Miracle-dz/UniFAR-FTUs huggingface dataset:
Preeclampsia Associated Differences in the Placenta, Fetal Brain, and Maternal Heart Can Be Demonstrated Antenatally: An Observational Cohort Study Using MRI

BACKGROUND: Preeclampsia is a multiorgan disease of pregnancy that has short- and long-term implications for the woman and fetus, whose immediate impact is poorly understood. We present a novel multiorgan approach to magnetic resonance imaging (MRI) investigation of preeclampsia, with the acquisition of maternal cardiac, placental, and fetal brain anatomic and functional imaging. METHODS: An observational study was performed recruiting 3 groups of pregnant women: those with preeclampsia, chronic hypertension, or no medical complications. All women underwent a cardiac MRI, and pregnant women underwent a placental-fetal MRI. Cardiac analysis for structural, morphological, and flow data were undertaken; placenta and fetal brain volumetric and T2* (which describes relative tissue oxygenation) data were obtained. All results were corrected for gestational age. A nonpregnant cohort was identified for inclusion in the statistical shape analysis. RESULTS: Seventy-eight MRIs were obtained during pregnancy. Cardiac MRI analysis demonstrated higher left ventricular mass in preeclampsia with 3-dimensional modeling revealing additional specific characteristics of eccentricity and outflow track remodeling. Pregnancies affected by preeclampsia demonstrated lower placental and fetal brain T2*. Within the preeclampsia group, 23% placental T2* results were consistent with controls, these were the only cases with normal placental histopathology. Fetal brain T2* results were consistent with normal controls in 31% of cases. CONCLUSIONS: We present the first holistic assessment of the immediate implications of preeclampsia on maternal heart, placenta, and fetal brain. As well as having potential clinical implications for the risk stratification and management of women with preeclampsia, this gives an insight into the disease mechanism.
The effect of hydrological and hydrochemical parameters on the microdistribution of aquatic fauna in drip water in the Velika Pasica Cave, Central Slovenia

Water quality and quantity significantly affects the aquatic fauna in the epikarst and conversely can also reflect the hydrological environment. Intensive, long‐term studies on hydrological and hydrochemical parameters were monitored at 4 permanent dripping sites (VP1–VP4) in the Velika Pasica Cave (Slovenian) over a period from 2006 to 2013 concurrently with aquatic fauna sampling. Multivariate methods were applied to elucidate the relationship between the environmental conditions and the resident aquatic ecosystem. Seven major aquatic taxa were collected from the drips, with Copepoda being the dominant taxa; however, the spatial distribution and composition of these species varied distinctively at the 4 study sites; the drip water from different sites had distinct hydrological and hydrochemical characteristics. The most significant parameters influencing community composition were drip water discharge volume and NO3− ion content. The results indicate that hydrology and hydrochemistry significantly influence the microdistribution of aquatic fauna within the epikarstic aquifer.
Person re-identification based on multi-branch visual transformer and self-distillation

Person re-identification technology has made significant progress in recent years with the development of deep learning. However, the recognition rate of models in this field is still lower than that of face recognition, which is challenging to implement in practical application scenarios. Therefore, improving the recognition rate of the pedestrian re-identification model is still a critical task. This paper mainly focuses on three aspects of this problem. The first is to use the characteristics of the multi-branch network structure of person re-identification to dig out the most effective online self-distillation scheme between branches without increasing additional resource requirements, making full use of the information contained in each branch. Secondly, this paper analyzes and verifies the pros and cons of knowledge distillation based on mean squared error (MSE) loss function and Kullback-Leibler (KL) divergence from theoretical and experimental perspectives. Finally, we verified through experiments that adding a specific value of noise perturbation to the model weights can further improve the recognition rate of the model. After several improvements in these areas, we obtained the current state-of-the-art performance on four public datasets for person re-identification.
Can coseismic stress variability suppress seismicity shadows? Insights from a rate-and-state friction model

[1] The paucity of detectable seismicity shadows in the days/months following a main shock has raised the question as to whether dynamic rather than static triggering could be the main mechanism driving the seismicity at these timescales. The lack of correlation between the stress unloading of the main fault and the typically vigourous aftershock production taking place on it, however, suggests that the spatial heterogeneity of the coseismic stress change could also be a factor in the apparent suppression of seismicity shadows, at least on the main fault. Here we study whether this stress variability can indeed be an important aspect in the stress modeling of earthquake sequences. A rate-and-state friction model of seismicity is used, in conjunction with realistic levels of stress heterogeneity at the earthquake nucleation scale (1–10 m, as predicted from these friction laws), based on scale-invariant models of coseismic slip distribution. In this model, a relatively weak on-fault quiescence is delayed by months to years because of the high stress heterogeneity. Delayed quiescences due to slip heterogeneity are similarly predicted at distances of up to about half the rupture length away from the fault. We also postulate that off-fault seismicity can be significantly controlled by stress variability that originates from small-scale crustal heterogeneity and the complexity in fault geometries. Rather than mapping stress changes by providing a single stress value at every grid point, these results suggest that an estimate of the likely stress variability (acting at the nucleation scale) is also needed, especially when trying to account for the seismicity occurring at relatively short timescales.
The Cross-linguistic Variations in Dependency Distance Minimization and its Potential Explanations

Dependency distance minimization (DDM) is a preference for short syntactic dependencies observed in natural languages. While DDM has been mainly studied as a language universal, languages also seem to exhibit considerable differences in the extent of DDM. The current study adopts quantitative methods and dependency treebanks of four distinct languages to investigate the cross-linguistic DDM variations and its possible explanations. It was found that: (i) the cross-linguistic DDM variations can be captured by a parameter in the fitting functions of the dependency distance distributions; (ii) there seems to be a trade-off relation between the syntactic complexity and morphological difficulty of a language. This attempt offers new insights into the study of DDM and points out the necessity of studying language as a multi-layer complex adaptive system.
An empirical, corpus-based, approach to Cantonese nominal expressions

This work focuses on the distribution and interpretation of Hong Kong Cantonese nominal expressions with the help of empirical, corpus-based, methods. We present the creation of a dataset containing all nominal expressions appearing in a corpus of naturally occurring Hong Kong conversations, along with annotations of inherent and contextual features of these expressions. We then compare the observed distribution of the different types of expressions with predictions made by existing theoretical analyses, and conclude that these predictions are largely supported by our data. We then focus on environments in which theory predicts that several types of expression are licensed, and thus in potential competition. For those cases, we find that inherent properties of the nominal head, in particular its length, play a significant role in determining which type of expression is used. We hypothesize that this constraint is related to the frequency of the head noun and to general linguistic principles that relate markedness and frequency in natural language production and interpretation.
The WEAVE 2.0 Corpus: Role Labelled Synthetic Chemical Procedures from Patents with Chemical Named Entities

Discovering new reaction pathways lies at the heart of drug discovery and chemical experimentation. A huge amount of drug reaction data lies in unannotated patent texts which are not machine readable. Reaction roles play an important role in analysing chemical pathways, and tracing chemicals through them, and while there is a vast body of chemical data available, the unavailability of reaction role annotated data is a blocker to effectively deploy deep learning methods for reaction discovery. This paper introduces a new dataset, WEAVE 2.0, obtained from chemical patents, along with full, manual, annotations of novel chemical reactions with reaction role information. We also provide baseline and state of the art models for chemical entity recognition from our raw dataset. Our dataset and associated models form the foundation of neural understanding of chemical reaction pathways via reaction roles.
Fly, fly little Comet! Exploring Subtoken-Level Metaphorical Patterns in Finnish and Hungarian Texts. New Results from the FiHuComet Corpus.

The FiHuComet Corpus was created to address the gap in the lack of a systematic comparison of metaphor research in Finnish and Hungarian (Bajzát and Simon, 2023). This study aims to: (i) expand the existing quasi-parallel corpus; (ii) explore subtoken-level metaphorical patterns comparatively in the examined languages with rich morphology. The analysis employs a MIPVU-inspired protocol for metaphor identification, the MetaID protocol (Simon et al., 2023). Although this endeavor is not new, the comparative study conducted on a small-scale corpus has only revealed a few aspects of the potential of comparative metaphor analysis in the context of Finno-Ugric languages selected.
Assamese Back Transliteration - An Empirical Study Over Canonical and Non-canonical Datasets

This study evaluates the performance of transformer-based state-of-the-art machine transliteration systems in processing noisy transliterated texts (non-canonical form) from social media in the context of the Assamese language. Fifteen different setups, using both canonical (manually forward transliterated) and non-canonical (manually back transliterated) datasets, are examined to identify suitable combinations for handling both the types of data. The ﬁndings indicate that existing transliteration models trained on manually transliterated Romanized datasets fail to handle highly ambiguous and noisy characteristics of social media data, resulting in degraded performance. This study systematically investigates how to combine noisy social media and manually transliterated clean datasets to build an effective transliteration model suitable for handling both clean and noisy text. To the best of our knowledge, this paper represents the pioneering effort in the domain of social media for the Assamese language machine transliteration.
Contrasting the Chinese bei-passive and the English be-passive through dependency distance

Many previous works have employed dependency distance to measure the syntactic difficulty of various languages. However, little is known concerning the properties of dependency distance within certain constructions across languages, such as the passive. The current study intends to contrast the Chinese bei -passive and the English be-passive using different measures of dependency distance against self-built treebanks. Results show that: (1) In terms of the dependency distance pointing from the predicate verb, the bei - passive exhibits a longer distance compared with the be-passive, consistent with general properties. (2) Regarding the dependency distance pointing from the head noun, both passives show a similar distance, following general properties. (3) With respect to the dependency distance between a head noun and the predicate verb, the bei -passive displays a shorter distance than the be - passive, contrary to general properties. Moreover, these findings suggest that the general properties of dependency distance in languages like Chinese and English may fail to fully explain those in certain language constructions, such as the passive.
UPOS-DEPREL Mismatches: Detecting Annotation Errors and Improving UD Guidelines based on Linguistic Knowledge

The current study explores the power of the UPOS-DEPREL combination in UD. By examining its distribution in 20 PUD treebanks, we found that most parts of speech have a matching dependency relation, which is supported by linguistic rationales. Based on the matches and minor combinations denoting the phenomenon of transcategorization, we set up a Possible Combination Zone. Those falling out of the zone are then considered errors or inconsistencies in annotation, or underdescribed special constructions in UD. Our findings thus provide a new way to detect annotation errors based on linguistic knowledge. In addition, we discuss two case studies on improving the current UD guideline enlightened by the combination rules.
The Association of Second Language Proficiency with Nonverbal Behaviors

This study investigates the association between nonverbal behaviors and second language proficiency. Proficient learners exhibit different nonverbal and verbal behaviors, such as gestures, speech rates, and vocabulary use. These distinctive properties can help analyze the language-learning process and develop evaluation tools for assessing proficiency levels. In this study, we compared the motion recognition results of learners’ nonverbal behaviors using Principal Component Analysis. The results suggest that motion recognition outcomes can depict the distinctive properties of nonverbal behaviors. This research suggests that future studies should employ motion recognition for further research in this field.
Study on the Domain Adaption of Korean Speech Act using Daily Conversation Dataset and Petition Corpus

In Korean, quantitative speech act studies have usually been conducted on single utterances with unspecified sources. In this study, we annotate sentences from the National Institute of Korean Language’s Messenger Corpus and the National Petition Corpus, as well as example sentences from an academic paper on contemporary Korean vlogging, and check the discrepancy between human annotation and model prediction. In particular, for sentences with differences in locutionary and illocutionary forces, we analyze the causes of errors to see if stylistic features used in a particular domain affect the correct inference of speech act. Through this, we see the necessity to build and analyze a balanced corpus in various text domains, taking into account cases with different usage roles, e.g., messenger conversations belonging to private conversations and petition corpus/vlogging script that have an unspecified audience.
Lyrics Generation Applying Metaphor Generation

The development of deep-learning technology has improved the quality of several tasks in the field of natural language processing. Examples of these tasks include response, dialogue, and text-generation systems. However, some contributions can be made to the literature. In particular, there are challenges in text generation in the music genre. In this study, we pro-pose a lyrics generation system that considers topics and includes metaphors. First, we analyzed the features of a corpus of Japanese lyrics based on the number of moras, Latent Dirichlet Allocation topic estimation, and semantic textual similarity using sentence-BERT(S-BERT). We thereafter propose a system based on the results of the analysis. In this system, succeeding lyrics are generated as input to the preceding lyrics. A figurative verb was generated as a noun in the extracted template lyrics. The template verb was replaced with a figurative verb and the replaced template was complemented by a pretrained BERT mask language model fine-tuned with the lyrics corpus to generate lyrics. The system outputs lyrics with metaphors that reflect the characteristics of the actual lyrics and their coherence with topics in the input lyrics.
Effectiveness of ChatGPT in Korean Grammatical Error Correction

As the first study in applying ChatGPT to L2 Korean grammar correction and learning, this study investigates the effectiveness of ChatGPT models as tools for Korean Grammatical Error Correction (GEC). The model comparison revealed that ChatGPT 4 outperforms ChatGPT 3.5 and the baseline model in all measures related to the precision of error detection and correction. Furthermore, a human evaluation shows that ChatGPT 4 outperforms its previous version in handling multiple grammatical errors and correcting semantic-level mistakes. The findings of the present study suggest that ChatGPT 4 as a self-learning tool would be more suitable for advanced to near-native level L2 learners of Korean to improve their semantic fluency of sentences with minimal errors than for less proficient L2 learners because the latest version of ChatGPT 4 still demonstrates a relatively lower accuracy rate in Korean GEC tasks.
Tbx1 haploinsufficiency causes brain metabolic and behavioral anomalies in adult mice which are corrected by vitamin B12 treatment

Introduction The brain-related phenotypes observed in 22q11.2 deletion syndrome (22q11.2DS) are highly variable and their origin is poorly understood. Changes in brain metabolism may cause or contribute to the phenotypes, given that many of the deleted genes (approx. 10%) are implicated in metabolic processes, but this is currently unknown. It is clearly important to address this knowledge gap, but in humans, the primary material required for studying brain metabolism is inaccessible. For this reason, we sought to address the issue using two mouse models of 22q11.2DS. Methods We used three independent approaches to investigate brain metabolism in young adult mice, namely, mass spectrometry, nuclear magnetic resonance spectroscopy and transcriptomics. We selected to study primarily Tbx1 single gene mutants because it is the primary candidate disease gene. We then confirmed key findings in the multi-gene deletion mutant Df1/+. Results We found that Tbx1 mutants have alterations of specific brain metabolites, including methylmalonic acid, which is highly brain-toxic, as well as a more general metabolomic imbalance. We provide transcriptomic evidence of an interaction genotype-vB12 treatment, and behavioural evidence of a response to vB12 treatment, which rescued some of the behavioural anomaly observed in Tbx1 mutants. We conclude that Tbx1 haploinsufficiency causes extensive brain metabolic anomalies, which are partially responsive to vB12 treatment. We suggest that alterations of glutamine-glutamate metabolism and fatty acid metabolism are key components of the metabolic phenotype in these mutants.
Conformational plasticity of a BiP-GRP94 chaperone complex

Hsp70/Hsp90-chaperones and their regulatory co-chaperones are critical for maintaining protein homeostasis. GRP94, the sole Hsp90-chaperone in the secretory pathway of mammalian cells, is essential for the maturation of important secretory and transmembrane proteins. Without the requirement of co-chaperones, the Hsp70-protein BiP controls regulatory conformational changes of GRP94 – the structural basis of which has remained elusive. Here, we biochemically and structurally characterize the formation of a BiP-GRP94 chaperone complex and its transition to a conformation expected to support the loading of substrate proteins from BiP onto GRP94. BiP initially binds to the open GRP94 dimer via an interaction interface that is conserved among Hsp70/90 paralogs. Subsequently, binding of a second BiP protein stabilizes a semi-closed GRP94 dimer, thereby advancing the chaperone cycle. Our findings highlight a fundamental mechanism of direct Hsp70/90 cooperation, independent of co-chaperones.
In vitro antimicrobial activity of non-aureus Staphylococci and Mammaliicocci against Staphylococcus aureus and Streptococcus uberis and their relationship with the presence of intramammary infections in organic dairy cows

This study aimed to: (1) Assess the in vitro antimicrobial activity of non-aureus Staphylococci and Mammaliicocci (NASM) isolates from organic cows teat apices with and without intramammary infections (IMI) by Staphylococcus aureus (SAU) or Streptococcus spp. and Streptococcus-like organisms (SSLO); (2) Investigate the relationship between IMI status and NASM counts on teat apex; (3) Explore the relationship between NASM species assigned taxonomy and the inhibitory activity against SAU and Streptococcus uberis (SUB). A total of 114 cows from 2 farms were sampled and followed from 8 weeks before to 5 weeks after calving. Weekly milk samples were cultured and used to design a case control study. Cases were defined as cows that had an IMI by SAU or SSLO, and controls were randomly chosen from cows without an IMI by these microorganisms. One teat apex sample from the week prior to IMI diagnosis was aerobically cultured. Taxonomy of recovered isolates was confirmed using mass spectrometry and whole genome sequencing. The inhibitory activity against SAU and SUB was investigated using agar dilution methods. Isolates were ranked based on in vitro inhibitory activity against SAU and SUB and categorized as “top 10” with the lowest minimum inhibitory concentration or not. The presence of NASM isolates classified as “top 10” against SAU was highest in controls (19.4%) compared to cases (5.8%). No difference in NASM counts on teat apex was observed between cases and controls. The inhibitory activity varied across NASM, with Staphylococcus succinus showing the highest inhibitory activity against both SAU and SUB.
ClimateBC v5.10 A program to generate climate normal, annual, seasonal and monthly data for historical and future periods in British Columbia

ClimateBC v5 is a standalone MS Windows application written in Visual Basic 6.0. It extracts and downscales PRISM (Daly et al. 2008) 1971-2000 monthly climate normal data (800 x 800 m) and monthly solar radiation normal data (800 x 800 m) to scale-free point locations, and calculates seasonal and annual climate variables for specific locations based on latitude, longitude and elevation (optional). ClimateBC covers the British Columbia and some surrounding areas (Figure 1).
Pre-existing Immunity to Influenza Aids Ferrets in Developing Stronger and Broader Vaccine-induced Antibody Responses

Influenza seasons occur annually, building immune history for individuals, but the influence of this history on subsequent influenza vaccine protection remains unclear. We extracted data from an animal trial to study its potential impact. The trial involved 80 ferrets, each receiving either one type of infection or a placebo before vaccination. We quantified the vaccine protection by evaluating hemagglutination inhibition (HAI) antibody titer responses. We tested whether hosts with different infection histories exhibited similar level of responses when receiving the same vaccine for all homologous and heterologous outcomes. We observed that different pre-existing immunities were generally beneficial to vaccine induced responses, but varied in magnitude. Without pre-immunity, post-vaccination HAI titers after the 1st dose of the vaccine were less likely to be above 1:40, and a booster shot was needed. Our study suggests that pre-existing immunity may strengthen and extend the homologous and heterologous vaccine protection.