Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relationship between Political Factors and the Development of E-Participatory Government

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Estimating Geographical Variation in the Risk of Zoonotic Plasmodium knowlesi Infection in Countries Eliminating Malaria

Background Infection by the simian malaria parasite, Plasmodium knowlesi, can lead to severe and fatal disease in humans, and is the most common cause of malaria in parts of Malaysia. Despite being a serious public health concern, the geographical distribution of P. knowlesi malaria risk is poorly understood because the parasite is often misidentified as one of the human malarias. Human cases have been confirmed in at least nine Southeast Asian countries, many of which are making progress towards eliminating the human malarias. Understanding the geographical distribution of P. knowlesi is important for identifying areas where malaria transmission will continue after the human malarias have been eliminated. Methodology/Principal Findings A total of 439 records of P. knowlesi infections in humans, macaque reservoir and vector species were collated. To predict spatial variation in disease risk, a model was fitted using records from countries where the infection data coverage is high. Predictions were then made throughout Southeast Asia, including regions where infection data are sparse. The resulting map predicts areas of high risk for P. knowlesi infection in a number of countries that are forecast to be malaria-free by 2025 (Malaysia, Cambodia, Thailand and Vietnam) as well as countries projected to be eliminating malaria (Myanmar, Laos, Indonesia and the Philippines). Conclusions/Significance We have produced the first map of P. knowlesi malaria risk, at a fine-scale resolution, to identify priority areas for surveillance based on regions with sparse data and high estimated risk. Our map provides an initial evidence base to better understand the spatial distribution of this disease and its potential wider contribution to malaria incidence. Considering malaria elimination goals, areas for prioritised surveillance are identified.
Virtual digital signal processing in an object-oriented system

We describe in this paper a prototype environment for digital signal processing in terms of a virtual processor and its associated virtual data. The basic operations of the processor are designed for fast vector operations on a variety of different types of hardware. One finished implementation is presented and others are planned. The modern digital signal processing environment is one of great flux. New chips are being developed and brought to market at ever faster rates. Those creating signal-processing software applications wish to take advantage of the speed of these hardware devices, but are faced with the problem of interfacing each application to each piece of hardware. The work required can be reduced immensely by using a signal-processing software environment that is common to all the low-level hardware devices, but such an environment must have hardware independence "designed in" from the very
Unified Direct-Flux Vector Control for AC Motor Drives

This paper introduces a unified direct-flux vector control scheme that is suitable for sinusoidal ac motor drives. The ac drives considered here are induction motor, synchronous reluctance, and synchronous permanent-magnet (PM) motor drives, including interior and surface-mounted PM types. The proposed controller operates in stator flux coordinates: The stator flux amplitude is directly controlled by the direct voltage component, while the torque is controlled by regulating the quadrature current component. The unified direct-flux control is particularly convenient when flux weakening is required since it easily guarantees maximum torque production under current and voltage limitations. The hardware for control is standard, and the control firmware is the same for all the motors under test with the only exception of the magnetic model used for flux estimation at low speed. Experimental results on four different drives are provided, showing the validity of the proposed unified control approach.
Cytoplasmic dynein participates in the centrosomal localization of the Golgi complex

The localization of the Golgi complex depends upon the integrity of the microtubule apparatus. At interphase, the Golgi has a restricted pericentriolar localization. During mitosis, it fragments into small vesicles that are dispersed throughout the cytoplasm until telophase, when they again coalesce near the centrosome. These observations have suggested that the Golgi complex utilizes a dynein-like motor to mediate its transport from the cell periphery towards the minus ends of microtubules, located at the centrosome. We utilized semi-intact cells to study the interaction of the Golgi complex with the microtubule apparatus. We show here that Golgi complexes can enter semi-intact cells and associate stably with cytoplasmic constituents. Stable association, termed here "Golgi capture," requires ATP hydrolysis and intact microtubules, and occurs maximally at physiological temperature in the presence of added cytosolic proteins. Once translocated into the semi-intact cell cytoplasm, exogenous Golgi complexes display a distribution similar to endogenous Golgi complexes, near the microtubule-organizing center. The process of Golgi capture requires cytoplasmic tubulin, and is abolished if cytoplasmic dynein is immunodepleted from the cytosol. Cytoplasmic dynein, prepared from CHO cell cytosol, restores Golgi capture activity to reactions carried out with dynein immuno-depleted cytosol. These results indicate that cytoplasmic dynein can interact with isolated Golgi complexes, and participate in their accumulation near the centrosomes of semi-intact, recipient cells. Thus, cytoplasmic dynein appears to play a role in determining the subcellular localization of the Golgi complex.
Sum rate maximization for full duplex wireless-powered communication networks

We consider a full duplex multiuser multiple-input multiple-output system and study the sum rate maximization in a wireless powered communication networks. We assume that the users of uplink (UL) channel have no available power supply and thus a harvest-then-transmit protocol is utilized. Specifically, the base station (BS) first conveys simultaneously the energy to all UL users via energy beamforming and also transmit the information to all users in the downlink (DL) channel via information beamforming. Then, the users in the UL channel send their independent information to the BS using their harvested energy in the second phase. Since the utility function of the sum rate maximization is nonconvex, and thus, the optimal solution is difficult to find in general. To solve this problem, we propose an iterative algorithm to obtain suboptimal solution based on semidefinite program in each iteration. Simulation results demonstrate that the proposed design outperforms the conventional design.
Interference Mitigation in LTE- Advanced MU-MIMO through Beamforming Technique

GPP LTE advanced is an evolving standard targeting 4G wireless system, LTE-Advanced introduces new functionalities such as Carrier aggregation, enhanced use of multi-antenna techniques. In this work, the different multi-antenna techniques such as single user MIMO (SU-MIMO) and multiuser MIMO (MU-MIMO) cases are investigated. In MU- MIMO case, interference is more when compared with SU-MIMO case. To overcome this challenge, the beamforming technique is used under the assumption of perfect channel knowledge at the transmitter availability. We evaluate the system performance of SU- MIMO and MU-MIMO cases in the different scenarios, with and without beamforming technique in terms of Bit-Error-Rate (BER) and Signal to Noise Ratio (SNR). For the stimulation purpose we use MATLAB software package.
An information-theoretic analysis of worst-case redundancy in database design

Normal forms that guide the process of database schema design have several key goals such as elimination of redundancies and preservation of integrity constraints, such as functional dependencies. It has long been known that complete elimination of redundancies and complete preservation of constraints cannot be achieved simultaneously. In this article, we use a recently introduced information-theoretic framework, and provide a quantitative analysis of the redundancy/integrity preservation trade-off, and give techniques for comparing different schema designs in terms of the amount of redundancy they carry. The main notion of the information-theoretic framework is that of an information content of each datum in an instance (which is a number in [0,1]): the closer to 1, the less redundancy it carries. We start by providing a combinatorial criterion that lets us calculate, for a relational schema with functional dependencies, the lowest information content in its instances. This indicates how good the schema design is in terms of allowing redundant information. We then study the normal form 3NF, which tolerates some redundancy to guarantee preservation of functional dependencies. The main result provides a formal justification for normal form 3NF by showing that this normal form pays the smallest possible price, in terms of redundancy, for achieving dependency preservation. We also give techniques for quantitative comparison of different normal forms based on the redundancy they tolerate.
Homology Modeling and Molecular Docking Studies of Bacillomycin and Iturin Synthetases with Novel Ligands for the Production of Therapeutic Lipopeptides.

BACKGROUND Lipopeptide synthetases play an important role in the production of lipopeptides. Lipopeptides are molecules made up of peptides and fatty acid moieties and have shown to have a broad range of antimicrobial activity. As infectious diseases have caused severe health problems mainly resulting from the development of antibiotic resistant strains of disease causing microorganisms there is a need of alternatives to antibiotics. The lipopeptide synthetase of the corresponding lipopeptides can be used as template to design these as drugs using computational techniques. OBJECTIVE The objective of this study was homology modeling and molecular docking of two lipopeptide synthetases, bacillomycin D synthetase and iturin A synthetase, with their ligands as a means of drug design. METHOD Schrödinger software was used for homology modeling and molecular docking. RESULTS After the identification of ligands, molecular docking of these ligands with the lipopeptide (bacillomycin and iturin) synthetases was performed. The docking was tested on the parameters of docking score and glide energy. 5 out of 21 ligands were found to dock with bacillomycin D synthetase whereas 8 out of 20 ligands docked with the iturin A synthetase. CONCLUSION The knowledge of the docking sites and docking characteristics of the lipopeptide synthetases mentioned in the paper with the ligands can provide advantages of high speed and reliability, reduced costs on chemicals and experiments and the ethical issues concerned with the use of animal models for screening of drug toxicity.
Low complexity image compression of capsule endoscopy images

Capsule endoscopy is a method for recording images of the digestive tract. A patient swallows a capsule containing a tiny camera, which captures images that are then transmitted wirelessly to an external receiver for examination by a physician. The images are captured using a Bayer filter mosaic, such that each pixel in the raw captured images represents only one color: red, green or blue. Due to limited computational capabilities in the capsule and bandwidth constraints, low-complexity and efficient compression of Bayer endoscopy images is required before transmission. In this paper, we focus on the JPEG algorithm, and show how to adapt it for compressing Bayer endoscopy images. We show that by transformation to the YCgCo color space and appropriate optimization of parameters, significant improvement is achieved over the standard JPEG.
Mental disorder and the outcome of HIV/AIDS in low-income and middle-income countries: a systematic review

Objectives:To conduct a systematic review of the literature to examine the interrelationship between mental health and treatment outcomes in low-income and middle-income settings; to update the work of Collins et al. (2006). Design:Systematic review of peer-reviewed articles that examined one of the following: the effects of mental disorders (including cognitive impairment) upon engagement with treatment and/or adherence; their influence upon HIV-related clinical outcomes; and the impact of interventions for mental disorder. Methods:Articles about mental health and HIV/AIDS were included if they were published after 2005 and addressed one of the areas of interest described above. Systematic methods were used for searching, screening, and data extraction. Studies employing quantitative measures of exposures and outcomes wherein all participants had a diagnosis of HIV/AIDS were included. Results:This review found ample and moderately consistent evidence that adverse mental health and alcohol consumption are associated with reduced adherence. Variation in measurement and the relative paucity of work meant that interpretation of studies examining engagement with care and other clinical outcomes was difficult. Evidence on the efficacy and effectiveness of mental health interventions in low-income and middle-income settings was very limited. Conclusion:This review suggests that psychosocial factors, namely, depression and alcohol may have adverse effects upon HIV-related outcomes. However, further large, high-quality studies examining outcomes other than adherence are needed. There is also an urgent need for randomized controlled trials of interventions for mental disorder and a need to investigate their impact upon HIV-related outcomes.
MULTITASK LEARNING TO IMPROVE ARTICULATORY FEATURE ESTIMATION AND PHONEME RECOGNITION

Speech sounds can be characterized by articulatory features. Articulatory features are typically estimated using a set of multilayer perceptrons (MLPs), i.e., a separate MLP is trained for each articulatory feature. In this report, we investigate multitask learning (MTL) approach for joint estimation of articulatory features with and without phoneme classification as subtask. The effect of number of subtasks in MTL is studied by selecting two different articulatory feature representations. Our studies show that MTL MLP can estimate articulatory features compactly and efficiently by learning the inter-feature dependencies through a common hidden layer representation, irrespective of number of subtasks. Furthermore, adding phoneme as subtask while estimating articulatory features improves both articulatory feature estimation and phoneme recognition. On TIMIT phoneme recognition task, articulatory feature posterior probabilities obtained by MTL MLP achieve a phoneme recognition accuracy of 73.8%, while the phoneme posterior probabilities achieve an accuracy of 74.2%.
Autonomy in Human-Robot Team Control

Human control of multiple robots has been characterized by the average demand of single robots on human attention or the distribution of demands from multiple robots. When robots are allowed to cooperate autonomously, however, demands on the operator should be reduced by the amount previously required to coordinate their actions. The present experiment compares control of small robot teams in which robots were controlled either independently or coordinated via Machinetta proxies. Coordinated robots found more victims and searched wider areas.
The Impact of Earnings Management on the Value-Relevance of Earnings and Book Value: A Comparison of Short-term and Long-term Discretionary Accruals

This paper examines the impact of earnings management on the value-relevance of earnings and book value. Two different sources of earnings management are investigated: short-term discretionary accruals and long-term discretionary accruals. A contribution of this paper is the development of models to estimate short-term and long-term discretionary accruals. The effect of earnings management on the relative value-relevance of earnings and book value depends on the source of earnings management. When examined independently, earnings management via either short-term or long-term discretionary accruals reduces the valuerelevance of earnings but has no impact on the value relevance of book value. Examination of both measures simultaneously reveals the differential impact of short-term versus long-term discretionary accruals. Earnings management via short-term discretionary accruals has no impact, whereas earnings management via long-term discretionary accruals reduces the valuerelevance of earnings and increases the value-relevance of book value. This supports the conclusion that earnings management via long-term discretionary accruals has a greater impact on the value-relevance of earnings and book value than earnings management via short-term discretionary accruals.
Prefrontal Cortex Fails to Learn from Reward Prediction Errors in Alcohol Dependence

Patients suffering from addiction persist in consuming substances of abuse, despite negative consequences or absence of positive consequences. One potential explanation is that these patients are impaired at flexibly adapting their behavior to changes in reward contingencies. A key aspect of adaptive decision-making involves updating the value of behavioral options. This is thought to be mediated via a teaching signal expressed as a reward prediction error (PE) in the striatum. However, to exert control over adaptive behavior, value signals need to be broadcast to higher executive regions, such as prefrontal cortex. Here we used functional MRI and a reinforcement learning task to investigate the neural mechanisms underlying maladaptive behavior in human male alcohol-dependent patients. We show that in alcohol-dependent patients the expression of striatal PEs is intact. However, abnormal functional connectivity between striatum and dorsolateral prefrontal cortex (dlPFC) predicted impairments in learning and the magnitude of alcohol craving. These results are in line with reports of dlPFC structural abnormalities in substance dependence and highlight the importance of frontostriatal connectivity in addiction, and its pivotal role in adaptive updating of action values and behavioral regulation. Furthermore, they extend the scope of neurobiological deficits underlying addiction beyond the focus on the striatum.
ORI-GENE: gene classification based on the evolutionary tree

MOTIVATION Genome projects have produced large amounts of data on the sequences of new genes whose functions are as yet unknown. The functions of new genes are usually inferred by comparing their sequences with those of known genes, but evaluation of the sequence homology of individual genes does not make the most of the available sequence information. Therefore, new methods and tools for extracting more biological information from homology searches would be advantageous. RESULTS We have developed a computational tool, ORI-GENE, to analyze the results of sequence homology searches from the perspective of the evolution of selected sets of new genes. ORI-GENE has a graphical interface and accomplishes two important tasks: first, based on the output of homology searches, it identifies species with similar genes and displays their pattern of distribution on the phylogenetic tree. This function enables one to infer the way in which a given gene may have propagated among species over time. Second, from the distribution patterns, it predicts the point at which a given gene may have been first acquired (i.e. its 'origin'), then classifies the gene on that basis. Because it makes use of available evolutionary information to show the way in which genes cluster among species, ORI-GENE should be an effective tool for the screening and classification of new genes revealed by genome analysis. AVAILABILITY ORI-GENE is retrievable via the Internet at: http://www.rtc. riken.go. jp/jouhou/ORI-GENE.
A generative genetic algorithm for evolving adaptation rules of software systems

The Internetware system is a complex and distributed self-adaptive system, which executes in an open, uncertain and dynamic environment, and adapts itself to changes in the environment. We hope that Internetware systems have the ability to automatically evolve in respond to changes. An important problem related to the development of Internetware systems is how to formulate proper adaptation rules. Because of the uncertainty of environment, the adaptation rules may not be suitable to the current system. Adaptation rules always need to be evolved to obtain better results. Some traditional methods can decide adaptation actions in different environmental conditions and evolve adaptation rules. But most of these methods bring about huge computation cost, which are not highly-efficient. To resolve these problems, we propose a method for evolving adaptation rules automatically, based on genetic algorithm and linear regression. We apply this method to evolve adaptation rules for a web application based on a widely used prototype --- RUBiS, which is an auction site similar to eBay. Experiments show that our method can evolve adaptation rules and improve the web application's performance in dynamic environment.
Effects of TDCS dosage on working memory in healthy participants

Background Transcranial direct current stimulation (tDCS) has been found to improve working memory (WM) performance in healthy participants following a single session. However, results are mixed and the overall effect size is small. Interpretation of these results is confounded by heterogeneous study designs, including differences in tDCS dose (current intensity) and sham conditions used. Aims We systematically investigated the effect of tDCS dose on working memory using behavioural and neurophysiological outcomes. Methods In a single-blind parallel group design, 100 participants were randomised across five groups to receive 15 minutes of bifrontal tDCS at different current intensities (2mA, 1mA, and three sham tDCS conditions at 0.034mA, 0.016mA, or 0mA). EEG activity was acquired while participants performed a WM task prior to, during, and following tDCS. Response time, accuracy and an event-related EEG component (P3) were evaluated. Results We found no significant differences in response time or performance accuracy between current intensities. The P3 amplitude was significantly lower in the 0mA condition compared to the 0.034mA, 1mA and 2mA tDCS conditions. Changes in WM accuracy were moderately correlated with changes in the P3 amplitude following tDCS compared to baseline levels (r = 0.34). Conclusions Working memory was not significantly altered by tDCS, regardless of dose. The P3 amplitude showed that stimulation at 1mA, 2mA and a sham condition (0.034mA) had biological effects, with the largest effect size for 1mA stimulation. These findings indicate higher sensitivity of neurophysiological outcomes to tDCS and suggests that sham stimulation previously considered inactive may alter neuronal function.