Miracle-dz/UniFAR-FTUs huggingface dataset:
Predict Pairwise Trust Based on Machine Learning in Online Social Networks: A Survey

Trust plays a crucial role in online social networks where users do not communicate or interact with each other in a direct face-to-face manner. Although many researchers have already conducted comprehensive studies on trust computing like trust evaluation, pairwise trust prediction is still relatively under explored especially with machine learning methods which can overcome the disadvantages of both linear predication and trust propagation. This survey aims to fill this gap and first provides an overview of state-of-the-art researches in pairwise trust prediction using machine learning techniques, especially in the context of social networking. Specifically, we present a workflow of trust prediction using machine learning and summarize current available trust-related datasets, classifiers and different metrics used to evaluate a trained classifier. Also, we review, compare, and contrast the literature for the purpose of identifying open issues and directing future research.
Diamonds in the Rough: Harnessing Tumor-Associated Myeloid Cells for Cancer Therapy

Therapeutic approaches that engage immune cells to treat cancer are becoming increasingly utilized in the clinics and demonstrated durable clinical benefit in several solid tumor types. Most of the current immunotherapies focus on manipulating T cells, however, the tumor microenvironment (TME) is abundantly infiltrated by a heterogeneous population of tumor-associated myeloid cells, including tumor-associated macrophages (TAMs), tumor-associated dendritic cells (TADCs), tumor-associated neutrophils (TANs), and myeloid-derived suppressor cells (MDSCs). Educated by signals perceived in the TME, these cells often acquire tumor-promoting properties ultimately favoring disease progression. Upon appropriate stimuli, myeloid cells can exhibit cytoxic, phagocytic, and antigen-presenting activities thereby bolstering antitumor immune responses. Thus, depletion, reprogramming or reactivation of myeloid cells to either directly eradicate malignant cells or promote antitumor T-cell responses is an emerging field of interest. In this review, we briefly discuss the tumor-promoting and tumor-suppressive roles of myeloid cells in the TME, and describe potential therapeutic strategies in preclinical and clinical development that aim to target them to further expand the range of current treatment options.
Risk factors associated with fecal shedding of Listeria monocytogenes by dairy cows and calves

Background Listeria monocytogenes (LM) is an important foodborne pathogen affecting animals and humans. Listeriosis outbreaks in humans caused by consumption of unpasteurized dairy products are of serious concern. Objective To determine risk factors associated with fecal shedding of LM in family dairy farms. Animals Fecal samples were collected from cows and calves on 20 family dairy farms in 2‐week intervals for a period of 1 year. Methods Longitudinal study. LM was detected using qPCR. Univariate mixed effect model and multivariate analyses were performed to associate risk factors (dietary change, breed, mastitis, other diseases, antibiotic treatment, other treatments, heat index, and meteorological season) with fecal shedding of LM. Results LM was isolated from all farms on at least 1 sampling day. The average yearly prevalence was 18.2% (98/540) and 8.4% (43/511) in cows and calves, respectively. Heat index (P = .05) and meteorological season (P = .04) affected fecal shedding of LM on a farm level. Meteorological season only influenced fecal shedding of LM in cows (P = .04), whereas heat index (P = .01) influenced fecal shedding of LM in calves. Spring season was identified as the major risk factor associated fecal shedding of LM on a farm level (P = .01) and in cows (P = .01). Dietary changes were associated with lower odds for fecal shedding of LM in calves (P < .01). Conclusions and Clinical Importance Fecal shedding of LM is associated with environmental temperatures and the meteorological season. Farmers and veterinarians should use this information when implementing strategies to reduce risks for LM dissemination in animals and in the community.
Effects of serotonin depletion and dopamine depletion on bimodal divided attention

Abstract Objectives: This study aimed to explore the effects of acute phenylalanine tyrosine depletion (APTD) and acute tryptophan depletion (ATD) on bimodal divided attention. A balanced amino acid mixture (BAL) served as control condition. Methods: Fifty-three healthy adults (final analyzed sample was N = 49, age: M = 23.8 years) were randomly assigned to APTD, ATD or BAL in a double-blind, between-subject approach. Divided attention was assessed after 4 h. Blood samples were taken before and 6 h after challenge intake. Results: Amino acid concentrations following challenge intake significantly decreased (all P ≤ 0.01). There was a significant difference in the mean reaction time (RT) towards auditory stimuli, but not towards visual stimuli between the groups. Post-hoc comparison of mean RTs (auditory stimuli) showed a significant difference between ATD (RT = 604.0 ms, SD = 56.9 ms) and APTD (RT = 556.4 ms, SD = 54.2 ms; P = 0.037), but no RT difference between ATD and BAL or APTD and BAL (RT = 573.6 ms, SD = 45.7 ms). Conclusions: The results indicate a possible dissociation between the effects of a diminished brain 5-HT and DA synthesis on the performance in a bimodal divided attention task. The difference was exclusively observed within the RT towards auditory signals.
Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model

Identifying and extracting building boundaries from remote sensing data has been one of the hot topics in photogrammetry for decades. The active contour model (ACM) is a robust segmentation method that has been widely used in building boundary extraction, but which often results in biased building boundary extraction due to tree and background mixtures. Although the classification methods can improve this efficiently by separating buildings from other objects, there are often ineluctable salt and pepper artifacts. In this paper, we combine the robust classification convolutional neural networks (CNN) and ACM to overcome the current limitations in algorithms for building boundary extraction. We conduct two types of experiments: the first integrates ACM into the CNN construction progress, whereas the second starts building footprint detection with a CNN and then uses ACM for post processing. Three level assessments conducted demonstrate that the proposed methods could efficiently extract building boundaries in five test scenes from two datasets. The achieved mean accuracies in terms of the F1 score for the first type (and the second type) of the experiment are 96.43 ± 3.34% (95.68 ± 3.22%), 88.60 ± 3.99% (89.06 ± 3.96%), and 91.62 ±1.61% (91.47 ± 2.58%) at the scene, object, and pixel levels, respectively. The combined CNN and ACM solutions were shown to be effective at extracting building boundaries from high-resolution optical images and LiDAR data.
A Multiple-Feature Reuse Network to Extract Buildings from Remote Sensing Imagery

Automatic building extraction from remote sensing imagery is important in many applications. The success of convolutional neural networks (CNNs) has also led to advances in using CNNs to extract man-made objects from high-resolution imagery. However, the large appearance and size variations of buildings make it difficult to extract both crowded small buildings and large buildings. High-resolution imagery must be segmented into patches for CNN models due to GPU memory limitations, and buildings are typically only partially contained in a single patch with little context information. To overcome the problems involved when using different levels of image features with common CNN models, this paper proposes a novel CNN architecture called a multiple-feature reuse network (MFRN) in which each layer is connected to all the subsequent layers of the same size, enabling the direct use of the hierarchical features in each layer. In addition, the model includes a smart decoder that enables precise localization with less GPU load. We tested our model on a large real-world remote sensing dataset and obtained an overall accuracy of 94.5% and an 85% F1 score, which outperformed the compared CNN models, including a 56-layer fully convolutional DenseNet with 93.8% overall accuracy and an F1 score of 83.5%. The experimental results indicate that the MFRN approach to connecting convolutional layers improves the performance of common CNN models for extracting buildings of different sizes and can achieve high accuracy with a consumer-level GPU.
Linagliptin/metformin HCl (fixed combination therapy) for the treatment of Type 2 diabetes mellitus

By the year 2030, diabetes mellitus will globally become the seventh leading cause of death. Currently, 382 million individuals worldwide have diabetes mellitus, with 80% residing in low- and middle-income countries. At present, Type 2 diabetes mellitus (T2DM) accounts for 85–95% of the diabetes burden in high-income countries. Alarmingly, this figure may be higher in the low- and middle-income nations. In an effort to combat this silent but deadly disease, pharmaceutical manufacturers have developed a host of different agents, each targeting a specific biochemical pathway. Among the most recent additions to this armament is linagliptin/metformin HCl, a fixed combination therapy compound manufactured by Boehringer Ingelheim. This novel formulation combines metformin, an insulin sensitizer, with linagliptin, a dipeptidyl peptidase-4 enzyme inhibitor. This review will discuss the pharmacokinetic properties of this molecule, assess clinical efficacy and gauge its place in the treatment algorithm for T2DM.
ISPAD Position Statement on Type 1 Diabetes in Schools

The ISPAD Position Statement on Type 1 Diabetes in Schools is based upon the 2018 ISPAD Clinical Guidelines for Management of Type 1 Diabetes in School and draws upon the published position statements on diabetes in schools of many countries. The input from less-resourced communities in Ghana, Pakistan, Haiti, Ecuador, Bolivia, and India is acknowledged and appreciated. The authors also acknowledge the input of parents, adolescents living with type 1 diabetes, school personnel, and experienced health care professionals who have contributed ideas and clarification of roles. The principles of this ISPAD Position Statement on Type 1 Diabetes in Schools should be applicable globally. ISPAD strongly supports compliance with legal protections for children and adolescents with type 1 diabetes to attend school, to be safe at school, and to receive optimal medical management at school and in all school-associated activities. Note:
Pedestrian Detection Based on YOLO Network Model

After going through the deep network, there will be some loss of pedestrian information, which will cause the disappearance of gradients, causing inaccurate pedestrian detection. This paper improves the network structure of YOLO algorithm and proposes a new network structure YOLO-R. First, three Passthrough layers were added to the original YOLO network. The Passthrough layer consists of the Route layer and the Reorg layer. Its role is to connect the shallow layer pedestrian features to the deep layer pedestrian features and link the high and low resolution pedestrian features. The role of the Route layer is to pass the pedestrian characteristic information of the specified layer to the current layer, and then use the Reorg layer to reorganize the feature map so that the currently-introduced Route layer feature can be matched with the feature map of the next layer. The three Passthrough layers added in this algorithm can well transfer the network's shallow pedestrian fine-grained features to the deep network, enabling the network to better learn shallow pedestrian feature information. This paper also changes the layer number of the Passthrough layer connection in the original YOLO algorithm from Layer 16 to Layer 12 to increase the ability of the network to extract the information of the shallow pedestrian features. The improvement was tested on the INRIA pedestrian dataset. The experimental results show that this method can effectively improve the detection accuracy of pedestrians, while reducing the false detection rate and the missed detection rate, and the detection speed can reach 25 frames per second.
Virtual reality and cardiac anatomy: Exploring immersive three‐dimensional cardiac imaging, a pilot study in undergraduate medical anatomy education

Cardiac anatomy can be challenging to grasp because of its complex three‐dimensional nature and remains one of the most challenging topics to teach. In light of some exciting technological advances in the field of virtual reality (VR), we sought to test the viability and the assess efficacy of this computer‐generated model for the purposes of teaching cardiac anatomy. Before learning cardiac anatomy, first‐year undergraduate medical students participated in an anatomically correct VR simulation of the heart. Students were randomly distributed into control and variable groups. Each student completed a pre‐intervention quiz, consisting of 10 multiple choice questions with 5 conventional cardiac anatomy questions and 5 visual–spatial (VS) questions. The control group continued to independent study, whereas the variable group subjects were exposed to a 30‐min immersive cardiac VR experience. At the end of the intervention, both the groups underwent a separate post‐intervention 10‐question quiz. Forty‐two students participated in the cardiac VR experiment, separated into 14 control and 28 variable subjects. They scored 50.9% on average on the pre‐intervention quiz (SD = 16.5) and 70.2% on the post‐intervention quiz (SD = 18.7). Compared to the control group, the students exposed to VR scored 21.4% higher in conventional content (P = 0.004), 26.4% higher in VS content (P < 0.001), and 23.9% higher overall (P < 0.001). VR offers an anatomically correct and immersive VS environment that permits learner to interact three‐dimensionally with the heart's anatomy. This study demonstrates the viability and the effectiveness of VR in teaching cardiac anatomy. Clin. Anat. 32:238–243, 2019. © 2018 Wiley Periodicals, Inc.
Improved Inapproximability of Rainbow Coloring

A rainbow $q$-coloring of a $k$-uniform hypergraph is a $q$-coloring of the vertex set such that every hyperedge contains all $q$ colors. We prove that given a rainbow $(k - 2\lfloor \sqrt{k}\rfloor)$-colorable $k$-uniform hypergraph, it is NP-hard to find a normal $2$-coloring. Previously, this was only known for rainbow $\lfloor k/2 \rfloor$-colorable hypergraphs (Guruswami and Lee, SODA 2015). We also study a generalization which we call rainbow $(q, p)$-coloring, defined as a coloring using $q$ colors such that every hyperedge contains at least $p$ colors. We prove that given a rainbow $(k - \lfloor \sqrt{kc} \rfloor, k- \lfloor3\sqrt{kc} \rfloor)$-colorable $k$ uniform hypergraph, it is NP-hard to find a normal $c$-coloring for any constant $c < k/10$. The proof of our second result relies on two combinatorial theorems. One of the theorems was proved by Sarkaria (J.~Comb.~Theory.~1990) using topological methods and the other theorem we prove using a generalized Borsuk-Ulam theorem.
Enrollment on clinical trials does not improve survival for children with acute myeloid leukemia: A population‐based study

It is questionable whether enrollment on clinical trials offers any survival advantage at the population level over standard‐of‐care treatment. The objectives of this study were to describe the impact of trial enrollment on event‐free survival and overall survival in pediatric acute myeloid leukemia (AML) using the Cancer in Young People in Canada (CYP‐C) database.
A Dual-Band Metasurface Antenna Using Characteristic Mode Analysis

A compact metasurface-based antenna is proposed for dual-band operations. The proposed metasurface is designed on a single-layered substrate including an array of modified $3 \times 3$ squared patches. Each of the four corner patches is split into four fractional patches while the four edge patches are evolved into Malta crosses and the center patch is scaled. A substrate integrated waveguide-based Y-junction cavity-fed dual slot drives the metasurface with multiple impedance resonances. Based on the predicted modal behaviors of metasurface using a characteristic mode analysis (CMA), as an example, an antenna operating at three resonant modes at 28, 33, and 36 GHz, respectively is designed for the dual-band operation for the coming 5G. The proposed design shows that the measured impedance bandwidths (return loss larger than 10 dB) are 23.7–29.2 GHz and 36.7–41.1 GHz with the achieved gain of 4.8–7.2 dBi and 8.9–10.9 dBi, respectively. The proposed dual-band antenna features the advantages of low profile and wideband, suitable for the coming dual-band 5G applications.
Antimicrobial resistance and virulence factors of Klebsiella pneumoniae affecting 30 day mortality in patients with bloodstream infection

Objectives To investigate the risk factors of patients with Klebsiella pneumoniae (KP) bloodstream infection (BSI) with a focus on antimicrobial resistance and virulence factors. Methods All KP BSI patients (n = 579) from six general hospitals during a 1 year period were included in this study. The risk factors of hosts and causative KP isolates were assessed to determine associations with the 30 day mortality of KP BSI patients by multivariate Cox hazards modelling. Results The 30 day mortality rate of KP BSI patients was 16.9% (98/579). Among the host-associated factors, increased SOFA score and leucopenia status exhibited strong associations with increased 30 day mortality. Among the pathogenic factors, carriage of the pks gene cluster (adjusted HR 1.80; 95% CI 1.16-2.79) was a risk factor, especially when accompanied by MDR. In this regard, KP isolates of the wzi50 capsular type (n = 22) frequently harboured pks (63.6%, 14/22) and ybtA (68.2%, n = 15) and mostly exhibited MDR (63.6%, n = 14), resulting in increased 30 day mortality. In contrast, hypermucoviscous KP isolates showed an inverse association with 30 day mortality (adjusted HR 0.55; 95% CI 0.33-0.90). Conclusions Despite the reported virulence of hypermucoviscous KP strains, they were associated with good prognoses in KP BSI patients. Importantly, carriage of the pks gene cluster, which is responsible for the synthesis of colibactin, was a relevant marker of early mortality.
Maternal Care among Madurese through the Form of Culture

To overcome the maternal's health problem, preventive action based on the culture's perspective was chosen compared to the medical's perspective. Madurese still believe in the myth about food and action which affect their pregnancy. Based on the theory of A.L Kroeber, there is 3 form of culture to identify a community's culture. This study aim is to identify maternal care among Madurese through the form of cultural: 1) ideas; 2) activities and 3) artefact. This study was a descriptive research with the qualitative approach. This study conducted in the area of Puskesmas Trageh (Bangkalan) and Puskesmas Omben (Sampang) in Madura. The total main informant was 18 persons that consist of 3 pregnant women for each area and the key informants consist of 1 midwife coordinator, 1 local midwife, 1 shaman and 1 family member of each pregnant women. The maternal care towards the form of cultural ideas: 1) They should have a good intention towards other people; 2) increase their worships through reading Holy Qur'an; 3) avoid the distraction of the spirits by pray and use a talisman. The maternal care towards the form of cultural activities: 1) have a celebration to show the gratitude feeling towards God; 2) drink herbal medicine and do massage at stomach by the shaman; 3) avoid several foods based on myth. The maternal care towards the form of the cultural artefact uses the talisman to avoid misfortune. It showed phenomena that Madurese still do traditional maternal care which often contradictory to the modern medicine.
2-Arachidonoylglycerol mobilizes myeloid cells and worsens heart function after acute myocardial infarction

AIMS Myocardial infarction (MI) leads to an enhanced release of endocannabinoids and a massive accumulation of neutrophils and monocytes within the ischaemic myocardium. These myeloid cells originate from haematopoietic precursors in the bone marrow and are rapidly mobilized in response to MI. We aimed to determine whether endocannabinoid signalling is involved in myeloid cell mobilization and cardiac recruitment after ischaemia onset. METHODS AND RESULTS Intravenous administration of endocannabinoid 2-arachidonoylglycerol (2-AG) into wild type (WT) C57BL6 mice induced a rapid increase of blood neutrophil and monocyte counts as measured by flow cytometry. This effect was blunted when using cannabinoid receptor 2 knockout mice. In response to MI induced in WT mice, the lipidomic analysis revealed significantly elevated plasma and cardiac levels of the endocannabinoid 2-AG 24 h after infarction, but no changes in anandamide, palmitoylethanolamide, and oleoylethanolamide. This was a consequence of an increased expression of 2-AG synthesizing enzyme diacylglycerol lipase and a decrease of metabolizing enzyme monoacylglycerol lipase (MAGL) in infarcted hearts, as determined by quantitative RT-PCR analysis. The opposite mRNA expression pattern was observed in bone marrow. Pharmacological blockade of MAGL with JZL184 and thus increased systemic 2-AG levels in WT mice subjected to MI resulted in elevated cardiac CXCL1, CXCL2, and MMP9 protein levels as well as higher cardiac neutrophil and monocyte counts 24 h after infarction compared with vehicle-treated mice. Increased post-MI inflammation in these mice led to an increased infarct size, an impaired ventricular scar formation assessed by histology and a worsened cardiac function in echocardiography evaluations up to 21 days. Likewise, JZL184-administration in a myocardial ischaemia-reperfusion model increased cardiac myeloid cell recruitment and resulted in a larger fibrotic scar size. CONCLUSION These findings suggest that changes in endocannabinoid gradients due to altered tissue levels contribute to myeloid cell recruitment from the bone marrow to the infarcted heart, with crucial consequences on cardiac healing and function.
A NOVEL DISPARITY-ASSISTED BLOCK MATCHING-BASED APPROACH FOR SUPER-RESOLUTION OF LIGHT FIELD IMAGES

Currently, available plenoptic imaging technology has limited resolution. That makes it challenging to use this technology in applications, where sharpness is essential, such as film industry. Previous attempts aimed at enhancing the spatial resolution of plenoptic light field (LF) images were based on block and patch matching inherited from classical image super-resolution, where multiple views were considered as separate frames. By contrast to these approaches, a novel super-resolution technique is proposed in this paper with a focus on exploiting estimated disparity information to reduce the matching area in the super-resolution process. We estimate the disparity information from the interpolated LR view point images (VPs). We denote our method as light field block matching super-resolution. We additionally combine our novel super-resolution method with directionally adaptive image interpolation from [1] to preserve sharpness of the high-resolution images. We prove a steady gain in the PSNR and SSIM quality of the super-resolved images for the resolution enhancement factor 8×8 as compared to the recent approaches and also to our previous work [2].