitis pigmentosa associated mutations in mouse Prpf8 cause misexpression of circRNAs and degeneration of cerebellar granule neurons

A subset of patients suffering from a familial retinitis pigmentosa (RP) carry mutations in several spliceosomal components including PRPF8 protein. Here, we established two novel alleles of murine Prpf8 that genocopy or mimic aberrant PRPF8 found in RP patients - the substitution p.Tyr2334Asn and an extended protein variant p.Glu2331ValfsX15. Homozygous mice expressing either of the aberrant Prpf8 variants developed within first 2 months progressive atrophy of the cerebellum due to extensive granule neuron loss. Comparison of transcriptome from pre-degenerative and degenerative tissues revealed a subset of circRNAs that were deregulated in all tissues and both Prpf8-RP mouse strains. To identify potential risk factors that sensitize cerebellum for Prpf8 mutations we monitored expression of several splicing proteins during first eight weeks. We observed downregulation of all selected splicing proteins in wild-type cerebellum, which coincided with neurodegeneration onset. The decrease in splicing protein expression was further pronounced in mouse strains expressing mutated Prpf8. Collectively, we propose a model where physiological reduction of spliceosomal components during postnatal tissue maturation sensitizes cells to expression of aberrant Prpf8 and the subsequent deregulation of circRNAs triggers neuron death.
Evaluating aquifer parameters for analytical groundwater models on contaminated sites

The estimation of area-specific hydro-geological parameters or coefficients (HGPCs) for groundwater modelling through conventional methods such as Pump Tests may not be suitable for contaminated sites, as these infiltration/extraction Tests may lead to inestimable additional risks of contaminant spread to protected zones and settlements. For early detection and control of contaminant plume transport behaviour, homogenized models of the affected site are important and reasonably accurate sources of information for decision makers. Measured data from bore profiles and groundwater monitoring stations and measuring wells provide the most reliable and formalised source of information on subsurface soil layers in most countries. This paper presents a method for estimating homogenized HGPCs for groundwater modelling and simulation, based on these data. The calculation method uses a spatially weighted averaging technique that incorporates an adaptation of the Voronoi algorithms in the plane as well as the thicknesses of soil layers recorded in each MWB. Each characteristic local HGPC is calculated separately. In a number of examples, the Paper underpins the assertion that a spatially weighted estimate of HGPCs provides a high accuracy for the modelling parameters, and for simulation results for the flow and transport of contaminant plumes in aquifer. Finally, a software tool for carrying out the estimation of groundwater parameters effectively is discussed, based on the presented method. © 2005 WIT Press WIT Transactions on Ecology and the Environment, Vol 80, www.witpress. com, ISSN 1743-3541 (on-line) Water Resources Management III 79
A Deep Learning-Based Approach for the Detection of Infested Soybean Leaves

We address the soybean leaves infestation problem by proposing a robust classification model that can reliably detect infests by Diabrotica speciosa and caterpillars. Our transfer-learning based model uses a VGG19 convolutional neural network to classify the soybean leaves and we achieve balanced accuracies between 93.71% and 94.16% on unseen testing data. This sets a new benchmark and outperforms previous work using the same dataset. Our work has theoretical and practical implications. The soybean plays a crucial role in the agricultural industry. Infestation of soybeans leads to enormous economic and environmental losses. With our model presented here, an early and accurate detection to control the spread of plant pests is possible, which reduces economic and ecological damages.
Paying Comprehensive Attention to the Temperature-Dependent Dual-Channel Excited-State Intramolecular Proton Transfer Mechanism of Fluorescence Ratio Probe BZ-DAM

The mechanism of fluorescence detection of diethyl chlorophosphate (DCP) based on 2-substituted benzothiazole (BZ-DAM) was studied by a theoretical calculation method. It should not be ignored that both the BZ-DAM and the detection product BZ-CHO have two excited-state intramolecular proton transfer (ESIPT) channels. Density functional theory (DFT) and time-dependent DFT (TDDFT) theory were used to study the photophysical mechanism of two compounds in two channels in (acetonitrile) ACN solvent, and the temperature dependence of the two channels was given. Channel 1 is more likely to exist at low temperatures and channel 2 is more likely to exist at high temperatures. By theoretical analysis of the constructed potential energy curve, the hydrogen bond energy and electron-hole analysis, we confirmed that both molecules undergo ESIPT and intramolecular charge transfer (ICT) processes in channel 1 and ESIPT and twisted intramolecular charge transfer (TICT) coupling processes in channel 2. The formation of product BZ-CHO molecules led to a significant fluorescence blue-shift phenomenon and inhibited the ICT process, which confirmed that BZ-DAM could be used as a fluorescence probe for fluorescence detection. We sincerely hope that this work will not only help to clarify the excited-state dynamics behavior of the BZ-DAM probe but also provide a new idea for designing and optimizing a new chemical dosimeter.
An Evaluation of the Naval Oceanic Vertical Aerosol Model During Key90

Abstract : An experiment was conducted in the Florida Keys from 2 - 19 July 1990 to test the performance of the Naval Oceanic Vertical Aerosol Model (NOVAM) in a weak-convective regime. Meteorological data collected by aircraft and boat was used to generate the surface and vertical profile information files required by NOVAM. Using this information, NOVAM predicts the aerosol extinction 1/km for a vertical cross-section of the atmosphere. Aircraft-observed aerosol extinction profiles were also obtained. Comparisons between observed and NOVAM aerosol extinction profiles revealed major deviations above the cloud top. From the surface to the top of the cloud layer, NOVAM generally did an excellent job in predicting profile shape, with the magnitude of aerosol extinction tied to the extinction matched at the surface. In a few cases, observed extinction increased more rapidly than NOVAM predicted extinction from the surface to the base of the cloud layer. This is attributed to rain scavenging associated with thunderstorm activity in the area. Comparison between different aerosol extinction profiles revealed much spatial and temporal variation that was verified by Lidar profiles of atmospheric structure. Thunderstorm activity, multiple cloud-layers, and the spatial variation in the atmospheric structure have led to a hypothesis that deep-convection was responsible for the major differences between observed and predicted aerosol extinction profiles. If this is the case, a simple modification to the weak-convective model may be made to apply to a deep- convection model.