Numerous Samples Research Articles

Analytical Methodologies for Anti-Infective Orphan Drugs: A Comprehensive Review of FDA Approvals (2013-2023) Part 1.

Most orphan diseases, which affect small patient populations, are chronic, incurable and often lead to early death. Due to small market size, orphan drugs developed to address these diseases receive little attention from the pharmaceutical industry. This lack of interest also applies to the development of analytical methods, which are crucial for drug analysis and quality control. Analysis of orphan drugs faces challenges, including a lack of reference standard and an inadequate number of samples for testing. In addition, constant adjustment of analytical techniques is demanded due to the lengthy development process. Financial constraints further hinder the advancement of analytical techniques since orphan drugs represents a narrow niche market and the pharmaceutical industry often focuses on research with greater impact, causing orphan drugs to be deprioritized. This review summarizes the analytical methods developed for US FDA-approved anti-infective orphan drugs (except antivirals) in the period between 2013 to 2023, covering in depth small molecules and broadly biologics in numerous dosage forms and biological samples. It covers the most common reported analytical methods, such as liquid chromatography, TLC, spectroscopy, and electrochemical analysis. This review highlights the crucial need for the continuous development of new analytical techniques to support the development and quality control of orphan drugs.

An Integrated 3D DEM Modeling Process for Bimrocks Considering Post‐Peak Behavior and Block Breakage

ABSTRACTBimrocks, a complex rock mass commonly found in geotechnical engineering, are often analyzed through the discrete element method (DEM) to understand their mechanical behavior from both macro and micro perspectives. However, there is limited research addressing the post‐peak behavior of bimrocks, particularly in terms of the uniaxial compression stress–strain curve and failure characteristics, with many studies overlooking the complex nature of their post‐peak behavior. This study proposes a comprehensive method for constructing three‐dimensional (3D) numerical samples of bimrocks and selecting appropriate parameters, focusing on accurately capturing both the post‐peak curve shape and failure characteristics. By combining laboratory tests with CT scanning techniques, numerical samples with structures matching those of the physical samples are created, addressing the issue of block stone breakage in traditional discrete element simulations. The study introduces the selection criteria for matrix and block stone parameters and analyzes the microscopic factors influencing the post‐peak curve and failure characteristics. Results indicate that the damping coefficient and loading rate are crucial in shaping the post‐peak curve, with complex curves requiring multiple damping coefficients. Additionally, the radius multiplier influences crack propagation direction, while the strength ratio affects crack penetration and secondary cracking, with these factors being dependent on the matrix strength.

A Revised Taxonomy of the Bassia scoparia Complex (Camphorosmoideae, Amaranthaceae s.l.) with an Updated Distribution of B. indica in the Mediterranean Region

Bassia scoparia is a widespread weedy species in the temperate regions of the world and is valued as a medicinal and ornamental plant. To date, the taxonomic concept of B. scoparia remains insufficiently studied due to a limited number of samples used in the previous phylogenetic analyses. To solve the taxonomy of the B. scoparia complex, we constructed a new phylogeny based on the nuclear ribosomal internal transcribed spacer (ITS), plastid intergenic spacer atpB-rbcL, and plastid region rpL16 intron sequences for numerous samples with diverse morphology. Our analysis revealed a close proximity and intermixed positions of the samples of the B. scoparia group with various morphology. Because of this polyphyly, we prefer to broadly delimit the species. An updated nomenclature of B. scoparia is provided including four new synonyms: Bassia angustifolia, B. littorea, Kochia albovillosa, and K. scoparia subsp. hirsutissima. In its new circumscription, B. scoparia encompasses populations with glabrous or variously hairy leaves and perianths. The original material of Kochia sieversiana, previously considered a species with hairy leaves and inflorescences, has the same diagnostic characters as in B. scoparia s.str. The correct name for more hairy-leaved plants is B. scoparia var. subvillosa. Plants with hairy perianths known as Kochia albovillosa and K. scoparia subsp. hirsutissima have a restricted distribution in Central Asia and South Siberia and have never been recorded as alien in other regions; they can be classified as a separate variety, B. scoparia var. hirsutissima. The ornamental variant of oblong or pyramidal shape may be called B. scoparia var. trichophila. Bassia scoparia is often confused with a similarly looking relative, B. indica, especially in North Africa, a region where secondary ranges of both species overlap. Phylogenetically, these species are sister groups; they share some morphological characters but have different primary distribution ranges. We traced a recent expansion of B. indica in the Mediterranean with the first record reported from the European continent (Spain) and uncovered various introduction pathways of the species in this region.

The Formation of Cavansite and Pentagonite in the Wagholi Quarries, Pune, India

Formation conditions of dimorphic minerals cavansite and pentagonite were previously based on theoretical assumptions. In doing so, the associations with other minerals, especially zeolites, that actually occur in nature, were disregarded or incorrectly taken into account. As a result, formation conditions were assumed that are not consistent with those for the associated minerals in the Deccan Volcanic Province. This relates in particular to overestimated high pressure and temperature values, as well as chronological processes of alteration. Long-term field studies and evaluations of numerous samples led to the conclusion that cavansite and pentagonite formed under temperature (approx. 120 °C to 200 °C) and pressure (0.01–0.03 GPa) conditions that are relevant for the associated low-temperature zeolites. Integration of geological and petrographic conditions, as well as crystallization sequences enabled the presentation of a multi-stage mineralization model. It is also explained that, contrary to the original assumption, characteristic pentagonite fivelings are not formed from five, but from six individuals.

Predicting Chern numbers in photonic crystals using generative adversarial network-based data augmentation.

The Chern number is the core of topological photonics, which is used to describe the topological properties of photonic crystals and other optical systems to realize the functional transmission and the control of photons within materials. However, the calculation process of Chern numbers is complex and time-consuming. To address this issue, we use the deep learning accompanied with Maxwell's equations to predict the Chern number of a two-dimensional photonic crystal with a square lattice in this paper. We propose a numerical-to-image generative adversarial networks (GANs) augmentation method to solve the problem of insufficient training data. Our method demonstrates excellent predictive performance on the test dataset, achieving an average accuracy of 92.25%. Besides that, the proposed data augmentation method can significantly improve the accuracy of Chern number predictions by 7.95%, compared with the method that did not use this approach. This method offers what we believe to be a novel solution to the challenge of limited numerical data samples in deep learning applications like complex calculations of physical quantities. It may also have certain potential to improve deep learning algorithms in other fields of science and engineering.

Exploring the Influence of Brand Authenticity on Consumer Behavior: Insights from Generation Y Consumers in Pakistan

This study investigates the influence of perceived brand authenticity (PBA) dimensions (quality commitment, heritage, and sincerity) on brand love and subsequent consumer behavior among Generation Y consumers in Pakistan. Additionally, this research introduces brand trust as a mediating variable and explores differences between local and global brands to enhance originality. A mixed-approach approach emerges as employed, combining quantitative surveys and qualitative interviews. Data were accumulated from a numerous sample of Generation Y clients in Pakistan through a hooked up questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM) emerges as used for hypothesis testing. Additionally, in-depth interviews were achieved to capture qualitative insights on brand authenticity perceptions. The results reveal that PBA dimensions significantly impact brand love, which, in turn, influences continuous purchase intention (CPI) and price premium (PP) willingness. Moreover, brand trust mediates the relationship between PBA and consumer behavior, strengthening the effect. Additionally, local brands (e.g., J., Khaadi, Daraz.pk) exhibit stronger authenticity perceptions than global brands (Adidas, KFC, Amazon) among Pakistani consumers. Digital marketing and influencer endorsements further enhance authenticity perceptions. This study contributes to branding literature by integrating Self-Determination Theory (SDT) and Consumer Culture Theory (CCT) to explain Pakistani consumers’ preference for authentic brands. It highlights the role of trust and digital branding in shaping PBA perceptions. For marketers, the findings suggest that authenticity-driven strategies, including transparent communication, cultural storytelling, and social media engagement, can enhance brand loyalty and justify premium pricing. This research contributes to the existing literature by examining the role of PBA in shaping consumer behavior within the Pakistani context. By understanding the specific factors that drive brand love and loyalty among Generation Y consumers, marketers can develop more effective branding strategies.

Ophiolite Association in the Rhodope Massif as indicator of the paleogeographical setting

All ophiolite associations mark epochs of active tectonic movements, which lead to significant petrological processes and modification of the relief of the Earth’s crust. Here we present a geological-petrographical characterization of one ophiolitic associations composed of: a) serpentinites; b) amphibolites-metamorphosed volcanic rocks and tuffs; c) metagabbros and metagabbrodiabases, placed among the Proterozoic metamorphic complex in the Rhodope Massif of Bulgaria on the Balkan Peninsula, South-Eastern Europе. The goal is to clarify the paleogeographical and geological setting during its creation. The methods of lithostratigraphic profiling and correlations on the database of geological field mapping were used, supplemented by microscopic, geochemical and isotopic studies of numerous rock samples. The summarized results confirm a certain stratigraphic level of the Ophiolite Association among the metamorphic complex and a complicated and protracted heterogenetic development, which is typical for the ophiolite associations created in eras of closing oceans, opposite movement of tectonic plates, subduction-obduction environment with appearance of autochthonous Neoproterozoic magmatism. Obducted fragments of serpentinites mark an old erosional continental surface, subsequently covered by transgressively deposited pelitic-carbonate sediments. The general conclusion of our study confirms the concept that the metamorphic complex of the Rhodope Massif represents a unified stratigraphic system consisting of two petrographic groups of different ages, with which we oppose the idea of a trust construction, launched by a group of geologists.

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease.

Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the characterization of microbial proteins present in low abundance relative to host proteins is difficult. Other significant challenges are attributed to using very large protein sequence databases, which impedes sensitivity and accuracy during peptide and protein identification from mass spectrometry data in addition to retrieving taxonomy and functional annotations and performing statistical analysis. To address these problems, we present an integrated bioinformatics workflow for mass spectrometry-based metaproteomics that combines custom protein sequence database generation, peptide-spectrum match generation and verification, quantification, taxonomic and functional annotations, and statistical analysis. This workflow also offers characterization of human proteins (while prioritizing microbial proteins), thus offering insights into host-microbe dynamics in disease. The tools and workflow are deployed in the Galaxy ecosystem, enabling the development, optimization, and dissemination of these computational resources. We have applied this workflow for metaproteomic analysis of numerous clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its utility via the analysis of residual fluid from cervical swabs. The complete workflow and accompanying training resources are accessible on the Galaxy Training Network to equip non-experts and experienced researchers with the necessary knowledge and tools to analyze their data.

Noninvasive, Presymptomatic Detection of Potato Cyst Nematode Infection in Tomato Using Chlorophyll Fluorescence Analysis.

Potato cyst nematodes (PCNs) are notorious pathogens in all major potato production areas worldwide. Mainly due to the low mobility of this soil pathogen, PCN infestations are mostly observed as patches ("foci") that only cover a fraction of the acreage. In-field presymptomatic localization of these pathogens is valuable, as it would allow for the localized application of control measures. Although the mapping of foci is technically feasible, it is unpractical, as it would require the analysis of numerous soil samples. We investigated whether chlorophyll fluorescence (Chl-F) could be suitable as a rapid, nondestructive method for early PCN detection. To this end, the impact of four Globodera pallida densities on the Chl-F of tomato was investigated in a phenotyping greenhouse for 26 days. Furthermore, the classical plant performance indicators of biomass and root surface area were compared with Chl-F. Thermal dissipation (NPQ) and an estimate of the photosynthetic rate (ΦPSII) responded at 1 day postinoculation, and ΦPSII was most sensitive to low PCN infection levels. Chl-F parameters responded more readily to PCN infection than biomass and root surface area. The maximum quantum yield of photosystem II (Fv/Fm) and the potential activity of photosystem II (Fv/F0) initially increased at low PCN infection levels, whereas a sharp decrease was observed at higher infestation levels. Hence, our data suggest that low PCN levels promoted plant performance before becoming detrimental at higher levels. Although Chl-F allowed for early and sensitive PCN detection, it remains to be investigated whether these signals can be distinguished from those produced by other belowground stressors in the field.

The BAMBOO method for correcting batch effects in high throughput proximity extension assays for proteomic studies

The proximity extension assay (PEA) enables large-scale proteomic investigations across numerous proteins and samples. However, discrepancies between measurements, known as batch-effects, potentially skew downstream statistical analyses and increase the risks of false discoveries. While implementing bridging controls (BCs) on each plate has been proposed to mitigate these effects, a clear method for utilizing this strategy remains elusive. Here, we characterized batch effects in PEA proteomics and identified three types: protein-specific, sample-specific, and plate-wide. We developed a robust regression-based method called BAMBOO (Batch Adjustments using Bridging cOntrOls) to correct them. Simulations comparing BAMBOO with established correction techniques (median centering, median of the difference (MOD), and ComBat) revealed that median centering and ComBat were significantly impacted by outliers within the BCs, whereas BAMBOO and MOD were more robust when no plate-wide effects were introduced. Optimal batch correction was achieved with 10–12 BCs. We validated the simulation results using experimental data and found that BAMBOO and MOD had a reduced incidence of false discoveries compared to alternative methods. Our findings emphasize the prevalence of batch effects in PEA proteomic studies and advocate for BAMBOO as a robust and effective tool to enhance the reliability of large-scale analyses in the proteomic field.

Drainage Pipeline Multi-Defect Segmentation Assisted by Multiple Attention for Sonar Images

Drainage pipeline construction projects are vulnerable to a range of defects, such as branch concealed joints, variable diameter, two pipe mouth significances, foreign object insertion, pipeline rupture, and pipeline end disconnection, generated during long-term service in a complex environment. This paper proposes two enhancements to multiple attention learning to detect and segment multiple defects. Firstly, we collected numerous samples of drainage pipeline sonar defect videos. Then, our multiple attention segmentation network was used for target segmentation. The test precision and accuracy of MAP@50 reach 96.0% and 90.9%, respectively, in the segmentation prediction. Compared to the coordinate attention and convolutional block attention module attention models, it had a significant precision advantage, and the weight file size is merely 7.0 MB, which is far smaller than the Yolov9 model segmentation weight size. The multiple attention method proposed in this paper was adopted for detection, instance segmentation, and pose detection in different public datasets, especially in the object detection of the coco128-seg dataset under the same condition. Map@50:95 has increased by 13.0% assisted by our multiple attention mechanism. The results indicated the memory efficiency and high precision of the integration of the multiple attention model on several public datasets.

Analytical validation of a novel bioassay for thyroid-stimulating immunoglobulin.

A novel and rapid cell-based bioassay, Turbo TSI, for measurement of thyroid-stimulating immunoglobulins (TSI) was recently reported. An assessment of the analytical performance of this TSI bioassay is described herein. Thawed cells from Turbo TSI kits were treated with different concentrations of a World Health Organization (WHO) international standard (IS) TSI-positive serum. TSI was measured as a function of luciferase activity measured as relative light units (RLU) and converted into international units per liter (IU/L). Analytical performance studies were performed on numerous samples, over multiple days, by two users at two sites. The limit of blank, limit of detection and limit of quantitation were determined to be 0.007 IU/L, 0.014 IU/L, and 0.021 IU/L, respectively. Receiver operator characteristics (ROC) analysis determined the cut-off to be 0.0241 IU/L with an area under the curve of 0.984. The linear range was shown to be from 0.015 to 11.958 IU/L. The intra-laboratory precision was ≤15%CV. The overall reproducibility of the assay was ≤20%CV for five concentrations (0.06 to 5.16 IU/L). Interference and cross reactivity studies with a variety of substances showed that the assay was robust. The Turbo TSI bioassay demonstrated 95.2% (95% CI 83.3-98.1) positive percent agreement and 94.8% (95% CI 90.9-97.1) negative percent agreement with an FDA-cleared bioassay (Thyretain ® TSI) using serum from 295 patients with autoimmune thyroid disease. The Turbo TSI bioassay exhibits excellent analytical performance and a high level of reproducibility. The performance compared well with Thyretain ® TSI, an FDA-cleared TSI bioassay.

Detection of Microcalcifications with Vessels, Using Linear Structure Analysis and Uncertainty Tracking Technique in Mammography

As a potential biomarker for women’s cardiovascular and chronic kidney diseases, breast arterial calcification (BAC) in mammography has become an emerging research topic in recent years. To provide a more objective measurement for vascular structures with calcium depositions in mammography, this paper introduces a new computerized method to delineate calcified vessels. Specifically, we leverage two underlying cues—calcification and vesselness—within a multiple seeded tracking with uncertainty scheme. This new vessel-tracking scheme generates numerous sampling paths to describe the complicated topology of vascular structures with calcium depositions. A compiling and linking process is further employed to organize these sampling paths into vessel segments that are likely part of the same vessel tract. The proposed method has been evaluated on 63 mammograms, comparing manual delineations from two experts using various assessment metrics. The experimental results confirm the efficacy and stability of the proposed method, indicating its potential use as a convenient BAC measurement tool, replacing the trivial and tedious manual delineation tasks

A predictive model for electrical conductivity of polymer carbon black nanocomposites

AbstractAlthough many studies have investigated the percolation threshold and conductivity in the polymer carbon black (CB) nanocomposites (PCBs) by experimental data, the modeling in this field is a topic previously overlooked in literature. This paper suggests a simple equation for percolation threshold in PCBs by the surface properties of polymer and CB a well as interphase depth (t). Also, the electrical conductivity of PCBs is simulated by CB radius (R), tunneling distance (λ) and interphase depth. The offered model is validated by experimented findings of numerous samples and parametric examinations. A thicker interphase and a higher interfacial tension positively reduce the percolation onset. The smaller CBs, lower percolation threshold, and smaller tunnels enhance the electrical conductivity of PCBs. The low percolation threshold of 0.01 is obtained by the low R (15 nm) and the thick interphase (35 nm). Also, R = 15 nm and t = 35 nm maximize the PCB conductivity to 6 S/m. These results signify the important roles of CB radius and interphase depth in the percolation threshold and conductivity of PCBs. In addition, network fraction (f) of 0.7 and λ = 1 nm produce the uppermost PCB conductivity of 1.6 S/m, but the system is insulated at CB network fraction less than 0.35 or tunneling distance more than 2 nm. Therefore, high network fraction and low tunneling distance are needed to enhance the conductivity of PCBs.Highlights An equation for percolation onset of PCBs is proposed by CB radius and interphase depth. The offered model is validated by experimented data and parametric examinations. A thicker interphase and a higher interfacial tension positively reduce the percolation onset. Smaller CBs, lower percolation onset, and smaller tunnels enhance the PCB conductivity. CB radius and interphase depth significantly manage the conductivity of PCBs.

Automated total nucleic acid extraction with magnetic beads for the detection of Plasmodium falciparum in large study cohorts

Abstract Background Molecular methods play an important role in clinical trials assessing anti-malarial drugs and vaccines, as well as in epidemiological studies aimed at detecting Plasmodium species, especially when dealing with large sample sizes. Molecular techniques are more sensitive and generally have a higher throughput compared to the gold standard microscopy. Further optimization can be achieved with automation of nucleic acid isolation, allowing for rapid and precise extraction. This study evaluated the isolation of total nucleic acids from Plasmodium falciparum mocked samples using an automated extraction method with a magnetic bead-based kit compared to a manual silica column-based kit. Additionally, two different RNA preservation solutions were compared. Methods Plasmodium falciparum Dd2 parasites were serially diluted and spiked into whole blood. The dilutions were stored in two different RNA preservation solutions and total nucleic acids extracted with an automated magnetic bead-based kit and a manual silica column-based kit. Subsequently, a reverse transcription (RT) qPCR for Plasmodium detection targeting Plasmodium 18S rRNA and DNA in a single reaction was performed and the quantification cycle (Cq) values across the different sample groups were compared. Results Comparable Cq values across the various sample preparations were obtained, suggesting minimal influence from RNA preservation solutions (p = 0.686) or extraction methods (p = 0.119) on RT-qPCR outcomes. Automated nucleic acids extraction allowed processing numerous samples in a shorter timeframe and showed similar efficiency in detecting Plasmodium in blood samples by RT-qPCR as manual extraction. Conclusions The automated method for nucleic acid isolation is a valuable tool for the detection of Plasmodium infections in large-scale studies. It is efficient, reliable, and cost-effective. Its potential applications extend to other molecular surveillance studies to support malaria control measures.

Astronomical Pointlike Source Detection via Deep Feature Matching

This study introduces PSDetNet, an innovative deep neural network tailored for the autonomous detection of pointlike astronomical sources by leveraging feature-matching techniques. PSDetNet comprises two primary modules: feature extraction and matching localization. The feature extraction module is built on residual blocks and adopts an encoder–decoder framework to distill features from images robustly. The matching localization module employs a patch-by-patch comparison against a preconstructed template, which is crafted through the alignment and weighted aggregation of numerous exemplar pointlike source samples, capturing the quintessential distribution characteristics of pointlike sources. The experimental results demonstrate that this network can accurately detect pointlike sources in astronomical imagery with high purity and completeness. It operates end to end and uses a fully convolutional architecture that allows for flexible processing of images of any size. This ability considerably enhances its applicability across various practical scenarios.

Eriophyes pouteriae sp. nov., a New Mite Species Infesting Pouteria sapota.

Pouteria sapota, or "mamey sapote", is a tropical fruit tree native to Central America and Southern Mexico, producing sweet, nutrient and vitamin-rich fruit. Several insect pests are known to infest P. sapota, but none have been associated with plant growth alterations. Eriophyoid mites are well known to cause plant malformations, but mites that cause this type of damage to mamey sapote have not been reported. Trees with abnormal leaf growth, including stunted leaves, outward curling, leaf yellowing, and diminishing overall tree vigor, were found in multiple locations in Southern Florida and one location in Brazil. Numerous plant samples were examined for the presence of minute eriophyoid-like mites, and a new species was found. It was morphologically described, and DNA fragments of the mitochondrial gene cytochrome c oxidase subunit I (COI), the nuclear subunit D2 region in 28S rDNA, and the ITS nuclear regions were PCR-amplified and sequenced. Morphological and molecular descriptions of the new species, named E. pouteriae sp. nov., are provided to aid the identification and future detection of this mite. Even though several species within the genus Eriophyes have been reported on other Sapotaceae species, this is the first eriophyoid mite known to be associated with mamey sapote.

Regulating Oxides Enhances the Mechanical Properties of Additively Manufactured Martensitic Stainless Steel

Laser powder bed fusion (LPBF), a subset of additive manufacturing, is used increasingly to produce martensitic stainless steels (MSSs) for aerospace and medical devices. However, excessive oxygen content in MSS powder generally impairs the mechanical properties of parts produced via LPBF. To investigate the effect of oxides on additively manufactured MSS, this study utilizes powders with various oxygen contents and printed numerous samples under different parameters. This article systematically analyzes the source, morphology, and distribution of oxides in MSS samples and their effects on the microstructure and mechanical properties. It is found that MSS samples printed with high‐oxygen powder exhibit higher strength and hardness. The average hardness of high‐oxygen powder‐printed samples increases by 14.2 HV3 over low‐oxygen samples. Additionally, their average tensile and yield strengths of 1025.97 and 809.18 MPa show increases of 3.36% and 17.33%, respectively. This provides a reference for regulating oxides in additively manufactured MSS and potentially benefiting from them.

Advancements of PAPI for the exascale generation

The Performance Application Programming Interface (PAPI) serves as a coherent, operating-system-independent interface for accessing performance counter data across a wide range of hardware and software components. PAPI can operate autonomously as a performance monitoring library and tool for application analysis. However, its true value emerges when it functions as a middleware for numerous third-party profiling, tracing, and sampling toolkits, establishing itself as a universal interface for hardware counter analysis. In this role, PAPI manages the intricacies of each hardware component, presenting a streamlined API to higher-level toolkits. Within the Exascale Computing Project (ECP), PAPI has expanded its capabilities in performance counter monitoring and incorporated support for power management across cutting-edge hardware and software technologies. This includes performance and power monitoring for AMD GPUs through integration with AMD ROCm and ROCm-SMI, Intel Ponte Vecchio GPUs via Intel’s oneAPI Level Zero, and NVIDIA GPUs through the CUPTI Profiling API. Additionally, PAPI is compatible with interconnects, the latest CPUs, and ARM chips. These enhancements have been implemented while preserving the standard PAPI interface and methodology for utilizing low-level performance counters in CPUs, GPUs, on/off-chip memory, interconnects, and the I/O system, encompassing energy and power management. To strengthen PAPI’s sustainability, ECP has facilitated its integration into Spack and E4S, ensuring software robustness through continuous integration and continuous deployment. In addition to hardware counter-based data, PAPI now supports the registration and monitoring of Software-Defined Events. This feature exposes the internal behavior of runtime systems and libraries like PaRSEC, SLATE, Magma, to applications utilizing those libraries, broadening the scope of performance events to include software-based information. Additionally, PAPI has been expanded with the Counter Analysis Toolkit, aiding in native performance counter disambiguation through micro-benchmarks. These micro-benchmarks probe various essential aspects of modern chips, contributing to the classification of raw performance events. In summary, ECP has enabled PAPI to include comprehensive counter analysis capabilities, advanced performance and power monitoring support for exascale hardware components, and broadened the scope of performance events to encompass not only hardware-related metrics but also software-based information.

The gut microbiome and resistome of yellow perch (Perca flavescens) living in Minnesota lakes under varying anthropogenic pressure

Anthropogenic activities can significantly impact wildlife in natural water bodies, affecting not only the host's physiology but also its microbiome. This study aimed to analyze the gut microbiome and antimicrobial resistance gene profile (i.e., the resistome) of yellow perch living in lakes subjected to different levels of anthropogenic pressure: wastewater effluent-impacted lakes and undeveloped lakes. Total DNA and RNA from gut content samples were extracted and sequenced for analysis. Results indicate that the gut resistome and microbiome of yellow perch differ between lakes, perhaps due to varying anthropogenic pressure. The resistome was predominated by macrolide resistance genes, particularly the MLS23S group, making up 53 % of resistome sequences from effluent-impacted lakes and 73 % from undeveloped lakes. The colistin resistance gene group (mcr) was detected in numerous samples, including variants associated with Aeromonas and the family Enterobacteriaceae. The gut microbiome across all samples was dominated by the phyla Proteobacteria, Firmicutes, and Actinobacteria, with the opportunistic pathogens Plesiomonas shigelloides and Aeromonas veronii more abundant in effluent-impacted lakes. Metagenomic analysis of wild fish samples offers valuable insights into the effects of anthropogenic pressures on microbial communities, including antimicrobial resistance genes, in water bodies.