Miracle-dz/UniFAR-FTUs huggingface dataset:
Pro-inflammatory markers are associated with response to sequential pharmacotherapy in major depressive disorder: a CAN-BIND-1 report

Abstract Objective There is limited literature on associations between inflammatory tone and response to sequential pharmacotherapies in major depressive disorder (MDD). Methods In a 16-week open-label clinical trial, 211 participants with MDD were treated with escitalopram 10–20 mg daily for 8 weeks. Responders continued escitalopram while non-responders received adjunctive aripiprazole 2–10 mg daily for 8 weeks. Plasma levels of pro-inflammatory markers—C-reactive protein, interleukin (IL)-1β, IL-6, IL-17, interferon-gamma (IFN)-Γ, tumor necrosis factor (TNF)-α, and Chemokine C–C motif ligand-2 (CCL-2)—measured at baseline, and after 2, 8 and 16 weeks were included in logistic regression analyzes to assess associations between inflammatory markers and treatment response. Results Pre-treatment IFN-Γ and CCL-2 levels were significantly associated with a lower of odds of response to escitalopram at 8 weeks. Increases in CCL-2 levels from weeks 8 to 16 in escitalopram non-responders were significantly associated with higher odds of non-response to adjunctive aripiprazole at week 16. Conclusion Higher pre-treatment levels of IFN-Γ and CCL-2 were associated with non-response to escitalopram. Increasing levels of these pro-inflammatory markers may be associated with non-response to adjunctive aripiprazole. These findings require validation in independent clinical populations.
The autism-associated loss of δ-catenin functions disrupts social behavior

Significance δ-catenin is important for the localization and function of glutamatergic AMPA receptors at synapses in many brain regions. The glycine 34 to serine (G34S) mutation in the δ-catenin gene has been found in autism patients and results in the loss of δ-catenin functions. δ-catenin expression is also closely linked to other autism-risk genes involved in synaptic structure and function, further implying that it is important for the development of autism. Importantly, social dysfunction is a key characteristic of autism. However, the links between δ-catenin functions and social behavior are largely unknown. The significance of the current research is thus predicated on filling this gap by identifying the molecular, cellular, and synaptic underpinnings of the role of δ-catenin in social behavior.
Attenuation of Dopaminergic Neurodegeneration in a C. elegans Parkinson’s Model through Regulation of Xanthine Dehydrogenase (XDH-1) Expression by the RNA Editase, ADR-2

Differential RNA editing by adenosine deaminases that act on RNA (ADARs) has been implicated in several neurological disorders, including Parkinson’s disease (PD). Here, we report results of a RNAi screen of genes differentially regulated in adr-2 mutants, normally encoding the only catalytically active ADAR in Caenorhabditis elegans, ADR-2. Subsequent analysis of candidate genes that alter the misfolding of human α-synuclein (α-syn) and dopaminergic neurodegeneration, two PD pathologies, reveal that reduced expression of xdh-1, the ortholog of human xanthine dehydrogenase (XDH), is protective against α-synuclein-induced dopaminergic neurodegeneration. Further, RNAi experiments show that WHT-2, the worm ortholog of the human ABCG2 transporter and a predicted interactor of XDH-1, is the rate-limiting factor in the ADR-2, XDH-1, WHT-2 system for dopaminergic neuroprotection. In silico structural modeling of WHT-2 indicates that the editing of one nucleotide in the wht-2 mRNA leads to the substitution of threonine with alanine at residue 124 in the WHT-2 protein, changing hydrogen bonds in this region. Thus, we propose a model where wht-2 is edited by ADR-2, which promotes optimal export of uric acid, a known substrate of WHT-2 and a product of XDH-1 activity. In the absence of editing, uric acid export is limited, provoking a reduction in xdh-1 transcription to limit uric acid production and maintain cellular homeostasis. As a result, elevation of uric acid is protective against dopaminergic neuronal cell death. In turn, increased levels of uric acid are associated with a decrease in ROS production. Further, downregulation of xdh-1 is protective against PD pathologies because decreased levels of XDH-1 correlate to a concomitant reduction in xanthine oxidase (XO), the form of the protein whose by-product is superoxide anion. These data indicate that modifying specific targets of RNA editing may represent a promising therapeutic strategy for PD.
MRI diffusion tensor reconstruction with PROPELLER data acquisition

MRI diffusion imaging is effective in measuring the diffusion tensor in brain, cardiac, liver, and spinal lesion imaging. Diffusion tensor tomography MRI (DTT MRI) method is based on reconstructing tensor field from measurements of projections of the diffusion tensor field. In the present paper we examine the potential of a novel data acquisition scheme, PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction), for its imaging capability and sufficiency for diffusion imaging. DTT MRI with PROPELLER data acquisition shows the significant potential to reduce a number of weighted measurements, avoid an ambiguity in reconstructing diffusion tensor parameters, increase SNR and decrease the influence of signal distortions.
Cryo-EM structure of the Mon1–Ccz1–RMC1 complex reveals molecular basis of metazoan RAB7A activation

Significance Rab GTPases are critical to specify cellular organelles and to regulate intracellular trafficking. As important molecular switches, the activity of Rab GTPases is spatiotemporally controlled by many proteins, including guanine nucleotide exchange factors (GEFs) and GTPase-activating proteins. The Mon1–Ccz1 complex is the only known RAB7A GEF, which is indispensable for endocytic trafficking and autophagy. In contrast to unicellular organisms, metazoans have a third member in the complex, RMC1. The cryogenic-electron microscopy structure of the Mon1–Ccz1–RMC1 complex and accompanying functional studies presented here, help to define the evolutionally conserved and metazoan-specific mechanisms in regulating RAB7A activation.
(Quasi) multitask support vector regression with heuristic hyperparameter optimization for whole-genome prediction of complex traits: a case study with carcass traits in broilers

Abstract This study investigates nonlinear kernels for multitrait (MT) genomic prediction using support vector regression (SVR) models. We assessed the predictive ability delivered by single-trait (ST) and MT models for 2 carcass traits (CT1 and CT2) measured in purebred broiler chickens. The MT models also included information on indicator traits measured in vivo [Growth and feed efficiency trait (FE)]. We proposed an approach termed (quasi) multitask SVR (QMTSVR), with hyperparameter optimization performed via genetic algorithm. ST and MT Bayesian shrinkage and variable selection models [genomic best linear unbiased predictor (GBLUP), BayesC (BC), and reproducing kernel Hilbert space (RKHS) regression] were employed as benchmarks. MT models were trained using 2 validation designs (CV1 and CV2), which differ if the information on secondary traits is available in the testing set. Models’ predictive ability was assessed with prediction accuracy (ACC; i.e. the correlation between predicted and observed values, divided by the square root of phenotype accuracy), standardized root-mean-squared error (RMSE*), and inflation factor (b). To account for potential bias in CV2-style predictions, we also computed a parametric estimate of accuracy (ACCpar). Predictive ability metrics varied according to trait, model, and validation design (CV1 or CV2), ranging from 0.71 to 0.84 for ACC, 0.78 to 0.92 for RMSE*, and between 0.82 and 1.34 for b. The highest ACC and smallest RMSE* were achieved with QMTSVR-CV2 in both traits. We observed that for CT1, model/validation design selection was sensitive to the choice of accuracy metric (ACC or ACCpar). Nonetheless, the higher predictive accuracy of QMTSVR over MTGBLUP and MTBC was replicated across accuracy metrics, besides the similar performance between the proposed method and the MTRKHS model. Results showed that the proposed approach is competitive with conventional MT Bayesian regression models using either Gaussian or spike–slab multivariate priors.
Diffusion tensor field restoration and segmentation

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is a relative new MRI modality that can be used to study tissue microstructure, e.g., white matter connectivity in brain in vivo. It has attracted vast attention during the past few years. In this dissertation, novel solutions to two important problems in DT-MRI analysis: diffusion tensor field (aka diffusion tensor image, DTI) restoration and segmentation are presented. For DTI restoration, we first develop a model for estimating the accuracy of a nonlinear estimator used in estimating the apparent diffusivity coefficient (ADC). We also use this model to design optimal diffusion weighting factors by accounting for the fact that the ground truth of the ADC is a distribution instead of a single value. The proposed method may be extended to study the statistical properties of DTI estimators and to design corresponding optimal acquisition parameters. We then present a novel constrained variational principle for simultaneous smoothing and estimation of the DTI from complex valued diffusion weighted images (DWI). The constrained variational principle involves the minimization of a regularization term of Lp smoothness norms, subject to a nonlinear inequality constraint on the data. The data term we employ is the original Stejskal-Tanner equation instead of the linearized version usually employed in literature. The complex valued nonlinear form leads to a more accurate (when compared to the linearized version) estimate of the DTI. The inequality constraint requires that the nonlinear least squares data term be bounded from above by a known tolerance factor. Finally, in order to accommodate the positive definite constraint on the diffusion tensor, it is expressed in terms of Cholesky factors and estimated. The constrained variational principle is solved using the augmented Lagrangian technique in conjunction with the limited memory quasi-Newton method. For DTI segmentation, we present a novel definition of tensor “distance” grounded in concepts from information theory and incorporate it in the segmentation of DTI. Diffusion tensor is a symmetric positive definite (SPD) tensor at each point of a DTI and can be interpreted as the covariance matrix of a local Gaussian distribution. Thus, a natural measure of dissimilarity between diffusion tensors would be the Kullback-Leibler (KL) divergence or its relative. In particular, we define a tensor “distance” as the square root of the J-divergence (symmetrized KL) between two Gaussian distributions corresponding to the tensors being compared. Our definition leads to novel closed form expressions for the “distance” itself and the mean value of a DTI. We then incorporate this new tensor “distance” in a region based active contour model for DTI segmentation and the closed expressions we derived greatly help the computation.
Analytic progress in open string field theory

Open string field theory provides an action functional for open string fields, and it is thus a manifestly off-shell formulation of open string theory. The solutions to the equation of motion of open string field theory are expected to describe consistent classical open string backgrounds. In this thesis, I present a number of analytic results in bosonic open string field theory. Firstly, I present analytic solutions to the equation of motion that describe an exactly marginal deformation of the chosen open string background. A prominent example in this class is the rolling-tachyon solution, which describes the decay of an unstable D-brane. Furthermore, I demonstrate that the Riemann surface geometry of string perturbation theory can be radically simplified using propagators of Schnabl gauge instead of Siegel gauge. In principle, this simplification allows the analytic computation of arbitrary off-shell one-loop open string amplitudes. Finally, I show that this simplicity of Schnabl gauge one-loop Riemann surfaces can be combined with the knowledge of analytic solutions to construct an analytically computable string field theory boundary state. For all known solutions, this boundary state precisely coincides with the BCFT boundary state of the open string background that the solution is expected to describe. This construction thus confirms the physical interpretation of known analytic solutions and thus provides a nice consistency check on open string field theory. Thesis Supervisor: Barton Zwiebach Title: Professor of Physics
A new probe guided mutation operator for more efficient exploration of the search space: an experimental analysis

This paper re-examines the classical polynomial mutation (PLM) operator and proposes a probe guided version of the PLM for more efficient exploration of the search space. The proposed probe guided mutation (PGM) operator applied to two well-known MOEAs, namely the non-dominated sorting genetic algorithm II (NSGAII) and strength Pareto evolutionary algorithm 2 (SPEA2), under two different sets of test functions. The relevant results are compared with the results derived by the same MOEAs by using their typical configuration with the PLM operator. The experimental results show that the proposed probe guided mutation operator outperforms the classical polynomial mutation operator, based on a number of different performance metrics that evaluate both the proximity of the solutions to the Pareto front and their dispersion on it.
A segmentation algorithm for noisy images

This paper presents a 2-D image segmentation algorithm and addresses issues related to its performance on noisy images. The algorithm segments an image by first constructing a minimum spanning tree representation of the image and then partitioning the spanning tree into subtrees representing different homogeneous regions. The spanning tree is partitioned in such a way that the sum of gray-level variations over all partitioned subtrees is minimized under the constraints that each subtree has at least a specified number of pixels and two adjacent subtrees have significantly different "average" gray-levels. Two types of noise, transmission errors and Gaussian additive noise, are considered and their effects on the segmentation algorithm are studied. Evaluation results have shown that the segmentation algorithm is robust in the presence of these two types of noise.
Superprism phenomena in planar photonic crystals

We utilize the anomalous dispersion of planar photonic crystals near the dielectric band edge to control the wavelength-dependent propagation of light. We typically observe an angular swing of up to 10/spl deg/ as the input wavelength is changed from 1290 nm to 1310 nm, which signifies an angular dispersion of 0.5 degree/nm. Such a strong angular dispersion is of the order required for wavelength-division multiplexing systems. This is the first demonstration of the "superprism" effect in a planar configuration with a small lattice.
SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs

Abstract Identifying the exact epitope positions for a monoclonal antibody (mAb) is of critical importance yet highly challenging to the Ab design of biomedical research. Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR), suitable for both experimental and modelled structures. In practice, SEPPA-mAb appended a fingerprints-based patch model to SEPPA 3.0, considering the structural and physic-chemical complementarity between a possible epitope patch and the complementarity-determining region of mAb and trained on 860 representative antigen-antibody complexes. On independent testing of 193 antigen-antibody pairs, SEPPA-mAb achieved an accuracy of 0.873 with an FPR of 0.097 in classifying epitope and non-epitope residues under the default threshold, while docking-based methods gave the best AUC of 0.691, and the top epitope prediction tool gave AUC of 0.730 with balanced accuracy of 0.635. A study on 36 independent HIV glycoproteins displayed a high accuracy of 0.918 and a low FPR of 0.058. Further testing illustrated outstanding robustness on new antigens and modelled antibodies. Being the first online tool predicting mAb-specific epitopes, SEPPA-mAb may help to discover new epitopes and design better mAbs for therapeutic and diagnostic purposes. SEPPA-mAb can be accessed at http://www.badd-cao.net/seppa-mab/.
DISTRIBUTED FEEDBACK CONTROL OF THE BURGERS EQUATION BY A REDUCED-ORDER APPROACH USING WEIGHTED CENTROIDAL VORONOI TESSELLATION

In this article, we study a reduced-order modelling for distributed feedback control problem of the Burgers equations. Brief review of the centroidal Voronoi tessellation (CVT) are provided. A weighted (nonuniform density) CVT is introduced and low-order approximate solution and compensator-based control design of Burgers equation is discussed. Through weighted CVT (or CVT-nonuniform) method, obtained low-order basis is applied to low-order functional gains to design a low-order controller, and by using the low-order basis order of control modelling was reduced. Numerical experiments show that a solution of reduced-order controlled Burgers equation performs well in comparison with a solution of full order controlled Burgers equation.
Therapeutic antagonism of the neurokinin 1 receptor in endosomes provides sustained pain relief

Significance Sustained GPCR signaling from endosomes mediates pathologies, including pain. Optimal therapy requires development of antagonists that penetrate cells, are retained in endosomes, and disrupt endosomal signaling. To enhance membrane penetration and retention in acidified endosomes, analogs of the neurokinin 1 receptor (NK1R) antagonist netupitant were synthesized with altered lipophilicity and acidity. Lipophilic and acidic analogs antagonized endosomal NK1R signaling and provided potent, efficacious, and long-lasting pain relief in mice expressing human NK1R. Mice expressing a truncated NK1R, corresponding to a naturally occurring variant with aberrant signaling and trafficking, showed diminished nociceptive responses to substance P. The results identify criteria that will facilitate the design of antagonists of endosomal receptors and provide evidence for the contribution of endosomal signaling to disease.
iPSC-derived reactive astrocytes from patients with multiple sclerosis protect cocultured neurons in inflammatory conditions

Multiple sclerosis (MS) is the most common chronic central nervous system inflammatory disease. Individual courses are highly variable, with complete remission in some patients and relentless progression in others. We generated induced pluripotent stem cells (iPSCs) to investigate possible mechanisms in benign MS (BMS), compared with progressive MS (PMS). We differentiated neurons and astrocytes that were then stressed with inflammatory cytokines typically associated with MS phenotypes. TNF-α/IL-17A treatment increased neurite damage in MS neurons from both clinical phenotypes. In contrast, TNF-α/IL-17A–reactive BMS astrocytes cultured with healthy control neurons exhibited less axonal damage compared with PMS astrocytes. Accordingly, single-cell transcriptomic BMS astrocyte analysis of cocultured neurons revealed upregulated neuronal resilience pathways; these astrocytes showed differential growth factor expression. Furthermore, supernatants from BMS astrocyte/neuronal cocultures rescued TNF-α/IL-17–induced neurite damage. This process was associated with a unique LIF and TGF-β1 growth factor expression, as induced by TNF-α/IL-17 and JAK-STAT activation. Our findings highlight a potential therapeutic role of modulation of astrocyte phenotypes, generating a neuroprotective milieu. Such effects could prevent permanent neuronal damage.
Highly sensitive multi-stage terahertz parametric upconversion detection based on a KTiOPO4 crystal.

In this Letter, we demonstrate a highly sensitive multi-stage terahertz (THz) wave parametric upconversion detector based on a KTiOPO4 (KTP) crystal pumped by a 1064-nm pulsed-laser (10 ns, 10 Hz). The THz wave was upconverted to near-infrared light in a trapezoidal KTP crystal based on stimulated polariton scattering. The upconversion signal was amplified in two KTP crystals based on non-collinear and collinear phase matching, respectively, to improve detection sensitivity. A rapid-response detection in the THz frequency ranges of 4.26-4.50 THz and 4.80-4.92 THz was achieved. Moreover, a dual-color THz wave generated from THz parametric oscillator using KTP crystal was detected simultaneously based on dual-wavelength upconversion. The minimum detectable energy of 2.35 fJ was realized with a dynamic range of 84 dB at 4.85 THz, which gives a noise equivalent power (NEP) of the order of 21.3 pW/Hz1/2. By changing the phase-matching angle or the wavelength of the pump laser, it is suggested that the detection of the THz frequency band of interest in a wide range from approximately 1 to 14 THz is possible.
MultiNEP: a multi-omics network enhancement framework for prioritizing disease genes and metabolites simultaneously

Abstract Motivation Many studies have successfully used network information to prioritize candidate omics profiles associated with diseases. The metabolome, as the link between genotypes and phenotypes, has accumulated growing attention. Using a ”multi-omics” network constructed with a gene–gene network, a metabolite–metabolite network, and a gene–metabolite network to simultaneously prioritize candidate disease-associated metabolites and gene expressions could further utilize gene–metabolite interactions that are not used when prioritizing them separately. However, the number of metabolites is usually 100 times fewer than that of genes. Without accounting for this imbalance issue, we cannot effectively use gene–metabolite interactions when simultaneously prioritizing disease-associated metabolites and genes. Results Here, we developed a Multi-omics Network Enhancement Prioritization (MultiNEP) framework with a weighting scheme to reweight contributions of different sub-networks in a multi-omics network to effectively prioritize candidate disease-associated metabolites and genes simultaneously. In simulation studies, MultiNEP outperforms competing methods that do not address network imbalances and identifies more true signal genes and metabolites simultaneously when we down-weight relative contributions of the gene–gene network and up-weight that of the metabolite–metabolite network to the gene–metabolite network. Applications to two human cancer cohorts show that MultiNEP prioritizes more cancer-related genes by effectively using both within- and between-omics interactions after handling network imbalance. Availability and implementation The developed MultiNEP framework is implemented in an R package and available at: https://github.com/Karenxzr/MultiNep