bioRxiv @biorxivpreprint@qoto.org

Hippocampal place cells in bats flying in a 200m tunnel have been shown to be active at multiple locations, with considerable variability in place field size and peak rate. We ask whether such disorderly representation of one's own position in a large environment could be stored in memory through Hebbian plasticity, and be later retrieved from a partial cue. Simulating an autoassociative network in which similarly variable place fields are encoded with a covariance rule, we find that it may serve spatial memory only within a certain variability range, in particular of field width. The working range is flanked by two dysfunctional regions, accessed through apparent phase transitions. For a large network, phase boundaries can be estimated analytically to depend only on the number of fields per cell in one case, and to be a pure number in the other, implying a maximal size of the environment that can be stored in memory. ### Competing Interest Statement The authors have declared no competing interest.

The brain constantly uses prior knowledge of the statistics of its environment to shape perception. These statistics are often implicit (not directly observable) and gradually learned from observation; but they can also be explicitly communicated to the observer, especially in humans. In value-based decision-making, these priors are treated differently depending on their implicit or explicit origin creating the "experience-description gap". Here, we show that the same distinction also applies to perception. We created a pair of categorization tasks with implicit and explicit priors respectively, and manipulated the strength of priors and sensory likelihood within the same human subjects. Perceptual decisions were influenced by priors in both tasks, and subjects updated their priors in the implicit task as the true statistics changed. Using Bayesian models of learning and perception, we found that the weight of the sensory likelihood in perceptual decisions was highly correlated across subjects between tasks, and slightly stronger in the implicit task. By contrast, the weight of priors was much less correlated across tasks, and it increased markedly from the explicit task to the implicit task. The same conclusion holds when using the subjects' reported priors. Model comparison also showed that different computations underpinned perceptual decisions depending on the origin of the priors. Taken together, those results support a dissociation in perceptual inference between the use of implicit and explicit priors. This conclusion could resolve conflicting results generated by the indiscriminate use of implicit and explicit priors when studying perception in healthy subjects and patients. ### Competing Interest Statement The authors have declared no competing interest.

Relatively little is known about the way vision is use to guide locomotion in the natural world. What visual features are used to choose paths in natural complex terrain? How do walkers trade off different costs such as getting to the goal, minimizing energy, and satisfying stability constraints? To answer these questions, it is necessary to monitor not only the eyes and the body, but also to represent the three dimensional structure of the terrain. We used photogrammetry techniques to do this, and found substantial regularities in the choice of paths. Walkers avoid paths that involve changes in height and choose more circuitous and flatter paths. This stable tradeoff is related to the walker's leg length and reflects both energetic and stability constraints. Gaze data and path choices suggest that subjects take into account the terrain approximately 5 steps ahead, and so are planning routes as well as particular foot plants. Such planning ahead allows the minimization of energetic costs. Thus locomotion in natural environments is controlled by decision mechanisms that attempt to optimize for multiple factors in the context of well-calibrated sensory and motor internal models ### Competing Interest Statement The authors have declared no competing interest.

One view of the neocortical architecture is that every region functions based on a universal computational principle. Contrary to this, we postulated that each cortical region has its own specific algorithm and functional properties. This idea led us to hypothesize that unique temporal patterns should be associated with each region, with the functional commonalities and variances among regions reflecting in the temporal structure of their neural signals. To investigate these hypotheses, we employed deep learning to predict electrodes locations in the macaque brain using single channel ECoG signals. To do this, we first divided the brain into seven regions based on anatomical landmarks, and trained a deep learning model to predict the electrode location from the ECoG signals. Remarkably, the model achieved an average accuracy of 33.6%, significantly above the chance level of 14.3%. All seven regions exhibited above-chance prediction accuracy. The feature vectors of models identified two main clusters: one including higher visual areas and temporal cortex, and another encompassing the remaining other regions. These results bolster the argument for unique regional dynamics within the cortex, highlighting the diverse functional specializations present across cortical areas. ### Competing Interest Statement The authors have declared no competing interest.

Since environments are constantly in flux, the brain's ability to identify novel stimuli that fall outside its own internal representation of the world is crucial for an organism's survival. Within the mammalian neocortex, inhibitory microcircuits are proposed to regulate activity in an experience-dependent manner and different inhibitory neuron subtypes exhibit distinct novelty responses. Discerning the function of diverse neural circuits and their modulation by experience can be daunting unless one has a biologically plausible mechanism to detect and learn from novel experiences that is both understandable and flexible. Here we introduce a learning mechanism, familiarity modulated synapses (FMSs), through which a network response that encodes novelty emerges from unsupervised synaptic modifications depending only on the presynaptic or both the pre- and postsynaptic activity. FMSs stand apart from other familiarity mechanisms in their simplicity: they operate under continual learning, do not require specialized architecture, and can distinguish novelty rapidly without requiring feedback. Implementing FMSs within a model of a visual cortical circuit that includes multiple inhibitory populations, we simultaneously reproduce three distinct novelty effects recently observed in experimental data from visual cortical circuits in mice: absolute, contextual, and omission novelty. Additionally, our model results in a set of diverse physiological responses across cell subpopulations, allowing us to analyze how their connectivity and synaptic dynamics influences their distinct behavior, leading to predictions that can be tested in experiment. Altogether, our findings demonstrate how experimentally-constrained cortical circuit structure can give rise to qualitatively distinct novelty responses using simple plasticity mechanisms. The flexibility of FMSs opens the door to computationally and theoretically investigating how distinct synapse modulations can lead to a variety of experience-dependent responses in a simple, understandable, and biologically plausible setup. ### Competing Interest Statement The authors have declared no competing interest.

Ascorbate plays an indispensable role in plants, functioning as both an antioxidant and a cellular redox buffer. It is widely acknowledged that the ascorbate biosynthesis in the photosynthetic tissues of land plants is governed by light-mediated regulation of the D-mannose/L-galactose (D-Man/L-Gal) pathway. At the core of this light-dependent regulation lies the VTC2 gene, encoding the rate-limiting enzyme GDP-L-Gal phosphorylase. The VTC2 expression is regulated by signals via the photosynthetic electron transport system. In this study, we directed our attention to the liverwort Marchantia polymorpha, representing one of the basal land plants, enabling us to conduct an in-depth analysis of its ascorbate biosynthesis. The M. polymorpha genome harbors a solitary gene for each enzyme involved in the D-Man/L-Gal pathway, including VTC2, along with three lactonase orthologs, which may be involved in the alternative ascorbate biosynthesis pathway. Through supplementation experiments with potential precursors, we observed that only L-Gal exhibited effectiveness in ascorbate biosynthesis. Furthermore, the generation of VTC2-deficient mutants through genome editing unveiled the inability of thallus regeneration in the absence of L-Gal supplementation, thereby revealing the importance of the D-Man/L-Gal pathway in ascorbate biosynthesis within M. polymorpha. Interestingly, gene expression analyses unveiled a distinct characteristic of M. polymorpha, where none of the genes associated with the D-Man/L-Gal pathway, including VTC2, showed upregulation in response to light, unlike other known land plants. This study sheds light on the exceptional nature of M. polymorpha as a land plant that has evolved distinctive mechanisms concerning ascorbate biosynthesis and its regulation. ### Competing Interest Statement The authors have declared no competing interest.

The advent of tyrosine kinase inhibitors (TKIs) as treatment of chronic myeloid leukemia (CML) is a paradigm in molecularly targeted cancer therapy. Nonetheless, TKI insensitive leukemia stem cells (LSCs) persist in most patients even after years of treatment. The sustained presence, heterogeneity and evolvability of LSCs are imperative for disease progression as well as recurrence during treatment-free remission (TFR). However, dynamic changes among LSC sub-populations upon TKI therapy impede their measurement and targeting. Here, we used cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to generate high-resolution single cell multiomics maps from CML patients at diagnosis, retrospectively stratified by BCR::ABL1IS (%) following 12 months of TKI therapy as per European LeukemiaNet (ELN) recommendations. Simultaneous measurement of global gene expression profiles together with >40 surface markers from the same cells revealed that each patient harbored a unique composition of stem and progenitor cells at diagnosis demonstrating that cellular heterogeneity is a hallmark of CML. The patients with treatment failure after 12 months of therapy had markedly higher abundance of molecularly defined primitive cells at diagnosis compared to the optimal responders. Furthermore, deconvolution of an independent dataset of CML patient-derived bulk transcriptomes (n=59) into constituent cell populations showed that the proportion of primitive cells versus lineage primed sub-populations significantly connected with the TKI-treatment outcome. The multiomic feature landscape enabled visualization of the primitive fraction as a heterogenous mixture of molecularly distinct Lin-CD34+CD38-/low BCR::ABL1+ LSCs and BCR::ABL1- hematopoietic stem cells (HSCs) in variable ratio across patients and guided their prospective isolation by a combination of CD26 and CD35 cell surface markers. We for the first time show that BCR::ABL1+ LSCs and BCR::ABL1- HSCs can be distinctly separated as CD26+CD35- and CD26-CD35+ respectively. In addition, we found the relative proportion of CD26-CD35+ HSCs to be higher in optimal responders when compared to treatment failures, at diagnosis as well as following 3 months of TKI therapy, and that the LSC/HSC ratio was increased in patients with prospective treatment failure. Collectively, the patient-specific cellular heterogeneity multiomics maps build a framework towards understanding therapy response and adapting treatment by devising strategies that either extinguish TKI-insensitive LSCs or engage the immune effectors to suppress the residual leukemogenic cells. ### Competing Interest Statement GK and PD are board members and have equity in Nygen Analytics AB, JR reports receiving honoraria and research funding from Novartis and Bristol-Myers Squibb (BMS) and honoraria from Ariad. HHjH has received honoraria from Pfizer, Novartis, BMS, and Incyte. SM has received honoraria and research funding from BMS, and research funding from Novartis, Janpix, and honoraria from Dren Bio. Sample collection from patients in the BosuPeg and BFORE clinical trials was supported by Pfizer investigator grant. Other honoraria, and research funds were for projects unrelated to this study. The remaining authors declare no conflicts of interest.

The process of osmosis, a fundamental phenomenon in life, drives water through a semi-permeable membrane in response to a solute concentration gradient across this membrane. In vitro, osmotic shocks are often used to drive shape changes in lipid vesicles, for instance, to study fission events in the context of artificial cells. While experimental techniques provide a macroscopic picture of large-scale membrane remodeling processes, molecular dynamics (MD) simulations are a powerful tool to study membrane deformations at the molecular level. However, simulating an osmotic shock is a time-consuming process due to the slow water diffusion across the membrane, making it practically impossible to examine its effects in classic MD simulations. In this paper, we present Shocker, a Python-based MD tool for simulating the effects of an osmotic shock by selecting and relocating water particles across a membrane over the course of several pumping cycles. Although this method is primarily aimed at efficiently simulating volume changes of vesicles it can handle membrane tubes and double bilayer systems as well. Additionally, Shocker is force field independent and compatible with both coarse-grained and all-atom systems. We demonstrate that our tool is applicable to simulate both hypertonic and hypotonic osmotic shocks for a range of vesicular and bilamellar setups, including complex multi-component systems containing membrane proteins or crowded internal solutions. ### Competing Interest Statement The authors have declared no competing interest.

Small interfering RNAs (siRNAs) exemplify the promise of genetic medicine in the discovery of novel therapeutic modalities. Their ability to selectively suppress gene expression makes them ideal candidates for development as oligonucleotide pharmaceuticals. Recent advancements in machine learning (ML) have facilitated unmodified siRNA design and efficacy prediction, but a model trained to predict the silencing activity of siRNAs with diverse chemical modification patterns has yet to be published, despite the importance of such chemical modifications in designing siRNAs with the potential to advance to the clinic. This study presents the first application of ML to classify efficient chemically modified siRNAs from sequence and chemical modification patterns alone. Three algorithms are evaluated at three classification thresholds and compared according to sensitivity, specificity, consistency of feature weights with empirical knowledge, and performance on an external validation dataset. Finally, possible directions for future research are proposed. ### Competing Interest Statement The authors have declared no competing interest.

Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish ( Danio rerio ), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo . Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species. ### Competing Interest Statement The authors declare the following financial and personal relationships that may be considered as potential competing interests: A. Begue, T. J. J. Doman, C. Dugo, J. Efromson, M. Harfouche, P. Reamey, and V. Saliu have a financial interest in Ramona Optics Inc.

Single-cell RNA sequencing (scRNA-seq) technology has been widely used to study the differences in gene expression at the single cell level, providing insights into the research of cell development, differentiation, and functional heterogeneity. Various pipelines and workflows of scRNA-seq analysis have been developed but few considered multi-timepoint data specifically. In this study, we develop CASi, a comprehensive framework for analyzing multiple timepoints' scRNA-seq data, which provides users with: (1) cross-timepoint cell annotation, (2) detection of potentially novel cell types emerged over time, (3) visualization of cell population evolution, and (4) identification of temporal differentially expressed genes (tDEGs). Through comprehensive simulation studies and applications to a real multi-timepoint single cell dataset, we demonstrate the robust and favorable performance of the proposal versus existing methods serving similar purposes. ### Competing Interest Statement The authors have declared no competing interest.

As a renewable, easily accessible, human-derived in vitro model, human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) are a promising tool for studying arrhythmia-related factors, including cardiotoxicity and congenital proarrhythmia risks. An oft-mentioned limitation of iPSC-CMs is the abundant cell-to-cell variability in recordings of their electrical activity. Here, we develop a new method, rapid ionic current phenotyping (RICP), that utilizes a short (10 s) voltage clamp protocol to quantify cell-to-cell heterogeneity in key ionic currents. We correlate these ionic current dynamics to action potential recordings from the same cells and produce mechanistic insights into cellular heterogeneity. We present evidence that the L-type calcium current is the main determinant of upstroke velocity, rapid delayed rectifier K+ current is the main determinant of the maximal diastolic potential, and an outward current in the excitable range of slow delayed rectifier K+ is the main determinant of action potential duration. We measure an unidentified outward current in several cells at 6 mV that is not recapitulated by iPSC-CM mathematical models but contributes to determining action potential duration. In this way, our study both quantifies cell-to-cell variability in membrane potential and ionic currents, and demonstrates how the ionic current variability gives rise to action potential heterogeneity. Based on these results, we argue that iPSC-CM heterogeneity should not be viewed simply as a problem to be solved but as a model system to understand the mechanistic underpinnings of cellular variability. ### Competing Interest Statement The authors have declared no competing interest.

Objective. Spiking neural networks (SNNs) are powerful tools that are well suited for brain machine interfaces (BMI) due to their similarity to biological neural systems and computational efficiency. They have shown comparable accuracy to state-of-the-art methods, but current training methods require large amounts of memory, and they cannot be trained on a continuous input stream without pausing periodically to perform backpropagation. An ideal BMI should be capable training continuously without interruption to minimize disruption to the user and adapt to changing neural environments. Approach. We propose a continuous SNN weight update algorithm that can be trained to perform regression learning with no need for storing past spiking events in memory. As a result, the amount of memory needed for training is constant regardless of the input duration. We evaluate the accuracy of the network on recordings of neural data taken from the premotor cortex of a primate performing reaching tasks. Additionally, we evaluate the SNN in a simulated closed loop environment and observe its ability to adapt to sudden changes in the input neural structure. Main results. The continuous learning SNN achieves the same peak correlation (ρ = 0.7) as existing SNN training methods when trained offline on real neural data while reducing the total memory usage by 92%. Additionally, it matches state-of-the-art accuracy in a closed loop environment, demonstrates adaptability when subjected to multiple types of neural input disruptions, and is capable of being trained online without any prior offline training. Significance. This work presents a neural decoding algorithm that can be trained rapidly in a closed loop setting. The algorithm increases the speed of acclimating a new user to the system and also can adapt to sudden changes in neural behavior with minimal disruption to the user. ### Competing Interest Statement The authors have declared no competing interest.

Extracellular vesicles (EVs) have recently emerged as pivotal mediators of intercellular communication, influencing disease progression in liver failure. EVs can carry diverse molecules, including proteins, RNAs, and linear double-stranded DNAs. However, the presence of extrachromosomal circular eccDNA (eccDNA) within EVs, as well as its potential relationship with the development of liver failure, remains to be definitively understood. In this study, we isolated and investigated eccDNA from plasma EVs of both liver failure patients (LFEVs-eccDNA) and healthy control individuals (HCEVs-eccDNA), comparing their characteristics between the two groups and conducting eccDNA functional assays. The findings demonstrated that LFEVs-eccDNA exhibited increased abundance, shorter length, and carried more coding genes, transposon genes, and cis-regulatory elements. Furthermore, LFEVs carried eccDNA (LFEVs-eccDNA) containing more segments of liver-specific expression genes. Additionally, through comparative analysis of eccDNAs with identical start-end sites between the two groups, we identified the over-represented eccDNAs in LFEVs, including eccZMIZ1-AS1 and eccZMYM6. Functional analysis through artificial eccDNA transfection and RNA-seq in HepG2 cells revealed that the introduction of synthetic eccZMIZ1-AS1 significantly activated the PI3K-Akt and HIF-1 signaling pathways, while eccZMYM6 had no influence on the RNA profile. Moreover, eccZMIZ1-AS1 prominently promoted the cell response to hypoxia, regulated lipid metabolism, and was related to vesicle formation. Together, our study revealed aberrant eccDNA hallmarks in plasma EVs of liver failure patients and suggested that over-represented LFEVs-eccDNA may exacerbate liver damage by disrupting the transcriptome of hepatocytes. It provides a potential noninvasive biomarker for diagnosing and monitoring liver failure. Moreover, targeting the LFEVs-eccDNA may present promising therapeutic strategies for treating liver failure. ### Competing Interest Statement The authors have declared no competing interest.

Warming is expected to increase abundances of wolf spider, the top predator in soil communities in the Arctic, but we have little understanding on how increased wolf spider density under warmer conditions affects soil microbial structure through trophic cascades. We tested the effects of wolf spider density and warming on bacterial and fungal community structure in litter through a fully factorial mesocosm experiment in Arctic tundra over two summers. Replicated litter bags were deployed at the soil surface and underground in the organic soil profile and collected at 2- and 14-month incubation. The litter samples were analyzed for community structure of bacteria and fungi and mass weight loss. After 2-month incubation, bacterial and fungal community compositions were already structured interactively by the spider density and warming treatments. Such interaction effect was also found in litter microbial community structure as well as litter mass loss rates after 14-month incubation. Our results show that wolf spiders have indirect, cascading effects on microbial community structure but that warming can alter these effects. The non-linear responses of microbial communities and litter decomposition to warming and increased spider density cast uncertainty in predicting structure and function of Arctic terrestrial ecosystem under warmer conditions in the future.

Chronic hyperinsulinemia and insulin resistance are prequels to type II diabetes mellitus (T2D), a disease that affects over 10% of Americans and is closely associated with obesity, as nearly 90% of diabetic patients are overweight or obese. Chronic excess nutrient exposure leads to glucolipotoxicity in pancreatic β-cells, which is characterized by a left shift in glucose concentration-dependent insulin secretion. Previous studies from our laboratory have demonstrated that the Bromodomain and Extra-Terminal (BET) protein inhibitor JQ1 (400 nM) increases fatty acid (FA) oxidation in clonal pancreatic β-cells (INS-1), leading to a partial reversal of glucolipotoxicity. The present research investigates the effect of JQ1 on the glucose sensitivity of INS-1 cells under hyperglycemic conditions. INS-1 cells were pre-treated with dimethyl sulfoxide-based 400 nM JQ1 for 3 days and cultured for 5 days in RPMI 1640 media (11 mM glucose). Subsequently, INS-1 cells were pre-incubated in a modified Krebs-Henseleit buffer (1 mM glucose) before adding test solutions (1-12 mM glucose). Samples for insulin release and cellular content were measured using a Homogeneous Time-Resolved Fluorescence insulin assay kit. Insulin secretion from INS-1 cells cultured in 11 mM glucose was maximally stimulated at 4 mM glucose. Treatment with JQ1 for one day right-shifted maximal glucose-stimulated insulin secretion (GSIS) to 6 mM glucose compared to control but did not prevent stimulated insulin secretion at 4 mM glucose. Three-day treatment with JQ1 reduced secretion at 4 mM glucose to basal level (1 mM) while maintaining the right-shifted concentration-dependent GSIS, an effect described herein for the first time. Total insulin content (TIC) and release as its percentage were also measured, indicating a higher TIC and lower percentage use in JQ1-treated cells. Additionally, lipid concentration was photographically fluorescent analyzed, showing significant depletion of lipid droplets in JQ1-treated cells. Results imply that epigenetic modulation of pancreatic β-cells with JQ1 beneficially alters signal transduction pathways that maintain insulin-glucose homeostasis by ameliorating glucolipotoxicity through the preservation of a low GSIS basal level, increment of GSIS maximum capacity, delay of GSIS cuspid level at 12 mM glucose, more efficient spend of total insulin content resources, and diminished lipid accumulation due to increased FA oxidation—thereby suggesting JQ1 returning hyperglycemic β-cells to physiological conditions. Suggested future directions include improving the efficacy and accuracy of JQ1 and similar small-molecule BET inhibitors through tests in human subjects' isolated islets, as this presents an innovative course of research for the prevention of T2D and comorbidities. ### Competing Interest Statement The authors have declared no competing interest.

Background: TNF-related apoptosis-inducing ligand (TRAIL) is a member of the TNF superfamily that can either induce cell death or activate survival pathways after binding to death receptors (DRs) DR4 or DR5. TRAIL is investigated as a therapeutic agent in clinical trials due to its selective toxicity to transformed cells. Macrophages can be polarized into pro-inflammatory/tumor-fighting M1 macrophages or anti-inflammatory/tumor-supportive M2 macrophages and an inbalance between M1 and M2 macrophages can promote diseases. Therefore, identifying modulators that regulate macrophage polarization is important to design effective macrophage-targeted immunotherapies. The impact of TRAIL on macrophage polarization is not known. Methods: Primary human monocyte-derived macrophages were pre-treated with either TRAIL or with DR4 or DR5-specific ligands and then polarized into M1, M2a, or M2c phenotypes in vitro. The expression of M1 and M2 markers in macrophage subtypes was analyzed by RNA sequencing, qPCR, ELISA, and flow cytometry. Furthermore, the cytotoxicity of the macrophages against U937 AML tumor targets was assessed by flow cytometry. TCGA datasets were also analyzed to correlate TRAIL with M1/M2 markers, and the overall survival of cancer patients. Results: TRAIL increased the expression of M1 markers at both mRNA and protein levels while decreasing the expression of M2 markers at the mRNA level in human macrophages. TRAIL also shifted M2 macrophages towards an M1 phenotype. Our data showed that both DR4 and DR5 death receptors play a role in macrophage polarization. Furthermore, TRAIL enhanced the cytotoxicity of macrophages against the AML cancer cells in vitro. Finally, TRAIL expression was positively correlated with increased expression of M1 markers in the tumors from ovarian and sarcoma cancer patients and longer overall survival in cases with high, but not low, tumor macrophage content. ### Competing Interest Statement The authors have declared no competing interest.

Field methods to diagnose bovine respiratory disease (BRD) do not accurately identify airway inflammation and lack clinical sensitivity. New diagnostic modalities, such as thoracic ultrasound (TU), computer-assisted lung auscultation (CALA), and transtracheal wash (TTW), have recently emerged which may deliver accurate diagnosis and prediction of BRD in clinical settings. Therefore, we sought to compare TU, CALA, and TTW fluid cytologic assessment in stocker cattle at risk for BRD in a pilot study. We enrolled 17 high-risk mixed-breed beef steers, sampled 10 and 21 days after arrival and conventional management, in a pilot cross-sectional observational study. Cattle were examined daily for 82 days for clinical BRD. On day 10, 16 cattle received CALA, and 10 and 8 of these received TU and TTW, respectively. On day 21, 12 cattle received CALA and TTW, and 10 received TU. CALA was scored as 1-5. Lung consolidation and/or comet tails were evaluated by TU. TTW was evaluated by 200-cell differential count, with inflammation defined as >20% neutrophils. Relationships between each diagnostic test, and between diagnostic tests and clinical BRD, were evaluated by logistic regression (P<0.10). Fourteen cattle were treated for BRD. CALA scores ranged 1-3; three cattle had lung consolidation. On day 10, 5 of 6 cattle previously treated for BRD and 0 of 3 not treated had >20% TTW neutrophils. On day 21, 5 of 9 treated cattle and 1 of 3 not treated had >20% TTW neutrophils. No significant relationship between CALA, TU, and TTW inflammation existed. TTW inflammation was associated with BRD diagnosis (P=0.0586). CALA and TU results were unrelated to TTW inflammation. Cytologic assessment of TTW may improve antemortem diagnosis of BRD. ### Competing Interest Statement The authors have declared no competing interest.

The discrepancy between chronological age and estimated brain age, known as the brain age gap, may serve as a biomarker to reveal brain development and neuropsychiatric problems. This has motivated many studies focusing on the accurate estimation of brain age using different features and models, of which the generalizability is yet to be tested. Our recent study has demonstrated that conventional machine learning models can achieve high accuracy on brain age prediction during development using only a small set of selected features from multimodal brain imaging data. In the current study, we tested the replicability of various brain age models on the Adolescent Brain Cognitive Development (ABCD) cohort. We proposed a new refined model to improve the robustness of brain age prediction. The direct replication test for existing brain age models derived from the age range of 8-22 years onto the ABCD participants at baseline (9 to 10 years old) and year-two follow-up (11 to 12 years old) indicate that pre-trained models could capture the overall mean age failed precisely estimating brain age variation within a narrow range. The refined model, which combined broad prediction of the pre-trained model and granular information with the narrow age range, achieved the best performance with a mean absolute error of 0.49 and 0.48 years on the baseline and year-two data, respectively. The brain age gap yielded by the refined model showed significant associations with the participants' information processing speed and verbal comprehension ability on baseline data. ### Competing Interest Statement The authors have declared no competing interest.

Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS) - BIDS Apps - have provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging - especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad - a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n=2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model. ### Competing Interest Statement The authors have declared no competing interest.