Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Electrical Stimulation Conditions on Neural Stem Cells Differentiation on Cross-Linked PEDOT:PSS Films

The ability to culture and differentiate neural stem cells (NSCs) to generate functional neural populations is attracting increasing attention due to its potential to enable cell-therapies to treat neurodegenerative diseases. Recent studies have shown that electrical stimulation improves neuronal differentiation of stem cells populations, highlighting the importance of the development of electroconductive biocompatible materials for NSC culture and differentiation for tissue engineering and regenerative medicine. Here, we report the use of the conjugated polymer poly(3,4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS CLEVIOS P AI 4083) for the manufacture of conductive substrates. Two different protocols, using different cross-linkers (3-glycidyloxypropyl)trimethoxysilane (GOPS) and divinyl sulfone (DVS) were tested to enhance their stability in aqueous environments. Both cross-linking treatments influence PEDOT:PSS properties, namely conductivity and contact angle. However, only GOPS-cross-linked films demonstrated to maintain conductivity and thickness during their incubation in water for 15 days. GOPS-cross-linked films were used to culture ReNcell-VM under different electrical stimulation conditions (AC, DC, and pulsed DC electrical fields). The polymeric substrate exhibits adequate physicochemical properties to promote cell adhesion and growth, as assessed by Alamar Blue® assay, both with and without the application of electric fields. NSCs differentiation was studied by immunofluorescence and quantitative real-time polymerase chain reaction. This study demonstrates that the pulsed DC stimulation (1 V/cm for 12 days), is the most efficient at enhancing the differentiation of NSCs into neurons.
Differential Longevity of Memory CD4 and CD8 T Cells in a Cohort of the Mothers With a History of ZIKV Infection and Their Children

Background: Zika virus (ZIKV) infection causes for mild and self-limiting disease in healthy adults. In newborns, it can occasionally lead to a spectrum of malformations, the congenital Zika syndrome (CZS). Thus, little is known if mothers and babies with a history of ZIKV infection were able to develop long-lasting T-cell immunity. To these issues, we measure the prevalence of ZIKV T-cell immunity in a cohort of mothers infected to the ZIKV during pregnancy in the 2016–2017 Zika outbreak, who gave birth to infants affected by neurological complications or asymptomatic ones. Results: Twenty-one mothers and 18 children were tested for IFN-γ ELISpot and T-cell responses for flow cytometry assays in response to CD4 ZIKV and CD8 ZIKV megapools (CD4 ZIKV MP and CD8 ZIKV MP). IFN-γ ELISpot responses to ZIKV MPs showed an increased CD4 and CD8 T-cell responses in mothers compared to children. The degranulation activity and IFN-γ-producing CD4 T cells were detected in most mothers, and children, while in CD8 T-cells, low responses were detected in these study groups. The total Temra T cell subset is enriched for IFN-γ+ CD4 T cells after stimulation of CD4 ZIKV MP. Conclusion: Donors with a history of ZIKV infection demonstrated long-term CD4 T cell immunity to ZIKV CD4 MP. However, the same was not observed in CD8 T cells with the ZIKV CD8 MP. One possibility is that the cytotoxic and pro-inflammatory activities of CD8 T cells are markedly demonstrated in the early stages of infection, but less detected in the disease resolution phase, when the virus has already been eliminated. The responses of mothers' T cells to ZIKV MPs do not appear to be related to their children's clinical outcome. There was also no marked difference in the T cell responses to ZIKV MP between children affected or not with CZS. These data still need to be investigated, including the evaluation of the response of CD8 T cells to other ZIKV peptides.
A Knowledge-Based Smart Trainer System for Transferring Knowledge From Coaches to Long Jump Students

Background: The constructive and specific feedback in guiding long jump athletes to improve their performance in each phase is part of the critical process for achieving desired long jump distance. However, to date, the potential approach for assisting a coach in capturing long jump movement and transferring their knowledge to long jump students is not well-established. Objectives: To investigate the performance of long jump students and evaluate transferring knowledge from coaches to long jump students using a Knowledge-Based Smart Trainer (KBST) System. Methods: Twenty-two participants (fifteen males, mean age = 15.33 ± 1.95 years; seven females, mean age = 14.57 ± 2.07 years) participated in the study. All participants were recruited from eleven sports schools in Thailand. Each participant was instructed to perform the long jump movement, including running, take-off, and landing, for three attempts (Test 1, Test 2, and Test 3). Test 1 was the conventional approach (coaches provided the feedback based on their experience). Test 2 and Test 3 were the KBST system approach (coaches provided the feedback based on the results from KBST system). Two cameras were used to record the participant movement from the starting position to the landing position. The capture data were analyzed by KBST system program. The outcome measures were starting position, maximum velocity, maximum velocity position, and take-off angle. Repeated-Measures ANOVA was conducted to compare the long jump performance across the three trials. The statistical significance was set at p-value < 0.05. Results: There was a statistically significant difference between Test 1 and Test 3 for long jump distance (mean difference = 0.292; Std. Error = 0.129; Sig. = 0.34). However, the mean of take-off angle was similar across the three trials (Test 1 = 12.16°, Test 2 = 12.71°, and Test 3 = 12.95°, respectively). Conclusion: The KBST system was effective in improving long jump students’ performance and also transferring knowledge from the coach to long jump students.
Evaluation of Task fMRI Decoding With Deep Learning on a Small Sample Dataset

Recently, several deep learning methods have been applied to decoding in task-related fMRI, and their advantages have been exploited in a variety of ways. However, this paradigm is sometimes problematic, due to the difficulty of applying deep learning to high-dimensional data and small sample size conditions. The difficulties in gathering a large amount of data to develop predictive machine learning models with multiple layers from fMRI experiments with complicated designs and tasks are well-recognized. Group-level, multi-voxel pattern analysis with small sample sizes results in low statistical power and large accuracy evaluation errors; failure in such instances is ascribed to the individual variability that risks information leakage, a particular issue when dealing with a limited number of subjects. In this study, using a small-size fMRI dataset evaluating bilingual language switch in a property generation task, we evaluated the relative fit of different deep learning models, incorporating moderate split methods to control the amount of information leakage. Our results indicated that using the session shuffle split as the data folding method, along with the multichannel 2D convolutional neural network (M2DCNN) classifier, recorded the best authentic classification accuracy, which outperformed the efficiency of 3D convolutional neural network (3DCNN). In this manuscript, we discuss the tolerability of within-subject or within-session information leakage, of which the impact is generally considered small but complex and essentially unknown; this requires clarification in future studies.
Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective

Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.
Characterization of the Signaling Modalities of Prostaglandin E2 Receptors EP2 and EP4 Reveals Crosstalk and a Role for Microtubules

Prostaglandin E2 (PGE2) is a lipid mediator that modulates the function of myeloid immune cells such as macrophages and dendritic cells (DCs) through the activation of the G protein-coupled receptors EP2 and EP4. While both EP2 and EP4 signaling leads to an elevation of intracellular cyclic adenosine monophosphate (cAMP) levels through the stimulating Gαs protein, EP4 also couples to the inhibitory Gαi protein to decrease the production of cAMP. The receptor-specific contributions to downstream immune modulatory functions are still poorly defined. Here, we employed quantitative imaging methods to characterize the early EP2 and EP4 signaling events in myeloid cells and their contribution to the dissolution of adhesion structures called podosomes, which is a first and essential step in DC maturation. We first show that podosome loss in DCs is primarily mediated by EP4. Next, we demonstrate that EP2 and EP4 signaling leads to distinct cAMP production profiles, with EP4 inducing a transient cAMP response and EP2 inducing a sustained cAMP response only at high PGE2 levels. We further find that simultaneous EP2 and EP4 stimulation attenuates cAMP production, suggesting a reciprocal control of EP2 and EP4 signaling. Finally, we demonstrate that efficient signaling of both EP2 and EP4 relies on an intact microtubule network. Together, these results enhance our understanding of early EP2 and EP4 signaling in myeloid cells. Considering that modulation of PGE2 signaling is regarded as an important therapeutic possibility in anti-tumor immunotherapy, our findings may facilitate the development of efficient and specific immune modulators of PGE2 receptors.
Optimized Culture Conditions for Improved Growth and Functional Differentiation of Mouse and Human Colon Organoids

Background & Aims Diligent side-by-side comparisons of how different methodologies affect growth efficiency and quality of intestinal colonoids have not been performed leaving a gap in our current knowledge. Here, we summarize our efforts to optimize culture conditions for improved growth and functional differentiation of mouse and human colon organoids. Methods Mouse and human colon organoids were grown in four different media. Media-dependent long-term growth was measured by quantifying surviving organoids via imaging and a cell viability readout over five passages. The impact of diverse media on differentiation was assessed by quantifying the number of epithelial cell types using markers for enterocytes, stem cells, Goblet cells, and enteroendocrine cells by qPCR and histology upon removal of growth factors. Results In contrast to Wnt3a-conditioned media, media supplemented with recombinant Wnt3a alone did not support long-term survival of human or mouse colon organoids. Mechanistically, this observation can be attributed to the fact that recombinant Wnt3a did not support stem cell survival or proliferation as demonstrated by decreased LGR5 and Ki67 expression. When monitoring expression of markers for epithelial cell types, the highest level of organoid differentiation was observed after combined removal of Wnt3a, Noggin, and R-spondin from Wnta3a-conditioned media cultures. Conclusion Our study defined Wnt3a-containing conditioned media as optimal for growth and survival of human and mouse organoids. Furthermore, we established that the combined removal of Wnt3a, Noggin, and R-spondin results in optimal differentiation. This study provides a step forward in optimizing conditions for intestinal organoid growth to improve standardization and reproducibility of this model platform.
Amperometric Gas Sensors of High Sensitivity

Amperometric gas sensors for the detection of ethylene, ethanol, acetaldehyde, and sulfur dioxide down to very low ppb concentrations are described. The design is based on porous Au electrodes supported on solid polymer electrolytes (SPE). Nafion as cation exchange membrane and a chemically grafted perfluorinated anion exchange membrane were employed as SPE. The sensors may be tailored to the highly sensitive detection of a specific gas by carefully selecting the component materials and the experimental conditions. The influence of gas flow rate, relative humidity of the gas streams and real surface area of the SPE electrodes are discussed.
Endurance Training Improves Oxygen Uptake and Endurance Capacity in Patients With Moderate to Severe Valvular Disease

Aim: Peak oxygen uptake (peakVO2) is one of the strongest predictors of survival in patients with valvular heart disease. The purpose of this study was to determine whether endurance training improves peakVO2 and endurance capacity in patients with moderate-severe aortic and mitral valve disease. Methods: 30 patients with moderate-severe valvular heart disease were randomly assigned to 12 weeks of endurance training (TG) (n = 16) or standard care (SC) (n = 14). PeakVO2 and maximum working capacity (Wattmax) were assessed by cardiopulmonary exercise testing, as well as submaximal endurance test at 80% of peakVO2 at baseline and after 12 weeks. Results: There was a significant improvement in peakVO2 from 27.2 ± 5.9 ml/kg to 30.4 ± 6.3 ml/kg (P < 0.001) in TG compared to the SC (peakVO2 from 24.6 ± 4.4 to 24.7 ± 3.8) and in the Wattmax from 151.8 ± 41.0 Watt to 171.2 ± 49.7 Watt in the TG compared to the SC (152.9 ± 35.6 Watt to 149.2 ± 28.4 Watt). The endurance capacity increased significantly from 17.0 ± 9.4 min to 32.8 ± 16.8 min (p = 0.003) in the TG compared to the SC (11.7 ± 6.2 min to 11.2 ± 7.6 min). The heart rate during the endurance test decreased in the TG from 154 ± 14 b/min to 142 ± 20 b/min for the same workload. No changes could be seen in the SC. Conclusion: Endurance training in patients with moderate to severe valvular heart disease increased significantly the peakVO2 as well as the endurance capacity.
Assembly Patterns of the Rhizosphere Microbiome Along the Longitudinal Root Axis of Maize (Zea mays L.)

It is by now well proven that different plant species within their specific root systems select for distinct subsets of microbiota from bulk soil – their individual rhizosphere microbiomes. In maize, root growth advances several centimeters each day, with the locations, quality and quantity of rhizodeposition changing. We investigated the assembly of communities of prokaryotes (archaea and bacteria) and their protistan predators (Cercozoa, Rhizaria) along the longitudinal root axis of maize (Zea mays L.). We grew maize plants in an agricultural loamy soil and sampled rhizosphere soil at distinct locations along maize roots. We applied high-throughput sequencing, followed by diversity and network analyses in order to track changes in relative abundances, diversity and co-occurrence of rhizosphere microbiota along the root axis. Apart from a reduction of operational taxonomic unit (OTU) richness and a strong shift in community composition between bulk soil and root tips, patterns of microbial community assembly along maize-roots were more complex than expected. High variation in beta diversity at root tips and the root hair zone indicated substantial randomness of community assembly. Root hair zone communities were characterized by massive co-occurrence of microbial taxa, likely fueled by abundant resource supply from rhizodeposition. Further up the root where lateral roots emerged processes of community assembly appeared to be more deterministic (e.g., through competition and predation). This shift toward significance of deterministic processes was revealed by low variability of beta diversity, changes in network topology, and the appearance of regular phylogenetic co-occurrence patterns in bipartite networks between prokaryotes and their potential protistan predators. Such patterns were strongest in regions with fully developed laterals, suggesting that a consistent rhizosphere microbiome finally assembled. For the targeted improvement of microbiome function, such knowledge on the processes of microbiome assembly on roots and its temporal and spatial variability is crucially important.
Methylacidimicrobium thermophilum AP8, a Novel Methane- and Hydrogen-Oxidizing Bacterium Isolated From Volcanic Soil on Pantelleria Island, Italy

The Favara Grande is a geothermal area located on Pantelleria Island, Italy. The area is characterized high temperatures in the top layer of the soil (60°C), low pH (3–5) and hydrothermal gas emissions mainly composed of carbon dioxide (CO2), methane (CH4), and hydrogen (H2). These geothermal features may provide a suitable niche for the growth of chemolithotrophic thermoacidophiles, including the lanthanide-dependent methanotrophs of the phylum Verrucomicrobia. In this study, we started enrichment cultures inoculated with soil of the Favara Grande at 50 and 60°C with CH4 as energy source and medium containing sufficient lanthanides at pH 3 and 5. From these cultures, a verrucomicrobial methanotroph could be isolated via serial dilution and floating filters techniques. The genome of strain AP8 was sequenced and based on phylogenetic analysis we propose to name this new species Methylacidimicrobium thermophilum AP8. The transcriptome data at μmax (0.051 ± 0.001 h−1, doubling time ~14 h) of the new strain showed a high expression of the pmoCAB2 operon encoding the membrane-bound methane monooxygenase and of the gene xoxF1, encoding the lanthanide-dependent methanol dehydrogenase. A second pmoCAB operon and xoxF2 gene were not expressed. The physiology of strain AP8 was further investigated and revealed an optimal growth in a pH range of 3–5 at 50°C, representing the first thermophilic strain of the genus Methylacidimicrobium. Moreover, strain AP8 had a KS(app) for methane of 8 ± 1 μM. Beside methane, a type 1b [NiFe] hydrogenase enabled hydrogen oxidation at oxygen concentrations up to 1%. Taken together, our results expand the knowledge on the characteristics and adaptations of verrucomicrobial methanotrophs in hydrothermal environments and add a new thermophilic strain to the genus Methylacidimicrobium.
Pattern Unifies Autism

Autism is a highly heterogeneous condition, genetically and phenotypically. This diversity of causation and presentation has impeded its definition, recognition, assessment, and treatment. Current diagnostic criteria for autism involve two domains, restricted interests and repetitive behavior (RRBs) and social deficits, whose relationship remains unclear. I suggest that the large suite of traits associated with autism can be usefully conceptualized under the single rubric of “pattern,” a term that connects autism with basic brain and cognitive functions and structures its phenotypes within a single theoretical framework. Autism thus involves increases and enhancements to pattern perception, pattern recognition, pattern maintenance, pattern generation, pattern processing, and pattern seeking. RRBs result from increased and imbalanced pattern-related perception and cognition, and social alterations result in part from the usual lack of clear pattern in social interactions, combined with the interference of RRBs with social development. This framework has strong implications for assessment of social and non-social autism-related traits, personalized therapy, and priorities for research.
Invisible Work Pre- and Post- Digitalisation. The case of a health authority in Norway

: In this paper, we argue that, despite the increasing opportunities for trans - parency and visibility that come with the digitalisation of workplaces, invisible work remains a highly relevant concept, as it represents practices that are vital to the effec - tive functioning of business operations. Drawing on a longitudinal qualitative case study, we examine invisible work practices before and after the digitalisation of a re - gional health authority in Norway following the implementation of an enterprise re - source planning (ERP) system. The study extends the literature in this field by showing both the changing status of invisible work and the emergence of new invisible work in digitalised workplaces. The theoretical and practical implications are then discussed.
A Dynamic Clause Specific Initial Weight Assignment for Solving Satisfiability Problems Using Local Search

For decades, the use of weights has proven its superior ability to improve dynamic local search weighting algorithms’ overall performance. This paper proposes a new mechanism where the initial clause’s weights are dynamically allocated based on the problem’s structure. The new mechanism starts by examining each clause in terms of its size and the extent of its link, and its proximity to other clauses. Based on our examination, we categorized the clauses into four categories: (1) clauses small in size and linked with a small neighborhood, (2) clauses small in size and linked with a large neighborhood, (3) clauses large in size and linked with a small neighborhood, and (4) clauses large in size and linked with a large neighborhood. Then, the initial weights are dynamically allocated according to each clause category. To examine the efficacy of the dynamic initial weight assignment, we conducted an extensive study of our new technique on many problems. The study concluded that the dynamic allocation of initial weights contributes significantly to improving the search process’s performance and quality. To further investigate the new mechanism’s effect, we compared the new mechanism with the state-of-the-art algorithms belonging to the same family in terms of using weights, and it was clear that the new mechanism outperformed the state-of-the-art clause weighting algorithms. We also show that the new mechanism could be generalized with minor changes to be utilized within the general-purpose stochastic local search state-of-the-art weighting algorithms.
DSCP: Depthwise Separable Convolution-Based Passive Indoor Localization Using CSI Fingerprint

Wi-Fi-based indoor localization has received extensive attention in wireless sensing. However, most Wi-Fi-based indoor localization systems have complex models and high localization delays, which limit the universality of these localization methods. To solve these problems, a depthwise separable convolution-based passive indoor localization system (DSCP) is proposed. DSCP is a lightweight fingerprint-based localization system that includes an offline training phase and an online localization phase. In the offline training phase, the indoor scenario is first divided into different areas to set training locations for collecting CSI. Then, the amplitude differences of these CSI subcarriers are extracted to construct location fingerprints, thereby training the convolutional neural network (CNN). In the online localization phase, CSI data are first collected at the test locations, and then, the location fingerprint is extracted and finally fed to the trained network to obtain the predicted location. The experimental results show that DSCP has a short training time and a low localization delay. DSCP achieves a high localization accuracy, above 97%, and a small median localization distance error of 0.69 m in typical indoor scenarios.