Miracle-dz/UniFAR-FTUs huggingface dataset:
A comprehensive review of antimalarial medicinal plants used by Tanzanians

Abstract Context Tanzania has rich medicinal plant (MP) resources, and most rural inhabitants rely on traditional healing practices for their primary healthcare needs. However, available research evidence on antimalarial MPs is highly fragmented in the country. Objective This systematic review compiles ethnomedicinal research evidence on MPs used by Tanzanians as antimalarials. Materials and methods A systematic web search was conducted using various electronic databases and grey materials to gather relevant information on antimalarial MPs utilized by Tanzanians. The review was per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The data were collected from 25 articles, and MS Excel software was used to analyse relevant ethnobotanical information using descriptive statistics. Results A total of 227 MPs belonging to 67 botanical families and 180 genera were identified. Fabaceae (15.9%) is the most frequently utilized family. The ethnobotanical recipes analysis indicated leaves (40%) and trees (44%) are the preferred MPs part and life form, respectively. Decoctions (67%) are the dominant preparation method of remedies. Of the recorded MPs, 25.9% have been scientifically investigated for antimalarial activities with positive results. However, 74.1% of MPs have no scientific records on antimalarial activities, but they could be potential sources of remedies. Conclusions The study discloses a wealth of antimalarial MPs possessed by Tanzanians and suggests a need for research to authenticate the healing potential of antimalarial compounds from the unstudied MPs. Additionally, it indicates that some of the presented MPs are potential sources for developing safe, effective and affordable antimalarial drugs.
THE DATA MINING TECHNIQUE - TREND ANALYSIS ON DIFFERENT WAGE WORKS OF NREGS DATA OF ANDHRA PRADESH

Data mining merged the ideas from statistics, machine learning, databases and parallel computing. The data mining techniques such as characterization, discrimination, classification, clustering, outlier and trend analysis, etc. may apply on National Rural Employment Guarantee Scheme (NREGS) data of Andhra Pradesh. The pattern discovery through data mining technique i.e. trend analysis on different wage works of NREGS data of AP is presented in this paper. From NREGS data, Expenditure on different wage works like water harvesting and conservation, drought proofing, irrigation provision, renovation, flood control works, and rural works are collected separately from 2006 to 2011 years about 22 districts of AP. Based on past history i.e. from 2006 to 2011 years about 22 districts of AP, trend analysis on expenditure for the above works is estimated for the years 2012 & 2013 and are presented in this paper. This information may be helpful to the government for budget allocation.
Controversies in the management of anti-TNF therapy in patients with Crohn’s disease: a Delphi consensus

Background Despite research, there are still controversial areas in the management of Crohn’s disease (CD). Objective To establish practical recommendations on using anti-tumour necrosis factor (TNF) drugs in patients with moderate-to-severe CD. Methods Clinical controversies in the management of CD using anti-TNF therapies were identified. A comprehensive literature review was performed, and a national survey was launched to examine current clinical practices when using anti-TNF therapies. Their results were discussed by expert gastroenterologists within a nominal group meeting, and a set of statements was proposed and tested in a Delphi process. Results Qualitative study. The survey and Delphi process were sent to 244 CD-treating physicians (response rate: 58%). A total of 14 statements were generated. All but two achieved agreement. These statements cover: (1) use of first-line non-anti-TNF biological therapy; (2) role of HLA-DQA1*05 in daily practice; (3) attitudes in primary non-response and loss of response to anti-TNF therapy due to immunogenicity; (4) use of ustekinumab or vedolizumab if a change in action mechanism is warranted; (5) anti-TNF drug level monitoring; (6) combined therapy with an immunomodulator. Conclusion This document sought to pull together the best evidence, experts’ opinions, and treating physicians’ attitudes when using anti-TNF therapies in patients with CD.
Managing uncertainty in RFID based tracking applications

Object or people based tracking systems that use RFID have seen increasing usage over the past decade. These systems provide an effective tracking solution by leveraging the non-line-of-sight precise identification capability of RFID technology, however they still have to overcome a number of challenges posed by the nature of the technology to improve their reliability and accuracy, such as uncertain data that leads to location uncertainty. In this thesis, two applications are been concentrated: i) asset tracking; and ii) tracking people. The goal was to develop a generalizable approach for tracking objects or people effectively by managing the location uncertainty problem caused by uncertain RFID data. In the context of an asset tracking application, we describe an optimized tracking algorithm to predict the locations of objects in the presence of missed reads using particle filters. To achieve high location accuracy we develop a model that characterizes the motion of objects in a supply chain. The model is also adaptable to the changing nature of a business, such as flow of goods, path taken by goods through the supply chain, and sales volumes. A scalable tracking algorithm is achieved by an object compression technique, which also leads to a significant improvement in accuracy. In the context of a people tracking application for addressing wandering off, one of the common behaviours among cognitively impaired patients, we have developed an approach for identifying the traversing direction and the traversing path used by the patients wearing an RFID tag integrated into clothing for the first time. Our approach uses a particle filtering (PF) based technique with Received Signal Strength Indicator (RSSI) maps obtained from scene analysis to continuously track a person wearing an RFID tag over their attire. Using real-time spatial and temporal data obtained from the PF based tracking approach, we develop two algorithms: i) tag traversing direction (TD) algorithm to identify the tag bearer’s moving direction (e.g. moving out of a room); and ii) tag traversing path detection algorithm (TPD) to estimate the traversal path used by the tag bearer. Furthermore, we propose a generic model for RFID sensing infrastructure using Kernel
Land Cover Change Impacts on Multidecadal Streamflow in Metropolitan Atlanta GA, USA

Urbanization has been associated with the degradation of streams, and a consequence of forest to urban land transition is a change in streamflow. Therefore, the purpose of this thesis is to examine the impacts of land-cover change in ten different watersheds in the rapidly urbanizing Atlanta, GA USA metropolitan area. Streamflow and precipitation data for a 30-year period (1986-2016) were analyzed in conjunction with land cover data from 1992, 2001, and 2011. Big Creek and Suwanee Creek experienced the most urbanization and increases (20%) in streamflow and runoff, and high flow (>95 percentile of flow) days doubled and increased 85%, respectively. Precipitation-adjusted streamflow for Peachtree Creek and Flint River decreased about 17%. Runoff ratios for South River were the highest among all watersheds, even the Etowah River, which remained moderately forested and had the most precipitation and slope. INDEX WORDS: Streamflow, Land cover, Urban streams, Baseflow, Watershed, Georgia, urbanization, precipitation LAND COVER CHANGE IMPACTS ON MULTIDECADAL STREAMFLOW IN METROPOLITAN ATLANTA, GA USA
Building back better: what does it imply for Jiri Technical School, Dolakha, Nepal?

A powerful earthquake of 7.8 magnitude struck on April 25, 2015 in Nepal. The impact was most severe where 8,790 casualties and 23,300 injured (National Planning Commission [NPC], 2015). Due to earthquake there was huge loss in education sector. In TVET sector Jiri Technical School, Dolakha, Nepal faced huge loss where almost all classroom including workshops, labs, hostel and cafeteria were damage. So, this study tries to portray the understanding perception of Jiri Technical School students on impact of earthquake on education and also educational resilience by using case study research design.
Driver Activity Recognition for Intelligent Vehicles: A Deep Learning Approach

Driver decisions and behaviors are essential factors that can affect the driving safety. To understand the driver behaviors, a driver activities recognition system is designed based on the deep convolutional neural networks (CNN) in this paper. Specifically, seven common driving activities are identified, which are the normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle radio device, texting, and answering the mobile phone, respectively. Among these activities, the first four are regarded as normal driving tasks, while the rest three are classified into the distraction group. The experimental images are collected using a low-cost camera, and ten drivers are involved in the naturalistic data collection. The raw images are segmented using the Gaussian mixture model to extract the driver body from the background before training the behavior recognition CNN model. To reduce the training cost, transfer learning method is applied to fine tune the pre-trained CNN models. Three different pre-trained CNN models, namely, AlexNet, GoogLeNet, and ResNet50 are adopted and evaluated. The detection results for the seven tasks achieved an average of 81.6% accuracy using the AlexNet, 78.6% and 74.9% accuracy using the GoogLeNet and ResNet50, respectively. Then, the CNN models are trained for the binary classification task and identify whether the driver is being distracted or not. The binary detection rate achieved 91.4% accuracy, which shows the advantages of using the proposed deep learning approach. Finally, the real-world application are analyzed and discussed.
Fine-Grained Accident Detection: Database and Algorithm

This paper presents a novel fine-grained task for traffic accident analysis. Accident detection in surveillance or dashcam videos is a common task in the field of traffic accident analysis by using videos. However, common accident detection does not analyze the specific particulars of the accident, only identifies the accident’s existence or occurrence time in a video. In this paper, we define the novel fine-grained accident detection task which contains fine-grained accident classification, temporal-spatial occurrence region localization, and accident severity estimation. A transformer-based framework combining the RGB and optical flow information of videos is proposed for fine-grained accident detection. Additionally, we introduce a challenging Fine-grained Accident Detection (FAD) database that covers multiple tasks in surveillance videos which places more emphasis on the overall perspective. Experimental results demonstrate that our model could effectively extract the video features for multiple tasks, indicating that current traffic accident analysis has limitations in dealing with the FAD task and that further research is indeed needed.
Temperature extremes in the Argentina central region and their monthly relationship with the mean circulation and ENSO phases

The aim of this study is to analyse the interannual variability of monthly climatic indices of extreme daily temperature in the central‐north region of Argentina throughout a year and its relationship with the atmospheric circulation. The impact of El Niño‐Southern Oscillation (ENSO) over different temperature indices throughout the year is specially analysed. Globally used indices in the 1970–2010 period are utilized for this purpose. The trends of temperature extremes show warming conditions in several months, especially in October and November. In order to find possible forcings of extreme temperature in the region at monthly scale, the co‐variability with other circulation monthly time series such as changes in the intensity of Southern Hemisphere semi‐permanent anticyclones and the intensity of the subtropical jet were studied. The main finding was a systematic shift of the South Atlantic anticyclone towards the west over the decades in June and August, which might hinder the cold advections over the region surveyed. Moreover, the influence of the variation on the intensity of the subtropical jet over extreme events showed significant positive (negative) correlations between the intensity of the jet and the frequency of cold (warm) indices in a great number of months. Finally, the influence of the ENSO phases on each of the temperature extremes analysed was studied. It was found that under El Niño conditions, minimum temperatures are affected quite evenly throughout the year, fostering the occurrence of warm nights. The impact of the El Niño event on the maximum extreme temperatures, however, shows seasonal differences. Between July and September warm days conditions are fostered, while between November and February the opposite (cold days) can be seen. This results in a decrease in the temperature range in the region surveyed during the summer months under El Niño conditions.
Status and trends in epidemiologic characteristics of diabetic end-stage renal disease: an analysis of the 2021 Korean Renal Data System

This article provides an update of the trends and characteristics of diabetic kidney disease stage 5D (CKD 5D) patients according to the Korean Renal Data System (KORDS), a nationwide registry database operated by the Korean Society of Nephrology. The KORDS Committee analyzed epidemiologic characteristics of diabetic CKD 5D patients using data from 2001 to 2021 in KORDS. In 2021, the dialysis adequacy of hemodialysis (HD) was lower in diabetic CKD 5D patients than non-diabetic CKD 5D patients, while that of peritoneal dialysis (PD) was similar. Diabetic CKD 5D patients had a higher proportion of cardiac and vascular diseases and were more frequently admitted to hospitals than non-diabetic CKD 5D patients, and the leading cause of death was cardiac disease. From 2001 to 2020, diabetic CKD 5D patients had a higher mortality rate than non-diabetic CKD 5D patients, but in 2021 this trend was reversed. Diabetic PD patients had the highest mortality rate over 20 years. The mortality rate of diabetic HD patients was higher than that of non-diabetic HD patients until 2019 but became lower starting in 2020. There was a decreasing trend in mortality rate in diabetic CKD 5D patients, but cardiac and vascular diseases were still prevalent with frequent admissions to hospitals. More specialized care is needed to improve the clinical outcomes of diabetic CKD 5D patients.
Feasibility of remote interviews in assessing disease severity in patients with major depressive disorder: A pilot study

Abstract Aim Interview quality is an important factor in the success of clinical trials for major depressive disorder (MDD). There is a substantial need to establish a reliable, remote clinical assessment interview system that can replace traditional in‐person interviews. Methods We conducted a multicenter, randomized, unblinded, prospective, cross‐sectional study to assess the reliability of remote interviews in patients with MDD (UMIN000041839). Eligible patients with MDD underwent remote and in‐person sessions of the Montgomery‐Åsberg Depression Rating Scale (MADRS) assessment performed by different raters within 28 days of providing consent. Patients were randomized to a group first assessed using in‐person interviews and secondarily using remote interviews (in‐person‐first group) or a group first assessed by remote interviews and secondarily using in‐person interviews (remote‐first group). Nineteen trained people (15 clinical psychologists, 3 nurses, and 1 clinical laboratory technologist) performed interviews. Results Of 59 patients (in‐person‐first group, n = 32; remote‐first group, n = 27) who completed both remote and in‐person interviews, 51% (n = 30) were women; the mean age was 41.6 years (range, 21–64 years). There was a strong association between remote and in‐person MADRS scores (r = 0.891, kappa = 0.901). An overall intraclass correlation coefficient (ICC) of 0.886 (95% confidence interval, 0.877–0.952) indicated good consistency between MADRS scores in remote and in‐person interviews. The ICC decreased as the severity of depression increased. Conclusion Our results suggest remote interviews are a feasible alternative option to in‐person interviews in assessing symptom severity in MDD patients and could promote clinical trials in Japan.
A temporal study of the macroinfauna of the beach most affected by the Prestige oil-spill (O Rostro, NW Spain)

Exposed beaches are considered poor habitats where wave action and sediment instability limit the development of biological assemblages, with low productivity compared with other coastal environments (McLachlan, 1983). In temperate latitudes, supralittoral levels of beaches are usually occupied by an assemblage constituted by semi-terrestrial amphipods, isopods and insects that are linked to the food supply from algal wrack and the tidal organic material. Intertidal levels are usually occupied by marine amphipods, isopods and polychaetes. Oil-spills are considered one of the most important environmental disasters on coastal areas and particularly, the “Prestige” oil-spill (autumn 2002 to spring 2003) was particularly harmful due to its magnitude. An important scientific effort was done in intertidal beaches and estuaries affected by the oil-spill, with special emphasis on macroinfaunal assemblages (Junoy et al. 2005; de la Huz et al. 2005; Puente et al. 2008, 2009). Medium and long-term studies are suitable to discern between the rates of natural and/or anthropogenic disturbances, however, these studies are scarce on sandy beaches. In the present study, a before-after environmental study was conducted in O Rostro beach (42o58 ́N9o15 ́W, Figure 1), the most affected beach by the “Prestige” oil-spill and pristinely characterized by low macroinfaunal abundances and diversity (Junoy et al. 2005; Lastra et al. 2006). The morphodynamic of the beach is well known after the monitoring plan and studies developed with occasion of the “Prestige” catastrophe (Bernabeu et al. 2006; CEPRECO, 2006; González et al. 2009).
Yeast eIF2A has a minimal role in translation initiation and uORF-mediated translational control in vivo

Initiating translation of most eukaryotic mRNAs depends on recruitment of methionyl initiator tRNA (Met-tRNAi) in a ternary complex (TC) with GTP-bound eukaryotic initiation factor 2 (eIF2) to the small (40S) ribosomal subunit, forming a 43S preinitiation complex (PIC) that attaches to the mRNA and scans the 5′-untranslated region (5′ UTR) for an AUG start codon. Previous studies have implicated mammalian eIF2A in GTP-independent binding of Met-tRNAi to the 40S subunit and its recruitment to specialized mRNAs that do not require scanning, and in initiation at non-AUG start codons, when eIF2 function is attenuated by phosphorylation of its α-subunit during stress. The role of eIF2A in translation in vivo is poorly understood however, and it was unknown whether the conserved ortholog in budding yeast can functionally substitute for eIF2. We performed ribosome profiling of a yeast deletion mutant lacking eIF2A and isogenic wild-type (WT) cells in the presence or absence of eIF2α phosphorylation induced by starvation for amino acids isoleucine and valine. Whereas starvation of WT confers changes in translational efficiencies (TEs) of hundreds of mRNAs, the eIF2AΔ mutation conferred no significant TE reductions for any mRNAs in non-starved cells, and it reduced the TEs of only a small number of transcripts in starved cells containing phosphorylated eIF2α. We found no evidence that eliminating eIF2A altered the translation of mRNAs containing putative internal ribosome entry site (IRES) elements, or harboring uORFs initiated by AUG or near-cognate start codons, in non-starved or starved cells. Thus, very few mRNAs (possibly only one) appear to employ eIF2A for Met-tRNAi recruitment in yeast cells, even when eIF2 function is attenuated by stress.
Copilot Refinement: Addressing Code Smells in Copilot-Generated Python Code

As one of the most popular dynamic languages, Python experiences a decrease in readability and maintainability when code smells are present. Recent advancements in Large Language Models have sparked growing interest in AI-enabled tools for both code generation and refactoring. GitHub Copilot is one such tool that has gained widespread usage. Copilot Chat, released on September 2023, functions as an interactive tool aims at facilitating natural language-powered coding. However, limited attention has been given to understanding code smells in Copilot-generated Python code and Copilot's ability to fix the code smells it generates. To this end, we built a dataset comprising 102 code smells in Copilot-generated Python code. Our aim is to first explore the occurrence of code smells in Copilot-generated Python code and then evaluate the effectiveness of Copilot in fixing these code smells employing different prompts. The results show that 8 out of 10 types of Python smells can be detected in Copilot-generated Python code, among which Multiply-Nested Container is the most common one. For these code smells, Copilot Chat achieves a highest fixing rate of 87.1%, showing promise in fixing Python code smells generated by Copilot itself. Besides, the effectiveness of Copilot Chat in fixing these smells can be improved with the provision of more detailed prompts. However, using Copilot Chat to fix these smells might introduce new code smells.
Performance of a Sagnac interferometer to observe vacuum optical nonlinearity

In Quantum Electrodynamics, vacuum becomes a nonlinear optical medium: its optical index should be modified in the presence of intense external electromagnetic fields. The DeLLight project (Deflection of Light by Light) aims to observe this effect using intense focused femtosecond laser pulses delivered by LASERIX. The principle is to measure with a Sagnac interferometer the deflection of a low-intensity focused pulse (probe) crossing the vacuum index gradient induced by a high-intensity pulse (pump). A Sagnac interferometer working with femtosecond laser pulses has been developed for the DeLLight project. Compared to previous prototypes, the interferometer now includes the focusing of the probe beam in the interaction area. In this article, we measure and characterize the critical experimental parameters limiting the sensitivity of the interferometer, namely the extinction factor, the spatial resolution, and the waist at focus of the probe pulse. We discuss future improvements.
Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses

We present a novel combination of dynamic embedded topic models and change-point detection to explore diachronic change of lexical semantic modality in classical and early Christian Latin. We demonstrate several methods for finding and characterizing patterns in the output, and relating them to traditional scholarship in Comparative Literature and Classics. This simple approach to unsupervised models of semantic change can be applied to any suitable corpus, and we conclude with future directions and refinements aiming to allow noisier, less-curated materials to meet that threshold.