Miracle-dz/UniFAR-FTUs huggingface dataset:
IFN Consensus Sequence Binding Protein/IFN Regulatory Factor-8 Guides Bone Marrow Progenitor Cells Toward the Macrophage Lineage1

IFN consensus sequence binding protein (ICSBP; IFN regulatory factor-8) is a transcription factor of the IFN regulatory factor family. Disruption of this gene results in a leukemia-like disease in mice. To investigate the role of ICSBP in myeloid cell development, lineage marker-negative (Lin−) bone marrow progenitor cells were purified from ICSBP+/+ and ICSBP−/− mice and tested for gene expression and colony-forming ability. ICSBP was expressed in Lin− progenitor cells, and its levels were markedly increased by IFN-γ. The colony-forming potential of ICSBP−/− progenitor cells was grossly abnormal, as they gave rise to a disproportionately high number of granulocyte colonies and many fewer macrophage colonies. IFN-γ inhibited colony formation, while promoting macrophage maturation in ICSBP+/+ cells. In contrast, the effects of IFN-γ were completely absent in ICSBP−/− progenitors. By retrovirus transduction we tested whether reintroduction of ICSBP restores a normal colony-forming potential in −/− progenitor cells. The wild-type ICSBP, but not transcriptionally defective mutants, corrected abnormal colony formation by increasing macrophage colonies and decreasing granulocyte colonies. Taken together, ICSBP plays a critical role in myeloid cell development by controlling lineage selection and is indispensable for IFN-γ-dependent modulation of progenitor cell maturation.
Temporal Dynamics of Sensorimotor Networks in Effort-Based Cost–Benefit Valuation: Early Emergence and Late Net Value Integration

Although physical effort can impose significant costs on decision-making, when and how effort cost information is incorporated into choice remains contested, reflecting a larger debate over the role of sensorimotor networks in specifying behavior. Serial information processing models, in which motor circuits simply implement the output of cognitive systems, hypothesize that effort cost factors into decisions relatively late, via integration with stimulus values into net (combined) value signals in dorsomedial frontal cortex (dmFC). In contrast, ethology-inspired approaches suggest a more active role for the dorsal sensorimotor stream, with effort cost signals emerging rapidly after stimulus onset. Here we investigated the time course of effort cost integration using event-related potentials in hungry human subjects while they made decisions about expending physical effort for appetitive foods. Consistent with the ethological perspective, we found that effort cost was represented from as early as 100–250 ms after stimulus onset, localized to dorsal sensorimotor regions including middle cingulate, somatosensory, and motor/premotor cortices. However, examining the same data time-locked to motor output revealed net value signals combining stimulus value and effort cost approximately −400 ms before response, originating from sensorimotor areas including dmFC, precuneus, and posterior parietal cortex. Granger causal connectivity analysis of the motor effector signal in the time leading to response showed interactions between these sensorimotor regions and ventrolateral prefrontal cortex, a structure associated with adjusting behavior–response mappings. These results suggest that rapid activation of sensorimotor regions interacts with cognitive valuation systems, producing a net value signal reflecting both physical effort and reward contingencies. SIGNIFICANCE STATEMENT Although physical effort imposes a cost on choice, when and how effort cost influences neural correlates of decision-making remains contested. This dispute reflects a larger disagreement between cognitive neuroscience and ethology over the role of sensorimotor systems in behavior: are sensorimotor circuits merely implementing the late-stage output of cognitive systems, or engaged rapidly and interactively from early in decision-making? We find that, although early representation of effort cost is associated with sensorimotor regions, these signals are also integrated with cognitive stimulus value representations in the time leading up to motor response. These data suggest that sensorimotor networks interact dynamically with cognitive systems to guide decision-making, providing a first step toward reconciling differing perspectives on sensorimotor roles in valuation and choice.
Very Early Detection of Alzheimer Neuropathology and the Role of Brain Reserve in Modifying Its Clinical Expression

Numerous studies show that the pathology of Alzheimer’s disease is present decades before a clinical diagnosis of dementia can be made. Given the likelihood that agents will become available that reliably delay onset and/or slow progression of Alzheimer’s disease, it will be important to detect preclinical Alzheimer’s disease as early as possible for maximal treatment effect. Detection of individuals by sensitive cognitive measures provides one way to identify people who are at high risk of developing clinical Alzheimer’s disease. However, it is likely that those with considerable brain or cognitive reserve will be able to mask cognitive deficits until very close to the onset of the dementia, rendering such cognitive measures insensitive. Optimum biomarkers for Alzheimer’s disease therefore need to target the severity of underlying brain pathology independently of brain reserve. Findings are presented showing the importance of higher education and larger brain size in masking the underlying disease pathology.
Precursors for cytochrome P450 profiling breath tests from an in silico screening approach

The family of cytochrome P450 enzymes (CYPs) is a major player in the metabolism of drugs and xenobiotics. Genetic polymorphisms and transcriptional regulation give a complex patient-individual CYP activity profile for each human being. Therefore, personalized medicine demands easy and non-invasive measurement of the CYP phenotype. Breath tests detect volatile organic compounds (VOCs) in the patients’ exhaled air after administration of a precursor molecule. CYP breath tests established for individual CYP isoforms are based on the detection of 13CO2 or 14CO2 originating from CYP-catalyzed oxidative degradation reactions of isotopically labeled precursors. We present an in silico work-flow aiming at the identification of novel precursor molecules, likely to result in VOCs other than CO2 upon oxidative degradation as we aim at label-free precursor molecules. The ligand-based work-flow comprises five parts: (1) CYP profiling was encoded as a decision tree based on 2D molecular descriptors derived from established models in the literature and validated against publicly available data extracted from the DrugBank. (2) Likely sites of metabolism were identified by reactivity and accessibility estimation for abstractable hydrogen radical. (3) Oxidative degradation reactions (O- and N-dealkylations) were found to be most promising in the release of VOCs. Thus, the CYP-catalyzed oxidative degradation reaction was encoded as SMIRKS (a programming language style to implement reactions based on the SMARTS description) to enumerate possible reaction products. (4) A quantitative structure property relation (QSPR) model aiming to predict the Henry constant H was derived from data for 488 organic compounds and identifies potentially VOCs amongst CYP reaction products. (5) A blacklist of naturally occurring breath components was implemented to identify marker molecules allowing straightforward detection within the exhaled air. Evident oxidative degradation reactions served as test case for the screening approach. Comparisons to metabolism data from literature support the results’ plausibility. Thus, a large scale screening for potential novel breath test precursor using the presented five stage work-flow is promising.
Selective compensation of voltage harmonics in an islanded microgrid

Recently, distributed generators are applied not only to inject power into the grid/local load, but also to compensate power quality problems. In this paper, a method for selective voltage harmonics compensation in microgrid based on the proper control of distributed generator (DG) interface converter is proposed. In this method, active and reactive power control loops are considered to control the power sharing among the DGs. Also, a virtual impedance loop and voltage and current proportional-resonant controllers are included. Simulation results show the effectiveness of the proposed method for compensating voltage harmonics to an acceptable level.
Small and medium enterprises and access to finance in Egypt

The purpose of this study is to put a spotlight on some of the problems of access to finance that stand in the way of small and midsize enterprises (SMEs) in Egypt and to draw the attention of policy makers to try to solve those issues. These problems vary from financial issues such as getting access to money to non-financial shortcomings in such areas as knowhow, marketing, management and accounting systems. Furthermore, SMEs in Egypt suffer from complex governmental regulations as well. To address the overall research question, “how do informal areas SMEs meet their financing needs and why do they make these choices,” the study used interviews and a written questionnaire both conducted with SME owners in the informal area of Imbaba in Giza. The study found that a large majority of SMEs have never used formal financial institutions due to lack of knowledge about how to deal with banks and the banks’ complicated procedures. A minority of those surveyed did use bank loans but suffered great difficulty in dealing with the system, repaying the loan, and dealing with high interest rates. The field work was complemented by a review of the literature, which found that banks' regulations and minimum loan size discourages SME owners from trying to obtain bank finance. Many interviewees expressed their anger at the complex and seemingly purposeless documentation required to obtain loans. Cut off from formal funding, most of the workshop owners interviewed depends on their social ties for financing, borrowing money from relatives and friends or participating in rotating credit associations. The study recommends that greater efforts be made by the government to ease regulations on starting a business. Banks should more aggressively pursue SME business, visiting shops and working with the owners to remove barriers to credit. NGOs should give assistance to shops facing difficulties in repayment to address the causes of their problems. If greater efforts are not made by the government and others to improve access to finance, SMEs will not be able to grow as quickly, with negative impacts on the Egyptian economy and on societal problems such as unemployment. Table of
A New Convolutional Formulation of the DFT and Efficient Systolic Implementation

A reduced-complexity circular-convolution-like formulation is presented for computation of the discrete Fourier transform. A systolic architecture is also derived for VLSI implementation of the proposed algorithm. The proposed architecture is fully-pipelined and contains regular and simple locally-connected processing elements. It is devoid of complex control structure and scalable for higher transform lengths. It is observed that the proposed systolic structure involves either less or nearly the same hardware-complexity compared with the corresponding existing systolic structures. Besides, it offers eight times more throughput and significantly low latency compared with the others.
Immunosuppressive drug monitoring – what to use in clinical practice today to improve renal graft outcome

Therapeutic drug monitoring (TDM) of immunosuppressive therapy is becoming an increasingly complex matter as the number of compounds and their respective combinations are continuously expanding. Unfortunately, in clinical practice, monitoring predose trough blood concentrations is often not sufficient for guiding optimal long‐term dosing of these drugs. The excellent short‐term results obtained nowadays in renal transplantation confer a misleading feeling of safety despite the fact that long‐term results have not substantially improved, definitely not to a point where longer graft survival could counteract the increasing need for transplant organs and less toxicity and side‐effects could ameliorate patient survival. It is therefore a challenging task to try to tailor immunosuppressive drug therapy to the individual patient profile and this in a time‐dependent manner. For the majority of currently used immunosuppressive drugs, measurement of total drug exposure by determination of the dose‐interval area under the concentration curve (AUC) seems to provide more useful information for clinicians in terms of concentration–exposure and exposure–response as well as reproducibility. To simplify this laborious way of measuring drug exposure, several validated abbreviated AUC profiles, accurately predicting the dose‐interval AUC, have been put forward. Together with an increasing knowledge of the time‐related pharmacokinetic behaviour of immunosuppressive drug and their metabolites, studies are focusing on how to apply abbreviated AUC sampling methods in clinical transplantation, taking into account the numerous factors affecting drug pharmacokinetics. Eventually, TDM using abbreviated AUC profiles has to be prospectively tested against classic methods of drug monitoring in terms of cost‐effectiveness, feasibility and clinical relevance with the ultimate goal of improving patient and graft survival.
Protein structure prediction (RMSD ≤ 5 Å) using machine learning models

Physical and chemical properties of protein help to determine the quality of protein structure. Here we explore the machine learning models using six physical and chemical properties, namely total empirical energy, secondary structure penalty, total surface area, pair number, residue length and Euclidean distance to predict the RMSD of a protein structure in the absence of its true native state. The Real Coded Genetic Algorithm is used to determine feature importance, and k-fold cross-validation is used to measure the robustness of the best predictive model. The experiments show that the random forest model outperforms the other machine learning approaches in RMSD prediction. The performance result shows that in the prediction of RMSD, the Root Mean Square Error RMSE is 0.48, correlation is 0.90, R² is 0.82 and accuracy is 97.02% with ±2 error on the testing data. The data set used in the study is available at http://bit.ly/PSP-ML.
Injection into pericardial sac and ligation of coronary artery of the rat.

ACTIVE interest in surgical methods by which deficient circulation in the coronary artery might be supported through collateral vessels was initated by Beck. This author, as a result of his experiences with the injection of diluted solution of sodium hypochlorite into the pericardial cavity of dogs 1 and of the observations of Hudson, Moritz and Wearn 2 and of Moritz, Hudson and Orgain 3 related to autopsy study of extracardiac circulation in human hearts, began the series of experiments leading to the operations on patients summarized by Beck 1 and by Feil. 4 In current surgical clinical efforts toward the development of extracardiac circulation, openings in the pericardium have been employed through which pectoral muscle or mediastinal fat, (Beck 5 ) or omentum (O'Shaughnessy 6 ) was brought to the heart. Many investigators have employed irritants to obliterate the pericardial sac by adhesions or to cause grafts to adhere. Beck 5 employed
Effect of secondary substrate binding in penicillopepsin: contributions of subsites S3 and S2' to kcat.

The kinetic parameters kcat, KM, and kcat/KM were determined at 25 degrees C and pH 4.5, 5.5, and 6.0 for the series of penicillopepsin substrates Ac-Alam-Lys-(NO2)Phe-Alan-amide, where (NO2)Phe is p-nitrophenylalanine and m and n equal 0-3. KM values at pH 6.0 were the same for all 12 peptides and averaged 0.088 +/- 0.02 mM but increased to different degrees at lower pH. In contrast, kcat values increased with increasing chain length. At pH 6 and at the pH optimum of kcat, the largest increases (about 37-fold on average) were obtained when alanine residues were added in positions P2' and P3. Only 1-2-fold increases were observed for positions P2, P3', P4, and P4'. These results show that occupation of subsites S2' and S3 is largely responsible for the rate enhancements caused by secondary substrate interactions with this series of peptides. Additional support for an important role of subsite S3 comes from the observation that the two peptides where m = 1 and n = 1 or 2, respectively, are cleaved not only between lysine and p-nitrophenylalanine but also between the latter and alanine, suggesting that occupation of subsite S3 by the N-terminal alanine overcomes the unfavorable interaction of alanine in subsite P1'. Subsite S3 is also important in the binding of pepstatin analogues and in transpeptidation reactions. It is proposed that the roles of subsites S3 and S2' are to facilitate the conversion of the first enzyme-substrate complex into a productive complex and to assist in the distortion of the scissile bond. (ABSTRACT TRUNCATED AT 250 WORDS)
The First-in-class Anti-EGFR Antibody Mixture Sym004 Overcomes Cetuximab Resistance Mediated by EGFR Extracellular Domain Mutations in Colorectal Cancer

Purpose: Approved anti-EGFR antibodies cetuximab and panitumumab provide significant clinical benefit in patients with metastatic colorectal cancer (MCRC). However, patients ultimately develop disease progression, often driven by acquisition of mutations in the extracellular domain (ECD) of EGFR. Sym004 is a novel 1:1 mixture of two nonoverlapping anti-EGFR mAbs that recently showed promising clinical activity in a phase I trial in MCRC. Our aim was to determine the efficacy of Sym004 to circumvent cetuximab resistance driven by EGFR ECD mutations. Experimental Design: Functional studies were performed to assess drug–receptor binding as well as ligand-dependent activation of individual EGFR mutants in the presence of cetuximab, panitumumab, and Sym004. Cell viability and molecular effects of the drugs were assayed in cetuximab-resistant cell lines and in tumor xenograft models. Efficacy of Sym004 was evaluated in patients progressing to cetuximab that harbored EGFR mutation in the post-cetuximab tumor sample. Results: Contrary to cetuximab and panitumumab, Sym004 effectively bound and abrogated ligand-induced phosphorylation of all individual EGFR mutants. Cells resistant to cetuximab harboring mutations in EGFR maintained sensitivity to Sym004, which was consistent with an effective suppression of EGFR downstream signaling, translating into profound and sustained tumor regression in the xenograft model. As proof-of-principle, a patient with a tumor harboring an EGFR mutation (G465R) following cetuximab therapy benefited from Sym004 therapy. Conclusions: Sym004 is an active drug in MCRC resistant to cetuximab/panitumumab mediated by EGFR mutations. EGFR mutations are potential biomarkers of response to Sym004 to be evaluated in ongoing large clinical trials. Clin Cancer Res; 22(13); 3260–7. ©2016 AACR.
A stochastic programming strategy in microgrid cyber physical energy system for energy optimal operation

This paper focuses on the energy optimal operation problem of microgrids (MGs) under stochastic environment. The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomness of unconventional energy resources. Therefore, it is necessary to develop a novel operation approach combining the uncertainty in the physical world with modeling strategy in the cyber system. This paper proposes an energy scheduling optimization strategy based on stochastic programming model by considering the uncertainty in MGs. The goal is to minimize the expected operation cost of MGs. The uncertainties are modeled based on autoregressive moving average (ARMA) model to expose the effects of physical world on cyber world. Through the comparison of the simulation results with deterministic method, it is shown that the effectiveness and robustness of proposed stochastic energy scheduling optimization strategy for MGs are valid.
User Preference and Channels Use in the Employment Seeking Process

Trust is a major issue for social capital theory and the word- of mouth concept. This paper compares social network sites with other media under consideration of trust and support. The research evaluates the difference between sources regarding trust and support to explain the situation of social network sites under consideration of the employment seeking process. The aim of this paper is the evaluation of the use of social network sites and private social networks by comparing these with job boards, company information, newspaper and official employment agency web pages. It is to identify the possible differences between information channels and the reasons for the use of different sources for the employment seeking process. It also inverstigate if organisations media offer the highest value to transfer information about employment opportunities to individuals.
Classical gluon radiation in ultrarelativistic nucleus-nucleus collisions

The classical Yang-Mills equations are solved perturbatively in covariant gauge for a collision of two ultrarelativistic nuclei. The nuclei are taken as ensembles of classical color charges on eikonal trajectories. The classical gluon field is computed in coordinate space up to cubic order in the coupling constant g. We construct the Feynman diagrams corresponding to this field and show the equivalence of the classical and diagrammatic approaches. An argument is given which demonstrates that at higher orders in g the classical description of the process breaks down. As an application, we calculate the energy, number, and multiplicity distributions of produced soft gluons and reproduce earlier results by Gunion and Bertsch and by Kovner, McLerran, and Weigert. {copyright} {ital 1997} {ital The American Physical Society}
Theory Of Financial Risk And Derivative Pricing

Risk control and derivative pricing have become of major concern to financial institutions, and there is a real need for adequate statistical tools to measure and anticipate the amplitude of the potential moves of the financial markets. Summarising theoretical developments in the field, this 2003 second edition has been substantially expanded. Additional chapters now cover stochastic processes, Monte-Carlo methods, Black-Scholes theory, the theory of the yield curve, and Minority Game. There are discussions on aspects of data analysis, financial products, non-linear correlations, and herding, feedback and agent based models. This book has become a classic reference for graduate students and researchers working in econophysics and mathematical finance, and for quantitative analysts working on risk management, derivative pricing and quantitative trading strategies.