Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward further improvement of the Flow of Funds Accounts

Compiling the flow-of-funds accounts (FFA) requires careful verification and validation of all primitive data and basic sources. New instruments, particularly in shadow banking and derivative markets, have been introduced one after another, as a result of the rapid evolution in financial markets. This paper presents the Bank of Japan’s framework of incorporating such new financial products into Japan’s FFA. Although some estimations are still adopted for classifying assets into sectors, without examining their unbiasedness, the improved FFA presents a fairly accurate picture of financial activities in the nation’s economy.
A comparison of feature selection algorithms for cancer classification through gene expression data: Leukemia case

In this study, three different feature selection algorithms are compared using Support Vector Machines as classifier for cancer classification through gene expression data. The ability of feature selection algorithms to select an optimal gene subset for a cancer type is evaluated by the classification ability of selected genes. A publicly available micro array dataset is employed for gene expression values. Selected gene subsets were able to classify subtypes of the considered cancer type with high accuracies and showed that these feature selection methods were applicable for bio-marker gene selection.
Implementation of Fuzzy Cognitive Maps Based on Fuzzy Neural Network and Application in Prediction of Time Series

The fuzzy cognitive map (FCM) has gradually emerged as a powerful paradigm for knowledge representation and a simulation mechanism that is applicable to numerous research and application fields. However, since efficient methods to determine the states of the investigated system and to quantify causalities that are the very foundations of FCM theory are lacking, constructing FCMs for complex causal systems greatly depends on expert knowledge. The manually developed models have a substantial shortcoming due to the model subjectivity and difficulties with assessing its reliability. In this paper, we proposed a fuzzy neural network to enhance the learning ability of FCMs. Our approach incorporates the inference mechanism of conventional FCMs with the determination of membership functions, as well as the quantification of causalities. In this manner, FCM models of the investigated systems can automatically be constructed from data and, therefore, operate with less human intervention. In the employed fuzzy neural network, the concept of mutual subsethood is used to describe the causalities, which provides more transparent interpretation for causalities in FCMs. The effectiveness of the proposed approach in handling the prediction of time series is demonstrated through many numerical simulations.
Herbal medications for anxiety, depression, pain, nausea and vomiting related to preoperative surgical patients: a systematic review and meta-analysis of randomised controlled trials

Objective To summarise the effects of herbal medications for the prevention of anxiety, depression, pain, and postoperative nausea and vomiting (PONV) in patients undergoing laparoscopic, obstetrical/gynaecological or cardiovascular surgical procedures. Methods Searches of MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials and LILACS up until January 2018 were performed to identify randomised controlled trials (RCTs). We included RCTs or quasi-RCTs evaluating any herbal medication among adults undergoing laparoscopic, obstetrical/gynaecological or cardiovascular surgeries. The primary outcomes were anxiety, depression, pain and PONV. We used the Grading of Recommendations Assessment, Development and Evaluation approach to rate overall certainty of the evidence for each outcome. Results Eleven trials including 693 patients were eligible. Results from three RCTs suggested a statistically significant reduction in vomiting (relative risk/risk ratio (RR) 0.57; 95% CI 0.38 to 0.86) and nausea (RR 0.69; 95% CI 0.50 to 0.96) with the use of Zingiber officinale (ginger) compared with placebo in both laparoscopic and obstetrical/gynaecological surgeries. Results suggested a non-statistically significantly reduction in the need for rescue medication for pain (RR 0.52; 95% CI 0.13 to 2.13) with Rosa damascena (damask rose) and ginger compared with placebo in laparoscopic and obstetrical/gynaecological surgery. None of the included studies reported on adverse events (AEs). Conclusions There is very low-certainty evidence regarding the efficacy of both Zingiber officinale and Rosa damascena in reducing vomiting (200 fewer cases per 1000; 288 fewer to 205 fewer), nausea (207 fewer cases per 1000; 333 fewer to 27 fewer) and the need for rescue medication for pain (666 fewer cases per 1000; 580 fewer to 752 more) in patients undergoing either laparoscopic or obstetrical/gynaecological surgeries. Among our eligible studies, there was no reported evidence on AEs. PROSPERO registration number CRD42016042838
Biomarker Strategies to Predict Need for Renal Replacement Therapy in Acute Kidney Injury

The early detection and diagnosis of acute kidney injury (AKI) with the standardization of novel kidney‐injury‐specific biomarkers is one of the highest research priorities in nephrology. Accordingly, the majority of studies of novel AKI biomarkers have focused on the early diagnosis of AKI using serum creatinine‐based definitions as the gold standard. However, another potential application of kidney‐injury‐specific biomarkers is for guiding decisions on when to initiate renal replacement therapy (RRT). The purpose of this review is to summarize recent findings concerning some of the more promising AKI biomarkers on their capacity, either alone or integrated with traditional surrogate measures of kidney injury, for early prediction of whether patients will develop severe AKI requiring RRT. Some studies that have examined neutrophil gelatinase‐associated lipocalin, cystatin‐C, N‐acetyl‐β‐d‐glucosaminidase, kidney injury molecule‐1, and α1‐microglobulin, among others, have suggested that these novel biomarkers have the potential to distinguish patients in whom RRT will be needed. This would imply that these biomarkers may be integrated into clinical decision algorithms and could synergistically improve our current ability to predict worsening AKI and need for RRT. However, published studies have many recognized limitations, which preclude our ability to adapt their findings into clinical practice today. While currently available data are not sufficient to conclude that biomarkers should be used routinely for clinical decision making for RRT initiation, additional data may in the future significantly modify the clinical variability for initiation of RRT, and potentially translate into improved outcomes and cost‐effectiveness. Finally, we propose a potential approach to future biomarker strategies for RRT initiation, integrating these biomarkers with “traditional” clinical factors.
microRNA‐124 attenuates isoflurane‐induced neurological deficits in neonatal rats via binding to EGR1

Isoflurane anesthesia induces neuroapoptosis in the development of the brain. In this study, neonatal rats and hippocampal neurons were subjected to isoflurane exposure, in which the effect of miR‐124 on the neurological deficits induced by isoflurane was evaluated. Isoflurane anesthesia models were induced in neonatal SD rats aged 7 days and then treated with miR‐124 agomir, miR‐124 antagomir, or LV‐CMV‐early growth response 1 (EGR1) plasmids. Then, the spatial learning and memory ability of rats were evaluated by Morris water maze. Furthermore, primary hippocampal neurons cultured 7 days were also exposed to isoflurane and transfected with miR‐124 agomir, miR‐124 antagomir, or LV‐CMV‐EGR1 plasmids. The targeting relationship of miR‐124 and EGR1 was verified by the dual‐luciferase reporter gene assay. To identify the effect of miR‐124 on neuron activities, the viability and apoptosis of hippocampal neurons were assessed. In response to isoflurane exposure, miR‐124 expression was reduced and EGR1 expression was increased in the hippocampal tissues and neurons. The isoflurane anesthesia damaged rats' spatial learning and memory ability, and reduced viability, and promoted apoptosis of hippocampal neurons. EGR1 was targeted and negatively regulated by miR‐124. The treatment of miR‐124 agomir improved rats' spatial learning and memory ability and notably increased hippocampal neuron viability and resistance to apoptosis, corresponding to an increased brain‐derived neurotrophic factor (BDNF) expression, inhibited expression of proapoptotic factors (cleaved‐Caspase‐3 and Bax), and enhanced the expression of antiapoptotic factor (Bcl‐2). Upregulated miR‐124 inhibited the expression of EGR1, by which mechanism miR‐124 reduced the neurological deficits induced by isoflurane in neonatal rats through inhibiting apoptosis of hippocampal neurons.
Wien Exploitation of Reciprocity in Measured MIMO Channels

In this work we study the feasibility of relative calibration to exploit channel reciprocity in a multipleinput multiple-output (MIMO) time division duplex (TDD) system. As an extension to the work of Guillaud et al. (2005), where the method was only applied to SISO and SIMO channels, real two-directional channel measurements were performed using the Eurecom MIMO Openair Sounder (EMOS). The setup considered here is more realistic, since a faster repetition rate, synchronization of transmitter and receiver over the air, and frequency offset, are accounted for. One of the major findings in the paper is a modification to the system model that correctly describes the non-negligible phase drift present in the measurements. We demonstrate that in a single-user MIMO channel and for low signal-to-noise (SNR) ratios, the relative calibration method can increase the capacity close to the theoretical limit.
Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds

P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly associated with the ADMET (absorption, distribution, metabolism, excretion and toxicity) properties of drugs/drug candidates and contributes to decreasing toxicity by eliminating compounds from cells, thereby preventing intracellular accumulation. Therefore, in the drug discovery and toxicological assessment process it is advisable to pay attention to whether a compound under development could be transported by P-gp or not. In this study, an in silico multiclass classification model capable of predicting the probability of a compound to interact with P-gp was developed using a counter-propagation artificial neural network (CP ANN) based on a set of 2D molecular descriptors, as well as an extensive dataset of 2512 compounds (1178 P-gp inhibitors, 477 P-gp substrates and 857 P-gp non-active compounds). The model provided a good classification performance, producing non error rate (NER) values of 0.93 for the training set and 0.85 for the test set, while the average precision (AvPr) was 0.93 for the training set and 0.87 for the test set. An external validation set of 385 compounds was used to challenge the model’s performance. On the external validation set the NER and AvPr values were 0.70 for both indices. We believe that this in silico classifier could be effectively used as a reliable virtual screening tool for identifying potential P-gp ligands.
Utility of time of onset of hypertension, ADMA and TAS in predicting adverse neonatal outcome in hypertensive disorders of pregnancy

Abstract Background: Limited studies have been conducted to evaluate the utility of indices for the prediction of the adverse neonatal outcomes in hypertensive disorders of pregnancy (HDP). Method: A total of 174 pregnant women with HDP (gestational hypertension, late onset preeclampsia, and early onset preeclampsia) and 49 controls were sampled during the third trimester. Preterm birth, low birth weight, fetal, and infant mortality and low Apgar scores were assessed. Results: Multivariate analysis confirmed systolic blood pressure (SBP), time of onset of hypertension (TOH), and total antioxidant status (TAS) as predictors of preterm births; TOH and diastolic blood pressure (DBP) to be predictors of low birth weight babies; TOH and asymmetric dimethyl arginine (ADMA) as predictors of fetal mortality and babies with low Apgar at 5 min. We found TOH as the single best predictor for adverse neonatal outcomes. Conclusion: This study identified TOH as an important predictor of most of the adverse neonatal outcomes.
Problems of testing of a highly specialized software

In this work the problems that arise when testing developed highly specialized software are considered. They were developed by students and teachers of different universities. The analysis, based on the example of TERMET, expediency of automated testing in developed educational or research systems, was performed. This system is used for manufacturing individual MEMS technologies.
An area and performance aware ECG encoder design for wireless healthcare services

Modern wearable devices demand low power high performance medical signal monitoring to achieve efficient and reliable health-care services. The electrocardiogram (ECG) signal which is used to diagnose heart diseases requires 24 hour monitoring. Efficient VLSI implementation of lossless ECG encoder is the critical requirement in wireless health care services. This paper presents an area efficient and high performance lossless ECG encoder that utilizes a single stage Huffman table to provide compressed ECG data. In the proposed ECG encoder architecture, low range of ECG data is encoded via small Huffman table whereas out of range data is segmented into upper and lower parts. These upper and lower parts are encoded by the same Huffman table in the two consecutive clock cycles. This architecture is implemented in MATLAB and simulated with MIT-BIH Arrhythmia database. The simulation results of the proposed ECG encoder show 72.87% more compression over the existing ECG encoder. To evaluate the hardware efficiency, the encoder is implemented in Verilog and synthesized with Synopsys Design Compiler using 90nm PDK. The results show that proposed encoder requires 12.11% less area and provides 2.1X improved performance over the existing encoder.
Coupling Genome-wide Transcriptomics and Developmental Toxicity Profiles in Zebrafish to Characterize Polycyclic Aromatic Hydrocarbon (PAH) Hazard

Polycyclic Aromatic Hydrocarbons (PAHs) are diverse environmental pollutants associated with adverse human health effects. Many studies focus on the carcinogenic effects of a limited number of PAHs and there is an increasing need to understand mechanisms of developmental toxicity of more varied yet environmentally relevant PAHs. A previous study characterized the developmental toxicity of 123 PAHs in zebrafish. Based on phenotypic responses ranging from complete inactivity to acute mortality, we classified these PAHs into eight bins, selected 16 representative PAHs, and exposed developing zebrafish to the concentration of each PAH that induced 80% phenotypic effect. We conducted RNA sequencing at 48 h post fertilization to identify gene expression changes as a result of PAH exposure. Using the Context Likelihood of Relatedness algorithm, we inferred a network that links the PAHs based on coordinated gene responses to PAH exposure. The 16 PAHs formed two broad clusters: Cluster A was transcriptionally more similar to the controls, while Cluster B consisted of PAHs that were generally more developmentally toxic, significantly elevated cyp1a transcript levels, and induced Ahr2-dependent Cyp1a protein expression in the skin confirmed by gene-silencing studies. We found that cyp1a transcript levels were associated with transcriptomic response, but not with PAH developmental toxicity. While all cluster B PAHs predominantly activated Ahr2, they also each enriched unique pathways like ion transport signaling, which likely points to differing molecular events between the PAHs downstream of Ahr2. Thus, using a systems biology approach, we have begun to evaluate, classify, and define mechanisms of PAH toxicity.
Unveiling the Structural Insights into the Inhibitor Binding Mechanism of Protein Kinase D1.

Although protein kinase D1 (PKD1) has been proved to be an efficient target for anticancer drug development, lack of structural details and substrate binding mechanisms are the main hinders for the development of selective inhibitors with therapeutic benefits. Thus the study considered the modeling of PKD1 and revealed the ligand binding mechanisms by using extensive molecular modeling approaches. Structural model of PKD1 was created by homology modeling and subsequently, active site identification and ATP binding characterization were accomplished to decipher the structural aspects of PKD1. Furthermore, known inhibitors were docked into the ATP binding site of PKD1 as well as the detailed ligand binding and molecular recognition mechanisms have been investigated by molecular dynamics and binding free energy calculations. The results confessed that, the conserved motifs including G-loop, hinge and catalytic subunits play important role in the ligand binding and selectivity is mediated by the polar interactions with less conserved residues in the hinge region, including Leu662, His663, and Asp665. The computed binding free energies of these inhibitors were in accordance with experimental bioactivity data. Taken together, these mechanistic understanding may help to put forward new hypotheses on new drug design on PKD1, for better therapies to overcome cancer and PKD1 dysregulated disorders.
5.1 POWER8TM: A 12-core server-class processor in 22nm SOI with 7.6Tb/s off-chip bandwidth

The 12-core 649mm2 POWER8™ leverages IBM's 22nm eDRAM SOI technology [1], and microarchitectural enhancements to deliver up to 2.5× the socket performance [2] of its 32nm predecessor, POWER7+™ [3]. POWER8 contains 4.2B transistors and 31. 5μF of deep-trench decoupling capacitance. Three thin-oxide transistor Vts are used for power/performance tuning, and thick-oxide transistors enable high-voltage I/O and analog designs. The 15-layer BEOL contains 5-80nm, 2-144nm, 3-288nm, and 3-640nm pitch layers for low-latency communication as well as 2-2400nm ultra-thick-metal (UTM) pitch layers for low-resistance distribution of power and clocks.
Capital Structure Dynamics and Stock Returns

Many finance theories predict that the capital structure affects firm value, which implies that the changes in leverage have an impact on stock returns. Most of the existing literature however has been focusing on the determinants of the capital structure. Using a sample of U.S. public firms during 1975-2002, we document a significantly negative effect of leverage changes on next-quarter stock returns. This effect remains significant after controlling for other firm characteristics such as ROE, book-to-market, firm size, and past returns. We propose and test several hypotheses to explain the observed effect. We find that the negative effect is stronger for the firms with a higher leverage level. This is consistent with a dynamic view of the pecking-order model that an increase in leverage reduces firms' debt capacity and may lead to future underinvestment. Further tests confirm the negative effect of current leverage change on future investment. In contrast, our results cannot be explained by the trade-off theory, default premium, the market timing theory, or the operational signaling story. Specifically, we find that deviation from the target leverage ratio has no impact on the stock returns, inconsistent with the trade-off theory (which implies an optimal, or partially optimal, leverage ratio). In addition, the change of long-term debt affects stock returns more than the change of short-term debt, and the one-year expected return following leverage change does not increase, both of which are inconsistent with the default risk premium hypothesis. Our results are not driven by firms' market timing activities. A firm times the market by issuing new equity (repurchasing stocks) when its equity is over- (under-) valued, which implies a positive relation between the leverage change and stock return. We also do not find support for the view that leverage increase signals poor future operating performance. Finally, we show that the return effect of leverage change contains information that cannot be explained by the popular pricing factors. This sheds new light on the link between capital structure choice and empirical asset pricing.
Multivariate Base Rates of Low Scores on Tests of Learning and Memory Among Latino Adult Populations

Abstract Objective: To determine the prevalence of low scores for two neuropsychological tests with five total scores that evaluate learning and memory functions. Method: N = 5402 healthy adults from 11 countries in Latin America and the commonwealth of Puerto Rico were administered the Rey–Osterrieth Complex Figure (ROCF) and the Hopkins Verbal Learning Test (HVLT-R). Two-thirds of the participants were women, and the average age was 53.5 ± 20.0 years. Z-scores were calculated for ROCF Copy and Memory scores and HVLT-R Total Recall, Delayed Recall, and Recognition scores, adjusting for age, age2, sex, education, and interaction variables if significant for the given country. Each Z-score was converted to a percentile for each of the five subtest scores. Each participant was categorized based on his/her number of low scoring tests in specific percentile cutoff groups (25th, 16th, 10th, 5th, and 2nd). Results: Between 57.3% (El Salvador) and 64.6% (Bolivia) of the sample scored below the 25th percentile on at least one of the five scores. Between 27.1% (El Salvador) and 33.9% (Puerto Rico) scored below the 10th percentile on at least one of the five subtests. Between 5.9% (Chile, El Salvador, Peru) and 10.3% (Argentina) scored below the 2nd percentile on at least one of the five scores. Conclusions: Results are consistent with other studies that found that low scores are common when multiple neuropsychological outcomes are evaluated in healthy individuals. Clinicians should consider the higher probability of low scores when evaluating learning and memory using various sets of scores to reduce false-positive diagnoses of cognitive deficits.