Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloning and identification of the CTLA-4IgV gene and functional application of vaccine in Xinjiang sheep

Abstract Cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) is an important surface molecule of activated T cells that has a strong affinity with the B7 molecule on the surface of antigen-presenting cells. Among these molecules, the CTLA-4 extracellular region (CTLA-4 IgV) may be used as a novel immune adjuvant molecule for delivering antigens and inducing strong humoral and cellular immune responses. In this study, bioinformatics analysis was performed to determine and clone the extracellular region of Xinjiang sheep CTLA-4 (NM_001009214). The CTLA-4 IgV gene was amplified and ligated into the pMD19-T vector, and the positive bacteria were screened by blue-white spots for sequencing and comparison. The correctly sequenced CTLA-4 IgV was digested and then ligated into the prokaryotic expression vector pET-30a(+). The plasmid pET30a–CTLA-4 IgV was constructed to induce the expression of the recombinant protein CTLA-4 IgV. Thereafter, CTLA-4 IgV was identified. Clustal X multiple sequence alignment revealed that the protein sequence of Xinjiang sheep CTLA-4 IgV was different from that of the known CTLA-4 extracellular region. The 3D protein structure of Xinjiang sheep CTLA-4 IgV was constructed via the bioinformatics method. Subsequently, molecular docking between the Xinjiang sheep CTLA-4 IgV protein and the B7 molecule was conducted. Results revealed multiple binding sites in the extracellular region of Xinjiang sheep CTLA-4, and two multiple interactions ensured stable binding after docking. The functionality of the Xinjiang sheep CTLA-4 IgV protein was further verified by fusing the CTLA-4 extracellular V region with EgG1Y162, a protective protein from Echinococcus granulosa, and the purified recombinant protein CTLA-4 IgV–EgG1Y162 was expressed with the mouse bone marrow-derived. The addition of the Xinjiang sheep CTLA-4 IgV protein at the amino terminus promoted the binding of EgG1Y162 to dendritic cells (DCs) and increased the maturation rate of these cells, further indicating that the protein could effectively improve the antigen presentation ability of DCs. The CTLA-4 extracellular domain protein of Xinjiang sheep is unique and has the potential to promote the presentation of the fusion protein by DCs as an adjuvant. The cloning and expression of this gene provide new measures and ideas for the preparation of the Xinjiang sheep vaccine to prevent zoonotic diseases. Graphical abstract
The role of Shunaoxin pills in the treatment of chronic cerebral hypoperfusion and its main pharmacodynamic components

Abstract Chronic cerebral hypoperfusion (CCH) is a frequent ischemic cerebrovascular disease that induces brain dysfunction. Shunaoxin pills (SNX) are traditional Chinese medicines (TCM), frequently used for the treatment of CCH. The purpose of this study was to develop an activity-based screening system to identify the active ingredients of SNX. We developed a model of CCH and revealed that SNX induces cerebrovascular dilatation and protects against CCH-induced nerve cell injury in rats. Using the transcriptome analysis, we found that Ca2+-related signaling pathways play a major role in the effect of SNX against CCH. We developed an activity-based screening system based on the ultra-high performance liquid chromatography with quadrupole time-of-flight mass spectrometry coupled with a dual-luciferase reporter calcium assay to identify the active components of SNX. As a result, SNX dilates cerebral blood vessels, increasing cerebral blood flow by modulating calcium-related signaling pathways and regulating calcium homeostasis. Two calcium antagonists, ligustilide and senkyunolide I, were identified as active ingredients in SNX. In conclusion, we developed a rapid screening method suitable for the discovery of active natural products in TCM by integrating genomics and target pathway-oriented spectroscopic analysis.
The Kalman Filter Approach for Time-varying ß Estimation

Beta parameter is used in finance in the form of market model to estimate systematic risk. Such βs are assumed to be time invariant. Literature shows that now there is a considerable evidence that β risk is not constant over time. The aim of this article is the estimation of time-varying Italian industry parameter βs using the Kalman filter technique. This approach is applied to returns of the Italian market over the period 1991-2001.
Forward planning under uncertainty in a population-based alcohol use disorder sample

Altered decision-making is a defining component of addiction, but there is little evidence whether these alterations affect multi-step planning in individuals with alcohol use disorder (AUD). We used a recently developed planning task in a cross-sectional approach to test the planning performance of 30 individuals diagnosed with AUD relative to 32 healthy control subjects, both sampled from the general population. To gain insight into the factors underlying behavioral performance, we inferred the parameters of a reinforcement learning agent performing rational planning, using a Bayesian inference scheme. This approach allowed us to differentiate between separate factors determining planning performance, e.g., planning depth, decision noise, and bias for certain action choices. Contrary to our hypothesis, we did not observe reduced planning depth in AUD subjects. Instead, our results show a small effect in the opposite direction: Healthy controls were slightly less efficient in the planning task. Importantly, subjects in the control group allocated less time for planning than AUD subjects, potentially indicative of motivational differences between groups. The group difference in planning depth persisted when controlling for both reaction times and general cognitive performance, albeit at a lesser magnitude. Altogether, our results do not favor the view that mild-to-moderate alcohol use disorder in general-population individuals generally involves impairments in cognitive tasks requiring forward planning across multiple steps.
Computational investigations in inhibition of alcohol/aldehyde dehydrogenase in lignocellulosic hydrolysates

Second generation alcoholic biofuels synthesis from lignocellulosic biomass (LB) consists three steps viz., pre-treatment, detoxification, and fermentation. This dilute acid pre-treatment process generates several compounds like acids, aldehydes, ketones, oxides and their phenolic derivatives that are potential inhibitors of some of the crucial enzymes in the metabolic pathway of ABE fermentation. With application of hybrid quantum mechanics/ molecular mechanics (QM/MM) approach, our aim is to discern the molecular mechanism of inhibition of key AADs across solventogenic species. The objectives of present study are: (1) identification and homology modelling of key AADs; (2) validation, quality assessment and physiochemical characterization of the modelled enzymes; (3) identification, construction and optimization of chemical structure of potent microbial inhibitors in LH; and (4) applications of hybrid QM/MM simulations to profile the molecular interactions between microbial inhibitors and key AADs. Our computational investigation has revealed various important facets of inhibition of the AAD enzymes, which could guide structural biologist in designing efficient and robust enzymes. Moreover, our methodology also provides a general framework which could applied for deciphering the molecular mechanism of inhibition behaviour of other enzymes. Highlights Homology modelling of 7 alcohol/aldehyde dehydrogenase (AAD) in solventogenic Clostridia Identification and structural optimization of potent microbial inhibitors in lignocellulosic hydrolysates QM/MM simulations to profile the molecular interactions between 10 inhibitors and 7 AADs Discernment of the molecular mechanism of inhibition of key alcohol/aldehyde dehydrogenase A methodological framework for deciphering the molecular mechanism of enzyme inhibition
SnRK1 inhibits anthocyanin biosynthesis through both transcriptional regulation and direct phosphorylation and dissociation of the MYB/bHLH/TTG1 MBW complex

Plants have evolved an extensive specialized secondary metabolism. The colorful flavonoid anthocyanins, for example, not only stimulate flower pollination and seed dispersal but also protect different tissues against high light, UV- and oxidative stress. Their biosynthesis is highly regulated by environmental and developmental cues and induced by high sucrose levels. Expression of the biosynthetic enzymes involved is controlled by a transcriptional MBW complex, comprising (R2R3) MYB- and bHLH-type transcription factors (TF) and the WD40 repeat protein TTG1. Anthocyanin biosynthesis is obviously useful but also carbon- and energy-intensive and non-vital. Consistently, the SnRK1 protein kinase, a metabolic sensor activated in carbon- and energy-depleting stress conditions, represses anthocyanin biosynthesis. Here we show that Arabidopsis SnRK1 represses MBW complex activity both at the transcriptional and post-translational level. In addition to repressing expression of the key transcription factor MYB75/PAP1, SnRK1 activity triggers MBW complex dissociation, associated with loss of target promoter binding, MYB75 protein degradation and nuclear export of TTG1. We also provide evidence for direct interaction with and phosphorylation of multiple MBW complex proteins. These results indicate that repression of expensive anthocyanin biosynthesis is an important strategy to save energy and redirect carbon flow to more essential processes for survival in metabolic stress conditions.
Quantization and the Hessian of Mabuchi energy

Let L→X be an ample bundle over a compact complex manifold. Fix a Hermitian metric in L whose curvature defines a Kahler metric on X. The Hessian of Mabuchi energy is a fourth-order elliptic operator D*D on functions which arises in the study of scalar curvature. We quantize D*D by the Hessian P*k Pk of balancing energy, a function appearing in the study of balanced embeddings. P*k Pk is defined on the space of Hermitian endomorphisms of H0(X,Lk) endowed with the L2-inner product. We first prove that the leading order term in the asymptotic expansion of P*k Pk is D*D. We next show that if Aut (X,L)/ℂ* is discrete, then the eigenvalues and eigenspaces of P*k Pk converge to those of D*D. We also prove convergence of the Hessians in the case of a sequence of balanced embeddings tending to a constant scalar curvature Kahler metric. As consequences of our results we prove that an estimate of Phong and Sturm is sharp and give a negative answer to a question posed by Donaldson. We also discuss some possible applications to the study of Calabi flow. © 2012.
Ion permeation pathway within the internal pore of P2X receptor channels

P2X receptor channels are trimeric ATP-activated ion channels expressed in neuronal and non-neuronal cells that are attractive therapeutic targets for human disorders. Seven subtypes of P2X receptor channels have been identified in mammals that can form both homomeric and heteromeric channels. P2X1-4 and P2X7 receptor channels are cation selective, whereas P2X5 has been reported to have both cation and anion permeability. P2X receptor channel structures reveal that each subunit is comprised of two transmembrane helices, with both N-and C-termini on the intracellular side of the membrane, and a large extracellular domain that contains the ATP binding sites at subunit interfaces. Recent structures of ATP-bound P2X receptors with the activation gate open reveal the unanticipated presence of a cytoplasmic cap over the central ion permeation pathway, leaving lateral fenestrations that may be largely buried within the membrane as potential pathways for ions to permeate the intracellular end of the pore. In the present study we identify a critical residue within the intracellular lateral fenestrations that is readily accessible to thiol reactive compounds from both sides of the membrane and where substitutions influence the relative permeability of the channel to cations and anions. Taken together, our results demonstrate that ions can enter or exit the internal pore through lateral fenestrations that play a critical role in determining the ion selectivity of P2X receptor channels. (223)
Maximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream Tasks

Objective Multi-site collaboration is essential for overcoming small-sample problems when exploring reproducible biomarkers in MRI studies. However, various scanner-specific factors dramatically reduce the cross-scanner replicability. Moreover, existing harmony methods mostly could not guarantee the improved performance of downstream tasks. Methods we proposed a new multi-scanner harmony framework, called ‘maximum classifier discrepancy generative adversarial network’, or MCD-GAN, for removing scanner effects in the original feature space while preserving substantial biological information for downstream tasks. Specifically, the adversarial generative network was utilized for persisting the structural layout of each sample, and the maximum classifier discrepancy module was introduced for regulating GAN generators by incorporating the downstream tasks. Results We compared the MCD-GAN with other state-of-the-art data harmony approaches (e.g., ComBat, CycleGAN) on simulated data and the Adolescent Brain Cognitive Development (ABCD) dataset. Results demonstrate that MCD-GAN outperformed other approaches in improving cross-scanner classification performance while preserving the anatomical layout of the original images. Significance To the best of our knowledge, the proposed MCD-GAN is the first generative model which incorporates downstream tasks while harmonizing, and is a promising solution for facilitating cross-site reproducibility in various tasks such as classification and regression. The codes of the MCD-GAN are available at https://github.com/trendscenter/MCD-GAN.
Investor Heuristics and Biases: Quantitative Effects on the U.S. Business Cycle

This paper presents a medium-scale quantitative New-Keynesian DSGE model with financial intermediaries that incorporates a variety of behavioral modifications to investors. Investors in this model are subject to the anchoring and adjustment heuristic, endogenous confidence bias, and exogenous confidence bias. A Bayesian MCMC approach is utilized to estimate various iterations of this model, including shock process parameters, for the U.S. economy. The estimation aims to fit the model to six macroeconomic time series from 1988 through 2019, along with a measure of investors’ expectations of future stock market performance. The estimated posterior means show that 18% or 31% of investor expectations arise from behavioral factors when modeled as exhibiting anchoring or confidence respectively. When exhibiting anchoring, investors are significantly pegged to 1-qtr and 1-year prior stock returns. In the model with endogenously propagated confidence, investors exhibit a confidence function that increases at a rate roughly halfway between a square and cubic root function. Both behavioral features are able to better fit the data as compared to the base model which includes only exogenous confidence shocks. In response to confidence shocks, the economy exhibits a dual-response fluctuation characterized by a large initial boom followed by a sustained recession. Models with behavioral features over-react to economic shocks compared to baseline, leading to more volatile business cycles.
Unintentional Inception: When a Premium Is Offered to Unintentional Creations

Creations can be fundamentally intended or unintended from their outset. Past work has focused on intentional creations, finding that people place a premium on effort. We examine the role of unintentionality in the inception of creations in six studies using a variety of stimuli (N = 1,965), finding that people offer a premium to unintentional creations versus otherwise identical intentional creations. We demonstrate that the unintentionality involved in the inception of a creation results in greater downward counterfactual thought about how the unintentional creation may have never been created at all, and this in turn heightens perceptions that the creation was a product of fate, causing people to place a premium on such creations. We provide evidence for this causal pathway using a combination of mediation and moderation approaches. Further, we illuminate that this premium is not offered when a negative outcome is ascribed to an unintentional creation.
Oscillation and a class of linear delay differential equations

The differential equation u(m)(t) + p(t)u(g(t)) 0, where p is one-signed, is broken into four cases, according to the parity of m and the sign of p. In each case, an analysis is given of the effect g can have on oscillation properties, and oscillation and nonoscillation criteria are given.
Modeling Gen Z Continuance Intention toward Ubiquitous Media System: Applying Technology Readiness and Technology Acceptance Model

The move from traditional technologies such as desktop computing environment to ubiquitous media systems (UMS) including ubiquitous and mobile computing essential to analyze decision-making process in order to forecast and understand the continued use of multi-context UMS. As these applications provide easy access across multiple channels, it is of particular interest to explore factors that may aﬀect the continuous intention toward UMS, particularly among generation Z. In this vein, continuous intention is a key indicator of long-term success for information system survival. This study proposed to employ the integrated model of technology readiness (TR) and technology acceptance model (TAM), as well as the mediation eﬀect of technology acceptance model on continuance intention. Understanding the inﬂuential factors of continuance intention towards using UMS would assist policymakers to support programs on promoting information technology (IT). Using a sample of 261 adult consumers of ubiquitous media systems in Taiwan and SEM analysis, signiﬁcant positive direct and indirect inﬂuences were found.
Comparing Stable Manifold and backward reachable set Method for Estimating the Region of Attraction

Numerous computational methods have been proposed for estimating the region of attraction (ROA) within the Mathematics, Power systems and Control community. In this paper, we consider the determination of the boundary of the ROA using the stable manifolds and the estimation of the ROA as backward reachable sets using a Hamilton-Jacobi-Isaacs partial differential equation. The stable manifolds methods discretize the ROA (or the boundary of the ROA) into a finite union of geometric elements and store them directly. The essential idea of the backward reachable set method is to present the ROA implicitly, meaning that it is feasible to find the ROA as the level set of some function of the state space variables. The stable manifold and backward reachable set method are compared and discussed through investigating numerical example.
Cocosvi: Single Snapshot Compressive Spectral Video Via Covariance Matrix Estimation

This paper proposes a snapshot spectral video imager based on the compressive covariance sampling (CCS) theory, named compressive covariance spectral video (CoCoS-Vi), and a low-rank optimization problem that exploits the covariance matrix (CM) spectrotemporal correlation to improve the reconstruction accuracy. Specifically, CoCoS-Vi relies on a lenslet array to simultaneously obtain multiple compressed measurements for each frame. Moreover, a dual-dispersive sensing geometry based on a single prism and the coded aperture is designed to recast the vectorial sensing problem into a matrix CCS model. Several computational simulations validate the CoCoSVi accuracy and speed, achieving up to 4 and 8 dB of PSNR compared to traditional ADMM and CM-based algorithms, respectively, and a speedup of up to 371x. A CoCoSVi testbed implementation was developed to validate the acquisition and reconstruction methodology. CoCoSVi experiments show a speedup of 231x against a traditional PnP-ADMMM algorithm.
A Deep Review and State-of-the-art Performance on Fingerprint Liveness Detection Databases

The need of effective Fingerprint Liveness Detection (FLD) has been arising due to advancements in spoofing fingerprint. The literature has been reported with numerous FLD techniques, which have been experimented on different datasets. However, the literature lags on deep details of benchmark databases and own databases of researchers. This poses a challenge to researchers in contributing and experimenting FLD techniques in a common platform. Hence, this paper extensively reviews the databases that have been used in the literature in the past decade. The review discusses the characteristics of the databases, volume of images and image acquisition environment. In addition, this review paper presents the state-of-the-art performance achieved on FLD databases and the methodology used to achieve them.
Bird Song Recognition Based on Deep Transfer Learning with XGBoost

Due to the influence of many factors such as geography, environment, body size and age, the performance of bird song classification depends heavily on the effectiveness of extracting differentiated features from the audio signal and building efficient audio classifier. In this paper, we first calculate delta and delta-delta for log-Mel spectrogram (log-Mels) to form 3-dimensional (3D) time-frequency spectrogram based on log-Mels not only preserves species differentiation but also reduces the influence of bird song-independent factors in the actual scene. Second, we propose a novel audio classifier based on deep transfer learning combining VGG16 with XGBoost. Specifically, the pre-trained VGG16 is fine-tuned and retrained, and the retrained VGG16 is used to extract high-level implicit features of the 3D time-frequency spectrogram based on log-Mels, and the extracted high-level implicit features are re-described and mapped to bird species using the XGBoost model. Experimental results on BirdsData based on real natural scenes show the proposed method effectively break through the limitations of complex environments and achieve an accuracy of 95.13% on the test set.
Smart Painting Software and Cross-Border Fusion based on Digital Image Processing Algorithms

Smart painting software and cross-border fusion based on digital image processing algorithms is studied in this paper. Early non-deep methods use shallow network model architectures, which are difficult to effectively mine complex nonlinear semantic correlation information between multimodal data. Hence, the deep structure is considered as the basis of model design. Therefore, the traditional CG concept has entered the profession of art creation and design. CG illustration is the product of the combination of advanced technology and art, with its own timeliness and shocking visual impact. Further, the novel digital image processing algorithms are integrated to achieve a smart platform. The efficiency of the model is demonstrated by performing a performance analysis.
Correspondence between neural threshold networks and Kauffman Boolean cellular automata

For an asymmetric version of the McCulloch-Pitts neural network (1943) and for Kauffman's infinite-range Boolean network model (1984), the time evolution of the Hamming distances between two different initial configurations are compared in the thermodynamic limit. It is shown that in both models phase transitions occur for corresponding values of the transition parameters and that their Hamming distances can have the same time evolution leading to quantitatively the same dynamics, as known from time-dependent Landau theory for phase transitions.