Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrative analysis of the Inflammatory Bowel Disease serum metabolome improves our understanding of genetic etiology and points to novel putative therapeutic targets.

BACKGROUND Polygenic and environmental factors are underlying causes of inflammatory bowel disease (IBD). We hypothesized that integration of the genetic loci controlling a metabolite's abundance, with known IBD genetic susceptibility loci, may help resolve metabolic drivers of IBD. METHODS We measured the levels of 1300 metabolites in the serum of 484 ulcerative colitis (UC) and 464 Crohn's disease (CD) patients and 365 controls. Differential metabolite abundance was determined for disease status, subtype, clinical and endoscopic disease activity as well as IBD phenotype including disease behavior, location and extent. To inform on the genetic basis underlying metabolic diversity, we integrated metabolite and genomic data. Genetic colocalization and Mendelian randomization (MR) analyses were performed using known IBD risk loci to explore whether any metabolite was causally associated with IBD. RESULTS We found 173 genetically controlled metabolites (mQTL, 9 novel) within 63 non-overlapping loci (7 novel). Furthermore, several metabolites significantly associated with IBD disease status and activity as defined by clinical and endoscopic indexes. This constitutes a resource for biomarker discovery and IBD biology insights. Using this resource, we show that a novel mQTL for serum butyrate levels containing ACADS was not supported as causal for IBD; replicate the association of serum omega-6 containing lipids with the fatty acid desaturase 1/2 locus and identify these metabolites as causal for CD through MR; and validate a novel association of serum plasmalogen and TMEM229B, which was predicted causal for CD. CONCLUSION An exploratory analysis combining genetics and unbiased serum metabolome surveys can reveal novel biomarkers of disease activity and potential mediators of pathology in IBD.
Role of Statins in the Treatment of Pancreatic Ductal Adenocarcinoma (PDAC)

Pancreatic ductal adenocarcinoma (PDAC) is a common cause of cancer-related mortality [1]. Moreover, a majority of patients (80%) are diagnosed at an advanced stage at presentation precluding surgical resection which is the mainstay of curative treatment options [1,2]. Multi-agent chemotherapy has been developed in recent years and have shown improved survival rates, even in patients with locally advanced and metastatic tumours [3,4]. In addition, adjuvant chemotherapy has also conferred a survival advantage in patients with resected pancreatic cancer [5,6]. However, despite these developments, PDAC related mortality remains high, with a 5 year survival rate of 6 7% across all stages [7,8]. This has Crimson Publishers Wings to the Research Research Article
A Discourse on Innovation of English Teaching in China from the Perspective of Artificial Intelligence

With the progression of economic globalisation, China’s expectations about the quality of English teaching in the country have increased, and because of the restrictive drawbacks of orthodox English teaching models, they cannot meet these requirements. The emergence of artificial intelligence (AI) technology can create new opportunities for the optimisation of English teaching. As such, this study aims to systematically review and analyse the current research on the innovations afforded by AI for China’s English teaching model, especially the application of AI in unquiesced English teaching. The descriptive–analytical approach is used to study and analyse the literature. The research results highlight evidence of success cases, illustrating that the implementation of AI in English teaching in China can provide new ideas and create new spaces for the innovation of teaching models. Keywords: artificial intelligence; English teaching in China; innovation in English teaching
Does globalisation have an impact on dual banking system productivity in selected Southeast Asian banking industry?

PurposeGlobalisation has influenced many countries, over the last few decades with financial globalisation and liberalisation bringing regulatory reforms in the banking sector. Thus, this study aims to fill a gap in the literature by examining the influence of globalisation on Islamic and conventional bank productivity in Southeast Asia.Design/methodology/approachThe sample comprised 155 banks (23 Islamic and 132 conventional) from 4 countries from 2008 to 2017. Panel data techniques will be used, together with data envelopment analysis (DEA)-based Malmquist productivity index (MPI), to investigate the impact of chosen main determinants on bank productivity. A panel regression analysis will be performed after generating the productivity index from the DEA-based MPI frontier.FindingsAccording to the findings, Islamic banks are statistically significantly more productive than conventional banks, and the findings of the t-test are corroborated by the findings of nonparametric tests. Furthermore, the findings of the panel regression model reveal that bank specific factors and macroeconomic variables are significant determinants to bank productivity. Surprisingly, the findings also show that the influence of social globalisation elements tends to be negatively related to conventional bank productivity.Originality/valueThis study adds to the existing literature by bridging the globalisation gap in the productivity of the dual banking industry, particularly in the specific context of Southeast Asia, given that the area is representative of Islamic and finance globally.
Stability analysis for SRAM cells with TSV induced stress in 3D ICs

Three-dimensional integrated circuits (3D-IC) have emerged as promising candidates to overcome the interconnect bottlenecks of nanometer scale designs while also helping to reduce wire delay and increase memory throughput. While this technology offers many potential advantages, it also produces large thermal mismatch stress in 3D-IC structures employing Through-Silicon-Via (TSV). The stress distribution in silicon and interconnect is affected by the via diameter and layout geometry. TSV-induced stress effects on electron/hole mobility and device performance will be studied for the widely used 6-transistor (6T) SRAM cell. Simulation results in this study show that static noise margin (SNM), Read Margin (RM) and write margin (WM) tend to increase with decreasing electron mobility or increasing hole mobility. Considering TSV-induced stress, we propose that for practical layouts of TSV-based 3DIC, p-type substrates should be placed further away from TSVs or closer to the smaller TSVs if multiple TSVs exist.
Synaptic self-organization of spatio-temporal pattern selectivity

Spiking model neurons can be set up to respond selectively to specific spatio-temporal spike patterns by optimization of their input weights. It is unknown, however, if existing synaptic plasticity mechanisms can achieve this temporal mode of neuronal coding and computation. Here it is shown that changes of synaptic efficacies which tend to balance excitatory and inhibitory synaptic inputs can make neurons sensitive to particular input spike patterns. Simulations demonstrate that a combination of Hebbian mechanisms, hetero-synaptic plasticity and synaptic scaling is sufficient for self-organizing sensitivity for spatio-temporal spike patterns that repeat in the input. In networks inclusion of hetero-synaptic plasticity leads to specialization and faithful representation of pattern sequences by a group of target neurons. Pattern detection is robust against a range of distortions and noise. The proposed combination of Hebbian mechanisms, hetero-synaptic plasticity and synaptic scaling is found to protect the memories for specific patterns from being overwritten by ongoing learning during extended periods when the patterns are not present. This suggests a novel explanation for the long term robustness of memory traces despite ongoing activity with substantial synaptic plasticity. Taken together, our result promote the plausibility of precise temporal coding in the brain. Author summary Neurons communicate using action potentials, that are pulses localized in time. There is evidence that the exact timing of these so called spikes carries information. The hypothesis, however, that computations in brains are indeed based on precise patterns of spikes is debated, particularly because this would require the existence of suitable detectors. While theoretically, individual neurons can perform spike pattern detection when their input synapses are carefully adjusted, it is not known if existing synaptic plasticity mechanisms indeed support this coding principle. Here, a combination of basic but realistic mechanisms is demonstrated to self-organize the synaptic input efficacies such that individual neurons become detectors of patterns repeating in the input. The proposed combination of learning mechanisms yields a balance of excitation and inhibition similar to observations in cortex, robustness of detection against perturbations and noise, and persistence of memory against plasticity during ongoing activity without the learned patterns. The proposed learning mechanism enables groups of neurons to incrementally acquire sets of patterns thereby faithfully representing their ’which’ and ’when’ in sequences. These results suggest that computations based on spatio-temporal spike patterns might emerge without any supervision from the synaptic plasticity mechanisms present in the brain.
Minimum-time manoeuvring in electric vehicles with four wheel-individual-motors

The coordinated control of vehicle actuators is gaining more and more importance as new platforms are becoming available, with chassis endowed with many different actuators that may help controlling the vehicle motion. Furthermore, wheel individual motors allow using a single system to apply both positive and negative torques at the wheels, which can be actuated independently one from the other. In electric vehicles (EVs), moreover, such a freedom in the actuation mechanisms opens the way to the combined optimisation of performance and energy consumption issues. In this paper, the problem of minimum-time manoeuvring in EVs is addressed, and the proposed strategy is compared against a benchmark, a-causal optimal solution showing that only a negligible loss of performance is experienced.
Basal plane thermal conductivity of thin germanane layers

vi List of Tables . vii List of Figures . viii Chapter 1: Introduction . 1 1.1 Thermoelectricity in Nanoscale Systems . 1 1.2 Motivation of This Work . 2 Chapter 2: Thermal Transport Measurements for Thin Films . 3 2.1 Raman Thermometry . 3 2.2 Time-Domain Thermoreflectance . 5 2.3 3-Omega Method . 6 2.4 Suspended Microheater Technique . 7 Chapter 3: Thermal Conductivity of Suspended Germanane . 12 3.1 Material Synthesis and Background . 12 3.2 Sample Assembly Method . 13 3.3 Contact Resistance and Experimental Results . 16 3.4 Uncertainty Analysis . 19 3.5 Phonon Mean Free Path . 22 Chapter 4: Conclusion . 24 Bibliography . 25
Gender Diversity on Board and Financial Reporting Quality in an Emerging Country

The board of directors’ monitoring function derived from the agency theory plays a crucial role in mitigating agency problems and monitoring managerial decisions to protect shareholders’ interests and to ensure high-quality financial reporting. There is a recent growing stream of research on the effects of gender differences that state that the presence of women on the board of directors could bring about positive outcomes in a firm’s performance, corporate monitoring, and oversight. Thus, the purpose of this study is to investigate whether the gender diversity of the board of directors has a significant impact on the firm’s financial reporting quality proxied by earnings management. This study employs panel data analysis of the financial data and qualitative data for the period from 2016 until the latest ones 2020 of Malaysian listed companies, as evidence from emerging countries. The empirical results of this study indicate that female directors on board mitigate earnings management. Thus, the results provide insights for regulators and policymakers; that the presence of female directors will affect management decisions and audit quality, therefore gender diversity on board should be further emphasized.
OM-MSCs Alleviate the Golgi Apparatus Stress Response following Cerebral Ischemia/Reperfusion Injury via the PEDF-PI3K/Akt/mTOR Signaling Pathway

The mechanism of Golgi apparatus (GA) stress responses mediated by GOLPH3 has been widely studied in ischemic stroke, and the neuroprotection effect of olfactory mucosa mesenchymal stem cells (OM-MSCs) against cerebral ischemia/reperfusion injury (IRI) has been preliminarily presented. However, the exact role of OM-MSCs in the GA stress response following cerebral IRI remains to be elucidated. In the present study, we used an oxygen-glucose deprivation/reoxygenation (OGD/R) model and reversible middle cerebral artery occlusion (MCAO) model to simulate cerebral IRI in vitro and in vivo. Our results showed that the level of GOLPH3 protein, reactive oxygen species (ROS), and Ca2+ was upregulated, SPCA1 level was downregulated, and GA fragmentation was increased in ischemic stroke models, and OM-MSC treatment clearly ameliorated these GA stress responses in vitro and in vivo. Subsequently, the knockdown of PEDF in OM-MSCs using PEDF-specific siRNA further demonstrated that secretion of PEDF in OM-MSCs protected OGD/R-treated N2a cells and MCAO rats from GA stress response. Additionally, rescue experiment using specific pathway inhibitors suggested that OM-MSCs could promote the phosphorylation of the PI3K/Akt/mTOR pathway, thereby mitigating OGD/R-induced GA stress response and excessive autophagy. In conclusion, OM-MSCs minimized the GA stress response following cerebral IRI, at least partially, through the PEDF-PI3K/Akt/mTOR pathway.
Specifications and Performances of Series Superfluid Helium Safety Relief Valves for the LHC

Protecting the LHC magnets requires safety relief valves operating with 1.9 K pressurized superfluid helium at their inlet. Following the evaluation of prototype valves, a specification for the production of the 360 safety relief valves needed for the LHC was issued. The production of the safety valves was then awarded to an industrial contractor. The performances of pre‐series valves were assessed for a variety of aspects including thermal performance, leak tightness in superfluid helium, dynamic behavior and resistance to intensive mechanical cycling. After the initial validation phase the series production was completed within the technical requirements of the specification. This paper describes the characteristics of the safety relief valves and the specifications for their industrial production. The performances of the pre‐series valves are presented and an overview of the series production phase is given.
Deuterium depletion in xylem water and soil isotopic effects complicate the assessment of riparian tree water sources in the seasonal tropics

Riparian trees located in seasonally dry environments may be reliant on groundwater supplies, but the prevalence and magnitude of groundwater uptake is often unclear. Using soil water matric potential and water stable isotopes, we examined the relative contributions of soil water and groundwater to the dry season water uptake of five riparian tree species along an intermittent river of tropical northern Australia. Because xylem water was depleted in deuterium relative to source water (average offset −14.0‰), we numerically removed this offset and assessed the effect of the correction on mixing model results. We also estimated the isotopic composition of unbound soil water (i.e., the portion of soil water not tightly bound to soil particles) from bulk soil water data by using an empirical formulation from the literature and tested whether considering unbound soil water as a source would affect our results. Despite the hot and dry surface environment, we found that soil moisture was available for trees at relatively shallow (~0.7–1.5 m) depths. When unbound soil water and corrected xylem water data were considered, most tree species used a combination of this soil moisture source and groundwater from the capillary fringe. However, not correcting for isotopic effects resulted in large underestimations of the groundwater contributions to tree water uptake. Our findings suggest that ignoring soil isotopic effects and deuterium depletion in xylem water may reduce the validity of source water partitioning assessments. Further research is needed on the likely causes for deuterium depletion in xylem water.
Ca2+ activity is required for injury-induced migration of microglia in zebrafish in vivo

Objectives Microglia are the resident immune cells in the brain. Brain injury can activate the microglia and induce its directional migration towards injury sites for exerting immune functions. While extracellular ATP released from the injury site mediates the directionality of activated microglia’s migration, what endows activated microglia with migration capability remains largely unexplored. Methods In the present study, we used the larval zebrafish as an in vivo model to visualize the dynamics of both morphology and Ca2+ activity of microglia during its migration evoked by local brain injury. Results We found that, in response to local injury, activated microglia exhibited an immediate Ca2+ transient and later elevated Ca2+ bursts frequency during its migration towards the local injury site (P < 0.01). Furthermore, suppression of Ca2+ activities significantly retarded microglial migration (P < 0.05). Conclusion Thus, our study suggests that intracellular Ca2+ activity is required for activated microglia’s migration.
Crime hotspot prediction based on dynamic spatial analysis

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.
Literature-based Discovery

Biomedical research is divided into highly specialized fields and subfields, with poor communication between them. The rate of growth of publications makes it difficult for a researcher to derive connections between Concepts from different research specialties. BioCAID's IMDI Profiler excels at quickly mining hidden connections among biomedical Concepts from large amounts of scientific literature and presenting them in as an intuitive visualization and navigation application.
The Impact of Gamified Instruction on Students’ Learning Outcomes: Systematic Review of Experimental Studies

This systematic review critically explores the intervention design and findings of the experimental studies that were published between January 2012-December 2020 in a number of digital libraries and databases and had the effect of a gamified instruction on students’ learning outcomes in their focus, with the aim of identifying what constitutes success or the lack thereof in the given context. The found effect(s) of gamified instruction on students’ learning engagement and achievement are discussed in relation to the a) intervention design, its flaws and their potential impact on reported outcomes and b) prevalent practice in gamification research. The discussion is structured around data collection sources, sample size, and intervention duration, but also the characteristics of learning technology, learning approach, course content, type of games and game elements. This study proposes a list of categories to be included in the description of a study context so that it is possible to a) systematically organise research findings, b) filter the variety of findings via means of replication studies. c) recognise the variant effect on different sub-populations, and d) suggest the way forward when designing and implementing gamified instruction within specific conditions. Furthermore, the study highlights the necessity of approaching the topic through a mixed-method approach involving a more intensive tracking schedule with new assessment instruments and a larger number of participants that are longitudinal or at least of a longer duration in order to obtain more comprehensive findings.
From statistical‐ to machine learning‐based network traffic prediction

Nowadays, due to the exponential and continuous expansion of new paradigms such as Internet of Things (IoT), Internet of Vehicles (IoV) and 6G, the world is witnessing a tremendous and sharp increase of network traffic. In such large‐scale, heterogeneous, and complex networks, the volume of transferred data, as big data, is considered a challenge causing different networking inefficiencies. To overcome these challenges, various techniques are introduced to monitor the performance of networks, called Network Traffic Monitoring and Analysis (NTMA). Network Traffic Prediction (NTP) is a significant subfield of NTMA which is mainly focused on predicting the future of network load and its behavior. NTP techniques can generally be realized in two ways, that is, statistical‐ and Machine Learning (ML)‐based. In this paper, we provide a study on existing NTP techniques through reviewing, investigating, and classifying the recent relevant works conducted in this field. Additionally, we discuss the challenges and future directions of NTP showing that how ML and statistical techniques can be used to solve challenges of NTP.