Miracle-dz/UniFAR-FTUs huggingface dataset:
Making topic modeling easy. A programming library in Python

Topic modeling, a method for the semantic analysis of large text collections, has been in the focus of interest in digital literary studies during the recent years. The method uses probabilistic procedures to generate probability distributions for words out of a collection of texts, sorting many single word distributions into distinct semantic groups called ‘topics’. These topics constitute groups of semantically related words, and the contribution of each topic to the composition of each text can be quantified mathematically (Blei 2012, Steyvers und Griffiths 2006). In digital literary studies, topic models can be interesting in themselves. For example their dynamic development either during the plot of single literary texts or over multiple texts in a stage of literary history can be analyzed (Jockers 2013, Blevins 2012, Rhody 2012, Schöch to appear), though comparing literary themes and the probabilistic concept of ‘topics’ described here is obviously not unproblematic. And topic models can also be interesting features for classifying or clustering texts (Blei 2012). There are currently two state-of-the-art implementations of the relevant algorithms: ‘Mallet’ (McCallum 2002) and ‘Gensim’ (Rehurek 2010). But usually more is required than simply running a topic modeling algorithm (Fig. 1): • Longer texts like novels need to be split into smaller parts (e.g. paragraphs, scenes, or a fixed amount of characters or words). • NLP based preprocessing is necessary • To achieve optimal results, texts must be reduced to content words, either by filtering out function words with stopword lists, or by using a part-of-speech tagger to exclude unwanted word classes. • Similarly, lemmatization and elimination of proper names can be useful. • After the topics have been generated, results are usually visualized based on the relevant metadata. • Results need to be evaluated with regard to internal or external criteria rather than just being left to interpretation.
Implementing STEADI in Academic Primary Care to Address Older Adult Fall Risk

Abstract Background and Objectives Falls are the leading cause of injury-related deaths in older adults. Objectives include describing implementation of the Centers for Disease Control and Prevention’s Stopping Elderly Accidents, Deaths, and Injuries (STEADI) initiative to help primary care providers (PCPs) identify and manage fall risk, and comparing a 12-item and a 3-item fall screening questionnaire. Design and Methods We systematically incorporated STEADI into routine patient care via team training, electronic health record tools, and tailored clinic workflow. A retrospective chart review of patients aged 65 and older who received STEADI measured fall screening rates, provider compliance with STEADI (high-risk patients), results from the 12-item questionnaire (Stay Independent), and comparison with a 3-item subset of this questionnaire (three key questions). Results Eighteen of 24 providers (75%) participated, screening 773 (64%) patients over 6 months; 170 (22%) were high-risk. Of these, 109 (64%) received STEADI interventions (gait, vision, and feet assessment, orthostatic blood pressure measurement, vitamin D, and medication review). Providers intervened on 85% with gait impairment, 97% with orthostatic hypotension, 82% with vision impairment, 90% taking inadequate vitamin D, 75% with foot issues, and 22% on high-risk medications. Using three key questions compared to the full Stay Independent questionnaire decreased screening burden, but increased the number of high-risk patients. Discussion and Implications We successfully implemented STEADI, screening two-thirds of eligible patients. Most high-risk patients received recommended assessments and interventions, except medication reduction. Falls remain a substantial public health challenge. Systematic implementation of STEADI could help clinical teams reduce older patient fall risks.
Drinking water and chronic kidney disease of unknown aetiology in Anuradhapura, Sri Lanka

ABSTRACT This paper examines how people in Anuradhapura District in Sri Lanka affected by endemic chronic kidney disease of unknown aetiology (CKDu) explain the factors causing the illness and their cultural meanings. The research found that the issue of contaminated water raised by the local community and the cultural meaning of water have influenced the government policy, health programmes, research agendas and the work of the media. Media reports on sociocultural, biomedical and epidemiological research into the aetiology of kidney disease have strengthened the perspective of the villagers who believe that polluted water has a direct relationship to kidney disease. This new understanding among villagers in Anuradhapura District has led to changes in their behaviours relating to the use and consumption of water, an important factor that has reinforced existing social hierarchies.
Toward A Typology Of Digital Thematic Research Collections

This paper considers an evolving genre of digital scholarship in the humanities, the thematic research collection, which is distinguished among other kinds of scholarly production as a collection of primary sources, gathered by scholarly effort and made available online to support research on a theme (Palmer, 2004). There are hundreds of such collections on the Web, ranging from well-known digital archives to small collections of historical or literary evidence within a thematic niche. Despite recognition of the genre (e.g., Price, 2009; Flanders, 2014; and Thomas, 2015), we do not know enough about this mode of production, how it contributes to humanities discourse, or how it relates to systems of peer review, discovery, and long-term maintenance. The evolution of public-facing humanities scholarship, long-term access to collections, and the completeness of the scholarly record depend in part on a more systematic understanding of this and other emergent genres. Through typological analysis, this research aims to build a foundation for rigorous study of thematic research collections. The goals of typology are to understand the breadth and variety of a genre, and identify unanticipated variations. We take up the following questions: What types of collections can we usefully distinguish, and what can these types and their characteristics reveal about the challenges and opportunities confronting the growth of digital scholarship in the humanities?
Alas1 is essential for neutrophil maturation in zebrafish

Neutrophils play essential roles in innate immunity and are the first responders to kill foreign micro-organisms, a function that partially depends on their granule content. The complicated regulatory network of neutrophil development and maturation remains largely unknown. Here we utilized neutrophil-deficient zebrafish to identify a novel role of Alas1, a heme biosynthesis pathway enzyme, in neutrophil development. We showed that Alas1-deficient zebrafish exhibited proper neutrophil initiation, but further neutrophil maturation was blocked due to heme deficiency, with lipid storage and granule formation deficiencies, and loss of heme-dependent granule protein activities. Consequently, Alas1-deficient zebrafish showed impaired bactericidal ability and augmented inflammatory responses when challenged with Escherichia coli. These findings demonstrate the important role of Alas1 in regulating neutrophil maturation and physiological function through the heme. Our study provides an in vivo model of Alas1 deficiency and may be useful to evaluate the progression of heme-related disorders in order to facilitate the development of drugs and treatment strategies for these diseases.
Coherent Coding of Enhanced Interaural Cues Improves Sound Localization in Noise With Bilateral Cochlear Implants

Bilateral cochlear implant (BCI) users only have very limited spatial hearing abilities. Speech coding strategies transmit interaural level differences (ILDs) but in a distorted manner. Interaural time difference (ITD) information transmission is even more limited. With these cues, most BCI users can coarsely localize a single source in quiet, but performance quickly declines in the presence of other sound. This proof-of-concept study presents a novel signal processing algorithm specific for BCIs, with the aim to improve sound localization in noise. The core part of the BCI algorithm duplicates a monophonic electrode pulse pattern and applies quasistationary natural or artificial ITDs or ILDs based on the estimated direction of the dominant source. Three experiments were conducted to evaluate different algorithm variants: Experiment 1 tested if ITD transmission alone enables BCI subjects to lateralize speech. Results showed that six out of nine BCI subjects were able to lateralize intelligible speech in quiet solely based on ITDs. Experiments 2 and 3 assessed azimuthal angle discrimination in noise with natural or modified ILDs and ITDs. Angle discrimination for frontal locations was possible with all variants, including the pure ITD case, but for lateral reference angles, it was only possible with a linearized ILD mapping. Speech intelligibility in noise, limitations, and challenges of this interaural cue transmission approach are discussed alongside suggestions for modifying and further improving the BCI algorithm.
An entirely enclosed scanning tunnelling microscope capable of being fully immersed in liquid helium

We present an ultrahigh‐vacuum (UHV)‐sealed high‐stability scanning tunnelling microscope (STM) that can be entirely immersed in liquid helium and readily used in a commercial Dewar or superconducting magnet. The STM head features a horizontal microscanner that can become standalone and ultrastable when the coarse approach inertial motor retracts. Low voltage is enough to operate the STM even at low temperature owing to the powerful motor. It is housed in a tubular chamber of 49 mm outer diameter, which can be pumped via a detachable valve (DV), UHV‐sealed and remain sealed after the DV is removed. The entire so‐sealed chamber can then be inserted into liquid helium, where in situ sample cleavage is done via vacuum bellow. This allows sample cleavage and STM measurements to take place in better UHV with higher cooling power. Quality atomic resolution images of graphite and charge density wave on 1T‐TiSe2 taken in ambient and 14 K conditions, respectively, are presented.
Construction of low contracted 3D skin equivalents by genipin cross‐linking

Continuous contraction of 3D skin equivalents in construction and use restricts their applications in clinical and pharmaceutical practices. So far, no effective method has been developed to inhibit such contraction. Hence, low cytotoxic cross‐linkers, 1‐ethyl‐3‐3‐dimethylaminopropylcarbodiimide hydrochloride (EDC) and genipin, are investigated to reduce the contraction in this study. As found, both genipin and EDC at 0.2 and 0.4 mmol/L are nontoxic to collagen‐entrapped fibroblasts and upregulate the extracellular matrix expression of fibroblasts in cross‐linked collagen. Particularly, collagen cross‐linking by intermediate concentrations of genipin, specifically 0.4 mmol/L, greatly reduces the contraction of 3D skin equivalents from 87% to 28% (n = 9, P < 0.05), while the collagen after EDC cross‐linking at 0.4 mmol/L still presented severe contraction of 64% over a 21‐day follow‐up period. The inhibited contraction might relate to the increased gel stiffness and slowed collagen degradation. Moreover, the genipin cross‐linking does not impair the formation of epidermal layers and improves the epidermal‐dermal junction of skin equivalents as well. In this regard, genipin cross‐linking might facilitate the applications of 3D skin equivalents in clinical practices and pharmacology testing.
LS-SVM's no-reference video quality assessment model under the Internet of things

The Internet of things, including Internet technology, including wired and wireless networks. The purpose of QOS-QOE energy saving optimization model for wireless sensor networks is to define and study the QOS-QOE energy saving optimization modeling problem of WSN networks. The main content is the research object of QOS-QOE wireless sensor network. The main research method is to find such key problems in QOS-QOE modeling of wireless sensor network, usually the objective function is the key issue, mainly uses the ant colony algorithm, genetic algorithm, SVM+PCA and LS-SVM and LIBSVM artificial neural network method. The four key technologies of the Internet are widely used, and these four technologies are mainly RFID, WSN, M2M, two kinds of integration. RFID can be implemented using MATLAB, NS2, and JAVA, and WSN can be implemented using NS2, and M2M can be developed using JAVA. In this paper, we investigate on the QOE and packet loss rate of the network because QOE is important in the network and packet loss rate is the key point in many papers. In order to have a better evaluate of video quality which through the network transmission, build NS2+MyEvalvid simulation platform, extract features, using Least squares support vector machine method to establish no-reference video quality assessment model considering the network packet loss. The experimental results show that, LS-SVM's training speed is fast, the model is more accurate than the other models.
Acute Effects of Dried Apple Consumption on Metabolic and Cognitive Responses in Healthy Individuals

The health‐protective effects of fruits and vegetables may be at least partly attributed to phytochemicals. Apples are known to be especially high in free phenolic compounds. Apples and apple products are associated with reduced risk of type II diabetes mellitus (T2DM), which is likely due to their rich content of fiber, nutrients and various phytochemicals. Insufficient evidence is available regarding the acute dietary effects apple intake on postprandial metabolic responses. We assessed the potential for dried apple to regulate blood glucose and insulin responses as well as satiety and total plasma antioxidant levels in an acute feeding study with healthy individuals and explored the acute effect of dried apple on cognitive responses using eight neurocognitive tests. A randomized crossover design with repeated measures and a minimum of one week washout period between tests was used. On each test day after a fast of 10 hours, a standardized serving size of either diced dried apple or two muffins, each providing 55 g carbohydrate, was consumed by the participants. Blood glucose, insulin and antioxidant concentrations were measured at 0, 15, 30, 45, 60, 90, and 120 minutes and satiety was assessed on 100 mm visual analog scales every 15 minutes for two hours. Cognitive tests were administered before and 2 hours after consumption of the test food. Twenty‐one healthy, normal weight subjects completed the study. Repeated‐measures analysis of variance evaluated the effects of dietary condition and time on blood glucose, insulin, satiety and total antioxidant levels. Paired t‐tests were used to compare the effects of diet at individual time points. Main effects for time (p ≤ 0.01) and dietary trial (p ≤ 0.01) were detected for plasma glucose responses. Dried apple produced lower (p ≤ 0.02) glucose concentrations at the 30, 45, 60 and 120‐minute time points than muffins. Main effects for time (p ≤ 0.01) were detected for insulin responses, and dried apple produced lower (p ≤ 0.03) insulin concentrations at the 30‐minute time point than muffins. No differences were detected for total plasma antioxidant capacity; however, the percent change from baseline for total plasma antioxidant levels was higher throughout the 120 minutes post food consumption for the dried apples versus muffins. Subjects reported that they could eat more food during the apple trial compared to the muffin trial; however, no differences in hunger, fullness or desire to eat were detected. Apple intake resulted in better post‐test performance (p ≤ 0.03) for psychomotor speed, and muffin intake resulted in better post‐test performance for executive function (p ≤ 0.01) and cognitive flexibility (p ≤ 0.02). These findings suggested that dried apple intake could reduce postprandial blood glucose and serum insulin responses in healthy individuals but little consistent difference antioxidant status, satiety and cognitive function.
Regional Patterns of Alcohol‐Induced Neuronal Loss Depend on Genetics: Implications for Fetal Alcohol Spectrum Disorder

BACKGROUND Alcohol exposure during pregnancy can kill developing neurons and lead to fetal alcohol spectrum disorder (FASD). However, affected individuals differ in their regional patterns of alcohol-induced neuropathology. Because neuroprotective genes are expressed in spatially selective ways, their mutation could increase the vulnerability of some brain regions, but not others, to alcohol teratogenicity. The objective of this study was to determine whether a null mutation of neuronal nitric oxide synthase (nNOS) can increase the vulnerability of some brain regions, but not others, to alcohol-induced neuronal losses. METHODS Immunohistochemistry identified brain regions in which nNOS is present or absent throughout postnatal development. Mice genetically deficient for nNOS (nNOS-/- ) and wild-type controls received alcohol (0.0, 2.2, or 4.4 mg/g/d) over postnatal days (PD) 4 to 9. Mice were sacrificed in adulthood (~PD 115), and surviving neurons in the olfactory bulb granular layer and brain stem facial nucleus were quantified stereologically. RESULTS nNOS was expressed throughout postnatal development in olfactory bulb granule cells but was never expressed in the facial nucleus. In wild-type mice, alcohol reduced neuronal survival to similar degrees in both cell populations. However, null mutation of nNOS more than doubled alcohol-induced cell death in the olfactory bulb granule cells, while the mutation had no effect on the facial nucleus neurons. As a result, in nNOS-/- mice, alcohol caused substantially more cell loss in the olfactory bulb than in the facial nucleus. CONCLUSIONS Mutation of the nNOS gene substantially increases vulnerability to alcohol-induced cell loss in a brain region where the gene is expressed (olfactory bulb), but not in a separate brain region, where the gene is not expressed (facial nucleus). Thus, differences in genotype may explain why some individuals are vulnerable to FASD, while others are not, and may determine the specific patterns of neuropathology in children with FASD.
Support Vector Machine based Feature Selection Method for Text Classification

Automatic text classification is one of the most effective tools used to sort out the increasing amount of textual content available online. High dimensionality remains one of the major obstacles observed in the text classification field in spite of the fact that there have been statistical methods available to face this issue. Still, none of them has proved to be effective enough to solve this problem. This paper proposes a feature ranking and selection method based on the Support Vector Machine (SVM) learning algorithm, also known as (SVM-FRM). This method assumes that weights given by the SVM learning algorithm to different features in feature space indicate the significance of these features. As such, the feature selection process can be established based on the referred to weights. The researchers tested the SVM proposed method using three text classification public datasets. Then, they compared the results to those of other statistical feature selection methods currently used for this purpose. In the light of this comparison, applying the proposed SVM-FRM method for text classification has proved to have a superior F-measure and accuracy performances than the rest of other methods applied for this purpose, when tested on balanced datasets, in spite of its size and the high competing performances on an unbalanced dataset. Keywords— Feature ranking; text classification; feature selection; SVM; dimensionality reduction;
Targeting the IL33–NLRP3 axis improves therapy for experimental cerebral malaria

Significance Cerebral malaria (CM) is a neurological complication of malaria infection that, despite antimalarial drug treatment, results in fatality or neurodisability in approximately 25% of cases. Thus, there is an urgent clinical need to develop therapies that can improve the efficacy of antimalarial drugs to prevent or reverse cerebral pathology. Here, we show in an experimental mouse model of CM (ECM) that IL33 administration can improve survival and reduce pathology in the brain over antimalarial drugs alone. Mechanistically, we demonstrate that IL33 enhances recovery from ECM by inhibiting NLRP3 inflammasome-induced inflammatory responses within the brain. These results suggest that IL33 and NLRP3 inflammasome inhibitors may be effective adjunctive therapies for CM. Cerebral malaria (CM) is a serious neurological complication caused by Plasmodium falciparum infection. Currently, the only treatment for CM is the provision of antimalarial drugs; however, such treatment by itself often fails to prevent death or development of neurological sequelae. To identify potential improved treatments for CM, we performed a nonbiased whole-brain transcriptomic time-course analysis of antimalarial drug chemotherapy of murine experimental CM (ECM). Bioinformatics analyses revealed IL33 as a critical regulator of neuroinflammation and cerebral pathology that is down-regulated in the brain during fatal ECM and in the acute period following treatment of ECM. Consistent with this, administration of IL33 alongside antimalarial drugs significantly improved the treatment success of established ECM. Mechanistically, IL33 treatment reduced inflammasome activation and IL1β production in microglia and intracerebral monocytes in the acute recovery period following treatment of ECM. Moreover, treatment with the NLRP3-inflammasome inhibitor MCC950 alongside antimalarial drugs phenocopied the protective effect of IL33 therapy in improving the recovery from established ECM. We further showed that IL1β release from macrophages was stimulated by hemozoin and antimalarial drugs and that this was inhibited by MCC950. Our results therefore demonstrate that manipulation of the IL33–NLRP3 axis may be an effective therapy to suppress neuroinflammation and improve the efficacy of antimalarial drug treatment of CM.