Miracle-dz/UniFAR-FTUs huggingface dataset:
Sestrin 2 and sestrin 3 suppress NK-92 cell-mediated cytotoxic activity on ovarian cancer cells through AMPK and mTORC 1 signaling

Ovarian cancer is one of the major cancer types. NK-92 cell line, which has consistently and reproducibly high anti-tumor cytotoxicity, may be used for immunotherapy against ovarian cancer. Understanding the mechanisms that regulate the anti-tumor activity of NK-92 cells is important for developing novel therapeutic strategies. In the current study, using an ovarian cancer xenograft mouse model, we identified the up-regulation of sestrin2 (SESN2) and sestrin3 (SESN3) in intratumoral NK-92 cells. Lentivirus-transduced NK-92 cells, which overexpressed SESN2 or SESN3 after doxycycline treatment, exhibited less expression of activating receptors, perforin and granzyme B. Overexpression of SESN2 and SESN3 impaired tumoricidal effect of NK-92 cells both in vitro and in vivo. Furthermore, overexpression of SESN2 and SESN3 inhibited mTORC1 signaling while promoting AMPK signaling in NK-92 cells. Taken together, our data highlights the crucial effects of SESN2 and SESN3 on NK-92 cell-mediated anti-ovarian cancer activity. This study might be valuable for designing a novel therapeutic strategy for ovarian cancer.
Association of ANRIL Expression with Coronary Artery Disease in Type 2 Diabetic Patients

Objective ANRIL is an important antisense noncoding RNA gene in the INK4 locus (9p21.3), a hot spot region associated with multiple disorders including coronary artery disease (CAD), type 2 diabetes mellitus (T2DM) and many different types of cancer. It has been shown that its expression is dysregulated in a variety of immune-mediated diseases. CAD is a major problem in T2DM patients and the cause of almost 60% of deaths in these patients worldwide. The aim of the present study was to compare the expression level of ANRIL between T2DM patients with and without CAD. Materials and Methods In this case-control study, we examined ANRIL expression in peripheral blood mononuclear cell samples by quantitative reverse transcriptionpolymerase chain reaction (RT-qPCR) in 64 T2DM patients with and without CAD (33 CAD+ and 31 CADpatients respectively, established by coronary angiography). Results Expression analysis revealed that ANRIL was up regulated (2.34-Fold, P=0.012) in CAD+ versus CAD diabetic patients. Data from receiver operating characteristic (ROC) curve analysis has shown that ANRIL could act as a potential biomarker for detecting CAD in diabetic patients. Conclusion The expression level of ANRIL is associated with presence of CAD in diabetic patients and could be considered as a potential peripheral biomarker.
Combining memory allocation and processor volatage scaling for energy-efficient IoT task scheduling

As IoT (Internet-of-things) technologies grow rapidly for emerging applications such as smart living and health care, reducing power consumption in battery-based IoT devices becomes an important issue. An IoT device is a kind of real-time systems, of which power-savings have been widely studied in terms of processor's dynamic voltage/frequency scaling. However, recent research has shown that memory subsystems are getting reached to a significant portion of power consumption in such systems. In this paper, we show that power consumption of real-time systems can be further reduced by combining voltage/frequency scaling with task allocation in hybrid memory. If a task set is schedulable in a low voltage mode of a processor, we can expect that the task set will still be schedulable even with slow memory. By considering this, we adopt non-volatile memory technologies that consume less power than DRAM but provide relatively slow access latency. Our aim is to allocate tasks in non-volatile memory if it does not violate the deadline constraint of real-time tasks, thereby reducing the power consumption of the system further. To do so, we incorporate the memory allocation problem into the problem model of processor's voltage scaling, and evaluate the effectiveness of the combined approach.
A Novel Framework for the Comparative Analysis of Biological Networks

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes.
NONLINEAR DYNAMIC RESPONSE AND DUCTILITY REQUIREMENTS OF A TYPICAL STEEL PLATE SHEAR WALL SUBJECTED TO EL CENTRO EARTHQUAKE

In the present research, an explicit dynamic analysis is conducted on a typical multistorey Steel Plate Shear Wall (SPSW) designed according to AISC requirements. The SPSW model is subjected to a base earthquake acceleration record to simulate the time history response of the structure. The analysis utilizes a finite element method involving both material and geometric nonlinearities. The energy dissipation behavior and distribution of storey shear over height of the model are investigated through time history response of the system. In addition, this paper proposes a new definition of energy ductility, which can be used in the investigation of the dynamic response of SPSWs. The ductility demand based on the proposed definition as well as shear resistance contribution of the web plate and Vertical Boundary Elements (VBE) of SPSW are evaluated. Keywords– Shear wall, steel plate, dynamic response, transient, energy dissipation, energy ductility
The Canary in the Coalmine; Bee Declines as an Indicator of Environmental Health

Bee declines have received much attention of late, but there is considerable debate and confusion as to the extent, significance and causes of declines. In part, this reflects conflation of data for domestic honeybees, numbers of which are largely driven by economic factors, with those for wild bees, many of which have undergone marked range contractions but for the majority of which we have no good data on population size. There is no doubt that bees are subject to numerous pressures in the modern world. The abundance and diversity of flowers has declined along with availability of suitable nest sites, bees are chronically exposed to cocktails of agrochemicals, and they are simultaneously exposed to novel parasites and pathogens accidentally spread by humans. Climate change is likely to exacerbate these problems in the future, particularly for cool-climate specialists such as bumblebees. Stressors do not act in isolation; for example pesticide exposure can impair both detoxification mechanisms and immune responses, rendering bees more susceptible to parasites. It seems certain that chronic exposure to multiple, interacting stressors is driving honeybee colony losses and declines of wild pollinators. Bees have a high profile and so their travails attract attention, but these same stressors undoubtedly bear upon other wild organisms, many of which are not monitored and have few champions. Those wild insects for which we do have population data (notably butterflies and moths) are overwhelmingly also in decline. We argue that bee declines are indicators of pervasive and ongoing environmental damage that is likely to impact broadly on biodiversity and the ecosystem services it provides.
Simple random search provides a competitive approach to reinforcement learning

A common belief in model-free reinforcement learning is that methods based on random search in the parameter space of policies exhibit significantly worse sample complexity than those that explore the space of actions. We dispel such beliefs by introducing a random search method for training static, linear policies for continuous control problems, matching state-of-the-art sample efficiency on the benchmark MuJoCo locomotion tasks. Our method also finds a nearly optimal controller for a challenging instance of the Linear Quadratic Regulator, a classical problem in control theory, when the dynamics are not known. Computationally, our random search algorithm is at least 15 times more efficient than the fastest competing model-free methods on these benchmarks. We take advantage of this computational efficiency to evaluate the performance of our method over hundreds of random seeds and many different hyperparameter configurations for each benchmark task. Our simulations highlight a high variability in performance in these benchmark tasks, suggesting that commonly used estimations of sample efficiency do not adequately evaluate the performance of RL algorithms.
RACIAL AND ETHNIC DISPARITIES IN ALZHEIMER ' S DISEASE : A Literature Review

iii ACRONYMS . iv INTRODUCTION . 1 METHODS . 2 EVIDENCE OF RACIAL AND ETHNIC DISPARITIES . 3 Prevalence, Incidence, and Diagnosis . 3 Health Outcomes . 4 Participation in Clinical Trials . 5 Use of Long-Term Services and Supports . 6 Use of Medications . 7 Health Care Expenditures . 8 Caregiving . 9 POSSIBLE REASONS FOR RACIAL AND ETHNIC DISPARITIES . 10 Factors Related to Measurement of Alzheimer's Disease Prevalence and Incidence . 10 Genetics . 11 Cardiovascular and Cerebrovascular Disease . 12 Socioeconomic Factors . 13 Cultural Differences . 14 Racial and Ethnic Discrimination . 15 INTERVENTIONS TO REDUCE RACIAL AND ETHNIC DISPARITIES . 17 Cultural Competence . 17 Outreach to Minority Communities . 19 CONCLUSIONS . 20 REFERENCES . 21
Infestation of parasitic rhizocephalan barnacles Sacculina beauforti (Cirripedia, Rhizocephala) in edible mud crab, Scylla olivacea

Screening of mud crab genus Scylla was conducted in four locations (Marudu Bay, Lundu, Taiping, Setiu) representing Malaysia. Scylla olivacea with abnormal primary and secondary sexual characters were prevalent (approximately 42.27% of the local screened S. olivacea population) in Marudu Bay, Sabah. A total of six different types of abnormalities were described. Crabs with type 1 and type 3 were immature males, type 2 and type 4 were mature males, type 5 were immature females and type 6 were mature females. The abdomen of all crabs with abnormalities were dented on both sides along the abdomen’s middle line. Abnormal crabs showed significant variation in their size, weight, abdomen width and/or gonopod or pleopod length compared to normal individuals. The mean body weight of abnormal crabs (type 1–5) were higher than normal crabs with smaller body size, while females with type 6 abnormality were always heavier than the normal counterparts at any given size. Sacculinid’s externa were observed in the abdomen of crabs with type 4 and type 6 abnormalities. The presence of embryos within the externa and subsequent molecular analysis of partial mitochondrial COI region confirmed the rhizocephalan parasite as Sacculina beauforti. Future in-depth descriptions of the life cycle and characteristics of S. beauforti are recommended as it involves a commercially important edible crab species and the effect on human health from the consumption of crabs is of crucial concern.
Prevalence of Epilepsy in Iran: A Meta-Analysis and Systematic Review

Objective Epilepsy is one of the most common diseases in Iran contributing to an array of health problems. In light of this, the aim of the present study is to examine the prevalence of epilepsy in Iran through a systematic review and meta-analysis. Materials & Methods A systematic search of several databases including PubMed, scientific information databases, Google, Google scholar, Elsevier and Scopus was conducted in June 2013. Observational studies were considered for inclusion if they were published in Iranian and examined epilepsy prevalence and/or related risk factors. Meta-analysis was conducted using a random effect model with the DerSimonian/Laird method. Heterogeneity was examined using the Breslow- Day test and inconsistency using the I2 statistic. Results A total of 45 studies were identified from the search strategy. Of these, nine published manuscripts with a total of 7,723 participants were included within the review. The pooled prevalence of epilepsy in Iran was estimated to be around 5% (95% confident interval (CI) 2 to 8). For each region the prevalence of epilepsy in central, northern and eastern Iran were 5% (95%CI 2 to 8), 1% (95%CI -1 to 3) and 4% (95%CI 3 to 11) respectively. The most common risk factors in order of prevalence were somatic diseases 39% (95%CI 15 to 62), convulsion 38% (95%CI 11 to 65), mental diseases 36% (95%CI 15 to 95) and hereditary development 26% (95%CI 9 to 42). A meta-regression model identified a declining trend in the prevalence of epilepsy within Iran for the last decade. Conclusion Pooled analyses from the nine included publications in this review estimate the prevalence of epilepsy in Iran to be around 5%. Although this result is much higher than rates in other countries, a declining trend in prevalence over the past decade was also identified.
Autonomous response of a third‐order digital filter with two's complement arithmetic realized in cascade form

In this letter, results on the autonomous response of a third‐order digital filter with two's complement arithmetic realized as a first‐order subsystem cascaded by a second‐order subsystem are reported. The behaviour of the second‐order subsystem depends on the pole location and the initial condition of the first‐order subsystem, because the transient behaviour is affected by the first‐order subsystem and this transient response can be viewed as an excitation of the original initial state to another state. New results on the set of necessary and sufficient conditions relating the trajectory equations, the behaviours of the symbolic sequences and the sets of the initial conditions are derived. The effects of the pole location and the initial condition of first‐order subsystem on the overall system are discussed. Some interesting differences between the autonomous response of the second‐order subsystem and the response due to the exponentially decaying input are reported. Some simulation results are given to illustrate the analytical results. Copyright © 2004 John Wiley & Sons, Ltd.
Validation of Memorial Sloan Kettering Cancer Center nomogram to detect non-sentinel lymph node metastases in a United Kingdom cohort.

AIM Axillary lymph node dissection, although associated with long-term morbidity, has been the standard of treatment for all nodepositive breast cancer patients. We assessed the risk prediction ability (validity) of Memorial Sloan Kettering Cancer Center (MSKCC) nomogram for non-sentinel lymph node metastases and analysed the outcome of patients with sentinel node metastases. PATIENTS AND METHODS All operable early breast cancer patients with sentinel node macro metastases (size > 2mm) who underwent axillary dissection from April 2009 to March 2015 were considered eligible. The risk of non-sentinel lymph node metastases was calculated using an online MSKCC calculator, and accuracy was determined based on the area under the receiver-operating characteristic curve (AUC-ROC). Tumour characteristics and overall survival were also analysed as secondary end points. RESULTS Of 1745 patients who were diagnosed with operable breast cancer during the study period, 114 patients were considered eligible. The AUC-ROC was 0.66 suggestive of lesser accuracy in prediction and not statistically significant (p value = 0.7303). Seventysix (50.7%) of these patients did not have any non-sentinel node metastases. At a mean follow up of four years, the disease-free survival was 86.4% and overall survival rate was 88.4%. CONCLUSIONS The MSKCC nomogram was unable to accurately predict the risk in our cohort of patients with more than half of this cohort of patients not requiring axillary dissection. These findings are consistent with other European studies. This study thus highlights the need for modified prediction model for European cohorts.
Learning about stress: neural, endocrine and behavioral adaptations

Abstract In this review, nonassociative learning is advanced as an organizing principle to draw together findings from both sympathetic-adrenal medullary and hypothalamic–pituitary–adrenocortical (HPA) axis responses to chronic intermittent exposure to a variety of stressors. Studies of habituation, facilitation and sensitization of stress effector systems are reviewed and linked to an animal’s prior experience with a given stressor, the intensity of the stressor and the appraisal by the animal of its ability to mobilize physiological systems to adapt to the stressor. Brain pathways that regulate physiological and behavioral responses to stress are discussed, especially in light of their regulation of nonassociative processes in chronic intermittent stress. These findings may have special relevance to various psychiatric diseases, including depression and post-traumatic stress disorder (PTSD).
Scalable Active Temporal Constrained Clustering

We introduce a novel interactive framework to handle both instance-level and temporal smoothness constraints for clustering large temporal data. It consists of a constrained clustering algorithm which optimizes the clustering quality, constraint violation and the historical cost between consecutive data snapshots. At the center of our framework is a simple yet effective active learning technique for iteratively selecting the most informative pairs of objects to query users about, and updating the clustering with new constraints. Those constraints are then propagated inside each snapshot and between snapshots via constraint inheritance and propagation to further enhance the results. Experiments show better or comparable clustering results than existing techniques as well as high scalability for large datasets.
‘‘Sequence space soup’’ of proteins and copolymers

To study the protein folding problem, we use exhaustive computer enumeration to explore ‘‘sequence space soup,’’ an imaginary solution containing the ‘‘native’’ conformations (i.e., of lowest free energy) under folding conditions, of every possible copolymer sequence. The model is of short self‐avoiding chains of hydrophobic (H) and polar (P) monomers configured on the two‐dimensional square lattice. By exhaustive enumeration, we identify all native structures for every possible sequence. We find that random sequences of H/P copolymers will bear striking resemblance to known proteins: Most sequences under folding conditions will be approximately as compact as known proteins, will have considerable amounts of secondary structure, and it is most probable that an arbitrary sequence will fold to a number of lowest free energy conformations that is of order one. In these respects, this simple model shows that proteinlike behavior should arise simply in copolymers in which one monomer type is highly solvent ave.
RiboProfiling: a Bioconductor package for standard Ribo-seq pipeline processing

The ribosome profiling technique (Ribo-seq) allows the selective sequencing of translated RNA regions. Recently, the analysis of genomic sequences associated to Ribo-seq reads has been widely employed to assess their coding potential. These analyses led to the identification of differentially translated transcripts under different experimental conditions, and/or ribosome pausing on codon motifs. In the context of the ever-growing need for tools analyzing Ribo-seq reads, we have developed ‘RiboProfiling’, a new Bioconductor open-source package. ‘RiboProfiling’ provides a full pipeline to cover all key steps for the analysis of ribosome footprints. This pipeline has been implemented in a single R workflow. The package takes an alignment (BAM) file as input and performs ribosome footprint quantification at a transcript level. It also identifies footprint accumulation on particular amino acids or multi amino-acids motifs. Report summary graphs and data quantification are generated automatically. The package facilitates quality assessment and quantification of Ribo-seq experiments. Its implementation in Bioconductor enables the modeling and statistical analysis of its output through the vast choice of packages available in R. This article illustrates how to identify codon-motifs accumulating ribosome footprints, based on data from Escherichia coli.
3D Room Geometry Inference Based on Room Impulse Response Stacks

Room geometry inference is concerned with the localization of reflective boundaries in an enclosed space. This paper outlines a method for inferring room geometry based on the positions of loudspeakers and real or image microphones, which are computed using sets of times of arrival (TOAs) obtained from room impulse responses (RIRs). These RIRs describe the acoustic propagation between the loudspeakers in an array and a single microphone. First, peaks corresponding to TOAs in these RIRs are detected and labeled using an automated method. Second, the labeled TOA sets are used to estimate the real and image microphone positions, with knowledge of the loudspeaker array geometry. Third, using all these positions, the positions of reflection points on the available reflectors in the room are determined. The reflection points determine the reflectors’ locations and orientations. This approach is largely automated and usable in real-world scenarios.
Prediction of Arctic Sea Ice Concentration Using a Fully Data Driven Deep Neural Network

The Arctic sea ice is an important indicator of the progress of global warming and climate change. Prediction of Arctic sea ice concentration has been investigated by many disciplines and predictions have been made using a variety of methods. Deep learning (DL) using large training datasets, also known as deep neural network, is a fast-growing area in machine learning that promises improved results when compared to traditional neural network methods. Arctic sea ice data, gathered since 1978 by passive microwave sensors, may be an appropriate input for training DL models. In this study, a large Arctic sea ice dataset was employed to train a deep neural network and this was then used to predict Arctic sea ice concentration, without incorporating any physical data. We compared the results of our methods quantitatively and qualitatively to results obtained using a traditional autoregressive (AR) model, and to a compilation of results from the Sea Ice Prediction Network, collected using a diverse set of approaches. Our DL-based prediction methods outperformed the AR model and yielded results comparable to those obtained with other models.
The Impact of Medical Liability Standards on Regional Variations in Physician Behavior: Evidence from the Adoption of National-Standard Rules

I explore the association between regional variations in physician behavior and the geographical scope of malpractice standards of care. I estimate a 30-50 percent reduction in the gap between state and national utilization rates of various treatments and diagnostic procedures following the adoption of a rule requiring physicians to follow national, as opposed to local, standards. These findings suggest that standardization in malpractice law may lead to greater standardization in practices and, more generally, that physicians may indeed adhere to specific liability standards. In connection with the estimated convergence in practices, I observe no associated changes in patient health. (JEL I11, I18, J44, K13)