Interpretation Of Complex Data Research Articles

Collectively Working towards Plant-Pollinator Interactions Data Interoperability and Reuse: Lessons Learned from the WorldFAIR Project

The WorldFAIR project worked with eleven case studies to advance the implementation of the FAIR data principles (Findable, Accessible, Interoperable, and Reusable; Wilkinson et al. 2016). The Case Study on Agricultural Biodiversity (Drucker et al. 2022) addressed the challenges of advancing interoperability and mobilizing plant-pollinator interaction data for reuse. In the first phase of the project we performed a landscape analysis and found that a significant amount of data on plant-pollinator interaction was available as supplementary files of research articles, in a range of formats such as PDFs, Excel® spreadsheets, and text files. The diversity of approaches and the lack of appropriate data vocabularies led to confusion, information loss, and the need for complex data interpretation and transformation. In the second phase of the project, we performed six studies to adopt standards recommended in the first phase. Our approach for plant-pollinator data standardization was based on Darwin Core (Darwin Core Task Group 2009) and Ecological Data Language (EML, Jones et al. 2019), in conjunction with a data model and vocabulary proposed by the Brazilian Network of Plant-Pollinator Interactions (REBIPP). The studies underwent a process of “FAIRification” (i.e., transforming data into a format that adheres to the FAIR data principles) using the Global Biotic Interactions (GloBI, Poelen et al. 2014) platform. Two studies also explored the publishing model for Biotic Interactions as part of the GBIF New Data Model. The standardization strategy was successful and increased the interoperability of plant-pollinator interaction data, resulting in a process that allows tracking the provenance of the data, as well as facilitates the reuse of datasets (Drucker et al. 2024). This effort led to the development of "FAIR best practices" and guidelines for sharing plant-pollinator interaction data, as well as a FAIR assessment tool designed to help researchers and institutions evaluate adherence to the FAIR principles (Drucker et al. 2024). A tutorial to standardize plant-pollinator datasets (Gonzalez-Vaquero et al. 2024) and a cookbook with guidelines and recommendations for publishing agriculture-related plant-pollinator data (Salim et al. 2024) were developed to help different actors standardize plant-pollinator interaction data in accordance with the FAIR principles. Moving from diverse approaches and siloed initiatives to widely available FAIR plant-pollination interaction data for scientists and decision-makers will enable the development of integrative studies that enhance our understanding of species biology, behavior, ecology, phenology, and evolution.

Advances in Platelet-Dysfunction Diagnostic Technologies.

The crucial role of platelets in hemostasis and their broad implications under various physiological conditions underscore the importance of accurate platelet-function testing. Platelets are key to clotting blood and healing wounds. Therefore, accurate diagnosis and management of platelet disorders are vital for patient care. This review outlines the significant advancements in platelet-function testing technologies, focusing on their working principles and the shift from traditional diagnostic methods to more innovative approaches. These improvements have deepened our understanding of platelet-related disorders and ushered in personalized treatment options. Despite challenges such as interpretation of complex data and the costs of new technologies, the potential for artificial-intelligence integration and the creation of wearable monitoring devices offers exciting future possibilities. This review underscores how these technological advances have enhanced the landscape of precision medicine and provided better diagnostic and treatment options for platelet-function disorders.

Electron Spectroscopy and Microscopy: A Window into the Surface Electronic Properties of Polycrystalline Metal Halide Perovskites.

In the past years, an increasing number of experimental techniques have emerged to address the need to unveil the chemical, structural, and electronic properties of perovskite thin films with high vertical and lateral spatial resolutions. One of these is angle-resolved photoemission electron spectroscopy which can provide direct access to the electronic band structure of perovskites, with the aim of overcoming elusive and controversial information due to the complex data interpretation of purely optical spectroscopic techniques. This perspective looks at the information that can be gleaned from the direct measurement of the electronic band structure of single crystal perovskites and the challenges that remain to beovercame to extend this technique to heterogeneous polycrystalline metal halide perovskites.

Developer perspectives on the ethics of AI-driven neural implants: a qualitative study

Convergence of neural implants with artificial intelligence (AI) presents opportunities for the development of novel neural implants and improvement of existing neurotechnologies. While such technological innovation carries great promise for the restoration of neurological functions, they also raise ethical challenges. Developers of AI-driven neural implants possess valuable knowledge on the possibilities, limitations and challenges raised by these innovations; yet their perspectives are underrepresented in academic literature. This study aims to explore perspectives of developers of neurotechnology to outline ethical implications of three AI-driven neural implants: a cochlear implant, a visual neural implant, and a motor intention decoding speech-brain-computer-interface. We conducted semi-structured focus groups with developers (n = 19) of AI-driven neural implants. Respondents shared ethically relevant considerations about AI-driven neural implants that we clustered into three themes: (1) design aspects; (2) challenges in clinical trials; (3) impact on users and society. Developers considered accuracy and reliability of AI-driven neural implants conditional for users’ safety, authenticity, and mental privacy. These needs were magnified by the convergence with AI. Yet, the need for accuracy and reliability may also conflict with potential benefits of AI in terms of efficiency and complex data interpretation. We discuss strategies to mitigate these challenges.

Physical Chemistry Education and Research in an Open-Sourced Future.

Proficiency in physical chemistry requires a broad skill set. Successful trainees often receive mentoring from senior colleagues (research advisors, postdocs, etc.). Mentoring introduces trainees to experimental design, instrumental setup, and complex data interpretation. In lab settings, trainees typically learn by customizing experimental setups, and developing new ways of analyzing data. Learning alongside experts strengthens these fundamentals, and places a focus on the clear communication of research problems. However, this level of input is not scalable, nor can it easily be shared with all researchers or students, particularly those that face socioeconomic barriers to accessing mentoring. New approaches to training will therefore progress the field of physical chemistry. Technology is disrupting and democratising scientific education and research. The emergence of free online courses and video resources enables students to learn in a style that suits them. Higher degrees of automation remove cumbersome and sometimes arbitrary technical barriers to learning new techniques, allowing one to collect high quality data quickly. Open sourcing of data and analysis tools has increased transparency, lowered barriers to access, and accelerated scientific dissemination. However, these advances also can lead to "black box" approaches to acquiring and analyzing data, where convenience replaces understanding and errors and misrepresentations become more common. The risk is a breakdown in education: if one does not understand the fundamentals of a technique or analysis, it is difficult to correctly discern the practical limits of an experiment, distinguish signal from noise, troubleshoot problems, or take full advantage of powerful analytical procedures. Our vision of the future of physical chemistry is built around democratized learning, where deep technical and analytical expertise from physical chemists is made freely available. Advancements in technical education through expert-generated educational resources and AI-based tools will enrich physical chemistry education. A holistic approach to education will prepare the physical chemists of 2050 to adapt to rapidly advancing technological tools, which accelerate the pace of research. Technical education will be enhanced by accessible open-source instrumentation and analysis procedures, which will provide instruments and analysis scripts specifically designed for education. High quality, comparable data from standardized open-source instruments will feed into accessible databases and analysis projects, providing others the opportunity to store and analyze both failed and successful experiments. The coupling of open-source education, hardware, and analysis will democratize physical chemistry while addressing risks associated with "black box" approaches.

Abstract 6222: Exposing the heterogeneity of the lipidome in the TME of cutaneous melanoma following treatment with NUC-7738 in combination with anti-PD-1 therapy

Abstract Background. Malignant melanoma exhibits distinct lipid profiles compared to normal tissue and changes in the lipidome influence multiple aspects of tumor biology. Lipidomic alterations within the tumor microenvironment (TME) may influence the efficacy of anti-PD-1 therapy. In combination with anti-PD-1 agent pembrolizumab, NUC-7738 (3'-deoxyadenosine [3’-dA] which is phosphorylated and protected with a phosphoramidate moiety attached at the 5’-position) has shown promising activity in anti-PD-1 refractory melanoma (Phase 2; NCT03829254). NUC-7738 generates sustained intracellular levels of 3’-dATP, which profoundly alters RNA regulatory processes in tumor cells, resulting in impaired cellular responses and changes in expression of proteins related to lipid biosynthesis. To interrogate components of the TME before and after NUC-7738 + pembrolizumab treatment, we developed a novel workflow incorporating Mass Spectrometry Imaging (MSI) to co-register lipidomic changes with molecular markers. Machine learning accelerated the interpretation of complex data obtained from paired patient biopsies. Methods. Snap frozen paired biopsies from patients with advanced cutaneous melanoma treated with NUC-7738 ±pembrolizumab were analyzed by desorption electrospray ionization (DESI)-MSI. Specific metabolic (e.g., ASAH1) and immune (e.g., CD8, CD20) markers were investigated by multiplex immunofluorescence (mIF) post DESI-MSI. Target selection was guided by bulk RNA-seq data. Raw MSI data was visualized using Waters HDI® software. Data extracted from selected regions of interest was exported into MetaboAnalyst 5.0 for statistical analysis. LIPID MAPS® Structure Database was used to assign lipid species. mIF/mIHC images were analyzed by HALO® AI. Results. Single 10 µm tissue sections allowed co-registered DESI-MSI and mIF, suitable for further informatic and AI-enabled image analysis. This facilitated direct comparison of lipids, cells, and protein expression in the TME, before and after treatment with NUC-7738, a compound known to cause profound changes in poly(A) tail regulation. MSI data analyses of the TME uncovered intra-tumoral heterogeneity of the lipidome and demonstrated changes following treatment. Conclusion. This platform provides a powerful investigative tool to simultaneously explore the myriad of factors in the TME that contribute to tumorigenesis and therapeutic response. Further analyses of the role of the lipidome in the TME and intra-tumoral heterogeneity will be explored in paired biopsies from patients with PD-1 inhibitor refractory melanoma. Citation Format: Alison L. Dickson, Greice M. Zickuhr, In Hwa Um, Ying Zhang, Ruth Plummer, Sarah P. Blagden, Stefan N. Symeonides, Natalie Cook, T.R. Jeffry Evans, Mustafa Elshani, David J. Harrison. Exposing the heterogeneity of the lipidome in the TME of cutaneous melanoma following treatment with NUC-7738 in combination with anti-PD-1 therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6222.

Mathematics in Data Science and Artificial Intelligence

Mathematics is a discipline that focuses on structure, order, and relation, derived from counting, measuring, and characterizing object shapes. Mathematics is necessary for professions in data science since machine learning algorithms, conducting analyses, and drawing conclusions from data all require it. A key component of data science is math. It can support problem-solving, model performance optimization, and the interpretation of complex data to address business-related queries. The technology known as artificial intelligence (AI) has come to revolutionize many facets of our existence. Mathematics plays a fundamental part in the astounding advances and capabilities of artificial intelligence. Mathematics contains various branches like algebra, geometry, Trigonometry, Calculus, Statistics and Probability. The foundation of mathematics gives artificial intelligence (AI) systems the ability to reason, learn, and make wise judgments. This article examines the relevance and use of mathematics in artificial intelligence. Large-scale data processing, analysis, and interpretation are made possible by machines thanks to mathematics, which forms the foundation of AI models and algorithms. Developing machine learning algorithms requires an understanding of concepts from statistics, probability theory, calculus, and linear algebra. These algorithms recognize patterns, forecast outcomes, and categorize data using mathematical equations and functions.

Rethinking an effective AV fistula-graft screening program. An "A B C".

The goal of a vascular access screening program is to detect and preemptively correct hemodynamically significant stenosis, however, a practice pattern allowing toimplementsuch a program still remains to be defined. Achieving balance between the increase in access-related procedures by adopting an aggressive screening program, and the risks associated with the absence of any screening program, i.e., failure or abandonment of the arterio-venousaccess with needforcentral venous catheter placement, can be extremely challenging. All major guidelines agree about the role of arterio-venous access monitoring, but the waysurveillance should be managedis still a controversial issue. Preserving long-term vascular access functionshould be a goal for all hemodialysis teams, yet it ideally requires a multidisciplinary effort with a monitoring program, calling fora great deal of involvement by hemodialysis health professionals. In this context, the engagement of skilled nurses and the role of patient empowerment with collaborative decision-making may be the key to a successfulvascular access screening program. Screening programs should be personalized, shared with thepatients, and tailored according to vascular access type and site. In the near future, new devices and the use of artificial intelligence mayallow to support interpretation of complex data and lead to the development of prediction modelsfor vascular access failure.

Visualization of automatically combined disease maps and pathway diagrams for rare diseases.

Introduction: Investigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower. Methods: In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer. Results: We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets. Discussion: In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.

Future Potential of E-Nose Technology: A Review

Electronic Nose (E-Nose) technology unlocks the fascinating world of electronic detection, identification, and analysis of scents and odors, paving the way for innovative research and promising applications. E-Nose mimics the human sense of smell and has gained significant attention and is applied in various fields, including the food, health and drug industries, safety and crime, and the environmental and agricultural sectors. This technology has the potential to improve quality control, medical diagnostics, and hazardous material detection processes. The E-Nose consists of a combination of gas sensors that mimic the olfactory receptors of the human nose. These sensors detect and respond to different scent molecules, resulting in unique response patterns that can be interpreted and analyzed. E-Nose has found application in the food industry to assess food quality, detect contamination, and monitor fermentation processes. In the health field, it has been used for disease diagnosis, monitoring patient health, and detecting cancerous tissue. In addition, E-Nose has been used for security purposes, such as detection of explosives and prohibited substances, as well as identification of counterfeit products. In addition, it has been used in environmental monitoring for air quality assessment and agriculture for disease detection in crops. Despite its promising potential, widespread adoption of E-Nose faces challenges related to sensor sensitivity, data analysis algorithms (complex data interpretation), response diversity, regulatory considerations, implementation complexity, and cost. This article reviews the latest developments in E-Nose technology, explores its applications and future potential, and highlights challenges that need to be addressed. This is considered important because E-Nose opens up a world of electronic scent identification, and analysis with the potential to improve quality control, diagnosis, and detection.

Nutritional Metabolomics in Diet-Breast Cancer Relations: Current Research, Challenges, and Future Directions-A Review.

Breast cancer is one of the most common types of cancer in women worldwide, and its incidence is increasing. Diet has been identified as a modifiable risk factor for breast cancer, but the complex interplay between diet, metabolism, and cancer development is not fully understood. Nutritional metabolomics is a rapidly evolving field that can provide insights into the metabolic changes associated with dietary factors and their impact on breast cancer risk. The review's objective is to provide a comprehensive overview of the current research on the application of nutritional metabolomics in understanding the relationship between diet and breast cancer. The search strategy involved querying several electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar. The search terms included combinations of relevant keywords such as "nutritional metabolomics", "diet", "breast cancer", "metabolites", and "biomarkers". In this review, both in vivo and in vitro studies were included, and we summarize the current state of knowledge on the role of nutritional metabolomics in understanding the diet-breast cancer relationship, including identifying specific metabolites and metabolic pathways associated with breast cancer risk. We also discuss the challenges associated with nutritional metabolomics research, including standardization of analytical methods, interpretation of complex data, and integration of multiple-omics approaches. Finally, we highlight future directions for nutritional metabolomics research in studying diet-breast cancer relations, including investigating the role of gut microbiota and integrating multiple-omics approaches. The application of nutritional metabolomics in the study of diet-breast cancer relations, including 2-amino-4-cyano butanoic acid, piperine, caprate, rosten-3β,17β-diol-monosulfate, and γ-carboxyethyl hydrochroman, among others, holds great promise for advancing our understanding of the role of diet in breast cancer development and identifying personalized dietary recommendations for breast cancer prevention, control, and treatment.

Rapid detection of EGFR mutation in CTCs based on a double spiral microfluidic chip and the real-time RPA method.

Circulating tumor cells (CTCs) are cells shed from primary or metastatic tumors and spread into the peripheral bloodstream. Mutation detection in CTCs can reveal vital genetic information about the tumors and can be used for "liquid biopsy" to indicate cancer treatment and targeted medication. However, current methods to measure the mutations in CTCs are based on PCR or DNA sequencing which are cumbersome and time-consuming and require sophisticated equipment. These largely limited their applications especially in areas with poor healthcare infrastructure. Here we report a simple, convenient, and rapid method for mutation detection in CTCs, including an example of a deletion at exon 19 (Del19) of the epidermal growth factor receptor (EGFR). CTCs in the peripheral blood of NSCLC patients were first sorted by a double spiral microfluidic chip with high sorting efficiency and purity. The sorted cells were then lysed by proteinase K, and the E19del mutation was detected via real-time recombinase polymerase amplification (RPA). Combining the advantages of microfluidic sorting and real-time RPA, an accurate mutation determination was realized within 2h without professional operation or complex data interpretation. The method detected as few as 3 cells and 1% target variants under a strongly interfering background, thus, indicating its great potential in the non-invasive diagnosis of E19del mutation for NSCLC patients. The method can be further extended by redesigning the primers and probes to detect other deletion mutations, insertion mutations, and fusion genes. It is expected to be a universal molecular diagnostic tool for real-time assessment of relevant mutations and precise adjustments in the care of oncology patients.

New data on palaeogeographical changes during the Holocene in the Varna Lake region

The article presents the results of an integrated study of geological data based on three shallow cores from the Varna Lake area. The sedimentary sequences and the related archaeological artifacts were examined through lithological descriptions, a biostratigraphical analysis of the molluscan fauna, and an analysis of the geomorphological, archaeological and radiocarbon data. Continental, liman and marine facies conditions of sedimentation have been established by examining the sedimentary textures, stratigraphic and palaoecological value of the molluscan subfossil fauna. This complex interpretation of data through different analyses allowed the highlighting of important stages in the palaeogeographical development of the Lake of Varna and in the human adaptation to changes in the coastal landscape at the beginning of the Holocene.

Visual genotyping from blood and saliva directly based on tracing phosphate ion generated during isothermal amplification

Genetic variation has been proven to be a promising biomarker for disease occurrence and development. However, current genotyping strategies are limited by cumbersome DNA purification, sophisticated apparatus, and complex data interpretation, which makes it difficult to satisfy the demand of speediness and convenience for precision medicine. This research is dedicated to establishing a visual genotyping method by tracing phosphate ions generated during loop-mediated isothermal amplification (LAMP) for point-of-care diagnostic test scenarios. By introducing pyrophosphatase, phosphate ions from pyrophosphate ion hydrolyzation serve as a signal generator firstly for genotyping, which not only enables the result to be interpreted with the naked eye through molybdophosphate generation, but also enhances the amplification efficiency to facilitate direct genotyping from blood and saliva without DNA purification. By virtue of the high sensitivity and the enhanced amplification efficiency of LAMP, as low as 0.1 μL of 20% blood or saliva is enough for clear result presentation within 30 min. In addition to visual interpretation, the result can also be determined with a microplate reader. As proof of concept, the genotype of anglotensin-converting enzyme has been successfully identified. Thus, we propose that this novel strategy may serve as a promising tool for straightforward genotyping.

Estimating Water Content in Water–Oil Mixtures and Porous MEDIA They Saturate: Joint Interpretation of NMR Relaxometry and Dielectric Spectroscopy

This article deals with the topical problem of estimating water content in water–oil mixtures within porous media they saturate, according to low-field NMR relaxometry and dielectric spectroscopy. The aim of the research is experimental validation of the capability of complex data interpretation to acquire information on the filtration-volumetric properties of drill cuttings, relaxation characteristics of oil-containing fluids, the water/oil ratio in water–oil mixtures, and their saturation of drill cuttings to control the composition of liquids produced from boreholes. The studies are carried out on samples of cuttings and oils taken from fields in the Northern regions of the West Siberian oil-and-gas province, where NMR studies have not been performed before. Based on the experimental data obtained, the possibility of water content assessment in water-in-oil mixtures and porous media they saturate were proved through NMR relaxometry. With the use of the proposed methodology, the amount of water in oil–water mixtures was established, and their main NMR characteristics were determined. The relative error in evaluating the proportion of water in mixtures based on high-viscosity oils is less than 10%, and about 20% for those based on light oils. When determining the oil–water ratio in the pore space of the drill cuttings, the error is about 15%. It was proven that joint use of these two techniques makes it possible to increase the reliability of the oil–water ratio assessment of all the samples studied. Furthermore, it was revealed that the NMR spectrum shifts to the right, and the spectrum of the complex permittivity shifts downwards during the transition from high-viscosity oils to light ones.

Definição de parâmetros de dinâmica e qualidade da água para o gerenciamento em reservatório profundo de hidrelétrica subtropical tipo cânion

ABSTRACT Spatial and temporal variability patterns of water quality were evaluated through monthly collection of water samples (surface, sub-surface and bottom) from 2005 to 2012. Principal Component Analysis was used to define the relative importance of each variable and Anova (two way) to analyze the significance of differences in water quality in the longitudinal axis of the reservoir. The variables: turbidity, Secchi transparency, residence time and temperature have greater importance on water quality. It was observed spatial and temporal gradients, related to the circulation, sedimentation and resuspension processes, and the influence of low flow, high residence time and winter mixing of water column on the cycling of solids and nutrients may explain the variation in these processes. The use of multivariate statistical analysis methods provided important information to understand these processes, it helps the interpretation of complex data to improve monitoring, and use of information to decision makers.

Design and Evaluation Study of Visual Analytics Decision Support Tools in Air Traffic Control

AbstractOperators in air traffic control facing time‐ and safety‐critical situations call for efficient, reliable and robust real‐time processing and interpretation of complex data. Automation support tools aid controllers in these processes to prevent separation losses between aircraft. Issues of current support tools include limited ‘what‐if’ and ‘what‐else’ probe functionalities in relation to vertical solutions. This work presents the design and evaluation of two visual analytics interfaces that promote contextual awareness and support ‘what‐if’ and ‘what‐else’ probes in the spatio‐temporal domain aiming to improve information integration and support controllers in prioritising conflict resolution. Both interfaces visualize vertical solution spaces against a time‐altitude graph. The main contributions of this paper are: (a) the presentation of two interfaces for supporting conflict solving; (b) the novel representation of how vertical information and aircraft rate of climb and descent affect conflicts and (c) an evaluation and comparison of the interfaces with a traditional air traffic control support system. The evaluation study was performed with domain experts to compare the effects of visualization concepts on operator engagement in processing solutions suggested by the tools. Results show that the visualizations support operators' ability to understand and resolve conflicts. Based on the results, general design guidelines for time‐critical domains are proposed.

Application of a Multidisciplinary Weight of Evidence Approach as a Tool for Monitoring the Ecological Risk of Dredging Activities

The use of multidisciplinary investigations for the evaluation of aquatic ecosystems status is recommended by the European Directives, but it is still a challenging practice. In this study, we apply a quantitative weight of evidence (WOE) approach (Sediqualsoft) for the integration of extensive data obtained from different typologies of investigations, obtained over a 4-year monitoring study of dredging activities in the harbor of Leghorn (Italy). During different phases of such operations, selected sites have been characterized in terms of levels of trace metals and polycyclic aromatic hydrocarbons in sediments, bioaccumulation of contaminants, and a wide battery of biomarkers in transplanted mussels, ecotoxicological effects of sediments through a battery of bioassays (algal growth inhibition, bioluminescence inhibition, and embryotoxicity tests), and the status of benthic communities. Each typology of data, line of evidence (LOE), has been initially elaborated through dedicated logical flowcharts and algorithms providing specific hazard indices, followed by their overall integration based on different weights assigned to each LOE. This approach allowed to summarize more than 10,000 results, reaching robust conclusions on environmental impact during various phases of dredging operations. This approach was confirmed as a useful tool for monitoring the risk, supporting a “site-oriented” decision making process by providing stakeholders simple interpretation of complex data.

Systematic assessment of acquisition and data-processing parameters in the suspect screening of veterinary drugs in archive matrices using LC-HRMS

ABSTRACT Monitoring strategies for veterinary drugs in products of animal origin are shifting towards a more risk-based approach. Such strategies not only target a limited number of predefined .substances but also facilitate detection of unexpected substances. By combining the use of archive matrices such as feather meal with suspect-screening methods, early detection of new hazards in the food and feed industry can be achieved. Effective application of such strategies is hampered by complex data interpretation and therefore, targeted data analysis is commonly applied. In this study, the performance of a suspect-screening data processing workflow using a suspect list or the online spectral database mzCloudTM was explored to facilitate detection of veterinary drugs in archive matrices. Data evaluation parameters specifically investigated for application of a suspect list were mass tolerance and the addition or omission of retention times. Application of a mass tolerance of 1.5 ppm leads to an increase in the number of false positives, as does omission of retention times in the suspect list. Different acquisition modes yielding different qualities of MS2 data were studied and proved to be a critical factor, where data-dependent acquisition is preferred when matching to the mzCloudTM database. Using this approach, it is possible to search for compounds on a dedicated suspect list based on the exact mass and retention times and, at the same time, detect unexpected compounds without a priori information. A pilot study was conducted and fourteen different antibiotics were detected (and confirmed by MS/MS). Three of these antibiotics were not included in the suspect list. The optimised suspect-screening method proved to be fit for the purpose of finding veterinary drugs in feather meal, which are not in the scope of the current monitoring methods and therefore, it gives added value in the perspective of a risk-based monitoring.

Machine Learning and Artificial Intelligence for Surgical Decision Making.

Background: The use of machine learning (ML) and artificial intelligence (AI) in medical research continues to grow as the amount and availability of clinical data expands. These techniques allow complex interpretation of data and capture non-linear relations not immediately apparent by classic statistical techniques. Methods: This review of the ML/AI literature provides a brief overview for practicing surgeons and clinicians of the current and future roles these methods will have within surgical infection research. Results: A conceptual overview of the techniques is provided along with concrete examples in the surgical infections literature. Further examples of ML/AI techniques in clinical decision support as well as therapy discovery with model-based deep reinforcement learning are illustrated. Conclusions: Artificial intelligence and ML are important and increasingly utilized techniques within the expanding body of surgical infection research. This article provides a minimal baseline literacy in ML/AI to be able to view such projects in an appropriately critical fashion.