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Prediction of a T-cell/MHC-I-based immune profile for colorectal liver metastases from CT images using ensemble learning. (arXiv:2303.04149v1 [q-bio.QM]) arxiv.org/abs/2303.04149

Prediction of a T-cell/MHC-I-based immune profile for colorectal liver metastases from CT images using ensemble learning

Colorectal cancer liver metastases (CLM) are the most common type of distant metastases originating from the abdomen and are characterized by a high recurrence rate after curative resection. It has been previously reported that CLM presenting a low cluster of differentiation 3 (CD3) positive T-cell infiltration density concurrent with a high major histocompatibility complex class I (MHC-I) expression were associated with poor clinical outcomes. In this study, we attempt to noninvasively predict whether a CLM exhibit the CD3LowMHCHigh immunological profile using preoperative CT images. To this end, we propose an ensemble network combining multiple Attentive Interpretable Tabular learning (TabNet) models, trained using CT-derived radiomic features. A total of 160 CLM were included in this study and randomly divided between a training set (n=130) and a hold-out test set (n=30). The proposed model yielded good prediction performance on the test set with an accuracy of 70.0% [95% confidence interval 53.6%-86.4%] and an area under the curve of 69.4% [52.9%-85.9%]. It also outperformed other off-the-shelf machine learning models. We finally demonstrated that the predicted immune profile was associated with a shorter disease-specific survival (p = .023) and time-to-recurrence (p = .020), showing the value of assessing the immune response.

arxiv.org

Conformational Fluctuations and Phases in Fused in Sarcoma (FUS) Low-Complexity Domain. (arXiv:2303.04215v1 [q-bio.BM]) arxiv.org/abs/2303.04215

Conformational Fluctuations and Phases in Fused in Sarcoma (FUS) Low-Complexity Domain

The well known phenomenon of phase separation in synthetic polymers and proteins has become a major topic in biophysics because it has been invoked as a mechanism of compartment formation in cells, without the need for membranes. Most of the coacervates (or condensates) are composed of Intrinsically Disordered Proteins (IDPs) or regions that are structureless, often in interaction with RNA and DNA. One of the more intriguing IDPs is the 526 residue RNA binding protein, Fused In Sarcoma (FUS), whose monomer conformations and condensates exhibit unusual behavior that are sensitive to solution conditions. By focussing principally on the N-terminus low complexity domain (FUS-LC comprising of residues 1-214) and other truncations, we rationalize the findings in solid state NMR experiments, which show that FUS-LC adopts a nonpolymorphic fibril (core-1) involving residues 39-95, flanked by fuzzy coats on both the N- and C- terminal ends. An alternate structure (core-2), whose free energy is comparable to core-1, emerges only in the truncated construct (residues 110-214). Both core-1 and core-2 fibrils are stabilized by a Tyrosine ladder as well as hydrophilic interactions. The morphologies (gels, fibrils, and glass-like behavior) adopted by FUS seem to vary greatly, depending on the experimental conditions. The effect of phosphorylation is site-specific and affects the stability of the fibril depending on the sites that are phosphorylated. Many of the peculiarities associated with FUS may also be shared by other IDPs, such as TDP43 and hnRNPA2. We outline a number of problems for which there is no clear molecular understanding.

arxiv.org

A normative theory of social conflict. (arXiv:2303.04285v1 [q-bio.NC]) arxiv.org/abs/2303.04285

A normative theory of social conflict

Social hierarchy in animal groups carries a crucial adaptive function by reducing conflict and injury while protecting valuable group resources. Social hierarchy is dynamic and can be altered by social conflict, agonistic interactions, and aggression. Understanding social conflict and aggressive behavior is of profound importance to our society and welfare. In this study, we developed a quantitative theory of social conflict. We modeled individual agonistic interactions as a normal-form game between two agents. We assumed that the agents use Bayesian inference to update their beliefs about their strength or their opponent's strength and to derive optimal actions. We compared the results of our model to behavioral and whole-brain neural activity data obtained for a large (n=116) population of mice engaged in agonistic interactions. We find that both types of data are consistent with the first-level Theory of Mind model (1-ToM) in which mice form both "primary" beliefs about their and their opponent's strengths as well as the "secondary" beliefs about the beliefs of their opponents. Our model helps identify brain regions that carry information about these levels of beliefs. Overall, we both propose a model to describe agonistic interactions and support our quantitative results with behavioral and neural activity data.

arxiv.org

Effect of Adult Neurogenesis on Sparsely Synchronized Rhythms of The Granule Cells in The Hippocampal Dentate Gyrus. (arXiv:2303.04319v1 [q-bio.NC]) arxiv.org/abs/2303.04319

Effect of Adult Neurogenesis on Sparsely Synchronized Rhythms of The Granule Cells in The Hippocampal Dentate Gyrus

We are concerned about the main encoding granule cells (GCs) in the hippocampal dentate gyrus (DG). Young immature GCs (imGCs) appear through adult neurogenesis. In comparison to the mature GCs (mGCs) (born during development), the imGCs show high activation due to lower firing threshold. On the other hand, they receive low excitatory drive from the entorhinal cortex via perforant paths and from the hilar mossy cells with lower connection probability $p_c~(=20~x~\%)$ ($x:$ synaptic connectivity fraction; $ 0 \leq x \leq 1$) than the mGCs with the connection probability $p_c~(=20~\%)$. Thus, the effect of low excitatory innervation (reducing activation degree) for the imGCs counteracts the effect of their high excitability. We consider a spiking neural network for the DG, incorporating both the mGCs and the imGCs. With decreasing $x$ from 1 to 0, we investigate the effect of young adult-born imGCs on the sparsely synchronized rhythms (SSRs) of the GCs (mGCs, imGC, and whole GCs). For each $x$, population and individual firing behaviors in the SSRs are characterized in terms of the amplitude measure ${\cal M}_a^{(X)}$ ($X=m,~im,~w$ for the mGCs, the imGCs, and the whole GCs, respectively) (representing the population synchronization degree) and the random phase-locking degree ${\cal L}_d^{(X)}$ (characterizing the regularity of individual single-cell discharges), respectively. We also note that, for $0 \leq x \leq 1,$ the mGCs and the imGCs exhibit pattern separation (i.e., a process of transforming similar input patterns into less similar output patterns) and pattern integration (making association between patterns), respectively. Quantitative relationship between SSRs and pattern separation and integration is also discussed.

arxiv.org

Many-core algorithms for high-dimensional gradients on phylogenetic trees. (arXiv:2303.04390v1 [stat.CO]) arxiv.org/abs/2303.04390

Many-core algorithms for high-dimensional gradients on phylogenetic trees

The rapid growth in genomic pathogen data spurs the need for efficient inference techniques, such as Hamiltonian Monte Carlo (HMC) in a Bayesian framework, to estimate parameters of these phylogenetic models where the dimensions of the parameters increase with the number of sequences $N$. HMC requires repeated calculation of the gradient of the data log-likelihood with respect to (wrt) all branch-length-specific (BLS) parameters that traditionally takes $\mathcal{O}(N^2)$ operations using the standard pruning algorithm. A recent study proposes an approach to calculate this gradient in $\mathcal{O}(N)$, enabling researchers to take advantage of gradient-based samplers such as HMC. The CPU implementation of this approach makes the calculation of the gradient computationally tractable for nucleotide-based models but falls short in performance for larger state-space size models, such as codon models. Here, we describe novel massively parallel algorithms to calculate the gradient of the log-likelihood wrt all BLS parameters that take advantage of graphics processing units (GPUs) and result in many fold higher speedups over previous CPU implementations. We benchmark these GPU algorithms on three computing systems using three evolutionary inference examples: carnivores, dengue and yeast, and observe a greater than 128-fold speedup over the CPU implementation for codon-based models and greater than 8-fold speedup for nucleotide-based models. As a practical demonstration, we also estimate the timing of the first introduction of West Nile virus into the continental Unites States under a codon model with a relaxed molecular clock from 104 full viral genomes, an inference task previously intractable. We provide an implementation of our GPU algorithms in BEAGLE v4.0.0, an open source library for statistical phylogenetics that enables parallel calculations on multi-core CPUs and GPUs.

arxiv.org

A Deep-Learning-Based Neural Decoding Framework for Emotional Brain-Computer Interfaces. (arXiv:2303.04391v1 [cs.HC]) arxiv.org/abs/2303.04391

A Deep-Learning-Based Neural Decoding Framework for Emotional Brain-Computer Interfaces

Reading emotions precisely from segments of neural activity is crucial for the development of emotional brain-computer interfaces. Among all neural decoding algorithms, deep learning (DL) holds the potential to become the most promising one, yet progress has been limited in recent years. One possible reason is that the efficacy of DL strongly relies on training samples, yet the neural data used for training are often from non-human primates and mixed with plenty of noise, which in turn mislead the training of DL models. Given it is difficult to accurately determine animals' emotions from humans' perspective, we assume the dominant noise in neural data representing different emotions is the labeling error. Here, we report the development and application of a neural decoding framework called Emo-Net that consists of a confidence learning (CL) component and a DL component. The framework is fully data-driven and is capable of decoding emotions from multiple datasets obtained from behaving monkeys. In addition to improving the decoding ability, Emo-Net significantly improves the performance of the base DL models, making emotion recognition in animal models possible. In summary, this framework may inspire novel understandings of the neural basis of emotion and drive the realization of close-loop emotional brain-computer interfaces.

arxiv.org

Bidirectional allostery mechanism of catch-bond effect in cell adhesion. (arXiv:2303.04443v1 [physics.bio-ph]) arxiv.org/abs/2303.04443

Bidirectional allostery mechanism of catch-bond effect in cell adhesion

Catch-bonds, whereby noncovalent ligand-receptor interactions are counterintuitively reinforced by tensile forces, play a major role in cell adhesion under mechanical stress. A basic prerequisite for catch-bond formation is that force-induced remodeling of ligand binding interface occurs prior to bond rupture. However, what strategy receptor proteins utilize to meet such specific kinetic control is still unclear, rendering the mechanistic understanding of catch-bond an open question. Here we report a bidirectional allostery mechanism of catch-bond for the hyaluronan (HA) receptor CD44 which is responsible for rolling adhesion of lymphocytes and circulating tumor cells. Binding of ligand HA allosterically reduces the threshold force for unlocking of otherwise stably folded force-sensing element (i.e., forward allostery), so that much smaller tensile force can trigger the conformational switching of receptor protein to high binding-strength state via backward allosteric coupling before bond rupture. The effect of forward allostery was further supported by performing atomistic molecular dynamics simulations. Such bidirectional allostery mechanism fulfills the specific kinetic control required by catch-bond and is likely to be commonly utilized in cell adhesion. We also revealed a slip-catch-slip triphasic pattern in force response of CD44-HA bond arising from force-induced repartitioning of parallel dissociation pathways. The essential thermodynamic and kinetic features of receptor proteins for shaping the catch-bond were identified.

arxiv.org

The evolution of cooperation and diversity by integrated indirect reciprocity. (arXiv:2303.04467v1 [q-bio.PE]) arxiv.org/abs/2303.04467

The evolution of cooperation and diversity by integrated indirect reciprocity

Indirect reciprocity is one of the major mechanisms for the evolution of cooperation in human societies. There are two types of indirect reciprocity: upstream and downstream. Cooperation in downstream reciprocity follows the pattern, 'You helped someone, and I will help you'. The direction of cooperation is reversed in upstream reciprocity, which instead follows the pattern, 'You helped me, and I will help someone else'. In reality, these two types of indirect reciprocity often occur in combination. However, upstream and downstream reciprocity have mostly been studied theoretically in isolation. Here, we propose a new model that integrates both types. We apply the standard giving-game framework of indirect reciprocity and analyze the model by means of evolutionary game theory. We show that the model can result in the stable coexistence of altruistic reciprocators and free riders in well-mixed populations. We also found that considering inattention in the assessment rule can strengthen the stability of this mixed equilibrium, even resulting in a global attractor. Our results indicate that the cycles of forwarding help and rewarding help need to be established for creating and maintaining diversity and inclusion in a society.

arxiv.org

3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction. (arXiv:2303.03543v1 [q-bio.BM]) arxiv.org/abs/2303.03543

3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction

Rich data and powerful machine learning models allow us to design drugs for a specific protein target \textit{in silico}. Recently, the inclusion of 3D structures during targeted drug design shows superior performance to other target-free models as the atomic interaction in the 3D space is explicitly modeled. However, current 3D target-aware models either rely on the voxelized atom densities or the autoregressive sampling process, which are not equivariant to rotation or easily violate geometric constraints resulting in unrealistic structures. In this work, we develop a 3D equivariant diffusion model to solve the above challenges. To achieve target-aware molecule design, our method learns a joint generative process of both continuous atom coordinates and categorical atom types with a SE(3)-equivariant network. Moreover, we show that our model can serve as an unsupervised feature extractor to estimate the binding affinity under proper parameterization, which provides an effective way for drug screening. To evaluate our model, we propose a comprehensive framework to evaluate the quality of sampled molecules from different dimensions. Empirical studies show our model could generate molecules with more realistic 3D structures and better affinities towards the protein targets, and improve binding affinity ranking and prediction without retraining.

arxiv.org

Quantum algorithm for position weight matrix matching. (arXiv:2303.03569v1 [quant-ph]) arxiv.org/abs/2303.03569

Quantum algorithm for position weight matrix matching

We propose two quantum algorithms for a problem in bioinformatics, position weight matrix (PWM) matching, which aims to find segments (sequence motifs) in a biological sequence such as DNA and protein that have high scores defined by the PWM and are thus of informational importance related to biological function. The two proposed algorithms, the naive iteration method and the Monte-Carlo-based method, output matched segments, given the oracular accesses to the entries in the biological sequence and the PWM. The former uses quantum amplitude amplification (QAA) for sequence motif search, resulting in the query complexity scaling on the sequence length $n$, the sequence motif length $m$ and the number of the PWMs $K$ as $\widetilde{O}\left(m\sqrt{Kn}\right)$, which means speedup over existing classical algorithms with respect to $n$ and $K$. The latter also uses QAA, and further, quantum Monte Carlo integration for segment score calculation, instead of iteratively operating quantum circuits for arithmetic in the naive iteration method; then it provides the additional speedup with respect to $m$ in some situation. As a drawback, these algorithms use quantum random access memories and their initialization takes $O(n)$ time. Nevertheless, our algorithms keep the advantage especially when we search matches in a sequence for many PWMs in parallel.

arxiv.org

Selecting Features for Markov Modeling: A Case Study on HP35. (arXiv:2303.03814v1 [q-bio.BM]) arxiv.org/abs/2303.03814

Selecting Features for Markov Modeling: A Case Study on HP35

Markov state models represent a popular means to interpret molecular dynamics trajectories in terms of memoryless transitions between metastable conformational states. To provide a mechanistic understanding of the considered biomolecular process, these states should reflect structurally distinct conformations and ensure a timescale separation between fast intrastate and slow interstate dynamics. Adopting the folding of villin headpiece (HP35) as a well-established model problem, here we discuss the selection of suitable input coordinates or `features', such as backbone dihedral angles and interresidue distances. We show that dihedral angles account accurately for the structure of the native energy basin of HP35, while the unfolded region of the free energy landscape and the folding process are best described by tertiary contacts of the protein. To construct a contact-based model, we consider various ways to define and select contact distances, and introduce a low-pass filtering of the feature trajectory as well as a correlation-based characterization of states. Relying on input data that faithfully account for the mechanistic origin of the studied process, the states of the resulting Markov model are clearly discriminated by the features, describe consistently the hierarchical structure of the free energy landscape, and$\unicode{0x2014}$as a consequence$\unicode{0x2014}$correctly reproduce the slow timescales of the process.

arxiv.org

Organelle-specific segmentation, spatial analysis, and visualization of volume electron microscopy datasets. (arXiv:2303.03876v1 [cs.CV]) arxiv.org/abs/2303.03876

Organelle-specific segmentation, spatial analysis, and visualization of volume electron microscopy datasets

Volume electron microscopy is the method of choice for the in-situ interrogation of cellular ultrastructure at the nanometer scale. Recent technical advances have led to a rapid increase in large raw image datasets that require computational strategies for segmentation and spatial analysis. In this protocol, we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis, and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. We specifically target researchers in the life sciences with limited computational expertise, who face the following tasks within their volume electron microscopy projects: i) How to generate 3D segmentation labels for different types of cell organelles while minimizing manual annotation efforts, ii) how to analyze the spatial interactions between organelle instances, and iii) how to best visualize the 3D segmentation results. To meet these demands we give detailed guidelines for choosing the most efficient segmentation tools for the specific cell organelle. We furthermore provide easily executable components for spatial analysis and 3D rendering and bridge compatibility issues between freely available open-source tools, such that others can replicate our full pipeline starting from a raw dataset up to the final plots and rendered images. We believe that our detailed description can serve as a valuable reference for similar projects requiring special strategies for single- or multiple organelle analysis which can be achieved with computational resources commonly available to single-user setups.

arxiv.org

Interactions between $\beta$-endorphin and kisspeptin neurons of the ewe arcuate nucleus are modulated by photoperiod. (arXiv:2303.03917v1 [q-bio.NC]) arxiv.org/abs/2303.03917

Interactions between $β$-endorphin and kisspeptin neurons of the ewe arcuate nucleus are modulated by photoperiod

Opioid peptides are well-known modulators of the central control of reproduction. Among them, dynorphin coexpressed in kisspeptin (KP) neurons of the arcuate nucleus (ARC) has been thoroughly studied for its autocrine effect on KP release through $κ$ opioid receptors. Other studies have suggested a role for $β$-endorphin (BEND), a peptide cleaved from the proopiomelanocortin (POMC) precursor, on food intake and central control of reproduction. Similarly to KP, BEND content in the ARC of sheep is modulated by day length and BEND modulates food intake in a dose-dependent manner. As KP levels in the ARC vary with photoperiodic and metabolic status, a photoperiod-driven influence of BEND neurons on neighboring KP neurons is plausible. The aim of this study was therefore to investigate a possible modulatory action of BEND on KP neurons located in the ovine ARC. Using confocal microscopy, numerous KP appositions on BEND neurons were found but there was no photoperiodic variation in the number of these interactions in ovariectomized, estradiol-replaced ewes. In contrast, BEND terminals on KP neurons were twice as numerous under short days (SD), in ewes having an activated gonadotropic axis, as compared to anestrus ewes under long days (LD). Injection of 5$μ$g BEND into the third ventricle of SD ewes induced a significant and specific increase of activated KP neurons (16% versus 9% in controls) while the percentage of overall activated (c-Fos positive) neurons, was similar between both groups. These data suggest a photoperiod-dependent influence of BEND on KP neurons of the ARC, which may influence GnRH pulsatile secretion and inform KP neurons on the metabolic status.

arxiv.org

Deciphering a Sleeping Pathogen: Uncovering Novel Transcriptional Regulators of Hypoxia-Induced Dormancy in Mycobacterium Tuberculosis. (arXiv:2303.04034v1 [q-bio.GN]) arxiv.org/abs/2303.04034

Deciphering a Sleeping Pathogen: Uncovering Novel Transcriptional Regulators of Hypoxia-Induced Dormancy in Mycobacterium Tuberculosis

Along the pathogenesis of Mycobacterium Tuberculosis (MTB), hypoxia-induced dormancy is a process involving the oxygen-depleted environment encountered inside the lung granuloma, where bacilli enter a viable, non-replicating state termed as latency. Affecting nearly two billion people, latent TB can linger in the host for indefinite periods of time before resuscitating, which significantly strains the accuracy of treatment options and patient prognosis. Transcriptional factors thought to mediate this process have only conferred mild growth defects, signaling that our current understanding of the MTB genetic architecture is highly insufficient. In light of these inconsistencies, the objective of this study was to characterize regulatory mechanisms underlying the transition of MTB into dormancy. The project methodology involved a three-part approach - constructing an aggregate hypoxia dataset, inferring a gene regulatory network based on those observations, and leveraging several downstream network analyses to make sense of it all. Results indicated dormancy to be functionally associated with cell redox homeostasis, metal ion cycling, and cell wall metabolism, all of which modulate essential host-pathogen interactions. Additionally, the crosstalk between individual regulons (Rv0821c and Rv0144; Rv1152 and Rv2359) was shown to be critical in facilitating bacterial persistence and allowing MTB to gain control over key micronutrients within the cell. Defense antioxidants and nutritional immunity were also identified as future avenues to explore further. In providing some of the first insights into the methods utilized by MTB to endure in a hypoxic state, this research suggests a range of strategies that might aid in improved clinical outcomes of TB treatment.

arxiv.org
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