Miracle-dz/UniFAR-FTUs huggingface dataset:
The Mayo Clinic Salivary Tissue-Organoid Biobanking: A Resource for Salivary Regeneration Research

The salivary gland (SG) is an essential organ that secretes saliva, which supports versatile oral function throughout life, and is maintained by elusive epithelial stem and progenitor cells (SGSPC). Unfortunately, aging, drugs, autoimmune disorders, and cancer treatments can lead to salivary dysfunction and associated health consequences. Despite many ongoing therapeutic efforts to mediate those conditions, investigating human SGSPC is challenging due to lack of standardized tissue collection, limited tissue access, and inadequate purification methods. Herein, we established a diverse and clinically annotated salivary regenerative biobanking at the Mayo Clinic, optimizing viable salivary cell isolation and clonal assays in both 2D and 3D-matrigel growth environments. Our analysis identified ductal epithelial cells in vitro enriched with SGSPC expressing the CD24/EpCAM/CD49f+ and PSMA-phenotype. We identified PSMA expression as a reliable SGSPC differentiation marker. Moreover, we identified progenitor cell types with shared phenotypes exhibiting three distinct clonal patterns of salivary differentiation in a 2D environment. Leveraging innovative label-free unbiased LC-MS/MS-based single-cell proteomics, we identified 819 proteins across 71 single cell proteome datasets from purified progenitor-enriched parotid gland (PG) and sub-mandibular gland (SMG) cultures. We identified distinctive co-expression of proteins, such as KRT1/5/13/14/15/17/23/76 and 79, exclusively observed in rare, scattered salivary ductal basal cells, indicating the potential de novo source of SGSPC. We also identified an entire class of peroxiredoxin peroxidases, enriched in PG than SMG, and attendant H2O2-dependent cell proliferation in vitro suggesting a potential role for PRDX-dependent floodgate oxidative signaling in salivary homeostasis. The distinctive clinical resources and research insights presented here offer a foundation for exploring personalized regenerative medicine.
The intracellular Ca2+ sensitivity of transmitter release from neocortical boutons

Synaptotagmin 1 (Syt1) and Syt2 are the main Ca2+ sensors triggering synchronous release in the brain. The Ca2+-sensitivity of Syt2-triggered release has been studied in detail. However, for Syt1, the dominating isoform in the neocortex, quantitative detail is lacking. We measured the Ca2+-dependency of Syt1-triggered release at layer 5 pyramidal neuron synapses by laser photolysis of caged Ca2+. Syt1-triggered release had high Ca2+ affinity and positive cooperativity (EC50, 20 μM; Hill coefficient, 3.57). It was steep in a dynamic range between ∼10 and ∼30 μM that was covered by action potential-evoked release. A kinetic model reveals significant differences to models of Syt2-triggered release. Our results suggest that Syt1 optimizes neocortical synapses for high reliability at moderate local Ca2+ elevations and for high plastic controllability.
Relationship between exclusions and final results in European Championships, World Championships and Olympic Games in men's handball 1982-2014

Situations of numerical inferiority in handball have influence on the development of the game since they produce an imbalance between players on both teams. The objective of this study is to establish the relationship between the exclusions against a team and the final result of the match during the 32 finals of European Championships, World Championships and Olympic Games between 1982 and 2014. The analysis is structured in four Grand Olympiads (GO) for eight years: 1982-1988, 1989-1996, 1997-2004 and 2005-2014. It was carried out the notational analysis of videotape recordings with SportCode Pro v.8. 5.2. software, leading to 185 situations of inferiority. The Kruskal-Wallis statistic test did not establish significant differences (p>0.05) between the number of exclusions per match and the victory of such match. On the other hand, the study establishes an increase of the number of exclusions from the half of the second time of the match during the last Grand Olympiad. This retrospective study can help trainers to give a tactical appropriate response to situations of inferiority as well as superiority for the benefit of the team.
Identifying HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements

Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-minute) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincaré SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearman’s correlations, and corrections for multiple comparisons. Only 8 files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.
Perinatal Brain Injury Triggers Niche-Specific Changes to Cellular Biogeography

Preterm infants are at risk for brain injury and neurodevelopmental impairment due, in part, to white matter injury following chronic hypoxia exposure. However, the precise molecular mechanisms by which perinatal hypoxia disrupts early neurodevelopment are poorly understood. Here, we constructed a brain-wide map of the regenerative response to newborn brain injury using high-resolution imaging-based spatial transcriptomics to analyze over 1.3 million cells in a mouse model of chronic neonatal hypoxia. Additionally, we developed a new method for inferring condition-associated differences in cell type spatial proximity, enabling the identification of niche-specific changes in cellular architecture. We observed hypoxia-associated changes in region-specific cell states, cell type composition, and spatial organization. Importantly, our analysis revealed mechanisms underlying reparative neurogenesis and gliogenesis, while also nominating pathways that may impede circuit rewiring following perinatal hypoxia. Altogether, our work provides a comprehensive description of the molecular response to newborn brain injury.
Heterogeneity and evolution of DNA mutation rates in microsatellite stable colorectal cancer

DNA sequence mutability in tumors with chromosomal instability is conventionally believed to remain uniform, constant, and low, based on the assumption that further mutational accrual in a context of marked aneuploidy is evolutionarily disadvantageous. However, this concept lacks robust experimental verification. We adapted the principles of mutation accumulation experiments, traditionally performed in lower organisms, to clonal populations of patient-derived tumoroids and empirically measured the spontaneous rates of accumulation of new DNA sequence variations in seven chromosomally unstable, microsatellite stable colorectal cancers (CRCs) and one microsatellite unstable CRC. Our findings revealed heterogeneous mutation rates (MRs) across different tumors, with variations in magnitude within microsatellite stable tumors as prominent as those distinguishing them from microsatellite unstable tumors. Moreover, comparative assessment of microsatellite stable primary tumors and matched synchronous metastases consistently documented a pattern of MR intensification during tumor progression. Therefore, wide-range diversity and progression-associated evolvability of DNA sequence mutational instability emerge as unforeseen hallmarks of microsatellite stable CRC, complementing karyotype alterations as selectable traits to increase genetic variation. One sentence summary Tumors with chromosomal instability accrue DNA sequence mutations at highly variable rates, which increase during metastatic progression.
Object motion representation in the macaque ventral stream – a gateway to understanding the brain’s intuitive physics engine

Effective interaction with moving objects and the ability to infer and predict their motion (a core component of “intuitive physics”) is essential for survival in the dynamic world. How does the primate visual system process such stimuli, enabling predictive capabilities for dynamic stimuli statistics like motion velocity and expected trajectories? In this study, we probed brain areas in the ventral visual pathway of rhesus macaques implicated in object recognition (areas V4 and inferior temporal, IT, cortex) to evaluate how they represent object motion speed and direction. We assessed the relationship between the distributed population activity in the ventral stream and two distinct object motion-based behaviors—one reliant on information directly available in videos (speed discrimination) and the other predicated on predictive motion estimates from videos (future event predictions). Further, employing microstimulation strategies, we confirm the causal, functional role of the IT cortex in these behaviors. Our results underscore the need to re-examine the traditional functional segregation of the primate visual cortices into “what” and “where” pathways and provide empirical constraints to model their interaction for a better circuit-level understanding of visual motion and intuitive physics.
EnnCore : End-to-End Conceptual Guarding of Neural Architectures

EnnCore addresses the fundamental security problem of guaranteeing safety, transparency, and robustness in neural-based architectures. Speciﬁcally, EnnCore aims at enabling system designers to specify essential conceptual/behavioral properties of neural-based systems, verify them, and thus safeguard the system against unpredictable behavior and attacks. In this respect, EnnCore will pioneer the dialogue be- tween contemporary explainable neural models and full-stack neural software veriﬁcation. This paper describes existing studies’ limitations, our research objectives, current achievements, and future trends towards this goal. In particular, we describe the development and evaluation of new methods, algorithms, and tools to achieve fully-veriﬁable intelligent sys- tems, which are explainable , whose correct behavior is guar-anteed, and robust against attacks. We also describe how En- nCore will be validated on two diverse and high-impact ap-plication scenarios: securing an AI system for (i) cancer di- agnosis and (ii) energy demand response.
Flexible communication between cell assemblies and ‘reader’ neurons

Cell assemblies are considered fundamental units of brain activity, underlying diverse functions ranging from perception to memory and decision-making. Cell assemblies have generally been studied in relation to specific stimuli or actions, but this approach does not readily extend to more abstract constructs. An alternative approach is to assess cell assemblies without making reference to external variables, and instead focus on internal brain processes — by defining assemblies by their endogenous ability to effectively elicit specific responses in downstream (‘reader’) neurons. However, this compelling idea currently lacks experimental support. Here, we provide evidence for assembly–reader communication. Reader activation was genuinely collective, functionally selective, yet flexible, implementing both pattern separation and completion. These processes occurred at the time scale of membrane integration, synaptic plasticity and gamma oscillations. Finally, assembly–reader couplings were selectively modified upon associative learning, indicating that they were plastic and could become bound to behaviorally relevant variables. These results support cell assemblies as an endogenous mechanism for brain function.
RAMEN Unveils Clinical Variable Networks for COVID-19 Severity and Long COVID Using Absorbing Random Walks and Genetic Algorithms

The COVID-19 pandemic has significantly altered global socioeconomic structures and individual lives. Understanding the disease mechanisms and facilitating diagnosis requires comprehending the complex interplay among clinical factors like demographics, symptoms, comorbidities, treatments, lab results, complications, and other metrics, and their relation to outcomes such as disease severity and long term outcomes (e.g., post-COVID-19 condition/long COVID). Conventional correlational methods struggle with indirect and directional connections among these factors, while standard graphical methods like Bayesian networks are computationally demanding for extensive clinical variables. In response, we introduced RAMEN, a methodology that integrates Genetic Algorithms with random walks for efficient Bayesian network inference, designed to map the intricate relationships among clinical variables. Applying RAMEN to the Biobanque québécoise de la COVID-19 (BQC19) dataset, we identified critical markers for long COVID and varying disease severity. The Bayesian Network, corroborated by existing literature and supported through multi-omics analyses, highlights significant clinical variables linked to COVID-19 outcomes. RAMEN’s ability to accurately map these connections contributes substantially to developing early and effective diagnostics for severe COVID-19 and long COVID.
Type‐Dependent Responses of Ice Cloud Properties to Aerosols From Satellite Retrievals

Aerosol‐cloud interactions represent one of the largest uncertainties in external forcings on our climate system. Compared with liquid clouds, the observational evidence for the aerosol impact on ice clouds is much more limited and shows conflicting results, partly because the distinct features of different ice cloud and aerosol types were seldom considered. Using 9‐year satellite retrievals, we find that, for convection‐generated (anvil) ice clouds, cloud optical thickness, cloud thickness, and cloud fraction increase with small‐to‐moderate aerosol loadings (<0.3 aerosol optical depth) and decrease with further aerosol increase. For in situ formed ice clouds, however, these cloud properties increase monotonically and more sharply with aerosol loadings. An increase in loading of smoke aerosols generally reduces cloud optical thickness of convection‐generated ice clouds, while the reverse is true for dust and anthropogenic pollution aerosols. These relationships between different cloud/aerosol types provide valuable constraints on the modeling assessment of aerosol‐ice cloud radiative forcing.
Agricultural pesticide use trends in Manitoba and 2,4-D fate in soil

In the last century, agricultural intensification on the Canadian prairies has resulted in increased pesticide use with the potential to expose non-target organisms to pesticides as a result of non-point source pollution. In order to minimize risk and implement programs and regulations that promote sustainable agricultural practices, information on the types of pesticides being used and their subsequent fate in soils is essential. In this study, pesticide use trends were summarized and Herbicide Risk Indicators (HRIs) were calculated for the 1996-2006 growing seasons; a time period in which genetically modified herbicide-tolerant (GMHT) crops were commercially adopted. This study also quantified the influence of soil moisture, temperature, slope position, and soil depth within the plough layer on 2,4-D [2,4-(dichlorophenoxy) acetic acid] fate in soil obtained from a cultivated undulating field in Southern Manitoba. Annual pesticide use varied slightly over the 11-year period, but overall, there were no significant increasing or decreasing temporal trends for herbicides, fungicides, or insecticides. Although the total mass of herbicides remained relatively consistent, there was a significant change in the types of herbicides applied associated with the increased adoption of GMHT-canola; the most significant trend being the increase of GLY, from 16% to 45% of the total herbicides used in 1996 to 2006, respectively. HRIs demonstrated that herbicides used in 2006, are on average, more soluble, but less persistent, less volatile, and less acutely toxic to mammals (inhalation and acceptable daily intake), aquatic invertebrates, fish and algae, than those applied in 1996. Although 2,4-D remains one of the top 10 herbicides applied to agricultural crops in Manitoba, there were no significant increasing or decreasing trends in 2,4-D use between 1996 and 2006. Results from the experimental studies revealed that 2,4-D mineralization half-lives (DT50) in soil varied from 3 days to 51 days with the total 2,4-D mineralization (MT) ranging from 5.8 to 50.9%, depending on soil moisture, temperature, slope position, and depth. Both DT50 and MT demonstrated a polynomial relationship with temperature, typical of a biological system with minimum, optimum, and maximum temperatures.
Deep adaptive dynamic programming for nonaffine nonlinear optimal control problem with state constraints

This paper presents a constrained deep adaptive dynamic programming (CDADP) algorithm to solve general nonlinear optimal control problems with known dynamics. Unlike previous ADP algorithms, it can directly deal with problems with state constraints. Both the policy and value function are approximated by deep neural networks (NNs), which directly map the system state to action and value function respectively without needing to use hand-crafted basis function. The proposed algorithm considers the state constraints by transforming the policy improvement process to a constrained optimization problem. Meanwhile, a trust region constraint is added to prevent excessive policy update. We first linearize this constrained optimization problem locally into a quadratically-constrained quadratic programming problem, and then obtain the optimal update of policy network parameters by solving its dual problem. We also propose a series of recovery rules to update the policy in case the primal problem is infeasible. In addition, parallel learners are employed to explore different state spaces and then stabilize and accelerate the learning speed. The vehicle control problem in path-tracking task is used to demonstrate the effectiveness of this proposed method.
The zone of influence of the subducting slab in the asthenospheric mantle

Due to the multidisciplinary nature of combined geodynamics and shear wave splitting studies, there is still much to be understood in terms of isolating the contributions from mantle dynamics to the shear wave splitting signal, even in a two‐dimensional (2‐D) mantle flow framework. This paper investigates the viscous flow, lattice preferred orientation (LPO) development, and predicted shear wave splitting for a suite of buoyancy‐driven subduction models using a non‐linear rheology to shed light on the nature of the slab‐driven asthenospheric flow and plate‐mantle coupling. The slab‐driven zone of influence in the mantle, LPO fabric, and resulting synthetic splitting are sensitive to slab strength and slab initial slab dip. The non‐linear viscosity formulations leads to dynamic reductions in asthenospheric viscosity extending over 600 km into the mantle wedge and over 300 km behind the trench, with peak flow velocities occurring in models with a weaker slab and moderate slab dip. The olivine LPO fabric in the asthenosphere generally increases in alignment strength with increased proximity to the slab but can be transient and spatially variable on small length scales. The results suggest that LPO formed during initial subduction may persist into the steady state subduction regime. Vertical flow fields in the asthenosphere can produce shear wave splitting variations with back azimuth that deviate from the predictions of uniform trench‐normal anisotropy, a result that bears on the interpretation of complexity in shear wave splitting observed in real subduction zones. Furthermore, the models demonstrate the corner flow paradigm should not be equated with a 2‐D subduction framework.
Biases with the Generalized Euclidean Distance measure in disparity analyses with high levels of missing data

The Generalized Euclidean Distance (GED) measure has been extensively used to conduct morphological disparity analyses based on palaeontological matrices of discrete characters. This is in part because some implementations allow the use of morphological matrices with high percentages of missing data without needing to prune taxa for a subsequent ordination of the data set. Previous studies have suggested that this way of using the GED may generate a bias in the resulting morphospace, but a detailed study of this possible effect has been lacking. Here, we test whether the percentage of missing data for a taxon artificially influences its position in the morphospace, and if missing data affects pre‐ and post‐ordination disparity measures. We find that this use of the GED creates a systematic bias, whereby taxa with higher percentages of missing data are placed closer to the centre of the morphospace than those with more complete scorings. This bias extends into pre‐ and post‐ordination calculations of disparity measures and can lead to erroneous interpretations of disparity patterns, especially if specimens present in a particular time interval or clade have distinct proportions of missing information. We suggest that this implementation of the GED should be used with caution, especially in cases with high percentages of missing data. Results recovered using an alternative distance measure, Maximum Observed Rescaled Distance (MORD), are more robust to missing data. As a consequence, we suggest that MORD is a more appropriate distance measure than GED when analysing data sets with high amounts of missing data.
Effect of shredded Colophospermum mopane wood inclusion as roughage on the performance of fattening Nguni cattle

A study was conducted to determine the effect of shredded Colophospermum (C.) mopane wood inclusion as roughage on the performance of fattening Nguni heifers. The heifers were randomly allocated to four diets with different shredded C. mopane wood inclusion levels (5, 8.5, 10 and 15%) in a completely randomized design. Shredded C. mopane wood inclusion level had effect (P less than 0.05) on intake and feed conversion ratio (FCR). Diet intakes per metabolic weight and FCR of Nguni heifers were optimised at different shredded C. mopane wood inclusion levels. However, shredded C. mopane wood inclusion levels did not affect (P greater than 0.05) diet in vitro digestibility, carcass weight, dressing percentage, meat pH, meat shear force values and meat colour intensities except for red colour intensity of rump steak. It was, thus, concluded that shredded C. mopane wood can be used as roughage without adversely affecting diet intake, FCR, and live weight of Nguni heifers.