Miracle-dz/UniFAR-FTUs huggingface dataset:
Candidate neuroinflammatory markers of cerebral autoregulation dysfunction in human acute brain injury

The loss of cerebral autoregulation (CA) is a common and detrimental secondary injury mechanism following acute brain injury and has been associated with worse morbidity and mortality. However patient outcomes have not as yet been conclusively proven to have improved as a result of CA-directed therapy. While CA monitoring has been used to modify CPP targets, this approach cannot work if the impairment of CA is not simply related to CPP but involves other underlying mechanisms and triggers, which at present are largely unknown. Neuroinflammation, particularly inflammation affecting the cerebral vasculature, is an important cascade that occurs following acute injury. We hypothesise that disturbances to the cerebral vasculature can affect the regulation of CBF, and hence the vascular inflammatory pathways could be a putative mechanism that causes CA dysfunction. This review provides a brief overview of CA, and its impairment following brain injury. We discuss candidate vascular and endothelial markers and what is known about their link to disturbance of the CBF and autoregulation. We focus on human traumatic brain injury (TBI) and subarachnoid haemorrhage (SAH), with supporting evidence from animal work and applicability to wider neurologic diseases.
Histological and biochemical evaluation of the effects of silver nanoparticles (AgNps) versus titanium dioxide nanoparticles (TiO2NPs) on rat parotid gland

ABSTRACT The unlimited use of nanoparticles (NPs) results in toxic impacts on different tissues. The current study aimed to compare the adverse effects of AgNPs and TiO2NPs on the parotid gland of adult male albino rats as regards the histopathological, immunohistochemical, and biochemical changes, exploring the possible underlying mechanisms and the degree of improvement after cessation of administration. Fifty-four adult male albino rats were divided into control group (I), AgNPs-injected group (II), and TiO2NPs-injected group (III). We measured the levels of tumor necrosis factor-alpha (TNF-α) and interleukin (IL-6) in the serum, and levels of MDA and GSH in parotid tissue homogenate. Quantitative real-time polymerase-chain reaction (qRT-PCR) was used to measure the expression levels of peroxisome proliferator-activated receptor-gamma coactivator 1-alpha (PGC1-α), nicotinamide adenine dinucleotide phosphate oxidase 4 (NOX4), mouse double minute 2 (MDM2), Caspase-3 Col1a1, and Occludin. Parotid tissue sections were examined by light microscope (Hematoxylin & Eosin and Mallory trichrome stains), electron microscope, and immunohistochemical examination of CD68 and anti-caspase-3 antibodies. Both NPs severely affected the acinar cells and damaged the tight junction between them by enhancing expression of the inflammatory cytokines, inducing oxidative stress, and disturbing the expression levels of the studied genes. They also stimulated fibrosis, acinar cell apoptosis, and inflammatory cells infiltration in parotid tissue. TiO2NPs effects were less severe than AgNPs. Cessation of exposure to both NPs, ameliorated the biochemical and structural findings with more improvement in TiO2NPs withdrawal. In conclusion: AgNPs and TiO2NPs adversely affected the parotid gland, but TiO2NPs were less toxic than AgNPs.
Elliptical Waves Transmission in a 1D Dispersive Nonlinear Electrical Lattice

A new version of the modified discrete nonlinear Schrödinger (MDNLS) equation that governs wave propagation in discrete dispersive nonlinear transmission lines is solved by the means of the Jacobi elliptic function method. The contribution of the linear dispersive capacitance is appreciated, and it is established that this equation possesses exact solutions whose nature and characteristics depend on the modulus of the Jacobian elliptic function. These solutions are either periodic waves, peak, or buddle solitons used in telecommunication and the medical domain. Propagation and stability of these solutions in the network are tested through direct simulations.
When I Don't Say … ‘Patients’

Institute of Health Sciences Education and Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada Centre for Health Education Scholarship and Department of Occupational Science and Occupational Therapy, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada Department of Family Practice, University of British Columbia, Vancouver, British Columbia, Canada
A rare combination: thrombotic and non-thrombotic pulmonary embolism

CT revealed multiple lesions consistent with metastasis and filling defects in bilateral pulmonary arteries. Fused PET/CT images revealed segments of filling defects with absolutely no FDG uptake, in contrast to others with high FDG avidity (Figure 1). This is in line with current knowledge in the literature that suggests that venous thromboemboli do not show FDG hypermetabolism. Conversely, tumor thrombi usually exhibit high FDG uptake. (1,2) Based on that, FDG-PET/CT can be a useful diagnostic imaging tool. A diagnosis of combined thrombotic and non-thrombotic pulmonary embolism was made. The patient received anticoagulation therapy and a new line of systemic oncologic treatment.
Moderate mechanical stimulation antagonizes inflammation of annulus fibrosus cells through YAP‐mediated suppression of NF‐κB signaling

Intervertebral disc degeneration (IDD) is a leading cause of low back pain. The inflammatory responses caused by aberrant mechanical loading are one of the major factors leading to annulus fibrosus (AF) degeneration and IDD. Previous studies have suggested that moderate cyclic tensile strain (CTS) can regulate anti‐inflammatory activities of AF cells (AFCs), and Yes‐associated protein (YAP) as a mechanosensitive coactivator senses diverse types of biomechanical stimuli and translates them into biochemical signals controlling cell behaviors. However, it remains poorly understood whether and how YAP mediates the effect of mechanical stimuli on AFCs. In this study, we aimed to investigate the exact effects of different CTS on AFCs as well as the role of YAP signaling involving in it. Our results found that 5% CTS inhibited the inflammatory response and promoted cell growth through inhibiting the phosphorylation of YAP and nuclear localization of NF‐κB, while 12% CTS had a significant proinflammatory effect with the inactivation of YAP activity and the activation of NF‐κB signaling in AFCs. Furthermore, moderate mechanical stimulation may alleviate the inflammatory reaction of intervertebral discs through YAP‐mediated suppression of NF‐κB signaling in vivo. Therefore, moderate mechanical stimulation may serve as a promising therapeutic approach for the prevention and treatment of IDD.
Efficient Large-Scale Virtual Screening Based on Heterogeneous Many-Core Supercomputing System

With the rapid growth of virtual drug data- bases, the need for efficient molecular docking tools for large-scale screening is also growing. We have developed Vina@QNLM 2.0, a novel molecular docking system that leverages the logical processing units and computational processing arrays of heterogeneous multicore architecture processors. Compared to Vina@QNLM, the new version optimizes the docking speed without sacrificing accuracy. This greatly improves the scoring capability for large molecules (molecular weight > 500). Simultaneously, the new system provides enhanced support for applications such as reverse target finding through an improved parallel strategy. Vina@QNLM 2.0 achieves a speedup 20 times higher than that, using logical processing units only during a single docking process. Additionally, we successfully scaled the reverse target finding a task to 122,401 kernel groups with a robust scalability of 80.01%. In practice, we completed a reverse target-seeking for nine glycan molecules with 10,094 proteins within 1 hour.
RIP analysis for quasi-Toeplitz measurement matrices

CS can be widely utilized in linear time-invariant(LTI)system identification problems through the use of Toeplitz matrices.However,measurements were usually need to be undersampling in the applications which signal frequency was high to reduce the requirement of sampling fequency,this led the measurement matrices turn into quasi-Toeplitz matrices which formed by extracting a part of row vectors from Toeplitz matrices.This paper considered quasi-Toeplitz matrices as sensing matrices.Theoretical derivation showed that the quasi-Toeplitz matrices satisfy the restricted isometry property(RIP). Compared the performance of quasi-Toeplitz matrices and other compressed sensing matrices in simulations.It shows that the quasi-Toeplitz matrices perform similar to other compressed sensing matrices and can be used as CS measurement matrices.
Stage-Terrain-Power Model Based on Analytic Hierarchy Process

As a means of transportation, bicycle has brought great convenience for human travel and exercise. With the popularization of bicycle, more and more cross-country time trial bicycle races have begun to rise. Based on this, this paper adopts Pontryagin's Maximum Principles and analytic hierarchy Process respectively to establish mechanical model and terrain analysis model, and evaluates the comprehensive quality of racers based on their race types, while considering the influence of various factors such as weather environment. In this paper, the power distribution of racers in different terrains and time periods is analyzed, and the model is applied to a typical track to obtain the power distribution diagram of racers achieving the best results.
Vocabulary expansion of compound words for domain adaptation of BERT

Pretraining models such as BERT, have achieved high accuracy in various natu-ral language processing tasks by pretraining on a large corpus and fine-tuning on downstream task data. However, BERT trains token-level inferences, which make it difficult to train unknown or compound words that are split by byte-pair encoding. In this paper, we propose an effective method for constructing word representations in vocabulary expansions for such compound words. The proposed method assumes domain adaptation by additional pretraining and expands the vocabulary by embedding a synonym as an approximate embedding of additional words. We conducted experiments using each vocabulary expansion method and evaluated these experiments for their accuracies in predicting additional vocabularies in the masked language model.
Vanilla Recurrent Neural Networks for Interpretable Semantic Textual Similarity

Semantic similarity systems assess to what extent two words, phrases, sentences are similar in their meaning. In this task, rule-based and neural network systems achieve the best results. The former requires intensive human workload and the latter needs heavy computing. Can we achieve high accuracy without hand-crafted rules and without intensive computing? In this paper we present three types of Vanilla Recurrent Neural Networks that fulfill the aforementioned requirements for In-terpretable Semantic Textual Similarity task and we compare them to the systems from Se-mEval competition.
Collection Methods and Data Characteristics of the PagkataoKo Dataset

We present the PagkataoKo Dataset, a new dataset for Filipino Automatic Personality Recognition (APR) containing demographic, personality trait, and social media data sourced from 3,128 Filipino Instagram and/or Twitter users. As APR is focused on processing an individual’s observable actions, we improve upon the previous Filipino APR dataset by collecting multimodal data, as well as similar data expressed in different environments. In our paper, we describe our collection methodology and detail the general characteristics of the dataset. We also report the language characteristics of the posts and highlight the presence of multiple languages (e.g. English, Tagalog) and code-switching – two aspects that make Filipino APR difficult. Lastly, we discuss how our dataset provides future work with multiple options to explore in order to navigate around the complexities found in Fil-ipinos’ language usage.
HMIST: Hierarchical Multilingual Isometric Speech Translation using Multi-Task Learning Framework and it’s influence on Automatic Dubbing

In this paper, we present an approach and impact of isometric neural machine translation on the automatic dubbing process. The length of generated isometric translated sentences ranges within a ± 10% of the source text. We propose a hierarchical and multilingual approach toward generating isometric translation via publicly available MUST-C, WMT, and IIT-Bombay(en-hi) datasets. Our experiments use namely, German(de), French(fr), Russian(ru), Italian(it), and Hindi(hi) languages. Additionally, we implement a paraphrasing module with Opuspar-cus(fr,de,ru), PAWS-X(fr,de) and Topaco(ru) datasets for German, French, and Russian languages to enhance the vocabulary and maintain the isometric constraints. In performance analysis, we report the average length range of source to translation, 55.15% for all languages, while ru exhibits the highest with 62.475% and a relative improvement of 23.04% from the baseline OPUS-MT model.
A Model-Theoretic Formalization of Natural Language Inference Using Neural Network and Tableau Method

Saji et al. (2021) integrated a neural-based NLI model and a symbolic one into a framework to get the best of both worlds. This framework is based on a tableau method, which is a proof system for formal logic; however, it has a remaining issue that its theoretical limitations have not been clariﬁed. To solve this issue, this paper formalizes the framework model-theoretically. On the basis of the formalization, we demonstrate that a certain kind of soundness holds for this framework, while the completeness does not.
Change of routine coagulation parameters in plasma samples with different hematocrit values

Abstract Aim There are many preanalytical variables affecting routine coagulation tests. Increased hematocrit (Htc) levels are one of these variables. However, no study has been conducted to determine the effect of low Htc values on coagulation tests. Therefore, in this study, we aimed to evaluate whether low Htc values affect coagulation tests besides high Htc values. Methods Standard human plasma was injected into coagulation tubes containing 3.2% sodium citrate to reflect hematocrit rates of 5% to 75% and prothrombin time (PT), active partial thromboplastin time (aPTT), thrombin time (TT) and fibrinogen measurements were performed. Results Three groups were formed according to Htc levels: A (5-25%), B (30-50%) and C (55-75%). PT (s) were found 13.7±0.30 in group A, 14.88±0.57 s in group B, 20.16 ±4.66 s in group C respectively. aPTT (s) results were 35.79±1.39 s in group A, 42.48 ± 3.51 s in group B and 76.47 ± 31.55 in group C. TT (s) results were found to be 26.42 ± 0.77 s (group A), 28.24±1.17 s (group B) and 32.02±2.60 (group C). Fibrinogen levels (g/L) were measured as 2.30 ± 0,05, 2.21 ± 0,07 and 1.90 ± 0.20 in groups A, B, C, respectively. For all measured parameters, group A reflecting low Htc was significantly (P < 0 ,0001) different from the other groups. Conclusion Previous studies have reported that high Htc (> 55%) levels affected routine coagulation tests. In our study, low Htc (5-25%) values were also shown to cause errors in the test results.
Classification of hyperspectral remote sensing data based on DNA computing

Some initial investigations are conducted to employ DNA computing for hyperspectral remote sensing data classification. As a novel branch of computational intelligence, DNA computing expresses rich information of spectral features with DNA encoding, and acquires the most typical DNA encoding of each class by DNA modulating and controlling mechanism. For each pixel of the hyperspectral image, computing the distance between the pixel and the typical DNA sequence, finding the class property of the minimum distance, set the class property of each pixel as the minimum distance class. An experiment was performed to evaluate the performance of the proposed algorithm in comparison with other traditional image matching classification algorithms: binary cording, spectral angles and spectral derivative feature coding (SDFC). It is demonstrated that the proposed algorithm is superior to the three traditional hyperspectral data classification algorithms based on the experiment results.
Night work and breast cancer risk in a cohort of female healthcare employees in Stockholm, Sweden

Objectives Night work has been classified as probably carcinogenic to humans by the International Agency for Research on Cancer, but epidemiological evidence was considered limited due to variability in findings and potential bias. This study aimed to investigate the risk of breast cancer in a cohort with detailed and registry-based data on night work. Methods The cohort comprised 25 585 women (nurses and nursing assistants) employed 1 year or more between 2008 and 2016 in the healthcare sector in Stockholm. Information on work schedules was obtained from employment records. Breast cancer cases were identified from the national cancer register. HRs were estimated by a discrete time proportional hazards model, adjusting for age, country of birth, profession and childbirth. Results There were 299 cases of breast cancer, 147 in premenopausal and 152 in postmenopausal women. The adjusted HR of postmenopausal breast cancer in association with ever versus never working nights was 1.31 (95% CI 0.91 to 1.85). Eight or more years of night work was associated with an increased risk of postmenopausal breast cancer, HR=4.33 (95% CI 1.45 to 10.57), based on five cases only, though. Conclusions This study is limited by a short period of follow-up and a lack of information on night work before 2008. Most exposure metrics showed no association with breast cancer risk, but there was an elevated risk of postmenopausal breast cancer in women after 8 or more years of night work.
Outcomes in adulthood among former child welfare services recipients: findings from a Norwegian registry study covering two decades

ABSTRACT This article presents outcomes for persons who have been supported by child welfare services (CWS) in Norway. This include persons who were placed in out-of-home care as well as persons who only received in-home support measures (i.e. not removed from the family home). Annual outcome-data was extracted from Norwegian population-based registers covering two decades for all persons receiving CWS in-home support and/or out-of-home care measures as well as for a comparison group of persons without any contact with CWS. This article presents outcomes related to upper secondary school, employment, financial assistance, and health-related benefits according different CWS supports. While the findings are consistent with previous research which highlight poor outcomes among persons who have been supported by CWS, this study provide a more nuanced account for upper secondary education, employment, health related benefits, and financial assistance for Norwegian CWS recipients. Being placed in residential care was a predictor for poorer outcomes. Conversely, being female and parents’ completion of upper secondary education were mitigating factors for some outcome variables. In broad terms, the gap in outcomes widened as persons were older (i.e. over time), chiefly as persons in the comparison group improved their outcomes. Further research into these nuances is warranted.
Emergence of new enhancers at late DNA replicating regions

Enhancers are fast-evolving genomic sequences that control spatiotemporal gene expression patterns. By examining enhancer turnover across mammalian species and in multiple tissue types, we uncovered a relationship between the emergence of novel enhancers and genome organization as a function of germline DNA replication time. While enhancers are most abundant in euchromatic regions, new enhancers emerged almost twice as often in late compared to early germline replicating regions, independent of transposable elements. Using a sequence model, we demonstrate that new enhancers are enriched for mutations that alter transcription factor (TF) binding. Recently evolved enhancers appeared to be mostly neutrally evolving and enriched in eQTLs. They also show more tissue specificity than conserved enhancers, and the TFs that bind to these elements, as inferred by binding sequences, also show increased tissue-specific gene expression. We find a similar relationship with DNA replication time in cancer, suggesting that these observations may be time-invariant principles of genome evolution. Our work underscores that genome organization has a profound impact in shaping mammalian gene regulation.
Molecular dissection of PI3Kβ synergistic activation by receptor tyrosine kinases, GβGγ, and Rho-family GTPases

The class 1A phosphoinositide 3-kinase (PI3K) beta (PI3Kβ) is functionally unique in the ability to integrate signals derived from receptor tyrosine kinases (RTKs), heterotrimeric guanine nucleotide-binding protein (G-protein)-coupled receptors (GPCRs), and Rho-family GTPases. The mechanism by which PI3Kβ prioritizes interactions with various membrane tethered signaling inputs, however, remains unclear. Previous experiments have not been able to elucidate whether interactions with membrane-tethered proteins primarily control PI3Kβ localization versus directly modulate lipid kinase activity. To address this gap in our understanding of PI3Kβ regulation, we established an assay to directly visualize and decipher how three distinct protein interactions regulate PI3Kβ when presented to the kinase in a biologically relevant configuration on supported lipid bilayers. Using single molecule Total Internal Reflection Fluorescence (TIRF) Microscopy, we determined the mechanism controlling membrane localization of PI3Kβ, prioritization of signaling inputs, and lipid kinase activation. We find that auto-inhibited PI3Kβ prioritizes interactions with RTK-derived tyrosine phosphorylated (pY) peptides before engaging either GβGγ or Rac1(GTP). Although pY peptides strongly localize PI3Kβ to membranes, stimulation of lipid kinase activity is modest. In the presence of either pY/GβGγ or pY/Rac1(GTP), PI3Kβ activity is dramatically enhanced beyond what can be explained by simply increasing the strength of membrane localization. Instead, PI3Kβ is synergistically activated by pY/GβGγ and pY/Rac1(GTP) through a mechanism consistent with allosteric regulation.
Some Discussions on Parallel Bounded Batch Scheduling to Minimize the Sum of Squared Machine Loads

We study the problem of scheduling n jobs on m parallel bounded batch machines to minimize the sum of squared machine loads. Each batch contains at most B jobs, and the processing time of a batch is equal to the longest processing time of the jobs in this batch. We prove this problem to be NP-hard. Furthermore, we present a polynomial time approximation scheme (PTAS) and a fully polynomial time approximation scheme (FPTAS) for this problem.