Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Sorghum bicolor leaf base extract for gastrointestinal effects

The leaf base of Sorghum bicolor (Family: Gramineae, Poaceae) was cold-macerated with 70% v/v methanol. The aqueous methanolic extract was further fractionated into non-polar, medium polar and very polar components using hexane, ethylacetate and water (aqueous), respectively. The gastrointestinal effects of these extracts were tested on intestinal motility (transit) in mice, castor oilinduced diarrhoeal model in rats, isolated rabbit jejunum, guinea pig ileum and rat stomach fundus strip. The oral and intraperitoneal LD50 values for the extracts were determined in mice and rats. The aqueous methanolic extract (100 – 400 mg/kg i.p) significantly (P contraction on rat stomach fundus strips. Both aqueous and ethylacetate fractions also reduced intestinal motility. However, ethylacetate fraction caused greater reduction than the aqueous fraction. The oral LD50 value for the aqueous methanolic extact in both rats and mice was found to be  2000 mg/kg while the intraperitoneal values are 1414.2 mg/kg in rats and 1341.6 mg/kg in mice. The intrapertoneal value for both aqueous and ethylacetate fractions is  2000 mg/kg in mice. The study provided scientific bases for the traditional use of S. bicolor for treatment of gastrointestinal related problems such as diarrhoea.
Making a Manageable Email Experience with Deep Learning Final Report

This paper uses Deep Learning algorithms in the context of Natural Language Processing (NLP) and more specifically applies them to the task of prioritizing e-mails based on their importance and whether they require an answer. We base our results on different datasets constructed from our own e-mails and from the ENRON dataset. We use the Bag of unigrams and bigrams model with SVM and Random Forests as a baseline and then develop an LSTM network and a Convolutional Neural Network as a way to improve our results. The CNN performs yields the highest accuracy and F1 for all datasets except those labeled by Google’s Importance label, in which case they are outperformed by Random forests, leading the authors to suspect a basis upon which Google assigns the ‘important’ label.
Performance-Related Sustained and Anticipatory Activity in Human Medial Temporal Lobe during Delayed Match-to-Sample

The medial temporal lobe (MTL)—hippocampus and surrounding perirhinal, parahippocampal, and entorhinal cortical areas—has long been known to be critical for long-term memory for events. Recent functional neuroimaging and neuropsychological data in humans performing short-delay tasks suggest that the MTL also contributes to performance even when retention intervals are brief, and single-unit data in rodents reveal sustained, performance-related delay activity in the MTL during delayed-non-match-to-sample tasks. The current study used functional magnetic resonance imaging to examine the relationship between activation in human MTL subregions and performance during a delayed-match-to-sample task with repeated (non-trial-unique) stimuli. On critical trials, the presentation of two faces was followed by a 30 s delay period, after which participants performed two-alternative forced-choice recognition. Functional magnetic resonance imaging revealed significant delay period activity in anterior hippocampus, entorhinal cortex, and perirhinal cortex over the 30 s retention interval, with the magnitude of activity being significantly higher on subsequently correct compared with subsequently incorrect trials. In contrast, posterior hippocampus, parahippocampal cortex, and fusiform gyrus activity linearly increased across the 30 s delay, suggesting an anticipatory response, and activity in parahippocampal cortex and hippocampus was greater during the probe period on correct compared with incorrect trials. These results indicate that at least two patterns of MTL delay period activation—sustained and anticipatory—are present during performance of short-delay recognition memory tasks, providing novel evidence that multiple processes govern task performance. Implications for understanding the role of the hippocampus and surrounding MTL cortical areas in recognition memory after short delays are discussed.
Matrix Converters Applied to Wind Energy Conversion Systems, Technologies and Investigation Trends

The matrix converter (MC) has been subject of investigation since 1980 and with the constant improvement of power electronics semiconductors along with the microelectronics it is now possible to efficiently and reliably implement such a topology. The MC finds its application wherever bidirectional power flow and controlled voltage and current in AC systems is needed and proves to be superior to its competitors when applied in certain specific environments and circumstances. Wind energy conversion systems (WECS) are a recent growing research topic where MC is found to be promisingly more efficient as power electronic controller. This paper discusses what a MC is, what are the different WECS configurations and the latest published advances and trends in this area of knowledge.
Gay and Lesbian Christians

Abstract Who are gay and lesbian churchgoing Christians? How do gay and lesbian Christians use their faith to cope with difficulties related to sexual orientation, such as discrimination and homophobia? Results of this study indicate that gay and lesbian churchgoers demonstrate many of the same features as their heterosexual peers. Participants indicated that church participation is important to them both for their own intrinsic religious values and for the social support they receive in church and parachurch activities. Overall, participants gave higher value to the use of logic/science and personal experience when making decisions about beliefs and behaviors than to the Bible or church tradition. Participants also demonstrated more positive than negative coping overall, with an increase in positive coping as stress increased.
A GPU Implementation of Generalized Graph Processing Algorithm GIM-V

Fast processing for extremely large-scale graph, which consists of millions to trillions of vertices and 100 billions to 100 trillions of edges, is becoming increasingly important in various domains such as health care, social networks, intelligence, system biology, and electric power grid, etc. The GIM-V algorithm based on MapReduce programing model is designed as general graph processing method for supporting petabyte-scale graph data. On the other hand, recent large-scale data-intensive computing systems tend to employ GPU accelerators to gain good peak performance and high memory bandwidth, however, the validity of acceleration, including optimization techniques, of the GIM-V algorithm using GPUs is an open problem. To address the problem, we implemented a GPU-based GIM-V application. We conducted our implementation using single node (12 hyper-threaded CPU cores, 1 GPU). The results showed that our GPU-based implementation performed 8.80 to 39.0 times faster than the original Hadoop-based GIM-V implementation (PEGASUS), and 2.72 times faster in the map stage than the CPU-based naive implementation. We also observed that the total elapsed time of our implementation introduces significant load imbalance between threads in a GPU, which causes 1.52 times performance degradation than the CPU-based implementation.
An integrated system for the segmentation of atherosclerotic carotid plaque ultrasound video

The robust border identification of atherosclerotic carotid plaque, the corresponding degree of stenosis of the common carotid artery (CCA), and also the characteristics of the arterial wall, including plaque size, composition, and elasticity, have significant clinical relevance for the assessment of future cardiovascular events. To facilitate the follow-up and analysis of the carotid stenosis in serial clinical investigations, we propose and evaluate an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound videos of the CCA based on video frame normalization, speckle reduction filtering, M-mode state-based identification, parametric active contours, and snake segmentation. Initially, the cardiac cycle in each video is identified and the video M-mode is generated, thus identifying systolic and diastolic states. The video is then segmented for a time period of at least one full cardiac cycle. The algorithm is initialized in the first video frame of the cardiac cycle, with human assistance if needed, and the moving atherosclerotic plaque borders are tracked and segmented in the subsequent frames. Two different initialization methods are investigated in which initial contours are estimated every 20 video frames. In the first initialization method, the initial snake contour is estimated using morphology operators; in the second initialization method, the Chan-Vese active contour model is used. The performance of the algorithm is evaluated on 43 real CCA digitized videos from B-mode longitudinal ultrasound segments and is compared with the manual segmentations of an expert, available every 20 frames in a time span of 3 to 5 s, covering, in general, 2 cardiac cycles. The segmentation results were very satisfactory, according to the expert objective evaluation, for the two different methods investigated, with true-negative fractions (TNF-specificity) of 83.7 ± 7.6% and 84.3 ± 7.5%; true-positive fractions (TPF-sensitivity) of 85.42 ± 8.1% and 86.1 ± 8.0%; and between the ground truth and the proposed segmentation method, kappa indices (KI) of 84.6% and 85.3% and overlap indices of 74.7% and 75.4%. The segmentation contours were also used to compute the cardiac state identification and radial, longitudinal, and shear strain indices for the CCA wall and plaque between the asymptomatic and symptomatic groups were investigated. The results of this study show that the integrated system investigated in this study can be successfully used for the automated video segmentation of the CCA plaque in ultrasound videos.
Reducing the Number of Annotations in a Verification-oriented Imperative Language

Automated software verification is a very active field of research which has made enormous progress both in theoretical and practical aspects. Recently, an important amount of research effort has been put into applying these techniques on top of mainstream programming languages. These languages typically provide powerful features such as reflection, aliasing and polymorphism which are handy for practitioners but, in contrast, make verification a real challenge. In this work we present Pest, a simple experimental, while-style, multiprocedural, imperative programming language which was conceived with verifiability as one of its main goals. This language forces developers to concurrently think about both the statements needed to implement an algorithm and the assertions required to prove its correctness. In order to aid programmers, we propose several techniques to reduce the number and complexity of annotations required to successfully verify their programs. In particular, we show that high-level iteration constructs may alleviate the need for providing complex loop annotations.
Antimicrobial Activity of Antrodia camphorata Extracts against Oral Bacteria

Antrodia camphorata (A. camphorata) is a unique, endemic and extremely rare mushroom species native to Taiwan, and both crude extracts of and purified chemical compounds from A. camphorata have been reported to have a variety of significant beneficial effects, such as anti-tumor and anti-inflammatory activity. However, reports on the effects of A. camphorata against dental pathogens have been limited. Oral health is now recognized as important for overall general health, including conditions such as dental caries, periodontal disease and rheumatoid arthritis. Streptococcus mutans (S. mutans) and Porphyromonas gingivalis (P. gingivalis) are the most common bacteria associated with dental plaque and periodontopathic diseases, respectively. Thus, our study examined the ability of five various crude extracts of A. camphorata to inhibit the growth of dental bacteria and anti-adherence in vitro. Among the extracts, the ethanol, ethyl acetate and chloroform extracts exhibited the lowest MICs against P. gingivalis and S. mutans (MIC = 4∼16 µg/mL). The MIC of the aqueous extract was greater than 2048 µg/mL against both P. gingivalis and S. mutans. In vitro adherence of S. mutans was significantly inhibited by the addition of either the ethyl acetate extract or chloroform extract (MIC = 16∼24 µg/mL), while the ethanol extract (MIC = 32∼64 µg/mL) exhibited moderate inhibitory activity. Based on the result of this study, the ethyl acetate and chloroform extracts of A. camphorata may be good candidates for oral hygiene agents to control dental caries and periodontopathic conditions.
A Complex Interplay of Tandem- and Whole-Genome Duplication Drives Expansion of the L-Type Lectin Receptor Kinase Gene Family in the Brassicaceae

The comparative analysis of plant gene families in a phylogenetic framework has greatly accelerated due to advances in next generation sequencing. In this study, we provide an evolutionary analysis of the L-type lectin receptor kinase and L-type lectin domain proteins (L-type LecRKs and LLPs) that are considered as components in plant immunity, in the plant family Brassicaceae and related outgroups. We combine several lines of evidence provided by sequence homology, HMM-driven protein domain annotation, phylogenetic analysis, and gene synteny for large-scale identification of L-type LecRK and LLP genes within nine core-eudicot genomes. We show that both polyploidy and local duplication events (tandem duplication and gene transposition duplication) have played a major role in L-type LecRK and LLP gene family expansion in the Brassicaceae. We also find significant differences in rates of molecular evolution based on the mode of duplication. Additionally, we show that LLPs share a common evolutionary origin with L-type LecRKs and provide a consistent gene family nomenclature. Finally, we demonstrate that the largest and most diverse L-type LecRK clades are lineage-specific. Our evolutionary analyses of these plant immune components provide a framework to support future plant resistance breeding.
Training of HMM with filtered speech material for hands-free recognition

This paper addresses the problem of hands-free speech recognition in a noisy office environment. An array of six omnidirectional microphones and a corresponding time delay compensation module are used to provide a beamformed signal as input to a HMM-based recognizer. Training of HMMs is performed either using a clean speech database or using a filtered version of the same database. Filtering consists in a convolution with the acoustic impulse response between the speaker and microphone, to reproduce the reverberation effect. Background noise is summed to provide the desired SNR. The paper shows that the new models trained on these data perform better than the baseline ones. Furthermore, the paper investigates on maximum likelihood linear regression (MLLR) adaptation of the new models. It is shown that a further performance improvement is obtained, allowing to reach a 98.7% WRR in a connected digit recognition task, when the talker is at 1.5 m distance from the array.
Randomized wait-free concurrent objects (extended abstract)

A concurrent object is a data structure shared by concurrent processes, A w aii-free implementation of a concurrent object guarantees that every operation completes in a finite number of steps, regardless of how processes interleave. It is known, however, that if concurrent processes communicate only by applying read and write operations to a shared memory, then it is impossible to construct wait-free implementations of many simple and useful data objects. In this paper we show how to construct randomized wait-free implementations of long-lived concurrent objects, implementations that guarantee that every operation completes in a finite ezpected number of steps, even against a powerful adversary.
The Molecular Component of the Galactic Center Arched Filaments H II Complex: OVRO Observations of the CS J = 2-1 Line

The Owens Valley Radio Observatory (OVRO) millimeter array was used to make observations of the CS (2–1) line (at 97.981 GHz) arising from the G0.07+0.04 region of the "-30 km s-1" molecular cloud near the Galactic center, with a spatial resolution of ∼8″. The ionized edge of this cloud forms the Arched Filaments H II regions, which are ionized by the adjacent Arches stellar cluster. The OVRO data were combined with single-dish data from 1986 obtained at the 30 m IRAM telescope by Serabyn & Güsten. A comparison of these CS (2–1) data and our recent H92α recombination line data reveals that the ionized and molecular gas are physically related, but that their velocities in this region differ by up to 35 km s-1. This difference in velocity can be understood if the gas that gave rise to the G0.07+0.04 H II region has been fully ionized. An overall comparison of the molecular and ionized gas across the entire -30 km s-1 cloud based on the single-dish CS (2–1) data and the H92α line data illustrates that such differences in velocity between the ionized and molecular gas are common and that the geometric arrangement of these components is complicated. Much of the ionized gas resides on the near side (to the observer) of the molecular cloud; however, in several regions some molecular material must lie in front of the H II region. The Arches stellar cluster therefore appears to be located in the midst of the molecular cloud, such that some of the near-side cloud surfaces along our line of sight have not been exposed to the ionizing radiation.
Primary Fibrinolysis Is Integral in the Pathogenesis of the Acute Coagulopathy of Trauma

Background:The existence of primary fibrinolysis (PF) and a defined mechanistic link to the “Acute Coagulopathy of Trauma” is controversial. Rapid thrombelastography (r-TEG) offers point of care comprehensive assessment of the coagulation system. We hypothesized that postinjury PF occurs early in severe shock, leading to postinjury coagulopathy, and ultimately hemorrhage-related death. Methods:Consecutive patients over 14 months at risk for postinjury coagulopathy were stratified by transfusion requirements into massive (MT) >10 units/6 hours (n = 32), moderate (Mod) 5 to 9 units/6 hours (n = 15), and minimal (Min) <5 units/6 hours (n = 14). r-TEG was performed by adding tissue factor to uncitrated whole blood. r-TEG estimated percent lysis was categorized as PF when >15% estimated percent lysis was detected. Coagulopathy was defined as r-TEG clot strength = G < 5.3 dynes/cm2. Logistic regression was used to define independent predictors of PF. Results:A total of 34% of injured patients requiring MT had PF, which was associated with lower emergency department systolic blood pressure, core temperature, and greater metabolic acidosis (analysis of variance, P < 0.0001). The risk of death correlated significantly with PF (P = 0.026). PF occurred early (median, 58 minutes; interquartile range, 1.2–95.9 minutes); every 1 unit drop in G increased the risk of PF by 30%, and death by over 10%. Conclusions:Our results confirm the existence of PF in severely injured patients. It occurs early (<1 hour), and is associated with MT requirements, coagulopathy, and hemorrhage-related death. These data warrant renewed emphasis on the early diagnosis and treatment of fibrinolysis in this cohort.
Incremental Structured ICP Algorithm

Variants of the ICP algorithm are widely used in many visionbased applications, such as visual odometry, structure from motion, 3D reconstruction, or object segmentation. Establishing correct correspondences between two sets of 3D points, generated by stereo vision, needs to take those uncertainties into account. We propose a novel variant of the traditional ICP algorithm for solving the mentioned alignment problem. Our method, named incremental structured iterative closest point, aims at improved registrations of 3D points calculated for real world data. Evaluations are carried out by measuring the distances between two inputs, in both local and global perspectives, and experimental results are visually presented in this paper.
Exploration of Heroin Use Behaviors and its Associated Risk Factors among Youth with Heroin-Dependence in Dar es Salaam, Tanzania

Objective: Heroin abuse among youth in Tanzania continued to increase over the past decades. This study sought to explore heroin abuse among youth in Dar es Salaam. Methodology: The study comprised of 16 participants (15 males) aged between 18 to 25 years who were obtained through snow ball sampling technique. In-depth interviews and focus group discussions were conducted to inquire their general knowledge, attitude, practices and the associated risks on heroin use. The sample size was determined by saturation principle; till when the researchers found no further new information emerging from in-depth interviews. Main findings: Most participants reported peer pressure and lack of formal employment as contributing factors for engaging in heroin use. Though most participants acknowledged heroin use to be accompanied with several consequences, but found difficult to abstain from it. The underlying reasons for these were continuous peer pressure, lack of effective drug rehabilitation services and withdrawal syndrome. The risk factors that reported to be associated with heroin use included mental illnesses, marital conflicts, risk to infections, robbery, legal problems, and rejection from families, friends and community at large. Conclusion and recommendations: This study demonstrated that individuals with heroin dependence encounter several difficulties towards abstinence including complexity of peer pressure and other patterns of social networks. Thus, future studies are warranted to explore the complex relationship between heroin use and associated risk factors in Tanzania.
A Concept-Driven Automatic Ontology Generation Approach for Conceptualization of Document Corpora

In the age of increasing information availability, many techniques, such as document clustering and information visualization, have been developed to ease understanding of information for users. However, most of these methods do not help users directly understand key concepts and their semantic relationships in document corpora, which are critical for capturing their conceptual structures. Therefore, we propose a novel approach called 'Clonto' to identify the key concepts and automatically generate ontologies based on these concepts for conceptualization of document corpora. Clonto applies latent semantic analysis to identify key concepts, allocates documents based on these concepts, and utilizes WordNet to automatically generate a corpus-related ontology. The documents are linked to the ontology through the key concepts. The experimental results show that Clonto can identify key concepts with a high precision and the clustering results of Clonto outperform the STC (Suffix Tree Clustering) algorithm, the Lingo clustering algorithm, the Fuzzy Ants clustering algorithm, and clustering based on TRS (Tolerance Rough Set). Moreover, based on the same document corpus, the ontology generated by Clonto shows a significant informative conceptual structure.
INSeRT: Protect Dynamic Code Generation against spraying

DCG (Dynamic Code Generation) technologies have found widely applications in the Web 2.0 era, and DCG-Spraying attack can easily circumvent DEP and ASLR protection mechanisms built in modern operating systems. We propose a new protection method, INSeRT, which combines randomization of intrinsic elements of machine instructions and randomly planted special trapping snippets. INSeRT practically renders the “sprayed code” ineffective, while alerts the host program of ongoing attacking attempts. We implemented a prototype of INSeRT on the V8 JavaScript engine with a performance overhead of less than 5%, which should be acceptable in practical application.