Miracle-dz/UniFAR-FTUs huggingface dataset:
E. coli Cell Lysis Induced by Lys394 Enzyme Assisted by Magnetic Nanoparticles Exposed to Non-Heating Low-Frequency Magnetic Field

The spreading of microbial pathogens with more and more resistance to traditional low-molecular antibiotic agents demands new approaches to antibacterial therapy. The employment of bacteriophage enzymes capable of breaking bacterial cell walls has attracted much interest within this context. The specific features of the morphology of Gram-negative bacteria prevent the effective direct usage of lytic enzymes and require assistance from additional helpers to facilitate cell lysis. The current work is devoted to the study of boosting the lysis of Escherichia coli (E. coli) JM 109 and MH 1 strains induced by Lys394 bacteriophage endolysin by means of rod-like (56 × 13 nm) magnetic nanoparticles (MNPs) activated by a non-heating low-frequency magnetic field (LF MF) with a frequency of 50 Hz and a flux density of 68.5 mT in a pulse–pause mode (1 s on and 0.3 s off). According to theoretical assumptions, the mechanism of MNP assistance is presumably based upon the disordering of the outer membrane that facilitates enzyme permeation into peptidoglycans to its substrate. It is found that the effect of the LF MF reaches an almost a twofold acceleration of the enzyme reaction, resulting in almost 80 and 70%, respectively, of lysed E. coli JM 109 and MH 1 cells in 21 min. An increase in the membrane permeability was proven by two independent experiments employing β-lactamase periplasmic enzyme leakage and Nile Red (NR) hydrophobic dye fluorescence. It is shown that the outer membrane disordering of E. coli caused by exposure to LF MF nanoparticle movement leads to almost complete (more than 80%) β-lactamase release out of the cells’ periplasm to the buffer suspension. Experiments with NR (displaying fluorescence in a non-polar medium only) reveal a drastic reduction in NR fluorescence intensity, reaching a change of an order of magnitude when exposed to LF MF. The data obtained provide evidence of changes in the bacterial cell wall structure. The result shown open up the prospects of non-heating LF MF application in enhancing enzyme activity against Gram-negative pathogens.
Une estimation asymptotique pour le modèle de Ginzburg-Landau

Etant donne Ω⊂⊂R 2 suffisamment regulier, g∈C 1 (∂Ω; S 1 ), et H g 1 ={u∈H 1,2 (Ω; C); u=g sur ∂Ω}, on demontre l'estimation. pour toute fonction minimisante u e EH g 1 l'energie de Ginzburg-Landau.
On the Motion of Typhoon (II)

In the previous paper(2), the author treated the motion of a typhoon by using a barotropic vorticity equation. The present short note is contributed to supplement some problems left to discuss. First, the physical meaning of ƒÀ-effect on a circular vortex is made clear. Second, integrating primitive equations of motion, the relation between pressure and velocity is obtained. Third, the balance equation by Charney is obtained from the pressure function. Fourth, ageostrophic pressure is estimated.
Knowledge Representation of Training Data With Adversarial Examples Supporting Decision Boundary

Deep learning (DL) has achieved tremendous success in recent years in many fields. The success of DL typically relies on a considerable amount of training data and the expensive model optimization process. Therefore, a trained DL model and its corresponding training data have become valuable assets whose intellectual property (IP) needs to be protected. Once a DL model or its training dataset is released, there is currently no mechanism for the entity that owns one part to establish a clear relationship with the other. In this paper, we aim to reveal the integrated relationship between a given DL model and the corresponding training dataset, by framing the problem of knowledge representation of a dataset with respect to DL models trained on it: how to effectively represent the knowledge transferred from a training dataset to a DL model? Our basic idea is that the knowledge transferred from a training dataset to a DL model can be uniquely represented by the model’s decision boundary. Therefore, we design a novel generation method that utilizes geometric consistency to find the samples supporting the decision boundary, which can serve as the proxy for the knowledge representation. We evaluate our method in three different cases: IP audit of training data, IP audit of DL models, and adversarial knowledge distillation. The experimental results show that our method can improve the performance of existing works in all cases, which confirm that our method can effectively represent the knowledge transferred from a training dataset to a DL model.
Comparative Proteomic Analysis of Toxoplasma gondii RH Wild-Type and Four SRS29B (SAG1) Knock-Out Clones Reveals Significant Differences between Individual Strains

In T. gondii, as well as in other model organisms, gene knock-out using CRISPR-Cas9 is a suitable tool to identify the role of specific genes. The general consensus implies that only the gene of interest is affected by the knock-out. Is this really the case? In a previous study, we generated knock-out (KO) clones of TgRH88_077450 (SRS29B; SAG1) which differed in the numbers of the integrated dihydrofolate-reductase-thymidylate-synthase (MDHFR-TS) drug-selectable marker. Clones 18 and 33 had a single insertion of MDHFR-TS within SRS29B. Clone 6 was disrupted by the insertion of a short unrelated DNA-sequence, but the marker was integrated elsewhere. In clone 30, the marker was inserted into SRS29B, and several other MDHFR-TS copies were found in the genome. KO and wild-type (WT) tachyzoites had similar shapes, dimensions, and vitality. This prompted us to investigate the impact of genetic engineering on the overall proteome patterns of the four clones as compared to the respective WT. Comparative shotgun proteomics of the five strains was performed. Overall, 3236 proteins were identified. Principal component analysis of the proteomes revealed five distinct clusters corresponding to the five strains by both iTop3 and iLFQ algorithms. Detailed analysis of the differentially expressed proteins revealed that the target of the KO, srs29B, was lacking in all KO clones. In addition to this protein, 20 other proteins were differentially expressed between KO clones and WT or between different KO clones. The protein exhibiting the highest variation between the five strains was srs36D encoded by TgRH_016110. The deregulated expression of SRS36D was further validated by quantitative PCR. Moreover, the transcript levels of three other selected SRS genes, namely SRS36B, SRS46, and SRS57, exhibited significant differences between individual strains. These results indicate that knocking out a given gene may affect the expression of other genes. Therefore, care must be taken when specific phenotypes are regarded as a direct consequence of the KO of a given gene.
Investigation of S1046 profile bladed vertical axis wind turbine and artificial intelligence-based performance evaluation

ABSTRACT It is very important to determine the parameters affecting the performance of the Darrieus-type wind turbine and its effects. In particular, it should be specified at which TSR value the peak power coefficient is obtained. In this study, standard and modified S1046 airfoils and aspect ratios (H/D), angle of attack (AoA), turbulent/non-turbulent flow (WT), number of blades (N), and chord length (C) were tested. Then, four different machines learning-based multi-output regression models (Decision Tree, Linear Regression, K-Nearest Neighbors, and Random Forest) were trained to make performance predictions with the data obtained from the evaluated test setup. Thirdly, feature selection based on the Random Forest algorithm, which is the best performing multi-output regression model, was performed using data due to changing parameter values on the established system. The importance of the parameters was determined. The operating range of the system was at relatively low TSR values. When analyzing the blade profile, the modified blade version performed better in certain combinations compared to the standard profile. Maximum power coefficient (Cp) was obtained from the modified turbine structure with 5 degrees of attack angle, H/D = 1.85, and C = 60 mm. The present study aims to increase the turbine’s power coefficient and aims to predict results as power coefficient without doing many different experiments.
Dynamics, topology and holomorphic curves.

In this paper we describe the intimate interplay between certain classes of dynamical systems and a holomorphic curve theory. There are many aspects touching areas like Gromov-Witten invariants, quantum cohomology, symplectic homology, Seiberg-Witten invariants, Hamilto-nian dynamics and more. Emphasized is this interplay in real dimension three. In this case the methods give a tool to construct global surfaces of section and generalizations thereof for the large class of Reeb vector elds. This class of vector elds, includes, in particular, all geodesic ows on surfaces. 1 Periodic orbits of dynamical systems Symplectic and contact geometry as well as Hamiltonian dynamics experienced in the last decade a tremendous growth. In order to cover some aspects in a certain depth one faces the serious dilemma of making a selection. Rather than touching many areas, it seems more appropriate to focus only on a few aspects. The choice made here was to describe the subtle relationship between Hamiltonian dynamics, topology and a theory of holomorphic curves. So many aspects are only brieey mentioned or even ignored. However, they are being dealt with in other papers contained in the proceedings of the ICM Berlin. In particular the contributions by The aim of this paper is to explain some of the recent progress at the interface of Hamiltonian dynamics and symplectic geometry. In order to appreciate the special features of (certain) Hamiltonian dynamics versus general dynamics we begin with the following classical problem. In 1950 Seifert, 79], raised the question if a given non-singular vector eld X on the three-sphere admits a periodic orbit: _ x = X(x) and x(0) = x(T); T > 0:
Operators With Hk Coefficients and Generalized Hodge - De Rham Decompositions

1. In t roduct ion Roughly speaking, the c l a s s i c a l Hodge de Rham decompos i t i o n i s the expression of a d i f f e r e n t i a b l e form as a sua of exact and coclosed forms. The Sobolev spaces of forms decompose in to the d i r ec t sums of subspaoes of exact and coclosed forms (see [5] and a lso [3]» [7J f o r t i l e decompositions of spaces of C forms) . The c r u c i a l point i n the proof of Hodge decomposition l i e s in the decomposition of space of t ensors in to the d i r e c t sum of image and kernel of l ap l ac i an . This property of l ap l ac i an i s a l so val id f o r a wide c l a s s of d i f f e r e n t i a l (and pseudod i f f e r e n t i a l ) opera tors , namely, f o r e l l i p t i c operators (see e . g . Pa l a i s Qp] , Thm 7 in Chapter XI). Moreover, the s imi la r behaviour as that of the e x t e r i o r d i f f e r e n t i a t i o n d i n p-covector bundles oan a lso be noticed f o r operators with i n f e c t i v e symbol ac t ing between vector bundles. D.G. Ebin [4] proved the analogous decomposition with respec t to a d i f f e r e n t i a l operator D with i n f e c t i v e symbol as the Hodge decompos i t i o n tha t was constructed by means of d. He bui lds the e l l i p t i c operator D*D as a s u b s t i t u t e of the l ap lac ian A.
Classification of lignocellulosic biomass by weighted‐covariance factor fuzzy C‐means clustering of mid‐infrared and near‐infrared spectra

The analysis of lignocellulosic materials is crucial to optimizing the conversion efficiencies in biorefineries and to studying crop residue input to soil nutrient cycles. Mid‐infrared (MIR) and near‐infrared (NIR) spectroscopies are rapid, simple, and nondestructive methods for the determination of biomass compositions. However, the analysis of a small set of plant biomass is not generally possible with conventional methods of data processing, such as partial least squares. Additionally, IR spectra do not distribute spherically in the data space. To overcome these problems, we propose a weighted‐covariance factor fuzzy C‐means clustering method combined with bootstrapping. The algorithm can classify spherical and nonspherical clusters, in contrast to classic fuzzy C‐means, which is only adapted to spherical clusters. Bootstrapping enables resampling of the available spectra to generate several datasets on which the classification is performed. This unsupervised clustering methodology was tested to classify a small set of maize roots in soil according to genotype or period of their biodegradation process based on their NIR and MIR spectra. This methodology is applied to determine the optimal pretreatment of IR spectra, to study the contribution of the combination of MIR and NIR spectra and to compare the results on spectral and chemical data. The results show that the best methods of pretreatment are the first‐order Savitzky‐Golay derivative followed by standard normal variate. The MIR spectra produce a better result than NIR spectra for the initial characterization and for dynamic samples, while MIR spectra acquired on raw samples, without soluble extraction, provided better classification than wet chemistry.
Engineering Two-Dimensional Multilevel Supramolecular Assemblies from a Bifunctional Ligand on Au(111)

Herein, we demonstrate the supramolecular assemblies from a bifunctional ligand on Au(111), towards engineering two-dimensional (metal-) organic multilevel nanostructures. The bifunctional ligand employed, including two Br atoms and one carboxylic terminal, offers multiple bonding motifs with different configurations and binding energies. These bonding motifs are highly self-selective and self-recognizable, and thus afford the formation of subunits that contribute to engineering multilevel self-assemblies. Our scanning tunneling microscopy experiments, in combination with the density functional theory calculations, revealed various hydrogen, halogen and alkali-carboxylate bonding motifs dictating the different levels of the assemblies. The multilevel assembly protocol based on a judicious choice of multiple bonding motifs guarantees a deliberate control of surface-confined (metal-) organic nanostructures. Our findings may present new opportunities for the fabrication of complex two-dimensional (metal-) organic nanostructures with potential in applications of functionally diverse nanomaterials.
Equilibrium equations in interval models of structures

A new interval model of linear equilibrium equations in mechanics is based on the algebraic completion of classical interval arithmetic and provides more realistic and accurate models involving interval uncertainties. It replaces straightforwardly a linear deterministic model by an interval model in terms of proper and improper intervals, fully conforms to the equilibrium principle, and provides sharper enclosure of a resultant force than the methods based on classical interval arithmetic. The present paper presents the interval algebraic approach to linear equilibrium equations and discusses its applications to some interval models of structures.
Addressing contingency in algorithmic (mis)information classification: Toward a responsible machine learning agenda

Machine learning (ML) enabled classification models are becoming increasingly popular for tackling the sheer volume and speed of online misinformation and other content that could be identified as harmful. In building these models, data scientists need to take a stance on the legitimacy, authoritativeness and objectivity of the sources of “truth” used for model training and testing. This has political, ethical and epistemic implications which are rarely addressed in technical papers. Despite (and due to) their reported high accuracy and performance, ML-driven moderation systems have the potential to shape online public debate and create downstream negative impacts such as undue censorship and the reinforcing of false beliefs. Using collaborative ethnography and theoretical insights from social studies of science and expertise, we offer a critical analysis of the process of building ML models for (mis)information classification: we identify a series of algorithmic contingencies—key moments during model development that could lead to different future outcomes, uncertainty and harmful effects as these tools are deployed by social media platforms. We conclude by offering a tentative path toward reflexive and responsible development of ML tools for moderating misinformation and other harmful content online.
Impact of Alteplase on Mortality in Critically Ill Patients with COVID-19 and Pulmonary Embolism

COVID-19 is an independent risk factor for pulmonary embolism (PE). Little is known about alteplase therapy in this patient group. A retrospective study analyzed 74 patients with PE and acute respiratory distress syndrome (ARDS) due to COVID-19 who were hospitalized in the intensive care unit in 2021. Patients with or without confirmed right heart thrombi (RHT) were treated with unfractionated heparin or alteplase. The mortality rate in patients with RHT treated with heparin was 100% compared to 37.9% and 55.2% in those treated with alteplase without RHT and alteplase with RHT, respectively. The risk of death in the alteplase group increased with delayed thrombolysis (p = 0.009, odds ratio (OR) = 1.73 95% CI (confidence interval) 1.14–2.62), increased D-dimer concentration (p = 0.02, OR = 1.43 95% CI 1.06–1.93), and decreased PaO2/FiO2 ratio (p = 0.001, OR = 0.56 95% CI 0.41–0.78). The receiver operating characteristic method determined that a 1-day delay in thrombolytic treatment, D-dimer concentration >5.844 mg/L, and PaO2/FiO2 <144 mmHg predicted a fatal outcome. The risk of death in patients with severe COVID-19 with ARDS and PE increases with higher D-dimer levels, decreased PaO2/FiO2, and delayed thrombolytic treatment. Thrombolysis seems to be treatment of choice in severe COVID-19 with PE and RHT. It should be carried out as soon as possible after the diagnosis is established.
Generation of attosecond electron pulses using petawatt lasers

Ionization of positive ions by relativistically-intense short laser pulses is analyzed in the framework of relativistic strong-field theory. We observe the appearance of broad interference-free patterns in the high-energy portion of the photoelectron spectra, which extend over hundreds of driving photon energies. These structures can be controlled by changing parameters of the driving laser field. As we also demonstrate, the electrons comprising these broad structures can form pulses of attosecond duration. While we present the fully numerical results for laser field intensities below 1020W=cm2, we also introduce the saddle point approximation to treat photoionization at larger intensities. In addition, the conditions enabling generation of ultrashort electron pulses are studied.
Data-Driven Resource Allocation for Deep Learning in IoT Networks

We consider an Internet of Things (IoT) network, where a large amount of sensor data is transmitted from wireless IoT devices to a central server for the classification of system behaviors. When the number of IoT devices and their sensors is very large, the redundancy in the collected data at the server can also be very high due to the high correlation between the data. By compressing the sensor data at IoT devices for reducing the redundancy, the transmit power can be largely reduced but the accuracy of the classification can also be decreased. Our objective is to maximize the classification accuracy by determining the compression ratio at each device, under the total transmit power constraint. In traditional resource allocation for multiuser wireless networks, the classification accuracy cannot be directly considered since it is usually data-agnostic. On the other hand, in deep learning, the classification accuracy can be maximized by training a neural network architecture using datasets. The deep learning, however, does not usually consider the wireless channel information, that is essential for the resource allocation. In this article, we propose a resource allocation scheme based on a binarized neural network in order to maximize the classification accuracy at the server while satisfying the total transmit power constraint by exploiting both the wireless channel state information and the data-driven approach. In experimental results, we show that the classification accuracy can largely be increased by the proposed scheme using MNIST and CIFAR-10 datasets.
Prediction of Cognitive Scores by Joint Use of Movie-Watching fMRI Connectivity and Eye Tracking via Attention-CensNet

Brain functional connectivity under the naturalistic paradigm has been illustrated to be better at predicting individual behaviors than other brain states, such as rest and task. Nevertheless, the state-of-the-art methods are difficult to achieve desirable results from movie-watching paradigm fMRI (mfMRI) induced brain functional connectivity, especially when the datasets are fewer. Incorporating other physical measurements into the prediction method may enhance accuracy. Eye tracking, becoming popular due to its portability and less expense, can provide abundant behavioral features related to the output of human's cognition, and thus might supplement the mfMRI in observing subjects’ subconscious behaviors. However, there are very few works on how to effectively integrate the multimodal information to strengthen the performance by a unified framework. To this end, a fusion approach with mfMRI and eye tracking, based on Convolution with Edge-Node Switching in Graph Neural Networks (CensNet), is proposed in this article, with subjects taken as nodes, mfMRI derived functional connectivity as node feature, different eye tracking features used to compute similarity between subjects to construct heterogeneous graph edges. By taking multiple graphs as different channels, we introduce squeeze-and-excitation attention module to CensNet (A-CensNet) to integrate graph embeddings from multiple channels into one. The experiments demonstrate that the proposed model outperforms the one using single modality, single channel and state-of-the-art methods. The results suggest that brain functional activities and eye behaviors might complement each other in interpreting trait-like phenotypes.