Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Characterization of WCK 5222 (Cefepime/Zidebactam)-Resistant Mutants Developed from a Carbapenem-Resistant Pseudomonas aeruginosa Clinical Isolate

Antibiotic resistance imposes a severe threat on human health. WCK 5222 is a β-lactam/β-lactamase inhibitor combination that is composed of cefepime and zidebactam. ABSTRACT WCK 5222 (cefepime/zidebactam) is a β-lactam/β-lactamase inhibitor combination that is effective against a broad range of highly drug-resistant bacterial pathogens, including those producing metallo-β-lactamase. In this study, we isolated a multidrug-resistant Pseudomonas aeruginosa clinical strain that is resistant to a variety of β-lactam antibiotics and the ceftazidime-avibactam combination. A metallo-β-lactamase gene blaDIM-2 was identified on a self-transmissible megaplasmid in the strain, which confers the resistance to β-lactam antibiotics, leaving WCK 5222 potentially one of the last treatment resorts. In vitro passaging assay combined with whole-genome sequencing revealed mutations in the pbpA gene (encoding the zidebactam target protein PBP2) in the evolved resistant mutants. Among the mutations, a V516M mutation increased the bacterial virulence in a murine acute pneumonia model. Reconstitution of the mutations in the reference strain PAO1 verified their roles in the resistance to zidebactam and revealed their influences on cell morphology in the absence and presence of zidebactam. Microscale thermophoresis (MST) assays demonstrated that the mutations reduced the affinity between PBP2 and zidebactam to various extents. Overall, our results revealed that mutations in the pbpA gene might be a major cause of evolved resistance to WCK 5222 in clinical settings. IMPORTANCE Antibiotic resistance imposes a severe threat on human health. WCK 5222 is a β-lactam/β-lactamase inhibitor combination that is composed of cefepime and zidebactam. It is one of the few antibiotics in clinical trials that are effective against multidrug-resistant Pseudomonas aeruginosa, including those producing metallo-β-lactamases. Understanding the mechanisms and development of bacterial resistance to WCK 5222 may provide clues for the development of strategies to suppress resistant evolvement. In this study, we performed an in vitro passaging assay by using a multidrug-resistant P. aeruginosa clinical isolate. Our results revealed that mutations in the zidebactam target protein PBP2 play a major role in the bacterial resistance to WCK 5222. We further demonstrated that the mutations reduced the affinities between PBP2 and zidebactam and resulted in functional resistance of PBP2 to zidebactam.
Penicillin Binding Proteins and β-Lactamases of Mycobacterium tuberculosis: Reexamination of the Historical Paradigm

M. tuberculosis, the causative agent of tuberculosis, kills more humans than any other bacterium. β-lactams are the most widely used class of antibiotics to treat bacterial infections. Unlike in the historical model that describes the relationship between β-lactams and M. tuberculosis, we find that M. tuberculosis penicillin binding proteins are able to inactivate select β-lactams with high efficiency. ABSTRACT Penicillin binding proteins (PBPs) have been extensively studied due to their importance to the physiology of bacterial cell wall peptidoglycan and as targets of the most widely used class of antibiotics, the β-lactams. The existing paradigm asserts that PBPs catalyze the final step of peptidoglycan biosynthesis, and β-lactams inhibit their activities. According to this paradigm, a distinct enzyme class, β-lactamases, exists to inactivate β-lactams. This paradigm has been the basis for how bacterial diseases are treated with β-lactams. We tested whether this historical view accurately reflects the relationship between β-lactams and the PBPs and the β-lactamase, BlaC, of Mycobacterium tuberculosis. BlaC was the major inactivator of the cephalosporin subclass of β-lactams. However, the PBPs PonA1 and PonA2 inactivated penicillins and carbapenems more effectively than BlaC. These findings demonstrate that select M. tuberculosis PBPs are effective at inactivating several β-lactams. Lesser-known PBPs, DacB, DacB1, DacB2, and Rv2864c, a putative PBP, were comparably more resistant to inhibition by all β-lactam subclasses. Additionally, Rv1730c exhibited low affinity to most β-lactams. Based on these findings, we conclude that in M. tuberculosis, BlaC is not the only source of inactivation of β-lactams. Therefore, the historical paradigm does not accurately describe the relationship between β-lactams and M. tuberculosis. IMPORTANCE M. tuberculosis, the causative agent of tuberculosis, kills more humans than any other bacterium. β-lactams are the most widely used class of antibiotics to treat bacterial infections. Unlike in the historical model that describes the relationship between β-lactams and M. tuberculosis, we find that M. tuberculosis penicillin binding proteins are able to inactivate select β-lactams with high efficiency.
Identification and Functional Characterization of Antifreeze Protein and Its Mutants in Dendroctonus armandi (Coleoptera: Curculionidae: Scolytinae) Larvae Under Cold Stress

Abstract Dendroctonus armandi (Tsai and Li) (Coleoptera: Curculionidae: Scolytinae) is considered to be the most destructive forest pest in the Qinling and Bashan Mountains of China. Low winter temperatures limit insect's populations, distribution, activity, and development. Insects have developed different strategies such as freeze-tolerance and freeze-avoidance to survive in low temperature conditions. In the present study, we used gene cloning, real-time polymerase chain reaction (PCR), RNA interference (RNAi), and heterologous expression to study the function of the D. armandi antifreeze protein gene (DaAFP). We cloned the 800 bp full-length cDNA encoding 228 amino acids of DaAFP and analyzed its structure using bioinformatics analysis. The DaAFP amino acid sequence exhibited 24–86% similarity with other insect species. The expression of DaAFP was high in January and in the larvae, head, and midgut of D. armandi. In addition, the expression of DaAFP increased with decreasing temperature and increasing exposure time. RNAi analysis also demonstrated that AFP plays an important role in the cold tolerance of overwintering larvae. The thermal hysteresis and antifreeze activity assay of DaAFP and its mutants indicated that the more regular the DaAFP threonine-cystine-threonine (TXT) motif, the stronger the antifreeze activity. These results suggest that DaAFP plays an essential role as a biological cryoprotectant in overwintering D. armandi larvae and provides a theoretical basis for new pest control methods. Graphical Abstract
Bacterial Endosymbionts of Bactericera maculipennis and Three Mitochondrial Haplotypes of B. cockerelli (Hemiptera: Psylloidea: Triozidae)

Abstract Insects harbor bacterial endosymbionts that provide their hosts with nutritional benefit or with protection against natural enemies, plant defenses, insecticides, or abiotic stresses.We used directed sequencing of 16S rDNA to identify and compare endosymbionts of Bactericera maculipennis (Crawford) and the western, central, and northwestern haplotypes of B. cockerelli (Šulc) (Hemiptera: Psylloidea: Triozidae). Both species are native to North America, are known to harbor the plant pathogen ‘Candidatus Liberibacter solanacearum’ and develop on shared host plants within the Convolvulaceae. The Old-World species Heterotrioza chenopodii (Reuter) (Psylloidea: Triozidae), now found in North America, was included as an outgroup. 16S sequencing confirmed that both Bactericera species harbor ‘Candidatus Liberibacter solanacearum’ and revealed that both species harbor unique strains of Wolbachia and Sodalis. However, the presence of Wolbachia and Sodalis varied among haplotypes of B. cockerelli. The central and western haplotypes harbored the same strains of Wolbachia, which was confirmed by Sanger sequencing of the wsp and ftsZ genes. Wolbachia was also detected in very low abundance from the northwestern haplotype by high-throughput sequencing of 16S but was not detected from this haplotype by PCR screening. The northwestern and central haplotypes also harbored Sodalis, which was not detected in the western haplotype. Heterotrioza chenopodii harbored an entirely different community of potential endosymbionts compared with the Bactericera spp. that included Rickettsia and an unidentified bacterium in the Enterobacteriaceae. Results of this study provide a foundation for further research on the interactions between psyllids and their bacterial endosymbionts.
Optimising the development and use of persuasive communication to influence behaviour in the Swan Canning River System. Final Report

Natural resource managers are regularly faced with the challenge of trying to influence people to behave in certain ways in order to meet certain conservation objectives. While approaches such as regulations and law enforcement can be quite successful, they are not always the most desirable, effective and efficient means of influencing behaviour. For this reason, managers are supplementing more restrictive and authoritative approaches with less intrusive behavioural influence tools that aim to influence people’s decision-making processes. A persuasive communication intervention informed by the social sciences is one such tool. Instead of relying on intuition or other arbitrary sources of information, persuasive communication relies on theoretical frameworks grounded in social psychology to identify and understand the underlying reasons why people decide to do certain behaviours. Understanding these reasons means it is more likely that a persuasive communication intervention will be successful in bringing about a desired behaviour change. However, natural resource managers often have a lack of exposure to these theoretical frameworks for analysing behaviour, making decisions with respect to communication efforts more challenging.
QIRO: A Static Single Assignment-based uantum Program Representation for Optimization

We propose an IR for quantum computing that directly exposes quantum and classical data dependencies for the purpose of optimization. The Quantum Intermediate Representation for Optimization (QIRO) consists of two dialects, one input dialect and one that is speciically tailored to enable quantum-classical co-optimization. While the irst employs a perhaps more intuitive memory-semantics (quantum operations act on qubits via side-efects), the latter uses value-semantics (operations consume and produce states) to integrate quantum datalow in the IR’s Static Single Assignment (SSA) graph. Crucially, this allows for a host of optimizations that leverage datalow analysis. We discuss how to map existing quantum programming languages to the input dialect and how to lower the resulting IR to the optimization dialect. We present a prototype implementation based on MLIR that includes several quantum-speciic optimization passes. Our benchmarks show that signiicant improvements in resource requirements are possible even through static optimization. In contrast to circuit optimization at run time, this is achieved while incurring only a small constant overhead in compilation time, making this a compelling approach for quantum program optimization at application scale.
Optimising the IgG‐degrading enzyme treatment regimen for enhanced adeno‐associated virus transduction in the presence of neutralising antibodies

Pre‐existing neutralising antibodies (NAbs) to adeno‐associated viruses (AAVs) remain an impediment for systemically administered AAV‐mediated gene therapy treatment in many patients, and various strategies are under investigation to overcome this limitation. Here, IgG‐degrading enzymes (Ides) derived from bacteria of the genus Streptococcus were tested for their ability to cleave human IgG and allow AAV‐mediated transduction in individuals with pre‐existing NAbs.
A Farming-for-Mining-Framework to Gain Knowledge in Supply Chains

Gaining knowledge from a given data basis is a complex challenge. One of the frequently used methods in the context of a supply chain (SC) is knowledge discovery in databases (KDD). For a purposeful and successful knowledge discovery, valid and preprocessed input data are necessary. Besides preprocessing collected observational data, simulation can be used to generate a data basis as an input for the knowledge discovery process. The process of using a simulation model as a data generator is called data farming. This paper investigates the link between data farming and data mining. We developed a Farming-for-Mining-Framework, where we highlight requirements of knowledge discovery techniques and derive how the simulation model for data generation can be configured accordingly, e.g., to meet the required data accuracy. We suggest that this is a promising approach and is worth further research attention.
Improving Simulation Optimization Run Time When Solving for Periodic Review Inventory Policies in a Pharmacy

Pharmaceutical drugs are critical to patient care, but demand and supply uncertainties in this inventory system make decision-making a challenging task. In this paper, we present a simulation-optimization model that determines near-optimal $(s, S)$ periodic review inventory policies that minimize the expected cost per day. The model accounts for perishability, positive lead time, stochastic demand, and supply disruptions. We implement a Binary Grid-Search algorithm which uses the structure of the objective function to quickly solve the simulation-optimization model. The numerical results illustrate how the Binary Grid-Search algorithm performs 21 times faster (when performing 10,000 replications) in terms of run time when compared to an Exhaustive Grid-Search, without sacrificing solution accuracy. This paper provides an efficient method to solve for the near-optimal $(s, S)$ periodic review inventory policies which is essential in the pharmacy inventory system that handles thousands of different drugs.
Machine Learning-Based Periodic Setup Changes for Semiconductor Manufacturing Machines

Semiconductor manufacturing machines, especially for photo-lithography processes, require large setup times when changing job types. Hence, setup operations do not often occur unless there is no job to be processed. In practice, a simulation-based method that predicts the incoming WIP is often used to determine whether changing machine setup states or not. The simulation-based method can provide useful information on the future production environment with a high accuracy but takes a long time, which can delay the setup change decisions. Therefore, this work proposes a machine learning-based approach that determines setup states of the machines. The proposed method shows better performance than several heuristic rules in terms of movement.
Field-Oriented Control of Five-Phase Induction Motor Fed From Space Vector Modulated Matrix Converter

This paper presents the field-oriented control (FOC) of a five-phase induction motor (FPIM) fed from a three-to-five phase direct matrix converter (DMC). The article focuses on the modulation and control of the three-to-five phase DMC, which discusses minimizing the problems associated with switching vector selection, sector identification, switching sequence selection, and dwell-time calculations. The DMC is controlled from the space vector pulse width modulation (SVPWM) technique, eliminating the x-y components of space vectors. Moreover, the matrix converter can perform unity power factor control at the input side. The FPIM is sourced from a DMC, and the FOC technique is applied to control the drive. The dynamic characteristics of the drive are succeeded for different loading conditions. The proposed work is simulated in Simulink/Matlab environment and further verified through practical experimentation. The control signals of the IGBTs are generated through FPGA embedded in dSPACE 1006. The experimental and the simulation results prove the practicability of the FOC to FPIM fed from a DMC.
Seeing Through Walls: Real-Time Digital Twin Modeling of Indoor Spaces

As the need for situational awareness rises in search and rescue tasks, systems for human spatial sensing augmentation in complex-built environments has become increasingly important. Sensor-based mapping and augmented reality (AR) techniques have been tested to provide enhanced visual assistance in indoor scenarios, such as “seeing through walls” abilities for occluded areas. Scholars have examined various scanning techniques and UI/UX designs for AR-based occlusion visualization. However, evidence is still needed to show how scene capturing, 3D mapping, rendering and visualization can be executed integratively, enabling an instantaneous “Digital Twins” modeling. This paper presents a robot-based system for mapping indoor environments and creating a near real-time virtual replica. A dynamically updated 3D map rendered with Unity generates egocentric “x-ray vision” views of the occluded objects using AR headsets. A case study was performed in a lab room, resulting in virtual regeneration of surroundings supporting search and rescue in occluded areas.
A Model-based Analysis of Evacuation Strategies in Hospital Emergency Departments

Evacuation planning for emergency incidents is an essential preparedness for Emergency Departments (ED) which normally contains patients with severe illness and limited mobility. However, the preparedness can be challenging due to a lack of empirical data and difficulties conducting physical drills. We propose an agent-based model to simulate the evacuation process in the EDs containing medical staff, rescuers, visitors and various types of patients. In a case study, we apply the model to a peak hour scenario of the ED of the largest hospital in Singapore. Two rescue strategies with different behavior sequences of medical staff as suggested by the practitioners are evaluated. The simulation results show that prioritizing preparation of all the patients generates less total evacuation time but leads to fewer evacuated cases in the first 20 minutes and more serious congestion compared to one-by-one transfer of individual patients.
Bbect: Bandit Based Ethical Clinical Trials

The aim of an Ethico-Optimal clinical trial is to randomly allocate the new drug (ND) and the standard of care (SOC) to patients in the sample, but with a greater fraction being administered ND if doing so is statistically justified. Such an adaptation is not possible in static trials, in which approximately half the patients would receive ND and the remaining patients SOC, despite evidence within the trial that ND is efficacious. We adapt a canonical stochastic multi-armed bandit algorithm named UCB1 to a clinical trials setting and analyse the resulting type-2 error (β), as also the minimum sample size required by such a trial for a certain β level. We also present simulations to establish that the ethical properties of such a trial are higher, both to verify our analysis and demonstrate an empirical advantage when compared to an existing method.
Machine Learning Methods for Indicating Cultural Biases in Spoken Russian Language: Dominants and Trends of Modern Society

The formation of a socio-cultural layer is based on a person's understanding of himself and the world around him and the translation of this under-standing into abstraction. This inevitably leads to the emergence of cultural biases in society as an extreme form of separation of one social group from another. In fact bias is nonrandom errors in thinking. The growing cultural biases in society is preserved in the consciousness of individuals and further affects the possible interpretation and perception of the neighboring social group, and, therefore, the public mood, in other words, the level of aggressiveness of the society. Thus the problem of identifying cultural shifts is relevant for the scientific community. There are many methods based on surveys and their subsequent analysis. In this paper, we propose to use machine learning and analysis of the large collection of text data from social networks (public Telegram chat). This approach can complement the standard methodology, including helping to reveal hidden patterns by being able to cover large amounts of data.
Incremental Nonnegative Tucker Decomposition with Block-Coordinate Descent and Recursive Approaches

Nonnegative Tucker decomposition (NTD) is a robust method used for nonnegative multilinear feature extraction from nonnegative multi-way arrays. The standard version of NTD assumes that all of the observed data are accessible for batch processing. However, the data in many real-world applications are not static or are represented by a large number of multi-way samples that cannot be processing in one batch. To tackle this problem, a dynamic approach to NTD can be explored. In this study, we extend the standard model of NTD to an incremental or online version, assuming volatility of observed multi-way data along one mode. We propose two computational approaches for updating the factors in the incremental model: one is based on the recursive update model, and the other uses the concept of the block Kaczmarz method that belongs to coordinate descent methods. The experimental results performed on various datasets and streaming data demonstrate high efficiently of both algorithmic approaches, with respect to the baseline NTD methods.
The Fulib solution to the TTC 2021 laboratory workflow case

This paper outlines the Fulib [1, 2] solution to the Laboratory Workflow Case of the Transformation Tool Contest 2021 [3]. Our analysis of the use case showed that it provides quite a number of different model elements that require individual treatment but the different cases are relatively simple. However, some parts of the predefined EMF metamodels do not work very well with the Fulib modeling approach. For example, the predefined metamodel uses index numbers to identify the tips of a liquid transfer job and these index numbers need to be mapped to the barcodes of the samples that are transported by a tip. Similarly, samples need to be mapped to cavities on micro-plates. Thus, we took the liberty to adapt the given metamodels by adding explicit associations between samples and some labware elements, cf. Fig. 1. Note, these adaptions connect elements from the source and from the target metamodel of our model to model transformation. For these adaptions, we loaded and combined the two given Ecore metamodels of the use case into the Fulib code generator and then did some manual modifications using Fulibs metamodeling API. Due to a misinterpretation, we also changed the cardinality of the previous-next association for Jobs from many-to-many to one-to-one. We felt this meets the semantics of the use case, too, and it resulted in a somewhat simpler model that can be processed easier and faster. The rest of the paper outlines the implementation of the different model processing steps and we conclude with some measurements.
Combining power of different methods to detect associations in large data sets

Exploring the relationship between factors of interest is a fundamental step for further analysis on various scientific problems such as understanding the genetic mechanism underlying specific disease, brain functional connectivity analysis. There are many methods proposed for association analysis and each has its own advantages, but none of them is suitable for all kinds of situations. This brings difficulties and confusions to practitioner on which one to use when facing a real problem. In this paper, we propose to combine power of different methods to detect associations in large data sets. It goes as combining the weaker to be stronger. Numerical results from simulation study and real data applications show that our new framework is powerful. Importantly, the framework can also be applied to other problems. Availability: The R script is available at https://jiangdata.github. io/resources/DM.zip.