Miracle-dz/UniFAR-FTUs huggingface dataset:
Hypoxia-induced switch in SNAT2/SLC38A2 regulation generates endocrine resistance in breast cancer

Significance The hypoxic microenvironment in solid tumors is known to reduce the efficacy of anticancer treatments in many cancer types, including breast cancer. This study shows hypoxia induces an amino acid transporter, SNAT2, which then causes resistance to antihormone therapy. We show major interplay between genes induced by estrogen receptor and hypoxia. Hypoxia-inducible factor 1α compensates for the loss of expression of estrogen receptor-α (ERα) for maintaining SNAT2 expression under hypoxia or endocrine therapies. SNAT2 overexpression produces complete resistance to antiestrogen therapy in vivo and is induced in tamoxifen resistance, and its expression is associated with poor survival in breast cancer and resistance to endocrine therapy in ERα+ luminal B patients. Our findings thus have revealed a previously unidentified mechanism for antiestrogen resistance driven by tumor metabolism. Tumor hypoxia is associated with poor patient outcomes in estrogen receptor-α–positive (ERα+) breast cancer. Hypoxia is known to affect tumor growth by reprogramming metabolism and regulating amino acid (AA) uptake. Here, we show that the glutamine transporter, SNAT2, is the AA transporter most frequently induced by hypoxia in breast cancer, and is regulated by hypoxia both in vitro and in vivo in xenografts. SNAT2 induction in MCF7 cells was also regulated by ERα, but it became predominantly a hypoxia-inducible factor 1α (HIF-1α)–dependent gene under hypoxia. Relevant to this, binding sites for both HIF-1α and ERα overlap in SNAT2’s cis-regulatory elements. In addition, the down-regulation of SNAT2 by the ER antagonist fulvestrant was reverted in hypoxia. Overexpression of SNAT2 in vitro to recapitulate the levels induced by hypoxia caused enhanced growth, particularly after ERα inhibition, in hypoxia, or when glutamine levels were low. SNAT2 up-regulation in vivo caused complete resistance to antiestrogen and, partially, anti-VEGF therapies. Finally, high SNAT2 expression levels correlated with hypoxia profiles and worse outcome in patients given antiestrogen therapies. Our findings show a switch in the regulation of SNAT2 between ERα and HIF-1α, leading to endocrine resistance in hypoxia. Development of drugs targeting SNAT2 may be of value for a subset of hormone-resistant breast cancer.
Mobile Network Traffic Prediction Based on Seasonal Adjacent Windows Sampling and Conditional Probability Estimation

Mobile operators collect and store the network generatedtraffic data for analysis. Time Series Prediction (TSP) has been used in mobile network traffic data analysis to produce predictive results for network planning and resource allocation. We propose a novel method of predicting mobile network traffic using neural networks based on conditional probability modeling between adjacent data windows. First, we develop a pre-processing method to aggregate the raw traffic log data and sample the aggregated time series to adjacent data windows, as training samples. Second, we use neural networks to parameterize the conditional probability between adjacent data windows and estimate the probability by training the neural networks with sampled data. The estimated conditional probability is then used to ensemble the prediction. Third, we show theoretically that the prediction based on all historical data is equivalent to the prediction based on just previous data window, given the estimation of conditional probability between adjacent data windows. We also analyze computation complexity and show that seasonality will reduce the computational complexity. In the experiment, we compare the prediction performance among the models with different seasonality, sample size and number of hidden layers, and show that the proposed schemes achieve better prediction accuracy than state-of-the-art.
Fine-Grained Trajectory Optimization of Multiple UAVs for Efficient Data Gathering from WSNs

The increasing availability of autonomous small-size Unmanned Aerial Vehicles (UAVs) has provided a promising way for data gathering from Wireless Sensor Networks (WSNs) with the advantages of high mobility, flexibility, and good speed. However, few works considered the situations that multiple UAVs are collaboratively used and the fine-grained trajectory plans of multiple UAVs are devised for collecting data from network including detailed traveling and hovering plans of them in the continuous space. In this paper, we investigate the problem of the Fine-grained Trajectory Plan for multi-UAVs (FTP), in which <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> UAVs are used to collect data from a given WSN, where <inline-formula> <tex-math notation="LaTeX">$m\geq 1$ </tex-math></inline-formula>. The problem entails not only to find the flight paths of multiple UAVs but also to design the detailed hovering and traveling plans on their paths for efficient data gathering from WSN. The objective of the problem is to minimize the maximum flight time of UAVs such that all sensory data of WSN is collected by the UAVs and transported to the base station. We first propose a mathematical model of the FTP problem and prove that the problem is NP-hard. To solve the FTP problem, we first study a special case of the FTP problem when <inline-formula> <tex-math notation="LaTeX">$m=1$ </tex-math></inline-formula>, called FTP with Single UAV (FTPS) problem. Then we propose a constant-factor approximation algorithm for the FTPS problem. Based on the FTPS problem, an approximation algorithm for the general version of the FTP problem when <inline-formula> <tex-math notation="LaTeX">$m> 1$ </tex-math></inline-formula> is further proposed, which can guarantee a constant factor of the optimal solution. Afterwards, the proposed algorithms are verified by extensive simulations.
Climate change and heatwaves in the main coastal cities of the Basque Country

In this paper we analyse the probabilistic behaviour of heatwaves (HWs) in the main coastal cities of the Basque Country (Bayonne, Bilbao and Donostia-San Sebastian) in the twentyfirst century. We estimate HW behaviour using data from eight climate circulation models under two representative concentration pathways (RCP 8.5 and RCP 4.5). We model HWs according to three factors: number per annum, duration and intensity, including correlations, and find very different results for each climate model. This highlights the problem of using a single model. Under RCP 8.5, we find an expected mean excess over the 30oC temperature threshold of 4.19oC for Bayonne, 4.05oC for Bilbao and 4.14oC for Donostia-San Sebastian in 2100. These expected values are based on incomplete information, so we also calculate several risk measures.
Factors Affecting the Characterization of Post-Exertional Malaise Derived from Patient Input

The National Institutes of Health/Center for Disease Control and Prevention (NIH/CDC) Common Data Elements (CDE) established a post-exertional malaise (PEM) workgroup with the task of describing PEM and recommending a standardized way of assessing it in patients with myalgic encephalomyelitis and chronic fatigue syndrome (ME/CFS). As a stigmatized group, patients with ME/CFS are in need of instruments which can properly describe their symptomatic experiences, which can help reduce the disparity between illness seriousness and appropriate attention from healthcare. The current study explored attitudes and preferences among 115 patients with ME/CFS who participated in the creation of a patient-driven instrument to measure PEM, the key symptom of the illness. Themes that emerged from the qualitative analyses of patient feedback focused on how their illness was experienced; their access to care; problems with physicians, researchers, and research methods; and expressions of gratitude for the collaborative process. Domains that were most important to the patient community were identified in the effort to create a comprehensive measure of PEM. Benefits of communitybased action research are discussed.
Mysteries of the trade? Skill-specific local agglomeration economies

ABSTRACT Do workers benefit from proximity to other workers with similar skill sets? This question dates back at least to Alfred Marshall. We use occupation groups to proxy skill sets and show that the answer likely depends on geographical levels, as well on regional hierarchy. Using longitudinal Swedish data, we document robust evidence consistent with highly localized spillovers at the level of sub-city districts between individuals in similar occupations. We further demonstrate less distance-sensitive benefits of working in districts and regions, characterized by high overall density (of employees in other occupations). We find no evidence of benefits from overall density outside Sweden’s three main metropolitan areas.
RELATIONSHIP BETWEEN ETHNIC DIVERSITY AND EMPLOYEE PERFORMANCE IN PUBLIC UNIVERSITIES IN WESTERN KENYA

The objective of this study was to assess the relationship between ethnic diversity and employee performance in public universities in Western Kenya. The study was guided by positivism research philosophy and descriptive survey research design was adopted. The target population included the chairpersons of departments of the four public universities in Western Kenya. A census of all the head of departments was conducted and primary data was collected using questionnaires pre tested for validity and reliability. Secondary data was collected from university documents and relevant publications in referred journals. Data was analyzed using descriptive and inferential statistics. Correlation and regression analysis were used to investigate the relationships between hypothesized variables. Out of the 120 questionnaires sent out, 93 questionnaires were received giving a return rate of 78%. Correlation and regression analysis established a statistically significant positive relationship between ethnic diversity variable and employee performance. The conclusion drawn from the study findings is that ethnic diversity positively influences employee performance in public universities in Western Kenya and majority of the employees in public universities are positive about ethnic diversity practices in their institutions. The study further recommends that public universities in Western Kenya should enhance ethnic diversity initiatives specifically in respecting employees’ custom, culture and values. Keyword: Workforce Diversity, Ethnic Diversity, Employee Performance, Public Universities, Human Resource Management.
Potential Bacterial Symbion of Sea Urchin As a Multi-Drug Resistant (MDR) Antibacterial Agent Against Staphylococcus aureus and Escherichia Coli Bacteria

Staphylococcus aureus and Escherichia coli are pathogenic bacteria agent of many human diseases. Those bacteria infect in various levels and also been antibiotic resistants. Bacterial resistance has become a serious global problem. The purposes of this study were to isolate and identify the symbiotic bacteria of the Sea Urchin that have an antibacterial activity of the Strain Multi-Drug Resistant (MDR) against Staphylococcus aureus and Escherichia coli. Sea Urchin was taken from Panjang island, Jepara Indonesia at 2-3 m depth. The symbiotic bacteria were isolated from Sea Urchin by using dilution method and spread plate method. Phenotypic characteristics was observed on colony shape, color and texture of growing bacteria. While the streak method was used to purify bacterial symbion. The antibacterial activity test was performed using overlay method. The results showed that 3 out of 37 isolates have antibacterial activity against Staphylococcus aureus and Escherichia coli. The BB.03. 35 isolate was selected for molecular identification due to the largest inhibitory zone diameter. The sequence of this bacterium showed 97% homology and closely related to Pseudoalteromonas flavipulchra.
Secondary Metabolites of Curvularia oryzae MTCC 2605

Curvularia oryzae MTCC 2605 was exploited for the production of secondary metabolites. The major compounds from the crude extract were purified by silica gel column chromatography and identified to be 11-�- methoxycurvularin and (S)-5-ethyl-8, 8-dimethylnonanal by NMR and Mass spectral data. Bioassays showed that 11-�-methoxycurvularin was active against bacteria, fungi and 4 th instar Spodoptera litura larvae.
Depression in Adults with a Physical Disability; An Institution Based Cross Sectional Study

Background: Depressions in person with physical disabilities are three times more common than in the general population. In Ethiopia, there are an estimated 15 million persons with disabilities. Despite the high burden of disability, very little is known about it; for this reason, the current study aimed at determining the magnitude of depression and its associated factors among adults with a physical disability. Methods: The study is a cross-sectional type conducted on individuals with a physical disability registered in Gondar physical disability’s rehabilitation and vocational center. All individuals with a physical disability attending the rehabilitation center were enrolled in the study. Face to face interview technique was used to evaluate the existence of depression and its determinants. In this study, the Beck Depression Inventory scale II was used to assess the status of depression. A multivariate logistic regression model was employed to see the strength, as well as the direction of the association between Depression and independent variables. Results: The study found that the frequency of depression was determined as 75.5%. Having a perceived internal stigma 5.03 (AOR=5.03 (95% CI: 2.30, 9.99), Poor social support (5.36 (AOR=5.36 (95% CI: 2.41, 10.8) and those who consume alcohol (2.77 (AOR=2.77 (1.24, 6.17) were associated likely to have depression. Conclusion: In this study, depression found to be a major health problem among adults with a physical disability. Early detection and regular screening for depression to give timely treatment as well as make the training fruitful, designing educational programs to build the community’s awareness to providing good social support and creating a sense of confidence and self-reliance in people with a physical disability would be helpful.
Reduced repertoire of cortical microstates and neuronal ensembles in medically-induced loss of consciousness

Medically-induced loss of consciousness (mLOC) has been linked to a macroscale break-down of brain connectivity, yet the neural microcircuit correlates of mLOC remain unknown. We applied non-linear t-stochastic neighbor embedding (t-SNE) and Lempel-Ziv-Welch complexity analysis to two-photon calcium imaging and local field potential (LFP) measurements of cortical microcircuit activity across anesthetic depth in mice, and to micro-electrode array recordings in human subjects. We find that mLOC disrupts population activity patterns by i) a reduction of discriminable network microstates and ii) a reduction of independent neuronal ensembles. These alterations are not explained by a simple reduction of neuronal activity and reveal abnormal functional microcircuits. Thus, normal neuronal ensemble dynamics could contribute to the emergence of conscious states.
A New Perspective on Adoption: Delivering Water Conservation Extension Programming to Nursery and Greenhouse Growers

Extension professionals help important agricultural sectors across the country resolve challenges using science-based practices that enhance environmental and social wellbeing while supporting businesses. Nursery and greenhouse growers comprise one of the largest sectors of U.S. agriculture, and this group is challenged to conserve water without compromising their economic viability. While Extension professionals educate and support nursery and greenhouse growers, there is a deficiency of research on adoption processes within this sector. To better understand this important Extension audience, this research examined the influence of critical thinking and problem-solving style on perceived characteristics of water conservation technologies and in turn the perceived characteristics relationship with their implementation. A route to adoption was established to inform effective Extension activities that promote water conservation. Problem-solving style predicts trialability while critical thinking style predicts none of the five characteristics of innovations. Of the five characteristics of water conservation innovations, relative advantage, trialability, and observability play a role in nursery and greenhouse growers’ implementation, and implementation does influence adoption. When designing water conservation programs for nursery and greenhouse growers, Extension professionals should consider participants’ problem-solving style and incorporate strategies to increase trialability, relative advantage, and observability.
A Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Series

Most electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated level and at high frequency rates. This data opens the possibility of developing new forecasting models with a potential positive impact on electricity systems. We present a general methodology that can process and forecast many smart-meter time series. Instead of using traditional and univariate approaches, we develop a single but complex recurrent neural-network model with long short-term memory that can capture individual consumption patterns and consumptions from different households. The resulting model can accurately predict future loads (short-term) of individual consumers, even if these were not included in the original training set. This entails a great potential for large-scale applications as once the single network is trained, accurate individual forecast for new consumers can be obtained at almost no computational cost. The proposed model is tested under a large set of numerical experiments by using a real-world dataset with thousands of disaggregated electricity consumption time series. Furthermore, we explore how geo-demographic segmentation of consumers may impact the forecasting accuracy of the model.
Watermelon, Kalium, and Kidney Health: A Review Literature

Kidney disease is one of the lifestyle conditions that affect several people around the world. Furthermore, the disease can increase the risk of other chronic disorders and make it difficult for patients to live a healthy and active life. Over the years, attempts have been made to identify new and alternative methods that can be used to manage such complications. One of the areas where researchers have focused on is the use of diet-based interventions. This paper aimed to explore the impact of watermelon consumption on kidney function and health. The study entailed conducting a systematic revive of articles related to the topic under investigation. The review yielded mixed results regarding the role of watermelon in the management of kidney disease. Most studies have shown that watermelon contains important nutrients and phytochemicals that protect the body from the adverse effects of free radical ions. In other studies, it was reported that overconsumption of watermelons might adversely affect renal function due to the high level of potassium in the fruit. Further investigations are required to examine the underlying mechanism through which the phytochemicals in watermelon affect kidney function. Furthermore, there is a need for additional empirical studies to assess the efficacy and safety of watermelon diets in the treatment and management of kidney-related complications.
Phosphorus solubilizing bacteria in combination with organic supplements improve phosphorus and chlorophyll content and growth parameters of maize

Sustainable crops production coupled with environmental safety measures has become a center of attention at current global canvas of science and research. Phosphorus solubilizing bacteria (PSB) are known to solubilize fixed phosphorus. Biochar is not only a nutrients rich organic amendment but also a very relevant input in relation to present climate change trend. Compost has been reported for a long time to improve soil physical properties in addition to having sufficient quantity of essential nutrients to sustain higher crops yield. Therefore, the application of two PSB strains, Achromobactor xylosoxides (S1) and Pseudomonas aeruginosa (S3), were tested in combination with and without a mixture of biochar and compost to improve some selective growth parameters of maize. The data obtained depicted that the mixture of biochar and compost significantly improved maize growth parameters as compared PSB strains and the control. Combination of PSB, biochar and compost remained more effective than their separate application. The PSB strain, Achromobactor xylosoxides was observed to be better than the Pseudomonas aeruginosa with and without organic supplements. Conclusively, the efficiency of PSB strains increases when used in conjunction with organic supplements like biochar and compost with respect to phosphorus and chlorophyll content and maize growth indices
A Method of State-of-Charge Estimation for EV Power Lithium-Ion Battery Using a Novel Adaptive Extended Kalman Filter

Battery management system (BMS) is one of the key subsystems of electric vehicle, and the battery state-of -charge (SOC) is a crucial input for the calculations of energy and power. Therefore, SOC estimation is a significant task for BMS. In this paper, a new method for online estimating SOC is proposed, which combines a novel adaptive extended Kalman filter (AEKF) and a parameter identification algorithm based on adaptive recursive least squares (RLS). Specifically, according to the first order R-C network equivalent circuit model, the battery model parameters are identified online using the RLS with multiple forgetting factors. Based on the identified parameters, the novel AEKF is used to accurately estimate the battery SOC. The online identification of parameter tracks the varying model. At the same time, due to the novel AEKF algorithm to dynamically adjust the system noise parameter, excellent accuracy of the SOC real-time estimation is obtained. Experiments are set to evaluate the accuracy and robustness of the proposed SOC estimation method. The simulation test results indicate that under DST and UDDS conditions, the maximum absolute errors are less than 0.015 after filtering convergence. In addition, the maximum absolute error is less than 0.02 in the simulation of DST with current and voltage measurement noise, so is in DST with current offset sensor error. The tests indicate that the proposed method can accurately estimate battery SOC and has strong robustness.
Frequency-Modulated Wireless Direct-Drive Motor Control

This article proposes and implements a frequency-modulated wireless direct-drive motor control (WDMC) for a promising application of underground in-pipe pumping. Accordingly, a novel urban drainage scheme is conceived, which offers high robustness, mobility, and flexibility. This scheme can help with the emergency response to prevent severe weather hazards, such as heavy rainfall. To power one energy-demanding unit of pumping network, a movable energy-carrying electric vehicle can park above the underground in-pipe pump and wirelessly drive the motor for accelerating the flow rate as required. Also, a pulse frequency modulation is newly used for wireless motor speed control with frequency-reduced zero-voltage switching. The system efficiencies can reach 87.6% and 83.9% with two tested power levels of 430 W at rated load and around 600 W at overload, respectively. Theoretical analysis, computer simulation, and practical experimentation are given to verify the feasibility of the proposed drainage system using the frequency-modulated WDMC.