Miracle-dz/UniFAR-FTUs huggingface dataset:
Abundance, vertical distribution, and community structure of benthic prokaryotes from permanently cold marine sediments (Svalbard, Arctic Ocean)

A detailed investigation of the benthic prokaryot~c community from 3 permanently cold stations near Svalbard (Arctic Ocean) and 1 site near Tromss (northern Norway) was conducted. Prokaryotic abundances, determined by DAPI-staining, were in the range of ca 2 X 108 to 4 X log cells cm-3 wet sediment. They showed little variation among sampling stations. Vertical profiles were characterized by a decrease of cell numbers with increasing sediment depth. The prokaryotic community composition was investigated employing rRNA (ribosomal RNA) slot-blot hybridization with domain-specific probes. Irrespective of station and vertical depth, Eubacteria always dominated the population, and the relative contribution of Archaea never exceeded 4 %. The measured total rRNA concentration and the prokaryotic cell counts in each sample were used to calculate per cell rRNA contents. Mean rRNA content (averaging all samples) was close to 3 fg rRNA cell-'. None of our data showed considerable differences to comparable results from temperate or warm habitats; therefore our findings do not allow conclusions on special adaptations of the prokaryotic community to their existence in permanently cold systems. In all samples, but most pronounced in the 3 coldest stations, per cell rRNA contents showed steep vertical gradients with maximum values at the sediment surface. Taking into account all stations, rRNA concentration and prokaryotic abundance were strongly positively correlated below ca 5.5 cm (r2 = 0.739), whereas in the upper sediment layers (0 to ca 5.5 cm) there was no significant correlation between these 2 parameters. This implies that there may be ddferent mechanisms involved in the control of prokaryotic rRNA contents in Wferent sedment horizons. Cellular rRNA concentrations can q v e an indication of growth rate and thereby the activity of prokaryotes. This is supported by the fact that we recorded the highest per cell rRNA contents in those stations and sediment depths where other studies conducted simultaneously with our investigation demonstrated the highest rates of metabolic processes.
AI-based Reference Ankle Joint Torque Trajectory Generation for Robotic Gait Assistance: First Steps

Robotic-based gait rehabilitation and assistance have been growing to augment and to recover motor function in subjects with lower limb impairments. There is interest in developing user-oriented control strategies to provide personalized assistance. However, it is still needed to set the healthy user-oriented reference joint trajectories, namely, reference ankle joint torque, that would be desired under healthy conditions. Considering the potential of Artificial Intelligence (AI) algorithms to model nonlinear relationships of the walking motion, this study implements and compares two offline AI-based regression models (Multilayer Perceptron and Long-Short Term Memory-LSTM) to generate healthy reference ankle joint torques oriented to subjects with a body height ranging from 1.51 to 1.83 m, body mass from 52.0 to 83.7 kg and walking in a flat surface with a walking speed from 1.0 to 4.0 km/h. The best results were achieved for the LSTM, reaching a Goodness of Fit and a Normalized Root Mean Square Error of 79.6 % and 4.31 %, respectively. The findings showed that the implemented LSTM has the potential to be integrated into control architectures of robotic assistive devices to accurately estimate healthy user-oriented reference ankle joint torque trajectories, which are needed in personalized and Assist-As-Needed conditions. Future challenges involve the exploration of other regression models and the reference torque prediction for remaining lower limb joints, considering a wider range of body masses, heights, walking speeds, and locomotion modes.
Brain volume prediction based on rs-fMRI time series

Rs-fMRI time series and volumes of different brain regions possess rich information about the functionality and the structure of the brain, respectively. Recently, artificial neural network emerges as a powerful tool to perform machine learning tasks, which empowers the exploration of the direct relationship between the two. We introduced the deep learning based method and proposed a 1-D convolution operation based deep structure that utilized the spatial and temporal features of the rs-fMRI time series to predict the volumes of different brain regions. The proposed method outperformed the baseline fully-connected neural network, in terms of mean square error. This indicated that the proposed method can utilize temporal and spatial information from rs-fMRI time series better than the fully- connected counterpart.
IMRL: An Improved Inertial-Aided KLT Feature Tracker

In recent years, visual simultaneous localization and mapping (SLAM) is widely used in robotic applications. Feature tracking is a fundamental problem in visual SLAM. Kanade-Lucas Tomasi (KLT) feature tracker is the most popular intensity-based feature tracking algorithm for its fast speed and easiness of use. However, it is vulnerable to large optical flow and accumulates error over time. To overcome the drawbacks, we propose a novel inertial-aided multi-reference and multi-level patch based feature tracking approach called IMRL feature tracker. A probabilistic approach is used to estimate the feature’s depth, and it is combined with the inertial measurements to provide a good initial feature position, which improves the robustness of our feature tracker to both fast camera rotation and translation. Furthermore, we propose a novel multi-reference and multi-level patch (MRL) based feature alignment method to improve the tracking accuracy. Thorough experiments were carried on open source datasets EuRoC and KITTI. The results show that comparing to the original KLT feature tracker, the proposed IMRL feature tracker achieves better robustness and accuracy with lower computational cost.
Evaluating Frameworks Assemblies In Microservices-based Systems Using Imperfect Information

Microservices is an architectural style that promotes the facility to build and maintain systems by breaking down its business capabilities into smaller and distributed services. Often, practitioners commonly use frameworks to provide generic functionalities to address recurring quality attribute concerns on microservices-based systems. Nevertheless, in practical settings, frameworks information is incomplete, imprecise, and changing as well as requirements. More realistically deployable approaches combine the exploration of candidate architectures with their evaluation regarding requirements satisfaction and the fuzziness and incompleteness available frameworks information. This article outlines a novel technique, called $\mu$Azimut, whose purpose is to generate, evaluate, and compare frameworks assemblies using potentially incomplete, imprecise, and changing descriptions of non-functional requirements and frameworks. The frameworks assemblies evaluation is based on a support score which allows modeling imperfect architectural knowledge. The technique is evaluated in an industrial case study. The results point out that $\mu$Azimut generates solutions that are close to those solutions that an architect selects for designing microservices architectures.
Development of a high throughput and low cost model for the study of semi-dry biofilms

Abstract The persistence of microorganisms as biofilms on dry surfaces resistant to the usual terminal cleaning methods may pose an additional risk of transmission of infections. In this study, the Centre for Disease Control (CDC) dry biofilm model (DBM) was adapted into a microtiter plate format (Model 1) and replicated to create a novel in vitro model that replicates conditions commonly encountered in the healthcare environment (Model 2). Biofilms of Staphylococcus aureus grown in the two models were comparable to the biofilms of the CDC DBM in terms of recovered log10 CFU well−1. Assessment of the antimicrobial tolerance of biofilms grown in the two models showed Model 2 a better model for biofilm formation. Confirmation of the biofilms’ phenotype with an extracellular matrix deficient S. aureus suggested stress tolerance through a non-matrix defined mechanism in microorganisms. This study highlights the importance of conditions maintained in bacterial growth as they affect biofilm phenotype and behaviour.
Real-time Wireless Channel Monitoring Cooperated with Roadside Sensors for Autonomous Mobility Assistance System (AMAS)

Research and development (R&D) for an autonomous and automatic mobility assistance system (AMAS) in cooperation with information communication technology (ICT) is undergoing. The AMAS method which efficiently updates and distributes the road information collected by mobility and roadside sensors to a dynamic map (DM) enables automatic mobility control with high reliability and accuracy. In order to verify the renewal and dispersion of DM, we have developed a wireless electronic traffic mirror (WETM) that acquires road information, recognizes road objects and transfers recognition information with a wireless communication system. Additionally, to meet the safety requirements, the AMAS must provide the wireless connection best suited for mobility. In order to provide optimum wireless connectivity, real-time wireless channel monitoring, which measures the quality of wireless channels with interference, is an important task in the AMAS. In this paper, the real-time wireless channel monitoring system in cooperation with the developed WETM is introduced, and in order to clarify the reason why the quality of the road was changed, clear evidence of the visibility of the road situation is clarified.
UAP56/DDX39B is a major cotranscriptional RNA–DNA helicase that unwinds harmful R loops genome-wide

In this study, Pérez-Calero et al. set out to investigate the role of UAP56/DDX39B in R-loop removal. Using in vitro and in vivo approaches, the authors demonstrate that UAP56 functions as a DNA–RNA helicase required to eliminate harmful cotranscriptional RNA structures that otherwise would block transcription and replication.
Research on Fault Diagnosis Method Based on Reciprocating Compressor

Based on the research and analysis of related fault diagnosis technologies at home and abroad, a fault diagnosis method for reciprocating compressors based on support vector machine and gravity search algorithm is proposed. This method optimizes the kernel parameters of SVM. Combined with an example of a fault diagnosis model of a reciprocating compressor, the analysis results show that the fault diagnosis method of a reciprocating compressor using the GSA-SVM algorithm has higher recognition accuracy than the SVM algorithm.
PD-L1 targeting high-affinity NK (t-haNK) cells induce direct antitumor effects and target suppressive MDSC populations

Background Although immune checkpoint inhibitors have revolutionized cancer treatment, clinical benefit with this class of agents has been limited to a subset of patients. Hence, more effective means to target tumor cells that express immune checkpoint molecules should be developed. For the first time, we report a novel natural killer (NK) cell line, programmed death-ligand 1 (PD-L1) targeting high-affinity natural killer (t-haNK), which was derived from NK-92 and was engineered to express high-affinity CD16, endoplasmic reticulum-retained interleukin (IL)-2, and a PD-L1-specific chimeric antigen receptor (CAR). We show that PD-L1 t-haNK cells also retained the expression of native NK receptors and carried a high content of granzyme and perforin granules. Methods NanoString, flow cytometry, and immunofluorescence analyses were performed to characterize the phenotype of irradiated PD-L1 t-haNK cells. In vitro PD-L1 t-haNK cell activity against cancer cell lines and human peripheral blood mononuclear cells (PBMCs) was determined via flow-based and 111In-release killing assays. The antitumor effect of PD-L1 t-haNK cells in vivo was investigated using MDA-MB-231, H460, and HTB1 xenograft models in NOD-scid IL2Rgammanull (NSG) mice. Additionally, the antitumor effect of PD-L1 t-haNK cells, in combination with anti-PD-1 and N-803, an IL-15 superagonist, was evaluated using mouse oral cancer 1 syngeneic model in C57BL/6 mice. Results We show that PD-L1 t-haNK cells expressed PD-L1-targeting CAR and CD16, retained the expression of native NK receptors, and carried a high content of granzyme and perforin granules. In vitro, we demonstrate the ability of irradiated PD-L1 t-haNK cells to lyse 20 of the 20 human cancer cell lines tested, including triple negative breast cancer (TNBC) and lung, urogenital, and gastric cancer cells. The cytotoxicity of PD-L1 t-haNK cells was correlated to the PD-L1 expression of the tumor targets and can be improved by pretreating the targets with interferon (IFN)-γ. In vivo, irradiated PD-L1 t-haNK cells inhibited the growth of engrafted TNBC and lung and bladder tumors in NSG mice. The combination of PD-L1 t-haNK cells with N-803 and anti-PD-1 antibody resulted in superior tumor growth control of engrafted oral cavity squamous carcinoma tumors in C57BL/6 mice. In addition, when cocultured with human PBMCs, PD-L1 t-haNK cells preferentially lysed the myeloid-derived suppressor cell population but not other immune cell types. Conclusion These studies demonstrate the antitumor efficacy of PD-L1 t-haNK cells and provide a rationale for the potential use of these cells in clinical studies.
Combat exposure and co-occurring mental health problems in UK Armed Forces personnel

ABSTARCT Background Mental disorders and alcohol misuse are often comorbid, and this comorbidity is more common in those who develop mental disorders following exposure to traumatic events. Aims To investigate the relationship between combat exposure and operational role (support versus combat) with mental disorders and associated comorbidity in a UK military cohort. Methods 4896 participants from a UK military cohort reported their operational role and frequency of exposure to combat events during deployment. Outcome measures included self-reported post-traumatic stress disorder, common mental disorder and alcohol misuse. Results Personnel reporting higher levels of combat exposure were more likely to meet criteria for two or more co-occurring mental disorders (odds ratio [OR] 3.90, 95% confidence interval [CI] 2.73–5.58). While having a combat role increased the risk of developing co-occurring disorders compared to having a support role (OR 1.67, 95% CI 1.26–2.23), this effect diminished following adjustment for variables including combat exposure (OR 0.89, 95% CI 0.62–1.27). Conclusions Combat exposure may play a greater role in the development of comorbid mental disorders than operational role, i.e. job title. Clinicians treating military personnel should be alert to the increased risk of comorbid mental disorders and alcohol misuse among those with a history of combat exposure.
Distributed Optimal Economic Dispatch for Smart Grids Based on Multi-Agent System

In this works, the economic dispatch problem for smart grids is simplified by the $\varepsilon$–exact penalty functions. A novel and systematic distributed optimization algorithm is introduced based on the multi-agent system via the algebraic graph theory. In order to minimize the operation cost in the entire grid, cooperative control of the power outputs of all power generators in the smart grid will be presented by using of the novel optimization algorithm. Furthermore, through the iteration of the penalty parameters and smooth parameters, the convergence of the algorithm is proved. The solution of the penalty optimization problem coincides with the optimal solution of the economic dispatch problem. A numerical example testifies the good performance of the new algorithm.
Analysis of High-efficiency Rectenna models at 2.45 GHz using Meandered Printed Dipole Antenna

This paper presents two models of high efficiency rectennas, resonant in the frequency of 2.45 GHz, which input power is -15 dBm. In the models it was used the same kinds of antennas and rectifier circuits that have been manufactured on FR4 substrate. The only difference between them is that one of them was built in a single board dismissing the usage of SMA connectors. Thus, this paper presents an approach that does not require the use of SMA connectors bringing improvements in power harvesting systems like output voltage 300 mV with input power -15 dBm and efficiency about 50%.
Monocular SLAM Initialization Using Epipolar and Homography Model

The performance of the initialization module for any visual Simultaneous Localization and Mapping (SLAM) algorithm is crucial in determining the overall accuracy of the navigation solution. This paper proposes an automatic real time initialization module based on a heuristic algorithm which is a model selection method that provides robustness for both non-planar and planar initial scenes using the epipolar and homography geometry respectively. Initialization is made more robust by utilizing features comprising of lines, edges and corner points in a scene adaptive method so that there is a balance between the trade-off of computation speed and accuracy of the algorithm. The proposed algorithm has been effectively used to initialize map and showed to improve the pose estimation and tracking of the state of the art visual odometry algorithm by simulation over benchmark datasets. The executable code implementation of the proposed initialization module has been made open source publically to provide as a valuable reference for the students of SLAM at https://github.com/maazmb/LEP-Hybrid-Visual-Odometry.git.
Effects of Environmental Stressors and Immunosuppressants on Immunity in Fundulus heteroclitus

Environmental stressors have been shown to alter immunocompetence in mammals. Similar effects have been reported in fish. The purpose of this paper is to review the literature concerning the white blood cells and immunity in Fundulus heteroclitus . Evidence for immunosuppression following prolonged captivity, handling, hypophysectomy, radiation, changes in temperature and salinity, and exposure to chemicals including environmental pollutants is discussed. New evidence for immunosuppression following exposure to benzo-a-pyrene, pentachlorophenol, and hexachlorobenzene is presented. Possible mechanisms of immune alteration and the consequences to F. heteroclitus are discussed.
Low collision energy fragmentation in structure-specific glycoproteomics analysis.

Glycosylation is a major post translational modification of proteins that regulates many biological processes including protein folding, structure stability, receptor activation and immune responses. The glycans attached to proteins represent an important determinant of the protein interaction-specificity and maintain the 3D structure of proteins. Mass spectrometry (MS) is one of the most efficient tools used in the current studies of glycoproteins and structure of their glycoforms. Collision energy (CE) is a crucial instrument parameter that can be exploited to improve structural resolution because different link-ages of glycan units show different stability under CID/HCD fragmentation. Here we report the utility of CE modulation for qualitative and quantitative analysis of site- and structure- specific glycoforms of proteins. Using CE modulation, we were able to break selectively specific glycan linkages on intact glycopeptides and get, to some degree, structure-specific mass spectrometric signals. Structure- and CE- specific oxonium ions provide sufficient information for the resolution of outer arm structure motifs with recognized biological functions. The complementary Y-ions, generated under optimized low CE (soft) conditions, provide additional structural information including features specific to the chitobiose core. This methodology of multiple CE fragmentation without merging spectral information can significantly improve confidence of glycopeptide identification and structural resolution by providing additional information to the established glycopeptide-search algorithms and tools.
Risk Factors for Failure of Direct Restorations in General Dental Practices

The aim of this retrospective noninterventional multicenter practice-based study was to analyze factors influencing the survival of direct restorations. Records from patients who visited 5 private practices regularly were searched for the presence of direct restorations. Data were recorded from 7,858 patients with 27,407 direct restorations being detected at least 6 mo before the last recall visit. Multilevel Cox proportional hazard models were used to evaluate the association between clinical factors and time until failure. Within 228 mo, 5,493 failures could be observed. Median survival time was 207 mo. The annual failure rates were 3.8%, 4.0%, 4.6%, 4.9%, and 3.9% for class I, II, III, IV, and V restorations, respectively. Class II and IV restorations showed a 1.1-times (95% CI, 1.0 to 1.2) and 1.2-times (95% CI, 1.1 to 1.2) higher failure rate than class I restorations (P ≤ 0.029). Patients aged <20 y and >60 y showed up to a 1.4-times higher failure rate than patients aged 20 to 60 y (P ≤ 0.015). Restorations that underwent check-up twice a year or more showed a significantly higher failure rate than those that did so less than twice a year (P < 0.001). Furthermore, the dentists significantly influenced time until failure (P < 0.001). Regarding the restorative material, composites showed up to a 2.1-times longer time until failure than GIC (P ≤ 0.020). Moderate failure rates were observed for direct restorations in the private practice setting after up to 18.5 y. Within the limitations of the present study, several factors on the levels of practice (i.e., dentist), patient (i.e., age), and tooth (i.e., restorative material, restored surfaces according to the classification of Black) were significant predictors for the failure rate. Therefore, treatment decision should take into account most relevant factors (German Clinical Trials Register DRKS00015228).
Research on Leakage Diagnosis of Heating Pipe Network Based on Deep Belief Network

Aiming at the large amount of data and complex structure of the heating pipe network system, this paper studies the fault diagnosis method of heating pipe network leakage based on deep belief network. Firstly, using the theory of graph theory to establish the hydraulic calculation model of the leakage condition of the heating pipe network, then according to the pressure change value of the pressure monitoring point in the pipe network; the deep belief network is used to establish the fault diagnosis model of the heating pipe network leakage, and the feasibility of the method is verified by experiments. The experimental results show that the proposed method has higher accuracy for the prediction of leakage pipe segments, and is superior to BP (Back Propagation Neural Network) neural network and support vector machine traditional fault diagnosis method.
Fungi associated with root and hypocotyl diseases of seedling lupins in Western Australia

Pleiochaeta setosa, Pythium irregulare, Fusarium spp. and Rhizoctonia spp. were frequently isolated from root lesions on lupin seedlings growing at 18 locations in the temperate south-west of Western Australia. P. setosa was isolated from all 13 sites where lupins had previously been grown (average isolation frequency 90%) but from none of the 5 sites with no lupin history. P. setosa was pathogenic in pot experiments using colonized millet or conidia as inoculum. Certain isolates of P. irregulare and Rhizoctonia spp. were pathogenic, but all Fusarium isolates appeared very weak or non-pathogenic. In a fungicide drench field experiment Rovral« reduced the isolation of P, setosa from roots by 97% and reduced root rot by 42%, providing complementary evidence for the importance of P. setosa as a root pathogen in Western Australia.Rhizoctonia solani was isolated from characteristic reddish-brown hypocotyl lesions and reproduced these symptoms in pathogenicity tests.