Miracle-dz/UniFAR-FTUs huggingface dataset:
The Risk Factors, Incidence and Prognosis of Postpartum Breast Cancer: A Nationwide Study by the SMARTSHIP Group

The term ‘pregnancy-associated breast cancer’ is no longer used as it has been consistently reported that breast cancer during pregnancy and breast cancer after delivery (postpartum breast cancer) have different characteristics and prognosis. The purpose of this study is to define postpartum breast cancer by analyzing the incidence rate, related factors, and prognosis according to the timing of breast cancer. Data from the Korean National Health Insurance Service were used to analyze 1,292,727 women aged 20-49 years who birthed their first child between 2007 and 2012. The annual incidence rate of breast cancer after delivery increased every year (7.7 per 10,000 person-years after 5 years, 19.36 per 10,000 person-years after 10 years). The risk of breast cancer was significantly higher (hazard ratio 1.15, 95% CI 1.05-1.27, P=0.0037) in women diagnosed with gestational diabetes, but that was not associated with overall survival (OS). Patients diagnosed with breast cancer within 5 years of delivery had a poorer prognosis than those diagnosed later (5-year OS, <5 years: 91.1% vs. 5-10 years: 96.0%). In multivariate analysis of OS, the hazard ratio of patients diagnosed within 5 years after delivery was twice as high as of patients diagnosed between 5 and 10 years. Women diagnosed with gestational diabetes had an increased risk of breast cancer. Breast cancer patients diagnosed within 5 years of delivery had a poorer prognosis than those diagnosed later. In this regard, careful screening for early diagnosis of high-risk patients and intensive research on new treatment strategies are needed.
Similarities and Differences in Fish Community Composition Accessed by Electrofishing, Gill Netting, Seining, Trawling, and Water eDNA Metabarcoding in Temperate Reservoirs

It is difficult to understand the composition and diversity of biological communities in complex and heterogeneous environments using traditional sampling methods. Recently, developments in environmental DNA metabarcoding have emerged as a powerful, non-invasive method for comprehensive community characterization and biodiversity monitoring in different types of aquatic ecosystems. In this study, water eDNA targeting fish (wf-eDNA) and four traditional fish sampling methods (electrofishing, gill netting, seining, trawling) were compared to evaluate the reliability and efficiency of wf-eDNA (vertebrate mitochondrial 12S ribosomal RNA (rRNA) as an alternative approach to assess the diversity and composition of freshwater fish communities. The results of wf-eDNA showed a consistency between the traditional sampling methods regarding species detection. However, some fish species detected using wf-eDNA assay were not detected using traditional sampling methods and vice versa. Comparison of wf-eDNA and traditional sampling methods revealed spatial homogeneity in fish community composition in all reservoirs. Ordination analysis showed that the wf-eDNA approach covers all traditional sampling methods and occupies an intermediate position. In addition, based on the Shannon diversity index, we found that in one reservoir the wf-eDNA method yielded similar fish community diversity to traditional sampling methods. However, in other reservoirs, the calculated Shannon diversity index of the wf-eDNA method was significantly higher than traditional sampling methods. In general, significant positive correlations were found between the wf-eDNA method and almost all traditional sampling methods. We conclude that wf-eDNA seems to be a reliable and complementary approach for biomonitoring and ecosystem management of freshwater ichthyofauna.
A System-Wide Investigation and Stratification of the Hemostatic Proteome in Premature Myocardial Infarction

Introduction Advancing understanding of key factors that determine the magnitude of the hemostatic response may facilitate the identification of individuals at risk of generating an occlusive thrombus as a result of an atherothrombotic event such as an acute Myocardial Infarction (MI). While fibrinogen levels are a recognized risk factor for MI, the association of thrombotic risk with other coagulation proteins is inconsistent. This is likely due to the complex balance of pro- and anticoagulant factors in any individual. Methods We compared measured levels of pro- and anticoagulant proteins in plasma from 162 patients who suffered an MI at an early age (MI <50 y) and 186 age- and gender-matched healthy controls with no history of CAD. We then used the measurements from these individuals as inputs for an established mathematical model to investigate how small variations in hemostatic factors affect the overall amplitude of the hemostatic response and to identify differential key drivers of the hemostatic response in male and female patients and controls. Results Plasma from the MI patients contained significantly higher levels of Tissue Factor (P = 0.007), the components of the tenase (FIX and FVIII; P < 0.0001 for both) and the prothrombinase complexes (FX; P = 0.003), and lower levels of Tissue Factor Pathway Inhibitor (TFPI; P = 0.033) than controls. The mathematical model, which generates time-dependent predictions describing the depletion, activation, and interaction of the main procoagulant factors and inhibitors, identified different patterns of hemostatic response between MI patients and controls, and additionally, between males and females. Whereas, in males, TF, FVIII, FIX, and the inhibitor TFPI contribute to the differences seen between case and controls, and in females, FII, FVIII, and FIX had the greatest influence on the generation of thrombin. We additionally show that further donor stratification may be possible according to the predicted donor response to anticoagulant therapy. Conclusions We suggest that modeling could be of value in enhancing our prediction of risk of premature MI, recurrent risk, and therapeutic efficacy.
The Achievement Motive in the Brain: BOLD Responses to Pictures of Challenging Activities Predicted by Implicit Versus Explicit Achievement Motives

The achievement motive refers to a preference for mastering challenges and competing with some standard of excellence. Along with affiliation and power motives, the achievement motive is typically considered to occur on the level of implicit versus explicit representations. Specifically, whereas implicit motives involve pictorial, emotional goal representations and facilitate corresponding action effortlessly, explicit motives involve propositional (“verbalized”) goal representations but need some effort to translate into action (McClelland et al., 1989). We used functional magnetic resonance imaging (fMRI) to investigate whether and to which degree the implicit and explicit achievement motives differentially predict blood-oxygen-level-dependent (BOLD) responses to pictures of individuals engaging in challenging activities. Whereas the implicit AM predicted activity in areas associated with emotion (orbitofrontal cortex) and visual processing (right dorsolateral prefrontal cortex, premotor and occipital cortices), the explicit AM predicted activity in areas associated with cognitive self-control or verbal goal processing (dorsal anterior cingulate cortex, left dorsolateral prefrontal cortex). The findings support the commonly assumed distinction between implicit and explicit motives with neuronal data. They also suggest that explicit motives require cognitive self-control to overcome potential lacks of motivation.
A multiscale experimental characterisation of Dutch unreinforced masonry buildings

In recent years, induced seismicity in the north of the Netherlands significantly increased. As a consequence, the seismic assessment of the built environment, which mainly consists of unreinforced masonry (URM) structures not designed for seismic loads, became of high relevance. Within this context, an extensive multiscale testing program has been performed at the laboratory of Delft University of Technology since 2014 to characterize the behavior of URM buildings from structural down to material level and provide benchmarks for the validation of numerical and analytical models. The paper presents an overview on the experimental campaign, which was structured in three phases: characterization of existing buildings; study of the structural response up to near collapse on replicated specimens; study of light damage state, also on replicated URM walls. The experimental campaign was characterized by a multiscale approach, with tests at structural, component, connection, and material level. At structural level, the campaign comprehended two quasi-static cyclic tests on full-scale two-story high assembled structures and a large number of both in-plane and out-of-plane tests performed either on single piers or on walls with openings. The in-plane stiffness and capacity of as-built and retrofitted timber floors was also assessed. At material level, destructive and slightly-destructive laboratory tests were performed on both existing and replicated masonry and timber specimens. Existing and retrofitting connections between the leaves of cavity walls and between concrete slabs and masonry veneers were studied. To study the initiation and propagation of cracking in URM structures, Digital Image Correlation (DIC) was used during dedicated in-plane tests.
LARQS: An Analogical Reasoning Evaluation Dataset for Legal Word Embedding

Applying natural language processing-related algorithms is currently a popular project in legal applications, for instance, document classification of legal documents, contract review and machine translation. Using the above machine learning algorithms, all need to encode the words in the document in the form of vectors. The word embedding model is a modern distributed word representation approach and the most common unsupervised word encoding method. It facilitates subjecting other algorithms and subsequently performing the downstream tasks of natural language processing vis-à-vis. The most common and practical approach of accuracy evaluation with the word embedding model uses a benchmark set with linguistic rules or the relationship between words to perform analogy reasoning via algebraic calculation. This paper proposes establishing a 1,256 Legal Analogical Reasoning Questions Set (LARQS) from the 2,388 Chinese Codex corpus using five kinds of legal relations, which are then used to evaluate the accuracy of the Chinese word embedding model. Moreover, we discovered that legal relations might be ubiquitous in the word embedding model.
Gender accessibility to credit among entrepreneurs: empirical evidence from women entrepreneurs in Kano Metropolis

This study was carried out to empirically assess women entrepreneurs’ access to credit in Kano metropolis. The study was aimed at analyzing the maximum willingness to accept loans by women entrepreneurs and exploring the major problems hindering women entrepreneurs from fundraising for businesses in Kano Metropolis. The study employed a multi-stage random sampling to select a total of three hundred and seventy-six (376) women entrepreneurs as the sample of the study. The analysis of the data was carried out using frequency count and simple percentages to analyze the demographic characteristics of the respondents. Furthermore, the inferential statistics were done using logit models to capture the specific objectives of the study. The findings of the study revealed that high interest and collateral requirement prevent women entrepreneurs from accessing a loan from financial institutions, while religious affiliation is a major factor affecting women entrepreneurs' access to credit facilities. Based on the findings of the study, the study recommends that financial institutions should introduce and operate an Islamic finance system to encourage women entrepreneurs to go for a loan as the interest rate is one of the most important constraints to women entrepreneurs seeking financial credits. Additionally, collateral demand attached to a loan for women should be removed to encourage women entrepreneurs to raise capital.
An Annotated Corpus of Textual Explanations for Clinical Decision Support

In recent years, machine learning for clinical decision support has gained more and more attention. In order to introduce such applications into clinical practice, a good performance might be essential, however, the aspect of trust should not be underestimated. For the treating physician using such a system and being (legally) responsible for the decision made, it is particularly important to understand the system’s recommendation. To provide insights into a model’s decision, various techniques from the field of explainability (XAI) have been proposed whose output is often enough not targeted to the domain experts that want to use the model. To close this gap, in this work, we explore how explanations could possibly look like in future. To this end, this work presents a dataset of textual explanations in context of decision support. Within a reader study, human physicians estimated the likelihood of possible negative patient outcomes in the near future and justified each decision with a few sentences. Using those sentences, we created a novel corpus, annotated with different semantic layers. Moreover, we provide an analysis of how those explanations are constructed, and how they change depending on physician, on the estimated risk and also in comparison to an automatic clinical decision support system with feature importance.
Leachate Characteristics and Pollution Index Assessment of an Active Dumpsite in Ibadan, Southwestern Nigeria

The study assessed the characteristics of the leachates from an active dumpsite as well as the extent of the leachate contamination using leachate pollution index (LPI). Monthly samples collected from three different leachate locations were subjected to physical and chemical tests using standard methods. Mean values of the determined parameters of the leachates from the three locations were significantly different (p<0.05). High pH, low chloride, BOD, COD and BOD/COD ratio values of the leachate samples suggest the age of the dumpsite, stability, and methanogenic phase. Significant and linear relationship that exists between the leachates characteristics and month of collection of samples indicated the influence of precipitation on the waste decomposition. The LPI (7.33) value of the dumpsite is lower when compared with previous studies. Results of study has thus shown that the dumpsite can pose a significant threat to the environment through percolation of contaminants from leachates into the soil, which in turn can infiltrate into the groundwater. Thus, remedial actions such as rehabilitation of the dumpsite are needed to reduce and control the environmental hazards.
Maintenance Operations of Airfield Pavement Markings

Pavement markings are an essential element in the navigational aids subsystem for any airfield. Most airfields still use waterborne paint as the primary marking material. However, several other materials are in use on roadways which asset managers could incorporate, providing more cost and time effective practices. An airfield experiences a host of maintenance operations which cause degradation of the pavement markings. Of particular concern are rubber removal operations, sweeping operations, and snowplowing operations. This research focuses on chemical rubber removal operations and sweeping operations. This study evaluates waterborne paint and thermoplastic markings to determine if marking materials perform differently from each other, and if maintenance operations cause different degradation rates among the same material. Evaluation criteria include retroreflectance, chromaticity, and coverage. The two materials experience different degradation characteristics under both treatments. Waterborne paint failed retroreflectance and chromaticity measurements after the first chemical rubber removal treatment. Thermoplastic failed chromaticity and coverage measurements after the third chemical rubber removal treatment. Neither material showed any appreciable amount of degradation in any of the three performance measurements when subjected to sweeping
Response of ashwagandha [Withania somnifera (L.) Dunal) tointegrated nutrient management system (INMS)

A field experiment was conducted during the (rainy) kharif season of 2008-09 at Jabalpur to study the effect of INMS on biochemical parameters, alkaloids, yield, nutrient uptake and status of nutrients of ashwagandha. INMS resulted in highest biochemical parameters (chlorophyll ‘a’, chlorophyll ‘b’ and carotenoids), alkaloids (withaferin A, withanoloide A and withanoloide B), yield (seed and root), nutrient uptake (N, P, K, S and Zn) and status of nutrients (N, P, K, S and Zn) were recorded with the application of 50% NPK/ha+5 t FYM+PSB+Azotobacter+5 kg Zn/ha which was at par with 10 t FYM/ha +PSB+Azotobacter 3 kg/ha+5 kg Zn/ha followed by 50% NPK/ha+5 t FYM+PSB +Azotobacter.
Neonatal Intensive Care Unit Network Neurobehavioral Scale Pro ﬁ les in Full-Term Infants: Associations with Maternal Adversity, Medical Risk, and Neonatal Outcomes

, Objectives To examine healthy, full-term neonatal behavior using the Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS) in relation to measures of maternal adversity, maternal medical risk, and infant brain volumes. Study design This was a prospective, longitudinal, observational cohort study of pregnant mothers followed from the ﬁrst trimester and their healthy, full-term infants. Infants underwent an NNNS assessment and high-quality magnetic resonance imaging 2-5 weeks after birth. A latent proﬁle analysis of NNNS scores categorized infants into neurobehavioral proﬁles. Univariate and multivariate analyses compared differences in maternal factors (social advantage, psychosocial stress, and medical risk) and neonatal characteristics between proﬁles. Results The latent proﬁle analysis of NNNS summary scales of 296 infants generated 3 proﬁles: regulated (46.6%), hypotonic (16.6%), and fussy (36.8%). Infants with a hypotonic proﬁle were more likely to be male ( c 2 = 8.601; P = .014). Fussy infants had smaller head circumferences (F = 3.871; P = .022) and smaller total brain (F = 3.522; P = .031) and cerebral white matter (F = 3.986; P = .020) volumes compared with infants with a hypotonic proﬁle. There were no differences between proﬁles in prenatal maternal health, social advantage, or psychosocial stress. Conclusions Three distinct neurobehavioral proﬁles were identiﬁed in healthy, full-term infants with hypotonic and fussy neurobehavioral features related to neonatal brain volumes and head circumference, but not prenatal exposure to socioeconomic or psychosocial adversity. Follow-up beyond the neonatal period will determine if identiﬁed proﬁles at birth are associated with subsequent clinical or developmental outcomes. (J Pediatr 2022; - :1-9) postulates
Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding

Pretrained models through self-supervised learning have been recently introduced for both acoustic and language modeling. Applied to spoken language understanding tasks, these models have shown their great potential by improving the state-of-the-art performances on challenging benchmark datasets. In this paper, we present an error analysis reached by the use of such models on the French MEDIA benchmark dataset, known as being one of the most challenging benchmarks for the slot filling task among all the benchmarks accessible to the entire research community. One year ago, the state-of-art system reached a Concept Error Rate (CER) of 13.6% through the use of a end-to-end neural architecture. Some months later, a cascade approach based on the sequential use of a fine-tuned wav2vec2.0 model and a fine-tuned BERT model reaches a CER of 11.2%. This significant improvement raises questions about the type of errors that remain difficult to treat, but also about those that have been corrected using these models pre-trained through self-supervision learning on a large amount of data. This study brings some answers in order to better understand the limits of such models and open new perspectives to continue improving the performance.
MemoSen: A Multimodal Dataset for Sentiment Analysis of Memes

Posting and sharing memes have become a powerful expedient of expressing opinions on social media in recent days. Analysis of sentiment from memes has gained much attention to researchers due to its substantial implications in various domains like finance and politics. Past studies on sentiment analysis of memes have primarily been conducted in English, where low-resource languages gain little or no attention. However, due to the proliferation of social media usage in recent years, sentiment analysis of memes is also a crucial research issue in low resource languages. The scarcity of benchmark datasets is a significant barrier to performing multimodal sentiment analysis research in resource-constrained languages like Bengali. This paper presents a novel multimodal dataset (named MemoSen) for Bengali containing 4417 memes with three annotated labels positive, negative, and neutral. A detailed annotation guideline is provided to facilitate further resource development in this domain. Additionally, a set of experiments are carried out on MemoSen by constructing twelve unimodal (i.e., visual, textual) and ten multimodal (image+text) models. The evaluation exhibits that the integration of multimodal information significantly improves (about 1.2%) the meme sentiment classification compared to the unimodal counterparts and thus elucidate the novel aspects of multimodality.
Unsupervised Embeddings with Graph Auto-Encoders for Multi-domain and Multilingual Hate Speech Detection

Hate speech detection is a prominent and challenging task, since hate messages are often expressed in subtle ways and with characteristics that may vary depending on the author. Hence, many models suffer from the generalization problem. However, retrieving and monitoring hateful content on social media is a current necessity. In this paper, we propose an unsupervised approach using Graph Auto-Encoders (GAE), which allows us to avoid using labeled data when training the representation of the texts. Specifically, we represent texts as nodes of a graph, and use a transformer layer together with a convolutional layer to encode these nodes in a low-dimensional space. As a result, we obtain embeddings that can be decoded into a reconstruction of the original network. Our main idea is to learn a model with a set of texts without supervision, in order to generate embeddings for the nodes: nodes with the same label should be close in the embedding space, which, in turn, should allow us to distinguish among classes. We employ this strategy to detect hate speech in multi-domain and multilingual sets of texts, where our method shows competitive results on small datasets.
A Multi-Party Dialogue Ressource in French

We presentDialogues in Games(DinG), a corpus of manual transcriptions of real-life, oral, spontaneous multi-party dialogues between French-speaking players of the board game Catan. Our objective is to make available a quality resource for French, composed of long dialogues, to facilitate their study in the style of (Asher et al., 2016). In a general dialogue setting, participants share personal information, which makes it impossible to disseminate the resource freely and openly. In DinG, the attention of the participants is focused on the game, which prevents them from talking about themselves. In addition, we are conducting a study on the nature of the questions in dialogue, through annotation (Cruz Blandon et al., 2019), in order to develop more natural automatic dialogue systems