Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomedical Concept Recognition Using Deep Neural Sequence Models

Background the automated identification of mentions of ontological concepts in natural language texts is a central task in biomedical information extraction. Despite more than a decade of effort, performance in this task remains below the level necessary for many applications. Results recently, applications of deep learning in natural language processing have demonstrated striking improvements over previously state-of-the-art performance in many related natural language processing tasks. Here we demonstrate similarly striking performance improvements in recognizing biomedical ontology concepts in full text journal articles using deep learning techniques originally developed for machine translation. For example, our best performing system improves the performance of the previous state-of-the-art in recognizing terms in the Gene Ontology Biological Process hierarchy, from a previous best F1 score of 0.40 to an F1 of 0.70, nearly halving the error rate. Nearly all other ontologies show similar performance improvements. Conclusions A two-stage concept recognition system, which is a conditional random field model for span detection followed by a deep neural sequence model for normalization, improves the state-of-the-art performance for biomedical concept recognition. Treating the biomedical concept normalization task as a sequence-to-sequence mapping task similar to neural machine translation improves performance.
A novel KRT1 c.1433A>G p.(Glu478Gly) mutation in a newborn with epidermolytic ichthyosis

Epidermolytic Ichthyosis is a rare genodermatosis related to point mutations affecting the genes encoding for keratin 1 or keratin 10. We report a case of Epidermolytic Ichthyosis in a newborn with a novel mutation (c.1433A>G) of KRT1 gene.
Qualitative Study on Nutrition- and Health-Related Knowledge Attitudes and Practices in Four High Stunting Regions of Lao PDR

Despite the Lao People’s Democratic Republic (Lao PDR)’s significant economic growth over the last decade, poor nutritional outcomes remain a concern. Rates of malnutrition are particularly high in remote, rural, and upland areas. Redressing these high rates of malnutrition is critical in reversing inequalities in mortality and ill-health throughout people’s lives. This report presents the results of a qualitative study to investigate the knowledge, attitudes, and practices related to maternal and child health and nutrition in local communities in four provinces of Lao PDR with high levels of childhood undernutrition. While study participants were aware of the importance and benefits of antenatal care, facilitybased birthing, and postnatal care, they also reported significant barriers to accessing these services. These barriers included poor road conditions and lack of transportation, long distances to health facilities, language difficulties, and lack of spousal support. With regard to infant and young child feeding, many mothers were aware of the benefits of colostrum for the first 24 hours and exclusive breastfeeding for the first six months of life and tried to put this into practice. However, some ethnic minority women stated that at times it was hard to prevent grandmothers from giving the infant water or a little chewed rice. There were mixed results when it came to growth monitoring of children. Even though mothers did participate in growth monitoring and promotion activities, most did not fully understand the reasons behind those activities. Access to clean water and appropriate sanitation facilities depended on location, but few families had access to safe water. While messages regarding handwashing and desisting from open defecation were well known in most villages, poverty and lack of support for providing safe and sustainable sanitation systems diminished the ability of many families to practice safe hygienic practices.
Personalized Monitoring Model for Electrocardiogram Signals: Diagnostic Accuracy Study

Background Due to the COVID-19 pandemic, the demand for remote electrocardiogram (ECG) monitoring has increased drastically in an attempt to prevent the spread of the virus and keep vulnerable individuals with less severe cases out of hospitals. Enabling clinicians to set up remote patient ECG monitoring easily and determining how to classify the ECG signals accurately so relevant alerts are sent in a timely fashion is an urgent problem to be addressed for remote patient monitoring (RPM) to be adopted widely. Hence, a new technique is required to enable routine and widespread use of RPM, as is needed due to COVID-19. Objective The primary aim of this research is to create a robust and easy-to-use solution for personalized ECG monitoring in real-world settings that is precise, easily configurable, and understandable by clinicians. Methods In this paper, we propose a Personalized Monitoring Model (PMM) for ECG data based on motif discovery. Motif discovery finds meaningful or frequently recurring patterns in patient ECG readings. The main strategy is to use motif discovery to extract a small sample of personalized motifs for each individual patient and then use these motifs to predict abnormalities in real-time readings of that patient using an artificial logical network configured by a physician. Results Our approach was tested on 30 minutes of ECG readings from 32 patients. The average diagnostic accuracy of the PMM was always above 90% and reached 100% for some parameters, compared to 80% accuracy for the Generalized Monitoring Models (GMM). Regardless of parameter settings, PMM training models were generated within 3-4 minutes, compared to 1 hour (or longer, with increasing amounts of training data) for the GMM. Conclusions Our proposed PMM almost eliminates many of the training and small sample issues associated with GMMs. It also addresses accuracy and computational cost issues of the GMM, caused by the uniqueness of heartbeats and training issues. In addition, it addresses the fact that doctors and nurses typically do not have data science training and the skills needed to configure, understand, and even trust existing black box machine learning models.
Get the Lowdown: The International Side of the Fall in the U.S. Natural Rate of Interest

Much consideration has been given among scholars and policymakers to the decline in the U.S. natural rate of interest since the 2007 – 09 global financial crisis. In this paper, I investigate its determinants and drivers through the lens of the workhorse two-country New Keynesian model that captures the trade and technological interconnectedness of the U.S. with the rest of the world economy. Using Bayesian techniques, I bring the set of binding log-linearized equilibrium conditions from this model to the data, but augmented with survey-based forecasts in order to align the solution with observed expectations incorporating the macro effects of the zero-lower bound constraint. With this structural framework, I recover a novel open-economy estimate of the U.S. natural rate. The paper’s main results are: (a) the decline in the U.S. natural rate largely follows the slide of the long-run real interest rate in the forecast data, but is partly cushioned in the short run by the contribution of domestic and to a significant extent also foreign productivity shocks; (b) the fall of U.S. measured labor productivity during this time contributed to a concomitant fall in U.S. output potential; (c) the past decade is also characterized by the compression of markups (negative cost-push shocks) which accounts for much of the cyclical upswing in U.S. output in spite of the fall in its potential; and (d) monetary policy has shown its efficacy boosting U.S. output and sustaining U.S. inflation close to its 2 percent target against the drag on inflation from the negative cost-push shocks during this time. Finally, I also argue that ignoring the international linkages may result in biased estimates and can distort the empirical inferences on U.S. monetary policy in important ways.
Occurrence and distribution of viruses infecting Zucchini and Watermelon in Morocco

Abstract Zucchini (Cucurbita pepo) and watermelon (Citrulus lanatus) rank among the most cultivated cucurbits in Morocco. These crops are prone to infection with a wide range of viral diseases responsible for tremendous losses. Field surveys carried out in 2016 and 2017 in four major cucurbit-growing areas revealed the presence of many virus-like symptoms on these crops, including severe yellowing and chlorosis. Therefore, symptomatic samples were collected and analysed serologically with DAS-ELISA for ZYMV, WMV, PRSV, CABYV and CMV and by The PCR and RT-PCR for ToLCNDV, and CVYV and MNSV, respectively. Results indicated that out of 136 samples, 79% of zucchini and 60% of watermelon samples were serologically positive for ZYMV, CMV, WMV, PRSV and CABYV. CVYV and MNSV were negatively amplified by RT-PCR using specific primer pairs. Interestingly, the PCR test revealed the occurrence of natural infection with ToLCNDV in Morocco.
Cryptic inoviruses are pervasive in bacteria and archaea across Earth’s biomes

Bacteriophages from the Inoviridae family (inoviruses) are characterized by their unique morphology, genome content, and infection cycle. To date, a relatively small number of inovirus isolates have been extensively studied, either for biotechnological applications such as phage display, or because of their impact on the toxicity of known bacterial pathogens including Vibrio cholerae and Neisseria meningitidis. Here we show that the current 56 members of the Inoviridae family represent a minute fraction of a highly diverse group of inoviruses. Using a new machine learning approach leveraging a combination of marker gene and genome features, we identified 10,295 inovirus-like genomes from microbial genomes and metagenomes. Collectively, these represent six distinct proposed inovirus families infecting nearly all bacterial phyla across virtually every ecosystem. Putative inoviruses were also detected in several archaeal genomes, suggesting that these viruses may have occasionally transferred from bacterial to archaeal hosts. Finally, we identified an expansive diversity of inovirus-encoded toxin-antitoxin and gene expression modulation systems, alongside evidence of both synergistic (CRISPR evasion) and antagonistic (superinfection exclusion) interactions with co-infecting viruses which we experimentally validated in a Pseudomonas model. Capturing this previously obscured component of the global virosphere sparks new avenues for microbial manipulation approaches and innovative biotechnological applications.
Influence of Transverse Cross‐Phases on Propagations of Optical Beams in Linear and Nonlinear Regimes

It is predicted that an orbital angular momentum (OAM) carried by an elliptic Gaussian beam (EGB) with a cross‐phase can induce an effective anisotropic diffraction (AD). At the so‐called critical OAM, such an OAM‐induced AD makes the spiraling elliptic Gaussian mode, which expands owing to the diffraction while keeping its elliptic degree changeless, exist in linearly isotropic media, where only the circular Gaussian mode is supposed to exist for a beam without an OAM. In this study, such a phenomenon is experimentally demonstrated via the propagations of the EGBs with the OAM in linearly and both‐linearly‐and‐nonlinearly isotropic media. In the former case, the spiraling elliptic Gaussian mode is observed at the critical OAM in the free space. In the latter case, the spiraling elliptic soliton predicted by Desyatnikov et al. is observed for the first time in cylindrical lead glass. The physics behind the spiraling elliptic soliton is the OAM‐induced AD, which preserves the elliptic degree of the EGB, whereas the nonlinearity only balances the diffraction of the EGB. The OAM‐induced AD is just a linear effect and has nothing to do with nonlinearity.
Seipin-Mediated Contacts as Gatekeepers of Lipid Flux at the Endoplasmic Reticulum–Lipid Droplet Nexus

Lipid droplets (LDs) are dynamic cellular hubs of lipid metabolism. While LDs contact a plethora of organelles, they have the most intimate relationship with the endoplasmic reticulum (ER). Indeed, LDs are initially assembled at specialized ER subdomains, and recent work has unraveled an increasing array of proteins regulating ER-LD contacts. Among these, seipin, a highly conserved lipodystrophy protein critical for LD growth and adipogenesis, deserves special attention. Here, we review recent insights into the role of seipin in LD biogenesis and as a regulator of ER-LD contacts. These studies have also highlighted the evolving concept of ER and LDs as a functional continuum for lipid partitioning and pinpointed a role for seipin at the ER-LD nexus in controlling lipid flux between these compartments.
Schwere Nahrungsmittelallergien – ist eine Toleranzinduktion möglich?

ZUSAMMENFASSUNG Die Standardtherapie bei schweren Nahrungsmittelallergien (NMA) umfasst Karenzdiät, Ernährungsberatung sowie Ausstattung mit Notfallmedikamenten und Schulung in den Notfallplan. Aufgrund der starken Einschränkung der Lebensqualität von Patienten mit schwerer Nahrungsmittelallergie sind kausale Therapieansätze wünschenswert, die eine orale Toleranz aktiv induzieren. In den letzten zwei Jahrzehnten wurden verschiedene Applikations-Routen (sublingual, oral, epikutan) einer spezifischen Immuntherapie bei Patienten mit Hühnerei-, Kuhmilch- und Erdnussallergie untersucht. Aktuelle Studien konzentrierten sich vor allem auf die orale Immuntherapie (OIT) mit Erdnuss und zeigten, dass die klinische Reaktionsschwelle durch eine wiederholte Zuführung definierter Allergenmengen tatsächlich angehoben werden konnte. Kürzlich hat die US-amerikanische Arzneimittelbehörde das Erdnuss-Pulver Palforzia als erstes Therapeutikum für Nahrungsmittelallergien bei Kindern und Jugendlichen zugelassen. Jedoch ist die Erdnuss-OIT auch mit systemischen Nebenwirkungen assoziiert. Eine Niedrigdosis-OIT zeigte ein besseres Sicherheitsprofil. Zusätzliche Studien mit direktem Vergleich verschiedener Allergendosen und Dosissteigerungs-Protokolle sind notwendig. Weiterhin müssen Langzeit-Wirksamkeit und Kosteneffektivität verschiedener Ansätze zur Induktion oraler Toleranz bewertet werden.
Studies on the effect of zinc and boron on growth and yield of linseed (Linum usitatissimum L.) under limited irrigation

An experiment was conducted during rabi 2018-19 at Oilseed Research Farm, Kalyanpur, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur to study the effect of zinc and boron on growth, yield attributes, seed yield and economics of linseed. The soil of experimental site was sandy loam in texture, low in organic carbon, available nitrogen and medium in available phosphorus and potash with slightly alkaline in reaction. The experiment was conducted in randomized block design with three replications. The treatments comprised soil application of ZnSO4 @ 25 kg ha-1, foliar application of ZnSO4 @ 0.5% at 45 DAS, soil application of ZnSO4 @ 25 kg ha-1 + foliar application of ZnSO4 @ 0.5% at 45 DAS, soil application of borax @ 1.5 kg ha-1, foliar application of borax @ 0.3% at 45 DAS, soil application of borax @ 1.5 kg ha-1 + foliar application of borax @ 0.3 at 45 DAS, soil application of ZnSO4 @ 25 kg ha-1 + borax @ 1.5 kg ha-1, foliar application of ZnSO4 @ 0.5% + borax @ 0.3% at 45 DAS compared with control treatment. The source of zinc and boron application were zinc sulphate hepta hydrate and borax. The experimental crop received a total rainfall of 35.2 mm which was exposed to mean minimum temperature 4.60C during fifty two standard week (24-31 December, 2018) and maximum temperature 39.40C during 15th meteorological week (9-15 April, 2019) respectively. Results exhibited that among the treatments foliar application of ZnSO4 @ 0.5% + borax @ 0.3% at 45 DAS found superior in terms of maximum plant growth, yield attributes and seed yield. The significantly maximum seed yield (2047 kg ha-1), gross monetary return (Rs.92115 ha-1) and net monetary return (Rs.65508 ha-1) were recorded with the foliar application of ZnSO4 @ 0.5% + borax @ 0.3% at 45 DAS and was followed by the soil application of ZnSO4@ 25 Kg ha-1 + borax@ 1.5 kg ha-1.
Delivery of Learning Modules through the Telegram Social Media Application: Views of Post Graduate Diploma in Education Distance Students of University of Education, Winneba

The study investigated the views of Post Graduate Diploma in Education (PGDE) Distance Students (2017/2018 Year Group of UEW, Winneba) Winneba Study Centre on the delivery of learning modules and leaning convenience in the use of the telegram application (app). The study adopted the sequential explanatory mixed methods design, characterized by the collection and analysis of quantitative data followed by collection and analysis of qualitative data. Questionnaire and interviews were instruments used to collect data for the study. The population for the study was all hundred (100) Post Graduate Diploma in Education (PGDE) Distance Students of the Winneba Study Centre. Simple random sampling technique was used to select two-thirds (75 students) of the population. Sixty (60) of the respondents successfully completed the online questionnaire. Ten (10) of the participants were traced to respond to the qualitative questions for the study. Data obtained from the online questionnaire were analyzed and presented in tables and charts. Data from the individual interviews were analyzed thematically. A major finding was, the telegram app facilitated the timely delivery of learning modules. The study concluded that, the telegram app proved to be efficient in not only the delivery of learning modules for the students but also very convenient an app for distance learning. The study recommends the adoption and integration of telegram app in the distance education programme in an attempt to resolve the late delivery of learning modules as well as learning convenience for the students.
A Novel Repertoire of Blood Transcriptome Modules Based on Co-expression Patterns Across Sixteen Disease and Physiological States

ABSTRACT Blood transcriptomics measures the abundance of circulating leukocyte RNA on a genome-wide scale. Dimension reduction is an important analytic step which condenses the number of variables and permits to enhance the robustness of data analyses and functional interpretation. An approach consisting in the construction of modular repertoires based on differential co-expression observed across multiple biological states of a given system has been described before. In this report, a new blood transcriptome modular repertoire is presented based on an expended range of disease and physiological states (16 in total, encompassing 985 unique transcriptome profiles). The input datasets have been deposited in NCBI public repository, GEO. The composition of the set of 382 modules constituting the repertoire is shared, along with extensive functional annotations and a custom fingerprint visualization scheme. Finally, the similarities and differences between the blood transcriptome profiles of this wide range of biological states are presented and discussed.
Exploring the topology of the maritime transport network in a large-scale archipelago: a complex network approach

In this study, we use a complex network analysis approach to investigate the topological structure of container shipping networks in the Indonesia archipelago to understand the network topology. Containerized cargo is responsible for more than half of the inter-island trade volume, making it one critical freight transport mode in the Indonesia archipelago. We summarize the network topological structure by measures such as degree distribution, average path length, and average clustering coefficient. Based on the initial result, we find that the degree distribution of the container shipping network in archipelago fits a hybrid distribution. The distribution proves the studied network is not scale-free. With regards to the network structure, the archipelago’s s hipping network exhibits a short path length and a low value of the clustering coefficient, potentially rejecting the small-world structure hypothesis. These initial findings provide evidence that the maritime shipping network in a large-scale archipelago shows a distinctive pattern compared to other maritime shipping networks in the existing literature.
The Folk Concept of Race

This chapter examines whether and how concepts vary across and within individuals (inter- and intra-individual variation) by examining what constrains variation of concepts. To address this issue, the chapter focuses on an independently interesting case study: inter- and intra-individual variation in the concept of race. The case study contrasts two competing hypotheses about the concept of race: the biological and the social hypotheses. According to the first hypothesis, the concept of race is a biological concept that is constrained by folk biology; according to the second, it is the concept of a social category. The chapter shows that people’s folk biology constrains the concept of race and thus limits how much concepts of race can vary within and across individuals.
In Vitro Comparative Study of Osteogenic Differentiation Ability between Adipose and Bone Marrow Mesenchymal Stem Cell Applied to Bovine Demineralized Bone Matrix

Ideal bone graft must possess the desirable trait such as osteoconductive, osteoinductive and osteogenesis. Demineralized Bone Matrix (DBM) provides both osteoconductive and osteoinductive trait. Referring to the tissue engineering principle, the addition of mesenchymal stem cell would add the osteogenic trait to this procedure. The design of this study is experimental using Bovine DBM. Bone Marrow Mesenchymal Stem Cell (BMSCs) and Adipose Mesenchymal Stem Cells (ASCs) were taken from New Zealand white rabbit. There are two groups of treatment, divided into DBM implanted with BMSCs and DBM implanted with ASCs. Each BMSCs and ASCs groups is incubated in the normal and osteogenic culture plate. Evaluation is performed by counting the osteoblast and immunohistochemistry stain using Alkaline Phosphate and Osteocalcin. After 4 weeks of incubation, we found that the osteoblast count in BMSCs groups is higher compared to the ASCs groups in both culture condition (p<0.01) along with Alkaline Phosphate staining (p<0.05), while the Osteocalcin staining showed insignificant differences (p>0.05). This study revealed that xenogenic bovine DBM can act as the potential osteoinductive scaffold for the MSCs to differentiate. The tissue engineering application by combining MSCs and Bovine DBM can be considered as an alternative in managing bone defect cases.
Real Time Action Recognition in Surveillance Video Using Machine Learning

Human gesture identification plays a crucial and key role in the surveillance and security domains. This technique is most wanted in today's world to identify the culprits or specific people over the surveillance cameras. In this proposed method the action identification is aided through the machine learning technology. Initially the frames of the subject under focus are segmented and modeled by the Gaussian modeling. The entire process of feature extraction and quantizing are done considering the characteristics of the area of interest. The proposed method has two phases testing and training phase using three datasets for validation and KNN classifier for classification. The output of the proposed algorithm is analyzed and is said to be have the correct gesture identification with accuracy rate of 95.568%.