Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of De Novo Dividing Stem Cells In Situ through Double Nucleotide Analogue Labeling

Tissue-specific somatic stem cells are characterized by their ability to reside in a state of prolonged reversible cell cycle arrest, referred to as quiescence. Maintenance of a balance between cell quiescence and division is critical for tissue homeostasis at the cellular level and is dynamically regulated by numerous extrinsic and intrinsic factors. Analysis of the activation of quiescent stem cells has been challenging because of a lack of methods for direct detection of de novo dividing cells. Here, we present and experimentally verify a novel method based on double labeling with thymidine analogues to detect de novo dividing stem cells in situ. In a proof of concept for the method, we show that memantine, a drug widely used for Alzheimer’s disease therapy and a known strong inducer of adult hippocampal neurogenesis, increases the recruitment into the division cycle of quiescent radial glia-like stem cells—primary precursors of the adult-born neurons in the hippocampus. Our method could be applied to assess the effects of aging, pathology, or drug treatments on the quiescent stem cells in stem cell compartments in developing and adult tissues.
Hydrological Modelling and Climate Adaptation under Changing Climate: A Review with a Focus in Sub-Saharan Africa

Empirical evidence continues to show that climate change remains a threat to the stability of the hydrologic system. As the climate system interacts with the hydrologic cycle, one significant repercussion of global warming includes changes in water availability at both regional and local scales. Climate change adaptation is intrinsically difficult to attain due to the dynamic earth system and lack of a comprehensive understanding of future climate and its associated uncertainties. Mostly in developing countries, climate adaptation is hampered by scarcity of good quality and adequate hydro-meteorological data. This article provides a synopsis of the modelling chain applied to investigate the response of the hydrologic system under changing climate, which includes choosing the appropriate global climate models, downscaling techniques, emission scenarios, and the approach to be used in hydrologic modelling. The conventional criteria for choosing a suitable hydrological model are discussed. The advancement of emission scenarios including the latest Shared Socioeconomic Pathways and their role in climate modelling, impact assessment, and adaptation, are also highlighted. This paper also discusses the uncertainties associated with modelling the hydrological impacts of climate change and the plausible approaches for reducing such uncertainties. Among the outcomes of this review include highlights of studies on the commonly used hydrological models for assessing the impact of climate change particularly in the sub-Saharan Africa region and some specific reviews in southern Africa. Further, the reviews show that as human systems keep on dominating within the earth system in several ways, effective modelling should involve coupling earth and human systems models as these may truly represent the bidirectional feedback experienced in the modern world. The paper concludes that adequate hydro-meteorological data is key to having a robust model and effective climate adaptation measures, hence in poorly gauged basins use of artificial neural networks and satellite datasets have shown to be successful tools, including for model calibration and validation.
Clonal Lineages and Virulence Factors of Carbapenem Resistant E. coli in Alameda County, California, 2017–2019

The prevalence of carbapenem-resistant Enterobacterales (CRE) has been increasing since the year 2000 and is considered a serious public health threat according to the Centers for Disease Control and Prevention. Limited studies have genotyped Carbapenem-resistant Escherichia coli using whole genome sequencing to characterize the most common lineages and resistance and virulence genes. The aim of this study was to characterize sequence data from carbapenem-resistant E. coli isolates (n = 82) collected longitudinally by the Alameda County Public Health Laboratory (ACPHL) between 2017 and 2019. E. coli genomes were screened for antibiotic resistance genes (ARGs) and extraintestinal pathogenic E. coli virulence factor genes (VFGs). The carbapenem-resistant E. coli lineages were diverse, with 24 distinct sequence types (STs) represented, including clinically important STs: ST131, ST69, ST95, and ST73. All Ambler classes of Carbapenemases were present, with NDM-5 being most the frequently detected. Nearly all isolates (90%) contained genes encoding resistance to third-generation cephalosporins; blaCTX-M genes were most common. The number of virulence genes present within pandemic STs was significantly higher than the number in non-pandemic lineages (p = 0.035). Virulence genes fimA (92%), trat (71%), kpsM (54%), and iutA (46%) were the most prevalent within the isolates. Considering the public health risk associated with CRE, these data enhance our understanding of the diversity of clinically important E. coli that are circulating in Alameda County, California.
Elastic constants of nematic solutions of rod-like and semi-flexible polymers

Abstract Analytical expressions for the elastic constants of nematic polymer solutions are derived when the macromolecules are monodisperse rod-like, bidisperse rodlike or semi-flexible. These expressions are derived in the gaussian approximation so that they constitute exact leading terms of a general asymptotic expansion for high degree of nematic ordering although the results are justifiable only within the range of validity of the second virial approximation. The limiting forms are in complete agreement with recent numerical work for monodisperse rods.
Data-Efficient Estimation of Remaining Useful Life for Machinery With a Limited Number of Run-to-Failure Training Sequences

Prognostics and Health Monitoring (PHM) of machinery is a research area with great relevance to industrial applications as it can serve as a foundation for safer, more cost-efficient operation and maintenance. The prediction of Remaining Useful Life (RUL) plays an important part in this field and has seen significant advances from the introduction of machine learning methods. However, these methods typically require model training with a large number of run-to-failure sequences, which are often not feasible to obtain due to the required time and cost investments. The present study addresses this issue by introducing a novel methodology, which first quantifies the deviation from the machine’s health and fault state and then calculates a machine Health Index (HI) prior to the prediction of RUL. In addition, the start of a degradation state is determined. Alternative implementations of the proposed methodology are compared utilising several methods, including Support Vector Regression (SVR), Long Short-Term Memory (LSTM) Neural Network (NN), Mahalanobis Distance (MD), and LSTM Autoencoder (AE) NN. The methodology is applied to the open turbofan degradation (C-MAPSS) and bearing vibration (FEMTO-ST PROGNOSTIA) datasets. When a reduced subset of training sequences is used, the prediction results demonstrate that the proposed methodology largely outperforms the baseline method without HI generation. For example, when comparing prediction errors of the C-MAPSS dataset at a reduction of the available number of training sequences to 5%, the proposed method shows an average prediction improvement by 6.5% - 19.2% relative to the baseline method. The presented approach is therefore suitable to improve model generalisation for cases with a limited number of training sequences. When the full training set is utilised, the most resource-saving variant of the proposed approach achieves an average training duration of 8.9% compared to the baseline method. Hence, an additional contribution of the presented data-efficient approach is the reduction of required computing resources, which has implications on training time, energy consumption, and environmental impact.
Post-anthesis photosynthetic attributes and N-P-K status of standard and emmer wheats respond differently to nitrogen supply

Abstract Impact of nitrogen (N) supply on photosynthesis and N, P, and K status of wheat, particularly ancient wheats lacks clarity. Hence, clarification of post-anthesis photosynthesis and N, P, and K status of ancient emmer compared to the improved durum and bread wheats was aimed at. Five field-grown ancient emmer, one improved durum and an improved bread wheat exposed to a wide range of N fertilizer (i.e. 0, 25, 50, and 75 kg ha−1 N) were tested in two consecutive years. Chlorophyll and carotenoids concentrations of the emmer and improved wheats were increased by 39–63% and 29–45%, respectively in response to N supply. Mesophyll conductance and net photosynthetic rate of the improved bread wheat were increased by 150% and 60%, respectively, with increase in N supply, but those of the emmer wheats were decreased in response to the medium (0–43%) and high N (0–37%) supplies. Emmer wheat out-numbered the improved wheats in terms of mesophyll conductance and net photosynthetic rate under the low N supply but the reverse was true with the high N supply. Leaf (1–2.2, 3.2–7.7, and 16.8–35 mg g−1, respectively) and grain (2.38–6.4, 5.6–12.1, and 20.5–28.9 mg g−1, respectively) P, leaf N, and grain K concentrations responded positively to the N supply and emmer wheats equaled or exceeded those of the improved wheats particularly when grown in the absence of high N supplies. The presented data indicates that ancient emmer wheats are potent to be employed to incorporate nutrient-acquiring traits into the modern wheats.
Results from on-Orbit Testing of the Fram Memory Test Experiment on the Fastsat Micro-Satellite

The Memory Test Experiment is a space test of a ferroelectric memory device on a low Earth orbit satellite that launched in November 2010. The memory device being tested is a commercial Ramtron Inc. 512K memory device. The circuit was designed into the FASTSAT avionics and is not used to control the satellite. The test consists of writing and reading data with the ferroelectric based memory device. Any errors are detected and are stored on board the satellite. The data is sent to the ground through telemetry once a day. Analysis of the data can determine the kind of error that was found and will lead to a better understanding of the effects of space radiation on memory systems. The test is one of the first flight demonstrations of ferroelectric memory in a near polar orbit which allows testing in a varied radiation environment. The initial data from the test is presented. This paper details the goals and purpose of this experiment as well as the development process. The process for analyzing the data to gain the maximum understanding of the performance of the ferroelectric memory device is detailed.
Detection of QTLs Regulating Six Agronomic Traits of Rice Based on Chromosome Segment Substitution Lines of Common Wild Rice (Oryza rufipogon Griff.) and Mapping of qPH1.1 and qLMC6.1

Wild rice is a primary source of genes that can be utilized to generate rice cultivars with advantageous traits. Chromosome segment substitution lines (CSSLs) are consisting of a set of consecutive and overlapping donor chromosome segments in a recipient’s genetic background. CSSLs are an ideal genetic population for mapping quantitative traits loci (QTLs). In this study, 59 CSSLs from the common wild rice (Oryza rufipogon Griff.) accession DP15 under the indica rice cultivar (O. sativa L. ssp. indica) variety 93-11 background were constructed through multiple backcrosses and marker-assisted selection (MAS). Through high-throughput whole genome re-sequencing (WGRS) of parental lines, 12,565 mapped InDels were identified and designed for polymorphic molecular markers. The 59 CSSLs library covered 91.72% of the genome of common wild rice accession DP15. The DP15-CSSLs displayed variation in six economic traits including grain length (GL), grain width (GW), thousand-grain weight (TGW), grain length-width ratio (GLWR), plant height (PH), and leaf margin color (LMC), which were finally attributed to 22 QTLs. A homozygous CSSL line and a purple leave margin CSSL line were selected to construct two secondary genetic populations for the QTLs mapping. Thus, the PH-controlling QTL qPH1.1 was mapped to a region of 4.31-Mb on chromosome 1, and the LMC-controlling QTL qLMC6.1 was mapped to a region of 370-kb on chromosome 6. Taken together, these identified novel QTLs/genes from common wild rice can potentially promote theoretical knowledge and genetic applications to rice breeders worldwide.
A Scalable Analytical Framework for Complex Event Episode Mining With Various Domains Applications

With the ubiquity of sensor networks and smart devices that continuously collect data, we face the challenge of analyzing the growing stream of data in real time. In recent years, there has been a huge need to gain useful knowledge by incrementally analyzing event sequence data. Although episode pattern mining techniques have existed for years, people have recently become more aware of their practical value in solving real-life domain problems such as manufacturing records, stock markets, and weather forecasts. The effective and efficient application of episode pattern mining techniques to analyze complex event data is becoming increasingly important for solving real-life problems in wide domains. However, few studies have focused on developing a scalable framework based on episode pattern mining of complex event sequences for applications in various domains. In this work, we propose a novel framework named SAAF (Scalable Analytical Application Framework) based on complex event episode mining techniques, including batch episode mining, delta episode mining, incremental episode mining, and pattern merging, to consider both efficiency and accuracy. Moreover, to enhance scalability, we adopt the lambda architecture with Apache Spark and Apache Spark Streaming as the system development framework. Finally, the experimental results on three real datasets of different domains and two benchmark datasets showed that the proposed SAAF framework exhibits excellent performance in terms of efficiency, accuracy, and scalability.
Comparison among U-17, U-20, and Professional Female Soccer in the GPS Profiles during Brazilian Championships

The purpose of this study was to compare and characterize the physical demand of official matches among under-17 (U-17), under-20 (U-20), and professional (Pro) female soccer players. All matches were from the U-17, U-20, and Pro National Brazilian Championships. Fourteen Pro matches, nine U-20 matches, and four U-17 matches were analyzed. The external load was measured by the global positioning system (GPS) and the internal workload was assessed by the ratings of perceived exertion (RPE) multiplied by the duration of the match. The activity profiles measured were total distance covered (km), total sprint distance (m) (speed > 18 km·h−1), number of accelerations and decelerations (between 1 and 2 m·s−2 and >3 m·s−2), and top speed (km·h−1). For the analysis, we standardized all the metrics (except the top speed) by the time (in minutes) played. The Pro group presented higher sprint distances, number of accelerations and decelerations, and top speeds, compared to U-20 and U-17. There was no difference in the total distance among groups, and there was no difference in any GPS metrics between U-20 and U-17. The RPE was higher in Pro and U-17, compared to U-20; however, the workload-RPE was higher in Pro, compared to both U-17 and U-20 groups. These findings provide important information for the evolution of physical performance according to age categories in elite female soccer players.
Contextual Multi-Armed Bandit Learning for Freshness-aware Cache Update in Vehicular Edge Networks

Vehicular edge caching (VEC) is expected to support real-time intelligent transportation systems by providing low-latency data services. However, dynamic vehicular environment, such as time-varying data freshness and dynamic data preference, may result in low cache efficiency. Based on the above motivation, this paper designs a system model of freshness-aware VEC system. Accordingly, we formulate the problem of Vehicular Edge Cache Update (VECU) by exploiting the concept of AoI and data heterogeneity for evaluating data freshness, which aims at maximizing the edge cache benefit. On this basis, the Contextual Multi-Armed Bandit for Caching Update (CMAB-CU) algorithm is designed to determine cache update decision by online estimating reward of each arm based on a linear function of dynamic vehicular features and historical observations. Finally, we modeling a simulation model and conduct simulation results, which demonstrates the effectiveness of the proposed algorithm in various service scenarios.
Nonparametric regression with missing data

Optimal estimation of a regression function, when either the response or the predictor may be missed at random, is considered. Missing at random (MAR) means that the conditional probability of missing, given response and predictor, does not depend on a variable whose values may be missed. Mean integrated squared error (MISE) is the used statistical criteria, and a nonparametric approach implies that no assumption about shape of the regression function is made. It is shown that optimal estimation depends on which variable, the response or the predictor, is missed. For a setting with missed responses, optimal estimation is based only on complete cases of observations and incomplete ones can be ignored. For a setting with missed predictors, optimal estimation is based on all cases, both complete and incomplete, and the procedure includes estimation of the conditional probability of missing the predictor given the response. Proposed estimators are completely data‐driven, do not involve imputation of missing values, and adapt to missing mechanism and smoothness of an estimated regression function. Theoretical results are complemented by the analysis of a credit score survey data. WIREs Comput Stat 2014, 6:265–275. doi: 10.1002/wics.1303
Origin of low dielectric constant of carbon-incorporated silicon oxide film deposited by plasma enhanced chemical vapor deposition

Dielectric properties of carbon-incorporated silicon oxide (SiOC) films deposited by plasma enhanced chemical vapor deposition (PECVD), using a bis-trimethylsilylmethane precursor, were compared with the dielectric properties of silicon oxide thermally grown in a furnace (thermal oxide) and also with oxide deposited by PECVD using a tetraethoxysilane (TEOS) precursor (PE-TEOS oxide). The electronic contribution to the dielectric constant of the three oxide films was calculated from their refractive indices measured at 632.8 nm by ellipsometry. Ionic contributions were computed from their IR reflection spectra, measured at 650–4000 cm−1, by using the Kramers–Kronig relation. The dipolar contribution was qualitatively analyzed from temperature dependence of the polarizability, on a per unit volume basis. The dielectric constant of the SiOC films, which was measured at 1 MHz, decreased from 4.2 to 2.3 as the carbon content increased from 0 to 19.6 at. %. Although there was a significant reduction of the diel.
Cricket Highlight Generation: Automatic Generation Framework Comprising Score Extraction and Action Recognition *

Generation of highlights is the method of pulling the most intriguing clips from a sports video. It is an important activity for sports broadcasters, especially for the sport of cricket. This research proposes a simple yet efficient approach for the automated generation of Cricket highlights from the entirety of a match video by key event detection. The proposed approach comprises of a score extraction technique with the help of the EAST model and OCR (Optical Character Recognition) techniques which helps detect the keyframes, followed by an action recognition pipeline using CNN and LSTM applied on those detected keyframes. The Observed outcomes exemplify that the precision of the proposed method for automated cricket highlights generation is productive and valuable.
Raptor: Mitigating CPU-GPU False Sharing Under Unified Memory Systems

The introduction of Unified Memory (UM) technology has greatly increased the programmability of CPU-GPU heterogeneous systems. At the same time, Unified Memory systems have given rise to new performance challenges. Achieving the desired performance and energy efficiency on such systems requires careful consideration of data allocation and migration. This paper looks at the problem of false sharing under UM. We present Raptor, a system for fast and accurate detection of page-level false sharing in heterogeneous applications. The system employs binary code instrumentation and leverages hardware performance counters to track UM allocations and data access patterns and pinpoint energy inefficiencies created by the occurrence of false sharing. Experiments on a suite of heterogeneous applications show false sharing can be a common occurrence in collaborative design paradigms with tight coupling of CPU-GPU tasks. When false sharing is eliminated via a padding scheme, applications are able to achieve higher performance at lower clock frequencies, leading to improved energy efficiency by as much as 2.96× and by 1.62× and 1.47× on average on two contemporary CPU-GPU platforms.
INFLUENCE SOCIAL MEDIA MARKETING ACTIVITIES AGAINST BRAND LOYALTY DIRECTLY OR THROUGH CONSUMER SATISFACTION AND BRAND EQUITY ON THE LARGEST E-COMMERCE IN INDONESIA

Social Media Marketing is all strategic formations in sales communication carried out through social media. Social Media Marketing is considered more effective than traditional marketing communications such as print or electronic media, so Social media marketing is considered to be able to increase brand loyalty which has an impact on customers who will consistently use the product repeatedly, will inform, and even recommend the product to others. This study analyzes the influence of Social Media Marketing on Brand Loyalty directly and its effect through the mediation of consumer satisfaction & Brand equity on Brand Loyalty. The sample was taken from 195 followers of the Instagram account of the largest e-commerce platform in Indonesia, including Tokopedia, Shopee, Lazada, Bukalapak, OLX, and others. The research uses quantitative methods with an explanatory research approach, while hypothesis testing is carried out with SEM (Structural Equation Model) analysts. The research shows that social media marketing has a direct and significant effect on brand equity and consumer satisfaction, but social media marketing has no effect on brand loyalty. Brand equity has a direct and significant effect on brand loyalty, but consumer satisfaction has no effect on brand loyalty. From this research, it was found that it is so important to optimize the use of social media marketing to reach a wider scope. Attractive social media marketing can influence consumer perceptions to form a good impression of a brand. Previous research stated that an increase in social media activity and brand equity affects consumers’ desire to buy products, an increase in brand equity will also impact high brand loyalty. However, it needs to be re-examined to find out the addition of the right variable as mediation to see the influence between customer satisfaction on brand loyalty and social media marketing on brand loyalty.