Miracle-dz/UniFAR-FTUs huggingface dataset:
Infer global, predict local: quantity-quality trade-off in protein fitness predictions from sequence data

Predicting the effects of mutations on protein function is an important issue in evolutionary biology and biomedical applications. Computational approaches, ranging from graphical models to deep-learning architectures, can capture the statistical properties of sequence data and predict the outcome of high-throughput mutagenesis experiments probing the fitness landscape around some wild-type protein. However, how the complexity of the models and the characteristics of the data combine to determine the predictive performance remains unclear. Here, based on a theoretical analysis of the prediction error, we propose descriptors of the sequence data, characterizing their quantity and quality relative to the model. Our theoretical framework identifies a trade-off between these two quantities, and determines the optimal subset of data for the prediction task, showing that simple models can outperform complex ones when inferred from adequately-selected sequences. We also show how repeated subsampling of the sequence data allows for assessing how much epistasis in the fitness landscape is not captured by the computational model. Our approach is illustrated on several protein families, as well as on in silico solvable protein models. Significance Statement Is more data always better? Or should one prefer fewer data, but of higher quality? Here, we investigate this question in the context of the prediction of fitness effects resulting from mutations to a wild-type protein. We show, based on theory and data analysis, that simple models trained on a small subset of carefully chosen sequence data can perform better than complex ones trained on all available data. Furthermore, we explain how comparing the simple local models obtained with different subsets of training data reveals how much of the epistatic interactions shaping the fitness landscape are left unmodeled.
110 Herculis: A Possible Prototype for Simultaneous Lithium and Beryllium Depletion, and Implications for Stellar Interiors

Standard stellar evolution predicts that late F stars should have retained their initial surface lithium abundance because their convection zones are too shallow to destroy it at their base. Yet observations reveal Li depletions, sometimes quite severe (the “Boesgaard Li gap”). Several physical mechanisms, which are not usually included in stellar evolution calculations, have been proposed to account for the Li depletions. These mechanisms include mass loss, microscopic diffusion, and various types of slow mixing driven by either waves or rotation. Identifying which of these (if any) might really be at work not only is of vital interest to advancing our knowledge of stellar interiors but also may have serious implications in other contexts, such as cosmology. We bring attention to beryllium (Be) observations in late F stars, and propose that they are crucial for discriminating between scenarios. Particularly important is the star 110 Her, which is depleted in surface Be by about a factor of 5-10, and yet enough surface Li has survived to be detected. The Li depletion is stronger than that of Be, about a factor of 100-200. Depleting surface Be without having depleted all of the surface Li (and depleting Li more strongly than Be) requires specific circumstances; we discuss how this Li/Be depletion pattern, if representative, would severely constrain or eliminate most of the proposed mechanisms. One mechanism, rotationally induced mixing, predicts relative Li and Be depletions that agree well with what is observed. It is important to establish whether or not the Li/Be depletion pattern observed in 110 Her is representative.
Scale Space Calibrates Present and Subsequent Spatial Learning in Barnes Maze in Mice

Abstract Animals are capable of representing different scale spaces from smaller to larger ones. However, most laboratory animals live their life in a narrow range of scale spaces like homecages and experimental setups, making it hard to extrapolate the spatial representation and learning process in large scale spaces from those in conventional scale spaces. Here, we developed a 3-m diameter Barnes maze (BM3), then explored whether spatial learning in the Barnes maze (BM) is calibrated by scale spaces. Spatial learning in the BM3 was successfully established with a lower learning rate than that in a conventional 1-m diameter Barnes maze (BM1). Specifically, analysis of exploration strategies revealed that the mice in the BM3 persistently searched certain places throughout the learning, while such places were rapidly decreased in the BM1. These results suggest dedicated exploration strategies requiring more trial-and-errors and computational resources in the BM3 than in the BM1, leading to a divergence of spatial learning between the BM1 and the BM3. We then explored whether prior learning in one BM scale calibrates subsequent spatial learning in another BM scale, and found asymmetric facilitation such that the prior learning in the BM3 facilitated the subsequent BM1 learning, but not vice versa. Thus, scale space calibrates both the present and subsequent BM learning. This is the first study to demonstrate scale-dependent spatial learning in BM in mice. The couple of the BM1 and the BM3 would be a suitable system to seek how animals represent different scale spaces with underlying neural implementation.
Inhibition of DNMT1 methyltransferase activity via glucose-regulated O-GlcNAcylation alters the epigenome

The DNA methyltransferase activity of DNMT1 is vital for genomic maintenance of DNA methylation. We report here that DNMT1 function is regulated by O-GlcNAcylation, a protein modification that is sensitive to glucose levels, and that elevated O-GlcNAcylation of DNMT1 from high glucose environment leads to alterations to the epigenome. Using mass spectrometry and complementary alanine mutation experiments, we identified S878 as the major residue that is O-GlcNAcylated on DNMT1. Functional studies further revealed that O-GlcNAcylation of DNMT1-S878 results in an inhibition of methyltransferase activity, resulting in a general loss of DNA methylation that is preferentially at partially methylated domains (PMDs). This loss of methylation corresponds with an increase in DNA damage and apoptosis. These results establish O-GlcNAcylation of DNMT1 as a mechanism through which the epigenome is regulated by glucose metabolism and implicates a role for glycosylation of DNMT1 in metabolic diseases characterized by hyperglycemia.
Exact Probability Distribution for the ROC Area under Curve

The Receiver Operating Characteristic (ROC) is a de facto standard for determining the accuracy of in vitro diagnostic (IVD) medical devices, and thus exactness in its probability distribution is crucial toward accurate statistical inference. We show the exact probability distribution of the ROC AUC-value, hence exact critical values and p-values are readily obtained. Because the exact calculations are computationally intense, we demonstrate a method of geometric interpolation which is exact in a special case but generally an approximation, vastly increasing computational speeds. The method is illustrated through open access data, demonstrating superiority of 26 composite biomarkers relative to a predicate device. Especially under correction for testing of multiple hypotheses, traditional asymptotic approximations are encumbered by considerable imprecision, adversely affecting IVD device development. The ability to obtain exact p-values will allow more efficient IVD device development.
Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations

Identifying subtypes of neuropsychiatric disorders based on characteristics of their brain activity has tremendous potential to contribute to a better understanding of those disorders and to the development of new diagnostic and personalized treatment approaches. Many studies focused on neuropsychiatric disorders examine the interaction of brain networks over time using dynamic functional network connectivity (dFNC) extracted from resting-state functional magnetic resonance imaging data. Some of these studies involve the use of either deep learning classifiers or traditional clustering approaches, but usually not both. In this study, we present a novel approach for subtyping individuals with neuropsychiatric disorders within the context of schizophrenia (SZ). We train an explainable deep learning classifier to differentiate between dFNC data from individuals with SZ and controls, obtaining a test accuracy of 79%. We next make use of cross-validation to obtain robust average explanations for SZ training participants across folds, identifying 5 SZ subtypes that each differ from controls in a distinct manner and that have different degrees of symptom severity. These subtypes specifically differ from one another in their interaction between the visual network and the subcortical, sensorimotor, and auditory networks and between the cerebellar network and the cognitive control and subcortical networks. Additionally, there are statistically significant differences in negative symptom scores between the subtypes. It is our hope that the proposed novel subtyping approach will contribute to the improved understanding and characterization of SZ and other neuropsychiatric disorders.
Extending eco-evolutionary theory with oligomorphic dynamics

Understanding the interplay between ecological processes and the evolutionary dynamics of quantitative traits in natural systems remains a major challenge. Two main theoretical frameworks are used to address this question, adaptive dynamics and quantitative genetics, both of which have strengths and limitations and are often used by distinct research communities to address different questions. In order to make progress, new theoretical developments are needed that integrate these approaches and strengthen the link to empirical data. Here, we discuss a novel theoretical framework that bridges the gap between quantitative genetics and adaptive dynamics approaches. “Oligomorphic dynamics” can be used to analyse eco-evolutionary dynamics across different time scales and extends quantitative genetics theory to account for multimodal trait distributions, the dynamical nature of genetic variance, the potential for disruptive selection due to ecological feed-backs, and the non-normal or skewed trait distributions encountered in nature. Oligomorphic dynamics explicitly takes into account the effect of environmental feedback, such as frequency- and density-dependent selection, on the dynamics of multi-modal trait distributions and we argue it has the potential to facilitate a much tighter integration between eco-evolutionary theory and empirical data.
Effect of field strength on RF power deposition near conductive leads: A simulation study of SAR in DBS lead models during MRI at 1.5 T—10.5 T

Background Since the advent of magnetic resonance imaging (MRI) nearly four decades ago, there has been a quest for ever-higher magnetic field strengths. Strong incentives exist to do so, as increasing the magnetic field strength increases the signal-to-noise ratio of images. However, ensuring patient safety becomes more challenging at high and ultrahigh field MRI (i.e., ≥3 T) compared to lower fields. The problem is exacerbated for patients with conductive implants, such as those with deep brain stimulation (DBS) devices, as excessive local heating can occur around implanted lead tips. Despite extensive effort to assess radio frequency (RF) heating of implants during MRI at 1.5 T, a comparative study that systematically examines the effects of field strength and various exposure limits on RF heating is missing. Purpose This study aims to perform numerical simulations that systematically compare RF power deposition near DBS lead models during MRI at common clinical and ultra-high field strengths, namely 1.5, 3, 7, and 10.5 T. Furthermore, we assess the effects of different exposure constraints on RF power deposition by imposing limits on either the B1+ or global head specific absorption rate (SAR) as these two exposure limits commonly appear in MRI guidelines. Methods We created 33 unique DBS lead models based on postoperative computed tomography (CT) images of patients with implanted DBS devices and performed electromagnetic simulations to evaluate the SAR of RF energy in the tissue surrounding lead tips during RF exposure at frequencies ranging from 64 MHz (1.5 T) to 447 MHz (10.5 T). The RF exposure was implemented via realistic MRI RF coil models created based on physical prototypes built in our institutions. We systematically examined the distribution of local SAR at different frequencies with the input coil power adjusted to either limit the B1+ or the global head SAR. Results The MRI RF coils at higher resonant frequencies generated lower SARs around the lead tips when the global head SAR was constrained. The trend was reversed when the constraint was imposed on B1+. Conclusion At higher static fields, MRI is not necessarily more dangerous than at lower fields for patients with conductive leads. Specifically, when a conservative safety criterion, such as constraints on the global SAR, is imposed, coils at a higher resonant frequency tend to generate a lower local SAR around implanted leads due to the decreased B1+ and, by proxy, E field levels.
Human intestinal epithelial cells can internalize luminal fungi via LC3-associated phagocytosis

Intestinal epithelial cells (IECs) are the first to encounter luminal microorganisms and actively participate in intestinal immunity. We reported that IECs express the β-glucan receptor Dectin-1, and respond to commensal fungi and β-glucans. In phagocytes, Dectin-1 mediates LC3 associated phagocytosis (LAP) utilizing autophagy components to process extracellular cargo. Dectin-1 can mediate phagocytosis of β-glucan-containing particles by non-phagocytic cells. We aimed to determine whether human IECs phagocytose β-glucan-containing fungal particles via LAP. Zymosan (β-glucan particle) and Heat-killed and UV inactivated C. albicans were phagocytosed by monolayers of human colonic (n=18) and ileal (n=4) organoids and IEC lines. LAP was identified by LC3 and Rubicon recruitment to phagosomes and lysosomal processing of internalized particles was demonstrated by co-localization with lysosomal dyes and LAMP2. Phagocytosis was significantly diminished by blockade of Dectin-1, actin polymerization and NAPDH oxidases. Our results show that human IECs sense luminal fungal particles and internalize them via LAP. This novel mechanism of luminal sampling suggests that IECs may contribute to the maintenance of mucosal tolerance towards commensal fungi.
Early human lung immune cell development and its role in epithelial cell fate

During human development, lungs develop their roles of gas exchange and barrier function. Recent single cell studies have focused on epithelial and mesenchymal cell types, but much less is known about the developing lung immune cells, although the airways are a major site of mucosal immunity after birth. An open question is whether tissue-resident immune cells play a role in shaping the tissue as it develops in utero. In order to address this, we profiled lung immune cells using scRNAseq, smFISH and immunohistochemistry. At the embryonic stage, we observed an early wave of innate immune cells, including ILCs, NK, myeloid cells and lineage progenitors. By the canalicular stage, we detected naive T lymphocytes high in cytotoxicity genes, and mature B lymphocytes, including B1 cells. Our analysis suggests that fetal lungs provide a niche for full B cell maturation. Given the abundance of immune cells, we investigated their possible effect on epithelial maturation and found that IL-1β drives epithelial progenitor exit from self-renewal and differentiation to basal cells in vitro. In vivo, IL-1β-producing myeloid cells were found adjacent to epithelial tips, suggesting that immune cells may direct the developing lung epithelium.
The scattering of electrons from inert gases. II. Absolute differential elastic cross sections for neon, krypton and xenon atoms

For pt.I see ibid., vol.8, no.10, p.1670 (1975). Measurements of the absolute differential cross sections for the elastic scattering of electrons from neon, krypton and xenon for a wide range of electron energies and scattering angles and with high angular resolution are presented. The range of electron energies is 20 to 400 eV, the range of scattering angles from 20 to 150 degrees and the angular resolution of both the incident and scattered electron beams is 1.5 degrees. The experimental method is based on the crossed electron and modulated atom beams techniques. Measured angular distributions have been made absolute by carrying out phase shift analyses over the 19.35 eV (1s2s2)2S resonant state of helium and the 11.10 and 11.27 eV (3s23p54s2), 2P1/2, 2P3/2 resonance doublet in argon coupled with accurate determinations of the beam density ratios of neon, krypton and xenon to helium and argon. The absolute differential cross sections are not consistently in agreement with any previous absolute determinations.
J Wave Detection Algorithm of the BCG in Chair and Bed using Continuous Spline Wavelet Transform

This study presents an algorithm for unsupervised beat-to-beat detection of the J-wave of the ballistocardiogram (BCG) in records of both lying (bed) and seated (chair) persons. The algorithm is based on the continuous wavelet transform (CWT) with splines, which offers the advantage of using a wide range of scales and the reduction of noise and mechanical interference. For J-wave detection, the most prominent negative modulus of the CWT is detected using adaptive time windows (the negative modulus provides more information about the location of the J-wave), and then a confirmation is performed from temporal and amplitude parameters. Seven records from a chair database and fifteen records from a bed database were used to evaluate the algorithm. To assess the J-wave detection, the Bland Altman test was used, measuring the heart rate (HR) from the ECG as a reference and considering a 95% confidence interval (±2 SD). For the bed database the mean error was -0.03 beats/min with a confidence interval of ±3.87 and for the chair database the mean error was -0.05 beats/min with a confidence interval of ±3.48 beats/min. Results satisfied the standards for HR meters recommended by the Association for the Advancement of Medical Instrumentation (AAMI).
Immersive Animation Authoring in Industrial VR Applications

Animation is an important aspect of most Virtual Reality (VR) applications including industrial applications such as VR training or product demonstration. Animations can be used to present the behavior or intended use of a component or product and can also be used as a visual cue or guide during VR training. Studies have shown that creating animations while immersed in the virtual space has several benefits including increasing the creativity and productivity of artists. Previous work on animation authoring in VR, however, has mostly been targeted towards animation artists, attempting to recreate traditional animation tools in VR, which are known for their complexity and steep learning curve. During this industrial PhD project I aim to investigate and create immersive animation authoring tools for industrial use cases targeted towards subject matter experts from the manufacturing industry. In this paper, I present the motivations and background for this project as well as the current status and planned future work.
Interaction Design of Fertility Tracking Application Using User-Centered Design

Fertility tracking application (FTA) is a type of digital health product in the form of a digital tracker that helps women manage their menstruation. Given that every woman has different menstrual characteristics, FTA has the potential to alienate certain groups of users. Thus, the aim of this research is to create an interaction design that can accommodate both normal and outlier users of FTA. The design methodology is user-centered design so that the iteration of design process is driven by feedbacks from existing user groups. Based on the user research, it is found that current FTAs, including but not limited to Flo and MyCalendar, have following problems: lack of ways for users to set up their preferred period tracking, lack of personalization, features with low visibility, and lack of explanation of unfamiliar medical terminologies in the application. Thus, an interaction is designed to address these problems. The solution allows each user to personalize various aspects of menstrual tracking. Usability goals of the interaction design solution are effective to use and easy to learn with helpful as user experience goals. The results of the interaction design evaluation using usability testing show that the solution is usable with a Single Ease Question score of 6.84 out of 7, a System Usability Score of 92 (Grade A/Excellent) out of 100, and an NPS score of +60 out of +100. Usability goal effective to use is achieved with 100% task completion and easy to learn is achieved with a score of 4.6 out of 5. Finally, helpful as user experience goal is achieved with a score of 5 out of 5.
Enhanced Pretreatment and Mapping Methods Based on Lightweight Lidar Odometry

Slam has important applications in the field of unmanned systems and robots. At present, most slam algorithms have satisfied results in indoor scenes, but the pose drift will occur in outdoor complex scenes, and most 3D SLAM can not generate effective navigation maps. Firstly, we introduce the basic method. Secondly, we propose the enhanced pretreatment and mapping methods based on lightweight lidar odometry, including a map dynamic update method based on double-layer boundary, which can generate 2D map without dynamic noise. And the ground point cloud segmentation method based on straight line fitting with ground points, which can reduce the pose drift. Thirdly, experiment results show that the SLAM algorithm proposed in this paper effectively solves the drift problem in the mapping of outdoor complex scenes and generates a navigable 2D-3D Hybrid map.
An Improved Method For Multi-agent Systems Avoiding Obstacle Based On Flocking Algorithm

In the traditional flocking algorithm, it is basically assumed that the properties of each agent are the same. In this paper, based on the flocking algorithm proposed by Olfati-Saber, the problem of individual differences of the agent is investigated. It is also found that when agents encounter a narrow intersection, it can fall into a local optimal solution causing stagnation due to the balance of the artificial potential field, so the obstacle avoidance of the flocking algorithm is improved to enable the agents to pass through such terrain smoothly and to reach convergence again after passing the terrain. It is demonstrated that the improved algorithm can perform the obstacle avoidance function through suitable parameter selection. Finally, computer simulations are given to verify the feasibility of the algorithm.