Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing validity of thoracic spine rotation range of motion measurement methods: comparison of magnetic resonance imaging and clinical measurements

[Purpose] This work was designed to establish criteria for assessing common clinical measurement methods for thoracic spinal rotation angles by comparing their results with magnetic resonance imaging measurements. [Participants and Methods] Twenty-six healthy participants underwent thoracic rotation angle assessments using an electronic goniometer in three positions: lumbar-locked, seated, and half-kneeling. We compared these results with measurements obtained by magnetic resonance imaging. [Results] A moderate but significant positive correlation was observed between the thoracic rotation angle measured by magnetic resonance imaging and the lumbar-locked rotation test. The respective 95% confidence intervals of these correlation coefficients were 0.09 and 0.72. Bland–Altman analysis revealed a fixed error in the lumbar-locked rotation test, suggesting that the test tended to overestimate thoracic rotation compared with magnetic resonance imaging, but proportional errors could not be definitively identified. [Conclusions] Thoracic spine rotation angles measured using magnetic resonance imaging aligned closely with previously reported results. Notably, although measurements obtained by the lumbar-locked rotation test correlated with magnetic resonance imaging results, they exhibited fixation errors. Functional tests (seated and half-kneeling) showed limited correlations with magnetic resonance imaging results. The influence of adjacent joints on clinical measurements should be considered.
Whole genome case-control study of central nervous system toxicity due to antimicrobial drugs

A genetic predisposition to central nervous system (CNS) toxicity induced by antimicrobial drugs (antibiotics, antivirals, antifungals, and antiparasitic drugs) has been suspected. Whole genome sequencing of 66 cases and 833 controls was performed to investigate whether antimicrobial drug-induced CNS toxicity was associated with genetic variation. The primary objective was to test whether antimicrobial-induced CNS toxicity was associated with seventeen efflux transporters at the blood-brain barrier. In this study, variants or structural elements in efflux transporters were not significantly associated with CNS toxicity. Secondary objectives were to test whether antimicrobial-induced CNS toxicity was associated with genes over the whole genome, with HLA, or with structural genetic variation. Uncommon variants in and close to three genes were significantly associated with CNS toxicity according to a sequence kernel association test combined with an optimal unified test (SKAT-O). These genes were LCP1 (q = 0.013), RETSAT (q = 0.013) and SFMBT2 (q = 0.035). Two variants were driving the LCP1 association: rs6561297 (p = 1.15x10-6, OR: 4.60 [95% CI: 2.51–8.46]) and the regulatory variant rs10492451 (p = 1.15x10-6, OR: 4.60 [95% CI: 2.51–8.46]). No common genetic variant, HLA-type or structural variation was associated with CNS toxicity. In conclusion, CNS toxicity due to antimicrobial drugs was associated with uncommon variants in LCP1, RETSAT and SFMBT2.
The effects of nonnative plants on food webs in residential landscapes

12 Introduction . 13 Methods . 16 Study Species . 16 Study Sites . 16 Caterpillar Sampling . 17 Foraging Behavior . 18 Bird Surveys . 18 Plant Surveys . 19 Caterpillar Analysis . 20 Foraging Behavior . 21 Chickadee Occupancy, Abundance, and Breeding. 22 Results . 23 Plant Diversity and Productivity . 24 Foraging Activity . 25 Detectability . 26 Chickadee Occupancy and Abundance . 26
A remnant never forgotten: the utility of circulating tumor DNA in treatment guidance of urachal cancer

Urachal cancer is a rare malignancy of the urachus that is treated with surgical resection if localized and systemic chemotherapy for metastatic disease. Circulating tumor DNA (ctDNA) is a single-stranded or double-stranded DNA released by tumor cells into the blood and harbored the mutations of the original tumor, shedding new light on molecular diagnosis and monitoring of cancer. We report a case of resected localized urachal cancer with clear surgical margins and negative lymph node dissection but elevated ctDNA that progressed to metastatic disease. We also highlight the possibility of using ctDNA levels to assist in adjuvant therapy.
Use of conventional flow resistance equations and a model for the Nikuradse roughness in vegetated flows at high submergence

Abstract This study examines the problem of flow resistance due to rigid vegetation in open channel flow. The reliability of the conventional flow resistance equations (i.e. Keulegan, Manning and Chézy-Bazin) for vegetated flows at high submergence, i.e. h/k >5, (where h = flow depth and k = vegetation height) is assessed. Several modern flow resistance equations based on a two-layer approach are examined, showing that they transform into the conventional equations at high submergences. To compare the conventional flow resistance equations at high submergences, an experimental methodology is proposed and applied to the experimental data reported in the literature and collected for this study. The results demonstrate the reliability of the Keulegan equation in predicting the flow resistance. Based on the obtained results, a model to evaluate the Nikuradse equivalent sand-grain roughness, kN, starting from the vegetation height and density, is proposed and tested.
A prospective phase II single-arm study and predictive factor analysis of irinotecan as third-line treatment in patients with metastatic gastric cancer

Background: Currently, there is no recommended standard third-line chemotherapy for metastatic gastric cancer. Objectives: In this study, we aimed to evaluate irinotecan’s efficacy and safety in treating metastatic gastric cancer after the failure of first- and second-line chemotherapy. Design: Prospective single-arm, two-center, phase II trial. Methods: Patients were aged 18–70 years, with histologically confirmed gastric adenocarcinoma and an Eastern Cooperative Oncology Group performance status of 0–1, progressed during or within 3 months following the last administration of second-line chemotherapy and had no other severe hematologic, cardiac, pulmonary, hepatic, or renal functional abnormalities or immunodeficiency diseases. Eligible patients received 28-day cycles of irinotecan (180 mg/m2 intravenously, days 1 and 15) and were assessed according to the RECIST 1.1 criteria every two cycles. Patients who discontinued treatment for any reason were followed up every 2 months until death. The primary endpoint was overall survival (OS), and the secondary endpoints were progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR), and toxicity. Results: A total of 98 eligible patients were enrolled in this study. In the intention-to-treat population, the median OS was 7.17 months, the median PFS was 3.47 months, and the ORR and DCR were 4.08% and 47.96%, respectively. In the per-protocol population, the median OS was 7.77 months, the median PFS was 3.47 months, and the ORR and DCR were 4.82% and 50.60%, respectively. The incidence of grade 3 or 4 hematological and non-hematological toxicities was 19.4%, and none of the patients died owing to adverse events. Cox regression analysis revealed neutropenia and baseline thrombocyte levels were independently correlated with PFS and OS. Conclusion: Irinotecan monotherapy is an efficient, well-tolerated, and economical third-line treatment for patients with metastatic gastric cancer as a third-line treatment. Trial registration: ClinicalTrials.gov identifier: NCT02662959.
Identi ﬁ cation and functional analysis of ovarian lncRNAs during different egg laying periods in Taihe Black-Bone Chickens

Introduction: Long non-coding RNA (lncRNA) refers to a category of non-coding RNA molecules exceeding 200 nucleotides in length, which exerts a regulatory role in the context of ovarian development. There is a paucity of research examining the involvement of lncRNA in the regulation of ovary development in Taihe Black-Bone Chickens. In order to further investigate the egg laying regulation mechanisms of Taihe Black-Bone Chickens at different periods, transcriptome analysis was conducted on the ovarian tissues at different laying periods. Methods: This study randomly selected ovarian tissues from 12 chickens for RNA-seq. Four chickens were selected for each period, including the early laying period (102 days, Pre), the peak laying period (203 days, Peak), and the late laying period (394 days, Late). Based on our previous study of mRNA expression pro ﬁ les in the same ovarian tissue, we identi ﬁ ed three differentially expressed lncRNAs (DE lncRNAs) at different periods and searched for their cis-and trans-target genes to draw an lncRNA-mRNA network. Results and discussion: In three groups of ovarian tissues, we identi ﬁ ed 136 DE lncRNAs, with 8 showing speci ﬁ c expression during the early laying period, 10 showing speci ﬁ c expression during the peak laying period, and 4 showing speci ﬁ c expression during the late laying period. The lncRNA-mRNA network revealed 16 pairs of lncRNA-target genes associated with 7 DE lncRNAs, and these 14 target genes were involved in the regulation of reproductive traits. Furthermore, these reproductive-related target genes were primarily associated with signaling pathways related to follicle and ovary development in Taihe Black-Bone Chickens, including cytokine-cytokine receptor interaction, TGF-beta signaling pathway, tyrosine metabolism, ECM-receptor interaction, focal adhesion, neuroactive ligand-receptor interaction, and cell adhesion molecules (CAMs). This study offers valuable insights for a comprehensive understanding of the in ﬂ uence of lncRNAs on poultry reproductive traits.
Impacts of Deforestation on Childhood Malaria Depend on Wealth and Vector Biology

Abstract Ecosystem change can profoundly affect human well‐being and health, including through changes in exposure to vector‐borne diseases. Deforestation has increased human exposure to mosquito vectors and malaria risk in Africa, but there is little understanding of how socioeconomic and ecological factors influence the relationship between deforestation and malaria risk. We examined these interrelationships in six sub‐Saharan African countries using demographic and health survey data linked to remotely sensed environmental variables for 11,746 children under 5 years old. We found that the relationship between deforestation and malaria prevalence varies by wealth levels. Deforestation is associated with increased malaria prevalence in the poorest households, but there was not significantly increased malaria prevalence in the richest households, suggesting that deforestation has disproportionate negative health impacts on the poor. In poorer households, malaria prevalence was 27%–33% larger for one standard deviation increase in deforestation across urban and rural populations. Deforestation is also associated with increased malaria prevalence in regions where Anopheles gambiae and Anopheles funestus are dominant vectors, but not in areas of Anopheles arabiensis. These findings indicate that deforestation is an important driver of malaria risk among the world's most vulnerable children, and its impact depends critically on often‐overlooked social and biological factors. An in‐depth understanding of the links between ecosystems and human health is crucial in designing conservation policies that benefit people and the environment.
DeepD3, an open framework for automated quantification of dendritic spines

Dendritic spines are the seat of most excitatory synapses in the brain, and a cellular structure considered central to learning, memory, and activity-dependent plasticity. The quantification of dendritic spines from light microscopy data is usually performed by humans in a painstaking and error-prone process. We found that human-to-human variability is substantial (inter-rater reliability 82.2±6.4%), raising concerns about the reproducibility of experiments and the validity of using human-annotated ‘ground truth’ as an evaluation method for computational approaches of spine identification. To address this, we present DeepD3, an open deep learning-based framework to robustly quantify dendritic spines in microscopy data in a fully automated fashion. DeepD3’s neural networks have been trained on data from different sources and experimental conditions, annotated and segmented by multiple experts and they offer precise quantification of dendrites and dendritic spines. Importantly, these networks were validated in a number of datasets on varying acquisition modalities, species, anatomical locations and fluorescent indicators. The entire DeepD3 open framework, including the fully segmented training data, a benchmark that multiple experts have annotated, and the DeepD3 model zoo is fully available, addressing the lack of openly available datasets of dendritic spines while offering a ready-to-use, flexible, transparent, and reproducible spine quantification method.
Autophagy differentially regulates tissue tolerance of distinct target organs in graft-versus-host disease models

Tissue-intrinsic mechanisms that regulate severity of systemic pathogenic immune-mediated diseases, such as acute graft-versus-host disease (GVHD), remain poorly understood. Following allogeneic hematopoietic stem cell transplantation, autophagy, a cellular stress protective response, is induced in host nonhematopoietic cells. To systematically address the role of autophagy in various host nonhematopoietic tissues, both specific classical target organs of acute GVHD (intestines, liver, and skin) and organs conventionally not known to be targets of GVHD (kidneys and heart), we generated mice with organ-specific knockout of autophagy related 5 (ATG5) to specifically and exclusively inhibit autophagy in the specific organs. When compared with wild-type recipients, animals that lacked ATG5 in the gastrointestinal tract or liver showed significantly greater tissue injury and mortality, while autophagy deficiency in the skin, kidneys, or heart did not affect mortality. Treatment with the systemic autophagy inducer sirolimus only partially mitigated GVHD mortality in intestine-specific autophagy-deficient hosts. Deficiency of autophagy increased MHC class I on the target intestinal epithelial cells, resulting in greater susceptibility to damage by alloreactive T cells. Thus, autophagy is a critical cell-intrinsic protective response that promotes tissue tolerance and regulates GVHD severity.
The effects of proprioceptive exercise training on physical fitness and performance of soccer skills in young soccer players

The purpose of this study is to analyze the effect of proprioceptive exercise training on soccer skills and physical fitness in young soccer players. Nineteen young soccer players participated in the study. Participants were divided into proprioceptive exercises training (PT) and control (Cont) groups. The physical fitness and soccer skill performance test assessed at pre- and posttraining. Balance ability and power significantly improved in both groups after 8 weeks of training (P<0.05). Dribble in the PT group was similar between pre- and posttraining, with a statistically significant difference in the Cont group (P<0.05). There were no significant differences within and between groups according to training in the left and right feet for juggling. The PT and Cont groups did not differ significantly regarding short passes posttraining. However, a significant improvement in long passes was observed in the PT group after training period (P<0.05). Shooting was no different between and within the groups. In conclusion, these inconsistent findings are thought to be due to the applied exercise method, training duration, and the specificity of the physical growth period of the young players. Therefore, it is necessary to increase the frequency and time of proprioceptive exercise training and apply factors for cognitive ability improvement to training programs for young soccer players in future studies. These studies could suggest appropriate training methods necessary to improve the performance of young soccer players.
Health inequalities at the intersection of multiple social determinants among under five children residing Nairobi urban slums: An application of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

In this analysis we examine through an intersectionality lens how key social determinants of health (SDOH) are associated with health conditions among under-five children (<5y) residing in Nairobi slums, Kenya. We used cross-sectional data collected from Nairobi slums between June and November 2012 to explore how multiple interactions of SDoH shape health inequalities in slums. We applied multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) approach. We constructed intersectional strata for each health condition from combinations of significant SDoH obtained using univariate analyses. We then estimated the intersectional effects of health condition in a series of MAIHDA logistic regression models distinguishing between additive and interaction effects. We quantified discriminatory accuracy (DA) of the intersectional strata by means of the variance partitioning coefficient (VPC) and the area under the receiver operating characteristic curve (AUC-ROC). The total participants were 2,199 <5y, with 120 records (5.5%) dropped because health conditions were recorded as “not applicable”. The main outcome variables were three health conditions: 1) whether a child had diarrhea or not, 2) whether a child had fever or not, and 3) whether a child had cough or not in the previous two weeks. We found non-significant intersectional effects for each health condition. The head of household ethnic group was significantly associated with each health condition. We found good DA for diarrhea (VPC = 9.0%, AUC-ROC = 76.6%) an indication of large intersectional effects. However, fever (VPC = 1.9%, AUC-ROC = 66.3%) and cough (VPC = 0.5%, AUC-ROC = 61.8%) had weak DA indicating existence of small intersectional effects. Our study shows pathways for SDoH that affect diarrhea, cough, and fever for <5y living in slums are multiplicative and shared. The findings show that <5y from Luo and Luhya ethnic groups, recent migrants (less than 2 years), and households experiencing CHE are more likely to face worse health outcomes. We recommend relevant stakeholders to develop strategies aimed at identifying these groups for targeted proportionate universalism based on the level of their need.
Linguistic Knowledge and Question Answering Gosse Bouma

In this chapter we present Joost, a Dutch QA system which has been used for both open (CLEF) and closed domain QA (medical questions). The availability of robust and deep syntactic parsing can improve the performance of Question Answering systems. This is illustrated using examples from our QA system. In particular, we demonstrate the application of syntactic information in all components of Joost, namely question analysis, passage retrieval, answer extraction and ranking, off-line extraction of facts and lexical acquisition for QA. MOTS-CLÉS : A définir par la commande
Asymptotic genealogies of interacting particle systems with an application to sequential Monte

We consider weighted particle systems of fixed size, in which a new generation of particles is formed by resampling particles from the current generation with probabilities proportional to their weights. This set-up covers a broad class of sequential Monte Carlo (SMC) methods, which are widely-used in applied statistics and cognate disciplines across a range of domains. We consider the genealogical tree embedded into such particle systems by the resampling operation, and identify conditions, as well as an appropriate time-scaling, under which it converges to the Kingman coalescent in the infinite system size limit in the sense of finite-dimensional distributions. This makes the plethora of distributional results known for the Kingman coalescent available for analysis of SMC algorithms, which we illustrate by characterising the limiting mean and variance of the tree height, as well as of the total branch length of the tree. It also greatly improves the tractability of genealogies of SMC methods, which are known to be closely connected to the performance of these algorithms. The conditions which we require to prove convergence are strong, but we demonstrate by simulation that they do not appear to be necessary.
Introduction to Information Retrieval by Christopher D. Manning

Introduction to Information Retrieval is the first textbook with a coherent treatment of classical and web information retrieval, including web search and the related areas of text classification and text clustering. Written from a computer science perspective, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents and of methods for evaluating systems, along with an introduction to the use of machine learning methods on text collections. Designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also interest researchers and professionals. A complete set of lecture slides and exercises that accompany the book are available on the web. Cambridge University Press has no responsibility for the persistence or accuracy of URLs for external or third-party Internet Web sites referred to in this publication and does not guarantee that any content on such Web sites is, or will remain, accurate or appropriate. Contents Table of Notation page xi Preface xv 1 Boolean retrieval 1 1.1 An example information retrieval problem 3 1.2 A first take at building an inverted index 6 1.3 Processing Boolean queries 9 1.4 The extended Boolean model versus ranked retrieval 13 1.5 References and further reading 16 2 The term vocabulary and postings lists 18 2.1 Document delineation and character sequence decoding 18 2.2 Determining the vocabulary of terms 21 2.3 Faster postings list intersection via skip pointers 33 2.4 Positional postings and phrase queries 36 2.5 References and further reading 43 3 Dictionaries and tolerant retrieval 45 3.
Avyaya Analyzer: Analysis of Indeclinables using Finite State Transducers

1. INTRODUCTIONLanguage Processing (NLP) is a branch of Artificial Intelligence and deals with the Analysis of Human Languages. Each language consists of a set of words called lexicon and a set of rules called grammar. These enable the use of words to form cognitional valid sentences. NLP deals with both the Spoken as well as Written Languages. The goal of a researcher in NLP is to develop a Parser to analyze any human language. A typical language parser can be divided into 3 components [8] viz., Morphological Analyzer, Local Word Grouper and Core Parser. A Sentence is the input for the Parser. A sentence is a group of interrelated words. Every word in the sentence has its own meaning and is encoded grammatically. Further it is related to other words in the sentence. Morphological Analyzer (MA) will take each word in the sentence and retrieve the information encoded in the word. With the help of the information produced by the MA, Local Word Grouper (LWG) will try to identify the phrases in the sentence by grouping the words which have mutual relationship in the sentence. Parser will generate the parsed structure of the sentence.
Genescript: DNA Sequence Annotation Pipeline

UNLABELLED Genescript uses a number of publicly available analysis programs to annotate a DNA sequence. It provides an integrated display of results from each program, and includes an evidence-based scoring system that gives informative summaries of predicted gene models. AVAILABILITY Genescript is available for download from http:://tcag.bioinfo. sickkids.on. ca/genescript/