Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Clinical Data and Attentional CT Imaging Features for Esophageal Fistula Prediction in Esophageal Cancer

Background and Purpose This study aims to develop a risk model to predict esophageal fistula in esophageal cancer (EC) patients by learning from both clinical data and computerized tomography (CT) radiomic features. Materials and Methods In this retrospective study, computerized tomography (CT) images and clinical data of 186 esophageal fistula patients and 372 controls (1:2 matched by the diagnosis time of EC, sex, marriage, and race) were collected. All patients had esophageal cancer and did not receive esophageal surgery. 70% patients were assigned into training set randomly and 30% into validation set. We firstly use a novel attentional convolutional neural network for radiographic descriptor extraction from nine views of planes of contextual CT, segmented tumor and neighboring structures. Then clinical factors including general, diagnostic, pathologic, therapeutic and hematological parameters are fed into neural network for high-level latent representation. The radiographic descriptors and latent clinical factor representations are finally associated by a fully connected layer for patient level risk prediction using SoftMax classifier. Results 512 deep radiographic features and 32 clinical features were extracted. The integrative deep learning model achieved C-index of 0.901, sensitivity of 0.835, and specificity of 0.918 on validation set with superior performance than non-integrative model using CT imaging alone (C-index = 0.857) or clinical data alone (C-index = 0.780). Conclusion The integration of radiomic descriptors from CT and clinical data significantly improved the esophageal fistula prediction. We suggest that this model has the potential to support individualized stratification and treatment planning for EC patients.
Contrasting Community Assembly Forces Drive Microbial Structural and Potential Functional Responses to Precipitation in an Incipient Soil System

Microbial communities in incipient soil systems serve as the only biotic force shaping landscape evolution. However, the underlying ecological forces shaping microbial community structure and function are inadequately understood. We used amplicon sequencing to determine microbial taxonomic assembly and metagenome sequencing to evaluate microbial functional assembly in incipient basaltic soil subjected to precipitation. Community composition was stratified with soil depth in the pre-precipitation samples, with surficial communities maintaining their distinct structure and diversity after precipitation, while the deeper soil samples appeared to become more uniform. The structural community assembly remained deterministic in pre- and post-precipitation periods, with homogenous selection being dominant. Metagenome analysis revealed that carbon and nitrogen functional potential was assembled stochastically. Sub-populations putatively involved in the nitrogen cycle and carbon fixation experienced counteracting assembly pressures at the deepest depths, suggesting the communities may functionally assemble to respond to short-term environmental fluctuations and impact the landscape-scale response to perturbations. We propose that contrasting assembly forces impact microbial structure and potential function in an incipient landscape; in situ landscape characteristics (here homogenous parent material) drive community structure assembly, while short-term environmental fluctuations (here precipitation) shape environmental variations that are random in the soil depth profile and drive stochastic sub-population functional dynamics.
Inequality in Social Support Associated With Mild Cognitive Impairment: A Cross-Sectional Study of Older (≥60 Years) Residents in Shanghai, China

Objective: Social support plays a critical role in the detection and management of mild cognitive impairment (MCI). However, socioeconomic inequalities exist in both social support and health care services. Our study aimed to compare the level of social support received by MCI patients in comparison with those without MCI and to determine its link with income. Methods: Secondary data analyses were performed. Social support was measured using the Duke Social Support Index (DSSI) and satisfaction ratings. Multivariate logistic regression models were constructed to determine the associations of personal income and MCI with social support after adjustment for variations in the sociodemographic and health characteristics of the respondents. The multiplicative and additive interaction effects of income and MCI were further examined through introducing the MCI*Income variable to the regression models and using the relative excess risk due to interaction (RERI) analysis, respectively. Results: The logistic regression models showed that the respondents with MCI had significantly lower social support as measured by the DSSI scores (AOR = 33.03, p < 0.001) and satisfaction ratings (AOR = 7.48, p < 0.001) compared with those without MCI. Similarly, social support decreased with lower personal income (p < 0.001). There existed a significant multiplicative interaction effect between personal income and MCI on social support (AOR = 0.30–0.32, p < 0.01). The gap in social support between those with and without MCI was higher in the higher income group compared with the lower income group (p < 0.001). No significant additive interaction effects on social support were found between MCI and income. Conclusions: There are significant disparities in social support between people living with and without MCI. Such a gap is more profound in people with higher income. The inequality in social support associated with MCI may present a significant challenge to the successful implementation of community MCI detection and management.
Increased Risk for Cerebral Hypoxia During Immediate Neonatal Transition After Birth in Term Neonates Delivered by Caesarean Section With Prenatal Tobacco Exposure

Introduction: Maternal tobacco smoking during pregnancy is a global health problem leading to an increased risk for fetal and neonatal morbidities. So far, there are no data of the potential impact of maternal smoking during pregnancy on the most vulnerable period after birth – the immediate postnatal transition. The aim of the present study was therefore, to compare cerebral oxygenation during immediate postnatal transition in term neonates with and without prenatal tobacco exposure. Methods: Included in this post-hoc analysis were healthy term neonates, with measurements of cerebral oxygenation (INVOS 5100C) during the first 15 min after birth, and for whom information on maternal smoking behavior during pregnancy was available. Neonates with prenatal tobacco exposure (smoking group) were matched 1:1 according to gestational age (±1 week), birth weight (±100 grams) and hematocrit (±5 %) to neonates without (non-smoking group). Cerebral regional tissue oxygen saturation (crSO2), cerebral fractional tissue oxygen extraction (cFTOE), arterial oxygen saturation (SpO2) and heart rate (HR) within the first 15 min after birth were compared between the two groups. Results: Twelve neonates in the smoking group with a median (IQR) gestational age of 39.1 (38.8–39.3) weeks and a birth weight of 3,155 (2,970–3,472) grams were compared to 12 neonates in the non-smoking group with 39.1 (38.7–39.2) weeks and 3,134 (2,963–3,465) grams. In the smoking group, crSO2 was significantly lower and cFTOE significantly higher until min 5 after birth. HR was significantly higher in the smoking group in min 3 after birth. Beyond this period, there were no significant differences between the two groups. Conclusion: Cerebral oxygenation within the first 5 min after birth was compromised in neonates with prenatal tobacco exposure. This observation suggests a higher risk for cerebral hypoxia immediately after birth due to fetal tobacco exposure.
Cloud-based Network Intrusion Detection System using Deep Learning

With the increase in the internet traffic worldwide, it has been observed that there is a major spike in the influx of network traffic into any system over the years. The internet traffic and the number of attempts at malicious access to any organization have increased over the years. The attacker can take advantage of this and over flood the organization with dummy traffic and make the systems unresponsive. The current existing models of Network Intrusion Detection System (NIDS) have a good prediction rate but somehow have high False Positive Rate (FPR). We propose a Deep Learning (DL) model to reduce the FPR which offers a scalable solution by deploying the model on cloud to increase the responsiveness of the NIDS during high loads, hence increasing the availability. The model which is running on docker containers on the cloud instance can be accessed using REST APIs as it is deployed as microservice. The experimental results showed that Deep Neural Networks (DNN) with five hidden layers achieved the best accuracy of 95.02% and a least accuracy of 88.75% by Long Short-Term Memory (LSTM) with two layers. In traditional ML algorithms, Random Forest approach have achieved 86% accuracy.
Acclimations to Cold and Warm Conditions Differently Affect the Energy Metabolism of Diapausing Larvae of the European Corn Borer Ostrinia nubilalis (Hbn.)

The European corn borer Ostrinia nubilalis is a pest species, whose fifth instar larvae gradually develop cold hardiness during diapause. The physiological changes underlying diapause progression and cold hardiness development are still insufficiently understood in insects. Here, we follow a complex of changes related to energy metabolism during cold acclimation (5°C) of diapausing larvae and compare this to warm-acclimated (22°C) and non-diapause controls. Capillary electrophoresis of nucleotides and coenzymes has shown that in gradually cold-acclimated groups concentrations of ATP/ADP and, consequently, energy charge slowly decrease during diapause, while the concentration of AMP increases, especially in the first months of diapause. Also, the activity of cytochrome c oxidase (COX), as well as the concentrations of NAD+ and GMP, decline in cold-acclimated groups, until the latter part of diapause, when they recover. Relative expression of NADH dehydrogenase (nd1), coenzyme Q-cytochrome c reductase (uqcr), COX, ATP synthase (atp), ADP/ATP translocase (ant), and prohibitin 2 (phb2) is supressed in cold-acclimated larvae during the first months of diapause and gradually increases toward the termination of diapause. Contrary to this, NADP+ and UMP levels significantly increased in the first few months of diapause, after gradual cold acclimation, which is in connection with the biosynthesis of cryoprotective molecules, as well as regeneration of small antioxidants. Our findings evidence the existence of a cold-induced energy-saving program that facilitates long-term maintenance of larval diapause, as well as gradual development of cold hardiness. In contrast, warm acclimation induced faster depletion of ATP, ADP, UMP, NAD+, and NADP+, as well as higher activity of COX and generally higher expression of all energy-related genes in comparison to cold-acclimated larvae. Moreover, such unusually high metabolic activity, driven by high temperatures, lead to premature mortality in the warm-acclimated group after 2 months of diapause. Thus, our findings strongly support the importance of low temperature exposure in early diapause for gradual cold hardiness acquisition, successful maintenance of the resting state and return to active development. Moreover, they demonstrate potentially adverse effects of global climate changes and subsequent increase in winter temperatures on cold-adapted terrestrial organisms in temperate and subpolar regions.
Does Team Integration Effect the Successful Implementation of Digital Transformation?

The research investigates the alignment of team integration and successful digital transformation in the UAE higher education sector. This research work identifies the key elements that define team integration from different prospects after a comprehensive literature review. In this research work, the relationship between the individuals and teams traits, technology aspects, and process aspects and successful digital transformation is identified. The findings of this research work will propose a framework that will be comprised of key team integration factors that can be used to enhance the implementation of digital transformation to the higher education sector in the UAE. Moreover, the education sector is slower is in adopting such new technologies more than other sectors which make making such an adoption difficult to implement unless a clear framework and practices are followed. The research findings will help decision makers and project managers to study and emphasize on such important factors emphasis and promote them push for such aspects to happen and to improve every time they start a transformation process or project. The conducted research uses a quantitative method, as data will be collected after distributing an online questionnaire distributed to different employees including leaders, project managers, academics and administration staff who worked or are working in digital transformation projects from different educational sectors in the United Arab Emirates.
A Serverless Academic Adviser Chatbot

Academic advising plays a vital role in students’ academic success; however, it is time consuming and difficult to maintain. Moreover, due to the COVID-19 outbreak and the sudden shift of education to the cyberspace, it has become even more challenging and time consuming for advisors to handle the drastically increasing numbers of queries received through online communication channels. To advise an enormous number of newly admitted students, the need arises for solutions that can handle the demands of a large number of students effectively without affecting student's academic success. This paper proposes an efficient, fast, scalable, and cost-effective solution using a serverless chatbot. The proposed Academic Advising chatbot, which can be integrated with Microsoft Teams, implements advanced semantic analysis techniques from Natural Language Processing (NLP) and analyzes the context of the student's queries, and responds accordingly.
Traversing Knowledge Graphs with Good Old (and New) Joins

Knowledge Graphs (KGs) provide a concise and intuitive abstraction for a variety of domains where edges capture the (potentially recursive) relationships between the entities [9]. This is leading to the rise of systems and tools able to facilitate graph data modeling, processing and analysis, with prominent AI companies developing core systems based on the property graph model [2]. In this context, Datalog-based languages are being re-discovered to be ductile to accomplish reasoning tasks over complex property graphs as they provide the essential elements to enable graph navigational operations [3]. The semantics of a Datalog program is usually specificed in an operational way via the chase procedure [7]. It entails multiple nondeterministic choices such as the rule application order and the fact binding order when multiple unification is possible [6]. In state-of-the-art reasoners, chase-based procedures are not directly adopted, but encoded in the form of engineered variations of the volcano iterator model [8] and so essentially within a pipe-and-filters architecture, where nodes (filters) are relational algebra operators and edges (pipes) are dependency connections between the rules. Such (potentially cyclic) structures, known as access plans, need to be translated into reasoning plans, where abstract relational algebra operators are transformed into specific project, select and join implementations: many implementations of each operator exist and it is up to the optimizer to choose the best one in terms of execution cost. This paper focuses on cases where the Datalog reasoning process involves a graph traversal task and investigates the connection between reasoning plans and graph traversal strategies. We move from the observation that the nondeterministic choices posed by the chase can be leveraged to control graph traversals —allowing to alternate breadth-first and depth-first strategies— and study the link of such choices with the reasoning plans. We conclude that in plans, specific join implementations and rule prioritization policies reflect the nondeterministic choices and exploit them to guide graph traversals in modern reasoners. Specifically, we implement our results in the vadalog System [3], a state-of-the-art knowledge graph management systems and conduct experimental evaluation.
The Effect of Rule Injectionin a Leakage Free Dataset

Knowledge graph embedding (KGE) has become a prominent topic for many AI-based tasks such as recommendation systems, natural language processing, and link prediction. Inclusion of additional knowledge such as ontology, logical rules and text improves the learning process of KGE models. One of the main characteristics of knowledge graphs (KGs) is the existence of relational patterns (e.g., symmetric and inverse relations) which usually remain unseen by the embedding models. Inclusion of logical rules provides embedding models with additional information about the patterns already present in the KGs. The injection of logical rules has not yet been studied in depth for KGE models. In this paper, we propose an approach for rule-based learning on top of the two embedding models namely RotatE and TransE within this scope of the paper. We ﬁrst study the eﬀect of rule injection in the performance of the selected models. Second, we explore how the removal of leakage from popular KGs such as FB15k and WN18 aﬀects the results. By leakage we are referring to the patterns exist in the training set from the test set (e.g. if the test set contains ( h, r, t ) then it also contains t , r , h in the training set which is considered as a symmetric leakage where t , r and h refers to tail , relation and head respectively). Empirical results suggest that incorporation of logical rules in the training process improves the performance of KGE models.
hPMSCs-Derived Exosomal miRNA-21 Protects Against Aging-Related Oxidative Damage of CD4+ T Cells by Targeting the PTEN/PI3K-Nrf2 Axis

Mesenchymal stem cells (MSCs)-derived exosomes were considered a novel therapeutic approach in many aging-related diseases. This study aimed to clarify the protective effects of human placenta MSCs-derived exosomes (hPMSC-Exo) in aging-related CD4+ T cell senescence and identified the underlying mechanisms using a D-gal induced mouse aging model. Senescent T cells were detected SA-β-gal stain. The degree of DNA damage was evaluated by detecting the level of 8-OH-dG. The superoxide dismutase (SOD) and total antioxidant capacity (T-AOC) activities were measured. The expression of aging-related proteins and senescence-associated secretory phenotype (SASP) were detected by Western blot and RT-PCR. We found that hPMSC-Exo treatment markedly decreased oxidative stress damage (ROS and 8-OH-dG), SA-β-gal positive cell number, aging-related protein expression (p53 and γ-H2AX), and SASP expression (IL-6 and OPN) in senescent CD4+ T cells. Additionally, hPMSC-Exo containing miR-21 effectively downregulated the expression of PTEN, increased p-PI3K and p-AKT expression, and Nrf2 nuclear translocation and the expression of downstream target genes (NQO1 and HO-1) in senescent CD4+ T cells. Furthermore, in vitro studies uncovered that hPMSC-Exo attenuated CD4+ T cell senescence by improving the PTEN/PI3K-Nrf2 axis by using the PTEN inhibitor bpV (HOpic). We also validated that PTEN was a target of miR-21 by using a luciferase reporter assay. Collectively, the obtained results suggested that hPMSC-Exo attenuates CD4+ T cells senescence via carrying miRNA-21 and activating PTEN/PI3K-Nrf2 axis mediated exogenous antioxidant defenses.
Addition of Fibroblast-Stromal Cell Markers to Immune Synovium Pathotypes Better Predicts Radiographic Progression at 1 Year in Active Rheumatoid Arthritis

Objectives This study aims to investigate if addition of fibroblast-stromal cell markers to a classification of synovial pathotypes improves their predictive value on clinical outcomes in rheumatoid arthritis (RA). Methods Active RA patients with a knee needle synovial biopsy at baseline and finished 1-year follow-up were recruited from a real-world prospective cohort. Positive staining for CD20, CD38, CD3, CD68, CD31, and CD90 were scored semiquantitatively (0-4). The primary outcome was radiographic progression defined as a minimum increase of 0.5 units of the modified total Sharp score from baseline to 1 year. Results Among 150 recruited RA patients, 123 (82%) had qualified synovial tissue. Higher scores of CD20+ B cells, sublining CD68+ macrophages, CD31+ endothelial cells, and CD90+ fibroblasts were associated with less decrease in disease activity and greater increase in radiographic progression. A new fibroblast-based classification of synovial pathotypes giving more priority to myeloid and stromal cells classified samples as myeloid-stromal (57.7%, 71/123), lymphoid (31.7%, 39/123), and paucicellular pathotypes (10.6%, 13/123). RA patients with myeloid-stromal pathotype showed the highest rate of radiographic progression (43.7% vs. 23.1% vs. 7.7%, p = 0.011), together with the lowest rate of Boolean remission at 3, 6, and 12 months. Baseline synovial myeloid-stromal pathotype independently predicted radiographic progression at 1 year (adjusted OR: 3.199, 95% confidence interval (95% CI): 1.278, 8.010). Similar results were obtained in a subgroup analysis of treatment-naive RA. Conclusions This novel fibroblast-based myeloid-stromal pathotype could predict radiographic progression at 1 year in active RA patients which may contribute to the shift of therapeutic decision in RA.
Breakfast Consumption and Academic Achievement Among Chinese Adolescents: A Moderated Mediation Model

The studies have shown that a healthy lifestyle has a significant impact on the academic achievement of adolescents. Behavior of breakfast eating is considered a hallmark of dietary patterns and an important component of a healthy lifestyle. The prior study explained that students had a lower level of achievement motivation at school because they were exposed to some militating factors in their families such as absenteeism, ill health, malnutrition, and hunger. This study examined the mediating role of achievement motivation and moderating role of socioeconomic status (SES) in the association between breakfast consumption and academic achievement. This study used a sample of 15-year-old Chinese students who participated in Program for International Student Assessment (PISA) 2015. In terms of gender, female students accounted for 47.2% and male students accounted for 52.8%. The results showed that (1) breakfast consumption had a positive predictive effect on academic achievement; (2) achievement motivation played a partial mediating role in the relationship between breakfast consumption and academic achievement; and (3) the direct and indirect effects were moderated by the SES of students, which meant that the effect of breakfast consumption on achievement motivation can differ depending on the SES of students. Besides, both the effects were stronger for individuals with higher SES. The conclusion of this study has an important theoretical value and reference value to guide the Chinese parents and Chinese adolescents to pay more attention to breakfast consumption and healthy lifestyles.