Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial and asymmetrical reproductive isolation between two sympatric tropical shrub species: Cnidoscolus aconitifolius and C. souzae (Euphorbiaceae)

Abstract Reproductive isolation is conferred by several barriers that occur at different stages of reproduction. Comprehensive reviews on the topic have identified that barriers occurring prior to zygote formation are often stronger than those that occur afterward. However, the overrepresentation of temperate perennial herbs in the current literature precludes any generalization of this pattern to plants that present other life forms and patterns of distribution. Here, we assessed reproductive isolation barriers and their absolute contribution to reproductive isolation and asymmetry in Cnidoscolus aconitifolius and C. souzae, two closely related tropical shrub species that co‐occur on the Yucatan peninsula. The reproductive barriers assessed were phenological mismatch, pollinator differentiation, pollen–pistil incompatibility (three pre‐zygotic barriers), fruit set failure, and seed unviability (post‐zygotic barriers). Reproductive isolation between the study species was found to be complete in the direction C. aconitifolius to C. souzae, but only partial in the opposite direction. One post‐zygotic barrier was the strongest example. Most barriers, particularly the pre‐zygotic examples, were asymmetrical and predicted the direction of heterospecific pollen flow and hybrid formation from C. souzae to C. aconitifolius. Both parental species, as well as the hybrids, were diploid and had a chromosome number 2n = 36. More studies with tropical woody perennials are required to fully determine whether this group of plants consistently shows stronger post‐zygotic barriers.
Use of Psychotropic Drugs during the COVID-19 pandemic in Minas Gerais, Brazil

ABSTRACT Objective: To describe the profile of dispensation of mental health drugs by analyzing trends in use before and during the COVID-19 pandemic within the Unified Health System (Sistema Único de Saúde [SUS]). Methods: Pharmacoepidemiological study based on the retrospective analysis of records regarding the dispensation of psychotropic medicines in the SUS database in the state of Minas Gerais between 2018 and 2021, considering the periods before (2018–2019) and during the COVID-19 pandemic (2020–2021). A database with the records of dispensation of municipalities was created, and the consistency of releases was verified using the Analysis of Variance (ANOVA) test. Medicine consumption was measured in a defined daily dose (DDD) per 1,000 inhabitants/day for SUS, and the difference between periods was evaluated using Student's t-test. Results: During the COVID-19 pandemic, there was an increase in the consumption of psychotropic drugs in SUS-MG. The most consumed medicines were fluoxetine hydrochloride, diazepam and phenobarbital sodium (DDD=5.89; 3.42; 2.49) in the Basic Pharmaceutical Services Component(CBAF), and olanzapine, risperidone and quetiapine hemifumarate (DDD=0.80; 0.47; 0.38) in the Specialized Pharmaceutical Services Component (CEAF). The highest percentage increase in consumption was attributed to clonazepam (75.37%) and lithium carbonate (35.35%), in CBAF, and levetiracetam (3,000.00%) and memantine hydrochloride (340.0%) in CEAF. Conclusion: The change in the psychotropic drug dispensation profile during the COVID-19 pandemic highlights the need to produce more studies to complete, confirm or rule out this profile and monitor the use of psychotropic drugs by the population in the post-pandemic context.
Identification of genes regulated by lipids from seaweed Susabinori (Pyropia yezoensis) involved in the improvement of hepatic steatosis: Insights from RNA-Seq analysis in obese db/db mice

Hepatic steatosis is an early stage in the progression of non-alcoholic fatty liver disease (NAFLD) and can lead to the development of non-alcoholic steatohepatitis (NASH), a major cause of liver-related morbidity and mortality. Identification of dietary components that can alleviate hepatic steatosis is crucial for developing effective therapeutic strategies for NAFLD. Recently, we demonstrated the impact of lipids extracted from the marine red alga Susabinori (Pyropia yezoensis) in a murine model of type 2-diabete (db/db). We found that Susabinori lipids (SNL), abundant in eicosapentaenoic acid (EPA)-containing polar lipids, protected against obesity-induced hepatic steatosis in db/db mice. To understand the specific genes or biological pathways underlying the effects of SNL, we conducted RNA-Seq analysis of the hepatic transcriptome. By performing comparative analysis of differentially expressed genes between normal mice and db/db mice consuming a control diet, as well as SNL-fed db/db mice, we identified the 15 SNL-dependent up-regulated genes that were down-regulated in db/db mice but up-regulated by SNL feeding. Gene ontology and pathway analysis on these 15 genes demonstrated a significant association with the metabolisms of arachidonic acid (AA) and linoleic acid (LA). Furthermore, we observed alterations in the expression levels of monoacylglycerol lipase (Magl) and fatty acid-binding protein 4 (Fabp4) in the SNL-fed db/db mice, both of which are implicated in AA and LA metabolism. Additionally, the livers of SNL-fed db/db mice exhibited reduced levels of AA and LA, but a high accumulation of EPA. In conclusion, the SNL diet might affect the metabolisms of AA and LA, which contribute to the improvement of hepatic steatosis. Our findings provide insights into the molecular mechanisms underlying the beneficial effects of SNL.
Patterns of metabolic syndrome and associated factors in women from the ELSA-Brasil: a latent class analysis approach

Abstract: This study aimed to identify patterns of metabolic syndrome among women and estimate their prevalence and relationship with sociodemographic and biological characteristics. In total, 5,836 women were evaluated using baseline data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Patterns of metabolic syndrome were defined via latent class analysis, using the following metabolic abnormalities as indicators: abdominal obesity, hyperglycemia, hypertension, hypertriglyceridemia, and reduced HDL cholesterol. The relationship between these patterns and individual characteristics was assessed using latent class analysis with covariates. Three patterns of metabolic syndrome were identified: high metabolic expression, moderate metabolic expression, and low metabolic expression. The first two patterns represented most women (53.8%) in the study. Women with complete primary or secondary education and belonging to lower social classes were more likely to have higher metabolic expression. Black and mixed-race women were more likely to have moderate metabolic expression. Menopausal women aged 50 years and older were more often classified into patterns of greater health risk. This study addressed the heterogeneous nature of metabolic syndrome, identifying three distinct profiles for the syndrome among women. The combination of abdominal obesity, hyperglycemia, and hypertension represents the main metabolic profile found among ELSA-Brasil participants. Sociodemographic and biological factors were important predictors of patterns of metabolic syndrome.
Mol-Zero-GAN: zero-shot adaptation of molecular generative adversarial network for specific protein targets

Drug discovery is a process that finds new potential drug candidates for curing diseases and is also vital to improving the wellness of people. Enhancing deep learning approaches, e.g., molecular generation models, increases the drug discovery process's efficiency. However, there is a problem in this field in creating drug candidates with desired properties such as the quantitative estimate of druglikeness (QED), synthetic accessibility (SA), and binding affinity (BA), and there is a challenge for training a generative model for specific protein targets that has less pharmaceutical data. In this research, we present Mol-Zero-GAN, a framework that aims to solve the problem based on Bayesian optimization (BO) to find the model optimal weights' singular values, factorized by singular value decomposition, and generate drug candidates with desired properties with no additional data. The proposed framework can produce drugs with the desired properties on protein targets of interest by optimizing the model's weights. Our framework outperforms the state-of-the-art methods sharing the same objectives. Mol-Zero-GAN is publicly available at https://github.com/cucpbioinfo/Mol-Zero-GAN.
EEG-Based Frequency Domain Separation of Upward and Downward Movements of the Upper Limb

One of the fundamental challenges encountered when implementing the Motor Imagery based Brain-Computer Interfacing (BCI) paradigm is accurately classifying the Electroencephalography (EEG) signals that originate due to the same joint movements. This emanates from the limited spatial proximity in the corresponding brain regions. Here, we explore the feasibility of distinguishing arm-reaching movements specific to the right hand using multiple frequency bands in EEG signals despite the limited spatial differentiation of induced potentials. To address this challenge, a channel averaging method was used combining six electrodes positioned in close proximity to the motor cortex, intending to isolate and enhance electromagnetic activity in the brain associated with arm movements. This study was further refined by focusing on two distinct frequency bands: mu (8-12Hz) and beta (12-30Hz), each associated with different cognitive and motor functions. The results of our study revealed promising outcomes across two classification methods. Utilizing the Support Vector Machine (SVM) classification method, our proposed approach achieved an average accuracy of 59.3% while the K-Nearest Neighbors (KNN) classification approach yielded an average accuracy of 61.63% in distinguishing between upward and downward movements of the right arm.
Tuning Colloidal Reactions

The precise control of complex reactions is critical for biological processes ranging from cell division to metabolism. Synthetic analogues of living materials suffer from our inability to tune chemical reactions with precise outcomes. Here, we leverage differentiable simulators to design nontrivial reaction pathways in colloidal assemblies. By optimizing interactions between reactants and substrates, we achieve controlled disassembly of octahedral and icosahedral shells. As a potential engineering target, we design a reaction that provokes the release of a small particle trapped in a shell.
Clique Supersaturation

We study how many copies of a graph $F$ that another graph $G$ with a given number of cliques is guaranteed to have. For example, one of our main results states that for all $t\ge 2$, if $G$ is an $n$ vertex graph with $kn^{3/2}$ triangles and $k$ is sufficiently large in terms of $t$, then $G$ contains at least \[\Omega(\min\{k^t n^{3/2},k^{\frac{2t^2}{3t-1}}n^{\frac{5t-2}{3t-1}}\})\] copies of $K_{2,t}$, and furthermore, we show these bounds are essentially best-possible provided either $k\ge n^{1/2t}$ or if certain bipartite-analogues of well known conjectures for Tur\'an numbers hold.
Global Latent Neural Rendering

A recent trend among generalizable novel view synthesis methods is to learn a rendering operator acting over single camera rays. This approach is promising because it removes the need for explicit volumetric rendering, but it effectively treats target images as collections of independent pixels. Here, we propose to learn a global rendering operator acting over all camera rays jointly. We show that the right representation to enable such rendering is a 5-dimensional plane sweep volume consisting of the projection of the input images on a set of planes facing the target camera. Based on this understanding, we introduce our Convolutional Global Latent Renderer (ConvGLR), an efficient convolutional architecture that performs the rendering operation globally in a low-resolution latent space. Experiments on various datasets under sparse and generalizable setups show that our approach consistently outperforms existing methods by significant margins.
Okapi: A Lightweight Architecture for Secure Speculation Exploiting Locality of Memory Accesses

This paper introduces Okapi, an innovative hardware/software cross-layer architecture designed to mitigate Transient Execution Side Channel (TES) attacks, including Spectre variants, in modern computing systems. A key contribution of Okapi is a set of security features building upon each other to offer various trade-offs between performance and security. At its core, Okapi allows for speculative data accesses if the targeted memory region has already been accessed non-speculatively before in the same trust domain. It delays first-time accesses until the speculation is resolved. Okapi stands out for its flexibility in security implementation. For environments with less stringent security needs, Okapi's features can be deactivated to eliminate performance overhead. When activated, the hardware modifications alone provide robust protection against transient execution attacks at a thread-level granularity, including all universal read gadgets like Spectre-PHT and Spectre-BTB. This incurs an average performance overhead of only 3.6 % for the SPEC CPU2017 benchmark suite. On top, Okapi introduces the OkapiReset instruction for additional software-level security support. This instruction, which can be manually inserted by developers or automatically via a compiler extension, allows for fully secure speculation and for trust domain sizes smaller than a thread. While the manual insertion of OkapiReset incurs an additional 0.6 % performance overhead, the automated compiler extension approach results in a 23.1 % overhead for making a cryptographic library fully secure. With an approximate 0.4 % hardware overhead, Okapi provides a highly scalable and adaptable solution for secure speculation in state-of-the-art processor design.
Understanding the Role of Four-Phonon Scattering in the Lattice Thermal Transport of Monolayer MoS$_{2}$

In the calculations of lattice thermal conductivity ($\kappa_{\text{L}}$), vital contributions stemming from four-phonon scattering are often neglected. The significance of four-phonon scattering in the thermal transport properties of monolayer (ML) MoS$_{2}$ has been unraveled using first-principles calculations combined with the Boltzmann transport equation. If only three-phonon scattering processes are considered then the $\kappa_{\text{L}}$ is found to be significantly overestimated ($\sim$ 115.8 Wm$^{-1}$K$^{-1}$ at 300 K). With the incorporation of the four-phonon scattering processes, the $\kappa_{\text{L}}$ reduces to 24.6 Wm$^{-1}$K$^{-1}$, which is found to be closer to the experimentally measured $\kappa_{\text{L}}$ of 34.5 Wm$^{-1}$K$^{-1}$. Four-phonon scattering significantly impacts the carrier lifetime ($\tau$) of the low-energy out-of-plane acoustic mode (ZA) phonons and thereby, suppresses its contribution in $\kappa_{\text{L}}$ from 64% (for three-phonon scattering) to 16% (for both three- and four-phonon scatterings). The unusually high four-phonon scattering rate ($\tau_{4}^{-1}$) of the ZA phonons is found to result from the simultaneous effect of the acoustic-optical frequency gap, strong anharmonicity, and the reflection symmetry imposed selection rule. The strong coupling between the quadratic dispersion of the ZA mode and the $\tau_{4}^{-1}$ is discovered by the application of mechanical strain. The strain induced increase in the linearity of the ZA mode dispersion dramatically reduces the significance of the four-phonon scattering in the strained ML-MoS$_{2}$, both qualitatively and quantitatively. These conclusions will provide significant insights into the thermal transport phenomena in ML-MoS$_{2}$, as well as any other 2D material.
On the Verification of Embeddings with Hybrid Markov Logic

The standard approach to verify representations learned by Deep Neural Networks is to use them in specific tasks such as classification or regression, and measure their performance based on accuracy in such tasks. However, in many cases, we would want to verify more complex properties of a learned representation. To do this, we propose a framework based on a probabilistic first-order language, namely, Hybrid Markov Logic Networks (HMLNs) where we specify properties over embeddings mixed with symbolic domain knowledge. We present an approach to learn parameters for the properties within this framework. Further, we develop a verification method to test embeddings in this framework by encoding this task as a Mixed Integer Linear Program for which we can leverage existing state-of-the-art solvers. We illustrate verification in Graph Neural Networks, Deep Knowledge Tracing and Intelligent Tutoring Systems to demonstrate the generality of our approach.
The classifying element for quotients of Fermat curves

Suppose $C$ is a cyclic Galois cover of the projective line branched at the three points $0$, $1$, and $\infty$. Under a mild condition on the ramification, we determine the structure of the graded Lie algebra of the lower central series of the fundamental group of $C$ in terms of a basis which is well-suited to studying the action of the absolute Galois group of $\mathbb{Q}$.
Privacy-Preserving Distributed Optimisation using Stochastic PDMM

Privacy-preserving distributed processing has received considerable attention recently. The main purpose of these algorithms is to solve certain signal processing tasks over a network in a decentralised fashion without revealing private/secret data to the outside world. Because of the iterative nature of these distributed algorithms, computationally complex approaches such as (homomorphic) encryption are undesired. Recently, an information theoretic method called subspace perturbation has been introduced for synchronous update schemes. The main idea is to exploit a certain structure in the update equations for noise insertion such that the private data is protected without compromising the algorithm's accuracy. This structure, however, is absent in asynchronous update schemes. In this paper we will investigate such asynchronous schemes and derive a lower bound on the noise variance after random initialisation of the algorithm. This bound shows that the privacy level of asynchronous schemes is always better than or at least equal to that of synchronous schemes. Computer simulations are conducted to consolidate our theoretical results.
Hierarchical Cognitive Spectrum Sharing in Space-Air-Ground Integrated Networks

In space-air-ground integrated networks (SAGINs), cognitive spectrum sharing has been regarded as a promising solution to improve spectrum efficiency by enabling a secondary network to access the spectrum of a primary network. However, different networks in SAGIN may have different quality of service (QoS) requirements, which can not be well satisfied with the traditional cognitive spectrum sharing architecture. For example, the aerial network typically has high QoS requirements, which however may not be met when it acts as a secondary network. To address this issue, in this paper, we propose a hierarchical cognitive spectrum sharing architecture (HCSSA) for SAGINs, where the secondary networks are divided into a preferential one and an ordinary one. Specifically, the aerial and terrestrial networks can access the spectrum of the satellite network under the condition that the caused interference to the satellite terminal is below a certain threshold. Besides, considering that the aerial network has a higher priority than the terrestrial network, we aim to use a rate constraint to ensure the performance of the aerial network. Subject to these two constraints, we consider a sum-rate maximization for the terrestrial network by jointly optimizing the transmit beamforming vectors of the aerial and terrestrial base stations. To solve this non-convex problem, we propose a penalty-based iterative beamforming (PIBF) scheme that uses the penalty method and the successive convex approximation technique. Moreover, we also develop three low-complexity schemes by optimizing the normalized beamforming vectors and power control. Finally, we provide extensive numerical simulations to compare the performance of the proposed PIBF scheme and the low-complexity schemes. The results also demonstrate the advantages of the proposed HCSSA compared with the traditional cognitive spectrum sharing architecture.
Homogenization of 2D materials in the Thomas-Fermi-von Weizsacker theory

We study the homogenization of the Thomas-Fermi-von Weizsacker (TFW) model for 2D materials. It consists in considering 2D-periodic nuclear densities with periods going to zero. We study the behavior of the corresponding ground state electronic densities and ground state energies. The main result is that these three dimensional problems converge to a limit model that is one dimensional. We also illustrate this convergence with numerical simulations and estimate the converging rate for the ground state electronic densities and the ground state energies.
Polarization dynamics of trapped polariton condensates with 
PT
 symmetry

We propose a grated microcavity setup to form trapped polariton condensates with parity-time ($\mathcal{PT}$) symmetry and study their polarization dynamics. The pseudo-conservative dynamics of the Stokes vector in proposed configuration is preserved in the presence of polariton-polariton interaction. In the case of weak gain-dissipation inbalance, as compared to the linear polarization splitting, the polarization Stokes spheres are deformed into ellipsoids. When linear polarization splitting becomes weak, i.e., when $\mathcal{PT}$-symmetry is broken for noninteracting system, the Stokes spheres are transformed into hyperboloids, but the dynamics is still described by closed trajectories, allowing manipulation of the polarization of polariton condensate by changing polarization splitting without losing its coherency.
Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4

Dynamic analysis methods effectively identify shelled, wrapped, or obfuscated malware, thereby preventing them from invading computers. As a significant representation of dynamic malware behavior, the API (Application Programming Interface) sequence, comprised of consecutive API calls, has progressively become the dominant feature of dynamic analysis methods. Though there have been numerous deep learning models for malware detection based on API sequences, the quality of API call representations produced by those models is limited. These models cannot generate representations for unknown API calls, which weakens both the detection performance and the generalization. Further, the concept drift phenomenon of API calls is prominent. To tackle these issues, we introduce a prompt engineering-assisted malware dynamic analysis using GPT-4. In this method, GPT-4 is employed to create explanatory text for each API call within the API sequence. Afterward, the pre-trained language model BERT is used to obtain the representation of the text, from which we derive the representation of the API sequence. Theoretically, this proposed method is capable of generating representations for all API calls, excluding the necessity for dataset training during the generation process. Utilizing the representation, a CNN-based detection model is designed to extract the feature. We adopt five benchmark datasets to validate the performance of the proposed model. The experimental results reveal that the proposed detection algorithm performs better than the state-of-the-art method (TextCNN). Specifically, in cross-database experiments and few-shot learning experiments, the proposed model achieves excellent detection performance and almost a 100% recall rate for malware, verifying its superior generalization performance. The code is available at: github.com/yan-scnu/Prompted_Dynamic_Detection.