julilili42/papers huggingface dataset:
Tracking spatial patterns and nocturnal arousal in an undisturbed natural population of the pulse-type weakly electric fishGymnotus omarorum
2024-07-02

SUMMARY Assessing animals’ locomotor and activity-rest patterns in natural populations is challenging. It requires individual identification and behavioral tracking in sometimes complex and inaccessible environments. Weakly electric fish are advantageous models for remote monitoring due to their continuous emission of electric signals (EODs). Gymnotus omarorum is a South American freshwater pulse-type weakly electric fish. Previous manual recordings of restrained individuals in the wild showed a spatial distribution compatible with territoriality and a nocturnal increase in EOD rate interpreted as arousal. This interdisciplinary study presents the development of low-cost amplifiers for remote EOD recordings and the refinement of tracking algorithms that provide individual recognition of Gymnotus omarorum in the wild. We describe natural daily spacing patterns of undisturbed individuals that are compatible with territoriality, although heterogeneous across sampling sites, and confirm that all resident fish showed a robust nocturnal increase of EOD rate likely associated with daily variations of water temperature. HIGHLIGHTS Successful remote individual tracking of wild pulse type weakly electric fish G. omarorum spacing patterns are compatible with known nocturnality and territoriality Residents keep their diurnal resting sites and move within small areas during the night The robust nocturnal electric arousal of residents is linked to water temperature peak
Lebertia litoralis sp. nov. a new water mite described on morphological and molecular characters (Acariformes, Hydrachnidia, Lebertiidae)
2024-07-02

An integrative taxonomic approach, combining new morphological data with the analysis of partial COI sequences (DNA-barcodes) is used to describe Lebertia litoralis Zawal & Szenejko sp. nov. from lake habitats in Poland and Norway. The obtained DNA-barcodes of Polish and Norwegian specimens differ only slightly (k = 4.142, Π = 0.006). Morphologically, the new species is most similar to Lebertia porosa Thor, 1900 and L. obscura Thor, 1900, from which it differs by a slenderer shape of the first leg and the position of the distoventral setae of p-4, inserted rather far away from the distal segment edge.http://zoobank.org/urn:lsid:zoobank.org:pub:27F6FFD0-CA98-4FAD-BC16-58FA654E36ED
New human footprints in the Middle Pleistocene of Doñana (Huelva, Spain)
2024-07-02

[EN] Research carried out in the Doñana coastline has revealed the existence of 78 new human footprints as several hundred footprints produced by artiodactyls, canids and likely rhinocerotids in an area of 6,000 m2 located on the El Coto beach in Matalascañas (Huelva). The age of this surface is 160.4 ± 9.0 ka, which places it in the Middle Pleistocene. This surface has so far yielded a total of 15 longitudinally complete footprints, which has made it possible to establish the biometric parameters, as well as the minimum number of individuals and their comparison with the footprints from the El Pichilín site located about 6 km NW of this point. Their study determined age classes, with two of them corresponding to adolescents or small-sized adult individuals and thirteen to a larger, adult individual. The minimum number of individuals represented in this association would be two. The footprints, whose length varies between 19.5 and 24.7 cm, could have been left by either an adolescent or a small-sized individual of 153.7-156.9 cm in stature. In turn, the footprints measuring 21.7 and 31.2 cm in length could have been left by an individual 168.2-179.6 cm tall, based on modern human comparative samples.
From Chalcogen Bonding to S–π Interactions in Hybrid Perovskite Photovoltaics
2024-07-03

Abstract The stability of hybrid organic–inorganic halide perovskite semiconductors remains a significant obstacle to their application in photovoltaics. To this end, the use of low‐dimensional (LD) perovskites, which incorporate hydrophobic organic moieties, provides an effective strategy to improve their stability, yet often at the expense of their performance. To address this limitation, supramolecular engineering of noncovalent interactions between organic and inorganic components has shown potential by relying on hydrogen bonding and conventional van der Waals interactions. Here, the capacity to access novel LD perovskite structures that uniquely assemble through unorthodox S‐mediated interactions is explored by incorporating benzothiadiazole‐based moieties. The formation of S‐mediated LD structures is demonstrated, including one‐dimensional (1D) and layered two‐dimensional (2D) perovskite phases assembled via chalcogen bonding and S–π interactions. This involved a combination of techniques, such as single crystal and thin film X‐ray diffraction, as well as solid‐state NMR spectroscopy, complemented by molecular dynamics simulations, density functional theory calculations, and optoelectronic characterization, revealing superior conductivities of S‐mediated LD perovskites. The resulting materials are applied in n‐i‐p and p‐i‐n perovskite solar cells, demonstrating enhancements in performance and operational stability that reveal a versatile supramolecular strategy in photovoltaics.
Evaluating regression and probabilistic methods for ECG-based electrolyte prediction
2024-07-03

Abstract Imbalances in electrolyte concentrations can have severe consequences, but accurate and accessible measurements could improve patient outcomes. The current measurement method based on blood tests is accurate but invasive and time-consuming and is often unavailable for example in remote locations or an ambulance setting. In this paper, we explore the use of deep neural networks (DNNs) for regression tasks to accurately predict continuous electrolyte concentrations from electrocardiograms (ECGs), a quick and widely adopted tool. We analyze our DNN models on a novel dataset of over 290,000 ECGs across four major electrolytes and compare their performance with traditional machine learning models. For improved understanding, we also study the full spectrum from continuous predictions to a binary classification of extreme concentration levels. Finally, we investigate probabilistic regression approaches and explore uncertainty estimates for enhanced clinical usefulness. Our results show that DNNs outperform traditional models but model performance varies significantly across different electrolytes. While discretization leads to good classification performance, it does not address the original problem of continuous concentration level prediction. Probabilistic regression has practical potential, but our uncertainty estimates are not perfectly calibrated. Our study is therefore a first step towards developing an accurate and reliable ECG-based method for electrolyte concentration level prediction—a method with high potential impact within multiple clinical scenarios.
Standard models of spatial vision mispredict edge sensitivity at low spatial frequencies
2024-07-03

One well-established characteristic of early visual processing is the contrast sensitivity function (CSF) which describes how sensitivity varies with the spatial frequency (SF) content of the visual input. The CSF prompted the development of a now standard model of spatial vision. It represents the visual input by activity in orientation- and SF selective channels which are nonlinearly recombined to predict a perceptual decision. The standard spatial vision model has been extensively tested with sinusoidal gratings at low contrast because their narrow SF spectra isolate the underlying SF selective mechanisms. It is less studied how well these mechanisms account for sensitivity to more behaviourally relevant stimuli such as sharp edges at high contrast (i.e. object boundaries) which abound in the natural environment and have broader SF spectra. Here, we probe sensitivity to edges (2-AFC, edge localization) in the presence of broadband and narrowband noises. We use Cornsweet luminance profiles with peak frequencies at 0.5, 3 and 9 cpd as edge stimuli. To test how well mechanisms underlying sinusoidal contrast sensitivity can account for edge sensitivity, we implement a single- and a multi-scale model building upon standard spatial vision model components. Both models account for most of the data but also systematically deviate in their predictions, particularly in the presence of pink noise and for the lowest SF edge. These deviations might indicate a transition from contrast- to luminance-based detection at low SFs. Alternatively, they might point to a missing component in current spatial vision models.
Effect of burst spikes on linear and nonlinear signal transmission in spiking neurons
2024-07-03

<title>Abstract</title> We study the impact of bursts on spike statistics and neural signal transmission.We propose a stochastic burst algorithm that is applied to a burst-free spike train and adds a random number of temporally-jittered burst spikes to each spike. This simple algorithm ignores any possible stimulus-dependence of bursting but allows to relate spectra and signal-transmission characteristics of burst-free and burst-endowed spike trains. By averaging over the various statistical ensembles, we find a frequency-dependent factor connecting the linear and also the second-order susceptibility of the spike trains with and without bursts. The relation between spectra involves an additional frequency-dependent offset. We confirm these relations for the (burst-free) spike trains of a stochastic integrate-and-fire neuron and identify frequency ranges in which the transmission is boosted or diminished by bursting. We then consider bursty spike trains of electroreceptor afferents of weakly electric fish and approach the role of burst spikes as follows. We compare the spectral statistics of the bursty spike train to (i) that of a spike train with burst spikes removed and to (ii) that of the spike train in (i) endowed by bursts according to our algorithm. Significant spectral features are explained by our signal-independent burst algorithm, e.g. the burst-induced boosting of the nonlinear response. A difference is seen in the information transfer for the original bursty spike train and our burst-endowed spike train. Our algorithm is thus helpful to identify different effects of bursting, in particular also those features only captured in a burst-generating dynamical neuron model.
Equilibrium points and stability around Psyche
2024-07-03

The asteroid Psyche is highlighted in the literature for being one of the largest ever observed,located in the main asteroid belt, with a diameter of approximately 232 km. Classified as typeM, it is speculated based on radar observations, that it is an exposed core of a primitive planet,composed mainly of iron-nickel. Although it was initially believed to have a high density,reaching values of 7.6 g/cm3, recent studies indicate lower values. Given such uncertainty, wesought not to use a specific density, but to explore this range of possible densities for Psyche.For each case, we compute the gravitational potential employing the polyhedral method, inwhich the irregular shape of the object is represented using several tetrahedra, maintaining aconstant density. From this potential, we determine the equilibrium points and their linearstabilities, looking for possible changes in behaviour according to the density. Since linearstability influences the time particles can remain close to the equilibrium point, we integrateorbits around Psyche and analyze the number and lifetime of particles that survive and collidewith the asteroid. The concentrated mass method (MASCONS) was employed and we also tookinto account the radiation pressure. This analysis is essential to assist in missions that aim toorbit the asteroid to collect information, such as the Psyche Mission launched by NASA in2023, which stands out for its objective of exploring a metallic object for the first time,unraveling the uncertainties surrounding this asteroid.
quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data
2024-07-04

Abstract The volume of public proteomics data is rapidly increasing, causing a computational challenge for large-scale reanalysis. Here, we introduce quantms ( https://quant,ms.org/ ), an open-source cloud-based pipeline for massively parallel proteomics data analysis. We used quantms to reanalyze 83 public ProteomeXchange datasets, comprising 29,354 instrument files from 13,132 human samples, to quantify 16,599 proteins based on 1.03 million unique peptides. quantms is based on standard file formats improving the reproducibility, submission and dissemination of the data to ProteomeXchange.
The usability of rollators as part of the human-centred quality of mobility devices: a systematic narrative literature review
2024-07-04

<b>Conclusion:</b> So far, no specific publications on the usability of rollators has been published, which made it necessary to summarize individual usability aspects using a deductive code catalogue. The results obtained, therefore, do not allow any generalized statement about the usability of rollators. However, this initiates discussions about the usability of rollators that should be studied in the future in a participatory and user-centred manner and, placing satisfaction more in the focus of usability engineering and evaluation of rollators.
The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction
2024-07-04

Abstract In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the intended function is captivating. However, a major challenge in this endeavor lies in accurately predicting the resulting protein structure. The exponential growth of protein databases has fueled the advancement of the field, while newly developed algorithms have pushed the boundaries of what was previously achievable in structure prediction. In particular, using deep learning methods instead of brute force approaches has emerged as a faster and more accurate strategy. These deep-learning techniques leverage the vast amount of data available in protein databases to extract meaningful patterns and predict protein structures with improved precision. In this article, we explore the recent developments in the field of protein structure prediction. We delve into the newly developed methods that leverage deep learning approaches, highlighting their significance and potential for advancing our understanding of protein design.
Expression and Prognostic Value of a Novel B7-H3 (CD276) Antibody in Acute Myeloid Leukemia
2024-07-04

Despite recent advances in immunophenotyping, the prognosis of acute myeloid leukemia (AML) is still mainly estimated using age and genetic markers. As the genetic heterogeneity of AML patients is high, flow cytometry-based classification with appropriate biomarkers can efficiently complement risk stratification and treatment selection. An increased expression of B7-H3 (CD276), an immune checkpoint protein, has been reported and associated with poor prognosis. However, the available data are limited and heterogeneous. Here, we used a novel, proprietary murine anti-B7-H3 8H8 antibody for the flow cytometric analysis of B7-H3 expression in AML blasts from 77 patients. Our antibody reliably detected substantial B7-H3 expression in 62.3% of AML patients. B7-H3 expression was higher in the monocytic French–American–British (FAB) M5 group and in intermediate and poor risk patients according to the European Leukemia Network. Using receiver operating characteristics (ROCs), we identified a specific fluorescence intensity cut-off of 4.45 to discriminate between B7-H3high and B7-H3low expression. High B7-H3 expression was associated with shorter overall survival (OS) and progression-free survival (PFS). In conclusion, we have developed a novel B7-H3 antibody that serves as a new tool for the detection of B7-H3 expression in AML and may help to facilitate risk stratification and treatment selection in AML patients.
Population-scale variability of the human UDP-glycosyltransferase gene family
2024-07-04

Human UDP-glycosyltransferases (UGTs) are responsible for the glycosylation of a wide variety of endogenous substrates and commonly prescribed drugs. Different genetic polymorphisms in UGT genes are implicated in interindividual differences in drug response and cancer risk. However, the genetic complexity beyond these variants has not been comprehensively assessed. We here leveraged whole-exome and whole-genome sequencing data from 141,456 unrelated individuals across 7 major human populations to provide a comprehensive profile of genetic variability across the human UGT gene family. Overall, 9666 exonic variants were observed, of which 98.9% were rare. To interpret the functional impact of UGT missense variants, we developed a gene family-specific variant effect predictor. This algorithm identified a total of 1208 deleterious variants, most of which were found in African and South Asian populations. Structural analysis corroborated the predicted effects for multiple variations in substrate binding sites. Combined, our analyses provide a systematic overview of UGT variability, which can yield insights into interindividual differences in phase 2 metabolism and facilitate the translation of sequencing data into personalized predictions of UGT substrate disposition.
On the analysis of two-time correlation functions: equilibrium versus non-equilibrium systems
2024-07-04

X-ray photon correlation spectroscopy (XPCS) is a powerful tool for the investigation of dynamics covering a broad range of timescales and length scales. The two-time correlation function (TTC) is commonly used to track non-equilibrium dynamical evolution in XPCS measurements, with subsequent extraction of one-time correlations. While the theoretical foundation for the quantitative analysis of TTCs is primarily established for equilibrium systems, where key parameters such as the diffusion coefficient remain constant, non-equilibrium systems pose a unique challenge. In such systems, different projections (`cuts') of the TTC may lead to divergent results if the underlying fundamental parameters themselves are subject to temporal variations. This article explores widely used approaches for TTC calculations and common methods for extracting relevant information from correlation functions, particularly in the light of comparing dynamics in equilibrium and non-equilibrium systems.
The role of adipose tissue dysfunction in hepatic insulin resistance and T2D
2024-07-05

The root cause of type 2 diabetes (T2D) is insulin resistance (IR), defined by the failure of cells to respond to circulating insulin to maintain lipid and glucose homeostasis. While the causes of whole-body insulin resistance are multifactorial, a major contributing factor is dysregulation of liver and adipose tissue function. Adipose dysfunction, particularly adipose tissue-IR (adipo-IR), plays a crucial role in the development of hepatic insulin resistance and the progression of metabolic dysfunction-associated steatotic liver disease (MASLD) in the context of T2D. In this review, we will focus on molecular mechanisms of hepatic insulin resistance and its association with adipose tissue function. A deeper understanding of the pathophysiological mechanisms of the transition from a healthy state to insulin resistance, impaired glucose tolerance, and T2D may enable us to prevent and intervene in the progression to T2D.
A 10-Year Longitudinal Study of Brain Cortical Thickness in People with First-Episode Psychosis Using Normative Models
2024-07-05

Abstract Background Clinical forecasting models have potential to optimize treatment and improve outcomes in psychosis, but predicting long-term outcomes is challenging and long-term follow-up data are scarce. In this 10-year longitudinal study, we aimed to characterize the temporal evolution of cortical correlates of psychosis and their associations with symptoms. Design Structural magnetic resonance imaging (MRI) from people with first-episode psychosis and controls (n = 79 and 218) were obtained at enrollment, after 12 months (n = 67 and 197), and 10 years (n = 23 and 77), within the Thematically Organized Psychosis (TOP) study. Normative models for cortical thickness estimated on public MRI datasets (n = 42 983) were applied to TOP data to obtain deviation scores for each region and timepoint. Positive and Negative Syndrome Scale (PANSS) scores were acquired at each timepoint along with registry data. Linear mixed effects models assessed effects of diagnosis, time, and their interactions on cortical deviations plus associations with symptoms. Results LMEs revealed conditional main effects of diagnosis and time × diagnosis interactions in a distributed cortical network, where negative deviations in patients attenuate over time. In patients, symptoms also attenuate over time. LMEs revealed effects of anterior cingulate on PANSS total, and insular and orbitofrontal regions on PANSS negative scores. Conclusions This long-term longitudinal study revealed a distributed pattern of cortical differences which attenuated over time together with a reduction in symptoms. These findings are not in line with a simple neurodegenerative account of schizophrenia, and deviations from normative models offer a promising avenue to develop biomarkers to track clinical trajectories over time.
MRI-Based Assessment of Risk for Stroke in Moyamoya Angiopathy (MARS-MMA): An MRI-Based Scoring System for the Severity of Moyamoya Angiopathy
2024-07-05

Before revascularization, moyamoya patients require hemodynamic evaluation. In this study, we evaluated the scoring system Prior Infarcts, Reactivity and Angiography in Moyamoya Disease (PIRAMID). We also devised a new scoring system, MRI-Based Assessment of Risk for Stroke in Moyamoya Angiopathy (MARS-MMA), and compared the scoring systems with respect to the capability to predict impaired [15O]water PET cerebral perfusion reserve capacity (CPR). We evaluated 69 MRI, 69 DSA and 38 [15O]water PET data sets. The PIRAMID system was validated by ROC curve analysis with neurological symptomatology as a dependent variable. The components of the MARS-MMA system and their weightings were determined by binary logistic regression analysis. The comparison of PIRAMID and MARS-MMA was performed by ROC curve analysis. The PIRAMID score correlated well with the symptomatology (AUC = 0.784). The MARS-MMA system, including impaired breath-hold-fMRI, the presence of the Ivy sign and arterial wall contrast enhancement, correlated slightly better with CPR impairment than the PIRAMID system (AUC = 0.859 vs. 0.827, Akaike information criterion 140 vs. 146). For simplified clinical use, we determined three MARS-MMA grades without loss of diagnostic performance (AUC = 0.855). The entirely MRI-based MARS-MMA scoring system might be a promising tool to predict the risk of stroke.