Tool For Selection Research Articles

Enzyme-Powered, Label-Free DNA Walker for Uracil-DNA Glycosylase Detection at Single-Cell Level.

Uracil-DNA glycosylase (UDG) plays a crucial role in the removal of damaged uracil bases, thereby upholding genetic stability and integrity. An enzyme-powered, label-free DNA walker was devised for UDG activity detection. Initially, a label-free DNA track, incorporating a gold nanoparticle (AuNP), multiple hairpin structures, and various swing arms, was engineered for walking mechanism. The hairpin structure was meticulously crafted to include a G-quadruplex sequence, enabling the generation of a label-free fluorescence signal. The swing arm remained inert in the absence of UDG, but became activated upon the introduction of UDG, thereby initiating the enzyme-powered walking process and generating significant dissociative G-quadruplex sequences. By integrating a selective fluorescent dye into the design, an enhanced label-free fluorescence response was achieved. The proposed DNA walker presented a direct and label-free approach for UDG detection, demonstrating exceptional sensitivity with a detection limit of 0.00004 U/mL. Using the uracil glycosylase inhibitor (UGI) as an inhibitory model, inhibitor assay was conducted with satisfactory precision. Furthermore, successful analysis of cellular UDG at the single-cell level was accomplished. Consequently, the developed DNA walker serves as a label-free, selective, and sensitive tool for UDG activity assessment, showing great potential for applications in disease diagnosis, inhibitor screening, and biomedical investigations.

Quantifying the resistance of Australian wheat genotypes to Pratylenchus thornei based on a continuous metric from a factor analytic linear mixed model

Genetic resistance to the parasitic root-lesion nematode, Pratylenchus thornei, is one of the main management strategies cereal growers can use to minimise the impact of nematodes on winter cereal cropping. Screening of genotypes in the presence of P. thornei populations must provide reliable resistance measures that are realised under field conditions. Adoption of the latest statistical methodologies can help to better differentiate between resistant and susceptible genotypes. In this study, post-harvest P. thornei population densities were measured from a collection of 17 field experiments, with varying starting P. thornei population densities, conducted between 2011 and 2018 in locations across the northern grain growing region of eastern Australia. The experiments primarily consisted of wheat genotypes. The post-harvest P. thornei population densities were analysed across multiple environments in a linear mixed model framework, with a factor analytic structure used to model genotype by environment (G ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} E) interaction effects exclusively for wheat genotypes. In general, genetic correlations between environments were found to be high, indicating limited G ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} E interaction for resistance to P. thornei. Post-processing of results using the factor analytic selection tools (FAST) method provided a measure of the overall performance for each wheat genotype, as well as a stability measure reflecting the consistency of the resistance status across environments. The FAST method quantified genotype resistance on a continuous scale, better reflecting the nature of genetic resistance based on a quantitative variable such as nematode population density, and provided a statistically robust and informative means of aiding selection decisions for resistance to P. thornei.

Statistical analysis of the transformation of agricultural production in various categoriesof farms and forms of management

Agriculture is a complex material process, where the starting point is the presence of certain factors of production, productive forces and a special factor — land resources. The functioning and development of agriculture are associated with numerous threats and risks that require their assessment, monitoring, identification, taking into account the size of the industry, which is important from the point of view of forming directions for further promising development, obtaining maximum profit at the lowest cost. The institutional component also remains extremely important: the optimal form of organization of agricultural production, in which the available resources can be used in the best way. For the most complete and comprehensively in-depth assessment of the quantitative and qualitative transformation of the agricultural sector, the methodology of statistics has been applied. Based on the use of Salai and integral Ryabtsev indicators, the article characterizes the structural differences in agricultural production in the context of business categories and organizational and legal forms over 30 years; substantiates approaches to the optimal selection and use of analytical tools to assess the size of production in order to identify the most effective. Based on the estimated data, the main trends in the development of the industry have been identified, the results of the functioning of the activities of a set of organizations by categories of farms and organizational and legal forms have been summarized. The provisions of the article can be used to model threats, risks and predict the further vector of agricultural development in the formation of directions of state support, adjustment of fiscal policy for different categories of farms.

Polyvalent Glycomimetic-Gold Nanoparticles Revealing Critical Roles of Glycan Display on Multivalent Lectin-Glycan Interaction Biophysics and Antiviral Properties.

Multivalent lectin-glycan interactions (MLGIs) are widespread and vital for biology, making them attractive therapeutic targets. Unfortunately, the structural and biophysical mechanisms of several key MLGIs remain poorly understood, limiting our ability to design spatially matched glycoconjugates as potential therapeutics against specific MLGIs. We have recently demonstrated that natural oligomannose-coated nanoparticles are powerful probes for MLGIs. They can provide not only quantitative affinity and binding thermodynamic data but also key structural information (e.g, binding site orientation and mode) useful for designing glycoconjugate therapeutics against specific MLGIs. Despite success, how designing parameters (e.g., glycan type, density, and scaffold size) control their MLGI biophysical and antiviral properties remains to be elucidated. A synthetic pseudodimannose (psDiMan) ligand has been shown to selectively bind to a dendritic cell surface tetrameric lectin, DC-SIGN, over some other multimeric lectins sharing monovalent mannose specificity but having distinct cellular functions. Herein, we display psDiMan polyvalently onto gold nanoparticles (GNPs) of varying sizes (e.g., ∼5 and ∼13 nm, denoted as G5- and G13 psDiMan hereafter) to probe how the scaffold size and glycan display control their MLGI properties with DC-SIGN and the closely related lectin DC-SIGNR. We show that G5/13 psDiMan binds strongly to DC-SIGN, with sub-nM K ds, with affinity being enhanced with increasing scaffold size, whereas they show apparently no or only weak binding to DC-SIGNR. Interestingly, there is a minimal, GNP-size-dependent, glycan density threshold for forming strong binding with DC-SIGN. By combining temperature-dependent affinity and Van't Hoff analyses, we have developed a new GNP fluorescence quenching assay for MLGI thermodynamics, revealing that DC-SIGN-Gx-psDiMan binding is enthalpy-driven, with a standard binding ΔH 0 of ∼ -95 kJ mol-1, which is ∼4-fold that of the monovalent binding and is comparable to that measured by isothermal titration calorimetry. We further reveal that the enhanced DC-SIGN affinity with Gx-psDiMan with increasing GNP scaffold size is due to reduced binding entropy penalty and not due to enhanced favorable binding enthalpy. We further show that DC-SIGN binds tetravalently to a single Gx-psDiMan, irrespective of the GNP size, whereas DC-SIGNR binding is dependent on GNP size, with no apparent binding with G5, and weak cross-linking with G13. Finally, we show that Gx-psDiMans potently inhibit DC-SIGN-dependent augmentation of cellular entry of Ebola pseudoviruses with sub-nM EC50 values, whereas they exhibit no significant (for G5) or weak (for G13) inhibition against DC-SIGNR-augmented viral entry, consistent to their MLGI properties with DC-SIGNR in solution. These results have established Gx-psDiMan as a versatile new tool for probing MLGI affinity, selectivity, and thermodynamics, as well as GNP-glycan antiviral properties.

Random forests with spatial proxies for environmental modelling: opportunities and pitfalls

Abstract. Spatial proxies, such as coordinates and distance fields, are often added as predictors in random forest (RF) models without any modifications being made to the algorithm to account for residual autocorrelation and improve predictions. However, their suitability under different predictive conditions encountered in environmental applications has not yet been assessed. We investigate (1) the suitability of spatial proxies depending on the modelling objective (interpolation vs. extrapolation), the strength of the residual spatial autocorrelation, and the sampling pattern; (2) which validation methods can be used as a model selection tool to empirically assess the suitability of spatial proxies; and (3) the effect of using spatial proxies in real-world environmental applications. We designed a simulation study to assess the suitability of RF regression models using three different types of spatial proxies: coordinates, Euclidean distance fields (EDFs), and random forest spatial prediction (RFsp). We also tested the ability of probability sampling test points, random k-fold cross-validation (CV), and k-fold nearest neighbour distance matching (kNNDM) CV to reflect the true prediction performance and correctly rank models. As real-world case studies, we modelled annual average air temperature and fine particulate air pollution for continental Spain. In the simulation study, we found that RFs with spatial proxies were poorly suited for spatial extrapolation to new areas due to significant feature extrapolation. For spatial interpolation, proxies were beneficial when both strong residual autocorrelation and regularly or randomly distributed training samples were present. In all other cases, proxies were neutral or counterproductive. Random k-fold cross-validation generally favoured models with spatial proxies even when it was not appropriate, whereas probability test samples and kNNDM CV correctly ranked models. In the case studies, air temperature stations were well spread within the prediction area, and measurements exhibited strong spatial autocorrelation, leading to an effective use of spatial proxies. Air pollution stations were clustered and autocorrelation was weaker and thus spatial proxies were not beneficial. As the benefits of spatial proxies are not universal, we recommend using spatial exploratory and validation analyses to determine their suitability, as well as considering alternative inherently spatial modelling approaches.

The Usefulness of Soil Penetration Resistance Measurements for Improving the Efficiency of Cultivation Technologies

The research results of soil penetration resistance (SPR) tests carried out on sandy clay using four cone probes with different dimensions of the measuring tip are presented in this study. It was indicated that the values of SPR can be used to diagnose the cultivation layer and, on this basis, determine whether it is necessary to cultivate it and select tools for the required treatment. Tests were carried out on three levels of soil density, 1.37, 1.43 and 1.51 g∙cm−3, and two moisture contents, 7.64% and 10.4%. The results show that the probe with the smallest cone with apex angles of 30° and 60° on the least dense soil indicated higher SPR by over 50% more than other probes with the highest cone and the same opening angles. The change in cone opening angle from 30° to 60° led to an increase in probe indications in the range of 10–25%, depending on the diameter of the cone tip. The statistical analysis shows that values of probe indications were statistically significant and were influenced by soil density, probe cone tip dimensions, the surface of the base and the apex angle. The values of SPR are fundamental in diagnosing the quality of the soil’s top layer, determining the necessity of breaking it up, and selecting the optimal tools for this procedure. To improve the efficiency of agricultural crop cultivation technologies. This is particularly important when carrying out cultivation procedures in an environmentally friendly manner. The measurements will help support the introduction of sustainable farming practices, including direct seeding, no-till cultivation, or precision agriculture, reducing soil degradation and increasing environmental benefits.

Digital engineering implementation in nuclear demonstration and nonproliferation projects at Idaho National Laboratory

Digital engineering and digital twins are increasingly being used in nuclear energy projects with important impacts. At Idaho National Laboratory, these approaches have been applied in a variety of nuclear energy research, development, and demonstration projects, with key lessons and evolutions occurring for each. In this paper, we describe the use of digital engineering and digital twins in the Versatile Test Reactor design, National Reactor Innovation Center test beds, and nonproliferation analysis of the AGN-201 reactor design. We share key lessons learned for these projects related to tool selection, adoption and training, and working with existing assets versus beginning at the design phase. We also share highlights of future potential uses of digital twins and digital engineering, including using artificial intelligence to perform repetitive design tasks and digital twins to move towards semiautonomous nuclear power plant operations.

Effect analysis of government intervention on scale-heterogeneous farmers' behavior of groundwater exploitation.

Government intervention has become an important measure to restrain groundwater overexploitation. This paper analyzes the effect of three types of government intervention measures, namely, guidance, incentive and constraint, on farmers' groundwater utilization behavior, from the perspective of scale-heterogeneity, using general quantile regression model, by survey data of 1122 households in well irrigation area of north China. The results showed that: (1) the incentive and guiding measures have negative effects on farmers' groundwater usage, while the effect of restrictive measures is not obvious. The guided policy is superior to the incentive measure as to governance effect. (2) With the increase of farmers' land scale, the influence of incentive measures shows a trend of weakening, and the effect of guided measures on groundwater demand reduction of farmers is stronger. When it comes to the different point of water consumption, when at the point level of 0.25, the incentive measures have the most obvious inhibitory effect. With the increase of water consumption of farmers, the guided measures begin to play a core role. The effect of restrictive measures is not obvious with the increase of water consumption. (3) In addition, farmers' irrigation water consumption also is affected by gender, cognition of water resources shortage, ecological cognitive level, acquisition ability of disaster information, village rain conditions, the degree of water rights market development, feelings of water fee increase, irrigated disputes in the village, collective economic level of village. The selection of policy tools is flexible according to the farmers' land scale for groundwater over-extraction control.

Multiobjective optimization of effect-directed planar chromatography as a promising tool for fast selection of polypotent natural products

A new method for efficiently selecting polypotent natural products is proposed in this study. The method involves using effect-directed HPTLC data and multiobjective optimization algorithms to extract chromatographic signals from HPTLC bioassay images. Three different multiobjective optimization methods, namely Derringer's desirability approach, Technique for order of preference by similarity to ideal solution (TOPSIS), and Sum of ranking differences (SRD), were applied to the chromatographic signals. In combination with jackknife cross-validation, Derringer's approach and TOPSIS demonstrated high similarity in finding the best (most polypotent), next to the best, next to the worst, and worst (least polypotent) extracts, while the SRD resulted in slightly different outcomes.Furthermore, a new method for identifying the chromatographic features that characterize the most polypotent extracts was proposed. This method is based on partial least square regression (PLS) and can be used in combination with HPTLC-chemical fingerprints to predict the desirability of new extracts. The resulting PLS models demonstrated high statistical performance with determination coefficients ranging from R2 = 0.885 in the case of Derringer's desirability, to 0.986 for TOPSIS. However, the PLS modeling of SRD values was not successful.

Comparative Study for DC-DC Converter Output Bank’s Reliability Evaluation Using Prediction Standards MIL-HDBK-217F vs. Telcordia SR-332

In the last decade, a higher level of reliability has become a compulsory demand when it comes to modern DC-DC converters. This work addresses the main reliability metrics: in many studies, the failure rate λ and MTBF of an output capacitor bank used within a high-current low-voltage buck converter have shown that the output capacitor bank is the most critical component within the converter. Many authors dealt with this issue by performing reliability predictions. The majority of studies use only one specific standard prediction to solve the problem. Herein, the calculation was performed using both the older standard, MIL-HDBK-217, and the latest one, Telcordia SR-332, providing a benchmark comparison between the two, which is a helpful tool for output capacitor selection in early-stage design. The military standard was well accepted for decades in reliability prediction, even in industrial electronics, and is still used today in a critical manner because there have been no more updates after the latest version, MIL-HDBK-217F—Notice 2, was released in 1995. Since then, newer prediction standards have appeared in the electronics reliability market. Over time, this standard was mostly used, but it does not accurately model the reliability because of a lack of taking account of the mission profile. The above-mentioned newer standard—i.e., Telcordia SR-332—also tries to compensate for the lack of the newest component technology in the older standard (which is the first standard released on the market), supplying useful design data for design engineers who use the so-called “design with reliability in mind” concept. This provides the designer of DC-DC converters with a comparison between the reliability values when the two mentioned standards are used. This paper establishes the environmental condition for the passive components by means of a point of load (PoL) buck converter that is used for both calculation methods. The influence of temperature and several specific concepts, like reference conditions, operating conditions, ripple, and internal self-heating, were taken into account in order to display the results. The temperature for the capacitor’s capsule needed in πT stress factor calculation was derived using PSPICE simulation. High-fidelity and dedicated SPICE models provided by the manufacturer were used for MOSFETs, polymer electrolytic, and MLCC capacitors that comprise the converter.

Factor analytic selection tools and environmental feature-integration enable holistic decision-making in Eucalyptus breeding

Understanding the genotype-by-environment interaction (GEI) and considering it in the selection process is a sine qua non condition for the expansion of Brazilian eucalyptus silviculture. This study’s objective is to select high-performance and stable eucalyptus clones based on a novel selection index that considers the Factor Analytic Selection Tools (FAST) and the clone’s reliability. The investigation explores the nuances interplay of GEI and extends its insights by scrutinizing the relationship between latent factors and real environmental features. The analysis, conducted across seven trials in five Brazilian states involving 78 clones, employs FAST. The clonal selection was performed using an extended FAST index weighted by the clone’s reliability. Further insights about GEI emerge from the integration of factor loadings with 25 environmental features through a principal component analysis. Ten clones, distinguished by high performance, stability, and reliability, have been selected across the target population of environments. The environmental features most closely associated with factor loadings, encompassing air temperature, radiation, and soil characteristics, emerge as pivotal drivers of GEI within this dataset. This study contributes insights to eucalyptus breeders, equipping them to enhance decision-making by harnessing a holistic understanding-from the genotypes under evaluation to the diverse environments anticipated in commercial plantations.

Evidence of Differential Attainment in Canadian Medical School Admissions: A Scoping Review.

In Canada, many groups (e.g., Black, Indigenous, rural backgrounds) have historically faced and continue to encounter systemic barriers in accessing the medical profession. These barriers often manifest in performance disparities, known as differential attainment, during medical school admissions. This scoping review summarizes the nature and extent of evidence on the association of differential attainment in medical school admissions selection tools and outcomes with applicant social identity in the Canadian context. The authors used Arksey and O'Malley's scoping review framework to summarize research studies published between 2000-2022 with empirical evidence of differential attainment in admissions selection tools and outcomes with respect to a range of applicant social identity categories. The authors recorded whether studies adopted a structuralist and/or intersectional perspective. Ultimately, 15 studies were included in the review. While the evidence on differential attainment associated with social identity in Canadian medical education was heterogeneous, numerous studies highlight differential attainment in the admissions process associated with applicant race and/or ethnicity (6 studies), age (5 studies), gender (4 studies), socioeconomic status (3 studies), geographic location (4 studies), and rural or urban background (5 studies). These attainment differences were reported at 3 phases of the admissions process (invitation to interview, offer of admission, and acceptance of offer), and were driven by several admissions selection tools, including grade point average, Medical College Admission Test score, and interview performance. The review highlights evidence that suggests systemic, structural inequities in admissions systems manifest as differential attainment in Canadian medical school admissions. Based on this evidence, those who identify as Black or Indigenous and those with low socioeconomic status or rural backgrounds were generally more adversely affected. Admission practices must be studied and improved so medical education systems can better avow equality and human dignity and achieve equity goals.

Realizing Textbook Outcomes Following Liver Resection for Hepatic Neoplasms with Development and Validation of a Predictive Nomogram

Background‘Textbook Outcome’ (TO) represents an effort to define a standardized, composite quality benchmark based on intraoperative and postoperative endpoints. This study aimed to assess the applicability of TO as an outcome measure following liver resection for hepatic neoplasms from a low- to middle-income economy and determine its impact on long-term survival. Based on identified perioperative predictors, we developed and validated a nomogram-based scoring and risk stratification system.MethodsWe retrospectively analyzed patients undergoing curative resections for hepatic neoplasms between 2012 and 2023. Rates of TO were assessed over time and factors associated with achieving a TO were evaluated. Using stepwise regression, a prediction nomogram for achieving TO was established based on perioperative risk factors.ResultsOf the 1018 consecutive patients who underwent liver resections, a TO was achieved in 64.9% (661/1018). The factor most responsible for not achieving TO was significant post-hepatectomy liver failure (22%). Realization of TO was independently associated with improved overall and disease-free survival. On logistic regression, American Society of Anesthesiologists score of 2 (p = 0.0002), perihilar cholangiocarcinoma (p = 0.011), major hepatectomy (p = 0.0006), blood loss >1500 mL (p = 0.007), and presence of lymphovascular emboli on pathology (p = 0.026) were associated with the non-realization of TO. These independent risk factors were integrated into a nomogram prediction model with the predictive efficiency for TO (area under the curve 75.21%, 95% confidence interval 70.69–79.72%).ConclusionTO is a realizable outcome measure and should be adopted. We recommend the use of the nomogram proposed as a convenient tool for patient selection and prognosticating outcomes following hepatectomy.

Bioisosteres at C9 of 2-Deoxy-2,3-didehydro-N-acetyl Neuraminic Acid Identify Selective Inhibitors of NEU3.

Human neuraminidases play critical roles in many physiological and pathological processes. Humans have four isoenzymes of NEU, making selective inhibitors important tools to investigate the function of individual isoenzymes. A typical scaffold for NEU inhibitors is 2-deoxy-2,3-didehydro-N-acetylneuraminic acid (DANA) where C9 modifications can be critical for potency and selectivity against human NEU. To design improved DANA analogues, we generated a library of compounds with either a short alkyl chain or a biphenyl substituent linked to the C9 position through one of six amide bioisosteres. Bioisostere linkers included triazole, urea, thiourea, carbamate, thiocarbamate, and sulfonamide groups. Within this library, we identified a C9 biphenyl carbamate derivative (963) that showed high selectivity and potency for NEU3 (Ki = 0.12 ± 0.01 μM). In contrast, NEU1 and NEU4 isoenzymes preferred amide and triazole linkers, respectively. Finally, analogues with urea, sulfonamide, and amide linkers showed enhanced inhibitory activity for a bacterial NEU, NanI from Clostridium perfringens.

Development and validation of stability indicating HPLC-DAD assay of adapalene, methylparaben and propylparaben in a cosmeceutical gel matrix with blueness, greenness and whiteness assessment via relative ranking: An ab initio-sub finem juxtaposed non-traditional approaches

Adapalene (ADA) is primarily used as a cosmeceutical gel in the treatment of mild-moderate acne and is also used off-label to treat keratosis pilaris as well as other skin conditions. A stability-indicating fit-for-purpose (FFP) method has been developed for co-determination of ADA, methylparaben (MPN), and propylparaben (PPN) combined in a cosmeceutical gel dosage form. This method was based on a reversed-phase RP-HPLC separation of the cited analytes. It avoided the use of tetrahydrofuran (THF), which is commonly used by both compendial and published procedures for determination of ADA, thus improving method blueness (applicability), greenness and whiteness. The method development and validation followed non-traditional eco-friendly approaches including chemical simulations and empirical modelling. The HPLC separation was performed on an RP Zorbax Eclipse XDB-C8 analytical column [150 × 4.6 mm, 5 µm (80 Å)] with gradient elution system of HPLC grade acetonitrile and 50 mM aqueous sodium dihydrogen phosphate buffer containing 5 mM ethylenediaminetetraacetic acid (EDTA) adjusted to pH 8.0 as the mobile phase at a flow rate of 2.5 mL min−1 and column oven was set at 25 °C. EDTA played an important role for reproducibility of retention time of the three analytes especially for ADA. It controlled deprotonation of the three acidic analytes under study and hence reproduce their ionization, hydrophobicity, and hence retention time. Quantitation was achieved with the photodiode array detection (DAD) at wavelengths of 256 nm for both MPN and PPN and 312 nm for ADA. Target Measurement Uncertainty (TMU) for the three analytes was determined via two approaches; misclassification rates approach employing 100,000 Monte Carlo simulations (MCS) and in-specification probability (ISP) to ascertain fitness-for-purpose of the proposed method. Microsoft Excel workbooks were developed for both TMU calculation approaches in addition to calculation of procedure capability index (Ppk) and sigma level for each analyte and attached in the Supplementary material. Moreover, linearity of the method underwent additional assessment using the previously mentioned TMU and procedure capability determination techniques. The robustness of the method was checked using Plackett-Burman experimental design (PBD) as an empirical modelling technique which was examined thoroughly to detect some interactions. The calibration graphs for each drug were rectilinear in the range of 0.34–7.08, 0.38–7.00 and 0.08–1.75 µg mL−1 for ADA, MPN, and PPN, respectively. The stress testing revealed photostability of the three components and variable degrees of degradation in the other conditions. The proposed HPLC-DAD method was successfully validated and applied for the determination of the investigated analytes in a gel matrix. In addition, method blueness and greenness were comparatively evaluated via very recent approaches of Blue Applicability Grade Index (BAGI), Green Solvents Selection Tool (GSST), Spider Diagram for Assessment of the Green Analytical Index (SDAGI), Analytical Method Greenness Score (AMGS), Carbon Footprint (CF), Modified Complementary Green Analytical Procedure Index (ComplexMoGAPI), and Analytical GREEnness Metric Approach (AGREE). Furthermore, the method whiteness was assessed via Red Green Blue Twelve (RGB 12) model following a relative ranking approach.

Quality of reporting health behaviors for multiple sclerosis (QuoRH-MS): A scoping review to inform intervention planning and improve consistency of reporting.

Multiple sclerosis (MS) is a neurological condition that necessitates a multidisciplinary approach to aid those living with MS in managing their disease. Health behavior, or lifestyle modification, is an emerging approach to MS self-management. MS researchers utilize measurement tools to ensure that interventions are best suited to the outcomes, thereby potentially influencing practice. The aim of this study was to investigate which tools are being used for health behavior management studies in people living with MS and develop an aid for tool selection. A scoping review guided by the PRISMA-Sc checklist and the JBI manual for evidence synthesis was employed with a systematic search strategy executed across four scientific databases: Medline, PubMed, CINAHL, and Cochrane Libraries. The types of assessment tools used were extracted from the included studies. Each tool was categorized into the health behavior intervention discipline (nutrition, exercise, and psychology) and then subcategorized by the tool's purpose. The frequency of use was determined for each tool. Reporting of validation of the assessment tools were collated to inform a tool selection checklist. The review identified a total of 248 tools (12 nutrition, 55 exercise, and 119 psychology unique reports) from 166 studies. Seventy-seven multidimensional tools were identified including measures of quality of life, fatigue, and functional scales. Only 88 studies (53%) referred to the validity of the tools. The most commonly reported tools were the dietary habits questionnaire (n=4, nutrition), 6-minute walk test (n=17, exercise), Symbol Digits and Modalities Test, and Hospital Anxiety and Depression Scale (n=15 each, psychology) with the Expanded Disability Status Scale reported 43 times. Evidence from interventions may inform practice for health professionals. This review provides insights into the range of tools reported across health behavior intervention studies for MS and offers a guide toward more consistent reporting of study methods.

Benchmarking pharmacogenomics genotyping tools: Performance analysis on short-read sequencing samples and depth-dependent evaluation.

Pharmacogenomics (PGx) investigates the influence of genetics on drug responses, enabling tailored treatments for personalized healthcare. This study assessed the accuracy of genotyping six genes using whole genome sequencing with four different computational tools and various sequencing depths. The effects of using different reference genomes (GRCh38 and GRCh37) and sequence aligners (BWA-MEM and Bowtie2) were also explored. The results showed generally minor variations in tool performance across most genes; however, more notable discrepancies were observed in the analysis of the complex CYP2D6 gene. Cyrius, a CYP2D6-specific tool, demonstrated the most robust performance, achieving the highest concordance rates for CYP2D6 in all instances, comparable to the consensus approach in most cases. There were rather small differences between the samples with 20× coverage depth and those with higher depth, but the decreased performance was more evident at lower depths, particularly at 5×. Additionally, variations in CYP2D6 results were observed when samples were aligned to different reference genomes using the same method, or to the same genome using different aligners, which led to reporting incorrect rare star alleles in several cases. These findings inform the selection of optimal PGx tools and methodologies as well as suggest that employing a consensus approach with two or more tools might be preferable for certain genes and tool combinations, especially at lower sequencing depths, to ensure accurate results. Additionally, we show how the upstream alignment can affect the performance of tools, an important factor to take into account.

The Impact of Digital Marketing on Consumer Behaviour

Abstract The use of communication techniques in marketing has become a fundamental component of effective marketing policy, representing an element of differentiation from the competition, which leads to obtaining, on the one hand, a competitive advantage, and on the other hand, to the consolidation of a long-term relationship with the consumer, ultimately determining their loyalty, with a notable effect on the financial outcomes of the organization. The selection of appropriate communication tools and channels, tailored to the interests of the targeted consumer segment requires a considerable effort on the part of marketers in the direction of deciphering consumer behaviour, understanding the influencing factors that shape it in the current economic and social climate, in which the use of digital technologies represents the most suitable means of initiating and conveying messages to consumers, as well as from consumers to the organization. The present study aims to emphasize the importance of using digital communication tools on the orientation of the purchase decision towards the products/services of an organization, without excluding, however, the need for an adequate use of traditional marketing communication tools. In the present article, the impact on consumer behaviour is analysed based on research from secondary data sources.

Guidelines to support the design, and selection and appraisal of multimedia teaching aids for microbiology education.

Microbiology education has a serious handicap - the lack of visibility of the players of the subject and their interactions - which engenders a disproportionate reliance upon multimedia teaching aids (MTAs). The International Microbiology Literacy Initiative (IMiLI) is creating educational resources in societally-relevant microbiology complemented by appropriate MTAs. However, proper guidance supporting microbiology educators in locating and selecting, or commissioning the creation of, adequate MTAs for different target audiences and learning objectives is lacking. The aims of this study were to (i) identify important considerations regarding educational/didactical standards and the design of educational multimedia and (ii) create an evidence-based guideline for selecting and appraising existing, and informing the creation of new, microbiology MTAs. This investigation is based on an exploratory, mixed-methods approach. The results of two literature reviews (covering educational and good practice multimedia design) informed the collation of a preliminary appraisal guideline for videos, animations, comics, and video games. A web-scraping approach was utilised to locate and retrieve existing exemplars of the four multimedia types and create four pertinent multimedia databases (including metadata). The preliminary guideline was piloted (and revised accordingly) by appraising quasi-random (or purposive) samples of each multimedia type. Educational multimedia experts were interviewed to discuss the findings. Finally, the guideline was updated to reflect the expert comments together with the results of the pilot appraisals. The final guideline has four components: (i) central considerations for selecting and appraising multimedia for specific audiences and educational purposes, (ii) multimedia selection tool, (iii) multimedia appraisal tools, and (iv) extensive background information as appendices linking all sections for further comprehension. Broad utilisation of the guideline has significant potential for simplifying and systematising multimedia selection/creation, leading to superior multimedia-based learning outcomes, establishing a rapid selection database (pre-appraised multimedia), reducing disparities in microbiology education and incentivising educational content creators.

Prediction of specific cutting energy consumption in eco-benign lubricating environment for biomedical industry applications: Exploring efficacy of GEP, ANN, and RSM models

This study emphasizes the criticality of measuring specific cutting energy in machining Hastelloy C276 for biomedical industry applications, offering valuable insights into machinability and facilitating the optimization of tool selection, cutting parameters, and process efficiency. The research employs artificial intelligence-assisted meta-models for cost-effective and accurate predictions of specific cutting energy consumption. Comparative analyses conducted on Hastelloy C276, utilizing a TiAlN-coated solid carbide insert across various media (dry, MQL, LN2, and MQL+LN2), reveal the superiority of hybrid LN2+MQL in reducing specific cutting energy consumption. Subsequently, the analysis of variance underscores the cutting speed as the most influential parameter as compared to other inputs. Finally, a statistical evaluation compares the Gene Expression Programming (GEP) model against the Artificial Neural Network (ANN), and Response Surface Methodology model, demonstrating the superior predictive performance of the GEP meta-model. The GEP model demonstrates validation results with an error range of 0.25%–1.52%, outperforming the ANN and RSM models, which exhibit an error range of 0.49%–8.33% and 2.68%–10.18%, respectively. This study suggests the potential integration of contemporary intelligent methodologies for sustainable superalloy machining in biomedical industry applications, providing a foundation for enhanced productivity and reduced environmental impact of surgical instrument and biomedical device machining.