Miracle-dz/UniFAR-FTUs huggingface dataset:
ChoruMM: a versatile multi-components mixed model for bacterial-GWAS

Genome-wide Association Studies (GWAS) have been central to studying the genetics of complex human outcomes, and there is now tremendous interest in implementing GWAS-like approaches to study pathogenic bacteria. A variety of methods have been proposed to address the complex linkage structure of bacterial genomes, however, some questions remain about to optimize the genetic modelling of bacteria to decipher causal variations from correlated ones. Here we examined the genetic structure underlying whole-genome sequencing data from 3,824 Listeria monocytogenes strains, and demonstrate that the standard human genetics model, commonly assumed by existing bacterial GWAS methods, is inadequate for studying such highly structured organisms. We leverage these results to develop ChoruMM, a robust and powerful approach that consists of a multi-component linear mixed model, where components are inferred from a hierarchical clustering of the bacteria genetic relatedness matrix. Our ChoruMM approach also includes post-processing and visualization tools that address the pervasive long-range correlation observed in bacteria genome and allow to assess the type I error rate calibration.
Bite force transmission and mandible shape in grasshoppers, crickets, and allies is largely dependent on phylogeny, not diet

Although organ systems evolve in response to many intrinsic and extrinsic factors, frequently one factor has a dominating influence. For example, mouthpart shape and mechanics are thought to correlate strongly with aspects of the diet. Within insects, this paradigm of a shape-diet connection is advocated for decades but the relationship has so far never been quantified and is mostly based on qualitative observations. Orthoptera (grasshoppers, crickets, and allies) are a prominent case, for which mandible shape and dietary preference are thought to correlate strongly and even lead to predictions of feeding preferences. Here, we analysed mandible shape, biting efficiency, and their potential correlation with dietary categories in a phylogenetic framework for a broad sampling of several hundred extant Orthoptera covering nearly all families. The mandibular mechanical advantage was used as a descriptor of gnathal edge shape and bite force transmission efficiency. We aimed to understand how mandible shape is linked to biting efficiency and diet, and how these traits are influenced by phylogeny and allometry. The investigation reveals that feeding ecology is not the unequivocal predictor of mandible shape that it was assumed to be. There is a strong phylogenetic signal suggesting that phylogenetic history does have a much more prevalent influence on gnathal edge shape and distal mechanical advantage, than, e.g., feeding guilds or the efficiency of the force transmission to the food. Being ancestrally phytophagous, Orthoptera evolved in an environment with abundant food sources so that selective pressures leading to more specialized mouthpart shapes and force transmission efficiencies were low.
Luminosity-line width relations and the extragalactic distance scale. I - Absolute calibration

Multicolor CCD photometry is presented for a sample of 15 spiral and irregular members of the Local, Sculptor, and M81 groups with M B ≤ -16.0 and relevant as potential calibrators of the luminosity-line width relations. The sample contains those galaxies for which a ground-based study of Cepheid variables could provide accurate distances and hence an absolute calibration for the relations. Unfortunately, only six of the systems presently have reliable independent distance estimates, but this number is sufficient to provide a reasonable calibration
Conversion of methionine biosynthesis in E. coli from trans- to direct-sulfurylation enhances extracellular methionine levels

Methionine is an essential amino acid in mammals and a critical metabolite in all organisms. As such, various applications, including food, feed, and pharmaceuticals, necessitate the addition of L-methionine. Although amino acids and other metabolites are commonly produced through bacterial fermentation, high-yield biosynthesis of L-methionine remains a significant challenge due to the strict cellular regulation of the biosynthesis pathway. As a result, methionine is produced primarily synthetically, resulting in a racemic mixture of D,L-methionine. This study aimed to enhance methionine bio-production yields in E. coli by replacing its highly regulated trans-sulfurylation pathway with the more common direct-sulfurylation pathway used by other bacteria. To this end, we generated an auxotroph E. coli strain (MG1655) by simultaneously deleting metA and metB genes and complementing them with metX and metY from different bacteria. Complementation of the genetically modified E. coli with metX/metY from Cyclobacterium marinum or Deinococcus geothermalis, together with the deletion of the global repressor metJ and overexpression of the transporter YjeH, resulted in a substantial increase of up to 126 and 160-fold methionine relative to the wild-type strain, respectively, and accumulation of up to 700 mg/L using minimal MOPS medium and 2 ml culture. Our findings provide a method to study methionine biosynthesis and a chassis for enhancing L-methionine production by fermentation. Highlights Replacement of E. coli metA and metB with metX and metY recovered its growth The engineered E. coli has a 160-fold increase in extracellular methionine levels Selection of different metX and metY leads to varying growth rates and enhanced methionine levels
New Quantum Search Model on Symmetric Ciphers and Its Applications

It has been a long-standing viewpoint that doubling the length of key seeds in symmetric cipher can resist the quantum search attacks. This paper puts forward a quantum key search model to deal with the post-quantum security of symmetric ciphers. The quantum search is performed in the punctured keystream/ciphertext space instead of the key space. On inputting the punctured keystreams/ciphertexts, we rule out the spurious keys and ﬁnd out the real key via the iterative use of the quantum singular value transformation search algorithm. We ﬁnd out several parameters, such as the length of the punctured keystream/ciphertext, the iterations, and the error in the search algo-rithm, and all of them can inﬂuence the resulting complexity. When these parameters are chosen properly, a better complexity can be obtained than Grover algorithm. Our search model can apply to any typical symmetric cipher. To demonstrate the power, we apply our model to analyze block ciphers AES family, stream ciphers Grain-128 and ZUC-like. The resulting complexity is ˜ O (2 31 . 1 ) of AES-128, ˜ O (2 32 . 4 ) of AES-192, ˜ O (2 33 . 0 ) of AES-256, ˜ O (2 27 . 7 ) of Grain-128, ˜ O (2 33 . 3 ) of ZUC-128, and ˜ O (2 34 . 7 ) of ZUC-256. Our results show that increasing the length of key seeds is not an eﬀective way anymore to resist the quantum search attacks, and it is necessary to propose new measures to ensure the post-quantum security of symmetric ciphers.
Single-cell RNA-seq differential expression tests within a sample should use pseudo-bulk data of pseudo-replicates

Single-cell RNA sequencing (scRNA-seq) has become a standard approach to investigate molecular differences between cell states. Comparisons of bioinformatics methods for the count matrix transformation (normalization) and differential expression (DE) analysis of these data have already highlighted recommendations for effective between-sample comparisons and visualization. Here, we examine two remaining open questions: (i) What are the best combinations of data transformations and statistical test methods, and (ii) how do pseudo-bulk approaches perform in single-sample designs? We evaluated the performance of 343 DE pipelines (combinations of eight types of count matrix transformations and ten statistical tests) on simulated and real-world data, in terms of precision, sensitivity, and false discovery rate. We confirm superior performance of pseudo-bulk approaches without prior transformation. For within-sample comparisons, we advise the use of three pseudo-replicates, and provide a simple R package DElegate to facilitate application of this approach.
Chaotic advection in bounded Navier–Stokes flows

We discuss mixing and transport in three-dimensional, steady, Navier–Stokes flows with the no-slip condition at the boundaries. The advective flux is related to the dynamics of the Navier–Stokes equations and a prediction is made of the scaling of the advective flux with the Reynolds number: the flux is expected to decay as the Reynolds number goes to infinity. This prediction is made via a Melnikov-type calculation together with boundary layer concepts through which the flow is split into an integrable and a small non-integrable part. The rate of decay is related to the details of viscous flow in boundary layers. The Melnikov function is related to the Bernoulli integral of the underlying Euler flow. The effects of molecular diffusivity are discussed and the effective axial diffusivity scaling predicted as a function of Reynolds and Péclet numbers. Using these ideas, we study the mass transport in the wavy vortex flow in the Taylor–Couette apparatus as a particular example. We propose an explanation of the observed non-monotonic behaviour of flux with increasing Reynolds number that was not captured in any of the previous models. It is shown that there is a Reynolds number at which the axial flux in the wavy vortex flow is maximized. At the low range of Reynolds numbers for which the wavy vortex flow is stable the flux increases, while for large Reynolds numbers it decreases. We compare these predictions with the available experimental and numerical data on the wavy vortex flow.
The immune response to a fungus in pancreatic cancer samples

Pancreatic ductal adenocarcinoma (PDAC) is a poor prognosis cancer with an . aggressive growth profile that is often diagnosed at late stage and that has few curative or therapeutic options. PDAC growth has been linked to alterations in the pancreas microbiome, which could include the presence of the fungus Malassezia. We used RNA-sequencing to compare 14 paired tumor and normal (tumor adjacent) pancreatic cancer samples and found Malassezia RNA in both the PDAC and normal tissues. Although the presence of Malassezia was not correlated with tumor growth, a set of immune- and inflammatory-related genes were up-regulated in the PDAC compared to the normal samples, suggesting that they are involved in tumor progression. Gene set enrichment analysis suggests that activation of the complement cascade pathway and inflammation could be involved in pro PDAC growth.
LMI-based criteria for synchronization of complex dynamical networks

This paper considers the problem of controlling a complex dynamical network by means of pinning. We point out that the synchronization criterion given in the form of matrix eigenvalues can be equivalent to a linear matrix inequality criterion. We further investigate network synchronization via pinning and prove several linear matrix inequality theorems. In particular, we theoretically provide two typical pinning strategies based on whether the graph which is made up of unpinned nodes and edges between them is irreducible or not. Numerical simulations including k-regular networks, star-shaped networks and BA scale-free networks, are shown for illustration and verification.
Wind direction effects on orographic drag

A series of idealised numerical simulations is performed to investigate the effect of wind direction on the pressure forces exerted on a high elliptical mesoscale ridge in the presence of Coriolis effects. At the Rossby number considered here (Ro ∼ 13), rotational effects have a significant impact on the flow fields, however the primary effect of rotation on the drag is to provide the asymmetry required to initiate vortex shedding when the flow is perpendicular to the mountain ridge. It is found that linear theory, although not valid for such high mountains, provides a useful scaling for the variation of drag with wind direction. For a large range of wind directions, the flow is in a high‐ (super‐linear) drag state and wave breaking, vortex shedding and upstream flow blocking are observed. However, when the flow is close to being parallel to the major axis of the mountain ridge, the drag becomes sub‐linear, and none of the above processes are seen. We show that the change from a high‐drag state to a low‐drag state can be explained in terms of the aspect ratio of the mountain, that is the ratio of the across‐flow mountain length to the along‐flow length. Finally we demonstrate that the results found for the idealised elliptical mountains also apply to a real mountain of similar dimensions. Copyright © 2008 Royal Meteorological Society and Crown Copyright 2008, published by John Wiley & Sons, Ltd.
AAQ-PEKS: An Attribute-based Anti-Quantum Public-Key Encryption Scheme with Keyword Search for E-healthcare Scenarios

—Electronic Medical Records (EMRs) have been utilized in plentiful medical institutions due to their superior convenience and low storage overhead. Nevertheless, it is difficult for medical departments with disparate management regulations to share EMRs through secure communication channels since sensitive EMRs are prone to be tampered with. Therefore, the EMRs should be encrypted before being outsourced to the network servers. Public key Encryption with Keyword Search (PEKS) has the ability for doctors to search encrypted EMRs, but traditional PEKS algorithms are susceptible to quantum computing attacks and without considering access control. To address the aforementioned issues, we proposed AAQ-PEKS scheme, named an attribute-based anti-quantum public-key encryption scheme with keyword search. Initially, based on the LWE hardness, we first introduce the attribute-based PEKS that can resist quantum attacks in E-health scenarios. Secondly, we combine Attribute-Based Encryption (ABE) into AAQ-PEKS to realize access control for sensitive EMRs. Thirdly, the computational security analysis illustrates that our scheme achieves correctness, Indistinguishability against Chosen Plaintext Attack (IND-CPA) and Indistinguishability against Chosen Keyword Attack (IND-CKA). Lastly, comprehensive performance evaluation in practice elaborates that our AAQ-PEKS is more efficient compared with other existing top-tier schemes. To conclude, our scheme has the characteristics of resisting quantum attacks and fine-grained access control for E-health scenarios.
Accurate simulation of linear viscoelastic properties by variance reduction through the use of control variates

An algorithm is demonstrated to yield accurate linear viscoelasticproperties in a nonequilibrium stochastic simulation by variance reduction. For noninteracting polymer dumbbells, multiple orders of magnitude improvements in computational accuracy for nonequilibrium viscoelasticproperties are easily realized. This simple and robust control variate scheme uses control variables defined as the viscoelasticproperties obtained from a parallel, equilibrium simulation. Results for strongly interactingcolloidal particles demonstrate some advantages as well as limitations of the method.
Exponential asymptotics and Stokes lines in nonlinear ordinary differential equations

A technique for calculating exponentially small terms beyond all orders in singularly perturbed ordinary differential equations is presented. The approach is based on the application of a WKBJ–type ansatz to the late terms in the naive asymptotic expansion and the identification of Stokes lines, and is closely related to the well–known Stokes line–smoothing phenomenon in linear ordinary differential equations. The method is illustrated by application to examples.
Remote Sensing Image Registration Based on SIFT and the Distance Between Generalized Tight Pair-wise Prototypes

SIFT(Scale Invariant Feature Transform) and its application on remote sensing image registration is discussed in this paper.First,the keypoint-based framework of remote sensing image registration is described;After analyzing robust feature extractor and keypoints matching which are two key problems in the image registration,a new approach based on SIFT and the distance between generalized tight pair-wise prototypes for remote sensing image registration is proposed;The relation between different matching methods based on SIFT is established by the concept of generalized tight pair-wise prototypes.Compared with the related work,our method can get more number of correct point matches and total number of point matches.The experimental results demonstrate the robustness and efficiency of the algorithm.
Multi-Agent Energy Management Strategy for Multi-Microgrids Using Reinforcement Learning

In this paper, an intelligent energy management framework is proposed to operate multiple grid-connected microgrids (MGs) using a cooperative control strategy. Each MG incorporates an intelligent agent, distributed energy resources (DERs), and residential loads. Multiple agents cooperate to optimize the control inputs using communication link-based state observation. The MGs are connected with two utility grids to participate in the electricity market through the power exchange using a real-time price signal. The secondary link supplies power to the MGs with a higher tariff when the primary grid link goes offline. Reinforcement learning (RL) is explored to build an intelligent energy management system (EMS) where the proximal policy optimization (PPO) algorithm is utilized. It is verified that the proposed energy management strategy reduces the operational cost of MGs significantly by exploiting a nearoptimal real-time scheduling policy. Moreover, the trained agents optimize the operation of DERs during the primary grid link offline scenario to compensate for the higher tariff.
Classification of Emotions from Speech Signal Using Machine Learning

Communication is essential for clearly expressing one's thoughts and ideas. Among all the different types of human communication, speech is the most preferred and powerful mode of expression. In terms of data, feature selection, and detection, this system outperforms an existing system. A methodology aiming at more precisely identifying speech percepts based on emotions. Recognition of emotion from speech signals is an essential but challenging task of Human-Computer Interaction. The task involves extracting the emotional content from the speech signals. It is quite challenging to extract emotion-oriented features from speech. In this case, we are using Mel Frequency Cepstral Coefficients to extract features. It is possible to classify natural emotions using various methods. In this paper we are recognized emotions using two different databases. One is RAVDESS database to extract emotions such as Neutral, Angry, Sad and happy and achieved recognition accuracy of 78.56%. Another one is Malayalam database created from different movie clips to extract emotions such as Neutral, Angry and Sad and achieved recognition accuracy of 84.3%. The aim of this paper is to classify models based on a MLP (multilayer perceptron) classifier. There are several intelligent services that consider the humanization of communication between humans and computers. Services such as chat-bots, cognitive diagnostics, medical intelligence, and marketing. As more and more people use mobile devices, the use of speech is becoming increasingly popular for interpersonal and human-to-machine communication. Among the applications are dialogue systems for spoken languages such as call center conversations and onboard vehicle driving systems that use emotion patterns in the speech.
Bidirectional Gated Recurrent Unit Based-Grey Wolf Optimizer for Interval Prediction of Voltage Margin Stability Index in Power Systems

Despite the importance of Photovoltaic (PV) generation systems for smart grids, the uncertainty of PV power outputs due to weather variations can cause power stability problems leading up to voltage collapse in power systems. Governed by load dynamics, data-driven short-term Voltage Stability Margin (VSM) assessment is a critical technique for power system design and operation. This paper addresses improving the VSM accuracy and quantifying the uncertainties associated with the predicted values using deep learning. Interval estimation of VSM is significant for monitoring short-term voltage stability in real-time against fast voltage collapse and sustained low voltage without recovery. The proposed solution lies in the use of a Bidirectional Gated Recurrent Unit (GRU) optimized by a multi-objective Grey Wolf optimizer (GWO) algorithm. Time domain simulation results are conducted using IEEE59 and IEEE Nordic-44 bus systems to provide the training samples and maintain a high level of grid observability for the grid stability evaluation. The generated N-l contingency test cases data were conducted using Power System Simulator for Engineering (PSS/E)-based time domain simulations. The generated features from fault-induced voltage events include the post-disturbance voltage magnitude, angles, frequencies, and active and reactive power trajectories of the system buses. The obtained results show that the proposed method can enable timely stability assessment with higher effectiveness.
Solar Powered Electric Drive-Train With Integrated Multifunctional Dual Power On-Board Charger Incorporating N-phase SRM

The article deals with developing a novel solar-powered electric drive-train (SPED) that integrates a multifunctional dual power on-board charger (DP-OBC) incorporating an N-phase switched reluctance motor (SRM). The re-leveraging of the drive-train power electronics and phase windings eliminates the requirement of a separate on-board charging module. The integrated multifunctional DP-OBC can simultaneously utilize two power sources for charging the battery storage system (BESS). When at a standstill, the electric vehicle (EV) BESS can be charged simultaneously via multiple sources, i.e., AC grid plus rooftop solar PV system or DC source plus rooftop solar PV system. Thus, dual power charging ensures a continuous power supply to the battery chargers even when solar energy is unavailable. The proposed SPED during propulsion is reconfigured as an N-phase asymmetrical half-bridge (AHB) configuration simultaneously powered via BESS and rooftop solar PV system, allowing simultaneous driving and charging operation, which increases the driving range. In addition, the proposed SPED employs the same number of power electronics components as in the N-phase AHB configuration, supernumerary reducing the component footprint of the EV charging system. Experiments on a prototype 1.1 kW 4-phase SRM are presented for validating propulsion and different charging modes of the proposed SPED.
Preparation of Aligned Polymer Micro/Nanofibres by Electrospinning

Polymer micro/nanofibres are prepared by typical and modified methods of electrospinning. The morphologies and microstructures of the electrospun micro/nanofibres are characterized by a scanning electron microscope (SEM). The micro/nanofibres prepared by the typical electrospinning are usually collected in the form of non-woven mats lacking of structural orientation. However, by modifying collector(s) of the electrospinning setup, the resulting polymer fibres show aligned structures to some extent. We analyse all the forces that the fibres experienced during electrospinning and find that the electrostatic force originating from the splitting electric field plays a key role in the alignment of the micro/nanofibres.