Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Models in Neuroscience

From interacting cellular components to networks of neurons and neural systems, interconnected units comprise a fundamental organizing principle of the nervous system. Understanding how their patterns of connections and interactions give rise to the many functions of the nervous system is a primary goal of neuroscience. Recently, this pursuit has begun to benefit from the development of new mathematical tools that can relate a system's architecture to its dynamics and function. These tools, which are known collectively as network science, have been used with increasing success to build models of neural systems across spatial scales and species. Here we discuss the nature of network models in neuroscience. We begin with a review of model theory from a philosophical perspective to inform our view of networks as models of complex systems in general, and of the brain in particular. We then summarize the types of models that are frequently studied in network neuroscience along three primary dimensions: from data representations to first-principles theory, from biophysical realism to functional phenomenology, and from elementary descriptions to coarse-grained approximations. We then consider ways to validate these models, focusing on approaches that perturb a system to probe its function. We close with a description of important frontiers in the construction of network models and their relevance for understanding increasingly complex functions of neural systems.
Questionnaires for Lung Health in Africa across the Life Course

Respiratory infections remain a leading cause of morbidity and mortality in many low and middle-income countries but non-communicable disease rates are rising fast. Prevalence studies have been primarily symptom-focused, with tools developed in countries in the Global North such as the United States and the United Kingdom. Systematic study in sub-Saharan African populations is necessary to accurately reflect disease risk factors present in these populations. We present tools for such studies, developed as part of the International Multidisciplinary Programme to Address Lung Health and TB in Africa (‘IMPALA’), which includes lay representatives. At a preliminary meeting, the adequacy and suitability of existing tools was discussed and a new questionnaire set proposed. Individual questionnaires were developed, and an expert panel considered content and criterion validity. Questionnaires underwent a cross-cultural adaptation process, incorporating translation and contextual ‘sense-checking’, through the use of pre-established lay focus groups in Malawi, before consensus-approval by project collaborators. The complete set of research questionnaires, providing information on lung health symptoms and a relevant range of potential risk factors for lung disease, is now available online. In developing the tools, cultural and contextual insights were important, as were translational considerations. The process benefitted from a foundation in expert knowledge, starting with validated tools and internationally respected research groups, and from a coordinated collaborative approach. We present and discuss a newly devised, contextually appropriate set of questionnaires for non-communicable lung disease research in Africa that are now available in open access for all to use.
Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors

Social isolation and loneliness are major health concerns in young and older people. Traditional approaches to monitor the level of social interaction rely on self-reports. The goal of this study was to investigate if wearable textile-based sensors can be used to accurately detect if the user is talking as a future indicator of social interaction. In a laboratory study, fifteen healthy young participants were asked to talk while performing daily activities such as sitting, standing and walking. It is known that the breathing pattern differs significantly between normal and speech breathing (i.e., talking). We integrated resistive stretch sensors into wearable elastic bands, with a future integration into clothing in mind, to record the expansion and contraction of the chest and abdomen while breathing. We developed an algorithm incorporating machine learning and evaluated its performance in distinguishing between periods of talking and non-talking. In an intra-subject analysis, our algorithm detected talking with an average accuracy of 85%. The highest accuracy of 88% was achieved during sitting and the lowest accuracy of 80.6% during walking. Complete segments of talking were correctly identified with 96% accuracy. From the evaluated machine learning algorithms, the random forest classifier performed best on our dataset. We demonstrate that wearable textile-based sensors in combination with machine learning can be used to detect when the user is talking. In the future, this approach may be used as an indicator of social interaction to prevent social isolation and loneliness.
Survival and Kidney Transplant Incidence on Home versus In-Center Hemodialysis, Following Peritoneal Dialysis Technique Failure

Background: Peritoneal dialysis (PD) technique failure is often accompanied by complications that increase risks of hospitalization and death. Planned transition to hemodialysis may improve outcomes. Transitioning patients from PD to home hemodialysis (HHD) may improve continuity of lifestyle and facilitate delivery of more frequent treatment. Methods: We analyzed United States Renal Data System (USRDS) data to compare the incidence of death and kidney transplant in patients who transferred from PD to HHD and matched patients who transferred from PD to in-center HD (IHD). We used Fine-Gray regression to estimate hazard ratios (HRs) of death and transplant for HHD versus IHD. Results: We identified 521 patients who transferred from PD to HHD. Survival in HHD patients was 89.1% at 1 year and 80.5% at 2 years. In intention-to-treat analysis, the HR of death for HHD versus matched IHD patients was 0.76 (95% confidence interval [CI] 0.65 – 0.90). In subsets of non-Medicare and Medicare patients, corresponding HRs were 0.57 (95% CI 0.43 – 0.75) and 0.92 (95% CI 0.75 – 1.13), respectively. Kidney transplant incidence in HHD patients was 10.6% at 1 year and 21.0% at 2 years. In modified intention-to-treat analysis, the HR of transplant for HHD versus matched IHD patients was 1.36 (1.14 – 1.61). Conclusions: Transfer to HHD after PD technique failure was rare, but associated with lower risk of death and higher incidence of transplant than transfer to IHD. Heterogeneity in relative risks by Medicare coverage suggests uncertainty about the magnitude of benefit. Still, these data suggest that clinical outcomes after PD technique failure can be improved.
Wasserstein GAN and Waveform Loss-Based Acoustic Model Training for Multi-Speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder

WaveNet, which learns directly from speech waveform samples, has been used as an alternative to vocoders and achieved very high-quality synthetic speech in terms of both naturalness and speaker similarity even in multi-speaker text-to-speech synthesis systems. However, the WaveNet vocoder uses acoustic features as local condition parameters, and these parameters need to be accurately predicted by another acoustic model. So far, it is not yet clear how to train this acoustic model, which is problematic because the final quality of synthetic speech is significantly affected by the performance of the acoustic model. Significant degradation occurs, especially when predicted acoustic features have mismatched characteristics compared to natural ones. In order to reduce the mismatched characteristics between natural and generated acoustic features, we propose new frameworks that incorporate either a conditional generative adversarial network (GAN) or its variant, Wasserstein GAN with gradient penalty (WGAN-GP), into multi-speaker speech synthesis that uses the WaveNet vocoder. The GAN generator performs as an acoustic model and its outputs are used as the local condition parameters of the WaveNet. We also extend the GAN frameworks and use the discretized-mixture-of-logistics (DML) loss of a well-trained WaveNet in addition to mean squared error and adversarial losses as parts of objective functions. Experimental results show that acoustic models trained using the WGAN-GP framework using back-propagated DML loss achieves the highest subjective evaluation scores in terms of both quality and speaker similarity.
Rhizoremediation of petroleum hydrocarbons: a model system for plant microbiome manipulation

Phytoremediation is a green and sustainable alternative to physico‐chemical methods for contaminated soil remediation. One of the flavours of phytoremediation is rhizoremediation, where plant roots stimulate soil microbes to degrade organic contaminants. This approach is particularly interesting as it takes advantage of naturally evolved interaction mechanisms between plant and microorganisms and often results in a complete mineralization of the contaminants (i.e. transformation to water and CO2). However, many biotic and abiotic factors influence the outcome of this interaction, resulting in variable efficiency of the remediation process. The difficulty to predict precisely the timeframe associated with rhizoremediation leads to low adoption rates of this green technology. Here, we review recent literature related to rhizoremediation, with a particular focus on soil organisms. We then expand on the potential of rhizoremediation to be a model plant‐microbe interaction system for microbiome manipulation studies.
Speed-sensorless state feedback control of induction machines with LC filter

A speed-sensorless state feedback controller for induction machines (IMs) with LC filter is proposed. The estimation of speed and remaining states is based on a speed-adaptive observer, requiring only the measurement of the filter input currents. The motor currents are controlled by a state-feedback controller with proportional and integral control action to achieve fast and asymptotic set point tracking. Observer \emph{and} controller gains are calculated offline using linear quadratic regulator (LQR) theory and updated online (gain-scheduling), in order to guarantee stability and improve control performance in the whole operation range. The proposed control scheme is validated by simulation and experimental results including several zero-crossings of the mechanical speed. It is shown that the overall control system performs well under various load- and speed conditions; while its tuning remains easy making it attractive also for industrial application.
Competing RNA pairings in complex alternative splicing of a 3′ variable region

Alternative pre-mRNA splicing remarkably expands protein diversity in eukaryotes. Drosophila PGRP-LC can generate three major 3′ splice isoforms that exhibit distinct innate immune recognition and defenses against various microbial infections. However, the regulatory mechanisms underlying the uniquely biased splicing pattern at the 3′ variable region remain unclear. Here we show that competing RNA pairings control the unique splicing of the 3′ variable region of Drosophila PGRP-LC pre-mRNA. We reveal three roles by which these RNA pairings jointly regulate the 3′ variant selection through activating the proximal 3′ splice site and concurrently masking the intron-proximal 5′ splice site, in combination with physical competition of RNA pairing. We also reveal that competing RNA pairings regulate alternative splicing of the highly complex 3′ variable regions of Drosophila CG42235 and Pip. Our findings will facilitate a better understanding of the regulatory mechanisms of highly complex alternative splicing as well as highly variable 3′ processing.
Short-term optimal load training vs a modified complex training in semi-professional basketball players

ABSTRACT This study investigated the effects on neuromuscular performance of a 6-week Optimal Load Training (OLT) and a novel modified Complex Training (MCT) (complex pairs: the same exercise using a moderate and an OL) in basketball players, in-season. Eighteen male athletes were randomly assigned to one of the protocols. Anthropometric measurements were taken to evaluate body composition. Lower- and upper-body maximum dynamic strength, countermovement jump (CMJ), standing long jump (SLJ), 10-m sprint and change of direction (COD) were also assessed. Moderate-to-large strength gains (presented as percentage change ± 90% confidence limits) were obtained for half-squat (OLT: 10.8 ± 5.3%; MCT: 17.2 ± 11.6%) and hip thrust (OLT: 23.5 ± 17.7%; MCT: 28.2 ± 19.0%). OLT athletes achieved likely small improvements in sprint (1.6 ± 1.6%) and COD (3.0 ± 3.2%). Players in the MCT attained likely moderate improvements in COD (3.0 ± 2.0%) and possibly small in SLJ (2.5 ± 4.6%). No protocol relevantly affected CMJ or body composition. An ANCOVA test revealed unclear between-group differences. In conclusion, both protocols increased basketball players’ strength without the use of heavy loads (> 85% 1RM) and without impairing sprint, CMJ and SLJ performance. These findings suggest that basketball strength and conditioning professionals may use either method to counteract strength losses during the season.
Chemical transport model simulations of organic aerosol in southern California: model evaluation and gasoline and diesel source contributions

Gasoline- and diesel-fueled engines are ubiquitous sources of air pollution in urban environments. They emit both primary particulate matter and precursor gases that react to form secondary particulate matter in the atmosphere. In this work, we updated the organic aerosol module and organic emissions inventory of a three-dimensional chemical transport model, the Community Multiscale Air Quality Model (CMAQ), using recent, experimentally derived inputs and parameterizations for mobile sources. The updated model included a revised volatile organic compound (VOC) speciation for mobile sources and secondary organic aerosol (SOA) formation from unspeciated intermediate volatility organic compounds (IVOCs). The updated model was used to simulate air quality in southern California during May and June 2010, when the California Research at the Nexus of Air Quality and Climate Change (CalNex) study was conducted. Compared to the Traditional version of CMAQ, which is commonly used for regulatory applications, the updated model did not significantly alter the predicted organic aerosol (OA) mass concentrations but did substantially improve predictions of OA sources and composition (e.g., POA–SOA split), as well as ambient IVOC concentrations. The updated model, despite substantial differences in emissions and chemistry, performed similar to a recently released research version of CMAQ (Woody et al., 2016) that did not include the updated VOC and IVOC emissions and SOA data. Mobile sources were predicted to contribute 30–40 % of the OA in southern California (half of which was SOA), making mobile sources the single largest source contributor to OA in southern California. The remainder of the OA was attributed to non-mobile anthropogenic sources (e.g., cooking, biomass burning) with biogenic sources contributing to less than 5 % to the total OA. Gasoline sources were predicted to contribute about 13 times more OA than diesel sources; this difference was driven by differences in SOA production. Model predictions highlighted the need to better constrain multi-generational oxidation reactions in chemical transport models.
MODEL-ORDER SELECTION IN STATISTICAL SHAPE MODELS

Statistical shape models enhance machine learning algorithms providing prior information about deformation. A Point Distribution Model (PDM) is a popular landmark-based statistical shape model for segmentation. It requires choosing a model order, which determines how much of the variation seen in the training data is accounted for by the PDM. A good choice of the model order depends on the number of training samples and the noise level in the training data set. Yet the most common approach for choosing the model order simply keeps a predetermined percentage of the total shape variation. In this paper, we present a technique for choosing the model order based on information-theoretic criteria, and we show empirical evidence that the model order chosen by this technique provides a good trade-off between over- and underfitting.
Multiple anthropogenic interventions drive puma survival following wolf recovery in the Greater Yellowstone Ecosystem

Abstract Humans are primary drivers of declining abundances and extirpation of large carnivores worldwide. Management interventions to restore biodiversity patterns, however, include carnivore reintroductions, despite the many unresolved ecological consequences associated with such efforts. Using multistate capture–mark–recapture models, we explored age‐specific survival and cause‐specific mortality rates for 134 pumas (Puma concolor) monitored in the Greater Yellowstone Ecosystem during gray wolf (Canis lupus) recovery. We identified two top models explaining differences in puma survivorship, and our results suggested three management interventions (unsustainable puma hunting, reduction in a primary prey, and reintroduction of a dominant competitor) have unintentionally impacted puma survival. Specifically, puma survival across age classes was lower in the 6‐month hunting season than the 6‐month nonhunting season; human‐caused mortality rates for juveniles and adults, and predation rates on puma kittens, were higher in the hunting season. Predation on puma kittens, and starvation rates for all pumas, also increased as managers reduced elk (Cervus elaphus) abundance in the system, highlighting direct and indirect effects of competition between recovering wolves and pumas over prey. Our results emphasize the importance of understanding the synergistic effects of existing management strategies and the recovery of large, dominant carnivores to effectively conserve subordinate, hunted carnivores in human‐dominated landscapes.
Incorporating Prior Knowledge of Principal Components in Genomic Prediction

Genomic prediction using a large number of markers is challenging, due to the curse of dimensionality as well as multicollinearity arising from linkage disequilibrium between markers. Several methods have been proposed to solve these problems such as Principal Component Analysis (PCA) that is commonly used to reduce the dimension of predictor variables by generating orthogonal variables. Usually, the knowledge from PCA is incorporated in genomic prediction, assuming equal variance for the PCs or a variance proportional to the eigenvalues, both treat variances as fixed. Here, three prior distributions including normal, scaled-t and double exponential were assumed for PC effects in a Bayesian framework with a subset of PCs. These developed PCR models (dPCRm) were compared to routine genomic prediction models (RGPM) i.e., ridge and Bayesian ridge regression, BayesA, BayesB, and PC regression with a subset of PCs but PC variances predefined as proportional to the eigenvalues (PCR-Eigen). The performance of methods was compared by simulating a single trait with heritability of 0.25 on a genome consisted of 3,000 SNPs on three chromosomes and QTL numbers of 15, 60, and 105. After 500 generations of random mating as the historical population, a population was isolated and mated for another 15 generations. The generations 8 and 9 of recent population were used as the reference population and the next six generations as validation populations. The accuracy and bias of predictions were evaluated within the reference population, and each of validation populations. The accuracies of dPCRm were similar to RGPM (0.536 to 0.664 vs. 0.542 to 0.671), and higher than the accuracies of PCR-Eigen (0.504 to 0.641) within reference population over different QTL numbers. Decline in accuracies in validation populations were from 0.633 to 0.310, 0.639 to 0.313, and 0.617 to 0.298 using dPCRm, RGPM and PCR-Eigen, respectively. Prediction biases of dPCRm and RGPM were similar and always much less than biases of PCR-Eigen. In conclusion assuming PC variances as random variables via prior specification yielded higher accuracy than PCR-Eigen and same accuracy as RGPM, while fewer predictors were used.
Inhibition of JNK and p38 MAPK‐mediated inflammation and apoptosis by ivabradine improves cardiac function in streptozotocin‐induced diabetic cardiomyopathy

Inflammation plays a critical role in the development of diabetic cardiomyopathy (DCM), which has been identified as a major predisposing factor for heart failure in diabetic patients. Previous studies indicated that ivabradine (a specific agent for heart rate [HR] reduction) has anti‐inflammatory properties, but its role in DCM remains unknown. This study investigated whether ivabradine exerts a therapeutic effect in DCM. C57BL/6J mice were injected intraperitoneally with streptozotocin (STZ) to induce diabetes; then administered with ivabradine or saline (control). After 12 weeks, the surviving mice were analyzed to determine the cardioprotective effect of ivabradine against DCM. Although treatment with ivabradine did not affect blood glucose levels, it attenuated tumor necrosis factor‐α, interleukin‐1β, and interleukin‐6 messenger RNA (mRNA) expression, inhibited c‐Jun N‐terminal kinase (JNK) and p38 mitogen‐activated protein kinase (p38 MAPK) activation, reduced histological abnormalities, myocardial apoptosis and collagen deposition, and improved cardiac function in the diabetic mice. Interestingly, the anti‐inflammatory and antiapoptotic properties of ivabradine, but not its inhibitory effect on JNK and p38 MAPK, were observed in high‐glucose‐cultured neonatal rat ventricular cardiomyocytes. Attenuating inflammation and apoptosis via intramyocardial injection of lentiviruses carrying short hairpin RNA targeting JNK and p38 MAPK validated that the anti‐inflammatory and antiapoptotic effects of ivabradine were partly attributed to JNK and p38 MAPK inactivation in diabetic mice. In summary, these data indicate that ivabradine‐mediated improvement of cardiac function in STZ‐induced diabetic mice may be partly attributed to inhibition of JNK/p38 MAPK‐mediated inflammation and apoptosis, which is dependent on the reduction in HR.
The benefits of pair bond tenure in the cooperatively breeding pied babbler (Turdoides bicolor)

Abstract The benefits of stable pair bonds (that persist between breeding attempts) have been well described, but are relatively less well known in cooperatively breeding species. If pair bonds are beneficial, then it is possible that the bond between the behaviorally and socially dominant pair may influence factors such as reproductive success and group stability in cooperative species. Here, we used long‐term data to investigate the relationships between pair bond tenure, reproductive success, and group stability in the cooperatively breeding pied babbler (Turdoides bicolor). Pair bond tenure positively influenced both the number of offspring recruited annually per pair and total reproductive success (over entire pair bond duration), indicating that pair bond tenure has an important influence on reproductive success. The likelihood of immigration into the group was lower for groups containing a bonded pair with long tenure, indicating that the duration of pair bonds may impact group stability. These findings suggest that pair tenure, a hitherto relatively unexplored factor in cooperative species, may have an important influence on group dynamics.