Miracle-dz/UniFAR-FTUs huggingface dataset:
Child and Adolescent Mental Health Services in South Africa—Senior Stakeholder Perceptions of Strengths, Weaknesses, Opportunities, and Threats in the Western Cape Province

Background: There is general consensus that child and adolescent mental health services in low- and middle-income countries have an urgent need to be strengthened. However, this require not only a universal understanding of services and service needs, but also in-depth local knowledge to inform relevant service strengthening. This study sought to explore the perspectives of senior child and adolescent mental health service providers and policy-makers in one South African province to identify strengths, weaknesses, opportunities, and threats to child and adolescent mental health services. Methods: A qualitative study was conducted with 13 purposively sampled senior child and adolescent mental health service providers, senior managers, and policy-makers from the Western Cape Province, using a half-day multi-stakeholder workshop format. Verbal and written data were recorded and coded for analysis. Two independent raters performed thematic analysis. Results: The comprehensive bio-psycho-social approach and strong specialist child and adolescent mental health service units were identified as strengths. Limited capacity, workload demands, inadequate and inequitable resource allocation, poor implementation of early detection and preventative policies, and overall neglect of child and adolescent mental health services, were identified as weaknesses. Collaborative working between child and adolescent mental health and pediatric services, and increased provincial government (Department of Health) involvement, were identified as potential opportunities to develop and strengthen child and adolescent mental health services. Silo working of agencies, societal stressors, inadequate infrastructure and other resources, and lack of dedicated funding for child and adolescent mental health, were identified as threats to the development of services. Conclusions: This analysis of strengths, weaknesses, opportunities, and threats reinforced the widespread neglect of child and adolescent mental health services in South Africa and highlighted areas for further research and advocacy. There is a clear need to explore the perspectives and experiences of service users and providers to generate comprehensive multi-stakeholder evidence that may identify positive "tipping points" for improvements and strengthening of child and adolescent mental health service delivery, training, and research.
TMT Based Proteomic Analysis of Human Follicular Fluid From Overweight/Obese and Normal-Weight Patients With Polycystic Ovary Syndrome

Background: Polycystic ovary syndrome (PCOS) is a major endocrine and metabolic disorder with heterogeneous manifestations and complex etiology. As a leading cause of anovulatory infertility, the molecular diversity of the follicular microenvironment has not been fully elucidated. The aim of the present study was to investigate the follicular fluid proteomic profiles of overweight/obese and normal-weight women with PCOS, to identify novel molecular mechanisms underlying PCOS and to determine the effect of obesity on the follicular fluid protein profiles. Methods: Follicular fluid samples were collected from 3 different groups: overweight/obese PCOS patients (n = 29), normal-weight PCOS patients (n = 29), and normo-ovulatory controls (n = 29). We used a quantitative approach with tandem mass tag labeling and liquid chromatography tandem mass spectrometry to identify the differentially expressed proteins. Differential abundance of four selected proteins was confirmed by ELISA. Gene Set Enrichment Analysis was also conducted to further explore our findings. Furthermore, we compared the clinical, hormonal, and biochemical characteristics of overweight/obese and normal-weight patients with PCOS to determine the effects of obesity. Results: A total of 1,153 proteins were identified, of which 41 and 19 proteins were differentially expressed in the overweight/obese PCOS group vs. the control group, and in the normal-weight PCOS group vs. the control group, respectively. Bioinformatics analyses showed that the inflammatory, immunological, and metabolic-related biological processes were co-enriched in both subgroups of PCOS. Apolipoprotein A-II, complement C5, fetuin-B, and stromal cell-derived factor 1 were found to be involved in various processes and were validated using the ELISA analysis. From clinical features and proteomic data, obesity was found to worsen follicular development disturbances in PCOS. Conclusion: In this proteomic study, a panel of proteins were found differentially expressed in the follicular fluid of PCOS. Inflammatory, immunological, and metabolic abnormalities were identified inside the intra-follicular environment, which could be aggravated by obesity. The identified proteins were correlated with follicular growth and may be considered as candidate biomarkers as well as therapeutic targets of PCOS.
Postoperative Outcomes and Functional Recovery After Preoperative Combination Chemotherapy for Pancreatic Cancer: A Propensity Score-Matched Study

Introduction: Previous studies show encouraging oncologic outcomes for neoadjuvant chemotherapy (NACT) in the setting of pancreatic ductal adenocarcinoma (PDAC). However, recent literature reported an increased clinical burden in patients undergoing pancreaticoduodenectomy (PD) following NACT. Therefore, the aim of our study was to assess the impact of NACT on postoperative outcomes and recovery after PD. Methods: A retrospective propensity score-matched study was performed including all patients who underwent PD for PDAC in a single center between 2015 and 2018. Patients treated with NACT for resectable, borderline resectable or locally advanced PDAC were matched based on nearest neighbor propensity scores in a 1:1 ratio to patients who underwent upfront resection. Propensity scores were calculated using 7 perioperative variables, including gender, age, BMI, ASA score, Charlson-Deyo comorbidity score, fistula risk score (FRS), vascular resection. Primary outcome was the number and severity of complications at 90-days after surgery measured by the comprehensive complication index (CCI). Data are reported as median (IQR) or number of patients (%). Results: Of 283 resected patients, 95 (34%) were treated with NACT. Before matching, NACT patients were younger, had less comorbidities (Charlson-Deyo score 0 vs. 1, p = 0.04), similar FRS [2 (0–3) for both groups], and more vascular resections performed [n = 28 (30%) vs. n = 26 (14%), p < 0.01]. After propensity-score matching, preoperative and intraoperative characteristics were comparable. Postoperatively, CCI was similar between groups [8.7 (0–29.6) for both groups, p = 0.59]. NACT patients had a non-statistically significant increase in superficial incisional surgical site infections [n = 12 (13%) vs. 6 (6%), p = 0.14], while no difference was found for overall infectious complications and organ-space SSI. The occurrence of clinically-relevant pancreatic fistula was similar between groups [10 (11%) vs. 13 (14%), p = 0.51]. No difference was found between groups for length of hospital stay [8 (7–15) vs. 8 (7–14) days, p = 0.62], and functional recovery outcomes. Conclusion: After propensity score adjustment for perioperative risk factors, NACT did not worsen postoperative outcomes and functional recovery following PD for PDAC compared to upfront resection.
Greenhouse- and Field-Measured Plant-Soil Feedbacks Are Not Correlated

Plant-soil feedbacks (PSFs) have become a commonly invoked mechanism of plant coexistence and abundance. Yet, most PSF experiments have been performed in greenhouse conditions. To test whether or not greenhouse-measured PSF values are of similar magnitude and positively correlated with field-measured PSFs, we compared PSF values from five different studies that measured PSF values in both greenhouse and field conditions. For 36 plant species, greenhouse-measured PSF values were larger than and not positively correlated with field-measured PSF values. Similarly, these 36 species produced 269 soil-specific PSF values, and for each site there was no positive correlation between these greenhouse- and field-measured PSF values. While PSFs were observed in both greenhouse and field conditions, results provided no support at the soil, site or species level that a positive correlation exists between greenhouse- and field-measured PSF. Further, greenhouse-measured PSF appear to overestimate field-measured PSF. Although from five studies, results strongly suggest that field experiments are needed to understand the role of PSFs in plant communities in natural settings.
Coupling the Paternò-Büchi (PB) Reaction With Mass Spectrometry to Study Unsaturated Fatty Acids in Mouse Model of Multiple Sclerosis

Lipid dysregulation has been implicated in multiple sclerosis due to its involvement during and after inflammation. In this study, we have profiled fatty acids (FAs) in the mouse model of multiple sclerosis with new capabilities of assigning carbon-carbon double bond (C=C) location(s) and quantifying C=C location isomers. These new capabilities are enabled by pairing the solution phase Paternò-Büchi (PB) reaction that modifies C=C bonds in FAs, with tandem mass spectrometry (MS/MS), termed as PB-MS/MS. A series of unsaturated FAs and C=C location isomers have been identified, including FA17:1 (Δ10), FA18:1 (Δ9 and Δ11), FA18:2 (Δ9 and Δ12), and FA 20:4 (Δ5, Δ8, Δ11, Δ14). Notable differences in saturated and unsaturated FAs between normal and experimental autoimmune encephalomyelitis (EAE) mice spinal cords have been detected. Furthermore, the effects of hydralazine, a scavenger of acrolein, on profile changes of FAs in mice were studied. Increased Δ11-to-Δ9 isomer ratios for FA 18:1 were noted in the diseased samples as compared to the control. The present work provides a facile and robust analytical method for the quantitation of unsaturated FAs as well as identification of FA C=C location isomers, which will facilitate discovering prospective lipid markers in multiple sclerosis.
Nitrogen and Phosphorus Additions Alter the Abundance of Phosphorus-Solubilizing Bacteria and Phosphatase Activity in Grassland Soils

Microorganisms mobilize phosphorus (P) in soil by solubilizing bound inorganic P from soil minerals and by mineralizing organic P via phosphatase enzymes. Nitrogen (N) inputs are predicted to increase through human activities and shift plants to be more P limited, increasing the importance of P mobilization processes for plant nutrition. We studied how the relative abundance of P-solubilizing bacteria (PSB), PSB community composition, and phosphatase activity respond to N and P addition (+N, +P, +NP) in grassland soils spanning large biogeographic gradients. The studied soils are located in South Africa, USA, and UK and part of a globally coordinated nutrient addition experiment. We show that the abundance of PSB in the topsoil was reduced by 18 % in the N and by 41 % in the NP treatment compared to the control. In contrast, phosphatase activity was significantly higher in the N treatment than in the control across all soils. Soil C:P ratio, sand content, pH, and water-extractable P together explained 71 % of the variance of the abundance of PSB across all study sites and all treatments. Further, the community of PSB in the N and NP addition treatment differed significantly from the control. Taken together, this study shows that N addition reduced the relative abundance of PSB, altered the PSB community, and increased phosphatase activity, whereas P addition had no impact. Increasing atmospheric N deposition may therefore increase mineralization of organic P and decrease solubilization of bound inorganic P, possibly changing P mobilization processes in grassland soils. Consequently, in ecosystems in which plant P nutrition depends on bound inorganic P, increased N inputs might diminish P supply and thus aggravate P limitation and constrain plant productivity.
Neurodegenerative Disease–Associated Variants in TREM2 Destabilize the Apical Ligand-Binding Region of the Immunoglobulin Domain

Single nucleotide variations in Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) have been linked to both late-onset Alzheimer's disease and behavioral variant frontotemporal dementia (FTD), the latter presenting either in isolation or with cystic bone lesions in a condition called Nasu-Hakola disease. Models of the extracellular domain of TREM2 show that Nasu-Hakola disease–associated mutations are grossly inactivating by truncation, frameshift, or unfolding, that Alzheimer's disease (AD)–associated variants localize to a putative ligand-interacting region (PLIR) on the extracellular surface, and that FTD-associated variants are found in the hydrophobic core. However, while these disease-associated residues are predicted to play some role in disrupting ligand binding to the extracellular domain of TREM2, how they ultimately lead to disease remains unknown. Here, we used in silico molecular modeling to investigate all-atom models of TREM2 and characterize the effects on conformation and dynamical motion of AD-associated R47H and R62H as well as FTD-associated T96K, D86V, and T66M variants compared to the benign N68K variant and the common variant. Our model, which is based on a published 2.2 Å resolution crystal structure of the TREM2 extracellular domain, finds that both AD- and FTD-associated variants cause localized instability in three loops adjacent to the PLIR that correspond to the complementarity-determining regions (CDRs) of antibodies. This instability ultimately disrupts tethering between these CDRs and the core of the immunoglobulin domain, exposing a group of otherwise-buried, negatively charged residues. This instability and exposure of negatively charged residues is most severe following introduction of the T66M variant that has been described as causing FTD even in the heterozygous state and is less severe following introduction of variants that are less strongly tied to FTD or of those associated with AD. Thus, our results provide further evidence that the proposed loss-of-function caused by neurodegenerative disease–associated variants may be driven by altered conformational stability of the ligand-interacting CDR and, ultimately, loss of affinity or specificity for TREM2 ligands.
Tissue-Specific Microbiomes of the Red Sea Giant Clam Tridacna maxima Highlight Differential Abundance of Endozoicomonadaceae

Giant clams (subfamily Tridacninae) are prevalent members of coral reef communities and engage in symbioses with algal photosymbionts of the family Symbiodiniaceae, similar to their scleractinian coral counterparts. However, we know little about their associated bacterial microbiome members. Here, we explored bacterial community diversity of digestive system, gill, and mantle tissues associated with the giant clam Tridacna maxima across a cross-shelf gradient (inshore, midshore, and offshore reef sites) in the central Red Sea using 16S rRNA gene amplicon sequencing. Different tissues harbor spatially stable and distinct microbial communities. Notably, diverse assemblages of bacteria affiliated to the family Endozoicomonadaceae were prevalent in all tissues, but particularly abundant in gills and to a lesser extent in digestive tissues. Besides Endozoicomonadaceae, bacteria in the families Pasteurellaceae, Alteromonadaceae, and Comamonadaceae were common associates, depending on the tissue queried. Taxonomy-based functional inference identified processes related to nitrogen cycling (among others) to be enriched in giant clam tissues and contributed by Endozoicomonadaceae. Our study highlights the tissue-specificity and broad taxonomic range of Endozoicomonadaceae associates, similar to other marine invertebrates, and suggests their contribution to nitrogen-related pathways. The investigation of bivalve-associated microbiome communities provides an important addition to the pathogen-focused studies for commercially important bivalves (e.g., oysters).
Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment

Attempts to develop flexible on-demand drone-assisted mobile network deployment are increasingly driven by cost-effective and energy-efficient innovations. The current stage opens up a wide range of theoretical discussions on the management of swarm processes, networks and other integrated projects. However, dealing with these complex issues remains a challenging task, although heuristic approaches are usually utilized. This article introduces a model of autonomous and adaptive drones that provide the function of aerial mobile base stations. Its particular goal is to analyze post-disaster recovery if the network failure takes place. We assume that a well-structured swarm of drones can re-establish the connection by spanning the residual functional, fixed infrastructure, and providing coverage of the target area. Our technique uses stochastic Langevin dynamics with virtual and adaptive forces that bind drones during deployment. The system characteristics of the swarms are a priority of our focus. The assessment of parametric sensitivity with the insistence on the manifestation of adaptability points to the possibility of improving the characteristics of the swarms in different dynamic situations.
Quantification of Information Exchange in Idealized and Climate System Applications

Often in climate system studies, linear and symmetric statistical measures are applied to quantify interactions among subsystems or variables. However, they do not allow identification of the driving and responding subsystems. Therefore, in this study, we aimed to apply asymmetric measures from information theory: the axiomatically proposed transfer entropy and the first principle-based information flow to detect and quantify climate interactions. As their estimations are challenging, we initially tested nonparametric estimators like transfer entropy (TE)-binning, TE-kernel, and TE k-nearest neighbor and parametric estimators like TE-linear and information flow (IF)-linear with idealized two-dimensional test cases along with their sensitivity on sample size. Thereafter, we experimentally applied these methods to the Lorenz-96 model and to two real climate phenomena, i.e., (1) the Indo-Pacific Ocean coupling and (2) North Atlantic Oscillation (NAO)–European air temperature coupling. As expected, the linear estimators work for linear systems but fail for strongly nonlinear systems. The TE-kernel and TE k-nearest neighbor estimators are reliable for linear and nonlinear systems. Nevertheless, the nonparametric methods are sensitive to parameter selection and sample size. Thus, this work proposes a composite use of the TE-kernel and TE k-nearest neighbor estimators along with parameter testing for consistent results. The revealed information exchange in Lorenz-96 is dominated by the slow subsystem component. For real climate phenomena, expected bidirectional information exchange between the Indian and Pacific SSTs was detected. Furthermore, expected information exchange from NAO to European air temperature was detected, but also unexpected reversal information exchange. The latter might hint to a hidden process driving both the NAO and European temperatures. Hence, the limitations, availability of time series length and the system at hand must be taken into account before drawing any conclusions from TE and IF-linear estimations.
Permutation Entropy: Enhancing Discriminating Power by Using Relative Frequencies Vector of Ordinal Patterns Instead of Their Shannon Entropy

Many measures to quantify the nonlinear dynamics of a time series are based on estimating the probability of certain features from their relative frequencies. Once a normalised histogram of events is computed, a single result is usually derived. This process can be broadly viewed as a nonlinear IRn mapping into IR, where n is the number of bins in the histogram. However, this mapping might entail a loss of information that could be critical for time series classification purposes. In this respect, the present study assessed such impact using permutation entropy (PE) and a diverse set of time series. We first devised a method of generating synthetic sequences of ordinal patterns using hidden Markov models. This way, it was possible to control the histogram distribution and quantify its influence on classification results. Next, real body temperature records are also used to illustrate the same phenomenon. The experiments results confirmed the improved classification accuracy achieved using raw histogram data instead of the PE final values. Thus, this study can provide a very valuable guidance for the improvement of the discriminating capability not only of PE, but of many similar histogram-based measures.
Evidence for Maintained Post-Encoding Memory Consolidation Across the Adult Lifespan Revealed by Network Complexity

Memory consolidation is well known to occur during sleep, but might start immediately after encoding new information while awake. While consolidation processes are important across the lifespan, they may be even more important to maintain memory functioning in old age. We tested whether a novel measure of information processing known as network complexity might be sensitive to post-encoding consolidation mechanisms in a sample of young, middle-aged, and older adults. Network complexity was calculated by assessing the irregularity of brain signals within a network over time using multiscale entropy. To capture post-encoding mechanisms, network complexity was estimated using functional magnetic resonance imaging (fMRI) during rest before and after encoding of picture pairs, and subtracted between the two rest periods. Participants received a five-alternative-choice memory test to assess associative memory performance. Results indicated that aging was associated with an increase in network complexity from pre- to post-encoding in the default mode network (DMN). Increases in network complexity in the DMN also were associated with better subsequent memory across all age groups. These findings suggest that network complexity is sensitive to post-encoding consolidation mechanisms that enhance memory performance. These post-encoding mechanisms may represent a pathway to support memory performance in the face of overall memory declines.
Modeling the Disorder of Closed System by Multi-Agent Based Simulation

Mess (disorder)—there are many different meanings related to this problem. The explicit majority comes from the area of philosophical, social and medical sciences. In our paper, we try to present the engineering aspect of the concept of disorder. We propose a mathematical model which describes the effects and consequences concerning the process of making the mess. We use Multi-Agent Modeling, where there are several independent agents with decision-making ability. Each agent has the ability to communicate and perceive for achieving its own aim. We use square grid n × n with objects which can be moved by agents to another places. The degree of disorder of the system is examined by the value of entropy. Using computer simulation, we investigate the time needed to find the desired thing in an environment in which agents (in real life, people) co-exist and they have different tendencies to tidiness. The cost of mess is counted as the number of attempts to access the object in the analyzed system and the time needed to locate the object.