Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Mobile Military Surveillance Based on Video Streaming by Employing Software Defined Networks

Situation awareness in surveillance systems benefits from high-quality video streaming service. This is even more important considering military systems, in which delays in image transmission may have a significant impact on the decision-making process. However, in order to deliver high-quality video streaming service, the required network infrastructure may be prohibitively complex, or even completely impossible to deploy, if mobile data providers are considered. Moreover, the demand for high network throughput poses extra requirements on the network. Considering this context, this paper addresses the problem of highly mobile networks composed of unmanned aerial vehicles (UAVs) as data providers of a military surveillance system. The proposed approach to tackle the problem is based on a Software Defined Networking (SDN) approach aiming at providing the best routes to deliver the data, enhancing the end-user quality of experience. An extensive experimental campaign was performed by means of simulations and the acquired results provide solid evidence of the usefulness of this proposal.
Who Said That? A Comparative Study of Non-Negative Matrix Factorisation and Deep Learning Techniques

When working with robots it is very important that the robot understands the user. This is more difﬁcult when the user is only able to speak to it. You do not want a robot to call for milk when the user said call for help. It is possible for a robot to get a clear understanding of the user in a lab environment where there is no noise or reverberation to distort the instructions. However, in a normal setting this is not always the case. We concentrate on speaker separation to improve speech recognition. To do this we use non-negative matrix factorisation (NMF) and deep learning techniques. For training and testing these techniques, we introduce a new corpus that is recorded with a microphone array. In this paper, we use different NMF and deep learning techniques for the speaker separation. We found that adding directional information improves the separation when there is no noise or reverberation. However, when reverberation is present we saw that the NMF technique with the Itakura-Saito cost function out performs the other techniques. With deep learning we found that a recurrent neural networks is able to perform the separation of the speakers.
A Second Order Multi-Stencil Fast Marching Method With a Non-Constant Local Cost Model

The fast marching method is widely employed in several fields of image processing. Some years ago a multi-stencil version (MSFM) was introduced to improve its accuracy by solving the equation for a set of stencils and choosing the best solution at each considered node. The following work proposes a modified numerical scheme for MSFM to take into account the variation of the local cost, which has proven to be second order. The influence of the stencil set choice on the algorithm outcome with respect to stencil orthogonality and axis swapping is also explored, where stencils are taken from neighborhoods of varying radius. The experimental results show that the proposed schemes improve the accuracy of their original counterparts, and that the use of permutation-invariant stencil sets provides robustness against shifted vector coordinates in the stencil set.
Computer-aided lateral ventricular and brain volume measurements in 3D ultrasound for assessing growth trajectories in newborns and neonates

For newborns and neonates, ultrasound (US) is the most common imaging modality used for examinations due to its accessibility and ease of use. However, precise volume measurements remain limited in 2D, while MRI in newborns is typically avoided because of immobilization issues which may require sedation. The objective of this study is to assess and validate the lateral ventricular and total brain volumes obtained with an automatic segmentation method using cerebral trans-fontanelle 3D US. Infants aged between 2 and 8.5 months old were recruited, with both MRI and 3D US acquired on the same day was used to validate ventricular and brain volume measurements in comparison to MRI. Lateral ventricles were segmented on both the US (manually and with a proposed automatic fusion-based approach) and MRI, while brain volumes were estimated with an automatic segmentation method. Volumetric 3D US measurements were then evaluated with respect to age distribution. For the comparison between MRI and 3D US, strong inter-class correlations (ICC) were found for the ventricle volumes (manual: 5.9% ± 2.5% difference (ICC = 0.99); automatic: 6.0% ± 2.6% difference (ICC = 0.98)), as well as the total brain size, with a 3.0% ± 1.3% difference (ICC = 0.98). There was no statistically significant difference based on t-test and f-test for the lateral ventricles volume (t-test: p = 0.542) and (f-test: p = 0.738) and for the total brain volume (t-test: p = 0.412) and (f-test: p = 0.685) between MRI and 3D US. This study demonstrates that 3D US can be used to automatically assess lateral ventricular and total brain volumes with no significant difference to the MRI acquisitions. The highest correlations were obtained for infants under 8 months when the fontanelle is open.
Sarcopenia as a predictor of all-cause mortality among older nursing home residents: a systematic review and meta-analysis

Objectives This study aims to review the evidence of sarcopenia as a predictor of all-cause mortality among nursing home residents. Design Systematic review and meta-analysis of observational cohort studies. Data sources PubMed, EMBASE and the Cochrane Library databases were searched for relevant articles. Participants Nursing home residents. Primary and secondary outcome measures All-cause mortality. Data analysis Summary-adjusted HRs or risk ratios (RRs) were calculated by fixed-effects model. The risk of bias was assessed by Newcastle-Ottawa Scale. Results Of 2292 studies identified through the systematic review, six studies (1494 participants) were included in the meta-analysis. Sarcopenia was significantly associated with a higher risk for all-cause mortality among nursing home residents (pooled HR 1.86, 95% CI 1.42 to 2.45, p<0.001, I2=0). In addition, the subgroup analysis demonstrated that sarcopenia was associated with all-cause mortality (pooled HR 1.87,95% CI 1.38 to 2.52, p<0.001) when studies with a follow-up period of 1 year or more were analysed; however, this was not found for studies with the follow-up period less than 1 year. Furthermore, sarcopenia was significantly associated with the risk of mortality among older nursing home residents when using bioelectrical impedance analysis to diagnosis muscle mass (pooled HR 1.88, 95% CI 1.39 to 2.53, p<0.001); whereas, it was not found when anthropometric measures were used to diagnosis muscle mass. Conclusion Sarcopenia is a significant predictor of all-cause mortality among older nursing home residents. Therefore, it is important to diagnose and treat sarcopenia to reduce mortality rates among nursing home residents. PROSPERO registration number CRD42018081668
Differentially Private Outlier Detection in a Collaborative Environment

Outlier detection is one of the most important data analytics tasks and is used in numerous applications and domains. The goal of outlier detection is to find abnormal entities that are significantly different from the remaining data. Often the underlying data is distributed across different organizations. If outlier detection is done locally, the results obtained are not as accurate as when outlier detection is done collaboratively over the combined data. However, the data cannot be easily integrated into a single database due to privacy and legal concerns. In this paper, we address precisely this problem. We first define privacy in the context of collaborative outlier detection. We then develop a novel method to find outliers from both horizontally partitioned and vertically partitioned categorical data in a privacy-preserving manner. Our method is based on a scalable outlier detection technique that uses attribute value frequencies. We provide an end-to-end privacy guarantee by using the differential privacy model and secure multiparty computation techniques. Experiments on real data show that our proposed technique is both effective and efficient.
Long non-coding RNA ZEB1-AS1 promotes cell invasion and epithelial to mesenchymal transition through inducing ZEB1 expression in cervical cancer

Background Long non-coding RNAs (lncRNAs) play important roles in cancer initiation and development. The purpose of the present study was to determine the functions and mechanisms of lncRNA ZEB1-AS1 in human cervical cancer (CC). Methods A total of 106 pairs of CC tissues and adjacent normal epithelial tissues were collected from CC patients who underwent resection. Three human CC cell lines (HeLa, C33A and SiHa) and a normal cervical cell line Crl-2614 and were transfected with human ZEB1-AS1 cDNA, or empty vector as the control. Then, cells were transfected with ZEB1-AS1-specific small interfering RNA (si-ZEB1-AS1), ZEB1-specific siRNA (si-ZEB1) or negative siRNA control (si-NC). The transfection efficiency was confirmed by RT-qPCR analysis. qPCR was applied to determine the qualification of RNA. Cell proliferation was investigated by MTT assay. The apoptosis rate of cells was detected by flow cytometer. Cell invasion was detected by transwell assay. Western blot was applied to determine the expression of proteins. CC xenografts in 12 male BALB/c athymic nude mice were established. And the tumor volumes were measured by vernier caliper. Results We found that ZEB1-AS1 expression was remarkably increased in human CC tissue samples and cell lines, and its expression levels were closely associated with poor prognosis of CC patients. Moreover, we found that knockdown of ZEB1-AS1 inhibited the proliferation, migration, invasion and epithelial–mesenchymal transition (EMT) of CC cells in vitro and suppressed CC xenograft tumor growth in vivo. Mechanistically, we found that knockdown of ZEB1-AS1 significantly inhibited ZEB1 expression, and knockdown of ZEB1 could rescue the effects of ZEB1-AS1 overexpression in CC cells. Conclusion In conclusion, our findings indicated that ZEB1-AS1 serves an oncogenic role in CC, which might become a potential prognostic indicator and therapeutic target in CC.
Relationship Between Sarcopenia and Both Physical Activity and Lifestyle in Patients With Chronic Liver Disease

Background Sarcopenia can affect the prognosis of patients with cirrhosis or hepatocellular carcinoma. Exercise therapy and nutritional therapy are carried out to prevent processing sarcopenia. In addition, changing lifestyle is also important. However, there are only few reports on the physical activities (PAs) and lifestyle of chronic liver disease patients and their association with sarcopenia. The aim of this study is to examine the relationship between sarcopenia in patients with chronic liver disease and both PA and lifestyle. Methods A total of 214 out-patients with chronic liver disease were enrolled into the present study. All patients were evaluated for with or without sarcopenia based on the sarcopenia diagnostic criteria of the Japan Society of Hepatology. Then, patient’s characteristics and laboratory parameters were divided into two groups with or without sarcopenia and compared. In continuous variable with significant difference in univariate analysis, cut-off value was calculated by receiver operating characteristic curve. We determined which factors were associated with sarcopenia in univariate analyses, and variables significant in the univariate analyses were entered in a multivariable logistic regression model. Results Patients with chronic liver disease had a prevalence of sarcopenia of 12.6% in this study. Sarcopenia patients were older (76.48 ± 6.69 versus 66.97 ± 11.19 years old; P < 0.01), had lower body mass index (BMI) (20.84 ± 2.44 versus 23.76 ± 3.72 kg/m2; P < 0.01), lower PA (6.6 (2.34 - 19.90) versus 16.5 (6.60 - 41.23) metabolic equivalents (METs)-h/week; P < 0.01) and longer total time sitting and lying on the day (7.43 ± 4.09 versus 5.68 ± 3.17 h/day; P = 0.01); retirement status (81.5% versus 48.1%; P < 0.01) and low frequency of driving (40% versus 20%; P = 0.01) were higher in sarcopenia patients than in non-sarcopenia patients. The independent predictive factors of sarcopenia, analyzed with logistic regression, were age (odds ratio (OR): 5.89, 95% confidence interval (CI): 2.15 - 16.20; P < 0.01), BMI (OR: 4.77, 95% CI: 1.87 - 12.10; P < 0.01) and PA (OR: 3.65, 95% CI: 2.15 - 16.20; P < 0.01). Conclusion Sarcopenia patients’ lifestyle characteristics were longer sedentary time and low frequency of driving, high retirement. Independent predictive factors of sarcopenia were elderly, low BMI and low PA. For these patients, intervention in the lifestyle for prevention of sarcopenia may be effective for patients with chronic liver disease.
Effect of 2, 6-Dichlorobenzonitrile on Amoebicidal Activity of Multipurpose Contact Lens Disinfecting Solutions

Multipurpose contact lens disinfecting solutions (MPDS) are widely used to cleanse and disinfect microorganisms. However, disinfection efficacy of these MPDS against Acanthamoeba cyst remain insufficient. 2, 6-dichlorobenzonitrile (DCB), a cellulose synthesis inhibitor, is capable of increasing the amoebical effect against Acanthamoeba by inhibiting its encystation. In this study, we investigated the possibility of DCB as a disinfecting agent to improve the amoebicidal activity of MPDS against Acanthamoeba cyst. Eight commercial MPDS (from a to h) were assessed, all of which displayed insufficient amoebicidal activity against the mature cysts. Solution e, f, and h showed strong amoebicidal effect on the immature cysts. Amoebicidal efficacy against mature cysts remained inadequate even when the 8 MPDS were combined with 100 μM DCB. However, 4 kinds of MPDS (solution d, e, f, and h) including 100 μM DCB demonstrated strong amoebicidal activity against the immature cysts. The amoebicidal activity of solution d was increased by addition of DCB. Cytotoxicity was absent in human corneal epithelial cells treated with either DCB or mixture of DCB with MPDS. These results suggested that DCB can enhance the amoebicical activity of MPDS against Acanthamoeba immature cyst in vitro.
Budget impact of antimicrobial wound dressings in the treatment of venous leg ulcers in the German outpatient care sector: a budget impact analysis

ABSTRACT Background: Hard-to-heal wounds are associated with high treatment costs and, in Germany, are mostly treated in the outpatient care sector. Wound dressings are the main cost-drivers in venous leg ulcer (VLU) care which prescription is budget-restricted. Objective: To determine to what extent the choice of antimicrobial dressing affects the spending in outpatient care by investigating the budget impact of the bioburden-reducing dressing Cutimed Sorbact. Methods: The budget impact analysis was performed comparing three different scenarios of the intervention mix of antimicrobial dressings. A Markov model was used to estimate the VLU progression during one year. The budget impact was determined by comparing the dressing and medicine resource use and costs of the three scenarios. Results: This analysis confirms the high treatment costs of VLU care. ScenarioA leads to a decreased resource use of antimicrobial dressings and results in 20.86% lower treatment costs after 12 months. The increased use of Cutimed Sorbact has a positive budget impact. Conclusion: This analysis indicates that the treatment of VLU patients may result in an exceedance of the budget per patient that is available to the treating practitioner. The choice of wound dressing, however, may positively affect the prescribers’ budget spending in outpatient care.
Exploiting high rate differential algebraic space-time block code in downlink multiuser MIMO systems

This paper considers a multiuser multiple-input multiple-output (MIMO) space-time block coded system that operates at a high data rate with full diversity. In particular, they propose to use a full rate downlink algebraic transmission scheme combined with a differential space-time scheme for multiuser MIMO systems. To achieve this, perfect algebraic space-time codes and Cayley differential transforms are employed. Since channel state information (CSI) is not needed at the differential receiver, differential schemes are ideal for multiuser systems to shift the complexity from the receivers to the transmitter, thus simplifying user equipment. Furthermore, orthogonal spreading matrices are employed at the transmitter to separate the data streams of different users and enable simple single user decoding. In the orthogonal spreading scheme, the transmitter does not require any knowledge of the CSI to separate the data streams of multiple users; this results in a system which does not need CSI at either end. With this system, to limit the number of possible codewords, a sphere decoder is used to decode the signals at the receiving end. The proposed scheme yields low complexity transceivers while providing full rate full diversity with good performance. Simulation results demonstrate the effectiveness of the proposed scheme.
Choice of high‐quality mates versus avoidance of low‐quality mates

Research in sexual selection assumes that individuals attempt to choose high‐quality mates, and that sexual signals evolve to indicate high quality. But it may often be more important to instead discriminate and avoid low‐quality mates, thus reducing the likelihood of large penalties in fitness. We show, using simulations, that avoidance of low‐quality mates (i.e., rejecting low‐quality and accepting either high‐ or medium‐quality mates) evolves in socio‐ecological circumstances such as monogamy with moderate opportunities for choice, costly choice, or abundant low‐quality mates. We also show that this strategy is qualitatively different from choosing high‐quality mates (i.e., preferring high‐quality over medium‐ and low‐quality mates). Rather than selecting signals that distinguish high‐ from low‐ and medium‐quality mates, avoiding low‐quality mates selects for signals or cues attuned at discriminating low‐quality mates from the remaining (e.g., low‐cost signals, absence of signaling mistakes). This may help explain the high diversity of sexual signals in nature, and their high evolutionary turnover with frequent losses and replacements (rather than reductions/increases of the same signal) over evolutionary time.
Analytical CPG model driven by limb velocity input generates accurate temporal locomotor dynamics

The ability of vertebrates to generate rhythm within their spinal neural networks is essential for walking, running, and other rhythmic behaviors. The central pattern generator (CPG) network responsible for these behaviors is well-characterized with experimental and theoretical studies, and it can be formulated as a nonlinear dynamical system. The underlying mechanism responsible for locomotor behavior can be expressed as the process of leaky integration with resetting states generating appropriate phases for changing body velocity. The low-dimensional input to the CPG model generates the bilateral pattern of swing and stance modulation for each limb and is consistent with the desired limb speed as the input command. To test the minimal configuration of required parameters for this model, we reduced the system of equations representing CPG for a single limb and provided the analytical solution with two complementary methods. The analytical and empirical cycle durations were similar (R2 = 0.99) for the full range of walking speeds. The structure of solution is consistent with the use of limb speed as the input domain for the CPG network. Moreover, the reciprocal interaction between two leaky integration processes representing a CPG for two limbs was sufficient to capture fundamental experimental dynamics associated with the control of heading direction. This analysis provides further support for the embedded velocity or limb speed representation within spinal neural pathways involved in rhythm generation.
Stance Detection in Russian: a Feature Selection and Machine Learning Based Approach

The huge scale and constant increase of the data volume in social media has led to a high demand for automatic means of such content analysis, specifically stance detection. This term stands for the task of assigning stance labels (“for” and “against”) with respect to a discussion topic. In the paper we tackle stance detection for Russian texts from social network “VKontakte” with the use of machine learning methods – the support vector machine, k-nearest neighbors, Naïve Bayes, AdaBoost, and decision trees. Also we apply the Recursive Feature Elimination (RFE) algorithm for feature selection and explore the impact of morphological analysis on the quality of the task solution. The best results (F1=84.3%) are achieved by using of the SVM and vector model with relatively small set of normalized words chosen by RFE.
Development of Molecular Diagnosis Using Multiplex Real-Time PCR and T4 Phage Internal Control to Simultaneously Detect Cryptosporidium parvum, Giardia lamblia, and Cyclospora cayetanensis from Human Stool Samples

This study aimed to develop a new multiplex real-time PCR detection method for 3 species of waterborne protozoan parasites (Cryptosporidium parvum, Giardia lamblia, and Cyclospora cayetanensis) identified as major causes of traveler’s diarrhea. Three target genes were specifically and simultaneously detected by the TaqMan probe method for multiple parasitic infection cases, including Cryptosporidium oocyst wall protein for C. parvum, glutamate dehydrogenase for G. lamblia, and internal transcribed spacer 1 for C. cayetanensis. Gene product 21 for bacteriophage T4 was used as an internal control DNA target for monitoring human stool DNA amplification. TaqMan probes were prepared using 4 fluorescent dyes, FAM™, HEX™, Cy5™, and CAL Fluor Red® 610 on C. parvum, G. lamblia, C. cayetanensis, and bacteriophage T4, respectively. We developed a novel primer-probe set for each parasite, a primer-probe cocktail (a mixture of primers and probes for the parasites and the internal control) for multiplex real-time PCR analysis, and a protocol for this detection method. Multiplex real-time PCR with the primer-probe cocktail successfully and specifically detected the target genes of C. parvum, G. lamblia, and C. cayetanensis in the mixed spiked human stool sample. The limit of detection for our assay was 2×10 copies for C. parvum and for C. cayetanensis, while it was 2×103 copies for G. lamblia. We propose that the multiplex real-time PCR detection method developed here is a useful method for simultaneously diagnosing the most common causative protozoa in traveler’s diarrhea.