Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing Inequalities Using an Unbiased Machine Learning Approach to Identify Births with the Highest Risk of Preventable Neonatal Deaths.

Background: Despite contemporaneous declines in neonatal mortality, recent studies show the existence of left-behind populations that continue to have higher mor- tality rates than the national averages. Additionally, many of these deaths are from preventable causes. This reality creates the need for more precise methods to iden- tify high-risk births so that policymakers can more precisely target them. This study fills this gap by developing unbiased machine-learning approaches to more accurately identify births with a high risk of neonatal deaths from preventable causes. Methods: We link administrative databases from the Brazilian health ministry to obtain birth and death records in the country from 2015 to 2017. The final dataset comprises 8,797,968 births, of which 59,615 newborns died before reaching 28 days alive (neonatal deaths). These neonatal deaths are categorized into preventable deaths (42,290) and non-preventable deaths (17,325). Our analysis identifies the death risk of the former group, as they are amenable to policy interventions. We train six machine- learning algorithms, test their performance on unseen data, and evaluate them using a new policy-oriented metric. To avoid biased policy recommendations, we also inves- tigate how our approach impacts disadvantaged populations. Results: XGBoost was the best performance algorithm for our task: the 5% births of the highest predicted risk from this model capture more than 85% of the actual deaths. Furthermore, the risk predictions exhibit no statistical differences in the pro- portion of actual preventable deaths from disadvantaged populations, defined by race, education, marital status, and maternal age. These results are similar for other thresh- old levels. Conclusions: We show that, by using publicly available administrative data sets and ML methods, it is possible to identify the births with the highest risk of preventable deaths with a high degree of accuracy. This is useful for policymakers as they can target health interventions to those who need them the most and where they can be effective without producing bias against disadvantaged populations. Overall, our approach can guide policymakers in reducing neonatal mortality rates and their health inequalities. Finally, it can be adapted to be used in other developing countries.
Brain morphology mediating the effect of genetic risk variants on Alzheimers disease

INTRODUCTION: Late-onset Alzheimers disease (LOAD) has been associated with alterations in the morphology of multiple brain structures and it is likely that disease mechanisms differ between brain regions. Coupling genetic determinants of LOAD with measures of brain morphology could localize and identify primary causal neurobiological pathways. METHODS: Mediation and Mendelian randomization (MR) analysis were performed using common genetic variation, T1 MRI and clinical data collected by UK Biobank and Alzheimers Disease Neuroimaging Initiative. RESULTS: Thickness of the entorhinal cortex and the volumes of the hippocampus, amygdala, choroid plexus and inferior lateral ventricle mediated the effect of APOE E4 on LOAD. MR showed that a thinner entorhinal cortex, a smaller hippocampus and amygdala, and a larger volume of the choroid plexus and inferior lateral ventricles, increased the risk of LOAD as well as vice versa. DISCUSSION: Combining neuroimaging and genetic data can give insight into the causal neuropathological pathways of LOAD.
SweatSkin

Sweat sensing affords monitoring essential bio-signals tailored for various well-being inspections. We present SweatSkin, the fabrication approach for customizable sweat-sensing on-skin interfaces. SweatSkin is unique in exploiting on-skin microfluidic channels to access bio-fluid secretes within the skin for personalized health monitoring. To lower the barrier to creating skin-conformable microfluidics capable of collecting and analyzing sweat, four fabrication methods utilizing accessible materials are proposed. Technical characterizations of paper- and polymer-based devices indicate that colorimetric analysis can effectively visualize sweat loss, chloride, glucose, and pH values. To support general to extreme sweating scenarios, we consulted five athletic experts on the SweatSkin devices' customization guidelines, application potential, and envisioned usages. The two-session fabrication workshop study with ten participants verified that the four fabrication methods are easy to learn and easy to make. Overall, SweatSkin is an extensible and user-friendly platform for designing and creating customizable on-skin sweat-sensing interfaces for UbiComp and HCI, affording ubiquitous personalized health sensing.
LocCams

Unlawful wireless cameras are often hidden to secretly monitor private activities. However, existing methods to detect and localize these cameras are interactively complex or require expensive specialized hardware. In this paper, we present LocCams, an efficient and robust approach for hidden camera detection and localization using only a commodity device (e.g., a smartphone). By analyzing data packets in the wireless local area network, LocCams passively detects hidden cameras based on the packet transmission rate. Camera localization is achieved by identifying whether the physical channel between our detector and the hidden camera is a Line-of-Sight (LOS) propagation path based on the distribution of channel state information subcarriers, and utilizing a feature extraction approach based on a Convolutional Neural Network (CNN) model for reliable localization. Our extensive experiments, involving various subjects, cameras, distances, user positions, and room configurations, demonstrate LocCams' effectiveness. Additionally, to evaluate the performance of the method in real life, we use subjects, cameras, and rooms that do not appear in the training set to evaluate the transferability of the model. With an overall accuracy of 95.12% within 30 seconds of detection, LocCams provides robust detection and localization of hidden cameras.
Patterns and function of pneumaticity in the vertebrae, ribs, and ilium of a titanosaur (Dinosauria, Sauropoda) from the Upper Cretaceous of Texas

ABSTRACT Many sauropod dinosaurs exhibit extensive postcranial skeletal pneumaticity that may have facilitated the evolution of extreme body sizes. Among titanosauriforms, complex, irregularly branching camellate chambers are found throughout the presacral vertebral column, often invading the ribs and ilium as well. To explore the function of these camellae, including reduction in bone volume, pneumaticity was examined in a titanosaur sauropod from the Upper Cretaceous Black Peaks Formation of Big Bend National Park, Texas, that includes pneumatic dorsal ribs and ilia. Using natural breaks to non-destructively observe the internal structure, patterns of camellate pneumaticity are described for the dorsal vertebrae, ribs, and ilium. The space occupied by camellae is quantified as the airspace proportion, which is reported here in a sauropod ilium for the first time. Airspace proportions exceed 70% in parts of the dorsal vertebrae and ilium, with lower values near the cotyles of the vertebral centra and the acetabulum. Values in the ribs decrease distally. These values are not appreciably different from those of sauropods with simpler camerate pneumaticity. If camellae did not offer greater weight reduction than camerae, they may have enhanced structural strength, as the chambers appear to align with stress in the vertebral centra and ilium. Apneumatic trabecular bone around the acetabulum, preacetabular process, and postzygapophyses, however, may indicate stresses too great for camellate bone to bear, although an ontogenetic influence cannot be ruled out.
Unraveling Glioblastoma Heterogeneity: Introducing SP2G Method for Identifying Invasive Sub-Populations

Glioblastomas exhibit remarkable heterogeneity at various levels, including motility modes and mechanoproperties that contribute to tumor resistance and recurrence. In a recent study using gridded micropatterns mimicking the brain vasculature, we linked glioblastoma cell motility modes, mechanical properties, formin content, and substrate chemistry. We now introduce SP2G (SPheroid SPreading on Grids), an analytic platform designed to identify the migratory modes of patient-derived glioblastoma cells and rapidly pinpoint the most invasive sub-populations. Tumorspheres are imaged as they spread on gridded micropatterns and analyzed by our semi-automated, open-source, Fiji macro suite that characterizes migration modes accurately. With SP2G, we could reveal intra-patient motility heterogeneity with molecular correlations to specific integrins and EMT markers. Thus, our system presents a versatile and potentially pan-cancer workflow to detect diverse invasive tumor sub-populations in patient-derived specimens and offers a valuable tool for therapeutic evaluations at the individual patient level. Teaser Cracking the inter and intra-patient diversity in Glioblastoma migration profiles
Traveling waves shape neural population dynamics enabling predictions and internal model updating

The brain generates predictions based on statistical regularities in our environment. However, it is unclear how predictions are optimized through iterative interactions with the environment. Because traveling waves (TWs) propagate across the cortex shaping neural excitability, they can carry information to serve predictive processing. Using human intracranial recordings, we show that anterior-to-posterior alpha TWs correlated with prediction strength. Learning about priors altered neural state space trajectories, and how much it altered correlated with trial-by-trial prediction strength. Learning involved mismatches between predictions and sensory evidence triggering alpha-phase resets in lateral temporal cortex, accompanied by stronger alpha phase-high gamma amplitude coupling and high-gamma power. The mismatch initiated posterior-to-anterior alpha TWs and change in the subsequent trial’s state space trajectory, facilitating model updating. Our findings suggest a vital role of alpha TWs carrying both predictions to sensory cortex and mismatch signals to frontal cortex for trial-by-trial fine-tuning of predictive models.
Detection and Removal of Hyper-synchronous Artifacts in Massively Parallel Spike Recordings

Current electrophysiology experiments often involve massively parallel recordings of neuronal activity using multi-electrode arrays. While researchers have been aware of artifacts arising from electric cross-talk between channels in setups for such recordings, systematic and quantitative assessment of the effects of those artifacts on the data quality has never been reported. Here we present, based on examination of electrophysiology recordings from multiple laboratories, that multi-electrode recordings of spiking activity commonly contain extremely precise (at the data sampling resolution) spike coincidences far above the chance level. We derive, through modeling of the electric cross-talk, a systematic relation between the amount of such hyper-synchronous events (HSEs) in channel pairs and the correlation between the raw signals of those channels in the multi-unit activity frequency range (250-7500 Hz). Based on that relation, we propose a method to identify and exclude specific channels to remove artifactual HSEs from the data. We further demonstrate that the artifactual HSEs can severely affect various types of analyses on spiking train data. Taken together, our results warn researchers to pay considerable attention to the presence of HSEs in spike train data and to make efforts to remove the artifacts from the data to avoid false results.
Phenotypic characterization of cryptic species in the fungal pathogen Histoplasma

Histoplasmosis is an endemic mycosis that often presents as a respiratory infection in immunocompromised patients. Hundreds of thousands of new infections are reported annually around the world. The etiological agent of the disease, Histoplasma, is a dimorphic fungus commonly found in the soil where it grows as mycelia. Humans can become infected by Histoplasma through inhalation of its spores (conidia) or mycelial particles. The fungi transitions into the yeast phase in the lungs at 37°C. Once in the lungs, yeast cells reside and proliferate inside alveolar macrophages. We have previously described that Histoplasma is composed of at least five cryptic species that differ genetically, and assigned new names to the lineages. Here we evaluated multiple phenotypic characteristics of 12 strains from five phylogenetic species of Histoplasma to identify phenotypic traits that differentiate between these species: H. capsulatum sensu stricto, H. ohiense, H. mississippiense, H. suramericanum, and an African lineage. We report diagnostic traits for two species. The other three species can be identified by a combination of traits. Our results suggest that 1) there are significant phenotypic differences among the cryptic species of Histoplasma, and 2) that those differences can be used to positively distinguish those species in a clinical setting and for further study of the evolution of this fungal pathogen. IMPORTANCE Identifying species boundaries is a critical component of evolutionary biology. Genome sequencing and the use of molecular markers have advanced our understanding of the evolutionary history of fungal pathogens, including Histoplasma, and have allowed for the identification of new species. This is especially important in organisms where morphological characteristics cannot be used for this purpose. In this study, we revise the taxonomic status of the four named species of the genus Histoplasma: H. capsulatum sensu stricto, H. ohiense, H. mississippiense, and H. suramericanum and propose the use of species-specific phenotypic traits to aid their identification when genome sequencing is not available. These results have implications not only for evolutionary study of Histoplasma, but also for clinicians, as the Histoplasma species could determine the outcome of disease and treatment needed.
Is ability-job fit important for work engagement? Evidence from the Irish civil service

ABSTRACT Few studies of work engagement have been undertaken within a civil service context. This study draws on data from an employee survey of the Irish civil service to explore the relationship between perceptions of ability-job fit and engagement and whether this relationship is mediated by meaningful work. It also examines whether the relationship between perceived ability-job fit and meaningful work is moderated by prosocial impact. The hypotheses for mediation are supported, but the findings for moderation suggest a tension between the ideals of doing public service work and those associated with making a difference to the lives of others.
EarSE

Speech enhancement is regarded as the key to the quality of digital communication and is gaining increasing attention in the research field of audio processing. In this paper, we present EarSE, the first robust, hands-free, multi-modal speech enhancement solution using commercial off-the-shelf headphones. The key idea of EarSE is a novel hardware setting---leveraging the form factor of headphones equipped with a boom microphone to establish a stable acoustic sensing field across the user's face. Furthermore, we designed a sensing methodology based on Frequency-Modulated Continuous-Wave, which is an ultrasonic modality sensitive to capture subtle facial articulatory gestures of users when speaking. Moreover, we design a fully attention-based deep neural network to self-adaptively solve the user diversity problem by introducing the Vision Transformer network. We enhance the collaboration between the speech and ultrasonic modalities using a multi-head attention mechanism and a Factorized Bilinear Pooling gate. Extensive experiments demonstrate that EarSE achieves remarkable performance as increasing SiSDR by 14.61 dB and reducing the word error rate of user speech recognition by 22.45--66.41% in real-world application. EarSE not only outperforms seven baselines by 38.0% in SiSNR, 12.4% in STOI, and 20.5% in PESQ on average but also maintains practicality.
A highly contiguous genome assembly for the Yellow Warbler (Setophaga petechia)

The Yellow Warbler (Setophaga petechia) is a small songbird in the New World Warbler family (Parulidae) that exhibits phenotypic and ecological differences across a widespread distribution and is important to California’s riparian habitat conservation. Here, we present a high-quality de novo genome assembly of a vouchered female Yellow Warbler from southern California. Using HiFi long-read and Omni-C proximity sequencing technologies, we generated a 1.22 Gb assembly including 687 scaffolds with a contig N50 of 6.80 Mb, scaffold N50 of 21.18 Mb, and a BUSCO completeness score of 96.0%. This highly contiguous genome assembly provides an essential resource for understanding the history of gene flow, divergence, and local adaptation and can inform conservation management of this charismatic bird species.
Do different types of VR influence pedestrian route choice behaviour? A comparison study of Desktop VR and HMD VR

Virtual Reality (VR) is a valuable tool for studying pedestrian behaviour in complex and realistic scenarios. However, it has remained unknown how different VR technology would influence pedestrian behaviour. This paper presents VR experiments that were conducted with 70 participants using a desktop VR or a HMD VR to perform four different wayfinding tasks in a multi-story building. Quantitative analysis of pedestrian behaviour data and user experience data were performed in order to investigate the impact of the technological differences between the two VR techniques. It was found that participants had better wayfinding task performance in the desktop group. However, the route and exit choice and user experience were overall similar between the two groups. The findings suggest that one could adopt more ‘simple’ VR technologies for studies featuring ‘simple’ wayfinding tasks.
Amyloid Morphology is Encoded in H-bonds and Electrostatics Interactions Ruling Protein Phase Separation

We report the mechanisms by which H-bonds and electrostatic interactions in ion- protein systems determine phase separation and amyloid formation. Using microscopy, small-angle X-ray scattering and atomistic molecular dynamics simulations, we found that anions interacting with insulin induced phase separation by neutralising the pro- tein charge and forming H-bonds between insulin molecules. The same interaction was responsible for an enhanced insulin conformational stability and resistance to oligomeri- sation. Under aggregation conditions, the anion-protein interaction translated into the activation of a coalescence process, leading to amyloid-like microparticles. This reaction is alternative to conformationally-driven pathways, which give rise to amyloid-like fibrils and core-shell structures, and occur instead in the absence of ion-protein binding. Our findings depict a scenario in which common interactions dictated both phase separation at low temperatures and the occurrence of heterogeneity in the amyloid morphology at high temperatures, similar to what has been reported for protein crystallisation.
USP50 suppresses alternative RecQ helicase use and deleterious DNA2 activity during replication

Mammalian DNA replication employs several RecQ DNA helicases to orchestrate the faithful duplication of genetic information. Helicase function is often coupled to the activity of specific nucleases, but how helicase and nuclease activities are co-directed is unclear. Here we identify the inactive ubiquitin-specific protease, USP50, as a ubiquitin-binding and chromatin-associated protein required for ongoing replication, fork restart, telomere maintenance and cellular survival during replicative stress. USP50 supports WRN:FEN1 at stalled replication forks, suppresses MUS81-dependent fork collapse and restricts double-strand DNA breaks at GC-rich sequences. Surprisingly we find that cells depleted for USP50 and recovering from a replication block exhibit increased DNA2 and RECQL4 foci and that the defects in ongoing replication, poor fork restart and increased fork collapse seen in these cells are mediated by DNA2, RECQL4 and RECQL5. These data define a novel ubiquitin-dependent pathway that promotes the balance of helicase: nuclease use at ongoing and stalled replication forks.
Effective assessment of CD4+ T cell Immunodominance patterns: impact of antigen processing and HLA restriction

Identifying T cell epitopes is essential for studying and potentially tuning immune responses to pathogens. The polymorphic nature of major histocompatibility complex of class II (MHCII)-genes, and the complexity of the antigen processing mechanisms hinders the effective prediction of immunodominant patterns in humans, specially at the population level. Here, we combined the output of a reconstituted antigen processing system and of in silico prediction tools for SARS-CoV-2 antigens considering a broad-population coverage DRB1* panel to gain insights on immunodominance patterns. The two methods complement each other, and the resulting model improves upon single positive predictive values (PPV) from each of them to explain known epitopes. This model was used to design a minimalistic peptide pool (59 peptides) matching the performance reported for large overlapping peptide pools (> 500 peptides). Furthermore, almost 70 % of the candidates (23 peptides) selected for a frequent HLA background (DRB1*03:01/*07:01) feature immunodominant responses ex vivo, validating our platform for accessing T cell epitopes at the population level. The analysis of the impact of processing constraints reveals distinct impact of proteolysis and solvent accessible surface area on epitope selection depending on the antigen. Thus, considering these properties for antigens in question should improve available epitope prediction tools.
Mechanism and regulation of cargo entry into the Commander recycling pathway

Commander is a multiprotein complex that orchestrates endosomal recycling of diverse integral cargo proteins and in humans is required for normal skeletal, brain, kidney, and cardiovascular development. While the structure of this complex has recently been described, the central question of how cargo proteins are selected for entry into the Commander recycling pathway remains unclear. Here using recombinant protein reconstitution and in silico predictions we identify the evolutionary conserved mechanism through which the unstructured carboxy-terminal tail of the integral protein adaptor sorting nexin-17 (SNX17) directly binds to the Retriever sub-complex of Commander. SNX17 adopts an autoinhibited conformation where its carboxy-terminal tail occupies the cargo binding groove. Competitive cargo binding overcomes this autoinhibition, promoting SNX17 endosomal residency and the release of the carboxy tail for Retriever association. Using molecular cell biology and high-resolution microscopy, we establish the central importance of SNX17-Retriever association in the handover of integrin and lipoprotein receptor cargoes into pre-existing endosomal retrieval sub-domains for entry into the recycling pathway. In describing the principal mechanism of cargo entry into the Commander recycling pathway we provide key insight into the function and regulation of this evolutionary conserved sorting complex.