Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of the R47H TREM2 variant on microglial exosome profiles

Variants in the triggering receptor expressed on myeloid cells 2 ( TREM2) gene are linked with an increased risk of dementia, in particular the R47H het TREM2 variant is linked to late-onset Alzheimer’s disease. Using human iPSC-derived microglia, we assessed whether variations in the dynamics of exosome secretion, including their components, from these cells might underlie some of this risk. We found exosome size was not altered between common variant controls and R47H het variants, but the amount and constitution of exosomes secreted were different. Exosome quantities were rescued by incubation with an ATP donor or with lipids via a phosphatidylserine TREM2 ligand. Following a lipopolysaccharide or phagocytic cell stimulus, exosomes Abbreviations used : AD = Alzheimer’s disease, CPZ = chlorpromazine, Cv = common variant, CytoD = Cytochalasin D, DIV = days in vitro, EB = embryoid bodies, ER = endoplasmic reticulum, FACs = fluorescence activated cell sorting, H 2 O 2 = hydrogen peroxide, iPSC = induced pluripotent stem cells, iPS-Mg = induced pluripotent stem cell-derived microglia, LC-MS = liquid chromatography mass spectrometry, LPS = lipopolysaccharide, NTA = nanoparticle tracking analysis, PBST = phosphate buffered saline solution with 1% TWEEN-20, PI = propidium iodide, PS + = phosphatidylserine, RA = retinoic acid, SDC = sodium deoxycholate, SN = supernatant, TREM2 = triggering receptor expressed on myeloid cells 2. ppm; Da; modifications: phosphorylation.
Establishment and Characterization of Feline Mammary Tumor Patient-Derived Xenograft Model

Simple Summary Feline mammary tumor (FMT) is a relatively commonly diagnosed neoplasm in the cat. Development of new veterinary cancer therapies is limited by the shortage of in vivo models that reproduce tumor microenvironment and metastatic progression. In this study, FMT patient-derived xenografts (PDX) model were established and primary cell line isolated from orthotopic PDX. The tumor grafts conserve original tumor essential features, including distant metastasis. FMT-PDX represents an available resource for bridging the biology of FMT with preclinical studies of FMT in cats. Abstract Feline mammary tumor is a relatively commonly diagnosed neoplasm in the cat. Development of new veterinary cancer therapies is limited by the shortage of in vivo models that reproduce tumor microenvironment and metastatic progression. Four feline mammary tumor orthotopic patient-derived xenograft model (PDX) successfully established in NOD-SCID gamma (NSG) mice. The overall success rate of PDX establishment was 36% (4/11). Histological, immunohistochemical, and short tandem repeat analysis showed a remarkable similarity between patient’s tumor and xenograft. The tumor grafts conserve original tumor essential features, including distant metastasis. Primary FMT-1807 cell line isolated from FMT-1807PDX tumor tissue. Tumorigenicity of FMT-1807 cells expanded from PDX was assessed by orthotopic injection into NSG mice. Mice yielded tumors which preserve the lung and liver metastasis ability. This work provides a platform for FMT translational investigation.
Does Two-Class Training Extract Real Features? A COVID-19 Case Study

Diagnosis aid systems that use image analysis are currently very useful due to the large workload of health professionals involved in making diagnoses. In recent years, Convolutional Neural Networks (CNNs) have been used to help in these tasks. For this reason, multiple studies that analyze the detection precision for several diseases have been developed. However, many of these works distinguish between only two classes: healthy and with a specific disease. Based on this premise, in this work, we try to answer the questions: When training an image classification system with only two classes (healthy and sick), does this system extract the specific features of this disease, or does it only obtain the features that differentiate it from a healthy patient? Trying to answer these questions, we analyze the particular case of COVID-19 detection. Many works that classify this disease using X-ray images have been published; some of them use two classes (with and without COVID-19), while others include more classes (pneumonia, SARS, influenza, etc.). In this work, we carry out several classification studies with two classes, using test images that do not belong to those classes, in order to try to answer the previous questions. The first studies indicate problems in these two-class systems when using a third class as a test, being classified inconsistently. Deeper studies show that deep learning systems trained with two classes do not correctly extract the characteristics of pathologies, but rather differentiate the classes based on the physical characteristics of the images. After the discussion, we conclude that these two-class trained deep learning systems are not valid if there are other diseases that cause similar symptoms.
Analisis Kerawanan Banjir sebagai Pendukung Perencanaan Model Water Sensitive Urban Design di Kabupaten Klaten

Klaten Regency is one of the regions that has a high level of flood vulnerability. The area of Klaten Regency which is huge and has diverse characteristics makes it difficult to determine an appropriate flood management model. Water Sensitive Urban Design (WSUD) is a model that focuses on handling water management problems with environmentally friendly infrastructure. Therefore, an analysis is carried out to determine the level of flood vulnerability and factors causing flooding to plan a WSUD design that is suitable for each sub-districts of Klaten Regency. The Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods are used to help the analysis. Aspects used as criteria are rainfall, slope, soil type, geological conditions, and land use. Based on the analysis, it could be concluded that Klaten Regency has two sub-districts with high flood hazard category, 21 sub-districts with medium category, and three sub-districts with low category. Bayat and Cawas are sub-districts that have a high level of flood vulnerability category. Meanwhile, Kemalang, Karangnongko and Polanharjo are districts with a low level of flood vulnerability category. The main factors causing flooding in Klaten Regency are slope and land use.
Inverse resonance problem with partial information on the interval

We consider the inverse resonance problem in scattering theory. In one-dimensional setting, the scattering matrix consists of entries of meromorphic functions. The resonances are defined as the poles of the meromorphic determinant. For the compactly supported perturbation, we are able to quantitatively estimate the zeros and poles of each meromorphic entry. The size of potential support is connected to the zero density of scattered wave field due to the form of Fourier transform. We will investigate certain properties of Fourier transforms in scattering theory and derive the inverse uniqueness on scattering source given certain knowledge on the perturbation and all the given resonances.
Formation Control and Obstacle Avoidance Algorithm of a Multi-USV System Based on Virtual Structure and Artificial Potential Field

This paper proposes a formation generation algorithm and formation obstacle avoidance strategy for multiple unmanned surface vehicles (USVs). The proposed formation generation algorithm implements an approach combining a virtual structure and artificial potential field (VSAPF), which provides a high accuracy of formation shape keeping and flexibility of formation shape change. To solve the obstacle avoidance problem of the multi-USV system, an improved dynamic window approach is applied to the formation reference point, which considers the movement ability of the USV. By applying this method, the USV formation can avoid obstacles while maintaining its shape. The combination of the virtual structure and artificial potential field has the advantage of less calculations, so that it can ensure the real-time performance of the algorithm and convenience for deployment on an actual USV. Various simulation results for a group of USVs are provided to demonstrate the effectiveness of the proposed algorithms.
A robust approach to estimate Q from surface seismic data and inverse Q filtering for resolution enhancement

Abstract Accurate estimation of seismic quality factor (Q) is important in seismic data processing to correct for the velocity dispersion effects and to compensate for absorption losses through inverse Q filtering. To estimate it, often logarithmic spectral ratios of the non-stationary seismic signal between two depth levels are linearly inverted. As these ratios are usually derived from the standard Fourier transform (FT) which has a poor time-frequency resolution, this can lead to biased Q estimation. We have calculated spectral ratios from the high-resolution time-frequency spectrum using the Stockwell transform (ST). We then non-linearly inverted these improved spectral ratios to estimate Q using the Levenberg-Marquardt (LM) method. For a synthetic wedge model, results demonstrate a reasonable improvement in the accuracy of Q estimation with the combined ST-LM approach. Application on a real P-wave reflection seismic dataset revealed an anomalous attenuation zone of very low Q (29–75) values. Inverse Q filtering using the estimated Q profile has enhanced the seismic resolution below it and minimized the differential viscous losses. Frequency slice filtering on Q compensated data has improved the S/N ratio and hence the amplitude fidelity of reflectors.
Climate Variability of Atmospheric Rivers and Droughts over the West Coast of the United States from 2006 to 2019

Water resources are crucial to the livelihood and sustainability of the general public across the western United States. This study covers the timespan of both the third driest drought in Californian history between 2012 and 2015 as well as the extreme atmospheric river year in 2016–2017. The evaluation of vertical moisture profiles using Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Radio Occultation (RO) data, National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis of 500 hPa geopotential heights, 1000–500 hPa thickness, Optimum Interpolation (OI) Sea Surface Temperature (SST), NOAA/NDBC buoy data, and NASA, MEaSUREs, Gridded Sea Surface Height Anomalies (SSHA) were performed. The daily COSMIC time evolution from 2006 through 2015 showed a flat to slightly upward trend of both temperature and water vapor profiles through the entirety of the western US drought. Subsequently, a significant increase of temperatures and water vapor were recorded in early 2016 before the extreme Atmospheric River (AR) season of 2016–2017. The quantitative analyses suggest that warmer SST and higher SSHA lead to an increase of heat fluxes from the ocean into the troposphere, which forces thickness changes and thus the position of troughs in the geopotential height field changes afterwards, consequently pushing the trough eastward over the Pacific Northwest and potentially leading to an active AR year in the western US. It appears that regional COSMIC RO moisture profiles, seasonal SST, and SLH anomalies may serve as a precursor for seasonal or sub-seasonal precipitation outlook along the western US.
A Circadian Rhythms Neural Network for Solving the Redundant Robot Manipulators Tracking Problem Perturbed by Periodic Noise

Redundant robot manipulators are usually applied to various complex scenarios where harmful noise especially periodic calculating noise always exists. In order to avoid the task failure affected by the periodic noise, a circadian rhythms neural network (CRNN) is applied to solve motion planning problems of redundant robot manipulators suffering from the periodic noise. First, in this article, we formulate a tracking problem of redundant manipulators as a convex quadratic programming (QP) problem. Second, the QP problem is converted into a matrix equation. Third, based on the neural dynamic design method, a CRNN model is exploited and developed to solve the matrix equation of the tracking problem. Comparative simulations between the proposed CRNN and the traditional zeroing neural network show that the CRNN has better robustness and performance on solving the end-effector tracking task. Two physical experiments are conducted to further certify the effectiveness, robustness, and practicability of the proposed CRNN.
Innovative design of traditional calligraphy costume patterns based on deep learning

With the global homogenization of today’s costume design, a new design phenomenon is derived, that is, the globalization of regional ethnic traditional costume. Chinese traditional style has become one of the global fashion trends. In order to digitally inherit the traditional costume culture and innovate and upgrade the design process in the costume industry, this paper takes the traditional calligraphy costume patterns as the research object and proposes an innovative design method based on deep learning Generative Adversarial Network. This study analyzes the artistic characteristics and cultural genes of traditional calligraphic costume patterns, establishes a Generative Adversarial Network model containing discrimination and generation modules, and optimizes the design of the model for the characteristics of traditional costume patterns, such as small samples, multiple specifications, and emphasis on meaning rather than form. Through comparative experiment, subjective evaluation and design application, the advanced and practical value of the model are verified. This research aims to provide new ideas and methods for the inheritance and innovation of traditional culture in costume arts.
Research on the Flow Stress of Thermal Compression Deformation of 5052 Aluminum Alloy Based on Computer

The design of 5052 aluminum alloy products is inseparable from the application of hot compression deformation technology, a considerable part of automotive aluminum alloy parts are composed of complex spatial curved surface rheological stress. CATIA thermal compression deformation technology in the design process not only to meet the structural characteristics of the parts, but also to ensure the smoothness, wear resistance of the product, so as to apply 5052 aluminum alloy products in a more comprehensive and effective application, and promote the design of automotive aluminum alloy more perfect, improve the efficiency of automotive aluminum alloy development.
The Importance of Religiousity and Resilience on Z-Generation and the Implication for School Counseling

The youngest generation needs to possess psychological resilience to deal with the Industry 5.0 challenge. Therefore, it is important to determine the contributing factors responsible for z-generation resilience. The purpose of this study is to examine the correlation between religiousity and resilience on z-generation. Data were obtained from 455 college students of Universitas Negeri Semarang, Indonesia. The Centrality of Religiosity Scale (CRS), developed by Huber, was used to determine the five aspects of religiosity, namely intellectual dimensions, ideology, public and private practice, as well as a religious experience. Meanwhile, the Brief Resilience Scale developed by Smith, Dalen, Wiggins, Tooley, Christopher, and Bernard (2008) was used to measure resilience. The result showed that religiousness significantly correlated to the resilience of the z-generation.
Remarks on Rule H

Fox (2000) argues that a single principle, Rule H, can account for (i) Strong Crossover, (ii) the ban on using co-binding to sneak around Condition B, and (iii) the Dahl paradigm. The focus of this paper is Fox’s analysis of the Dahl paradigm. Though elegant and appealing, the analysis faces both conceptual and empirical problems. On the conceptual side, the analysis assumes that a bound pronoun within an elided VP must be bound in a structurally parallel configuration to its counterpart in the antecedent VP. This requirement does not follow from independently-motivated constraints on VP ellipsis. On the empirical side, Roelofsen (2011) has turned up additional ellipsis phenomena that do not pattern as Fox’s analysis predicts. I will argue that a relatively minor modification to Fox’s analysis suffices to solve both the conceptual and empirical problems. Taking inspiration from Kehler & Buring (2008), I increase the domain of application of Rule H to include syntactic structures which underlie Focus Semantic Values, so that Rule H acts as a filter on Focus Semantic Values. The only relevant constraint on VP ellipsis is an independently-motivated Rooth-style contrast constraint.
Analysis of Quarantine Service Quality on Ship Crew Satisfaction during the Covid-19 Pandemic at Belawan Sea Port

This study aims to analyze the quality of quarantine services on ship crew satisfaction during the Covid-19 pandemic at the Belawan Sea port. This study is a quantitative study with a cross sectional method which aims to analyze the quality of quarantine services on satisfaction during the COVID-19 pandemic at the Belawan Sea Port using the ServQual approach which consists of physical evidence, reliability, responsiveness, assurance, and empathy. The results of the study are expected to provide input regarding the quality of quarantine services provided by the Class I Medan KKP to ship crews during the COVID-19 pandemic. Based on the ServQual analysis, the results show that the satisfaction of the crew of the quarantine service at Belawan Port is very satisfying with a suitability level of 107.56% (the average service / reality score is 4.00 and the expectation score is 3.72). It is suggested to improve coordination and more intensive advocacy with shipping agents as intermediaries between officers and skippers as crew so as to reduce the length of service waiting time, in order to increase the number of human resources carrying out document checks on board, involving female medical officers for health checks The crew who came from affected countries/ports to facilitate information retrieval, ordered the boarding officers to bring Personal Protective Equipment when interacting with the crew.
Effects of Green Cardamom Supplementation on Obesity and Diabetes Gene Expression Among Obese Women With Polycystic Ovary Syndrome; A Double Blind Randomized Controlled Trial

BackgroundPolycystic ovary syndrome (PCOS) is an endocrine disease in which related obesity, metabolic disorders and is considered as one of the main causes of infertility in women. This trial was investigated the effects of green cardamom on obesity and diabetes genes expression among obese women with PCOS.Methods194 PCOS women were randomly divided two groups: intervention (n= 99; 3 g/day green cardamom) and control groups (n=95). All of them were given low calorie diet. Anthropometric, glycemic and androgen hormones were assessed before and after 16 weeks’ intervention. Fat mass and obesity-associated (FTO), Peroxisome proliferative activating Receptor-γ (PPAR-γ) Carnitine Palmitoyltransferase 1A (CPT1A), Acetyl-CoA Carboxylase Beta (ACAB), Leptin Receptor (LEPR), Gherlin, and lamin A/C (LAMIN) genes in PBMC were measured in each group using reverse transcription-polymerase chain reaction (RT-PCR) method.ResultsAnthropometric indices were significantly decreased after intervention in both two studied groups. Glycemic indices and androgen hormones were significantly improved in the intervention group. The expression level of FTO, CPT1A, LEPR, and LAMIN were significantly down-regulated (P<0.001), as well as, PPAR-y was significantly up-regulated in the intervention group after intervention with green cardamom (P<0.001). ConclusionThis current study showed that the administration of green cardamom is a beneficial approach for improving of anthropometric, glycemic and androgen hormones, as well as, obesity and diabetes genes expression in PCOS women under low calorie diet. This trial was registered with the Iranian Clinical Trials Registry (registration number: IRCT20200608047697N1). 1 August, 2020; https://www.irct. ir/trial/48748
Diagnosis of Long Sightedness Using Neural Network and Decision Tree Algorithms

Long-sightedness occurs provided the eyes don’t concentrate properly on the retina that is the delicate illuminated portion at the back of the eyes. It influences the capability to recognise nearby items. Numerous researches have been conducted on eye disease, but the outcome hasn’t been 100% accurate. What motivated the development of this system was that most people are not aware of the warning signs of the disease as a result of negligence, ignorant, and time constraint involved in awaiting an ophthalmologist for diagnosis or detection. Therefore, this study examined the diagnosis of long-sightedness using three algorithms which include Neural Network, Decision Tree, and Back Propagation, and this led to the development of an Expert System. Backpropagation and Decision tree algorithms were employed to train the Neural Network. A decision tree was implemented using a knowledge extraction rule to classify and categorise the disease based on the patient’s symptoms. The C# programming language was used for the system implementation, and MySQL was used for the database. The outcome of the developed system explained how the sickness was identified so eradicating the impenetrability in the Neural network only, and finally, the plan was tested after development.
Comparison of CMIP6 and CMIP5 models in simulating mean and extreme precipitation over East Africa

This study examines the improvement in Coupled Model Intercomparison Project Phase Six (CMIP6) models against the predecessor CMIP5 in simulating mean and extreme precipitation over the East Africa region. The study compares the climatology of the precipitation indices simulated by the CMIP models with the CHIRPS data set using robust statistical techniques for 1981–2005. The results display the varying performance of the general circulation models (GCMs) in the simulation of annual and seasonal precipitation climatology over the study domain. CMIP6 multi‐model ensemble mean (hereafter MME) shows improved performance in the local annual mean cycle simulation with a better representation of the rainfall within the two peaks, especially the MAM rainfall relative to their predecessor. Moreover, simulation of extreme indices is well captured in CMIP6 models relative to CMIP5. The CMIP6‐MME performed better than the CMIP5‐MME with lesser biases in simulating Simple Daily Intensity Index (SDII), consecutive dry days (CDD), and very heavy precipitation days >20 mm (R20mm) over East Africa. Remarkably, most CMIP6 models are unable to simulate extremely wet days (R95p). Some CMIP6 models (e.g., NorESM2‐MM and CNRM‐CM6‐1) depict robust performance in reproducing the observed indices across all analyses. OND season shows wet biases for some indices (i.e., R95p, PRCPTOT), except for SDII, CDD, and R20mm in CMIP6 models. Consistent with other studies, the mean ensemble performance for both CMIP5/6 shows better performance as compared with individual models due to the cancellation of some systematic errors in the individual models. Generally, CMIP6 depicts improved performance in the simulation of MAM rainfall compared with CMIP5 models. However, the new model generation is still marred by uncertainty, thereby depicting unsatisfactory performance over the East Africa domain. This calls for further investigation into the sources of persistent systematic biases and a methodology for identifying individual models with robust features that can accurately simulate observed patterns for future usage.