Miracle-dz/UniFAR-FTUs huggingface dataset:
Fintech Key-Phrase: A New Chinese Financial High-Tech Dataset Accelerating Expression-Level Information Retrieval

Expression-level information extraction is a challenging task in natural language processing (NLP), which aims to retrieve crucial semantic information from linguistic documents. However, there is a lack of up-to-date data resources for accelerating expression-level information extraction, particularly in the Chinese financial high technology field. To address this gap, we introduce Fintech Key-Phrase: a human-annotated key-phrase dataset for the Chinese financial high technology domain. This dataset comprises over 12K paragraphs along with annotated domain-specific key-phrases. We extract the publicly released reports, Chinese management’s discussion and analysis (CMD&A), from the renowned Chinese research data services platform (CNRDS) and then filter the reports related to high technology. The high technology key-phrases are annotated following pre-defined philosophy guidelines to ensure annotation quality. In order to better understand the limitations and challenges in the purposed dataset, we conducted comprehensive noise evaluation experiments for the Fintech Key-Phrase, including annotation consistency assessment and absolute annotation quality evaluation. To demonstrate the usefulness of our released Fintech Key-Phrase in retrieving valuable information in the Chinese financial high technology field, we evaluate its significance using several superior information retrieval systems as representative baselines and report corresponding performance statistics. Additionally, we further applied ChatGPT to the text augmentation approach of the Fintech Key-Phrase dataset. Extensive comparative experiments demonstrate that the augmented Fintech Key-Phrase dataset significantly improved the coverage and accuracy of extracting key phrases in the finance and high-tech domains. We believe that this dataset can facilitate scientific research and exploration in the Chinese financial high technology field. We have made the Fintech Key-Phrase dataset and the experimental code of the adopted baselines accessible on Github: https://github.com/albert-jin/Fintech-Key-Phrase. To encourage newcomers to participate in the financial high-tech domain information retrieval research, we have developed a series of tools, including an open website1 and corresponding real-time information retrieval APIs.2
MAGNETIC FIELDS IN BRIGHT-RIMMED CLOUDS AND COMETARY GLOBULES TRACED USING R-BAND POLARIZATION OBSERVATIONS

We present results of our R−band polarimetry of a bright-rimmed cloud, IC1396A (with BRC 36), associated with the H II region S131 and the cometary globule LDN 1616 to study their magnetic field geometry. The distances of these clouds have been reported to be ∼ 750 pc and ∼ 450 pc, respectively in the literature. The young open cluster Trumpler 37 in the vicinity of IC1396A and the high mass stars in the Orion belt near L1616 are found to be responsible for the structure of these clouds. We made polarimetry of foreground stars inferred from their distances measured by the Hipparcos satellite to subtract the foreground contribution to the observed polarization results. We discuss the optical polarimetric results and compare our findings with MHD simulations towards BRCs and CGs.
Detection of COVID19 in Chest X-Ray Images Using Transfer Learning

COVID19 is a highly contagious disease infected millions of people worldwide. With limited testing components, screening tools such as chest radiography can assist the clinicians in the diagnosis and assessing the progress of disease. The performance of deep learning-based systems for diagnosis of COVID-19 disease in radiograph images has been encouraging. This paper investigates the concept of transfer learning using two of the most well-known VGGNet architectures, namely VGG-16 and VGG-19. The classifier block and hyperparameters are fine-tuned to adopt the models for automatic detection of Covid-19 in chest x-ray images. We generated two different datasets to evaluate the performance of the proposed system for the identification of positive Covid-19 instances in a multiclass and binary classification problems. The experimental outcome demonstrates the usefulness of transfer learning for small-sized datasets particularly in the field of medical imaging, not only to prevent over-fitting and convergence problems but also to attain optimal classification performance as well.
The Alcator C-Mod Program

Abstract To introduce this special issue, this paper gives a brief overview of the key themes in the Alcator C-Mod tokamak research program. These include energy, particle and momentum transport, wave-plasma interactions, boundary and divertor physics and technology, macrostability including disruptions and their mitigation, and advanced tokamak scenarios with profile control and noninductive current drive.
Automatic sign language recognition: from video corpora to gloss sentences

This PhD thesis investigates large vocabulary, continuous automatic sign language recognition (ASLR) from single view video using hidden Markov models (HMMs) with Gaussian mixture models (GMMs) as state emission functions and n-gram, statistical language models. We go beyond the state-of-the-art by investigating continuous sign language instead of isolated signs and extract features and object locations from video via object tracking foregoing invasive data acquisition methods such as bulky cyber gloves. Overall, we make contributions in three major areas. In the first part of this thesis, we develop best practices for sign language corpus creation and introduce the large vocabulary, single view, continuous sign language corpus RWTH-PHOENIX-Weather which has been created in the context of this work. RWTH-PHOENIX-Weather is annotated in gloss notation and features several subsets usable for object tracking, single signer as well as multi signer recognition. The second part of this thesis focuses on object tracking of the signer’s hands and face. Extending an existing model-free dynamic programming tracking (DPT) framework, we introduce spatial pruning and multi-pass tracking techniques to enhance tracking robustness by taking the kinematic constraints of the human body into account. These approaches are quantitatively evaluated on hand and face location annotations of more than 140k video frames created as part of this work. More than 50k of those frames are part of the RWTH-PHOENIX-Weather corpus. In the third and longest part of this thesis, we focus on ASLR. We present extensive, experimental analyses of standard computer vision (CV) features such as scale invariant feature transformation (SIFT) and histogram of oriented gradients (HOG) in the context of ASLR as well as investigate impact factors such as error propagation from object tracking, temporal context, hidden Markov model (HMM) state alignment quality, and the language model. Methods to improve alignment quality via sign length and non-gesture modeling are discussed and found to be effective in improving recognition results for single signer recognition. Addressing the multimodal nature of sign languages, we investigate modality combination techniques applied during decoding finding that synchronous and asynchronous combination without re-training improve recognition results in the context of single signer and multi signer recognition. Furthermore, we investigate which techniques transfer from single signer to multi signer recognition. All proposed modelling and recognition techniques are experimentally evaluated on publicly available, continuous German Sign Language (GSL) corpora or the novel RWTH-PHOENIX-Weather corpus created in the context of his work. In either case, we achieve either competitive results or results that clearly outperform results found in the literature.
Association between triglyceride-glucose index and risk of end-stage renal disease in patients with type 2 diabetes mellitus and chronic kidney disease

Aims It has been suggested that the triglyceride-glucose (TyG) index is a novel and reliable surrogate marker of insulin resistance (IR). However, its relationship with the risk of end-stage renal disease (ESRD) in patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) remains uncertain. Accordingly, we sought to examine the relationship between the TyG index and ESRD risk in patients with T2DM and CKD. Methods From January 2013 to December 2021, 1,936 patients with T2DM and CKD hospitalized at Peking University Third Hospital (Beijing, China) were enrolled into the study. The formula for calculating the TyG index was ln[fasting triglyceride (mg/dL) × fasting blood glucose (mg/dL)/2]. ESRD was defined as an estimated glomerular filtration rate of less than 15 mL/min/1.73 m2 or the commencement of dialysis or renal transplantation. The relationship between the TyG index and ESRD risk was analyzed using Cox proportional hazard regression. Results 105 (5.42%) participants developed ESRD over a mean follow-up of 41 months. The unadjusted analysis revealed a 1.50-fold (95% confidence interval [CI] 1.17-1.93; P = 0.001) increased risk for ESRD per one unit rise in the TyG index, and the positive association remained stable in the fully adjusted model (hazard ratio, 1.49; 95% CI, 1.12-1.99; P = 0.006). Analysis using restricted cubic spline revealed a significant positive association between the TyG index and ESRD risk. In addition, Kaplan-Meier analysis revealed significant risk stratification with a TyG index cutoff value of 9.5 (P = 0.003). Conclusion In individuals with T2DM and CKD, a significant and positive association was shown between an elevated TyG index and the risk of ESRD. This conclusion provides evidence for the clinical importance of the TyG index for evaluating renal function decline in individuals with T2DM and CKD.
Identification of cucumber S-adenosylmethionine decarboxylase genes and functional analysis of CsSAMDC3 in salt tolerance

As one of the key enzymes in the biosynthesis of polyamines, S-adenosylmethionine decarboxylase (SAMDC) plays an important role in plant stress resistance. In this study, four SAMDC genes (CsSAMDC1-4) were identified in cucumber (Cucumis sativus L.) and divided into three groups (I, II, and III) by phylogenetic analysis. Motif analysis suggested the existence of many conserved motifs, which is compatible with SAMDC protein classification. Gene structure analysis revealed that CsSAMDC2 and CsSAMDC3 in group I have no intron, which showed a similar response to salt stress by gene expression analysis. CsSAMDC3 responded differently to hormone and stress treatments, and was more susceptible to salt stress. Compared with wild-type (WT) tobacco, the activities of superoxide dismutase, peroxidase, and catalase were increased in CsSAMDC3-overexpressing tobacco under salt stress, but the content of electrolyte leakage, malondialdehyde, and hydrogen peroxide were decreased, which alleviated the inhibition of growth induced by salt stress. Under salt stress, overexpression of CsSAMDC3 in transgenic tobacco plants exhibited salt tolerance, mainly in the form of a significant increase in dry and fresh weight, the maximal quantum yield of PSII photochemistry, the net photosynthetic rate and the content of spermidine and spermine, while the content of putrescine was reduced. In addition, the expression levels of antioxidase-related coding genes (NtSOD, NtPOD, NtCAT) and PAs metabolism-related coding genes (NtSAMS, NtSPDS, NtSPMS, NtPAO) in transgentic plants was lower than WT under salt stress, which suggested that overexpression of CsSAMDC3 affected the expression of these genes. In summary, our results showed that CsSAMDC3 could be used as a potential candidate gene to improve salt tolerance of cucumber by regulating polyamine and antioxidant metabolism.
Identification of food and nutrient components as predictors of Lactobacillus colonization

A previous double-blind, randomized clinical trial of 42 healthy individuals conducted with Lactobacillus johnsonii N6.2 found that the probiotic’s mechanistic tryptophan pathway was significantly modified when the data was stratified based on the individuals’ lactic acid bacteria (LAB) stool content. These results suggest that confounding factors such as dietary intake which impact stool LAB content may affect the response to the probiotic treatment. Using dietary intake, serum metabolite, and stool LAB colony forming unit (CFU) data from a previous clinical trial, the relationships between diet, metabolic response, and fecal LAB were assessed. The diets of subject groups with high vs. low CFUs of LAB/g of wet stool differed in their intakes of monounsaturated fatty acids, vegetables, proteins, and dairy. Individuals with high LAB consumed greater amounts of cheese, fermented meats, soy, nuts and seeds, alcoholic beverages, and oils whereas individuals with low LAB consumed higher amounts of tomatoes, starchy vegetables, and poultry. Several dietary variables correlated with LAB counts; positive correlations were determined for nuts and seeds, fish high in N-3 fatty acids, soy, and processed meats, and negative correlations to consumption of vegetables including tomatoes. Using machine learning, predictors of LAB count included cheese, nuts and seeds, fish high in N-3 fatty acids, and erucic acid. Erucic acid alone accurately predicted LAB categorization, and was shown to be utilized as a sole fatty acid source by several Lactobacillus species regardless of their mode of fermentation. Several metabolites were significantly upregulated in each group based on LAB titers, notably polypropylene glycol, caproic acid, pyrazine, and chondroitin sulfate; however, none were correlated with the dietary intake variables. These findings suggest that dietary variables may drive the presence of LAB in the human gastrointestinal tract and potentially impact response to probiotic interventions.
Validation of the Grief Support in Healthcare Scale among frontline nursing professionals working in COVID-19 inpatient wards in Korea

Introduction During the COVID-19 pandemic, healthcare workers (HCWs) have been exposed to higher levels of anxiety and psychological stress than the general population. Nurses who cared for COVID patients could not avoid repeated mourning as they witnessed the deaths of their patients. Therefore, tools are needed to evaluate whether there is adequate support for the grieving process of HCWs in both qualitative and quantitative manners. Methods Data from 229 nurses who witnessed the deaths of COVID-19 inpatients were analyzed using an online survey of nurses working in three tertiary hospitals. Factor analysis was conducted to validate the 10-item Korean version of Grief Support in Healthcare Scale (GSHCS). Stress and Anxiety to Viral Epidemics-9 was used to measure stress and anxiety caused by coronavirus, Generalized Anxiety Disorder-7 was used to measure overall anxiety, and Patient Health Questionnaire-9 was used for depression. Convergent validity correlation analysis was also performed with GSHCS. Results The two-factor model showed a good fit for the 10-item GSHCS (χ2 = 35.233, df = 34, p = 0.410, CFI = 0.999, TLI = 0.990, RMSEA = 0.013, SRMR = 0.064). Cronbach’s alpha is 0.918 and McDonald’s omega is 0.913, suggesting that the 10-item version of the GSHCS is reliable for determining psychometric properties. Conclusion According to this study, the 10-item Korean version of the GSHCS is a reliable and valid measure of psychological support for grief among frontline nursing professionals who have witnessed the deaths of patients they cared for while working in COVID-19 inpatient wards. A two-factor model of the GSHCS has a good model fit and good convergent validity with other rating scales that measure viral anxiety, depression, and general anxiety.
Naturally acquired antibody response to Plasmodium falciparum and Plasmodium vivax among indigenous Orang Asli communities in Peninsular Malaysia

Malaria remains a public health problem in many parts of the world. In Malaysia, the significant progress towards the national elimination programme and effective disease notification on malaria has resulted in zero indigenous human malaria cases since 2018. However, the country still needs to determine the extent of malaria exposure and transmission patterns, particularly in high-risk populations. In this study, a serological method was used to measure transmission levels of Plasmodium falciparum and Plasmodium vivax among indigenous Orang Asli communities in Kelantan, Peninsular Malaysia. A community-based cross-sectional survey was conducted in three Orang Asli communities (i.e., Pos Bihai, Pos Gob, and Pos Kuala Betis) in Kelantan from June to July 2019. Antibody responses to malaria were assessed by enzyme-linked immunosorbent assay (ELISA) using two P. falciparum (PfAMA-1 and PfMSP-119) and two P. vivax (PvAMA-1 and PvMSP-119) antigens. Age-adjusted antibody responses were analysed using a reversible catalytic model to calculate seroconversion rates (SCRs). Multiple logistic regression was used to investigate factors associated with malaria exposure. The overall malaria seroprevalence was 38.8% for PfAMA-1, 36.4% for PfMSP-119, 2.2% for PvAMA-1, and 9.3% for PvMSP-119. Between study areas, the proportion of seropositivity for any P. falciparum and P. vivax antigens was significantly highest in Pos Kuala Betis with 34.7% (p < 0.001) and 13.6% (p < 0.001), respectively. For all parasite antigens except for PvAMA-1, the proportion of seropositive individuals significantly increased with age (all p < 0.001). Based on the SCR, there was a higher level of P. falciparum transmission than P. vivax in the study area. Multivariate regression analyses showed that living in Pos Kuala Betis was associated with both P. falciparum (adjusted odds ratio [aOR] 5.6, p < 0.001) and P. vivax (aOR 2.1, p < 0.001) seropositivities. Significant associations were also found between age and seropositivity to P. falciparum and P. vivax antigens. Analysis of community-based serological data helps describe the level of transmission, heterogeneity, and factors associated with malaria exposure among indigenous communities in Peninsular Malaysia. This approach could be an important adjunct tool for malaria monitoring and surveillance in low malaria transmission settings in the country.
ONT long-read WGS for variant discovery and orthogonal confirmation of short read WGS derived genetic variants in clinical genetic testing

Technological advances in Next-Generation Sequencing dramatically increased clinical efficiency of genetic testing, allowing detection of a wide variety of variants, from single nucleotide events to large structural aberrations. Whole Genome Sequencing (WGS) has allowed exploration of areas of the genome that might not have been targeted by other approaches, such as intergenic regions. A single technique detecting all genetic variants at once is intended to expedite the diagnostic process while making it more comprehensive and efficient. Nevertheless, there are still several shortcomings that cannot be effectively addressed by short read sequencing, such as determination of the precise size of short tandem repeat (STR) expansions, phasing of potentially compound recessive variants, resolution of some structural variants and exact determination of their boundaries, etc. Therefore, in some cases variants can only be tentatively detected by short reads sequencing and require orthogonal confirmation, particularly for clinical reporting purposes. Moreover, certain regulatory authorities, for example, New York state CLIA, require orthogonal confirmation of every reportable variant. Such orthogonal confirmations often involve numerous different techniques, not necessarily available in the same laboratory and not always performed in an expedited manner, thus negating the advantages of “one-technique-for-all” approach, and making the process lengthy, prone to logistical and analytical faults, and financially inefficient. Fortunately, those weak spots of short read sequencing can be compensated by long read technology that have comparable or better detection of some types of variants while lacking the mentioned above limitations of short read sequencing. At Variantyx we have developed an integrated clinical genetic testing approach, augmenting short read WGS-based variant detection with Oxford Nanopore Technologies (ONT) long read sequencing, providing simultaneous orthogonal confirmation of all types of variants with the additional benefit of improved identification of exact size and position of the detected aberrations. The validation study of this augmented test has demonstrated that Oxford Nanopore Technologies sequencing can efficiently verify multiple types of reportable variants, thus ensuring highly reliable detection and a quick turnaround time for WGS-based clinical genetic testing.
The impact of digital healthcare on vulnerable pregnant women: A review of the use of the MyCare app in the maternity department at a central London tertiary unit

Introduction Digitalisation offers innovative solutions within maternity services; however, vulnerable groups risk being overlooked. University College London Hospital's (UCLH) successful implementation of a digital maternity app, MyCare, gives women access to test results, information about appointments, and enables communication with healthcare professionals (HCPs). Yet, little is known about access and engagement among vulnerable pregnant women. Methodology Research was conducted over a 3-month period (April–June 2022) in the Maternity Department at UCLH, UK. MyCare datasets were analysed, and anonymised surveys completed by vulnerable pregnant women and HCPs. Results Lower rates of utilisation and engagement with MyCare were seen in vulnerable pregnant women especially among refugee/asylum seekers, those with mental health issues, and those facing domestic violence. Non-users were also more likely to be individuals from ethnic minority backgrounds, with a lower average social-deprivation-index decile, whose first language was not English, and with a significant history of non-attendance to appointments. Patient and HCP surveys highlighted various barriers to MyCare engagement, including a lack of motivation, limited language options, low e-literacy levels, and complex app interfaces. Conclusion The use of a single digital tool, without a formulated pathway to identify and assist those not accessing or engaging with it, risks unequal care provision which may exacerbate health inequalities. This research advances the idea that digital exclusion is not necessarily a matter of access to technology, but an issue of a lack of engagement with these tools. Therefore, vulnerable women and HCPs must be integral to the implementation of digital strategies, to ensure no one is left behind.