Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure-Kinetics Relationships of Opioids from Metadynamics and Machine Learning

The nation’s opioid overdose deaths reached an all-time high in 2021. The majority of deaths are due to synthetic opioids represented by fentanyl. Naloxone, which is a FDA-approved reversal agent, antagonizes opioids through competitive binding at the μ-opioid receptor (mOR). Thus, knowledge of opioid’s residence time is important for assessing the effectiveness of naloxone. Here we estimated the residence times of 15 fentanyl and 4 morphine analogs using metadynamics, and compared them with the most recent measurement of the opioid kinetic, dissociation, and naloxone inhibitory constants (Mann, Li et al, Clin. Pharmacol. Therapeut. 2022). Importantly, the microscopic simulations offered a glimpse at the common binding mechanism and molecular determinants of dissociation kinetics for fentanyl analogs. The insights inspired us to develop a machine learning (ML) approach to analyze the kinetic impact of fentanyl’s substituents based on the interactions with mOR residues. This proof-of-concept approach is general; for example, it may be used to tune ligand residence times in computeraided drug discovery. Graphical TOC Entry
The neurocellular implementation of representational geometry in primate prefrontal cortex

Modern neuroscience has seen the rise of a population-doctrine that represents cognitive variables using geometrical structures in activity space. Representational geometry does not, however, account for how individual neurons implement these representations. Here, leveraging the principle of sparse coding, we present a framework to dissect representational geometry into biologically interpretable components that retain links to single neurons. Applied to extracellular recordings from the primate prefrontal cortex in a working memory task with interference, the identified components revealed disentangled and sequential memory representations including the recovery of memory content after distraction, signals hidden to conventional analyses. Each component was contributed by small subpopulations of neurons with distinct electrophysiological properties and response dynamics. Modelling showed that such sparse implementations are supported by recurrently connected circuits as in prefrontal cortex. The perspective of neuronal implementation links representational geometries to their cellular constituents, providing mechanistic insights into how neural systems encode and process information.
Whole genome analyses reveal weak signatures of population structure and environmentally associated local adaptation in an important North American pollinator, the bumble bee Bombus vosnesenskii

Studies of species that experience environmental heterogeneity across their distributions have become an important tool for understanding mechanisms of adaptation and predicting responses to climate change. We examine population structure, demographic history, and environmentally associated genomic variation in Bombus vosnesenskii, a common bumble bee in the western U.S.A. , using whole genome resequencing of populations distributed across a broad range of latitudes and elevations. We find that B. vosnesenskii exhibits minimal population structure and weak isolation by distance, confirming results from previous studies using other molecular marker types. Similarly, demographic analyses with Sequentially Markovian Coalescent (SMC) models suggest that minimal population structure may have persisted since the last interglacial period, with genomes from different parts of the species range showing similar historical effective population size (Ne) trajectories and relatively small fluctuations through time. Redundancy analysis revealed a small amount of genomic variation explained by bioclimatic variables, and environmental association analysis with latent factor mixed modeling (LFMM2) identified few outlier loci that were sparsely distributed throughout the genome. Some outlier loci were in genes with known regulatory relationships, suggesting the possibility of weak selection, although compared to other species examined with similar approaches, evidence for extensive local adaptation signatures in the genome was relatively weak. Overall, results indicate Bombus vosnesenskii is an example of a generalist with a high degree of flexibility in its environmental requirements that may ultimately benefit the species under periods of climate change.
Study of Regional-Scale Boundary Layer Characteristics over Northern India with a Special Reference to the Role of the Thar Desert in Regional-Scale Transport

Abstract Extensive contrasts of land surface heterogeneities have a pivotal role in modulating boundary layer processes and consequently, the regional-scale dispersion of air pollutants. The Weather Research and Forecasting (WRF) modeling system has been used to analyze the regional-scale boundary layer features over northern India. Two cases, 9–11 December 2004 and 20–22 May 2005, representing the winter and summer season, respectively, are chosen for the simulations. The model results have been compared with the observations from the India Meteorological Department (IMD) and Wyoming Weather Web data archive over three cities: Delhi, Ahmedabad, and Jodhpur. The simulations show that the thermal stratifications and the associated wind pattern are very well supported by land surface characteristics over the region. The results signify that the underlying land surface along with the prevailing hemispheric-scale meteorological processes (synoptic conditions) is the driver of the simulated patterns. The study.
JET and COMPASS asymmetrical disruptions

Asymmetrical disruptions may occur during ITER operation and they may be accompanied by large sideways forces and rotation of the asymmetry. This is of particular concern because resonance of the rotating asymmetry with the natural frequencies of the vacuum vessel (and other in-vessel components) could lead to large dynamic amplification of the forces. A significant fraction of non-mitigated JET disruptions have toroidally asymmetric currents that flow partially inside the plasma and partially inside the surrounding vacuum vessel (‘wall’). The toroidal asymmetries (otherwise known as the appearance of 3D structures) are clearly visible in the plasma current (Ip) and the first plasma current moments. For the first time we present here the asymmetries in toroidal flux measured by the diamagnetic loops and also propose a physical interpretation. The presented data covers the period of JET operation with a C-wall (JET-C from 2005 until late 2009) and with an ITER-like wall (JET-ILW from 2011 until late 2014), during which pick-up coil and saddle loop data at four toroidally orthogonal locations were routinely recorded. The observed rotations of the Ip asymmetries are in the range from −5 turns to +10 turns (a negative value is counted to the negative plasma current). Initial observations on COMPASS of asymmetric disruptions are presented, which are in line with JET data. The whole of the JET-ILW disruption database and the limited number of COMPASS disruptions examined confirm that the development of the toroidal asymmetry precedes the drop to unity of q95. It is shown that massive gas injection (MGI), which is routinely used to mitigate disruptions, significantly reduces the Ip asymmetries in JET. However, MGI produces fast plasma current quench and consequently high vessel eddy currents, which expose the machine to additional stresses. The effect of the large gas quantity used during the injection is of particular concern as well.
Naturally-acquired and Vaccine-induced Human Monoclonal Antibodies to Plasmodium vivax Duffy Binding Protein Inhibit Invasion of Plasmodium knowlesi (PvDBPOR) Transgenic Parasites

The Duffy antigen receptor for chemokines (DARC) expressed on erythrocytes is central to Plasmodium vivax (Pv) invasion of reticulocytes. Pv expresses a Duffy binding protein (PvDBP) on merozoites, a DARC ligand, and their protein-protein interaction is central to vivax blood stage malaria. Here we compared the functional activity of humAbs derived from naturally exposed and vaccinated individuals for the first time using easily cultured P. knowlesi (Pk) that had been genetically modified to replace its endogenous PkDBP orthologue with PvDBP to create a transgenic parasite, PkPvDBPOR. This transgenic parasite requires DARC to invade human erythrocytes but is not reticulocyte restricted. Using this model, we evaluated the invasion inhibition potential of 12 humAbs (9 naturally acquired; 3 vaccine-induced) targeting PvDBP individually and in combinations using growth inhibition assays (GIAs). The PvDBP-specific humAbs demonstrated 70-100% inhibition of PkPvDBPOR invasion with the IC50 values ranging from 51 to 338 μg/mL for the 9 naturally acquired (NA) humAbs and 33 to 99 μg/ml for the 3 vaccine-induced (VI) humAbs. To evaluate antagonistic, additive, or synergistic effects, six pairwise combinations were performed using select humAbs. Of these combinations tested, one NA/NA (099100/094083) combination demonstrated relatively strong additive inhibition between 10-100 μg/mL; all combinations of NA and VI humAbs showed additive inhibition at concentrations below 25 μg/mL and antagonism at higher concentrations. None of the humAb combinations showed synergy. This PkPvDBPOR model system enables efficient assessment of NA and VI humAbs individually and in combination. IMPORTANCE Given the importance of Duffy blood group antigen and P. vivax Duffy binding protein (PvDBP) interaction leading to blood stage vivax malaria, development of vaccines or therapeutic human monoclonal antibodies (humAbs) targeting PvDBP are key strategies for treating and controlling Pv. The P. knowlesi-based PkPvDBPOR transgenic model system enables efficient assessment of NA and VI humAbs individually and in combination. As such, this model could prioritize specific humAb combinations ahead of clinical trials of these reagents.
A sample preparation procedure enables acquisition of 2-channel super-resolution 3D STED image of an entire oocyte

Super-resolution (SR) microscopy is a cutting-edge method that can provide detailed structural information with high resolution. However, the thickness of the specimen has been a major limitation for SR methods, and larger structures have posed a challenge. To overcome this, the key step is to optimize sample preparation to ensure optical homogeneity and clarity, which can enhance the capabilities of SR methods for the acquisition of thicker structures. Oocytes are the largest cells in the mammalian body and are crucial objects in reproductive biology. They are especially useful for studying membrane proteins. However, oocytes are extremely fragile and sensitive to mechanical manipulation and osmotic shocks, making sample preparation a critical and challenging step. We present an innovative, simple, and sensitive approach to oocyte sample preparation for 3D STED acquisition. This involves alcohol dehydration and mounting into a high refractive index medium. This extended preparation procedure allowed us to successfully obtain a unique 2-channel 3D STED super-resolution image of an entire mouse oocyte. By optimizing sample preparation, we can overcome the limitations of SR methods and obtain high-resolution images of larger structures, such as oocytes, Knowledge of which are important for understanding fundamental biological processes. RESEARCH HIGHLIGHTS This study aimed to develop a successful sample preparation protocol for imaging mouse oocytes using 3D STED super-resolution microscopy. The results showed that the oocyte sample was optically homogenous, enhancing the capability of the 3D STED method to capture high-resolution images throughout the full depth of the sample, resulting in highly similar SR images. The 3D STED point spread function (PSF) pattern of the depletion laser was successfully secured throughout the entire volume of the sample, including the top and bottom. This study presents the first-ever full volume 3D image of the mouse oocyte, which was acquired using a 2-channel 3D STED method. GRAPHICAL ABSTRACT Introducing an extended sample preparation procedure resulted in an outstanding optical quality sample environment pronounced by high refractive index, high transparency, and minimal spherical aberration. This procedure allowed 3D STED within the entire oocyte.
A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology

A major goal of cancer biology is to understand the mechanisms underlying tumorigenesis driven by somatically acquired mutations. Existing computational approaches focus on either scoring the pathogenicity of mutations or characterizing their effects at specific scales. Here, we established a unified computational framework, NetFlow3D, that systematically maps the multiscale mechanistic effects of somatic mutations in cancer. The establishment of NetFlow3D hinges upon the Human Protein Structurome, a complete repository we first compiled that incorporates the 3D structures of every single protein as well as the binding interfaces for all known PPIs in humans. The vast majority of 3D structural information was resolved by recent deep learning algorithms. By applying NetFlow3D to 415,017 somatic protein-altering mutations in 5,950 TCGA tumors across 19 cancer types, we identified 1,656 intra- and 3,343 inter-protein 3D clusters of mutations throughout the Human Protein Structurome, of which ~50% would not have been found if using only experimentally-determined protein structures. These 3D clusters have converging effects on 377 cellular subnetworks. Compared to canonical PPI network analyses, NetFlow3D achieved a 5.5-fold higher statistical power for identifying significantly dysregulated subnetworks. The majority of identified subnetworks were previously obscured by the overwhelming background noise of non-clustered passenger mutations, including portions of non-canonical PRC1, mediator complex, MCM2-7 complex, neddylation of cullins, complement system, TRiC, etc. NetFlow3D and our pan-cancer results can be accessed from http://netflow3d.yulab. org. This work shows that mapping how individual mutations act across scales requires the integration of their local spatial organization on protein structures and their global topological organization in the PPI network. One-Sentence Summary Integrating the arrangement of somatic mutations on 3D protein structures and throughout the protein-protein interaction network provides unique insights of their multiscale mechanistic effects in cancer.
Inhibitory effect of Ephedra herba on human norovirus infection in human intestinal organoids

Human norovirus (HuNoV) is a major cause of acute gastroenteritis and foodborne diseases worldwide with public health concern, yet no antiviral therapies have been developed. In this study, we aimed to screen crude drugs, which are components of Japanese traditional medicine, ‘‘Kampo” to see their effects on HuNoV infection using a reproducible HuNoV cultivation system, stem-cell derived human intestinal organoids/enteroids (HIOs). Among the 23 crude drugs tested, Ephedra herba significantly inhibited HuNoV infection in HIOs. A time-of-drug addition experiment indicated that this crude drug likely targets post-entry step for the inhibition. To our knowledge, this is the first anti-HuNoV inhibitor screen targeting crude drugs, and Ephedra herba was identified as a novel inhibitor candidate that merits further study.
Noise reduction in computed tomography using a multiplicative continuous-time image reconstruction method

In clinical X-ray computed tomography (CT), filtered back-projection as a transform method and iterative reconstruction such as the maximum-likelihood expectation-maximization (ML-EM) method are known methods to reconstruct tomographic images. As the other reconstruction method, we have presented a continuous-time image reconstruction (CIR) system described by a nonlinear dynamical system, based on the idea of continuous methods for solving tomographic inverse problems. Recently, we have also proposed a multiplicative CIR system described by differential equations based on the minimization of a weighted Kullback–Leibler divergence. We prove theoretically that the divergence measure decreases along the solution to the CIR system, for consistent inverse problems. In consideration of the noisy nature of projections in clinical CT, the inverse problem belongs to the category of ill-posed problems. The performance of a noise-reduction scheme for a new (previously developed) CIR system was investigated by means of numerical experiments using a circular phantom image. Compared to the conventional CIR and the ML-EM methods, the proposed CIR method has an advantage on noisy projection with lower signal-to-noise ratios in terms of the divergence measure on the actual image under the same common measure observed via the projection data. The results lead to the conclusion that the multiplicative CIR method is more effective and robust for noise reduction in CT compared to the ML-EM as well as conventional CIR methods.
Mathematical instantons with maximal order jumping lines

A mathematical instanton bundle on P (over an algebraically closed field) is a rank two vector bundle E on P with c1 = 0 and with H0(E) = H1(E(−2)) = 0. Let c2(E) = n. Then n > 0. A jumping line of E of order a, (a > 0), is a line ` in P on which E splits as O`(−a)⊕O`(a). It is easy to see that the jumping lines of E all have order ≤ n. We will say that E has a maximal order jumping line if it has a jumping line of order n. Our goal is to show that such an E is unobstructed in the moduli space of stable rank two bundles, i.e., H2(E ⊗ E) = 0. The technique can be slightly extended. We show that when c2 = 5, any E with a jumping line of order 4 is unobstructed. We describe at the end how mathematical instantons with maximal order jumping lines arise and estimate the dimension of this particular smooth locus of bundles. It is known that every mathematical instanton bundle on P with c2 ≤ 4 is unobstructed ([L]). In [H], it is shown that bundles built by the Serre construction from the union of n+1 skew lines and the bundles built from elliptic curves in P of degree n + 4 are unobstructed (with c2 = n.) The SU(2)instanton bundles obtained from physics are also known to be unobstructed using analytic arguments ([D-V]). Recently Nusler and Trautmann [N-T] have shown that any mathematical instanton with a section in degree 1 is unobstructed, extending the case of bundles obtained from skew lines and also extending a result of Hirschowitz and Narasimhan on ’t Hooft bundles [H-N]. A preprint of Ancona and Ottaviani ([A-O]) produces a singular point on the moduli space of stable bundles with c1 = 0, c2 = 5. This singular point is in the closure of the open set of mathematical instantons but is not itself a mathematical instanton bundle. Still open is the general Question: for fixed Chern classes c1 = 0, c2 = n, is the moduli space Mmi(0, n) of mathematical instanton bundles on P irreducible and smooth? A construction of stable bundles on P (not necessarily mathematical instanton) with jumping lines of large order was first done by C. Peskine.
Optimum detection of small targets in a cluttered background

We discuss an optimum method for target detection where the input data consist of a time series of images in one or more data channels. Backgrounds are assumed to be complicated by nonstationary Gaussian clutter that is correlated over time and across channels and may be further corrupted by highly localized non-Gaussian interference terms that appear target-like. Because of the nonstationary clutter, methods based on the Fourier transform are impractical. Instead, we use a pixel-based autoregressive (AR) model. To deal with the inhomogeneous clutter, we segment the data into locally stationary regions; each region is then whitened using its estimated AR parameters, and an optimum matched filter is applied to the whitened data. The main contributions are the following. First, we develop a generalized AR model to describe multiple frames of multiple-channel data. Second, we introduce a novel automated method for the detection of small targets in nonstationary background. Third, we discuss some special applications such as the detection of small targets in non-Gaussian background clutter. We describe in detail the implementation of these techniques, and demonstrate their performance using both synthetic data and real data obtained from the Compact Airborne Spectrographic Imager (CASI).
Improved magnetoelectric performance of the Ni-P/Ni/Pb(Zr,TiO)3 cylindrical layered composites

The Ni-P/Ni/Pb(Zr,TiO)3 (PZT) cylindrical layered magnetoelectric (ME) composites have been prepared by electroless deposition and electrodeposition. The ME effect in the axial mode was researched in this paper. Compared with that of the Ni/PZT and Ni-P/PZT composites, the Ni-P/Ni/PZT composites have both lower optimal magnetic field and higher ME voltage coefficient. The reason for the ME improvement can be ascribed to the flux concentration effect and the compressive stress in the Ni layers induced by the high permeability Ni-P layers. These results open up a suitable way to enhance the sensitivity and optimize the design of ME devices for practical application.
Neural Geometric Fabrics: Efficiently Learning High-Dimensional Policies from Demonstration

: Learning dexterous manipulation policies for multi-ﬁngered robots has been a long-standing challenge in robotics. Existing methods either limit themselves to highly constrained problems and smaller models to achieve extreme sample efﬁciency or sacriﬁce sample efﬁciency to gain capacity to solve more complex tasks with deep neural networks. In this work, we develop a structured approach to sample-efﬁcient learning of dexterous manipulation skills from demonstrations by leveraging recent advances in robot motion generation and control. Specifically, our policy structure is induced by Geometric Fabrics - a recent framework that generalizes classical mechanical systems to allow for ﬂexible design of expressive robot motions. To avoid the cumbersome manual design required by existing motion generators, we introduce Neural Geometric Fabric (NGF) - a framework that learns Geometric Fabric-based policies from data. NGF policies are provably stable and capable of encoding speed-invariant geometries of complex motions in multiple task spaces simultaneously. We demonstrate that NGFs can learn to perform a variety of dexterous manipulation tasks on a 23-DoF hand-arm physical robotic platform purely from demonstrations. Results from comprehensive comparative and ablative experiments show that NGF’s structure and action spaces help learn acceleration-based policies that consistently out-perform state-of-the-art baselines like Riemannian Motion Policies (RMPs), and other commonly used networks, such as feed-forward and recurrent neural networks. More importantly, we demonstrate that NGFs do not rely on often-used and expertly-designed operational-space controllers, promoting an advancement towards efﬁciently learning safe, stable, and high-dimensional controllers.