Variation In Response Research Articles

Hearing ability of prairie voles (Microtus ochrogaster).

The hearing abilities of mammals are impacted by factors such as social cues, habitat, and physical characteristics. Despite being used commonly to study social behaviors, hearing of the monogamous prairie vole (Microtus ochrogaster) has never been characterized. In this study, anatomical features are measured and auditory brainstem responses (ABRs) are used to measure auditory capabilities of prairie voles, characterizing monaural and binaural hearing and hearing range. Sexually naive male and female voles were measured to characterize differences due to sex. It was found that prairie voles show a hearing range with greatest sensitivity between 8 and 32 kHz, binaural hearing across interaural time difference ranges appropriate for their head sizes. No differences are shown between the sexes in binaural hearing or hearing range (except at 1 kHz), however, female voles have increased amplitude of peripheral ABR waves I and II and longer latency of waves III and IV compared to males. The results confirm that prairie voles have a broad hearing range, binaural hearing consistent with rodents of similar size, and differences in amplitudes and thresholds of monaural physiological measures between the sexes. These data further highlight the necessity to understand sex-specific differences in neural processing that may underly variability in responses between sexes.

A Systematic Review of the Impact of Growth Hormone Therapy on Final Adult Height of Children with Idiopathic Short Stature

Abstract Introduction This review aims to systematically determine the effect of growth hormone (GH) therapy on adult height of children and adolescents with idiopathic short stature (ISS). Methods A systematic review was conducted to assess the effectiveness of GH therapy in children with ISS. Databases like ProQuest Central, journal @ Ovoid, EBSCOhost Medline Complete, Oxford University Press Journals, KB + JISC Collections Elsevier Science Direct Freedo, and BMJ, and cross-referencing of bibliographies were searched electronically. The randomized trials from 1989 to January 2023 were retrieved. Randomized trials with final adult height measurements and fit the inclusion criteria (height >2 standard deviation [SD] score below the mean with no comorbid conditions that would impair growth, peak growth hormone responses >10 μg/L, no previous history of GH therapy) were included in this review. The exclusion criteria are nonrandomized trials; trials include causes of short stature other than ISS, and studies include interventions other than GH and gonadotropin-releasing hormone analog (GnRH-a). A structured approach to the critical appraisal program by Oxford was used to analyze and extract the data. Results The study reviewed 14 eligible randomized trials, which recruited 2,206 assessable children for analysis. Seven trials compared different GH doses, four trials compared GH therapy with controls, and three trials compared a combination of GH and GnRH therapy with GH alone. Apart from one study, the overall dropout rate was not high. The high percentage of boys was a potential source of heterogeneity between trials. The change in height (HT)-SD score was 1.06 ± 0.30 and 0.18 ± 0.27 with treatment and control children, respectively, and the difference is statistically significant (p < 0.001). The overall mean height gain was 5 cm (0.84 SD score) more in treated children. The height velocity was found to be decreased significantly (p < 0.001) in the second and third years of treatment in the GH + GnRH-a treated group from 7 cm/year during the first year of treatment to 5.4 cm during the second year and 4.9 cm/year during the third year. They also found that using a higher dose of GH at 9 mg/m2/week leads to approximately 7 cm mean final height gain; however, lower dose regimens are less effective. Conclusion Although the magnitude of the effectiveness of GH therapy is, on average, less than that achieved in other conditions for which GH is licensed, GH therapy seems to be effective in children with idiopathic short stature, and it reduces the deficit in height as adults. Moreover, the effect seems to be dose-dependent, with better results at high GH doses, and the response variability is seen in different individuals. The use of combined GH with GnRH therapy needs to be balanced with their side effects.

Personalized Feature Importance Ranking for Affect Recognition From Behavioral and Physiological Data

Designing affect-based personalized technology involves dealing with large datasets. Machine learning (ML) algorithms are employed to predict affect and similar human factors from in-game metrics, behavioral patterns, or physiological responses. The classification performance is usually presented as a global point estimate without providing user-specific interpretations. This approach is incompatible with effective personalization in games because it disregards the variability of body responses between players. This paper proposes a methodology to classify subjective human factors from large multimodal data. A public VR dataset (CEAP-360VR) was used to extensively compare three ML classifiers and five feature importance techniques. The produced models could reduce the original feature space by 82% (from 113 to 20 features) without compromising predictive performance (F1 score). A random forest (RF) using forward sequential feature selection (fSFS) yielded the best prediction of binary valence (F1=0.761) and arousal (F1=0.748). Finally, feature importance rankings are discussed with emphasis on global and user-specific patterns that may improve affect recognition. The proposed methodology is envisioned to help game designers and researchers create customized user-centric games and VR experiences inferring possible explanations from multimodal datasets.

Rapid-IAF: Rapid Identification of Individual Alpha Frequency in EEG Data Using Sequential Bayesian Estimation.

Rapid and robust identification of the individual alpha frequency (IAF) in electroencephalogram (EEG) is an essential factor for successful brain-computer interface (BCI) use. Here we demonstrate an algorithm to determine the IAF from short-term resting-state scalp EEG data. First, we outlined the algorithm to determine IAF from short-term resting scalp EEG data and evaluated its reliability using a large-scale dataset of scalp EEG during motor imagery-based BCI use and independent dataset for generalizability confirmation (N = 147). Next, we characterized the relationship between IAF and responsive frequency band of sensorimotor rhythm, which exhibits prominent event-related desynchronization (SMR-ERD) while attempting unilateral and movement. The proposed sequential Bayesian estimation algorithm (Rapid-IAF) determined IAF from less than 26-second resting EEG data among 95% of participants, indicating a clear advance over the conventional methods, which uses 2-15 minutes of data in previous literatures. We confirmed that the determined IAF corresponded to the frequency of SMR, which exhibits the most prominent event-related desynchronization during BCI use (individual SMR-ERD frequency, ISF). Moreover, intraclass correlation revealed that the estimated IAF was more stable than ISF across sessions, suggesting its reliability and utility for robust BCI use without intermittent recalibration. Conclusion In summary, our method rapidly and reliably determined IAF compared to the conventional method using the spectral power change based on task-related response. The method can be utilized to quick BCI initialization. The demonstration of rapid, task-free parametrization of individual variability of neural responses would be of importance for future BCI systems including neural communication via a cursor, an avatar or robots, and closed-loop neurofeedback training.

(Poly)phenol-related gut metabotypes and human health: an update.

Dietary (poly)phenols have received great interest due to their potential role in the prevention and management of non-communicable diseases. In recent years, a high inter-individual variability in the biological response to (poly)phenols has been demonstrated, which could be related to the high variability in (poly)phenol gut microbial metabolism existing within individuals. An interplay between (poly)phenols and the gut microbiota exists, with (poly)phenols being metabolised by the gut microbiota and their metabolites modulating gut microbiota diversity and composition. A number of (poly)phenol metabolising phenotypes or metabotypes have been proposed, however, potential metabotypes for most (poly)phenols have not been investigated, and the relationship between metabotypes and human health remains ambiguous. This review presents updated knowledge on the reciprocal interaction between (poly)phenols and the gut microbiome, associated gut metabotypes, and subsequent impact on human health.

ENLIGHTENMENT: A Scalable Annotated Database of Genomics and NGS-Based Nucleotide Level Profiles.

The revolution in sequencing technologies has enabled human genomes to be sequenced at a very low cost and time leading to exponential growth in the availability of whole-genome sequences. However, the complete understanding of our genome and its association with cancer is a far way to go. Researchers are striving hard to detect new variants and find their association with diseases, which further gives rise to the need for aggregation of this Big Data into a common standard scalable platform. In this work, a database named Enlightenment has been implemented which makes the availability of genomic data integrated from eight public databases, and DNA sequencing profiles of H. sapiens in a single platform. Annotated results with respect to cancer specific biomarkers, pharmacogenetic biomarkers and its association with variability in drug response, and DNA profiles along with novel copy number variants are computed and stored, which are accessible through a web interface. In order to overcome the challenge of storage and processing of NGS technology-based whole-genome DNA sequences, Enlightenment has been extended and deployed to a flexible and horizontally scalable database HBase, which is distributed over a hadoop cluster, which would enable the integration of other omics data into the database for enlightening the path towards eradication of cancer.

Association of Burnout With Primary Care Clinician Perception of Team-Based Scheduling Support.

Primary care clinician burnout is pervasive and detrimental. How components of teamwork and clinic culture might contribute to burnout remains unsettled. To examine associations between primary care clinician perceptions of specific components of teamwork and of organizational culture, and perceived stress and burnout. Cross-sectional survey study of primary care clinicians from 5 county health system clinics. Measures: Perceptions of teamwork related to coordination of care, and clinic provision of chronic disease self-management support; values alignment and workplace equity; and demographics. Descriptive statistics and Spearman's correlations to examine associations, controlling for clinic and examining response variability by clinic. Of 72 clinicians, 64% were female and 32% non-white. About 56% had worked at least 4 years and half worked 5 to 6 half days/week or more in their clinic. Clinicians who reported having someone on the clinician's care team routinely schedule follow-up appointments for patients with complex chronic illnesses reported lower stress and burnout. Those who perceived greater values alignment with their clinic and greater personal and employee equitable treatment had lower stress and burnout. Teamwork among clinicians and non-clinical staff, a component of teamwork that is not well-considered in current literature, could be an important piece of the puzzle to decrease the persistent and challenging issue of stress and burnout among primary care clinicians.

Exploring the Potential of ChatGPT-4 in Predicting Refractive Surgery Categorizations: Comparative Study.

Refractive surgery research aims to optimally precategorize patients by their suitability for various types of surgery. Recent advances have led to the development of artificial intelligence-powered algorithms, including machine learning approaches, to assess risks and enhance workflow. Large language models (LLMs) like ChatGPT-4 (OpenAI LP) have emerged as potential general artificial intelligence tools that can assist across various disciplines, possibly including refractive surgery decision-making. However, their actual capabilities in precategorizing refractive surgery patients based on real-world parameters remain unexplored. This exploratory study aimed to validate ChatGPT-4's capabilities in precategorizing refractive surgery patients based on commonly used clinical parameters. The goal was to assess whether ChatGPT-4's performance when categorizing batch inputs is comparable to those made by a refractive surgeon. A simple binary set of categories (patient suitable for laser refractive surgery or not) as well as a more detailed set were compared. Data from 100 consecutive patients from a refractive clinic were anonymized and analyzed. Parameters included age, sex, manifest refraction, visual acuity, and various corneal measurements and indices from Scheimpflug imaging. This study compared ChatGPT-4's performance with a clinician's categorizations using Cohen κ coefficient, a chi-square test, a confusion matrix, accuracy, precision, recall, F1-score, and receiver operating characteristic area under the curve. A statistically significant noncoincidental accordance was found between ChatGPT-4 and the clinician's categorizations with a Cohen κ coefficient of 0.399 for 6 categories (95% CI 0.256-0.537) and 0.610 for binary categorization (95% CI 0.372-0.792). The model showed temporal instability and response variability, however. The chi-square test on 6 categories indicated an association between the 2 raters' distributions (χ²5=94.7, P<.001). Here, the accuracy was 0.68, precision 0.75, recall 0.68, and F1-score 0.70. For 2 categories, the accuracy was 0.88, precision 0.88, recall 0.88, F1-score 0.88, and area under the curve 0.79. This study revealed that ChatGPT-4 exhibits potential as a precategorization tool in refractive surgery, showing promising agreement with clinician categorizations. However, its main limitations include, among others, dependency on solely one human rater, small sample size, the instability and variability of ChatGPT's (OpenAI LP) output between iterations and nontransparency of the underlying models. The results encourage further exploration into the application of LLMs like ChatGPT-4 in health care, particularly in decision-making processes that require understanding vast clinical data. Future research should focus on defining the model's accuracy with prompt and vignette standardization, detecting confounding factors, and comparing to other versions of ChatGPT-4 and other LLMs to pave the way for larger-scale validation and real-world implementation.

Forehead Flap Practices: A Cross-Sectional Survey of Facial Plastic and Reconstructive Surgeons.

Background: The paramedian forehead flap (PMFF) is a well-established technique utilized for reconstruction of complex nasal defects. Objective: To identify the different techniques and management of patients undergoing PMFF reconstruction and compare these with current literature. Methods: Members of the American Academy of Facial Plastic and Reconstructive Surgery were sent a practice survey highlighting various nuances in PMFF reconstruction. The survey included questions about flap design, operative techniques, and perioperative care. Results: In total, 172 responses were received (14% response rate). Mean years of practice after fellowship was 15.8 years with most respondents performing either 1-5 (33.1%) or 6-10 (27.3%) PMFFs per year. Common practices included the use of general anesthesia, elevation of PMFF in the subgaleal plane (59.6%), and pedicle division at 3 weeks (80%) (p < 0.001). Complication rates ranged between 1% and 5%. The nose was the most common site for revision (p < 0.001) and the average number of secondary procedures after forehead flap division was 1.1 (standard deviation 0.81). The most variability in responses was seen for methods of internal lining reconstruction. Conclusion: Reconstructive surgeons frequently divide the PMFF pedicle at 3 weeks or later and have variable approaches to reconstruction of the internal lining with low complication rates overall.

Comparing likely effectiveness of urban Nature-based Solutions worldwide: The example of riparian tree planting and water quality

Amongst a spectrum of benefits, Nature-based Solutions (NBS) are increasingly being advocated as improving the quality of aquatic environments in urban areas. Of these, a widely adopted measure is tree planting. Yet, because of the local complexities and spatial variability of urban hydrological response, it is difficult to predict to what extent improvements in water quality will arise. To overcome this barrier, a standardised approach to process-based model simulation of urban river quality is described (QUESTOR-YARDSTICK (QUESTOR-YS)). The approach eliminates the influence of point sources of pollution and harmonises the way in which river hydrodynamics and contributory catchment size are represented. Thereby, it focuses on differences in water quality between cities due solely to climate, river discharge and urban diffuse nutrient pollution factors. The relative sensitivity to NBS establishment between urban water bodies in different cities anywhere across the world can also potentially be quantified. The method can be readily extended to include wastewater effluents. The validity of the approach is demonstrated for a small river in Birmingham, UK; and thence demonstrated for the case of 10 km of riparian tree planting in Birmingham, Oslo (Norway) and Aarhus (Denmark). Modelling suggests that riparian tree planting can substantially improve water quality in each example city for three key indicators of water quality in sensitive summer conditions (water temperature, chlorophyll-a and dissolved oxygen). Results show the level of benefit achievable in response to a fixed amount of planting will depend on the existing level of riparian tree occupancy.

Impact of CYP2D6 polymorphisms on the efficacy of tamoxifen in Iraqi women with breast cancer

Objectives: The aim of the present study was to investigate the impact of the CYP2D6 genetic polymorphism on clinical outcome in Iraqi breast cancer patients who were candidates for Tamoxifen therapy. Methods: Comprehensive CYP2D6 genotyping was performed in 140 Iraqi women with breast cancer who were women on adjuvant treatment with tamoxifen. Breast cancer patients recruited into the study were divided into two groups: seventy breast cancer women who had no history of recurrence at the time of sampling and had a long time on tamoxifen without recurrence and seventy breast cancer women who had recurrence at the time of sampling after one year of treatment with tamoxifen therapy. Recurrence free survival (RFS) was determined in the recruited patients. Results: Multiple genetic variants of the gene encoding the CYP2D6 enzyme were detected with significant differences in their frequencies and percentages in both recurrent and non-recurrent groups of breast cancer patients. The findings of this study suggest that interindividual variation in clinical outcome may be related to genetic variation in CYP2D6 enzyme, which is characterized by variable RFS periods. Conclusions: This study revealed that the CYP2D6 enzyme of breast cancer patients who participated in this study is highly polymorphic. The CYP2D6 gene of study participants exhibited different allelic combinations with variable frequencies. The multiple genetic variants (alleles) of the gene encoding the CYP2D6 enzyme exhibited significant differences in their frequencies and percentages in both recurrent and non-recurrent groups of breast cancer patients. .The study revealed variable Recurrence free survival (RFS) with highly polymorphic gene. Our study quotes the presence of increased CYP2D6 enzyme polymorphism is associated with variable clinical response.

Assessment of the sensitivity of model responses to urban emission changes in support of emission reduction strategies

AbstractThe sensitivity of air quality model responses to modifications in input data (e.g. emissions, meteorology and boundary conditions) or model configurations is recognized as an important issue for air quality modelling applications in support of air quality plans. In the framework of FAIRMODE (Forum of Air Quality Modelling in Europe, https://fairmode.jrc.ec.europa.eu/) a dedicated air quality modelling exercise has been designed to address this issue. The main goal was to evaluate the magnitude and variability of air quality model responses when studying emission scenarios/projections by assessing the changes of model output in response to emission changes. This work is based on several air quality models that are used to support model users and developers, and, consequently, policy makers. We present the FAIRMODE exercise and the participating models, and provide an analysis of the variability of O3 and PM concentrations due to emission reduction scenarios. The key novel feature, in comparison with other exercises, is that emission reduction strategies in the present work are applied and evaluated at urban scale over a large number of cities using new indicators such as the absolute potential, the relative potential and the absolute potency. The results show that there is a larger variability of concentration changes between models, when the emission reduction scenarios are applied, than for their respective baseline absolute concentrations. For ozone, the variability between models of absolute baseline concentrations is below 10%, while the variability of concentration changes (when emissions are similarly perturbed) exceeds, in some instances 100% or higher during episodes. Combined emission reductions are usually more efficient than the sum of single precursor emission reductions both for O3 and PM. In particular for ozone, model responses, in terms of linearity and additivity, show a clear impact of non-linear chemistry processes. This analysis gives an insight into the impact of model’ sensitivity to emission reductions that may be considered when designing air quality plans and paves the way of more in-depth analysis to disentangle the role of emissions from model formulation for present and future air quality assessments.

High-Quality Brain and Bone Marrow Nuclei Preparation for Single Nuclei Multiome Assays.

Single-cell analysis has become the approach of choice for unraveling the complexity of biological processes that require assessing the variability of individual cellular responses to treatment or infection with single-cell resolution. Many techniques for single-cell molecular profiling have been developed over the past 10 years, and several dedicated technologies have been commercialized. The 10X Genomics droplet-based single-cell profiling is a widespread technology that offers ready-to-use reagents for transcriptomic and multi-omic single-cell profiling. The technology includes workflows for single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, respectively), scATAC-Seq, single-cell immune profiling (BCR/TCR sequencing), and multiome. The latter combines transcriptional (scRNA-Seq) and epigenetic information (scATAC-Seq) coming from the same cell. The quality (viability, integrity, purity) of single-cell or single-nuclei suspensions isolated from tissues and analyzed by any of these approaches is critical for generating high-quality data. Therefore, the sample preparation protocols should be adapted to the particularities of each biological tissue and ensure the generation of high-quality cell and nuclei suspensions. This article describes two protocols for preparing brain and bone marrow samples for the downstream multiome 10X Genomics pipeline. The protocols are performed stepwise and cover tissue dissociation, cell sorting, nuclei isolation, and quality control of prepared nuclei suspension that is used as starting material for cell partitioning and barcoding, library preparation, and sequencing. These standardized protocols produce high-quality nuclei libraries and robust and reliable data.

Clinical Implications of the Relationship Between Naltrexone Plasma Levels and the Subjective Effects of Heroin in Humans.

Extended-release naltrexone (NTX) is an opioid antagonist approved for relapse prevention after medical withdrawal. Its therapeutic effect is dependent on the NTX plasma level, and as it decreases, patients may lack protection against relapse and overdose. Therefore, identifying the minimally effective NTX level needed to block opioid-induced subjective effects has important clinical implications. This secondary, individual-level analysis of data collected in a human laboratory study was conducted to evaluate the relationship between NTX levels and subjective effects of an intravenously administered 25-mg challenge dose of heroin in non-treatment-seeking participants with opioid use disorder (N = 12). Subjective ratings of drug liking using a 100-mm visual analog scale (VAS) and NTX levels were measured across 6 weeks after participants received a single injection of either extended-release NTX 192 mg (N = 6) or 384 mg (N = 6). Cubic spline mixed-effects models were used to provide 95% prediction intervals for individual changes in liking scores as a function of NTX levels. Naltrexone levels above 2 ng/mL blocked nearly all VAS ratings of drug liking after intravenous heroin administration. Participants with NTX levels ≥ 2 ng/mL had minimal (≤20 mm) changes from placebo in VAS ratings of drug liking based on 95% prediction intervals. In contrast, NTX levels < 2 ng/mL were associated with greater variability in individual-level subjective responses. In clinical practice, a plasma level range of 1 to 2 ng/mL is considered to be therapeutic in providing heroin blockade. The current findings suggest that a higher level (>2 ng/mL) may be needed to produce a consistent blockade.

Machine Learning-Assisted Characterization of Pore-Induced Variability in Mechanical Response of Additively Manufactured Components

Manufacturing defects, such as porosity and inclusions, can significantly compromise the structural integrity and performance of additively manufactured parts by acting as stress concentrators and potential initiation sites for failure. This paper investigates the effects of pore system morphology (number of pores, total volume, volume fraction, and standard deviation of size of pores) on the material response of additively manufactured Ti6Al4V specimens under a shear–compression stress state. An automatic approach for finite element simulations, using the J2 plasticity model, was utilized on a shear–compression specimen with artificial pores of varying characteristics to generate the dataset. An artificial neural network (ANN) surrogate model was developed to predict peak force and failure displacement of specimens with different pore attributes. The ANN demonstrated effective prediction capabilities, offering insights into the importance of individual input variables on mechanical performance of additively manufactured parts. Additionally, a sensitivity analysis using the Garson equation was performed to identify the most influential parameters affecting the material’s behaviour. It was observed that materials with more uniform pore sizes exhibit better mechanical properties than those with a wider size distribution. Overall, the study contributes to a better understanding of the interplay between pore characteristics and material response, providing better defect-aware design and property–porosity linkage in additive manufacturing processes.

Quantifying modeling uncertainties in seismic analysis of dams: Insights from an international benchmark study

AbstractAdvances in nonlinear dynamic analysis of dams have not completely resolved concerns over modeling confidence and analysis accuracy. Verification and validation offer accuracy assessment, but uncertainties persist during performance evaluation due to both epistemic (modeling) and aleatory (parametric) sources. Epistemic uncertainties arise from simplifications and modeling techniques. This paper addresses epistemic uncertainties in a gravity dam seismic analysis using data from the International Comnission on Large Dams (ICOLD) benchmark study. While the benchmark formulation included the finite element model of the dam, mechanical material properties, and dynamic loads, participants retained the flexibility to opt for best‐practice modeling assumptions, simplifications, and other specifics. Notable response variability emerged, particularly in crack profiles and damage predictions. This study examines sources of variability, quantifying modeling uncertainty for the benchmark problem. More specifically, the modeling variability is quantified using the logarithmic standard deviation, also known as dispersion. This metric enables its incorporation into other seismic risk assessment and fragility studies. Under relatively low‐intensity motion (peak ground acceleration [PGA] of 0.18 g in this case), modeling dispersion of 0.45, 0.30, 0.32, and 0.30 were calculated for the maximum dynamic crest displacement, maximum hydrodynamic pressure at the heel, heel and crest maximum acceleration, respectively. Additionally, the dispersion of the failure PGA was determined to be 0.7. Findings underscore the need for systematic seismic response modeling in dam engineering to enhance prediction accuracy. A better understanding of the sources and magnitudes of modeling uncertainties can help improve the reliability of dam seismic analysis and contribute to the development of more effective risk assessment and mitigation strategies.

Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea

As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events(ADE) resulting from warfarin overdose can be critical, so that typically physicians adjust the warfarin dosage through the INR monitoring twice a week when starting warfarin. Our study aimed to develop machine learning (ML) models that predicts the discharge dosage of warfarin as the initial warfarin dosage using clinical data derived from electronic medical records within 2 days of hospitalization. During this retrospective study, adult patients who were prescribed warfarin at Asan Medical Center (AMC) between January 1, 2018, and October 31, 2020, were recruited as a model development cohort (n = 3168). Additionally, we created an external validation dataset (n = 891) from a Medical Information Mart for Intensive Care III (MIMIC-III). Variables for a model prediction were selected based on the clinical rationale that turned out to be associated with warfarin dosage, such as bleeding. The discharge dosage of warfarin was used the study outcome, because we assumed that patients achieved target INR at discharge. In this study, four ML models that predicted the warfarin discharge dosage were developed. We evaluated the model performance using the mean absolute error (MAE) and prediction accuracy. Finally, we compared the accuracy of the predictions of our models and the predictions of physicians for 40 data point to verify a clinical relevance of the models. The MAEs obtained using the internal validation set were as follows: XGBoost, 0.9; artificial neural network, 0.9; random forest, 1.0; linear regression, 1.0; and physicians, 1.3. As a result, our models had better prediction accuracy than the physicians, who have difficulty determining the warfarin discharge dosage using clinical information obtained within 2 days of hospitalization. We not only conducted the internal validation but also external validation. In conclusion, our ML model could help physicians predict the warfarin discharge dosage as the initial warfarin dosage from Korean population. However, conducting a successfully external validation in a further work is required for the application of the models.

Research on Optimal Group-Purchase Threshold and Pricing Strategy of Community Group Purchase

This study delves into the rapidly evolving community group-buying model, specifically focusing on the determination of optimal group-buying thresholds and pricing strategies for merchants. Aiming to bridge the gap in the existing literature, the methodology employs optimization models, integrating a numerical analysis to construct and evaluate a single merchant model. The findings reveal a nuanced relationship: within a specific threshold interval, a unique group-purchase threshold exists where merchants can maximize profits by balancing group and ordinary sales. The study shows that factors like ordinary selling price, group-buying publicity, and associated costs significantly influence these thresholds and pricing strategies. A critical insight is the threshold’s variability in response to market conditions, highlighting a strategic balance for maximizing profitability. The research underscores the need for merchants to adapt their strategies in response to evolving market dynamics and consumer behaviors. However, the study acknowledges its limitations due to its theoretical nature and focus on the Chinese market, suggesting the potential for future empirical studies in diverse cultural and economic contexts. Overall, this research contributes both theoretically and practically by providing a foundational framework for merchants to optimize group-purchase thresholds and pricing strategies in the dynamic realm of community group buying.

Historical variability of cotton yield and response to climate and agronomic management in Xinjiang, China

Cotton is the primary fibre crop in the world with high economic value, and its yield can be affected by climate and agronomic management. Xinjiang, the largest cotton-producing province in China, contributes to approximately 90 % of the national and over 20 % of global production. Earlier studies focused on cotton yield variability and/or drivers on the site scale, for only one to several counties or cities within Xinjiang, in several years before 2000, or provincial production for a short period. It remains poorly understood how and why cotton yields change in Xinjiang. This study analyzes the spatiotemporal variability of cotton yields at the provincial and county (73 counties) levels from 1989 to 2017 using yield statistics, and identifies the dominant climate and agronomic management factors as well as their optimal ranges, historical states, and interaction effects using the geographical detector method (Geodetector). The results show that the Xinjiang cotton yield has increased markedly over the past decades, with the long-term trend outweighing the interannual variability. High yields are mostly found in southern and northwestern Xinjiang. Yield has increased significantly in over 95.6 % of cotton-planting areas, primarily in the west. Nitrogen fertilization is the leading driver of cotton yield changes, mainly impacting long-term trends. The combination of nitrogen fertilization and agricultural mechanization enhances the explanatory power in a bivariate way and is the strongest in the interaction effect between factors. Temperature variability has the greatest influence on detrended yield variability, and the explanatory power is enhanced and the highest when combined with precipitation. In addition, historical states of these factors are generally lower than their optimal ranges, indicating potential for cotton yield enhancement through improved agronomic management practices and in the context of global warming. This study could enhance understanding of cotton Xinjiang yield variability and drivers, and provides scientific guidance for local cotton cultivation.

Photophysiological responses of bottom sea-ice algae to fjord dynamics and rapid freshening

Sea ice algae have a broad salinity tolerance but can experience stress during rapid decreases in salinity that occur with seasonal ice melt and during ice sample melt. This study investigated the impact of salinity on the photophysiological responses of bottom-ice algal communities from two Svalbard fjords (Tempelfjorden and Van Mijenfjorden). To further investigate the impact of salinity alone, and particularly to rapid freshening, the responses of a lab-cultured ice algal community from Van Mijenfjorden were assessed. Photophysiological responses were mainly determined via 14C-based incubations which provided photosynthesis-irradiance curves. Main findings showed that i) the bottom-ice algal community in Tempelfjorden was characterized by lower photosynthetic efficiency and chlorophyll a biomass than the Van Mijenfjorden communities, and ii) a lab-cultured ice algal community from Van Mijenfjorden dominated by pennate diatoms had significantly lower photosynthetic efficiency, maximum photosynthesis and photoacclimation index after a decrease in salinity from 33 to 10. The lower photosynthetic efficiency and chlorophyll a biomass at Tempelfjorden may be attributed to the almost two-fold lower bulk-ice salinity in Tempelfjorden compared to Van Mijenfjorden, which was likely associated with freshwater inputs from the tidewater glacier Tunabreen during sea ice formation. Other factors such as under-ice light intensities, brine volume fraction and brine nutrient concentrations likely also contributed to variability in ice algal response. Furthermore, experimental results indicated that the cultured Van Mijenfjorden community was negatively impacted by a rapid (within 4 to 24 h) reduction in salinity from 33 to 10. We further documented a significant start of recovery of these algae after 168 h. From this work, we surmise that decreases in surface water salinity, for example arising from the intensifying freshening of fjord waters, may only cause temporary changes in ice algal photoacclimation state and thus in chlorophyll a biomass. Further, this study also supports the need for salinity buffered melt of sea ice samples to reduce artificial bias in biological measurements.