Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanisms of neurodegeneration and regeneration in alcoholism.

AIMS This is a review of preclinical studies covering alcohol-induced brain neuronal death and loss of neurogenesis as well as abstinence-induced brain cell genesis, e.g. brain regeneration. Efforts are made to relate preclinical studies to human studies. METHODS The studies described are preclinical rat experiments using a 4-day binge ethanol treatment known to induce physical dependence to ethanol. Neurodegeneration and cognitive deficits following binge treatment mimic the mild degeneration and cognitive deficits found in humans. Various histological methods are used to follow brain regional degeneration and regeneration. RESULTS Alcohol-induced degeneration occurs due to neuronal death during alcohol intoxication. Neuronal death is related to increases in oxidative stress in brain that coincide with the induction of proinflammatory cytokines and oxidative enzymes that insult brain. Degeneration is associated with increased NF-kappaB proinflammatory transcription and decreased CREB transcription. Corticolimbic brain regions are most sensitive to binge-induced degeneration and induce relearning deficits. Drugs that block oxidative stress and NF-kappaB transcription or increase CREB transcription block binge-induced neurodegeneration, inhibition of neurogenesis and proinflammatory enzyme induction. Regeneration of brain occurs during abstinence following binge ethanol treatment. Bursts of proliferating cells occur across multiple brain regions, with many new microglia across brain after months of abstinence and many new neurons in neurogenic hippocampal dentate gyrus. Brain regeneration may be important to sustain abstinence in humans. CONCLUSIONS Alcohol-induced neurodegeneration occurs primarily during intoxication and is related to increased oxidative stress and proinflammatory proteins that are neurotoxic. Abstinence after binge ethanol intoxication results in brain cell genesis that could contribute to the return of brain function and structure found in abstinent humans.
Smoothing for ZUPT-aided INSs

Due to the recursive and integrative nature of zero-velocity-update-aided (ZUPT-aided) inertial navigation systems (INSs), the error covariance increases throughout each ZUPT-less period followed by a drastic decrease and large state estimate corrections as soon as ZUPTs are applied. For dead-reckoning with foot-mounted inertial sensors, this gives undesirable discontinuities in the estimated trajectory at the end of each step. However, for many applications, some degree of lag can be tolerated and the information provided by the ZUPTs at the end of a step can be made available throughout the step, eliminating the discontinuities. For this purpose, we propose a smoothing algorithm for ZUPT-aided INSs. For near real-time applications, smoothing is applied to the data in a step-wise manner requiring a suggested varying-lag segmentation rule. For complete off-line processing, full data set smoothing is examined. Finally, the consequences and impact of smoothing are analyzed and quantified based on real-data.
Validation of hospital register-based diagnosis of Parkinson's disease.

INTRODUCTION Denmark has a long-standing tradition of maintaining one of the world's largest health science specialized register data bases as the National Hospital Register (NHR). To estimate the prevalence and incidence of diseases, the correctness of the diagnoses recorded is critical. Parkinson's disease (PD) is a neurodegenerative disorder and only 75-80% of patients with parkinsonism will have idiopathic PD (iPD). It is necessary to follow patients in order to determine if some of them will develop other neurodegenerative diseases and a one-time-only diagnostic code for iPD reported in the register may be incorrect. MATERIAL AND METHODS This was a large nationwide population-based study of risk factors for iPD, called Parkinson's disease in Denmark (PASIDA). We evaluated the iPD diagnosis reported in the NHR. Medical records with primary diagnoses of iPD from six neurological departments were collected and abstracted using a standardized system to review the diagnostic accuracy of the ICD codes. RESULTS Among the 1,040 medical records abstracted, 857 (82.4%) patients met our criteria for iPD. 183(17.6%) of the patients suffered from other diagnoses such as atypical PD (66 patients), secondary PD (60 patients) and other diagnoses (46 patients). CONCLUSION Possibly only about 82% of the patients with the primary diagnosis of iPD in the Danish NHR actually suffered from iPD. To improve diagnostic validity, we appeal to update the ICD code and to identify the correct parkinsonian phenotype to reduce biased case sampling in register-based studies and appropriate treatment for these rare diseases. FUNDING This study was supported by a grant from the National Institutes of Environmental Health Sciences, USA (grant No R01 ES013717). The funding source had no role in the design or analysis of the study or in the decision to submit the manuscript for publication. TRIAL REGISTRATION Ethical approval: The study protocol was approved by the Danish Data Protection Agency (No 2006-41-7323) and by UCLA-IRB.
Gaseous Decontamination Methods in High-containment Laboratories

Decontamination of high-containment laboratories is an important aspect in containing high-risk pathogens and protecting workers from the risk of infection. In the event of accidental spills and in day-to-day clean-up, all areas with the potential to be contaminated must be disinfected. As well, all items leaving the containment suite must be decontaminated. Often the entire containment suite must be decontaminated when a change in the pathogens being manipulated occurs. Large areas and equipment must also be decontaminated prior to maintenance, repairs, and recertification. Surface decontamination using a liquid disinfectant is often the simplest method and is ideal for day-to-day cleaning. However, when large areas and complex equipment require decontaminating, gaseous methods or fumigants are often a better choice. In these situations, liquid disinfectant wipe-down techniques can be time-consuming and difficult to standardize and may simply not be feasible (Krause et al., 2001). In these cases, gas decontamination methods are advantageous because of their ability to cover large surface areas and more difficult to reach areas (Rogers et al., 2008a). Many factors can influence the efficacy of gaseous decontamination methods. The microorganisms present as well as their concentration and the level of soiling are all important as different organisms have various levels of resistance to inactivation, with bacterial spores being the most resistant. Further, the environmental conditions of the area must be monitored as the temperature and relative humidity may influence the inactivation efficacy of some fumigants. The most important factors influencing inactivation are the concentration of the fumigant and length of the exposure. In general less exposure time is required to inactivate microorganisms when a higher concentration of fumigant is used and vice versa. Along with the previously mentioned influences, other factors that may also be important when choosing a fumigant and developing protocols include: the size and layout of the room; the compatibility with the materials present; the ability to seal or isolate the room; the toxicity of the fumigant; and the method of releasing the fumigant into the area as well as the neutralization and aeration requirements. This review provides a comparison among methodologies for common area decontamination currently employed in high-containment laboratories. Factors influencing decontamination efficacy, application, advantages, and limitations are discussed in the context of high-containment laboratories.
Partial discharge detection and diagnosis in gas insulated switchgear: State of the art

Power utilities are struggling to reduce power failure incidents in substations and their components to operate more reliably and economically [1]. Many power failures are produced directly or indirectly because of the insulation system of utility components [2], [3]. The selection of the insulation should ensure power plant operational continuity along with completely resolving or significantly limiting the actual power system's failures [4]. Gas insulated substations (GIS) have the best insulation performance which ensures achieving minimum failure incidents, although at high installation cost. The most common insulating gas used in GIS is Sulfur hexafluoride (SF6) gas, which is widely used as an effective electrical insulation as well as an arc-quenching medium [5]. Basic GIS and gas insulated transmission lines (GITL or GIL) consist of a conductor supported by solid insulators inside an enclosure filled with SF6 gas or its mixture [6].
Genetic admixture and lineage separation in a southern Andean plant

Mountain orogeny has been a major factor in plant evolution in all continents by changing the landscape and climate, creating new habitats and ecological opportunities. In this study we found that diversity in two southern Andean Escallonia species is geographically structured and there is a deep divergence between infraspecific groups that could be associated with ancient evolutionary events like orogeny. We also found evidence of admixture, likely the result of hybridization at the margins of the parental species' distribution range.
Talent Development for Faculty: The Case of Vietnam

The Problem With the rise of a globalized knowledge-based economy, the Vietnamese government has demonstrated a determination to build world-class universities by making higher education development one of the top national priorities. The Vietnamese higher education system is an interesting case of a latecomer’s development in an emerging market economy regulated by a single-party socialist state espousing communism. Although they have invested in higher education for decades, the system is still in a trial phase. For example, only two national universities cracked the top 150 in the Asian region, not in the world. Although the faculty talent pool can significantly help achieve the goals of the Vietnamese higher education system, the quality of Vietnamese faculty members is a key limitation. Furthermore, literature on talent development (TD) for faculty in the Vietnamese context is rare. The Solution This study aims to explore faculty TD trends and issues in the Vietnamese higher education system. This comprehensive review of the literature (a) provides an overview of the Vietnamese higher education system; (b) presents findings on the scope, foci, activities, and outcomes of faculty TD activities, and (c) provides implications for policy, practice, and further research on TD for faculty in Vietnam. As few published reviews of faculty TD in Vietnamese universities are available, the current comprehensive literature review can set the foundation for more effective practices and further research on TD and human resources development (HRD) for faculty in Vietnam. The Stakeholders The recommendations provided from this study will help HRD practitioners, institutional and ministerial decision makers, and faculty members in Vietnam.
A novel semi-supervised face recognition for video

Video-based face recognition has been one of the hot topics in the field of pattern recognition in the last few decades. In this paper, incorporating Support Vector Machines (SVM) and Locality Preserving Projections (LPP), we propose a novel semi-supervised face recognition algorithm for video, which can discover more space-time semantic information hidden in video face sequence, simultaneously make full use of the small amount of labeled data with the plentiful unknown information and the intrinsic nonlinear structure information to extract discriminative manifold features. We also compare our algorithm with other algorithms on UCSD/Honda Video Database. The experimental results show that the proposed algorithm can outperform state-of-the-art solutions for videobased face recognition.
The TIPSTER SUMMAC Text Summarization Evaluation

The TIPSTER Text Summarization Evaluation (SUMMAC) has established definitively that automatic text summarization is very effective in relevance assessment tasks. Summaries as short as 17% of full text length sped up decision-making by almost a factor of 2 with no statistically significant degradation in F-score accuracy. SUMMAC has also introduced a new intrinsic method for automated evaluation of informative summaries.
Iterative Algorithm for Solving Two-player Zero-sum Extensive-form Games with Imperfect Information

We develop and evaluate a new exact algorithm for finding Nash equilibria of two-player zero-sum extensive-form games with imperfect information. Our approach is based on the sequence-form representation of the game, and uses an algorithmic framework of double-oracle methods that have been used successfully in other classes of games. The algorithm uses an iterative decomposition, solving restricted games and exploiting fast best-response algorithms to add additional sequences to the game over time. We demonstrate our algorithm on a class of adversarial graph search games motivated by real world border patrolling scenarios. The results indicate that our framework is a promising way to scale up solutions for extensive-form games, reducing both memory and computation time requirements.
Cross-Fertilizing Deep Web Analysis and Ontology Enrichment

Deep Web databases, whose content is presented as dynamically- generated Web pages hidden behind forms, have mostly been left unindexed by search engine crawlers. In order to automatically explore this mass of information, many current techniques assume the existence of domain knowledge, which is costly to create and maintain. In this article, we present a new perspective on form understanding and deep Web data acquisition that does not require any domain-specific knowledge. Unlike previous approaches, we do not perform the various steps in the process (e.g., form under- standing, record identification, attribute labeling) independently but integrate them to achieve a more complete understanding of deep Web sources. Through information extraction techniques and using the form itself for validation, we reconcile input and output schemas in a labeled graph which is further aligned with a generic ontology. The impact of this alignment is threefold: first, the resulting seman- tic infrastructure associated with the form can assist Web crawlers when probing the form for content indexing; second, attributes of response pages are labeled by matching known ontology instances, and relations between attributes are uncovered; and third, we enrich the generic ontology with facts from the deep Web.
Sample Spaces and Feature Models: There and Back Again

We present probabilistic feature models (PFMs) and illustrate their use by discussing modeling, mining and interactive configuration. PFMs are formalized as a set of formulas in a certain probabilistic logic. Such formulas can express both hard and soft constraints and have a well defined semantics by denoting a set of joint probability distributions over features. We show how PFMs can be mined from a given set of feature configurations using data mining techniques. Finally, we demonstrate how PFMs can be used in configuration in order to provide automated support for choice propagation based on both hard and soft constraints. We believe that these results constitute solid foundations for the construction of reverse engineering tools for software product lines and configurators using soft constraints.
MuGeN: Simultaneous Exploration of Multiple Genomes and Computer Analysis Results

MOTIVATION The availability of increasing amounts of sequence data about completely sequenced genomes spurs the development of new methods in the fields of automated annotation, and of comparative genomics. Tools allowing the visualization of results produced by analysis methods, superimposed on possibly annotated sequence data, and enabling synchronized navigation in multiple genomes, provide new means for interactive genome exploration. This kind of visual inspection can be used as a basis to assess the quality of new analysis algorithms, or to discover genome portions to be subjected to in-depth studies. RESULTS We propose a software package, MuGeN, built for navigating through multiple annotated genomes. It is capable of retrieving annotated sequences in several formats, stored in local files, or available in databases over the network. From these, it then generates an interactive display, or an image file, in most common formats suitable for printing, further editing or integrating in Web pages. Genome maps may be mixed with computer analysis results loaded from XML files, whose format is generic enough to be adapted to a majority of sequence oriented analysis methods. AVAILABILITY MuGeN is available at http://www-mig.jouy. inra.fr/bdsi/MuGeN.
Fiducial Marker Approach for Biomechanical Smartphone-Based Measurements

Marker-based measurement has been used to assess human body positioning, but human marker tracking has yet to make the transition from the laboratory to personal computing devices, such as smartphones. A novel smartphone-based approach could use a fiducial marker system. Fiducial markers are applicable to augmented reality, robotics, and other applications where a camera-object pose is required and tracked. However, few fiducial systems can be implemented on a mobile phone because of the processing requirements for identifying and tracking the tags in realtime. In augmented reality, virtual information is shared with the real world to further enhance a person’s view of the environment; therefore, this illusion is directly associated with good registration of both virtual and real worlds. Measurement applications also require accurate and fast registration so that real objects are in alignment with virtual objects in real-time. Our research reviewed and evaluated various fiducial marker systems by developing an Android mobile application for real-time biomechanical measurement. A test was designed for two nominated fiducial systems to compare their speed and robustness on the mobile phone. AprilTag2 was selected as the best fiducial marker option for this application.
Building holistic descriptors for scene recognition: a multi-objective genetic programming approach

Real-world scene recognition has been one of the most challenging research topics in computer vision, due to the tremendous intraclass variability and the wide range of scene categories. In this paper, we successfully apply an evolutionary methodology to automatically synthesize domain-adaptive holistic descriptors for the task of scene recognition, instead of using hand-tuned descriptors. We address this as an optimization problem by using multi-objective genetic programming (MOGP). Specifically, a set of primitive operators and filters are first randomly assembled in theMOGP framework as tree-based combinations, which are then evaluated by two objective fitness criteria i.e., the classification error and the tree complexity. Finally, the best-so-far solution selected by MOGP is regarded as the (near-)optimal feature descriptor for scene recognition. We have evaluated our approach on three realistic scene datasets: MIT urban and nature, SUN and UIUC Sport. Experimental results consistently show that our MOGP-generated descriptors achieve significantly higher recognition accuracies compared with state-of-the-art hand-crafted and machine-learned features.
A novel balanced differential-pair multi-output CMOS CCCII

A novel structure, based on balanced differential-pair, is proposed to implement the single/multiple output CMOS second-generation current-controlled conveyors (CCCII). A balanced-output CCCII is demonstrated and its performances are simulated based on the AMS's 0.35 mu CMOS process by the HSPICE simulations. The large-signal simulations present the compatibility of the proposed structure and the theorem of CCCII. The bandwidth of transresistance up to over 600 MHz is noticed via small-signal simulations, which cause the device earns its place as one of the attractive RF analog building blocks especially to current-mode circuits. A current-mode filter based on Tow-Thomas (TT) structure is synthesized to verify the working capability and the usefulness of current-controlled transresistance.
Transfer learning approach for learning of unstructured data from structured data in medical domain

Transfer learning is a most important research area within information retrieval. As we know, there are different types of data available everywhere and among those, dealing with unstructured data is quite difficult. This paper focuses on dealing with unstructured data. Social challenge is, any nonmedical background person also uses this system for prediction of patient disease. This paper utilizes a bisecting k-means algorithm for the purpose of disease prediction. We have proposed a model for identifying more relevant disease using readings mentioned in patient's pathology lab test report. Our model is influenced by clustering and unsupervised transfer learning. We demonstrate the effectiveness of our model using patient pathology lab report dataset and dataset used for storing different test names (hemoglobin, sugar, etc.) of four diseases (Diabetes, Lipid profile cholesterol, Liver profile and Kidney profile). Our basic aim is to improve performance of the system by transferring knowledge, learned in one or multiple source tasks and use the same to improve learning in a related target task.
An economic assistance strategy for autonomous driving system

Recently autonomously driven vehicle technologies become the potential researches in order to increase road safety, driving comfort, fuel efficiency and so on. This study focuses on the fuel-saving issue in automatic vehicle within the scope of the decision maker layer. An intelligent economic assistance strategy (EAS) is explained and aims to reduce fuel cost when the vehicle is driving autonomously. The EAS has performed in decision marker layer which includes three components, mission component, behavior component and motion component. First, the mission component decides the shortest route from present location to destination. Next, in behavior component, the EAS receives the scene information and ego-vehicle information from data acquisition unit in perception. Due to the development the different economy schemes based on different driving scenarios can provide more fuel efficiency, the system analysis the whole information and adopts the driving scenario classifier to classify the immediately driving behavior. Then, in motion component, the EAS presents an economic driving pattern to guidance automatic vehicle to achieve low fuel consumption. Based on the result of the EAS system, the action layer drives the automatic vehicle according to suggest of economy driving speed and adjusts the speed and acceleration within limited of safe and economy. Finally, the proposed economy assistant strategy can successfully guide automatic vehicle in an economic driving style.