Miracle-dz/UniFAR-FTUs huggingface dataset:
The association between childcare arrangements and risk of overweight and obesity in childhood: a systematic review

Over 80% of preschool‐aged children experience non‐parental childcare. Childcare type has the potential to influence weight outcomes, but its impact on childhood overweight/obesity is not well established. This review aims to (i) systematically evaluate the effects of childcare type on childhood overweight/obesity risk and (ii) investigate the impact of childcare intensity and age at commencement. Five electronic databases were searched for observational studies quantifying an association between childcare type ≤5 years and weight outcomes <18 years. Twenty‐four studies were included (n = 127,529 children). Thirteen studies reported increased risk of overweight/obesity in children attending informal care (n = 9) or centre care (n = 4) vs. parental care. Seven studies reported decreased risk of overweight/obesity for children in centre vs. ‘non‐centre’ care (parental and informal). Four studies reported no association between informal or centre care and overweight/obesity. Early (<3 years) informal care, especially by a relative, was associated with increased risk of overweight/obesity. Higher intensity childcare, especially when commenced early (<1 year), increased overweight/obesity risk. Later (≥3 years) centre care was associated with decreased risk of overweight/obesity. Early informal care, earlier commencement age and higher intensity represent a risk for childhood obesity. Exploration of the obesogenic aspects of these contexts is essential to inform preventative measures.
Extracellular ATP Acts on Jasmonate Signaling to Reinforce Plant Defense1[OPEN]

Extracellular ATP signaling, mediated by the P2K1 receptor, directly changes the stability of JAZ1 protein in JA signaling to boost plant defense response. Damaged cells send various signals to stimulate defense responses. Recent identification and genetic studies of the plant purinoceptor, P2K1 (also known as DORN1), have demonstrated that extracellular ATP is a signal involved in plant stress responses, including wounding, perhaps to evoke plant defense. However, it remains largely unknown how extracellular ATP induces plant defense responses. Here, we demonstrate that extracellular ATP induces plant defense mediated through activation of the intracellular signaling of jasmonate (JA), a well-characterized defense hormone. In Arabidopsis (Arabidopsis thaliana) leaves, ATP pretreatment induced resistance against the necrotrophic fungus, Botrytis cinerea. The induced resistance was enhanced in the P2K1 receptor overexpression line, but reduced in the receptor mutant, dorn1-3. Mining the transcriptome data revealed that ATP induces a set of JA-induced genes. In addition, the P2K1-associated coexpression network contains defense-related genes, including those encoding jasmonate ZIM-domain (JAZ) proteins, which play key roles as repressors of JA signaling. We examined whether extracellular ATP impacts the stability of JAZ1 in Arabidopsis. The results showed that the JAZ1 stability decreased in response to ATP addition in a proteasome-dependent manner. This reduction required intracellular signaling via second messengers—cytosolic calcium, reactive oxygen species, and nitric oxide. Interestingly, the ATP-induced JAZ1 degradation was attenuated in the JA receptor mutant, coi1, but not in the JA biosynthesis mutant, aos, or upon addition of JA biosynthesis inhibitors. Immunoprecipitation analysis demonstrated that ATP increases the interaction between COI1 and JAZ1, suggesting direct cross talk between extracellular ATP and JA in intracellular signaling events. Taken together, these results suggest that extracellular ATP signaling directly impacts the JA signaling pathway to maximize plant defense responses.
Blood pressure goal for the elderly Chinese: the findings from the Northern Shanghai Study

ABSTRACT Background: The Eighth Joint National Committee Panel (JNC8) recommended a novel blood pressure (BP) goal for individuals with hypertension, which was less stringent than the Seventh Joint National Committee (JNC7) guideline and is still under debate. In our study, we aimed at finding a better BP goal for the elderly Chinese. Methods: About 1599 community-based elderly participants were recruited in the northern Shanghai and were classified by chronic kidney disease or diabetes mellitus to investigate their BP control conditions based on both the JNC7 and JNC8. Then, participants were categorized into four groups: normotensive individuals (Group 1), individuals at BP goal by JNC7 (Group 2), individuals at BP goal by JNC8 but not by JNC7 (Group 3), and individuals not at-goal by both guidelines (Group 4). Patients’ hypertensive target organ damages as left ventricular mass index (LVMI), peak transmitral pulsed Doppler velocity/early diastolic tissue Doppler velocity (E/Ea), pulse wave velocity (PWV), etc. were evaluated. Results: According to the JNC8, 19.0% of the population were reclassified as at-goal. Group 4 had significantly greater LVMI than Group 2 (96.5 vs 91.5 g/m2, p < 0.05) and also had significantly greater E/Ea (10.3 vs 9.7 and 10.3 vs 9.7, p < 0.05) and PWV (10.3 vs 9.3 m/s and 10.3 vs 9.7 m/s, p < 0.05) than both Group 2 and Group 3; however, there were no significant differences of these variables between Group 2 and Group 3. Conclusion: In the community-based elderly Chinese, the JNC8 hypertension guideline may set a better BP goal than the JNC7 in identifying patients’ left ventricular diastolic dysfunction and arterial stiffening.
Prevalence and Implications of Bone Marrow Involvement in Patients with Gastric Mucosa-Associated Lymphoid Tissue Lymphoma

Background/Aims Mucosa-associated lymphoid tissue (MALT) lymphoma of the stomach is an uncommon disease. Bone marrow involvement is reported even in patients with only a mucosal lesion. We evaluated the prevalence and risk factors of marrow involvement and its implications for diagnosis and treatment. Methods In total, 132 patients who were diagnosed with gastric MALT lymphoma at the National Cancer Center in Korea between January 2001 and December 2016 were enrolled in the study. The patient data were collected and analyzed retrospectively. Results Of the 132 patients, 47 (35.6%) were male, with a median age of 52 years (range, 17 to 81 years). The median follow-up duration was 48.8 months (range, 0.5 to 169.9 months). Helicobacter pylori infection was detected in 82 patients (62.1%). Most patients (80.3%) had stage IE1 according to the modified Ann Arbor staging system. Ninety-two patients underwent bone marrow evaluation, and four patients (4.3%) had marrow involvement. Of these patients, one presented with abdominal lymph node involvement, while the other three had stage IE1 disease if marrow involvement was disregarded. All three patients had no significant symptoms and were monitored after local treatment without evidence of disease aggravation. Conclusions Bone marrow involvement was found in 4.3% of the patients with gastric MALT lymphoma. Bone marrow examination may be deferred because marrow involvement does not change the treatment options or outcome in gastric MALT lymphoma confined to the stomach wall.
Stenotrophomonas-Like Bacteria Are Widespread Symbionts in Cone Snail Venom Ducts

ABSTRACT Cone snails are biomedically important sources of peptide drugs, but it is not known whether snail-associated bacteria affect venom chemistry. To begin to answer this question, we performed 16S rRNA gene amplicon sequencing of eight cone snail species, comparing their microbiomes with each other and with those from a variety of other marine invertebrates. We show that the cone snail microbiome is distinct from those in other marine invertebrates and conserved in specimens from around the world, including the Philippines, Guam, California, and Florida. We found that all venom ducts examined contain diverse 16S rRNA gene sequences bearing closest similarity to Stenotrophomonas bacteria. These sequences represent specific symbionts that live in the lumen of the venom duct, where bioactive venom peptides are synthesized. IMPORTANCE In animals, symbiotic bacteria contribute critically to metabolism. Cone snails are renowned for the production of venoms that are used as medicines and as probes for biological study. In principle, symbiotic bacterial metabolism could either degrade or synthesize active venom components, and previous publications show that bacteria do indeed contribute small molecules to some venoms. Therefore, understanding symbiosis in cone snails will contribute to further drug discovery efforts. Here, we describe an unexpected, specific symbiosis between bacteria and cone snails from around the world.
Trialling exudate-based enrichment efforts to improve the welfare of rescued slow lorises Nycticebus spp.

Slow lorises Nycticebus spp. are heavily targeted in the illegal animal trade through- out Southeast Asia. Because lorises are venomous, many have their teeth inhumanely removed by animal traders in order to 'tame' them to live as pets or to be used for tourist photography. Confis- cated animals are brought to rescue centres where captive care and rehabilitation has proven challenging. Many rescue centres report high mortality, high rates of abnormal behaviour, and low rates of successful reintroductions. Most centres, however, provide little or no gums or saps to these highly specialised exudativorous primates. Our study evaluated the effects of exudate- based enrichment efforts on confiscated wild-born slow lorises at a rescue centre in Java, Indone- sia, including methods to provide exudates to animals lacking teeth. We found that these enrich- ment efforts prolonged foraging and feeding times, but did not reduce abnormal behaviour or promote social behaviour. We found a strong relationship between the amount of time spent on the ground and the time spent performing abnormal behaviour. We discuss the positive implica- tions of prolonging feeding periods, the practicality of each of the enrichment efforts for different types of captive settings, and methods for improving the welfare of captive slow lorises via dietary changes.
Online Deep Metric Learning

Metric learning learns a metric function from training data to calculate the similarity or distance between samples. From the perspective of feature learning, metric learning essentially learns a new feature space by feature transformation (e.g., Mahalanobis distance metric). However, traditional metric learning algorithms are shallow, which just learn one metric space (feature transformation). Can we further learn a better metric space from the learnt metric space? In other words, can we learn metric progressively and nonlinearly like deep learning by just using the existing metric learning algorithms? To this end, we present a hierarchical metric learning scheme and implement an online deep metric learning framework, namely ODML. Specifically, we take one online metric learning algorithm as a metric layer, followed by a nonlinear layer (i.e., ReLU), and then stack these layers modelled after the deep learning. The proposed ODML enjoys some nice properties, indeed can learn metric progressively and performs superiorly on some datasets. Various experiments with different settings have been conducted to verify these properties of the proposed ODML.
Changes over time in head and neck cancer patients’ and caregivers’ illness perceptions and relationships with quality of life

Objective: This study investigated changes in illness perceptions from diagnosis to six months later in patients with head and neck cancer (HNC) and their caregivers. The study also examined whether discrepancy in patient and caregiver perceptions at diagnosis predicted patient health-related quality of life (HRQL) at six months. Design: Forty-two patient–caregiver dyads completed the Brief Illness Perception Questionnaire (Brief IPQ) at diagnosis and again six months later. Patients also completed a HRQL questionnaire at both time points. Analyses were performed using the Actor–Partner Interdependence Model. Main Outcome Measure: Total patient HRQL assessed by the Functional Assessment of Cancer Therapy (FACT-H&N). Results: Perceptions of emotional impact and illness concern reduced over time in patients and caregivers. Perceptions of treatment control and identity increased in caregivers only. After controlling for the effects of baseline HRQL, and the individual contribution of patient and caregiver illness perceptions, greater discrepancy in perceptions of timeline, personal control, and illness identity among dyads at diagnosis predicted lower patient HRQL at six-month follow-up. Conclusion: Patients’ and their caregivers’ perceptions of HNC are dynamic over time. Greater discrepancy between patients’ and caregivers’ illness perceptions at diagnosis predict poorer subsequent patient HRQL.
Arabic Data Science Toolkit: An API for Arabic Language Feature Extraction

We introduce Arabic Data Science Toolkit (ADST), a framework for Arabic language feature extraction, designed for data scientists that may not be familiar with Arabic or natural language processing. The functions in the toolkit allow data scientists to extend their algorithms beyond lexical or statistical methods and leverage Arabic-speciﬁc linguistic and stylistic features to enhance their systems and enable them to reach performance levels they might receive on languages with more resources, or languages with which they have more familiarity
Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding

The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: “Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP? ”. In answer, we develop novel methods to exploit the rich expressiveness of REs at different levels within a NN, showing that the combination significantly enhances the learning effectiveness when a small number of training examples are available. We evaluate our approach by applying it to spoken language understanding for intent detection and slot filling. Experimental results show that our approach is highly effective in exploiting the available training data, giving a clear boost to the RE-unaware NN.
Building a List of Synonymous Words and Phrases of Japanese Compound Verbs

We started to construct a database of synonymous expressions of Japanese “Verb + Verb” compounds semi-automatically. Japanese is known to be rich in compound verbs consisting of two verbs joined together. However, we did not have a comprehensive Japanese compound lexicon. Recently a Japanese compound verb lexicon was constructed by the National Institute for Japanese Language and Linguistics(NINJAL)(2013-15). Though it has meanings, example sentences, syntactic patterns and actual sentences from the corpus that they possess, it has no information on relationships with another words, such as synonymous words and phrases. We automatically extracted synonymous expressions of compound verbs from corpus which is “five hundred million Japanese texts gathered from the web” produced by Kawahara et.al. (2006) by using word2vec and cosine similarity and find suitable clusters which correspond to meanings of the compound verbs by using k-means++ and PCA. The automatic extraction from corpus helps humans find not only typical synonyms but also unexpected synonymous words and phrases. Then we manually compile the list of synonymous expressions of Japanese compound verbs by assessing the result and also link it to the “Compound Verb Lexicon” published by NINJAL.
Pseudo maximum likelihood approach for the analysis of multivariate left‐censored longitudinal data

The linear mixed effects model based on a full likelihood is one of the few methods available to model longitudinal data subject to left censoring. However, a full likelihood approach is complicated algebraically because of the large dimension of the numeric computations, and maximum likelihood estimation can be computationally prohibitive when the data are heavily censored. Moreover, for mixed models, the complexity of the computation increases as the dimension of the random effects in the model increases. We propose a method based on pseudo likelihood that simplifies the computational complexities, allows a wide class of multivariate models, and that can be used for many different data structures including settings where the level of censoring is high. The motivation for this work comes from the need for a joint model to assess the joint effect of pro‐inflammatory and anti‐inflammatory biomarker data on 30‐day mortality status while simultaneously accounting for longitudinal left censoring and correlation between markers in the analysis of Genetic and Inflammatory Markers for Sepsis study conducted at the University of Pittsburgh. Two markers, interleukin‐6 and interleukin‐10, which naturally are correlated because of a shared similar biological pathways and are left‐censored because of the limited sensitivity of the assays, are considered to determine if higher levels of these markers is associated with an increased risk of death after accounting for the left censoring and their assumed correlation. Copyright © 2016 John Wiley & Sons, Ltd.
Extended Pipeline for Content-Based Feature Engineering in Music Genre Recognition

We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification. The key idea is to extend the traditional two-step process of extraction and classification with additive stand-alone phases which are no longer organized in a waterfall scheme. The whole system is realized by traversing backtrack arrows and cycles between various stages. In order to give a compact and effective representation of the features, the standard early temporal integration is combined with other selection and extraction phases: on the one hand, the selection of the most meaningful characteristics based on information gain, and on the other hand, the inclusion of the nonlinear correlation between this subset of features, determined by an autoencoder. The results of the experiments conducted on GTZAN dataset reveal a noticeable contribution of this methodology towards the model's performance in classification task.
Response to discussion on “Roadway asset inspection sampling using high‐dimensional clustering and locality‐sensitivity hashing,” Computer‐Aided Civil and Infrastructure Engineering, 34:2, 116–129, 2019

The hashing approach as well as how it works in theory has been explained in the published manuscript. The eligible hash function has been defined and proved in Datar, Immorlica, Indyk, and Mirrokni (2004), as highlighted on pages 3–4 under section 2 “Literature Review. ” In fact, both localitysensitive hashing (LSH) and inner product hash function used in our sampling method are well-established concepts in computer science. In our study, we applied these concepts in the context of highway asset management to design a new sampling method and further justified its suitability. We understand the concern of the discusser in terms of the randomized hashing approach. However, interested readers should really refer to relevant papers that are cited in our manuscript regarding the theoretical proof. As long as the hash function can fulfill the criteria (bullet points in the second section), it is considered eligible to be used for the LSH approach—in our case, the inner product hash function.
Dry Matter Partitioning in Tomato: Validation of a Dynamic Simulation Model

Abstract A model for dynamic simulation of dry matter distribution between reproductive and vegetative plant parts and the distribution among individual fruit trusses in glasshouse tomato, is validated. The model is part of the crop growth model TOMSIM and is based on the hypothesis that dry matter distribution is regulated by the sink strengths of the plant organs, quantified by their potential growth rates, i.e. the growth rates at non-limiting assimilate supply. Within the plant, individual fruit trusses are distinguished and sink strength of a truss is described as a function of its development stage. Truss development rate is a function of temperature only. The same potential growth curve, proportional to the number of fruits per truss, is adopted for all trusses. In a simple version of the model, vegetative plant parts are lumped together as one sink with a constant sink strength. In a more detailed version, vegetative sink strength is calculated as the sum of sink strengths of vegetative units (three leaves and stem internodes between two trusses). The model was validated for six glasshouse experiments, covering effects of planting date, plant density, number of fruits per truss (pruning at anthesis), truss removal (every second truss removed at anthesis), single- and double-shoot plants and a temperature experiment conducted in climate rooms at 17, 20 or 23 °C. Daily increase in above-ground dry weight, average daily temperatures and number of set fruits per truss were inputs to the model. Both the simple and the more detailed model showed good agreement between measured and simulated fraction of dry matter partitioned into the fruits over time. For the simple version of the model, the slope of the lines relating simulated to measured fraction partitioned into the fruits (16 data sets), varied between 0.92 and 1.11, on average it was 1.04, implying 4% over-estimation for this fraction. For the detailed model these numbers were slightly better: 0.89, 1.08 and 1.01, respectively. The temperature experiment revealed no important direct influence of temperature on the ratio between generative and vegetative sink strength. Simulated truss growth curves showed reasonable agreement with the measurements, although both models over-estimated (17% on average) final dry weight of the lower trusses (truss 1 –3) on a plant. Modelling dry matter partitioning based on sink strengths of organs is promising, as it is a general, dynamic and flexible approach, showing good agreement between measurements and simulation for a range of conditions. Applicability of the model is, however, still limited as long as the number of fruits per truss (flower and /or fruit abortion) is not simulated, as this is a major feedback mechanism in plant growth.
Building a Macro Chinese Discourse Treebank

Discourse structure analysis is an important research topic in natural language processing. Discourse structure analysis not only helps to understand the discourse structure and semantics, but also provides strong support for deep applications of natural language processing, such as automatic summarization, statistical machine translation, question and answering, etc. At present, the analyses of discourse structure are mainly concentrated on the micro level, while the analyses on macro level are few. Therefore, this paper focuses on the construction of representation schema and corpus resources on the macro level of discourse structure. This paper puts forward a macro discourse structure framework and constructs the logical semantic structure and functional pragmatic structure respectively. On this basis, a macro Chinese discourse structure treebank is annotated, consisting of 147 Newswire articles. Preliminary experimental results show that the representation schema and corpus resource constructed in this paper can lay the foundation for further analysis of macro discourse structure.