Conventional Analysis Research Articles

A high-precision automatic extraction method for shedding diseases of painted cultural relics based on three-dimensional fine color model

In recent years, with the development of 3D digitization of cultural relics, most cultural sites contain a large number of fine 3D data of cultural relics, especially complex geometric objects such as painted cultural relics. At present, how to automatically extract surface damage information from the fine 3D color model of painted cultural relics and avoid the loss of accuracy caused by reducing the dimension using conventional methods is an urgentproblem. In view of the above issues, this paper proposes an automatic and high-precision extraction method for cultural relics surface shedding diseases based on 3D fine data. First, this paper designs a 2D and 3D integrated data conversion model based on OpenSceneGraph, a 3D engine, which performs mutual conversion between 3D color model textures and 2D images. Second, this paper proposes a simple linear iterative clustering segmentation algorithm with an adaptive k value, which solves the problem of setting the superpixel k value and improves the accuracy of image segmentation. Finally, through the 2D and 3D integrated models, the disease is statistically analyzed and labeled on the 3D model. Experiments show that for painted plastic objects with complex surfaces, the disease extraction method based on the 3D fine model proposed in this paper has improved geometric accuracy compared with the current popular orthophoto extraction method, and the disease investigation is more comprehensive. Compared with the current 3D manual extraction method in commercial software, this method greatly improves the efficiency of disease extraction while ensuring extraction accuracy. The research method of this paper activates many existing 3D fine data of cultural protection units and converts conventional 2D data mining and analysis into 3D, which is more in line with the scientific utilization of data in terms of accuracy and efficiency and has certain scientific research value, leading value and practical significance.

Combination of on-line sample preconcentration by large-volume dual preconcentration by isotachophoresis and stacking (LDIS) with field-amplified sample injection (FASI) on Y-channel microchips.

In our previous study, the combination of two on-line sample preconcentration techniques, large-volume sample stacking with an electroosmotic flow (EOF) pump (LVSEP) and transient isotachophoresis (tITP), in microchip electrophoresis (MCE) was developed, which was named large-volume dual preconcentration by isotachophoresis and stacking (LDIS). LDIS was apparently effective for improving the sensitivity and the peak shape. In LDIS, however, there was a limit to the improvement of the sensitivity enhancement factor (SEF) since the amount of analytes to be concentrated was limited to the channel volume. To overcome this issue, in the present article, LDIS was coupled with field-amplified sample injection (FASI) technique on Y-shaped channel microchips. The use of a Y-channel in LDIS-FASI allowed consecutive LVSEP, FASI and tITP enrichments with a simple voltage control. In conventional LVSEP and LDIS analyses of a standard analyte, the SEFs were evaluated to be 2630 and 13,100, respectively, whereas in LDIS-FASI that was increased to 27,900 even at the FASI injection time of 0s. To achieve higher SEFs, furthermore, the FASI injection time was increased to 150s, resulting in the best SEF of 58,500. It should be emphasized that the peak width in LDIS-FASI was quite narrow, only 0.3-3.1s, while in normal LVSEP that was 13s. Furthermore, the LDIS-FASI technique was applied to the analysis of oligosaccharide mixture. Due to the focusing effect by LDIS-FASI, the resolutions were improved from 0.97-1.57 to 2.08-2.73.

Enhancing spectral analysis in nonlinear dynamics with pseudoeigenfunctions from continuous spectra

The analysis of complex behavior in empirical data poses significant challenges in various scientific and engineering disciplines. Dynamic Mode Decomposition (DMD) is a widely used method to reveal the spectral features of nonlinear dynamical systems without prior knowledge. However, because of its infinite dimensions, analyzing the continuous spectrum resulting from chaos and noise is problematic. We propose a clustering-based method to analyze dynamics represented by pseudoeigenfunctions associated with continuous spectra. This paper describes data-driven algorithms for comparing pseudoeigenfunctions using subspaces. We used the recently proposed Residual Dynamic Mode Decomposition (ResDMD) to approximate spectral properties from the data. To validate the effectiveness of our method, we analyzed 1D signal data affected by thermal noise and 2D-time series of coupled chaotic systems exhibiting generalized synchronization. The results reveal dynamic patterns previously obscured by conventional DMD analyses and provide insights into coupled chaos’s complexities.

Brain Evoked Response Qualification Using Multi-Set Consensus Clustering: Toward Single-Trial EEG Analysis.

In event-related potential (ERP) analysis, it is commonly assumed that individual trials from a subject share similar properties and originate from comparable neural sources, allowing reliable interpretation of group-averages. Nevertheless, traditional group-level ERP analysis methods, including cluster analysis, often overlook critical information about individual subjects' neural processes due to using fixed measurement intervals derived from averaging. We developed a multi-set consensus clustering pipeline to examine cognitive processes at the individual subject level. Initially, consensus clustering from diverse methods was applied to single-trial EEG epochs of individual subjects. Subsequently, a second level of consensus clustering was performed across the trials of each subject. A newly modified time window determination method was then employed to identify individual subjects' ERP(s) of interest. We validated our method with simulated data for ERP components N2 and P3, and real data from a visual oddball task to confirm the P3 component. Our findings revealed that estimated time windows for individual subjects provide precise ERP identification compared to fixed time windows across all subjects. Additionally, Monte Carlo simulations with synthetic single-trial data demonstrated stable scores for the N2 and P3 components, confirming the reliability of our method. The proposed method enhances the examination of brain-evoked responses at the individual subject level by considering single-trial EEG data, thereby extracting mutual information relevant to the neural process. This approach offers a significant improvement over conventional ERP analysis, which relies on the averaging mechanism and fixed measurement interval.

Minimum spanning tree analysis of unimpaired individuals at risk of Alzheimer's disease.

Identifying early and non-invasive biomarkers to detect individuals in the earliest stages of the Alzheimer's disease continuum is crucial. As a result, electrophysiology and plasma biomarkers are emerging as great candidates in this pursuit due to their low invasiveness. This is the first magnetoencephalography study to assess the relationship between minimum spanning tree parameters, an alternative to overcome the comparability and thresholding problem issues characteristic of conventional brain network analyses, and plasma phosphorylated tau231 levels in unimpaired individuals, with different risk levels of Alzheimer's disease. Seventy-six individuals with available magnetoencephalography recordings and phosphorylated tau231 plasma determination were included. The minimum spanning tree for the theta, alpha and beta bands for each subject was obtained, and the leaf fraction, tree hierarchy and diameter were calculated. To study the relationship between these topological parameters and phosphorylated tau231, we performed correlation analyses, for the whole sample and considering the two risk sub-groups separately. Increasing concentrations of phosphorylated tau231 were associated with greater leaf fraction and tree hierarchy values, along with lower diameter values, for the alpha and theta frequency bands. These results emerged for the whole sample and the higher risk group, but not for the lower risk group. Our results indicate that the network topology of cognitively unimpaired individuals with elevated plasma phosphorylated tau231 levels, a marker of Alzheimer's disease pathology and amyloid-β accumulation, is already altered, shifting towards a more integrated network increasing its vulnerability and hub-dependency, mostly in the alpha band. This is indicated by increases in leaf fraction and tree hierarchy, along with reductions in diameter. These results match the initial trajectory proposed by theoretical models of disease progression and network disruption and suggest that changes in brain function and organization begin early on.

Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model.

Immune cells undergo cytokine-driven polarization in respond to diverse stimuli. This process significantly modulates their transcriptional profiles and functional states. Although single-cell RNA sequencing (scRNA-seq) has advanced our understanding of immune responses across various diseases or conditions, currently there lacks a method to systematically examine cytokine effects and immune cell polarization. To address this gap, we developed Single-cell unified polarization assessment (Scupa), the first computational method for comprehensive immune cell polarization analysis. Scupa is trained on data from the Immune Dictionary, which characterizes 66 cytokine-driven polarization states across 14 immune cell types. By leveraging the cell embeddings from the Universal Cell Embeddings model, Scupa effectively identifies polarized cells in new datasets generated from different species and experimental conditions. Applications of Scupa in independent datasets demonstrated its accuracy in classifying polarized cells and further revealed distinct polarization profiles in tumor-infiltrating myeloid cells across cancers. Scupa complements conventional single-cell data analysis by providing new insights into immune cell polarization, and it holds promise for assessing molecular effects or identifying therapeutic targets in cytokine-based therapies.

Risk perception regarding social determinants of health among women with breast cancer in Iran: a qualitative study

ObjectivesBreast cancer is the most common cancer among women all around the world. Today, in addition to factors including hormones and genetics that are involved in the occurrence of breast...

Machine learning and fluorosensing for estimation of maize nitrogen status at early growth-stages

Potential of mobile fluorescence sensor measurements have been in focus for quantifying plant nitrogen (N) variability early in the crop growing season. Real time estimation of such N status indicators at field scale would enable precision management of N fertilizers. In standard practice, linear regression analysis involves the use of several fluorescence channels and indices as predictive variables for estimating plant nitrogen content. Considering the multi-collinearity between these predictor variables, the conventional regression analysis (multiple linear regression) often fails to deliver a good range of prediction accuracies. Hence, machine learning regression techniques are utilized in this study to estimate N status indicators i.e., %N, above ground biomass, and N uptake at V6 and V9 growth stages of maize across three site-years in 2012 and 2013 crop growing seasons. The Multiplex®3 (FORCE-A) portable active fluorescence system was used to capture fluorescence information. Derived indices including four N balance indices (NBI_R, NBI_B, NBI_B, and NBI1), two chlorophyll indices (CHL and CHL1), and one flavonoid index (FLAV) were used as predictors. The independent site data were first utilized in a Support Vector Regression (SVR) model to assess the training and test accuracies in estimation of N status indicators considering a comparative analysis between V6 and V9 growth stages. The current research also involved assessing how well the machine learning-trained model could be applied to a different dataset and validated its performance in a cross-site experimental setting. Subsequently, cross-site comparisons of nitrogen status estimates were conducted to recommend the selection of machine learning strategies. These strategies include (1) Partial Least Square Regression, (2) Support Vector Regression, (3) Gaussian Process Regression, (4) Random Forest Regression, and (5) Artificial Neural Network. The comparative investigation demonstrated promising accuracy in estimating plant nitrogen content, above-ground biomass, and nitrogen uptake at the V6 stages of maize, with correlation coefficients in the moderate range (r = 0.72 ± 0.03) and Root Mean Square Error. Superior prediction accuracies were obtained at V9 growth stages than at V6. Among the various machine learning models assessed, Support Vector Regression consistently outperformed the others across the three site-year datasets, delivering error estimates well within acceptable ranges and demonstrating the lowest RMSE values for maize nitrogen indicators.

Preserving the integrity of personhood in people with advanced cancer: An in-depth qualitative study among patients, relatives, and care professionals

Background: Every advanced cancer diagnosis brings enormous challenges to patients and their relatives on numerous levels: be it physical, practical, social challenges, or on a more personal level. While specific aspects have been researched before, an overarching approach is lacking. Aim: To understand the lived experiences of people with advanced cancer, to identify gaps along the cancer care continuum, to identify potential opportunities for meaningful interventions and to develop a theoretical framework for practitioners and researchers. Design: A qualitative study using in-depth interviews with patients, relatives, and care professionals. Interviews were transcribed verbatim and analysed using a conventional content analysis. Setting/participants: Fifty-four interviews with 17 patients from a university oncology department and palliative care service, 15 relatives and 22 care professionals from physicians to funeral directors. All interviewees were recruited by a German university hospital. Results: We developed a novel model describing the diagnosis with advanced cancer as a highly disruptive experience that threatens to challenge the integrity of personhood in cancer patients through four areas: communication, knowledge, relationships, and confidence. We were able to identify factors leading to disintegration in these areas and factors supporting a restoration of integrity of personhood. Conclusions: The developed model provides a more thorough understanding of patients lived experiences. It can help to develop new interventions along the cancer care continuum to support patients in the complex challenges they face. These interventions should focus on supporting the integrity of personhood.

Dissecting glial scar formation by spatial point pattern and topological data analysis

Glial scar formation represents a fundamental response to central nervous system (CNS) injuries. It is mainly characterized by a well-defined spatial rearrangement of reactive astrocytes and microglia. The mechanisms underlying glial scar formation have been extensively studied, yet quantitative descriptors of the spatial arrangement of reactive glial cells remain limited. Here, we present a novel approach using point pattern analysis (PPA) and topological data analysis (TDA) to quantify spatial patterns of reactive glial cells after experimental ischemic stroke in mice. We provide open and reproducible tools using R and Julia to quantify spatial intensity, cell covariance and conditional distribution, cell-to-cell interactions, and short/long-scale arrangement, which collectively disentangle the arrangement patterns of the glial scar. This approach unravels a substantial divergence in the distribution of GFAP+ and IBA1+ cells after injury that conventional analysis methods cannot fully characterize. PPA and TDA are valuable tools for studying the complex spatial arrangement of reactive glia and other nervous cells following CNS injuries and have potential applications for evaluating glial-targeted restorative therapies.

Comparative molecular and conventional cytogenetic analyses of three species of Rhinella (Anura; Bufonidae).

The genus Rhinella corresponds to a group of anurans characterized by numerous taxonomic and systemic challenges, leading to their organization into species complexes. Cytogenetic data for this genus thus far are limited to the diploid number and chromosome morphology, which remain highly conserved among the species. In this study, we analyse the karyotypes of three species of the genus Rhinella (Rhinella granulosa, Rhinella margaritifera, and Rhinella marina) using both classical (conventional staining and C-banding) and molecular (FISH-fluorescence in situ hybridization with 18S rDNA, telomeric sequences, and microsatellite probes) cytogenetic approaches. The aim of this study is to provide data that can reveal variations in the distribution of repetitive sequences that can contribute to understanding karyotypic diversification in these species. The results revealed a conserved karyotype across the species, with 2n = 22 and FN = 44, with metacentric and submetacentric chromosomes. C-banding revealed heterochromatic blocks in the pericentromeric region for all species, with a proximal block on the long arms of pairs 3 and 6 in R. marina and on the short arms of pairs 4 and 6 in R. margaritifera. Additionally, 18S rDNA probes hybridized to pair 5 in R. granulosa, to pair 7 in R. marina, and to pair 10 in R. margaritifera. Telomeric sequence probes displayed signals exclusively in the distal region of the chromosomes, while microsatellite DNA probes showed species-specific patterns. These findings indicate that despite a conserved karyotypical macrostructure, chromosomal differences exist among the species due to the accumulation of repetitive sequences. This variation may be attributed to chromosome rearrangements or differential accumulation of these sequences, highlighting the dynamic role of repetitive sequences in the chromosomal evolution of Rhinella species. Ultimately, this study emphasizes the importance of the role of repetitive DNAs in chromosomal rearrangements to elucidate the evolutionary mechanisms leading to independent diversification in the distinct phylogenetic groups of Rhinella.

Patient Preferences and Priorities for the Design of an Acute Kidney Injury Prevention Trial: Findings from a Consensus Workshop.

High-quality clinical trials are needed to establish the efficacy and safety of novel therapies for acute kidney injury (AKI) prevention. In this consensus workshop, we identified patient and caregiver priorities for recruitment, intervention delivery, and outcomes of a clinical trial of cilastatin to prevent nephrotoxic AKI. We included adults with lived experience of AKI, chronic kidney disease, or risk factors for AKI (e.g., critical care hospitalization), and their caregivers. Using a modified nominal group technique approach, we conducted a series of hybrid in-person/virtual discussions covering 3 clinical trial topic areas: (1) consent and recruitment; (2) intervention delivery; and (3) trial outcomes. Participants voted on their top preferences in each topic area, and discussion transcripts were analyzed inductively using conventional content analysis. Thirteen individuals (11 patients, 2 caregivers) participated in the workshop. For consent and recruitment, participants prioritized technology enabled pre-screening and involvement of family members in the consent process. For intervention delivery, participants prioritized measures to facilitate ease of intervention administration and return visits. For trial outcomes, participants identified kidney-related and other clinical outcomes (e.g., AKI, chronic kidney disease, cardiovascular events) as top priorities. Analysis of transcripts provided insight into care team and family involvement in trial-related decisions, implications of allocation to a placebo arm, and impact of participants' experiences of AKI and critical illness. Findings from our workshop will directly inform development of a clinical trial protocol of cilastatin for nephrotoxic AKI prevention and can assist others in patient-centered approaches to AKI trial design.

Conventional and genetic associations of BMI with major vascular and non-vascular disease incidence and mortality in a relatively lean Chinese population: U-shaped relationship revisited.

Higher body mass index (BMI) is associated with higher incidence of cardiovascular and some non-cardiovascular diseases (CVDs/non-CVDs). However, uncertainty remains about its associations with mortality, particularly at lower BMI levels. The prospective China Kadoorie Biobank recruited >512 000 adults aged 30-79years in 2004-08 and genotyped a random subset of 76 000 participants. In conventional and Mendelian randomization (MR) analyses, Cox regression yielded adjusted hazard ratios (HRs) associating measured and genetically predicted BMI levels with incident risks of major vascular events (MVEs; conventional/MR 68 431/23 621), ischaemic heart disease (IHD; 50 698/12 177), ischaemic stroke (IS; 42 427/11 897) and intracerebral haemorrhage (ICH; 7644/4712), and with mortality risks of CVD (15 427/6781), non-CVD (26 915/4355) and all causes (42 342/6784), recorded during ∼12years of follow-up. Overall, the mean BMI was 23.8 (standard deviation: 3.2) kg/m2 and 13% had BMIs of <20 kg/m2. Measured and genetically predicted BMI showed positive log-linear associations with MVE, IHD and IS, but a shallower positive association with ICH in conventional analyses. Adjusted HRs per 5 kg/m2 higher genetically predicted BMI were 1.50 (95% CI 1.41-1.58), 1.49 (1.38-1.61), 1.42 (1.31-1.54) and 1.64 (1.58-1.69) for MVE, IHD, IS and ICH, respectively. These were stronger than associations in conventional analyses [1.21 (1.20-1.23), 1.28 (1.26-1.29), 1.31 (1.29-1.33) and 1.14 (1.10-1.18), respectively]. At BMIs of ≥20 kg/m2, there were stronger positive log-linear associations of BMI with CVD, non-CVD and all-cause mortality in MR than in conventional analyses. Among relatively lean Chinese adults, higher genetically predicted BMI was associated with higher risks of incident CVDs. Excess mortality risks at lower BMI in conventional analyses are likely not causal and may reflect residual reverse causality.

Experiences of unemployed and/or work-disabled cancer survivors who have pursued to return to paid employment: a focus group study.

To explore experiences of unemployed and/or work-disabled cancer survivors who have pursued to return to paid employment. Four digital focus group interviews were conducted with 16 cancer survivors (< 10 years post-diagnosis) who have pursued to return to work within the last 2 years. Interview topics included motivations, facilitators of and barriers to job seeking, and returning to and maintaining paid employment. Interview audio recordings were transcribed verbatim and analyzed using conventional content analyses. Participants were mostly female (94%), and the majority had successfully returned to paid employment (56%). Both intrinsic factors (e.g., sense of purpose, social interactions) and extrinsic factors (e.g., financial necessity) motivated their return to paid employment. During job seeking, participants experienced facilitators including support, personal qualities (e.g., life experience), and trial workplaces. Barriers included inadequate support, perceived employer discrimination, and work ability uncertainty. Returning to and maintaining employment was facilitated by flexible work, supportive colleagues, and intrinsic drive, while barriers included side effects (e.g., fatigue) and overly demanding work. Unemployed and/or work-disabled cancer survivors are generally motivated to return to paid employment by both intrinsic and extrinsic factors, but uncertainty about their ability and inadequate support may hinder this. These findings highlight the need for trial workplaces, support during every phase of return to paid employment, and a flexible, supportive workplace. Tailored interventions addressing the needs identified in this study are urgently needed. The recommendations provided offer strategies for various stakeholders to enhance support for unemployed and work-disabled cancer survivors.

Molecular and ultrastructural morphological analyses of highly metamorphosed Aspergillus fumigatus on human formalin-fixed paraffin-embedded tissue.

Invasive fungal infections including invasive pulmonary aspergillosis (IPA) generally have a poor prognosis, because the fungi spread throughout various organs. Therefore, it is important to accurately identify the fungal species for treatment. In this article, we present the results of pathological and molecular morphological analyses that were performed to elucidate the cause of respiratory failure in a patient who died despite suspicion of IPA and treatment with micafungin (MCFG). Pathological analysis revealed the existence of cystic and linear fungi in lung tissue. The fungi were identified as Aspergillus fumigatus (A. fumigatus) by partial sequencing of genomic DNA. Correlative light microscopy and electron microscopy (CLEM) analysis confirmed that fungi observed with light microscopy can also be observed with scanning electron microscopy (SEM) using formalin-fixed paraffin-embedded tissue sections. SEM revealed an atypical ultrastructure of the fungi including inhomogeneous widths, rough surfaces, and numerous cyst-like structures of various sizes. The fungi showed several morphological changes of cultured A. fumigatustreated with MCFG that were previously reported. Our results indicate that integrated analysis of ultrastructural observation by SEM and DNA sequencing may be an effective tool for analyzing fungi that are difficult to identify by conventional pathological analysis.

Fish size structures in lakes of the Lower Mississippi River floodplain

Abstract The Lower Mississippi River has a floodplain that includes &gt;1350 perennial lakes carved by shifts in river courses and other hydro‐fluvial processes over eons. Notwithstanding their similar provenances, these waterbodies exhibit an immense variety of morphologies and successional stages that illustrate their natural trajectory from aquatic to forested wetlands. A result of this geographical, morphological and temporal diversity is dynamic and varied fish communities. We examined how size structures of fish communities in these floodplain lakes were associated with key in‐lake and off‐lake environmental drivers. Fish lengths were collected with standardised procedures in a sample of 30 of these lakes to construct a lake‐by‐length group matrix. Likewise, in‐lake and off‐lake environmental descriptors were collected to construct a lake‐by‐covariate matrix. Distance‐based linear models were used to assess associations between fish size structure and environmental descriptors. Smaller fish were typically associated with increasing levels of turbidity, chlorophyll‐a, phycocyanin and surrounding agriculture. Shallow, hypereutrophic floodplain lakes associated with agricultural landscapes and reduced connectivity experience harsh physicochemical environments. These conditions appeared to hinder the formation of sustained fish communities but may confer a survival advantage to juveniles or small short‐lived species. Conversely, larger fish were associated with increasing lake depth, water clarity, connectivity, and extent of surrounding forests‐wetlands. Enhanced stability and size structure were observed in communities residing in deeper and clearer lakes, suggesting that these conditions facilitated the development of longer‐lived species spanning multiple age groups. The enhanced connectivity that facilitated this increased stability also permitted the presence of larger itinerant species. Size‐structure assessments can serve as a valuable ecological and biodiversity indicator in floodplain lakes. Size‐structure assessments could supplement and, depending on objectives, even supplant conventional taxonomic analyses, and enhance surveillance of this vast and important natural resource.

Comprehensive spatial distribution profiling of bioactive emodin in mouse organs using mass spectrometry imaging.

Emodin is an important anthraquinone compound with good anti-inflammatory activity in Chinese traditional medicine rhubarb. Detailed spatial distribution information in bio-tissues plays an important role in revealing the pharmacodynamics, toxicology and chemical mechanism of emodin. Herein, the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry imaging (MALDI-TOF-MSI) analytical method was established to obtain information on the spatial and temporal changes of emodin in multiple mouse tissue sections (heart, liver, spleen, lung, kidney, and brain) after intraperitoneal injection of emodin in mice. The measurements were accomplished in the negative ion mode in the range of m/z 250-285Da with a spatial resolution on 40µm. It was found that emodin was predominantly distributed in the arteriolar vascular region of the heart, the capsule region of the spleen, and the cortex of the kidney. Moreover, the MALDI-TOF-MSI result implied that emodin might be distributed in the brain. These more detailed spatial distribution information provides the significant reference for investigating the action mechanism of emodin, which cannot be obtained from conventional LC-MS analysis. The distribution trend of emodin in the results of MALDI-TOF-MSI analysis agreed with the ultra-performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS) results well, demonstrating the complementarity and reliability of the established MALDI-TOF-MSI method. Our work provided a label-free molecular imaging method to investigate the precise spatial distribution of emodin in various organs, which prove great potential in studying the effective substances and mechanism of rhubarb.

Quantitative Hydrogen-Deuterium Exchange Mass Spectrometry for Simultaneous Structural Characterization and Affinity Indexing of Single Target Drug Candidate Libraries.

Hydrogen-deuterium eXchange mass spectrometry (HDX-MS) is increasingly used in drug development to locate binding sites and to identify allosteric effects in drug/target interactions. However, the potential of this technique to quantitatively analyze drug candidate libraries remains largely unexplored. Here, a collection of 13 WDR5-targeting small molecules with surface plasmon resonance (SPR) dissociation coefficients (KD) ranging from 20 nM to ∼116 μM were characterized using differential HDX-MS (ΔHDX-MS). Conventional qualitative analysis of the ΔHDX-MS data set revealed the binding interfaces for all compounds and allosteric effects where present. We then demonstrated that ΔHDX-MS signal-to-noise (S/N) not only can rank library-relative affinity but also can accurately predict KD from a calibration curve constructed from high-quality SPR data. Three methods for S/N calculation are explored, each suitable for libraries with different characteristics. Our results demonstrate the potential for ΔHDX-MS use in drug candidate library affinity validation and/or determination while simultaneously characterizing structure.

Attributes of Good Faculty as Perceived by Health Professions Students: A Qualitative Study

Background: Diverse perspectives have been documented regarding the attributes of outstanding university faculty members. It is crucial to consider the evolving demands and expectations of students at the University of Medical Sciences, which change with age and educational stages. Extracting students' opinions and expectations can yield valuable insights into the attributes required of faculty members in academic institutions. Objectives: This study aimed to identify health professions students’ perceptions of the attributes of a good faculty member. Methods: A qualitative study was conducted using a conventional content analysis approach. The participants in this study were health professions students (medicine, nursing, midwifery, laboratory sciences, operating room technology, anesthesia, prehospital emergency, pharmacy, dentistry, and health) at Alborz University of Medical Sciences, who were purposefully selected. Study data were gathered by conducting in-depth, face-to-face, and semi-structured personal interviews with 23 students, as well as a group interview with seven students. The data analysis followed the conventional content analysis approach, using the steps proposed by Lundman and Granheim. Results: Study participants’ perceptions of good faculty attributes fell into two main categories: Personality attributes and professional attributes. Personality attributes included appearance and personality traits. Professional attributes encompassed scientific and scholarly qualities, as well as teaching skills. Conclusions: Our findings reveal that study participants placed significant emphasis on both personality and professional attributes when defining good faculty characteristics. Sub-categories such as appearance, personality traits, scientific expertise, and teaching skills emerged as important factors shaping student perceptions of faculty members. This underscores the multifaceted nature of what constitutes an effective faculty member in students' eyes and highlights the importance of cultivating a diverse range of attributes within academia. Future research could delve deeper into each sub-category to refine our understanding of what truly defines effective faculty members.

Decomposition and Dynamics of Unit Labor Costs in a Context of International Fragmentation of Production: Evidence from the German Automotive Sector

Conventional analysis has sought to explain the determinants of price competitiveness by way of national factors, but given the current degree of international fragmentation of production, a more holistic perspective needs to be explored. This document develops a specific proposal that permits the indicator on vertically integrated unit labor costs (VIULCs) to be broken down into its fundamental components; also, the evolution of this indicator is explored for the German automotive sector, to determine which component might best elucidate the evolution of the VIULCs. The results show a decrease in VIULCs deriving from the German sector as well as from its suppliers, confirming the relevant effects of outsourcing and offshoring strategies. Moreover, wage restraint among suppliers coupled with productivity gains are found to explain the overall reduction in VIULCs. JEL classification: P16, F15, F23,