Miracle-dz/UniFAR-FTUs huggingface dataset:
Climatic drivers of seasonal dynamics for Respiratory Syncytial Virus (RSV) in Antananarivo, Madagascar, 2011-2021

Introduction Respiratory Syncytial Virus (RSV) is a primary source of acute lower respiratory tract infection (ALRTI), the leading cause of death in children under five. Over 99% of RSV attributed deaths occur in low -income countries, including Madagascar. RSV transmission is linked to climate, driving highly seasonal dynamics. Methods We used generalized additive models (GAMs) to identify correlates of reported RSV infections in Antananarivo, Madagascar from January 2011 -December 2021, then fit catalytic models to cumulative age -structured incidence to estimate age -specific force of infection (FOI). We fit a time series Susceptible Infected Recovered (TSIR) model to the dataset to estimate weekly RSV transmission, then evaluated associations with precipitation, humidity, and temperature using generalized linear models. We used GAMs to quantify interannual trends in climate and assess whether significant deviations in RSV burden occurred in years representing climatic anomalies. Results Reported RSV infections in Antananarivo were significantly associated with patient ages [≤]2 years. Highest FOI was estimated in patients [≤]1 year, with transmission declining to near-zero by age five before rising in older (60+) cohorts. TSIR models estimated a January-February peak in RSV transmission, which was strongly positively associated with precipitation and more weakly with temperature but negatively related to relative humidity. Precipitation, humidity, and temperature all increased across the study period in Antananarivo, while reported RSV infections remained stable. Significant deviations in RSV burden were not associated with clear climate anomalies. Conclusions Stable rates of reported RSV infections in Antananarivo across the past decade may reflect contrasting impacts of elevated precipitation and increased humidity on transmission. If future climate changes yield more rapidly accelerating precipitation than humidity, this could accelerate RSV burden. Introduction of recently-developed public health interventions to combat RSV in low income settings like Madagascar is essential to mitigating burden of disease (RSV), in particular any future climate-driven increases in transmission or severity.
Endocrine disruption in teleosts and amphibians is mediated by anthropogenic and natural environmental factors: implications for risk assessment

Environmental variation in the Anthropocene involves several factors that interfere with endocrine systems of wildlife and humans, presenting a planetary boundary of still unknown dimensions. Here, we focus on chemical compounds and other impacts of anthropogenic and natural origins that are adversely affecting reproduction and development. The main sink of these endocrine disruptors (EDs) is surface waters, where they mostly endanger aquatic vertebrates, like teleost fish and amphibians. For regulatory purposes, EDs are categorized into EATS modalities (oestrogenic, androgenic, thyroidal, steroidogenesis), only addressing endocrine systems being assessable by validated tests. However, there is evidence that non-EATS modalities—and even natural sources, such as decomposition products of plants or parasitic infections—can affect vertebrate endocrine systems. Recently, the disturbance of natural circadian light rhythms by artificial light at night (ALAN) has been identified as another ED. Reviewing the knowledge about EDs affecting teleosts and amphibians leads to implications for risk assessment. The generally accepted WHO-definition for EDs, which focuses exclusively on ‘exogenous substances' and neglects parasitic infections or ALAN, seems to require some adaptation. Natural EDs have been involved in coevolutionary processes for ages without resulting in a general loss of biodiversity. Therefore, to address the ‘One Health’-principle, future research and regulatory efforts should focus on minimizing anthropogenic factors for endocrine disruption. This article is part of the theme issue ‘Endocrine responses to environmental variation: conceptual approaches and recent developments’.
Thyroid fine-needle aspiration cytology: malignancy rate in the category of indeterminate significant atypia/indeterminate significant follicular lesion

BACKGROUND: Fine needle aspiration cytology (FNAC) is a standard preoperative diagnostic modality for thyroid nodules. The Bethesda Thyroid Cytopathology Reporting System (TBSRTC) defines the FNAC atypia group as atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS). OBJECTIVES: Determine the risk of malignancy after surgical resection in patients with AUS/FLUS. DESIGN: Retrospective SETTING: Pathology department of a tertiary care center PATIENTS AND METHODS: All thyroid FNACs between 2015 and 2023 that were diagnosed as AUS/FLUS in Turkey. Patient demographics, preoperative ultrasonographic features, and follow-up data were collected. MAIN OUTCOME MEASURES: Relationship between AUS/FLUS diagnosis and final histopathological diagnosis. SAMPLE SIZE: 562 RESULTS: In total, 562 thyroid nodules were diagnosed as AUS/FLUS, and 267 (47.5%) were surgically excised. A malignant histopathological diagnosis was given in 28 cases (10.4%). Malignancy risk sensitivity of AUS/FLUS diagnosis was 75.68% (95% CI=58.80–88.23%), specificity was 55.24% (95% CI=50.91–59.52%), positive predictive value was 10.49% (95% CI=8.71–12.58%), and negative predictive value was 97.04% (95% CI=94.86–98.31%). In the ultrasonographic data, having symptomatic nodules, nodule calcification, and irregular nodule borders were all statistically significant signs of cancer in a one-variable analysis (P<.01). The presence of a family history emerged as a statistically significant prognostic marker for malignancy (P=.012). Although not statistically significant, the malignancy rate for nodules with nuclear atypia was 11.9%, significantly higher than the rate of 8.3% for nodules with architectural atypia only (P=0.32). CONCLUSIONS: The diagnosis of AUS/FLUS has a high rate of predicting the risk of malignancy and should continue to be offered. In addition to cytopathological features, ultrasound data and family history should be taken into consideration when evaluating the case. LIMITATIONS: Retrospective design and no molecular studies.
Characteristics of Community Structure of Zooplankter in Autumn at Estuary of Dadu River

In September 2010,the investigation on community structure of zooplankter was carried out in the main stem and left branch of Dadu River,Emei River,and Qingyi River.The study found out 165 categories of 92 genera of zooplankter.The average density and average biomass of the zooplankter were 376.19 ind. /L and 0.0604 mg /L respectively.In terms of the density,biomass and characteristics of community structure in different water areas,the results showed that Emei River Qingyi River left branch of Dadu River main stem of Dadu River and there was a low similarity among the categories of zooplankter in different water areas.The cluster analysis indicated that 31 sampling stations of the Dadu River Estuary were grouped into three categories,including the main stem and left branch river of Dadu River,Qingyi River,Emei River.The relevance between the water environment and characteristics of community structure of zooplankter was analyzed,which concluded that temperature was an important influencing factor.Zooplankter,playing a critical role in the material circulation and energy flow in the water ecological environment,is an important component of aquatic organism in rivers and a vital assessing indicator for river ecological restoration.
Water storage efficiency in no-till dryland cropping systems

ABSTRACT: Wheat-fallow (W-F) is the predominant cropping system in the Great Plains, but the percent of precipitation stored as soil water (WSE) during fallow is frequently less than 25% with conventional tillage. No-till technology has improved potential WSE. Our objectives were to determine the effects of cropping system, landscape position(soil), and evaporative gradient(location) on WSE during inter-crop periods in intensified no-till cropping systems. Water storage efficiency was 48% during the wheat to corn fallow period in the 3- or 4- year rotational systems, contrasting sharply with the 22% WSE for the W-F system. The 3-year system, with a shorter fallow period (11 months), was just as effective in storing water as the long fallow period (14 months) in the WF system. Water storage efficiency was the lowest at the southern location, which had the highest potential evapotranspiration, but the contrasts among cropping systems remained. Toeslope soils had the highest WSE compared to summit or sidelslope positions because of their opportunity to catch runoff water. The possibility exists for using even more intensive cropping systems than those examined in this study, and this may mean that summer fallow could be eliminated with no-till practices.
Estimates of genetic parameters for rumination time, feed efficiency, and methane production traits in first lactation Holstein cows.

The large-scale recording of traits such as feed efficiency and methane emissions for use in genetic improvement programs is complex, costly, and time-consuming. Therefore, heritable traits that can be continuously recorded in dairy herds and are correlated to feed efficiency and methane emission traits could provide useful information for genetic evaluation. Rumination time has been suggested to be associated with feed efficiency, methane production (methane emission in g/day), and production traits at the phenotypic level. Therefore, the objective of this study was to investigate the genetic relationships among rumination time, feed efficiency, methane and production traits using 7,358 records from 656 first lactation Holstein cows. The estimated heritabilities were moderate for rumination time (0.45 ± 0.14), methane production (0.36 ± 0.12), milk yield (0.40 ± 0.08), fat yield (0.29 ± 0.06), protein yield (0.32 ± 0.07), and energy corrected milk (0.28 ± 0.07), while low and non-significant for feed efficiency (0.15 ± 0.07), which was defined as the residual of the multiple linear regression of DMI on ECM and MBW. A favorable negative genetic correlation was estimated between rumination time and methane production (-0.53 ± 0.24), while a positive favorable correlation was estimated between rumination time and energy corrected milk (0.49 ± 0.11). The estimated genetic correlation of rumination time with feed efficiency (-0.01 ± 0.17) was not significantly different from zero but showed a trend of a low correlation with dry matter intake (0.21 ± 0.13, P = 0.11). These results indicate that rumination time is genetically associated with methane production and milk production traits, but high standard errors indicate that further analyses should be conducted to verify these findings when more data for rumination time, methane production and feed efficiency become available.
Vocabulary Replacement in SentencePiece for Domain Adaptation

Large language models (LLMs) show poor performance due to excessive subword segmentation. This problem is particularly evident when the vocabularies of the pretrained domain differ from the domain of the target task. Traditionally, researchers extend the domain-speciﬁc vocabulary of the target task by increasing the LLMs’ vocabulary size. This approach is problematic because the number of parameters in the LLMs increases signiﬁ-cantly. In contrast, we propose a new method that does not change the vocabulary size, instead replacing it with a domain-speciﬁc vocabulary. The vocabulary replacement method is proposed as follows. First, we select words that consist of multiple subwords in the vocabulary model. Next, we replace the less frequently used words with ones from the domain-speciﬁc vocabulary. Finally, we up-date the vocabulary model. In this study, we conducted our experiments on the Sentence-Piece vocabulary model. The vocabulary replacement on the Japanese T5 and multilingual T5 shows that our proposed method produces a space of 6 k ∼ 21 k words that can be replaced. In addition, we also compare the performance of the LLMs with Python code generation as the target task. The results showed almost no performance degradation on the Japanese General Language Understanding Evaluation tasks and improved performance of the code generation tasks.
Multi-Task Learning for Emotion Recognition in Conversation with Emotion Shift

As human-computer interaction continues to evolve, the importance of emotion recognition in conversation is becoming more apparent. For applications like chatbots to provide more human-like responses, it is essential for machines to understand the emotions embedded in conversations. Although most of the recent research has focused on extracting contextual information from conversations, the subtleties of emotion shifts (ES) within local conversations are often overlooked. However, acquiring knowledge about ES between interactions can reduce the rate of emotion recognition errors in dialogues with fluctuating emotions. As a solution for ES detection, we define ES be-tween the same speaker as well as between different speakers using Emotion Recognition in Conversation (ERC) datasets. We propose a novel multi-task learning model, called Mtl-ERC-ES, which identifies three tasks simultaneously: Emotion Recognition in Conver-sation (ERC), Emotion Shift (ES), and Sentiment Classification (SC). Our approach provides high-quality performance on the ERC task, consistently ranking among the top per-formers across multiple datasets. Our approach also demonstrates the effectiveness of custom ES tasks.
Paper Recommendation Using Citation Contexts in Scholarly Documents

This paper proposes a citation recommendation method that considers and utilizes citation contexts in scholarly papers. Citation contexts describe the purpose of the citation and how the cited paper is related to the citing paper. Citation contexts allow citation recommendation systems to select relevant papers based on the relationships between other existing papers. In the proposed method, the candidate selection model ﬁrst projects the input paper and existing papers in the database to a vector space using the words appearing in the title and abstract. Then, a recommendation decision model estimates the probability that each candidate is a suitable citation for the input paper using a feed-forward network. Experiments conducted on scholarly papers in the medical domain demonstrate the beneﬁts of using citation contexts for the citation recommendation task.
Machine Reading Comprehension for Vietnamese Customer Reviews: Task, Corpus and Baseline Models

Customers spend much time researching product information before making a purchase. This problem can be partially addressed through Machine Reading Comprehension (MRC) on customer reviews. Nonetheless, to implement MRC effectively, benchmark corpora specific to the review domain in Vietnamese are lacking. Therefore, we proposed ViRe4MRC , the first benchmark corpus for evaluating review-based machine reading comprehension on customer reviews in Vietnamese. This corpus comprises 6,603 human-generated question-answer pairs from 2,174 customer reviews on smartphone and restaurant domains. We also evaluate the experimental results of monolingual language models: ViBERT, PhoBERT, and vELECTRA; multilingual language models: mBERT and XLM-RoBERTa (XLM-R). As a result, the XLM-R Large model, as the best model, achieved 44.25% Exact Match (EM) and 78.13% F1. Our corpus 1 is available for research purposes.
Automated Generation of Multiple-Choice Cloze Questions for Assessing English Vocabulary Using GPT-turbo 3.5

A common way of assessing language learners’ mastery of vocabulary is via multiple-choice cloze (i.e., fill-in-the-blank) questions. But the creation of test items can be laborious for individual teachers or in large-scale language programs. In this paper, we evaluate a new method for automatically generating these types of questions using large language models (LLM). The VocaTT (vocabulary teaching and training) engine is written in Python and comprises three basic steps: pre-processing target word lists, generating sentences and candidate word options using GPT, and finally selecting suitable word options. To test the efficiency of this system, 60 questions were generated targeting academic words. The generated items were reviewed by expert reviewers who judged the well-formedness of the sentences and word options, adding comments to items judged not well-formed. Results showed a 75% rate of well-formedness for sentences and 66.85% rate for suitable word options. This is a marked improvement over the generator used earlier in our research which did not take advantage of GPT’s capabilities. Post-hoc qualitative analysis reveals several points for improvement in future work including cross-referencing part-of-speech tagging, better sentence validation, and improving GPT prompts.
Image Caption Synthesis for Low Resource Assamese Language using Bi-LSTM with Bilinear Attention

The task of Automatic Image Caption-ing (AIC) involves the synthesis of semantically and syntactically correct nat-ural language descriptions for an input image. Most existing works on AIC have focused on caption generation in the English language. In contrast, very few efforts have been made to synthe-size captions in Indian languages, especially the low-resource ones. The first contribution of this paper is the creation of two image caption datasets for As-samese language. This includes Flickr30K-Assamese Caption (Flickr30K-AC) and COCO-Assamese Caption (COCO-AC) dataset. These datasets are created by translating Flickr30K and MSCOCO English captions to Assamese. The semantic and syntactic errors in translated captions are further corrected manually. The manual correction was performed to preserve the linguistic characteristics of Assamese language for training caption generators. Second, a Bi-directional LSTM (Bi-LSTM) based model with bilinear attention is proposed for generating the Assamese cap-tions. The model performance is evaluated through qualitative and quantitative measures (BLEU-n and CIDEr scores) and benchmarked against three baseline models.
Estimating Lexical Complexity in Multi-Domain Settings for the Russian Language

During the last few years, the tasks of Complex Word Identification (CWI) and Lexical Complexity Prediction (LCP) have received growing attention from researchers who studied the quality of complexity estimation with modern Transformer-based models in different setups and for various languages: English, German, French, Spanish, Chinese, Japanese, Swedish and Russian. The crucial step in creating a large and robust modern language model for any language is a collection of representative data. Supporting the trend of providing the researchers with the ability to study the task of LCP for various languages and aiming to increase the representativity for the Russian language, we extend the number of available domains with two new ones: biomedical and sociopolitical. The data was collected and annotated in accordance with the original methodology, originally described for the CompLex dataset. We conduct a comparative study on data from both domains and perform a cross-domain study. We present the results of experiments with the RuBERT-based model in mono-domain and cross-domain settings for both domains. The results of our work are two novel corpora consisting of 756 distinct words for the biomedical domain with 3050 contexts and 669 distinct words for the sociopolitical domain with 3025 contexts.
Explorative study on verbalizing students’ skills with NLP/AI-tool in Digital Living Lab at Laurea UAS, Finland

This explorative study tested Laurea UAS students’ (N=16) abilities to verbalize their skills, before and after the study unit “Digital Analytics and Consumer Insights”. Before the study unit the students listed their skills unaided and afterwards with help of Careerbot AI -service. The findings indicate that the intervention increased both quantity and quality of the skills verbalized, relevant to the learning objectives and generic, 21st century skills.
Automatic Identification of Assistance Needs in Disaster Situations Using Hybrid Word Embedding Techniques

Social media platforms have become increasingly important channels for affected people to request urgent assistance during disaster situations. However, given the huge amount of data shared on these platforms, it has proved challenging to identify the specific types of assistance needed. The reason behind this challenge lies in the complexity of the task of designing and representing features from existing data. Herein, we propose to combine two types of feature extraction techniques, namely syntactic and semantic, using TF-IDF, Glove, and Fast-Text, in order to extract highly discriminating features from tweets relating to requests for assistance. The purpose of this study is to evaluate the effectiveness of different feature extraction techniques and their combination when applied to various classifiers. We will perform this evaluation using the Purohit dataset, which consists of Hurricane Sandy tweets classified into six different classes. Experimental results show that the TF-IDF combined with FasTtext, fed into an SVM classifier outperformed the other models in terms of accuracy (86.11%) and F1-score (84.38%). According to this study, combining feature extraction techniques can improve machine learning performance.
Benchmarks for Atlantic Ocean Circulation 1

Oceanographers frequently decompose the total oceanic circulation into two components: a wind-driven component existing primarily in the horizontal plane and a thermohaline-driven component existing primarily in the vertical plane (also called the meridional overturning circulation, MOC). Although this decomposition is a simplification of the dynamics of the motion in the ocean (the two components are not separable in the complete equations of motion), it provides a framework for describing responses of the ocean to different surface forcing functions. Numerical modelling, paleoclimate and observational studies indicate that both the wind-driven and thermohaline circulation can play an important role in longer-term (greater than decadal) climate variability. The U.S. National Oceanic and Atmospheric Administration addresses both components to satisfy its missions of detecting, attributing and forecasting long-term climate change. We contribute to NOAA’s mission by developing and providing observational benchmarks (i.e., indices) for various components of the wind-driven circulation (hereinafter WDC) and MOC in the Atlantic