Miracle-dz/UniFAR-FTUs huggingface dataset:
RNA-dependent proteome solubility maintenance in Escherichia coli lysates analysed by quantitative mass spectrometry: Proteomic characterization in terms of isoelectric point, structural disorder, functional hub, and chaperone network

ABSTRACT Protein aggregation, a consequence of misfolding and impaired proteostasis, can lead to cellular malfunctions such as various proteinopathies. The mechanisms protecting proteins from aggregation in complex cellular environments have long been investigated, often from a protein-centric viewpoint. However, our study provides insights into a crucial, yet overlooked actor: RNA. We found that depleting RNAs from Escherichia coli lysates induces global protein aggregation. Our quantitative mass spectrometry analysis identified over 900 statistically significant proteins from the Escherichia coli proteome whose solubility depends on RNAs. Proteome-wide characterization showed that the RNA dependency is particularly enriched among acidic proteins, intrinsically disordered proteins, and structural hub proteins. Moreover, we observed distinct differences in RNA-binding mode and Gene Ontology categories between RNA-dependent acidic and basic proteins. Notably, the solubility of key molecular chaperones [Trigger factor, DnaJ, and GroES] is largely dependent on RNAs, suggesting a yet-to-be-explored hierarchical relationship between RNA-based chaperone (termed as chaperna) and protein-based chaperones, both of which constitute the whole chaperone network. These findings provide new insights into the RNA-centric role in maintaining healthy proteome solubility in vivo, where proteins associate with a variety of RNAs, either stably or transiently.
Healthcare Resource Utilization and Costs in an At-Risk Population With Diabetic Retinopathy

Purpose Several investigators have suggested the cost-effectiveness of earlier screening, management of risk factors, and early treatment for diabetic retinopathy (DR). We aimed to evaluate the extent of health care utilization and cost of delayed care by insurance type in a vulnerable patient population. Methods A retrospective analysis of patients with DR was conducted using electronic medical record (EMR) data from January 2014 to December 2020 at Denver Health Medical Center, a safety net institution. Patients were classified by disease severity and insurance status. DR-specific costs were assessed via Current Procedural Terminology (CPT) codes over a 24-month follow-up period. Results Among the 313 patients, a higher proportion of non-English speaking patients were uninsured. Rates of proliferative DR at presentation differed across insurance groups (62% of uninsured, 42% of discount plan, and 33% of Medicare/Medicaid, P = 0.016). There was a significant difference in the total median cost between discount plan patients ($1258, interquartile range [IQR] = $0 – $5901) and both Medicare patients ($751, IQR = $0, $7148, P = 0.037) and Medicaid patients ($593, IQR = $0 – $6299, P = 0.025). Conclusions There were higher rates of proliferative DR at presentation among the uninsured and discount plan patients and greater total median cost in discount plan patients compared to Medicare or Medicaid. These findings prioritize mitigating gaps in insurance coverage and barriers to preventative care among vulnerable populations. Translational Relevance Advanced diabetic disease and increased downstream health care utilization and cost vary across insurance type, suggesting improved access to preventative care is needed in these specific at-risk populations.
Go with the flow: Impacts of high and low flow conditions on freshwater mussel assemblages and distribution

Understanding the drivers of distribution and assemblage composition of aquatic organisms is an important aspect of management and conservation, especially in freshwater systems that are inordinately facing increasing anthropogenic pressures and decreasing biodiversity. For stream organisms, habitat conditions during high flows may be impossible to measure in the field, but can be an important factor for their distribution, especially for less mobile organisms like freshwater mussels. Hence, the objective of this study was to use a two dimensional HEC-RAS model to simulate hydraulic conditions during high and baseline flows (flows approx. 10–600 x and 0.7 x median daily flows respectively) in a 20 km segment in the San Saba River, Texas in combination with existing mussel survey data from 200 sites (collected every 100m) to 1) examine whether hydraulic conditions differed between areas of increased mussel richness and diversity (referred to as hotspots) and other sites, and 2) understand how well site occupancy and species abundance could be explained by hydraulic conditions occurring under different flow conditions. The results showed that richness and diversity hotspots occurred in deeper areas with lower shear stress, stream power, and Froude number during both high and low flows. Occupancy could be predicted with 67–79% accuracy at the site scale and 60–70% accuracy at the mesohabitat scale (∼20 to 1200 m long). In addition, hydraulic conditions across flow scenarios explained up to 55% of variation in species abundances, but predictions were less successful for species often observed to occupy micro-scale flow refuges such as bedrock crevices. The results indicate that pools may serve as important refuge for all species during both high and low flow events, which may be relatively unique to bedrock-dominated systems. Understanding hydraulic conditions that occur at extreme flows such as these is important given that the frequency and magnitude of such events are increasing due to climate change.
Does bilevel optimization result in more competitive racing behavior?

Two-vehicle racing is natural example of a competitive dynamic game. As with most dynamic games, there are many ways in which the underlying information pattern can be structured, resulting in different equilibrium concepts. For racing in particular, the information pattern assumed plays a large impact in the type of behaviors that can emerge from the two interacting players. For example, blocking behavior is something that cannot emerge from static Nash play, but could presumably emerge from leader-follower play. In this work, we develop a novel model for competitive two-player vehicle racing, complete with simplified aerodynamic drag and drafting effects, as well as position-dependent collision-avoidance responsibility. We use this model to explore the impact that different information patterns have on the resulting competitiveness of the players. A solution approach for solving bilevel optimization problems is developed, which allows us to run a large-scale empirical study comparing how bilevel strategy generation (both as leader and as follower) compares with Nash equilibrium strategy generation as well as a single-player, constant velocity prediction baseline. Each of these choices are evaluated against different combinations of opponent strategy selection method. The somewhat surprising results of this study are discussed throughout.
The interplay of trophic interactions and game dynamics gives rise to life-history trade-offs, consistent personalities, and predator-prey and aggression power laws

By introducing a simple model of ecological interactions where individuals interact trophically, and through a game dynamic, I show that the dissipative flow of resources can derive evolution and lead to the emergence of a scale-invariant ecosystem exhibiting a wide range of mean and fluctuation scaling laws that govern trophic interactions and game dynamics. The eco-evolutionary approach suggests life history trade-offs are a natural consequence of ecological dynamics and, combined with the non-equilibrium dynamic, lead to the evolution of consistent personalities. Aggressiveness and personality consistency depend on trophic position, and predators evolve a higher aggressiveness and starker personality differences. Author summary Throughout the history of life, the flow of energy across ecosystems has contributed to the evolution of complex forms of life and strikingly universal patterns on a large scale. However, it is not clear what factors lead to such universal patterns and how they relate to evolution. Simple mathematical models suggest that the dissipative flow of resources through ecosystems leads to self-organization in a critical state with scale-invariant avalanches of activity. The scale-invariant structure of ecosystems results in a complex set of scaling laws governing the structure and dynamics of populations. The same non-equilibrium ecological dynamics derive evolution and account for the evolution of individuals’ behavioral differences and consistent personalities.
Time-varying Spatial Propagation of Brain Networks in fMRI data

Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro-scale level as measured by resting-state functional magnetic resonance imaging (rsfMRI). Previous studies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags. However, to our knowledge, no studies have examined spatial propagation patterns evolving with time across multiple overlapping 4D networks. Here, we propose a novel approach to study how dynamic states of the brain networks spatially propagate and evaluate whether these propagating states contain information relevant to mental illness. We implement a lagged windowed correlation approach to capture voxel-wise network-specific spatial propagation patterns in dynamic states. Results show systematic spatial state changes over time, which we confirmed are replicable across multiple scan sessions using human connectome project data. We observe networks varying in propagation speed; for example, the default mode network (DMN) propagates slowly and remains positively correlated to blood oxygenation level-dependent (BOLD) signal for 6-8 seconds, whereas the visual network propagates much quicker. We also show that summaries of network specific propagative patterns are linked to schizophrenia. More specifically, we find significant group differences in multiple dynamic parameters between schizophrenia patients and controls within four large-scale networks: default mode, temporal lobe, subcortical, and visual network. Individuals with schizophrenia spend more time in certain propagating states. In summary, this study introduces a promising general approach to exploring the spatial propagation in dynamic states of brain networks and their associated complexity and reveals novel insights into the neurobiology of schizophrenia.
Trial-by-trial detection of cognitive events in neural time-series

Measuring the time-course of neural events that make up cognitive processing is crucial to understand the relation between brain and behavior. To this aim, we formulated a method to discover a trial-wise sequence of events in multivariate neural signals such as electro- or magneto-encephalograpic (E/MEG) recordings. This sequence of events is assumed to be represented by multivariate patterns in neural time-series, with inter-event durations following probability distributions. By estimating event-specific multivariate patterns, and between-event duration distributions, the method allows to recover the by-trial onsets of brain responses. We demonstrate the properties and robustness of this hidden multivariate pattern (HMP) method through simulations, including robustness to low signal-to-noise ratio, as typically observed in EEG recordings. The applicability of HMP is illustrated using previously published data from a speed-accuracy trade-off task. We show how HMP provides, for any experiment or condition, an estimate of the number of events, the sensors contributing to each event (e.g. EEG scalp topography), and the durations between each event. Traditional exploration of tasks’ cognitive architectures can thus be enhanced by HMP estimates.
A Curated Cell Life Imaging Dataset of Immune-enriched Pancreatic Cancer Organoids with Pre-trained AI Models

Tumor organoids are three-dimensional in vitro models which can recapitulate the complex mutational landscape and tissue architecture observed in cancer patients, providing a realistic tumor microenvironment for testing novel therapies, including immunotherapies. A significant challenge in organoid research in oncology lies in developing efficient and reliable methods for segmenting organoid images, quantifying organoid growth, regression and response to treatments, as well as predicting the behavior of organoid systems. Up to now, a curated dataset of organoids co-cultured with immune cells is not available. To address this gap, we present a new public dataset, comprising both phase-contrast images of murine and patient-derived tumor organoids of one of the deadliest cancer types, the Pancreatic Ductal Adenocarcinoma, co-cultured with immune cells, and state-of-the-art algorithms for object detection and segmentation. Our dataset, OrganoIDNetData, encompassing 190 images with 33906 organoids, can be a potential common benchmark for different organoids segmentation protocols, moving beyond the current practice of training and testing these algorithms on isolated datasets.
Atlas for the Lateralized Visuospatial Attention Networks (ALANs): Insights from fMRI and Network Analyses

Hemispheric specialization is central to human evolution and fundamental to human cognitive abilities. While being a defining feature of functional brain architecture, hemispheric specialization is overlooked to derive brain parcellations. Alongside language, which is typically lateralized in the left hemisphere, visuospatial attention is set to be its counterpart in the opposite hemisphere. However, it remains uncertain to what extent the anatomical and functional underpinnings of lateralized visuospatial attention mirror those supporting language. Building on our previous work, which established a lateralized brain atlas for language, we propose a comprehensive cerebral lateralized atlas delineating the anatomo-functional bases of visuospatial attention, ALANs. Combining task and resting-state functional connectivity analyses, we identified 95 lateralized brain areas comprising five networks supporting visuospatial attention processes. Among them, we can find two large-scale networks: the ParietoFrontal and TemporoFrontal networks. We identify hubs playing a pivotal role in the intra-hemispheric interaction within visuospatial attentional networks. The rightward lateralized ParietoFrontal encompasses one hub, the inferior frontal sulcus, while the TemporoFrontal network encompasses two right hubs: the inferior frontal cortex (pars triangularis and the anterior insula) and the posterior part of the superior temporal sulcus. Together, these networks encompass the homotope of the language network from the left hemisphere. This atlas of visuospatial attention provides valuable insights for future investigations into the variability of visuospatial attention and hemispheric specialization research. Additionally, it facilitates more effective comparisons among different studies, thereby enhancing the robustness and reliability of research in the field of attention.
The cellular representation of temperature across the somatosensory thalamus

Although distinct thalamic nuclei encode sensory information for almost all sensory modalities, the existence of a thalamic representation of temperature with a role in thermal perception remains unclear. To address this, we performed high-density electrophysiological recordings across the entire forelimb somatosensory thalamus in awake mice, and identified an anterior and a posterior representation of temperature that spans three thalamic nuclei. We found that these parallel representations show fundamental differences in the cellular encoding of temperature which reflects their cortical output targets. While the anterior representation encodes cool only and the posterior both cool and warm; in both representations cool was more densely represented and showed shorter latency, more transient responses as compared to warm. Moreover, thalamic inactivation showed a major role in thermal perception. Our comprehensive dataset identifies the thalamus as a key structure in thermal processing and highlights a novel posterior pathway in the thalamic representation of warm and cool.
High-throughput nano-flow proteomics using a dual-column electrospray source

With current trends in proteomics, especially regarding clinical and low input (to single cell) samples, it is increasingly important to both maximize the throughput of the analysis and maintain as much sensitivity as possible. The new generation of mass spectrometers (MS) are taking a huge leap in sensitivity, allowing to analyze samples with shorter liquid chromatography (LC) methods while digging as deep in the proteome. However, the throughput can be doubled by implementing a dual column nano-LC-MS configuration. For this purpose, we used a dual-column setup with a two-outlet electrospray source, and compared it to a classic dual-column setup with a single-outlet source.
Individual differences reveal similarities in serial dependencies across perceptual tasks, but no relation to serial dependencies for oculomotor behavior

Serial dependence effects from one trial to the next have been observed across a wide range of perceptual tasks, as well as for oculomotor behavior. This opens up the question of whether the effects observed across all of these studies share underlying mechanisms. Here we measured the same group of observers across four different tasks, two perceptual (color judgments and orientation judgments) and two oculomotor (tracking of moving targets and the pupil light reflex). On the group level, we observed significant attractive serial dependence effects for all tasks, except the pupil response. The rare absence of a serial dependence effect for the reflex like pupil light response suggests that sequential effects require cortical processing or even higher-level cognition. In the following step, we leveraged reliable individual differences between observers in the other tasks to test whether there is a trait-like behavior of some observers showing stronger serial dependence effects across all of these tasks. We observed a significant relationship in the strength of serial dependence for the two perceptual experiments, but no relation between the perceptual tasks and oculomotor behavior. This indicates, differences in processing between perception and oculomotor control and the absence of a general trait-like behavior that affects all tasks similarly. However, the shared variance in the strength of serial dependence effects across different perceptual tasks indicates the importance of a similar positive decision bias present, that is reliably different between observers and consistent across different serial dependence tasks.
Cell-free expression and biochemical characterization of polysaccharide-synthesizing glycosyltransferases

Polysaccharides are a major class of natural polymers found abundantly across all major life forms and play a critical role as structural, metabolic, or functional components in biomolecular processes. Some polysaccharides like cellulose and hyaluronan are synthesized by membrane-bound family-2 glycosyltransferases (GTs). Despite the fact that the GT-2 family has the maximum number of deposited sequences, the biochemistry of GT-2 family enzymes is still poorly understood due to difficulties associated with GT membrane protein expression, purification, and reconstitution in lipid carriers. Here, we chose Populus tremula x tremuloides cellulose synthase 8 (PttCesA8) and Streptococcus equisimilis hyaluronan synthase (SeHas) as putative family-2-GTs to be expressed in a wheat-germ-based cell-free expression (CFE) system as proteoliposomes. The cell-free products were obtained as reconstituted liposomes directly from CFE reactions at high yields and short processing times compared to other approaches. GT enzymes expression was confirmed using SDS-PAGE and immunoblotting, and the integration of GTs in lipid layers was observed using cryogenic electron microscopy. Both GTs tested were catalytically active when incubated with their respective substrates and cofactors. The Michalis-Menten kinetic constants, Km for PttCesA8, was 295.8 µM, and SeHas was 321.51 µM (toward UDP N-Acetyl Glucosamine) and 207.88 µM (toward UDP Glucuronic Acid), respectively. UDP was found to actively inhibit both these GTs with apparent inhibition constants of 10.08 µM and 24.38 µM. Mutation of specific conserved residues in structure-deficit SeHas confirmed the importance of lysine-139, glutamine-248, and threonine-283 residues in hyaluronan biosynthesis. In summary, wheat-germ-based CFE can be used to express functionally active and liposome-reconstituted family-2 GTs at high yields with relative ease to enable classical enzymology assays and will also enable more detailed structural studies in the near future.
Modeling of landslide volume estimation

Abstract Mass displacement of materials such as landslide is considered among problematic phenomena in Baqi Basin located at southern slopes of Binaloud, Iran; since, it destroys agricultural lands and pastures and also increases deposits at the basin exit. Therefore, it is necessary to identify areas which are sensitive to landslide and estimate the significant volume. In the present study, in order to estimate the volume of landslide, information about depth and area of slides was collected; then, considering regression assumptions, a power regression model was given which was compared with 17 suggested models in various regions in different countries. The results showed that values of estimated mass obtained from the suggested model were consistent with observed data (P value= 0.000 and R = 0.692) and some of the existing relations which implies on efficiency of the suggested model. Also, relations that were created in small-area landslides were more suitable rather than the ones created in large-area landslides for using in Baqi Basin. According to the suggested relation, average depth value of landslides was estimated 3.314 meters in Baqi Basin which was close to the observed value, 4.609 m.
Android – Based Smart Nutrition Application for Children Nutritional Awareness

Malnutrition in children has emerged as a critical public health concern in the modern era, with significant implications for their long-term health and well-being. Lack of proper nutrition knowledge and awareness among parents often contributes to this issue. While prior research has created food identification systems to help people know the food nutrition, there is a glaring lack of applications expressly made to help parents understand their children's nutrition. To address this gap, we propose a smart nutrition application that aims to provide parents with essential information about the nutritional content of their children's food, thereby increasing their awareness and understanding of nutrition. The application's main feature utilizes an AI camera equipped with the You Only Look Once (YOLO) algorithm, enabling it to scan a meal and display detailed nutrient information. Furthermore, a UI-based and text-based ingredients calculator is provided to help parents calculate calories and food nutrition. Moreover, the article feature is intended to serve as a resource for parents to gain knowledge about children's nutrition. By leveraging TensorFlow, a machine learning framework, the proposed application achieves efficient and accurate food recognition with an overall accuracy rate of 90%.