Miracle-dz/UniFAR-FTUs huggingface dataset:
A Frame Work for Learning Drug Designing through Molecular Modelling Software Techniques and Biological Databases for Protein-Ligand Interactions

Applications of computer and information technology are indispensable in various fields especially in the field of biology. The use of computer aided tools plays a key role in solving biological problems. The spontaneous process of molecular docking is important for finding potentially strong candidate of drug for various viruses. The binding of protein receptors with ligand molecules is essential in drug discovery process. The aim of molecular docking tools is to predict the interaction between protein and ligand. This review outlines the major tools for protein - ligand docking which in turn emphasize the importance of molecular docking in modern drug discovery process.
Subunit Stoichiometry of a Heteromultimeric G protein-coupled Inward-rectifier K+ Channel*

We investigated the stoichiometry of the heteromultimeric G protein-coupled inward-recitfier K+ channel (GIRK) formed from GIRK1 and GIRK4 subunits. Multimeric GIRK constructs with several concatenated channel subunits were expressed in Xenopus oocytes. Coexpression of various trimeric constructs with different monomers clearly showed that the functional channel has stoichiometry (GIRK1)2(GIRK4)2. Efforts to establish a preferred arrangement of subunits around the channel pore suggest that more than one arrangement may be viable.
Correlation among environmental parameters using an online Smart Weather Station System

In recent years, the evolving wireless technology, cheaper micro-controllers, smart cities concept and `Internet of Things' (IoTs) have given way to the need of online wireless management systems for smart weather stations (SWS). In this paper, we develop an `Online Smart Weather Station System' for studying the correlation amongst multiple weather parameters data, collected over a period of 18 months. The system consists of sensors that generate weather data, local storage, wireless transfer to control centre, and web-based online representation and analysis of data. Six weather parameters such as air temperature, relative humidity, wind-speed, rain, rain-rate and solar energy are studied. Statistical measures such as mean, median, standard deviation, min, max and normalized standard deviation are computed from the weather data. The correlation among temperature, humidity and wind-speed parameters is presented. Few important observations are made, for possible applications in agriculture, construction and manufacturing activities. Further a web-based service can be aimed at automated analysis of weather data generated by the smart weather station.
Continuing Medical Education: A New Vision of the Professional Development of Physicians

The authors describe their vision of what continuing medical education (CME) should become in the changing health care environment. They first discuss six types of literature (e.g., concerning learning and adult development principles, problem-based/practice-based learning, and other topics) that contribute to ways of thinking about and understanding CME. They then state their view that the Association of American Medical Colleges (AAMC) has made a commitment to helping CME be more effective in the professional development of physicians. In presenting their new vision of CME, the authors describe their interpretation of the nature and values of CME (e.g., optimal CME is highly self-directed; the selection and design of the most relevant CME is based on data from each physician's responsibilities and performance; etc.). They then present seven action steps, suggestions to begin them, and the institutions and organizations they believe should carry them out, and recommend that the AAMC play a major role in supporting activities to carry out these steps. (For example, one action step is the generation and application of new knowledge about how and why physicians learn, select best practices, and change their behaviors). Six core competencies for CME educators are defined. The authors conclude by stating that collaboration among the appropriate academic groups, professional associations, and health care institutions, with leadership from the AAMC, is essential to create the best learning systems for the professional development of physicians.
Chronic Alcohol-Induced microRNA-155 Contributes to Neuroinflammation in a TLR4-Dependent Manner in Mice

Introduction Alcohol-induced neuroinflammation is mediated by pro-inflammatory cytokines and chemokines including tumor necrosis factor-α (TNFα), monocyte chemotactic protein-1 (MCP1) and interleukin-1-beta (IL-1β). Toll-like receptor-4 (TLR4) pathway induced nuclear factor-κB (NF-κB) activation is involved in the pathogenesis of alcohol-induced neuroinflammation. Inflammation is a highly regulated process. Recent studies suggest that microRNAs (miRNAs) play crucial role in fine tuning gene expression and miR-155 is a major regulator of inflammation in immune cells after TLR stimulation. Aim To evaluate the role of miR-155 in the pathogenesis of alcohol-induced neuroinflammation. Methods Wild type (WT), miR-155- and TLR4-knockout (KO) mice received 5% ethanol-containing or isocaloric control diet for 5 weeks. Microglia markers were measured by q-RTPCR; inflammasome activation was measured by enzyme activity; TNFα, MCP1, IL-1β mRNA and protein were measured by q-RTPCR and ELISA; phospho-p65 protein and NF-κB were measured by Western-blotting and EMSA; miRNAs were measured by q-PCR in the cerebellum. MiR-155 was measured in immortalized and primary mouse microglia after lipopolysaccharide and ethanol stimulation. Results Chronic ethanol feeding up-regulated miR-155 and miR-132 expression in mouse cerebellum. Deficiency in miR-155 protected mice from alcohol-induced increase in inflammatory cytokines; TNFα, MCP1 protein and TNFα, MCP1, pro-IL-1β and pro-caspase-1 mRNA levels were reduced in miR-155 KO alcohol-fed mice. NF-κB was activated in WT but not in miR-155 KO alcohol-fed mice. However increases in cerebellar caspase-1 activity and IL-1β levels were similar in alcohol-fed miR-155-KO and WT mice. Alcohol-fed TLR4-KO mice were protected from the induction of miR-155. NF-κB activation measured by phosphorylation of p65 and neuroinflammation were reduced in alcohol-fed TLR4-KO compared to control mice. TLR4 stimulation with lipopolysaccharide in primary or immortalized mouse microglia resulted in increased miR-155. Conclusion Chronic alcohol induces miR-155 in the cerebellum in a TLR4-dependent manner. Alcohol-induced miR-155 regulates TNFα and MCP1 expression but not caspase-dependent IL-1β increase in neuroinflammation.
Achieving Performance and Availability Guarantees with Spot Instances

In the Infrastructure-as-a-Service (IaaS) cloud computing market, spot instances refer to virtual servers that are rented via an auction. Spot instances allow IaaS providers to sell spare capacity while enabling IaaS users to acquire virtual servers at a lower price than the regular market price (also called "on demand" price). Users bid for spot instances at their chosen limit price. Based on the bids and the available capacity, the IaaS provider sets a clearing price. A bidder acquires their requested spot instances if their bid is above the clearing price. However, these spot instances may be terminated by the IaaS provider impromptu if the auction's clearing price goes above the user's limit price. In this context, this paper addresses the following question: Can spot instances be used to run paid web services while achieving performance and availability guaran-tees? The paper examines the problem faced by a Software-as-a-Service (SaaS) provider who rents spot instances from an IaaS provider and uses them to provide a web service on behalf of a paying customer. The SaaS provider incurs a monetary cost for renting computing resources from the IaaS provider, while charging its customer for executing web service transactions and paying penalties to the customer for failing to meet performance and availability objectives. To address this problem, the paper proposes a bidding scheme and server allocation policies designed to optimize the average revenue earned by the SaaS provider per time unit. Experimental results show that the proposed approach delivers higher revenue to the SaaS provider than an alternative approach where the SaaS provider runs the web service using "on demand" instances. The paper also shows that the server allocation policies seamlessly adapt to varying market conditions, traffic conditions, penalty levels and transaction fees.
Understanding the Behavior of Spark Workloads from Linux Kernel Parameters Perspective

Despite a number of innovative computer systems with high capacity memory have been built, the design principles behind an operating system kernel have remained unchanged for decades. We argue that kernel parameters is a kind of special interface of operating system and must be factored into the operation and maintenance of datacenters. To shed some light on the effectiveness of tuning Linux parameters of virtual memory subsystem when running Spark workloads, we evaluate the benchmarks in a simple standalone deploy mode. Our performance results reveal that some of the Linux memory parameters must be carefully set to efficiently support these processing workloads. We hope this work yields insights for datacenter system operators.
PaDIS emulator: an emulator to evaluate CDN-ISP collaboration

We present PaDIS Emulator, a fully automated platform to evaluate CDN-ISP collaboration for better content delivery, traffic engineering, and cost reduction. The PaDIS Emulator enables researchers as well as CDN and ISP operators to evaluate the benefits of collaboration using their own operational networks, configuration, and cost functions. The PaDIS Emulator consists of three components: the network emulation, the collaboration mechanism, and the performance monitor. These layers provide scalable emulation of the interaction between an ISP or a number of ISPs with multiple CDNs and vice versa. PaDIS Emulator design is flexible in order to implement a wide range of collaboration mechanisms on virtualized or real hardware, and evaluate them before introduction to operational networks.
Hydrogeologic assessment of groundwater under direct influence of surface water

Crypfosporidium parvum is a waterborne pathogen known to reside in surface waters throughout the US. Recent Cryptosporidiosis outbreaks from drinking water wells are the result of this contaminant transporting between surface and ground water systems. With a high resistance to typical groundwater treatment procedures, aquifer infiltration by Crypfosporidium poses a serious threat. Since groundwater wells are the main source of drinking water supply in the State of Florida, understanding certain factors that affect the passage of C. parvum into the subsurface would prevent future outbreaks. This study examines Florida’s surficial geology, recharge capacity, and possible sinkhole development as they influence the risk of groundwater contamination. Microscopic Particulate Analysis (MPA) sampling was performed on 71 9 public water systems distributed throughout Florida. Sampling results defined the wells as having high, moderate, or low risk to surface water influence. The results of this study indicated that the hydrogeology of an area tends to influence the MPA Risk Index (RI) of a well. Areas of higher aquifer recharge generally contained the majority of the moderate and highrisk wells. Surficial geologic regions of shelly sand and clay, limestone, and clayey sand contained over 80% of the high-risk wells. The results also suggested that areas prone to sinkhole development are likely to contain wells with positive RI. KEY TERMS: Cryptosporidium parvum; groundwater contamination; karst region; hydrogeology; microscopic particulate analysis; Safe Drinking Water Act.
Face recognition and gender classification in personal memories

Face annotation is an important concept for personal memories retrieval. Using automatic face recognition to annotate and find people in those memories provides an improvement of a personal memories management system. However, its results are limited by the uncontrolled conditions inherent to personal memories. In this paper, we propose a face recognition method to address these limitations which includes techniques of skin detection and pose estimation. It is also proposed a gender classification method to provide more information about the detected faces. Experimental results are presented that show that these methods improve the overall performance of face recognition and gender classification in personal memories.
A Hybrid Microgrid With DC Connection at Back to Back Converters

The necessity of an AC or DC microgrid is governed by available micro sources and connected loads. A hybrid structure can ensure a sustainable configuration blending both the forms. In this paper, a hybrid microgrid structure for a grid connected microgrid with DC connection at back to back (B2B) converters is proposed. While a B2B connection between two AC systems could bestow a reliable, isolated and efficient coupling, an extra DC bus connection can facilitate use of the DC micro sources. The DC bus can supply the local DC loads and can also trade part of the power with the AC grids. The voltage support at the DC link (of the B2B converters) can be used for the DC bus formation. Different power management strategies with fixed power references or decentralized power distribution in AC/DC sides are proposed and validated with simulations in PSCAD.
Get the Rhythm: Modeling Neuronal Activity

The simulation system NEURON is a common research tool for constructing structurally and functionally realistic models of neuronal systems. NEURON allows the development of simulations at any level of complexity, from subcellular components to single cells, cellular networks, and system-level models. Focusing on an in vitro cell model of a single, acutely isolated thalamic neuron, we used the simulation environment to address and to discuss the following questions in an undergraduate course: (i) Which parts are required to design a single compartment with passive electrical properties? (ii) Which components are necessary to model a single action potential or a train of action potentials? (iii) What can we learn from voltage-clamp and current-clamp experiments? (iv) What kind of cellular parameters are accessible from the modeling data? (v) What are the differences between single-compartment models and multi-compartment models? (vi) What are the advantages and disadvantages of artificial cell models? (vii) Can realistic modeling open up new strategies to discover the way that neurons process information?
Multiple Biological Model Classification: From System Biology to Synthetic Biology

This paper describes how to classify a family of biochemical pathways and circuits in terms of their temporal behavior by systematic application of time-frequency analysis and temporal logic based model checking. There are two immediate pay-offs for this approach. First, a family of models obtained by systematic perturbations of an archetypal (e.g., wild-type or ancestral) model, when classified in this manner, can help in identifying various incorrect or implausible features of the model. Second, specific hypotheses about various features of a given model can be automatically generated from such analysis and then subjected to experimental verification. We illustrate, by two examples, how our approach can be used (1) to understand the behavior of any individual topologically distinct circuit among the set of 125 synthetic biological circuits created out of similar elements, or (2) to ascertain the correctness of a well known Yeast Cell Cycle model by checking a family of perturbed models created through single and double mutations. We also show how this tool interacts with a propositional temporal logic model-checking system to present qualitative distinctions among the groups within the family of biological circuits or among the different multi-modal behaviors of a single pathway. The paper also poses several challenging open problems in computer science, related to our basic generate and test framework (with heuristics) used for systematically producing a set of temporal logic sentences that can be tested against different datasets.
On the extinction of Continuous State Branching Processes with catastrophes

We consider continuous state branching processes (CSBP's) with additional multiplicative jumps modeling dramatic events in a random environment. These jumps are described by a Levy process with bounded variation paths. We construct the associated class of processes as the unique solution of a stochastic differential equation. The quenched branching property of the process allows us to derive quenched and annealed results and make appear new asymptotic behaviors. We characterize the Laplace exponent of the process as the solution of a backward ordinary differential equation and establish when it becomes extinct. For a class of processes for which extinction and absorption coincide (including the $\alpha$ stable CSBP's plus a drift), we determine the speed of extinction. Four regimes appear, as in the case of branching processes in random environment in discrete time and space.The proofs rely on a fine study of the asymptotic behavior of exponential functionals of Levy processes. Finally, we apply these results to a cell infection model and determine the mean speed of propagation of the infection.
A motion accuracy evaluator based on body parts movement by MapReduce video processing

This paper presents a healthcare application that tracks body parts movement in video recording persons who exercise, analyzes the motor performance (e.g., motion speed and space), and evaluates fitness status (e.g., motion accuracy and abnormality). We design a MapReduce video processing for collecting the data of body parts movement from a large number of video files and successfully shorten the execution time. Analyzing the video files, the application correctly detects persons who have difficulties in their motion speed and space.
JNK2 and JNK3 combined are essential for apoptosis in dopamine neurons of the substantia nigra, but are not required for axon degeneration

Activation of c‐jun N‐terminal kinase (JNK) by the mitogen‐activated protein kinase cascade has been shown to play an important role in the death of dopamine neurons of the substantia nigra, one of the principal neuronal populations affected in Parkinson’s disease. However, it has remained unknown whether the JNK2 and JNK3 isoforms, either singly or in combination, are essential for apoptotic death, and, if so, the mechanisms involved. In addition, it has been unclear whether they play a role in axonal degeneration of these neurons in disease models. To address these issues we have examined the effect of single and double jnk2 and jnk3 null mutations on apoptosis in a highly destructive neurotoxin model, that induced by intrastriatal 6‐hydroxydopamine. We find that homozygous jnk2/3 double null mutations result in a complete abrogation of apoptosis and a prolonged survival of the entire population of dopamine neurons. In spite of this complete protection at the cell soma level, there was no protection of axons. These studies provide a striking demonstration of the distinctiveness of the mechanisms that mediate cell soma and axon degeneration, and they illustrate the need to identify and target pathways of axon degeneration in the development of neuroprotective therapeutics.
A New Control Strategy to Mitigate the Impact of Inverter-Based DGs on Protection System

Despite their undoubted advantages, Distributed Generation (DG) systems can negatively impact some aspects of the distribution system operation. In this paper, impacts of inverter-based DGs on fuse-recloser coordination in the fuse-saving protection scheme are thoroughly studied. Various fault conditions with different fault resistances and the effects of different DG locations are investigated. Also, the effects of DG reactive power injection, known as a DG potential ancillary service, on the protection scheme are studied. Furthermore, in order to mitigate the impact of DG on the protection coordination, a simple and effective control strategy is proposed. This strategy limits the DG output current according to the DG terminal voltage. Extensive simulations at different fault conditions and different DG penetration levels showed that the proposed control method is able to eliminate DG's contribution during the fault, and consequently eliminate its impact on the fuse-recloser coordination. In comparison to other methods, this strategy is inexpensive, easy to implement, does not limit DG capacity during normal condition, and does not require any change in the original protection system. The simulation results also demonstrated that the proposed method is robust against non-fault transient disturbances such as load switching, starting of induction motors, etc.
PCA Based Spatial Spectrum Sensing for MIMO Cognitive Radios

In the recent years spatial spectrum sensing become a promising approach due to the convergence of almost all wireless standards to incorporate spatial dimensions and use of multiple antennas at both transmitter and receiver. Keeping in consideration such wireless environment, we proposed a spectrum sensing algorithm based on principal component (PC) of spatially received signals. The proposed algorithm is analyzed under SISO (single input single output), SIMO (single input multiple output) and MIMO (multiple input multiple output) (employing stream multiplexing and Alamouti space time coding) scenario. Performance comparison was done by receiver operating curve (ROC) with other proposed algorithms in literature i.e. Maximum minimum Eigen value (MME). No prior information about the channel or primary user's (PU) signal is assumed. Simulations show the improved performance when info about spatial diversity of PU is incorporated in the proposed PCA. All the algorithms were tested using experimental data while using USRP (universal software radio peripheral) test bed that was controlled by GNU radio software.
Post-meal glucose peaks at home associate with carotid intima-media thickness in type 2 diabetes.

CONTEXT Two-hour postprandial hyperglycemia is related to chronic complications of diabetes and is currently used in the international guidelines to drive the therapy. OBJECTIVE Our objective was to assess the size and timing of post-meal glucose peaks in the everyday life of type 2 diabetic patients and the relationship with carotid atherosclerosis. DESIGN, SETTING, AND PATIENTS This was an observational study performed in 644 outpatients with type 2 diabetes attending diabetes clinics located in the area of the Campania County, South Italy, who provided complete home blood glucose profiles and centralized carotid intima-media thickness (CIMT) assessment. The study was conducted from 2001-2005. MAIN OUTCOME MEASURES Incremental glucose peak (IGP) was the maximal incremental increase in blood glucose obtained at any point after the meal. CIMT was assessed by carotid sonography. RESULTS The level of glycosylated hemoglobin and CIMT progressively increased across quintiles of IGP (P for trend = 0.01 for both). In univariate analysis, all examined glycemic parameters were significantly correlated with CIMT. IGP (r = 0.40; P = 0.006) showed the strongest correlation with CIMT, which remained significant in multiple linear regression analysis (R(2) = 0.26; P = 0.01). IGP was associated with a significant increase of CIMT in tertiles of glycosylated hemoglobin. IGP occurred within 1 h from the start of the meal in 95% of the entire diabetic population. CONCLUSION IGPs are frequent in the everyday life of patients with type 2 diabetes, occur for most (95%) within 1 h after meal, timing of IGPs is not influenced by treatment (diet or drugs), and IGPs correlate with CIMT.