Miracle-dz/UniFAR-FTUs huggingface dataset:
Intra-Articular Injection of Synovium-Derived Mesenchymal Stem Cells with Hyaluronic Acid Can Repair Articular Cartilage Defects in a Canine Model

Objective: The purpose of this study was to evaluate effectiveness of intra-articular injection of synovium-derived mesenchymal stem cells (SMSCs) and hyaluronic acid (HA) for the treatment of articular cartilage defects in a canine model. Methods: Forty-eight knees of 24 beagle dogs were randomly assigned to 16 groups (n=3) according to both the number of injected SMSCs (0.5×105 cells, 5×106 cells, 5×107 cells) and the concentration of HA (0%, 0.01%, 0.1%, 0.5%). A partial-thickness cartilage defect was created in the medial femoral condyle under arthroscopy. After seven weeks, autologous SMSCs with or without 1 ml HA were percutaneously injected into the injured knee. In the control group, 1 ml saline was injected. Twelve weeks after the injection, evaluation was performed using the International Cartilage Repair Society (ICRS) visual assessment scale and the modified O’Driscoll histological score. Results: The mean ICRS visual assessment scale and the mean modified O’Driscoll histological score were 1.0 ± 0.00 and 11.0 ± 0.00 in the control group. On the other hand, the mean ICRS visual assessment scale and the mean modified O’Driscoll histological score were 7.3 ± 3.21 and 30.7 ± 7.23 in the group injected with 5×106 cells and 0.01% HA. Conclusion:Intra-articular injection of SMSCs with HA may be effective for stimulating articular cartilage repair. Our results suggest that there is an ideal combination of the number of SMSCs and the concentration of HA for promoting particular cartilage repair.
Exact outage probability of two-way decode-and-forward NOMA scheme with digital network coding

In this paper, we propose a two-way decode and forward non-orthogonal multiple access (NOMA) scheme (called a TWNOMA protocol) in which an inter-mediate relay helps two source nodes to communicate with each other. In the proposed protocol, we implement digital network coding (DNC) to compress received data from these source nodes. We analyze and evaluate the system performance in terms of exact closed-form outage probability over Rayleigh fading channels. Simulation and analysis results discover contributions as follows. Firstly, the outage performance of the proposed protocol TWNOMA is improved when comparing with a conventional two-way scheme using DNC (called a TWDNC protocol), two-way scheme without using DNC (called a TWNDNC protocol) and two-way scheme in amplify-and-forward relay systems (called a TWANC protocol). Finally, the theoretical analyses are validated by performing Monte Carlo simulations.
Further Results on Stabilization of Chaotic Systems Based on Fuzzy Memory Sampled-Data Control

This note investigates sampled-data control for chaotic systems. A memory sampled-data control scheme that involves a constant signal transmission delay is employed for the first time to tackle the stabilization problem for Takagi–Sugeno fuzzy systems. The advantage of the constructed Lyapunov functional lies in the fact that it is neither necessarily positive on sampling intervals nor necessarily continuous at sampling instants. By introducing a modified Lyapunov functional that involves the state of a constant signal transmission delay, a delay-dependent stability criterion is derived so that the closed-loop system is asymptotically stable. The desired sampled-data controller can be achieved by solving a set of linear matrix inequalities. Compared with the existing results, a larger sampling period is obtained by this new approach. A simulation example is presented to illustrate the effectiveness and conservatism reduction of the proposed scheme.
Divergent effects of first-generation and second-generation antipsychotics on cortical thickness in first-episode psychosis

Background Whether there are differential effects of first-generation antipsychotics (FGAs) and second-generation antipsychotics (SGAs) on the brain is currently debated. Although some studies report that FGAs reduce grey matter more than SGAs, others do not, and research to date is limited by a focus on schizophrenia spectrum disorders. To address this limitation, this study investigated the effects of medication in patients being treated for first-episode schizophrenia or affective psychoses. Method Cortical thickness was compared between 52 first-episode psychosis patients separated into diagnostic (i.e. schizophrenia or affective psychosis) and medication (i.e. FGA and SGA) subgroups. Patients in each group were also compared to age- and sex-matched healthy controls (n = 28). A whole-brain cortical thickness interaction analysis of medication and diagnosis was then performed. Correlations between cortical thickness with antipsychotic dose and psychotic symptoms were examined. Results The effects of medication and diagnosis did not interact, suggesting independent effects. Compared with controls, diagnostic differences were found in frontal, parietal and temporal regions. Decreased thickness in FGA-treated versus SGA-treated groups was found in a large frontoparietal region (p < 0.001, corrected). Comparisons with healthy controls revealed decreased cortical thickness in the FGA group whereas the SGA group showed increases in addition to decreases. In FGA-treated patients cortical thinning was associated with higher negative symptoms whereas increased cortical thickness in the SGA-treated group was associated with lower positive symptoms. Conclusions Our results suggest that FGA and SGA treatments have divergent effects on cortical thickness during the first episode of psychosis that are independent from changes due to illness.
Pleiotropic Effect of Common Variants at ABO Glycosyltranferase Locus in 9q32 on Plasma Levels of Pancreatic Lipase and Angiotensin Converting Enzyme

For forty-three clinical test values presumably associated to common complex human diseases, we carried out a genome-wide association study using 600K SNPs in a general Japanese population of 1,639 individuals (1,252 after quality control procedures) drawn from a regional cohort, followed by a replication study for statistically significant SNPs (p = 1.95×10−9–8.34×10−39) using an independent population of 1,671 from another cohort. In this single two-stage study, we newly found strong and robust associations of common variants at the ABO histo-blood glycosyltransferase locus in 9q32 with the plasma levels of pancreatic lipase (P-LIP), in addition to successful confirmation of the known ABO association of angiotensin converting enzyme (ACE) independent of the ACE1 gene in 17q23.2 with the ACE level. Our results are compatible with the previously reported association between the ABO gene and pancreatic cancer, and show that the effect of these common variants at the ABO locus on the P-LIP and ACE levels is largely opposing and pleiotropic.
Loss of MicroRNA-21 Influences the Gut Microbiota, Causing Reduced Susceptibility in a Murine Model of Colitis

Background and Aims microRNAs regulate gene expression and influence the pathogenesis of human diseases. The present study investigated the role of microRNA-21 [miR-21] in the pathogenesis of intestinal inflammation, because miR-21 is highly expressed in inflammatory bowel disease. Inflammatory bowel disease is associated with intestinal barrier dysfunction and an altered gut microbiota. Recent studies have demonstrated that host microRNAs can shape the microbiota. Thus, we determined the influence of miR-21 on the gut microbiota and observed the subsequent impact in a dextran sodium sulphate [DSS]-induced colitis model. Methods The influence of miR-21 on the gut microbiota and inflammation was assessed in wild-type [WT] and miR-21-/- mice, in co-housed mice, following antibiotic depletion of the microbiota, or by colonization of germ-free [GF] mice with fecal homogenate, prior to DSS administration. We carried out 16S rRNA sequencing on WT and miR-21-/- mice to dissect potential differences in the gut microbiota. Results miR-21-/- mice have reduced susceptibility to DSS-induced colitis compared with WT mice. Co-housing conferred some protection to WT mice, while GF mice colonized with fecal homogenate from miR-21-/- were protected from DSS colitis compared with those colonized with WT homogenate. Further supporting a role for the microbiota in the observed phenotype, the protection afforded by miR-21 depletion was lost when mice were pre-treated with antibiotics. The 16S rRNA sequencing revealed significant differences in the composition of WT and miR-21-/- intestinal microbiota. Conclusions These findings suggest that miR-21 influences the pathogenesis of intestinal inflammation by causing propagation of a disrupted gut microbiota.
An Anti-Human Thymocyte Globulin-Based Reduced-Intensity Conditioning Regimen Is Associated with a Higher Quality of Life and Lower Organ Toxicity without Affecting Lymphocyte Reconstitution

Reduced-intensity (RIT) conditioning regimens are gaining increased attention as a result of their advantages and efficacy. However, no data are available regarding whether these regimens improve patient quality of life (QoL). In our study, health-related QoL (HRQoL) was retrospectively assessed in 111 patients with hematological malignancies. Analysis of the Quality of Life Questionnaire indicated that 35 of the RIT patients were able to perform their normal work and returned to their baseline levels of function 2 to 3 months after transplantation. In the myeloablative (MA) group, only 24 patients were able to resume work, and these patients returned to their baseline levels of function 6 to 8 months after transplantation (68.6% vs. 40.0%, P = 0.004). Grade III–IV organ toxicity occurred in 20% of the RIT patients and in 52% of the MA patients (P = 0.001), and the cumulative incidences of grades III–IV acute graft-versus-host disease (GVHD) were 13.7% and 35.0% in RIT and MA patients, respectively (P = 0.015). In conclusion, the RIT conditioning regimens were well tolerated by the patients, with a low incidence of transplant-related mortality (TRM) and serious acute GVHD. In addition, these regimens minimized procedure-related toxicity, improved QoL and did not influence lymphocyte reconstitution; however, OS was similar for both regimens because the relapse rate was relatively increased in the RIT groups.
A Survey and Recommendations for Distributed, Parallel, Single Pass, Incremental Bayesian Classification Based on MapReduce for Big Data

In the emerging digital age, massive production of data is occurred actively or passively by collecting data from users and environment via applications, sensor devices and so on. That makes it important and crucial to have the ability to process big data efficiently and effectively utilize it. The challenge to process big data is that it has high volume, velocity, variety, as well as veracity and value. In this paper, we present a survey of related work and prescribe our recommendations towards building Bayesian classification for big data environments. It is based on MapReduce and is distributed, parallel, single pass and incremental which makes it feasible to be deployed and executed on cloud computing platform We also carry out scalability analysis of the proposed solution that it can train Bayesian classifier to perform predictive analytics by processing big data with large volume, velocity and variety.
Improved performance of solid oxide fuel cell operating on biogas using tin anode-infiltration

This work presents a novel method of Sn-infiltration on SOFC anodes for SOFC operation in biogas dry reforming. Using commercially available NiYSZ-based anode supported half cells with hand-painted LSM/YSZ cathode layers, Sn-infiltrated NiYSZ SOFCs containing different amounts of Sn were manufactured. These SOFCs were tested for their electrochemical performance and quantity of deposited carbon during operation on simulated biogas of 1:2 volume ratio of CO2:CH4 without humidification but with 25% Helium added to the feed stream to enable measurements of the fuel cell outlet gas composition using a quadrupole mass spectrometer. Most of the SOFCs were tested in biogas for 1 day (22 hours), but several cells were tested for 6 days (150 hours) to evaluate performance degradation. The electrochemical performance tests at 750 oC showed that with H2 as fuel the non-infiltrated NiYSZ SOFCs were able to reliably generate a moderate level of current of 350 mA cm-2 at 0.7 V; however when simulated biogas was introduced, current dropped significantly to 90-200 mA cm-2. Contrary to non-infiltrated cells, a series of Sn-infiltrated cells under the same operating conditions performed equally well both on H2 and biogas producing 310 to 420 mA cm-2 at 0.7 V. Several cells showed stable electrochemical performance over 150 hours of operation both on H2 and biogas. Using Temperature Programmed Oxidation (TPO), both Sn-infiltrated and non-infiltrated SOFCs showed low quantities of carbon formed during 22 hours operation on biogas. Visual observation and SEM images of the anode surface after 150 hours operation on biogas showed no sign of deposited carbon. The conclusion is that Sn-infiltrated NiYSZ-based SOFC can be operated on simulated biogas with significantly higher electrochemical performance and low carbon deposition, given the anode is adequately modified.
Deep learning powered automated tool for generating image based datasets

During the last decade, rapid growth of the social media usage and the Internet in general led to the drastic expansion of the publicly available data. This gives the great opportunity for research and analysis in many fields of machine learning and data science. Even though this data is open to use, often it is distributed over the various sources on the web and in order to be used in data science experiments it requires to be gathered, properly labeled and pre-processed. This is the essential step of every research and it is generally time consuming. In this paper, the automated system for gathering and preparing the dataset is proposed. The system consists of numerous features which embrace cutting edge approaches in order to reduce the time for developing the dataset. It is primarily developed for image based datasets, and provides possibility for researchers to collect the images from the different sources: social media by official APIs or online image libraries using web scraping techniques. Along with the data gathering, proposed system provides an intelligent image labeling feature by using state-of-the-art deep learning methods which use trained models for object recognition and label them in standardized formats. The system is modular and could be extended for desired objects detection by simply adding new trained models. Finally, it provides the possibility to speed up the researchers by setting their focus more on the essential parts of their studies than to generate the dataset by themselves.
TwinNet: A Double Sub-Network Framework for Detecting Universal Adversarial Perturbations

Deep neural network has achieved great progress on tasks involving complex abstract concepts. However, there exist adversarial perturbations, which are imperceptible to humans, which can tremendously undermine the performance of deep neural network classifiers. Moreover, universal adversarial perturbations can even fool classifiers on almost all examples with just a single perturbation vector. In this paper, we propose TwinNet, a framework for neural network classifiers to detect such adversarial perturbations. TwinNet makes no modification of the protected classifier. It detects adversarially perturbated examples by enhancing different types of features in dedicated networks and fusing the output of the networks later. The paper empirically shows that our framework can identify adversarial perturbations effectively with a slight loss in accuracy when predicting normal examples, which outperforms state-of-the-art works.
Robust state-dependent computation in neuromorphic electronic systems

State-dependent computation is one of the main signatures of cognition. Recently, it has been shown how it can be used as a computational primitive in spiking neural networks for constructing complex cognitive behaviors in neuromorphic agents. However, to achieve the desired computations and behaviors in mixed signal analog-digital neuromorphic electronic systems, these computational primitives should be able to cope with noisy and imprecise components, such as silicon neurons and synapses, with noisy and unreliable external signals, and with interference from the environment. Here we present a spiking neural network model that addresses all these issues while exhibiting both analog signal processing properties and digital symbolic computational abilities. We show how this Neural State Machine (NSM) model can be used for realizing robust state-dependent computation on neuromorphic hardware, and we validate it with experimental results obtained from a recently developed multi-neuron multi-core neuromorphic computing architecture.
Realizing Highly-Available, Scalable, and Protocol-Independent vSDN Slicing With a Distributed Network Hypervisor System

In this paper, we design and implement a distributed network virtualization hypervisor (NVH) system, namely, DPVisor, which can provide superior network programmability based on protocol-oblivious forwarding to realize highly-available, scalable, and protocol-independent virtual software-defined network slicing. The experimental comparisons indicate that DPVisor achieves comparable performance as ONVisor (i.e., an OpenFlow-based ONOS benchmark), in terms of the message processing latency, message processing throughput, and failure recovery time. Moreover, to optimize the performance of the distributed NVH systems, we carefully adjust the consistency model used in the state synchronization of NVH instances and propose a local cache-based scheme to balance the tradeoff between the consistency and availability of network status. Our experimental results confirm that the message processing throughput of the distributed NVH systems can be greatly improved (i.e., for both DPVisor and ONVisor), while the data consistency among NVH instances is still maintained well.
Zika virus alters the microRNA expression profile and elicits an RNAi response in Aedes aegypti mosquitoes

Zika virus (ZIKV), a flavivirus transmitted primarily by Aedes aegypti, has recently spread globally in an unprecedented fashion, yet we have a poor understanding of host-microbe interactions in this system. To gain insights into the interplay between ZIKV and the mosquito, we sequenced the small RNA profiles in ZIKV-infected and non-infected Ae. aegypti mosquitoes at 2, 7 and 14 days post-infection. ZIKA induced an RNAi response in the mosquito with virus-derived short interfering RNAs and PIWI-interacting RNAs dramatically increased in abundance post-infection. Further, we found 17 host microRNAs (miRNAs) that were modulated by ZIKV infection at all time points. Strikingly, many of these regulated miRNAs have been reported to have their expression altered by dengue and West Nile viruses, while the response was divergent from that induced by the alphavirus Chikungunya virus in mosquitoes. This suggests that conserved miRNA responses occur within mosquitoes in response to flavivirus infection. This study expands our understanding of ZIKV-vector interactions and provides potential avenues to be further investigated to target ZIKV in the mosquito host.
Efficient Identity-Based Encryption Scheme with Equality Test in Smart City

To support a sustainable development of smart city, smart grid is an indispensable part. Sensor technology in smart gird enables interactive real-time data transmission between cloud and the edge of the network. There are a number of research challenges in the design of smart grids. One of these research challenges is balancing customer privacy and the cloud-based power system’s function optimization. Identity-based encryption with equality test (IBEET) scheme has recently been identified as a viable solution, in which customers can delegate a trapdoor to the power system control server and the server then searches on the encrypted data to determine whether two different ciphertexts are encryptions of the same plaintext. Unfortunately, existing schemes are inefficient and the trapdoor could be used to perform equality test on any message; thus, leakage of privacy. In this paper, we propose an efficient IBEET scheme with bilinear pairing, which reduces the need for time-consuming HashToPoint function and each trapdoor could only be used to perform the equality test on a particular keyword. We then prove the security of our scheme for one-way chosen-ciphertext security against a chosen identity (OW-ID-CCA) attack in the random oracle model (ROM). The performance evaluation of our scheme demonstrates that in comparison to the scheme of Ma (2016), our scheme achieves a reduction of 36.7 and 39.24 percent in computation costs during the encryption phase and test phase, respectively.
Genome-by-genome approach for fast bacterial genealogical relationship evaluation

Motivation Large-scale whole-genome sequencing dataset-based studies are becoming increasingly common in pathogen surveillance and outbreak investigations. A highly discriminative and time-efficient bioinformatics tool is needed to transform large amounts of sequencing data into usable biological information. To replace the intuitive, yet inefficient, way of gene-by-gene allele calling algorithm, a new algorithm using genome-by-genome approach was developed. Results Tests showed that the program equipped with the new algorithm achieved significant improvements in allele calling efficiency compared to a conventional gene-by-gene approach. The new program, Fast-GeP, rendered a fast and easy way to infer high-resolution genealogical relationships between bacterial isolates using whole-genome sequencing data. Availability and implementation FAST-GeP is freely available from: https://github.com/jizhang-nz/fast-GeP. Supplementary information Supplementary data are available at Bioinformatics online.
Diagnosing and Minimizing Semantic Drift in Iterative Bootstrapping Extraction

Semantic drift is a common problem in iterative information extraction. Previous approaches for minimizing semantic drift may incur substantial loss in recall. We observe that most semantic drifts are introduced by a small number of questionable extractions in the earlier rounds of iterations. These extractions subsequently introduce a large number of questionable results, which lead to the semantic drift phenomenon. We call these questionable extractions Drifting Points (DPs). If erroneous extractions are the “symptoms” of semantic drift, then DPs are the “causes” of semantic drift. In this paper, we propose a method to minimize semantic drift by identifying the DPs and removing the effect introduced by the DPs. We use isA (concept-instance) extraction as an example to describe our approach in cleaning information extraction errors caused by semantic drift, but we perform experiments on different relation extraction processes on three large real data extraction collections. The experimental results show that our DP cleaning method enables us to clean around 90 percent incorrect instances or patterns with about 90 percent precision, which outperforms the previous approaches we compare with.