Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of dietary fibre intake and gut microbiota in adults

Abstract Increasing evidence indicates that gut microbiota may influence colorectal cancer risk. Diet, particularly fibre intake, may modify gut microbiota composition, which may affect cancer risk. We investigated the relationship between dietary fibre intake and gut microbiota in adults. Using 16S rRNA gene sequencing, we assessed gut microbiota in faecal samples from 151 adults in two independent study populations: National Cancer Institute (NCI), n 75, and New York University (NYU), n 76. We calculated energy-adjusted fibre intake based on FFQ. For each study population with adjustment for age, sex, race, BMI and smoking, we evaluated the relationship between fibre intake and gut microbiota community composition and taxon abundance. Total fibre intake was significantly associated with overall microbial community composition in NYU (P=0·008) but not in NCI (P=0·81). In a meta-analysis of both study populations, higher fibre intake tended to be associated with genera of class Clostridia, including higher abundance of SMB53 (fold change (FC)=1·04, P=0·04), Lachnospira (FC=1·03, P=0·05) and Faecalibacterium (FC=1·03, P=0·06), and lower abundance of Actinomyces (FC=0·95, P=0·002), Odoribacter (FC=0·95, P=0·03) and Oscillospira (FC=0·96, P=0·06). A species-level meta-analysis showed that higher fibre intake was marginally associated with greater abundance of Faecalibacterium prausnitzii (FC=1·03, P=0·07) and lower abundance of Eubacterium dolichum (FC=0·96, P=0·04) and Bacteroides uniformis (FC=0·97, P=0·05). Thus, dietary fibre intake may impact gut microbiota composition, particularly class Clostridia, and may favour putatively beneficial bacteria such as F. prausnitzii. These findings warrant further understanding of diet–microbiota relationships for future development of colorectal cancer prevention strategies.
Optimisation and asymptotic stability

ABSTRACT The problems of unconstrained optimisation and establishing asymptotic stability have much in common. Understanding the analogy between these two sheds light on their interconnection and may lead to a number of new results. For instance, in this paper, we provide estimates of the rate of convergence when analysing asymptotic stability of differential equations, rather than just ascertain the very fact of stability. Also, standard methods for the design of Lyapunov functions (e.g. those having the meaning of the full energy of the system) turn out to be unsatisfactory from this point of view and have to be modified. These claims are exemplified in the paper by considering the heavy-ball method for function minimisation ‘in parallel’ with the problem of asymptotic stability for the synchronous motor equation.
SpiderBoost: A Class of Faster Variance-reduced Algorithms for Nonconvex Optimization

There has been extensive research on developing stochastic variance reduced methods to solve large-scale optimization problems. More recently, novel algorithms SARAH \citep{Lam2017a,Lam2017b} and SPIDER \citep{Fang2018} of such a type have been developed. In particular, SPIDER has been shown in \cite{Fang2018} to outperform existing algorithms of the same type and meet the lower bound in certain regimes for nonconvex optimization. Though appealing in theory, SPIDER requires $\epsilon$-level stepsize to guarantee the convergence, and consequently runs slow in practice. This paper proposes SpiderBoost as an improved scheme, which comes with two major advantages compared to SPIDER. First, it allows much larger stepsize without sacrificing the convergence rate, and hence runs substantially faster in practice. Second, it extends much more easily to proximal algorithms with guaranteed convergence for solving composite optimization problems, which appears challenging for SPIDER due to stringent requirement on per-iteration increment to guarantee its convergence. Both advantages can be attributed to the new convergence analysis we develop for SpiderBoost that allows much more flexibility for choosing algorithm parameters. As further generalization of SpiderBoost, we show that proximal SpiderBoost achieves a stochastic first-order oracle (SFO) complexity of $\mathcal{O}(\min \{n^{1/2}\epsilon^{-2},\epsilon^{-3}\})$ for composite optimization, which improves the existing best results by a factor of $\mathcal{O}(\min\{n^{1/6},\epsilon^{-1/3}\})$. Moreover, for nonconvex optimization under the gradient dominance condition and in an online setting, we also obtain improved oracle complexity results compared to the corresponding state-of-the-art results.
Determinants of Intensive Care Unit Telemedicine Effectiveness. An Ethnographic Study

Rationale: Telemedicine is an increasingly common care delivery strategy in the ICU. However, ICU telemedicine programs vary widely in their clinical effectiveness, with some studies showing a large mortality benefit and others showing no benefit or even harm. Objectives: To identify the organizational factors associated with ICU telemedicine effectiveness. Methods: We performed a focused ethnographic evaluation of 10 ICU telemedicine programs using site visits, interviews, and focus groups in both facilities providing remote care and the target ICUs. Programs were selected based on their change in risk‐adjusted mortality after adoption (decreased mortality, no change in mortality, and increased mortality). We used a constant comparative approach to guide data collection and analysis. Measurements and Main Results: We conducted 460 hours of direct observation, 222 interviews, and 18 focus groups across six telemedicine facilities and 10 target ICUs. Data analysis revealed three domains that influence ICU telemedicine effectiveness: 1) leadership (i.e., the decisions related to the role of the telemedicine, conflict resolution, and relationship building), 2) perceived value (i.e., expectations of availability and impact, staff satisfaction, and understanding of operations), and 3) organizational characteristics (i.e., staffing models, allowed involvement of the telemedicine unit, and new hire orientation). In the most effective telemedicine programs these factors led to services that are viewed as appropriate, integrated, responsive, and consistent. Conclusions: The effectiveness of ICU telemedicine programs may be influenced by several potentially modifiable factors within the domains of leadership, perceived value, and organizational structure.
A Lightweight Online Multiple Object Vehicle Tracking Method

In this paper, multiple-object vehicle tracking system by affinity matching using min-cost linear cost assignment is proposed. This tracking system is targeted to scene recordings acquired from cameras mounted on a moving ego vehicle. Vehicle tracking on the road scene and images acquired from moving ego vehicle’s camera amplifies the problem of greater bounding box geometry change in comparison with other low speed tracking applications such as traditional pedestrian tracking. This perturbation occurs in many tracking scenarios such as when a high speed object is approaching from an opposing lane. Since autonomous driving algorithms need to use the processing resources in an efficient manner even while satisfying the requirements of computationally complex tasks like localization, object detection, occupancy grid update, sensor-fusion and trajectory planning, our study is particularly focused on the development and benchmarking of an computationally lightweight online multiple object tracking model. To test and evaluate our model, we use KITTI Object Tracking - Car Benchmark dataset and our model statistical metric values are comparably higher; our model outperforms the state-of-the-art methods on ML and MT and places second on MOTA and MOTP metric evaluations, and processing time is 6 to 20 times faster compared to other methods.
Metapopulation dynamics of roseate terns: Sources, sinks and implications for conservation management decisions

Habitat management to restore or create breeding sites may allow metapopulations to increase in size and reduce the risk of demographic stochasticity or disasters causing metapopulation extinction. However, if newly restored or created sites are of low quality, they may act as sinks that draw individuals away from better quality sites to the detriment of metapopulation size. Following intensive conservation effort, the metapopulation of roseate tern (Sterna dougallii) in NW Europe is recovering from a large crash in numbers, but most former colonies remain unoccupied and hence are potential targets for restoration. To inform conservation efforts, we studied the dynamics of this metapopulation with a multistate integrated population model to assess each of the three main colonies for important demographic contributors to population growth rate, source/sink status and possible density dependence. All three study colonies are managed for roseate terns (and other tern species) in similar ways, but the demographic processes vary considerably between colonies. The largest colony is a source involved in almost all dispersal, and its growth is determined by survival rates and productivity. Productivity and juvenile apparent survival at the largest colony appear to be density-dependent. Although the mechanisms are unclear, this may provide an increasing impetus for emigration of recruits to other colonies in future. The smallest of the three colonies is a sink, relying on immigration for its growth. Simulation models suggest the metapopulation would be c. 10% larger in the absence of dispersal to the sink colony. This work indicates that, due to variable site quality, aims to enhance both distribution and size of metapopulations may be mutually exclusive. In this case, before future attempts to encourage recolonisation of former sites, assessments of site suitability should be undertaken, focusing on food availability and isolation from predators to maximise the likelihood of attaining levels of productivity and survival that avoid creation of a sink population to the detriment of the overall metapopulation size.
Group and kin recognition via olfactory cues in chimpanzees (Pan troglodytes)

Primates were traditionally thought to have a reduced sense of smell. Although there is now evidence that olfaction plays a greater role in primate social life than previously assumed, research on the sense of smell in non-human apes is scarce. Chimpanzees sniff the ground and vegetation on boundary patrols, but the function of this behaviour is unclear. Since chimpanzees are highly territorial and can kill individuals that do not belong to their own community, sniffing might function to gather information about conspecifics, particularly concerning group membership and kinship. To investigate whether chimpanzees recognize group members and kin via olfactory cues, we conducted behavioural bioassays on two groups of chimpanzees at Leipzig Zoo. In a pilot study, we found that chimpanzees responded more strongly to urine than to faeces or body odour. We then presented urine from group members, outgroup individuals and an unscented control in aerated boxes using a simultaneous discrimination task. The first behaviour after a chimpanzee first approached a box was related to olfaction (sniffing, nose within 20 cm, licking) in 83% of cases, highlighting the importance of olfaction as a general investigation mechanism in this species. Chimpanzees sniffed significantly longer at urine stimuli than the control and significantly longer at odours from outgroup individuals than those from group members. Furthermore, the duration of sniffing was positively correlated with relatedness. Our results suggest that chimpanzees use olfactory cues to obtain information about social relationships and fill a gap in our understanding of primate chemical communication.
ARL4C stabilized by AKT/mTOR pathway promotes the invasion of PTEN‐deficient primary human glioblastoma

Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) deficiency in primary human glioblastoma (GBM) is associated with increased invasiveness and poor prognosis with unknown mechanisms. Therefore, how loss of PTEN promotes GBM progression remains to be elucidated. Herein, we identified that ADP‐ribosylation factor like‐4C (ARL4C) was highly expressed in PTEN‐deficient human GBM cells and tissues. Mechanistically, loss of PTEN stabilized ARL4C protein due to AKT/mTOR pathway‐mediated inhibition of ARL4C ubiquitination. Functionally, ARL4C enhanced the progression of GBM cells in vitro and in vivo. Moreover, microarray profiling and GST pull‐down assay identified that ARL4C accelerated tumor progression via RAC1‐mediated filopodium formation. Importantly, targeting PTEN potently inhibited GBM tumor progression in vitro and in vivo, whereas overexpression of ARL4C reversed the tumor progression impaired by PTEN overexpression. Clinically, analyses with patients' specimens validated a negative correlation between PTEN and ARL4C expression. Elevated ARL4C expression but PTEN deficiency in tumor was associated with poorer disease‐free survival and overall survival of GBM patients. Taken together, ARL4C is critical for PTEN‐deficient GBM progression and acts as a novel prognostic biomarker and a potential therapeutic candidate. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Lane Detection and Road Surface Reconstruction Based on Multiple Vanishing Point & Symposia

Lane detection algorithm based on monocular camera is one of the most popular methods in recent years, which can meet the requirement of real-time and robust for autonomous vehicle. In this way, the position of lane markers can be transferred from perspective space to road space base on the planar road assumption. However, large numbers of road scenes, especially the up and down slope road environment, cannot meet this requirement.In this paper, we propose a multiple vanishing point detection method to reconstruct the road space in slope scenes. In order to improve the accuracy of vanishing point estimation, the road images are decomposed into near and far regions. We extract candidate lane markers in near region by using multiscale convolution kernel and Hough Transform at first. Then, the lane markers in far region can be detected based on the result of near region. At last, different vanishing points are extracted in near and far regions. With the help of a vanishing point based on camera model, we can project both of near and far regions into road space. The experiment is conducted on our self-driving car ‘TuLian’ in campus environment.
Role of NOTCH3 Mutations in the Cerebral Small Vessel Disease Cerebral Autosomal Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalopathy.

In this review article, we discuss recent progress in establishing the molecular basis for cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL; OMIN No. 125310), which is a cerebral small vessel disease (SVD) caused by mutations in the NOTCH3 gene. SVDs are a group of diseases defined by pathological changes affecting small vessels in the brain (small perforating arteries, arterioles, capillaries, and venules). Damage to cerebral white and deep gray matter, enlarged perivascular (VirchowRobin) spaces, cerebral microbleeds, and lacunes are lesions associated with SVDs. SVDs cause ≈20% of all strokes and >40% of dementia cases in elderly and are on the rise in an aging population. SVDs are difficult to diagnose because current imaging modalities only recognize vessels that are ≈10× larger than those primarily affected by SVD. SVDs can be classified into 6 different subtypes: arteriolosclerosis, cerebral amyloid angiopathy, genetic SVD distinct from cerebral amyloid angiopathy, inflammatory and immunologically mediated SVDs, venous collagenosis, and other SVDs (eg, postradiation angiopathy). The third most common category consists of SVD for which there is a genetic basis. This group includes cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy, Fabry disease, and, the main subject of this review, CADASIL. We provide an overview of recent discoveries in CADASIL and Notch research, with a focus on the molecular underpinnings of the disease. This is a research area with interesting yet sometimes conflicting findings, and we discuss whether CADASIL should be viewed solely as an aggregation disease or whether dysregulated Notch signaling also is central to the vascular pathology in the disease.
Flying Ad Hoc Networks: A New Domain for Network Communications

The advent of flying ad hoc networks (FANETs) has opened an opportunity to create new added-value services. Even though it is clear that these networks share common features with its predecessors, e.g., with mobile ad hoc networks and with vehicular ad hoc networks, there are several unique characteristics that make FANETs different. These distinctive features impose a series of guidelines to be considered for its successful deployment. Particularly, the use of FANETs for telecommunication services presents demanding challenges in terms of quality of service, energy efficiency, scalability, and adaptability. The proper use of models in research activities will undoubtedly assist to solve those challenges. Therefore, in this paper, we review mobility, positioning, and propagation models proposed for FANETs in the related scientific literature. A common limitation that affects these three topics is the lack of studies evaluating the influence that the unmanned aerial vehicles (UAV) may have in the on-board/embedded communication devices, usually just assuming isotropic or omnidirectional radiation patterns. For this reason, we also investigate in this work the radiation pattern of an 802.11 n/ac (WiFi) device embedded in a UAV working on both the 2.4 and 5 GHz bands. Our findings show that the impact of the UAV is not negligible, representing up to a 10 dB drop for some angles of the communication links.
JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information.

OBJECTIVE When treatment decisions are being made for patients with acute ischemic stroke, timely and accurate outcome prediction plays an important role. The optimal rehabilitation strategy also relies on long-term outcome predictions. The decision-making process involves numerous biomarkers including imaging features and demographic information. The objective of this study was to integrate common stroke biomarkers using machine learning methods and predict patient recovery outcome at 90 days. MATERIALS AND METHODS A total of 512 patients were enrolled in this retrospective study. Extreme gradient boosting (XGB) and gradient boosting machine (GBM) models were used to predict modified Rankin scale (mRS) scores at 90 days using biomarkers available at admission and 24 hours. Feature selections were performed using a greedy algorithm. Fivefold cross validation was applied to estimate model performance. RESULTS For binary prediction of an mRS score of greater than 2 using biomarkers available at admission, XGB and GBM had an AUC of 0.746 and 0.748, respectively. Adding the National Institutes of Health Stroke Score at 24 hours and performing feature selection improved the AUC of XGB to 0.884 and the AUC of GBM to 0.877. With the addition of the recanalization outcome, XGB's AUC improved to 0.807 for nonrecanalized patients and dropped to 0.670 for recanalized patients. GBM's AUC improved to 0.781 for nonrecanalized patients and dropped to 0.655 for recanalized patients. CONCLUSION Decision tree-based GBMs can predict the recovery outcome of stroke patients at admission with a high AUC. Breaking down the patient groups on the basis of recanalization and nonrecanalization can potentially help with the treatment decision process.
VianniaTopes: a database of predicted immunogenic peptides for Leishmania (Viannia) species

Abstract Leishmania is a protozoan parasite causing several disease presentations collectively known as leishmaniasis. Pathogenic species of Leishmania are divided into two subgenera, L. (Leishmania) and L. (Viannia). Species belonging to the Viannia subgenus have only been reported in Central and South America. These species predominantly cause cutaneous leishmaniasis, but in some cases, parasites can migrate to the nasopharyngeal area and cause a highly disfiguring mucocutaneous presentation. Despite intensive efforts, no effective antileishmanial vaccine is available for use in humans, although a few candidates mainly designed for L. (Leishmania) species are now in clinical trials. After sequencing the genome of Leishmania panamensis, we noticed a high degree of sequence divergence among several orthologous proteins from both subgenera. Consequently, some of the previously published candidates may not work properly for species of the Viannia subgenus. To help in vaccine design, we predicted CD4+ and CD8+ T cell epitopes in the theoretical proteomes of four strains belonging to the Viannia subgenus. Prediction was performed with at least two independent bioinformatics tools, using the most frequent human major histocompatibility complex (MHC) class I and class II alleles in the affected geographic area. Although predictions resulted in millions of peptides, relatively few of them were predicted to bind to several MHC alleles and can therefore be considered promiscuous epitopes. Comparison of our results to previous applications to species of the Leishmania subgenus confirmed that approximately half of the reported candidates are not present in Viannia proteins with a threshold of 80% sequence similarity and coverage. However, our prediction methodology was able to predict 70–100% of the candidates that could be found in Viannia. All the prediction data generated in this study are publicly available in an interactive database called VianniaTopes.
Learning Neural Emotion Analysis from 100 Observations: The Surprising Effectiveness of Pre-Trained Word Representations

Deep Learning has drastically reshaped virtually all areas of NLP. Yet on the downside, it is commonly thought to be dependent on vast amounts of training data. As such, these techniques appear ill-suited for areas where annotated data is limited, like emotion analysis, with its many nuanced and hard-to-acquire annotation formats, or other low-data scenarios encountered in under-resourced languages. In contrast to this popular notion, we provide empirical evidence from three typologically diverse languages that today's favorite neural architectures can be trained on a few hundred observations only. Our results suggest that high-quality, pre-trained word embeddings are crucial for achieving high performance despite such strong data limitations.