Miracle-dz/UniFAR-FTUs huggingface dataset:
Fecal Microbiota Transplantation Is Effective in Relieving Visceral Hypersensitivity in a Postinfectious Model

Aim To investigate the effect of fecal microbiota transplantation on visceral hypersensitivity compared with Bifidobacterium longum. Methods Mice visceral hypersensitivity was induced by Trichinella spiralis. After 8 weeks, they were divided into three groups (controls, Bifidobacterium longum, and fecal microbiota transplantation) and were daily treated by gavage with 0.2 ml PBS, Bifidobacterium longum HB55020, or fecal microbiota for 7 days. Visceral hypersensitivity was tested with abdominal withdrawal reflex. Permeability of colon epithelium was assessed with Ussing chamber. Results After administration of Bifidobacterium longum, compared with mice in postinfectious group, mice had higher pain threshold (p < 0.05). After administration of fecal microbiota, compared with mice in postinfectious group, mice had higher pain threshold (p < 0.05). Fecal microbiota transplantation was as effective as Bifidobacterium in relieving visceral hypersensitivity. Administration of Bifidobacterium longum or fecal microbiota transplantation improved colon epithelium permeability. Expression of occluding-1 was increased. Conclusion Manipulation of microbiota is effective in relieving visceral hypersensitivity. Fecal microbiota transplantation is as effective as Bifidobacterium longum administration.
Two Strategies of Pseudomonas syringae to Avoid Recognition of the HopQ1 Effector in Nicotiana Species

Pseudomonas syringae employs a battery of type three secretion effectors to subvert plant immune responses. In turn, plants have developed receptors that recognize some of the bacterial effectors. Two strain-specific HopQ1 effector variants (for Hrp outer protein Q) from the pathovars phaseolicola 1448A (Pph) and tomato DC3000 (Pto) showed considerable differences in their ability to evoke disease symptoms in Nicotiana benthamiana. Surprisingly, the variants differ by only six amino acids located mostly in the N-terminal disordered region of HopQ1. We found that the presence of serine 87 and leucine 91 renders PtoHopQ1 susceptible to N-terminal processing by plant proteases. Substitutions at these two positions did not strongly affect PtoHopQ1 virulence properties in a susceptible host but they reduced bacterial growth and accelerated onset of cell death in a resistant host, suggesting that N-terminal mutations rendered PtoHopQ1 susceptible to processing in planta and, thus, represent a mechanism of recognition avoidance. Furthermore, we found that co-expression of HopR1, another effector encoded within the same gene cluster masks HopQ1 recognition in a strain-dependent manner. Together, these data suggest that HopQ1 is under high host-pathogen co-evolutionary selection pressure and P. syringae may have evolved differential effector processing or masking as two independent strategies to evade HopQ1 recognition, thus revealing another level of complexity in plant – microbe interactions.
Injecting hypervisor-based software partitions into Design Space Exploration activities considering mixed-criticality requirements

This work faces the role of HW/SW Design Space Exploration for heterogeneous parallel embedded systems subject to mixed-criticality requirements, extended to consider also hypervisor technologies. In particular, it presents an evolutionary approach integrated into a reference Electronic System-Level HW/SW Co-Design flow able to consider and evaluate design alternatives while exploiting also Hypervisor-based SW partitions. Finally, some experimental results show the effectiveness of the proposed approach.
A Variational Time Series Feature Extractor for Action Prediction

We propose a Variational Time Series Feature Extractor (VTSFE), inspired by the VAE-DMP model of Chen et al., to be used for action recognition and prediction. Our method is based on variational autoencoders. It improves VAE-DMP in that it has a better noise inference model, a simpler transition model constraining the acceleration in the trajectories of the latent space, and a tighter lower bound for the variational inference. We apply the method for classification and prediction of whole-body movements on a dataset with 7 tasks and 10 demonstrations per task, recorded with a wearable motion capture suit. The comparison with VAE and VAE-DMP suggests the better performance of our method for feature extraction. An open-source software implementation of each method with TensorFlow is also provided. In addition, a more detailed version of this work can be found in the indicated code repository. Although it was meant to, the VTSFE hasn't been tested for action prediction, due to a lack of time in the context of Maxime Chaveroche's Master thesis at INRIA.
Camera based pedestrian detection for railway driver support systems

Pedestrian Detection (PD) is one the most studied application areas of object detection. Although an important effort is already given to develop advanced classifiers for self-driving cars (SDC) especially in recent years, studies about onboard railway systems remain very limited. Although the similarity of railway and highway scenarios suggests that systems developed for SDC-PD can be used for rail vehicles, it is also necessary to examine the low level of generalization ability and the high degree of dependence of the PD test performance on the training set. In this paper, the feasibility of PD systems, whose effectiveness on SDC is proved, was examined on railway systems. For this purpose, SDC-PD systems, created with different training benchmarks such as Caltech and INRIA, have been applied to railway system scenes with various difficulties and the results have been examined. The findings show that the direct implementation of classifiers for SDC does not provide sufficient success to come up with specific challenges to railway systems.
Bednet vzw: een virtuele leeromgeving voor langdurig en chronisch zieke kinderen

Dit onderzoek focust op de mogelijkheden van Bednet vzw, een initiatief gesteund door het Departement Onderwijs en Vorming en gericht op het bieden van een oplossing voor schoolproblemen van langdurig en chronisch zieke kinderen. Bednet tracht hierbij aandacht te hebben voor zowel de didactische als de sociale kant van het schoolse gebeuren. Om de noden en vereisten van een dergelijk programma voor zieke kinderen in kaart te brengen werd een explorerend onderzoek uitgevoerd. Hiertoe werden 59 leerlingen en veertien leerkrachten uit verschillende lagen van het secundair onderwijs bevraagd (respectievelijk met behulp van een enquete en semigestructureerde interviews). Resultaten werden gekaderd in de ecologische theorie van Bronfenbrenner (Bronfenbrenner & Morris, 1998) en zijn over het algemeen vrij coherent te noemen over de verschillende respondenten heen. De resultaten wijzen uit dat Bednet zowel mogelijkheden biedt als een aantal beperkingen heeft. Op basis van deze resultaten wordt een aantal suggesties ter optimalisatie aangeboden. Verder onderzoek dient de verschillende componenten van de ecologische theorie in dit en soortgelijke initiatieven te kaderen. Wat is Bednet?
Poster abstract: NMEMD: A novel method for efficiently measuring software-defined network delay

Currently, many QoS-sensitive software-defined network applications expect the network end-to-end delay to meet certain constraints. In this context, it is significant to measure and monitor network delay. The only way traditional measurement methods can improve their accuracy is by increasing the number of measurement times or frequencies. However, the measurement process itself considerably consumes network resource. To address this problem, a novel method for efficiently measuring software-defined network delay, called NMEMD, is proposed to automatically optimize the measurement frequency and cycle based on the software-defined network delay. Simulation results demonstrate the ability of the proposed method to improve measurement accuracy of network delay and reduce resource consumption.
From Text to Topics in Healthcare Records: An Unsupervised Graph Partitioning Methodology

Electronic Healthcare Records contain large volumes of unstructured data, including extensive free text. Yet this source of detailed information often remains under-used because of a lack of methodologies to extract interpretable content in a timely manner. Here we apply network-theoretical tools to analyse free text in Hospital Patient Incident reports from the National Health Service, to find clusters of documents with similar content in an unsupervised manner at different levels of resolution. We combine deep neural network paragraph vector text-embedding with multiscale Markov Stability community detection applied to a sparsified similarity graph of document vectors, and showcase the approach on incident reports from Imperial College Healthcare NHS Trust, London. The multiscale community structure reveals different levels of meaning in the topics of the dataset, as shown by descriptive terms extracted from the clusters of records. We also compare a posteriori against hand-coded categories assigned by healthcare personnel, and show that our approach outperforms LDA-based models. Our content clusters exhibit good correspondence with two levels of hand-coded categories, yet they also provide further medical detail in certain areas and reveal complementary descriptors of incidents beyond the external classification taxonomy.
A comprehensive review of web-based resources of non-coding RNAs for plant science research

Non-coding RNAs (ncRNAs) are transcribed from genome but not translated into proteins. Many ncRNAs are key regulators of plants growth and development, metabolism and stress tolerance. In order to make the web-based ncRNA resources for plant science research be more easily accessible and understandable, we made a comprehensive review for 83 web-based resources of three types, including genome databases containing ncRNA data, microRNA (miRNA) databases and long non-coding RNA (lncRNA) databases. To facilitate effective usage of these resources, we also suggested some preferred resources of miRNAs and lncRNAs for performing meaningful analysis.
Quantized feedback control based on event-triggered observer

In this paper, we propose a quantized feedback control approach based on event-triggered observer. First, according to the characteristics of event-triggering mechanism and observer, an event-triggered observer is put forward to estimate the unmeasured states. Next, we introduce quantizer and controller to quantify data and construct closed-loop system respectively. In this framework, besides the observer, quantizer and controller are only updated at the event-triggered sampling instants so that it can reduce the computational effort and sampling frequency. Moreover, the condition in terms of linear matrix inequalities is provided to ensure the closed-loop system as well as the observer error system and quantization error system asymptotic stability under the event-triggered strategy. Finally, the simulation example is given to illustrate the proposed method.
Learning and detection mechanisms of spectral-activity information towards energy efficient 5G communication

The context information on spectral occupation and prediction of communication activity over various frequency bands will play a key role in the energy-efficient operation and co-existence of future networks, consisting of human-centric systems along with machine-to-machine communications. When addressing the spectrum-scarcity challenge, the scenarios can be dynamic in space and time, so self-adapting systems will prevail in 5G networks. The main goal of this article is to overview the benefits and drawbacks of sensing and learning methods in the context of future 5G communications.
Caterpillar-inspired crawling robot on a stick using active-release and passive-grip elastic legs

This paper presents a lightweight caterpillar-inspired soft-bodied robot that produces crawling locomotion on a stick. The significant features are passive-grip/active-release opposable legs and dual elastic arch structure to couple the segment contraction and the leg opening. These actuations are driven by a shape memory alloy (SMA) coil attached along the body axis. The mechanical design allows the robot to hold the unstable substrate without energy consumption. We find that adequate activation time lag between the legs/segments exists but the system is not sensitive to the parameter changes thanks to the softness of the body. The robot can build very cheap (less than 30 US dollars) to 3d-print using hard and rubber-like materials together, which doesn't require any assembly labor to build the body.
A new Gated -ground- sleep architecture for ultra low leakage of SRAM cell

As down-scaling of transistors and up-scaling of technology creating an overwhelming inversely proportional scenario in today's VLSI era, three major components taking into account while designing of CMOS memory cells. As the technology shrinks down, the power dissipation widely impacts to SRAM cells. Along with the power consumption, delay and stability of SRAM cell is also becoming a challenging issue day by day. In this paper, a low power designing approach is being introduced taking advantages of combination architectures of Transmission Gate logic and Gated ground sleep logic. The Transmission Gate invokes a leakage free sub-threshold scenario when Gated ground sleep logic provides a swiftly stacking effect which jointly can induce lowering down power consumption in SRAM cell. This new approach can significantly reduce power consumption upto 75% compared to conventional 6T-SRAM cell. All the experimental works are done by tSPICE 16 with 0. 25μm technology.
Magnetic resonance angiography for the primary diagnosis of pulmonary embolism: A review from the international workshop for pulmonary functional imaging

Pulmonary contrast enhanced magnetic resonance angiography (CE-MRA) is useful for the primary diagnosis of pulmonary embolism (PE). Many sites have chosen not to use CE-MRA as a first line of diagnostic tool for PE because of the speed and higher efficacy of computerized tomographic angiography (CTA). In this review, we discuss the strengths and weaknesses of CE-MRA and the appropriate imaging scenarios for the primary diagnosis of PE derived from our unique multi-institutional experience in this area. The optimal patient for this test has a low to intermediate suspicion for PE based on clinical decision rules. Patients in extremis are not candidates for this test. Younger women (< 35 years of age) and patients with iodinated contrast allergies are best served by using this modality We discuss the history of the use of this test, recent technical innovations, artifacts, direct and indirect findings for PE, ancillary findings, and the effectiveness (patient outcomes) of CE-MRA for the exclusion of PE. Current outcomes data shows that CE-MRA and NM V/Q scans are effective alternative tests to CTA for the primary diagnosis of PE.
Exploring patients’ experiences of the whiplash injury-recovery process – a meta-synthesis

Purpose The aim of this study was to conduct a meta-synthesis to analyze qualitative research findings and thereby understand patients’ experiences of whiplash-associated disorders (WAD) and the injury-recovery process. Materials and methods A qualitative meta-synthesis, which is an interpretive integration of existing qualitative findings, was performed. The databases PubMed, PsychINFO, Scopus, and Web of Science were searched. The Critical Assessment Skills Programme was used to assess the quality of the included studies. Results Four studies were included. The synthesis resulted in several codes, 6 categories, and 3 themes (distancing from normalcy, self-efficacy in controlling the life situation after the injury, and readjustment and acceptance) that described the participants’ pain beliefs, their WAD-related life situation and their future expectations and acceptance. Changes in self-image were difficult to cope with and likely led to perceived stigmatization. Struggling with feelings of loss of control appeared to lead to low confidence and insecurity. Focusing on increasing knowledge and understanding the pain and its consequences were believed to lead to better strategies for handling the situation. Furthermore, recapturing life roles, including returning to work, was challenging, but an optimistic outlook reinforced symptom improvements and contributed to feelings of happiness. Conclusion The results of the present study provide a comprehensive understanding of patients’ complex, multifaceted experiences of WAD, and the injury-recovery process. The findings can guide us in the development of new ways to evaluate and manage WAD. The results also indicate that a more patient-centered approach is needed to determine the depth and breadth of each patient’s problems.
Joint load management and resource allocation in the energy harvesting powered small cell networks with mobile edge computing

Considering the advantages of small cell networks (SCNs) and mobile edge computing (MEC), it is natural to combine small cells with MEC, which can not only enhance the computation capacity of the mobile user equipment (UE) but also provide the users with a high quality of services (QoS). However, the high energy consumption becomes a rigorous problem to the operators and the environment with the small cell base stations (SBSs) densely deployed. Hence, it is crucial to exploit off-grid and green energy resources to power the SBSs, for which energy harvesting (EH) is one of the viable solutions. But there impose formidable challenges combining EH-based SCNs with MEC, due to the fact that the energy arrival and traffic are not uniformly distributed across space and time domain. In this work, we jointly study the green load management and resource allocation scheme in such a network for achieving green computation offloading under the constraints of network cost and users' QoS. First, we formulate the main problem. To reduce the complexity of the proposed problem, we adopt a distributed three-stage iteration approach to solve it. To avoid overload and make full use of the resources of SBSs, cell range expansion (CRE) is used to adjust the coverage of each SBS to match the resource state and traffic pattern. After that, channel allocation is performed considering interference migration. Based on the results of the former two steps, the MEC servers dynamically distribute the computation resources to the UEs. Simulation results verified the performance of the proposed algorithm with different system parameters.
Solve game of the amazons with neural networks and distributed computing

A training model was constructed with convolution neural network. Its policy network was trained by supervised learning. Then the parameters of the policy network were constantly adjusted by self-play and the game strength was enhanced. So it made it surpassed the existing game programs that simply used - pruning algorithm or Monte Carlo algorithm. By using distributed computing to update the data asynchronously, multiple game groups were allocated to different machines at the same time. In this way, a number of common configuration computers could be made full use of.
Segmentation of objects in RGB-D scenes by clustering surfaces

Object recognition, detection and segmentation tasks using RGB-D data are frequently used in the field of machine vision and robotics. In this work, a method for object segmentation on RGB-D data is proposed. In the first step, filtering steps are applied to obtain a better representation of the depth data. Surface normals are calculated for all points in the depth data. In the next step, a modified version of the region-growing algorithm is used, in which surface normals are compared. Thus, surfaces of objects are obtained. In the last step, with the help of the features extracted from the surfaces, objects are segmented by clustering surfaces with X-means. The proposed method has been tested with data sets containing complex scenes and is comparable to the related studies in the literature.
Increase of Albinistic Hosts Caused by Gut Parasites Promotes Self-Transmission

Paranosema locustae is a gut parasite that has been applied widely in the control of grasshoppers in many parts of the world. Usually, P. locustae is transmitted horizontally via passive modes under natural conditions but in the current study, a positive transmission strategy of P. locustae was demonstrated. First, infection by P. locustae resulted in the cuticula of infected Locusta migratoria nymphs to become lighter in color: normally only a small proportion of locusts are pale with most either being partly or mostly black; but locusts infected with P. locustae became pale. And it was found that the change to pale occurred even among uninfected black and partly black nymphs reared with infected locusts. The eumelanin of the thorax and abdomen of infected individuals decreased significantly, as did the level of dopamine. In addition, there was a decrease in phenol oxidase activity and the expression of henna and pale, which are involved in the synthesis of cuticle melanin, decreased. What is the ecological significance of this increase in light-colored hosts caused by P. locustae? We discovered that light-colored locusts were more susceptible to the microsporidian pathogen than dark-colored individuals were, because of their weaker melanization. Phenol oxidase activity in pale locusts was lower than that of black locusts, but the serpin expression level of pale locusts was higher than that of black individuals. When examined for infection, it was found that initially uninfected nymphs had picked up P. locustae infections indicating that infections are readily passed from one pale locust to another. The infection rate of healthy locusts reared with light-colored locusts infected with P. locustae was 100% which was more than with black-colored ones. The increase in albinistic locusts clearly promoted the prevalence of P. locustae in the total population. In conclusion, these results elucidated a new strategy of positive self-transmission in P. locustae. Importance: Mother Nature always grants wisdom to her creatures and feeds them carefully. This wisdom is particularly apparent in the relationships between two interacting species. In this study, our team focused on the interaction between L. migratoria and P. locustae. In a previous study, it was found that L. migratoria isolate infected individuals, reducing avoiding the spread of P. locustae, in a previous study. The solitary, pale individuals infected by P. locustae were left behind as locust groups marched ahead, leading to a kind of behavioral immunity in the insects. Here, we reported that P. locustae promotes pigmentation loss in L. migratoria, causing a larger proportion of light-colored individuals, and these lighter individuals which possessed weaker immunity against pathogens. This strategy is advantageous to P. locustae, as it promotes its propagation and spread. These extraordinary abilities of L. migratoria and P. locustae have accumulated over millennia of years of interaction.
Biological Mechanisms of Cancer-Induced Depression

Patients with cancer are more likely to develop depression than the general population, which negatively impacts their quality of life and prognosis. In order to identify effective antidepressants catered toward cancer patients, the biology of depression in the context of cancer must be well-understood. Many theories have emerged postulating the mechanisms underlying the development of depressive disorder. Here, we review the role inflammation, a hyperactive hypothalamic-pituitary-adrenal (HPA) axis, and glutamate excitotoxicity may play in cancer-induced depression. Hopefully, novel therapeutics targeting these dysregulated pathways may be potent in ameliorating depressive symptoms in the cancer population.