Differences in competitors' market influence due to market structure: Evidence from Japanese gambling market.

This study investigates how differences in the market structure between the Japanese horse racing and Keirin1 racing markets affects the influence exercised by high-turnover operators (major operators) in both markets on low-turnover operators (minor operators) in those markets.2 In the horse racing market structure, there are few competitors, and the difference in turnover3 between major and minor operators is large. In contrast, in the Keirin racing market structure, there are many competitors, and the difference in turnover between major and minor operators is small. Panel analysis results show that in horse racing, operators with low turnover are significantly affected by those with high turnover, while in Keirin racing, operators with low turnover are less affected by competitors with high turnover. The results not only indicate that firms are affected differently by competitors due to the market structure but also suggest that this has an impact on market segmentation policies and firms' marketing efforts.

Multiomics sequencing and immune microenvironment characteristics define three subtypes of small cell neuroendocrine carcinoma of the cervix.

Small cell cervical carcinoma (SCCC) is the most common neuroendocrine tumor in the female genital tract, with an unfavorable prognosis and lacking an evidence-based therapeutic approach. Until now, the distinct subtypes and immune characteristics of SCCC combined with genome and transcriptome have not been described. We performed genomic (n = 18), HPV integration (n = 18), and transcriptomic sequencing (n = 19) of SCCC samples. We assessed differences in immune characteristics between SCCC and conventional cervical cancer, and other small cell neuroendocrine carcinomas, through bioinformatics analysis and immunohistochemical assays. We stratified SCCC patients through non-negative matrix factorization and described the characteristics of these distinct types. We further validated it using multiplex immunofluorescence (n = 77) and investigated its clinical prognostic effect. We confirmed a high frequency of PIK3CA and TP53 alterations and HPV18 integrations in SCCC. SCCC and other small cell carcinoma had similar expression signatures and immune cell infiltration patterns. Comparing patients with SCCC to those with conventional cervical cancer, the former presented immune excluded or 'desert' infiltration. The number of CD8+ cells in the invasion margin of SCCC patients predicted favorable clinical outcomes. We identified three transcriptome subtypes: an inflamed phenotype with high-level expression of genes related to the MHC-II complex (CD74) and IFN-α/ß (SCCC-I), and two neuroendocrine subtypes with high-level expression of ASCL1 or NEUROD1, respectively. Combined with multiple technologies, we found that the neuroendocrine groups had more TP53 mutations and SCCC-I had more PIK3CA mutations. Multiplex immunofluorescence validated these subtypes and SCCC-I was an independent prognostic factor of overall survival. These results provide insights into SCCC tumor heterogeneity and potential therapies. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Evaluating the safety and efficiency impacts of forced lane change with negative gaps based on empirical vehicle trajectories.

A lane-changing (LC) maneuver may cause the follower in the target lane (new follower) to decelerate and give up space, potentially affecting crash risk and traffic flow efficiency. In congested flow, a more aggressive LC maneuver occurs where the lane changer is partially next to the new follower and creates negative gaps, namely negative gap forced LC (NGFLC). Although NGFLC forms the foundation of sideswipe crashes, little has been done to address its impacts and the contributing factors. To tackle this issue, a total of 15,810 LC trajectory samples are extracted from three drone videos at different locations. These samples are categorized into NGFLC and normal LC groups for comparative analysis. Five commonly used conflict indicators are extended into two-dimensional to evaluate the crash risk of LC maneuver. The change of time gaps during LC maneuver are examined to quantify the impact of LC on traffic flow efficiency. We find that NGFLCs significantly increase crash risk, reflected by the number of hazardous LC events and potential crash areas compared to normal LC. Additionally, results reveal that both the lane changer and the new follower tend to maintain a larger time gap after NGFLCs. Factors including time headway, relative speed, and historical gaps in the target lane significantly affect NGFLC incidence. Once the movement of the leader in the original lane is taken into account, the prediction accuracy improves from 81% to 91%. The transferability tests indicate that the findings about the negative impact of NGFLC and the accuracy of its prediction model are consistent across different locations. These findings hold implications for driving assistance systems to better predict and mitigate NGFLCs.

Life course predictors of child emotional distress during the COVID-19 pandemic: Findings from a prospective intergenerational cohort study.

BACKGROUND: We examine precursors of child emotional distress during the COVID-19 pandemic in a prospective intergenerational Australian cohort study. METHODS: Parents (N = 549, 60% mothers) of 934 1-9-year-old children completed a COVID-19 specific module in 2020 and/or 2021. Decades prior, a broad range of individual, relational and contextual factors were assessed during parents' own childhood, adolescence and young adulthood (7-8 to 27-28 years old; 1990-2010) and again when their children were 1 year old (2012-2019). RESULTS: After controlling for pre-pandemic socio-emotional behaviour problems, COVID-19 child emotional distress was associated with a range of pre-pandemic parental life course factors including internalising difficulties, lower conscientiousness, social skills problems, poorer relational health and lower trust and tolerance. Additionally, in the postpartum period, pre-pandemic parental internalising difficulties, lower parental warmth, lower cooperation and fewer behavioural competencies predicted child COVID-19 emotional distress. CONCLUSIONS: Findings highlight the importance of taking a larger, intergenerational perspective to better equip young populations for future adversities. This involves not only investing in child, adolescent, and young adult emotional and relational health, but also in parents raising young families.

Effects of geographical origin and post-harvesting processing on the bioactive compounds and sensory quality of Brazilian specialty coffee beans.

Specialty coffee beans are those produced, processed, and characterized following the highest quality standards, toward delivering a superior final product. Environmental, climatic, genetic, and processing factors greatly influence the green beans' chemical profile, which reflects on the quality and pricing. The present study focuses on the assessment of eight major health-beneficial bioactive compounds in green coffee beans aiming to underscore the influence of the geographical origin and post-harvesting processing on the quality of the final beverage. For that, we examined the non-volatile chemical profile of specialty Coffea arabica beans from Minas Gerais state, Brazil. It included samples from Cerrado (Savannah), and Matas de Minas and Sul de Minas (Atlantic Forest) regions, produced by two post-harvesting processing practices. Trigonelline, theobromine, theophylline, chlorogenic acid derivatives, caffeine, caffeic acid, ferulic acid, and p-coumaric acid were quantified in the green beans by high-performance liquid chromatography with diode array detection. Additionally, all samples were roasted and subjected to sensory analysis for coffee grading. Principal component analysis suggested that Cerrado samples tended to set apart from the other geographical locations. Those samples also exhibited higher levels of trigonelline as confirmed by two-way ANOVA analysis. Samples subjected to de-pulping processing showed improved chemical composition and sensory score. Those pulped coffees displayed 5.8% more chlorogenic acid derivatives, with an enhancement of 1.5% in the sensory score compared to unprocessed counterparts. Multivariate logistic regression analysis pointed out altitude, ferulic acid, p-coumaric acid, sweetness, and acidity as predictors distinguishing specialty coffee beans obtained by the two post-harvest processing. These findings demonstrate the influence of regional growth conditions and post-harvest treatments on the chemical and sensory quality of coffee. In summary, the present study underscores the value of integrating target metabolite analysis with statistical tools to augment the characterization of specialty coffee beans, offering novel insights for quality assessment with a focus on their bioactive compounds.

Determinants of depressive and alcohol use disorders among female sex workers in Ethiopia: evidence from a national bio-behavioral survey, 2020.

BACKGROUND: Female sex workers (FSWs) face an elevated risk of developing mental health disorders and alcohol use disorders (AUD), which in turn increase their vulnerability to HIV and other sexually transmitted infections (STIs) and other negative outcomes. To effectively address both of these health issues, it is crucial to understand the shared key determinants underlying these illnesses, which is a substantial knowledge gap in Ethiopia and elsewhere in the world. Therefore, this study aimed to identify the common key determinants of depression and AUD among FSWs in Ethiopia using a bivariate multivariable ordinal logistic model. METHODS: We analyzed cross-sectional biobehavioral data collected in 2020 from 16 cities and major towns in Ethiopia using the respondent-driven sampling (RDS) technique, which involved a total of 6,085 FSWs. FSWs who had lived at the study sites for at least a month before the study period were deemed eligible for recruitment. Major depressive disorder (DD) and AUD were screened using the Patient Health Questionnaire (PHQ9) and alcohol use disorder identification test (AUDIT), respectively. We used descriptive statistics to summarize study population characteristics and bivariate multivariable ordinal logistic regression (BMOLR) to identify common determinants of DD and AUD combined and their nonnormal correlation. RESULTS: Among 6085 FSWs screened for DD and AUD, 13.5% and 4.0% have met the criteria for moderate and severe depressive disorder, respectively, and 20.3% and 34.7% have met the AUDIT criteria for harmful or hazardous behavior and alcohol dependence, respectively. FSW with experience of inconsistent condom use, condom failure, violence, mobility, use of any drugs, non-paying partners, abortion, and selling sex for more than five years were associated with an increase in the severity of both disorders. A high average income from selling sex and the number of paying partners reduced the severity of depression and increased the level of alcohol dependence. Being HIV positive and ever having anal sex were associated only with an increase in depression. CONCLUSION: Major DD and AUD are prevalent among FSWs in Ethiopia. The findings revealed that common key determinants, which exacerbated the severity of both disorders, were also risk factors for HIV and other STIs. Consequently, integrated STI strategies are essential in the screening, referral, and treatment of depression and AUD. Intervention packages should encompass determinants of depression and AUD, including condom utilization, drug use, mobility between towns, abortion, violence, and counseling services. Additionally, strategies to ensure economic security should be incorporated.

Classification of acacia, rape and multifloral Hungarian honey types.

The composition of honey is mostly determined by the species-specific characteristics of flowering plants, which is reflected in the significant deviations in composition of honey varieties. The high-quality acacia honey is assessed based on both physical-chemical parameters and melissopalynology. The appearance of rape pollen in acacia honey makes the acacia honey be sorted into the multifloral honey category. Over carrying out melissopalynology, the149 samples of various honeys (acacia, rape and multifloral) have also been analysed by using physical-chemical and elemental analysis. Multivariate data analysis revealed that multifloral honey is much closer to acacia honey than to rape honey, as it can be observed from the examined unique parameters. By the PCA (Principal Component Analysis) analysis based on united set of physico-chemical and melissopalynology results the acacia and rape honey samples are entirely separated for each other, while multifloral honey samples are very close to acacia honey group and partially overlap with it. On ignoring the pollen analysis and based on the rest of the results, the multifloral honey category is almost indistinguishable from the declared and verified acacia honey category.

Using isometric log-ratio in compositional data analysis for developing a groundwater pollution index.

This study introduces a novel groundwater pollution index (GPI) formulated through compositional data analysis (CoDa) and robust principal component analysis (RPCA) to enhance groundwater quality assessment. Using groundwater quality monitoring data from sites impacted by the 2010-2011 foot-and-mouth disease outbreak in South Korea, CoDa uncovers critical hydrochemical differences between leachate-influenced and background groundwater. The GPI was developed by selecting key subcompositional parts (NH4+-N, Cl-, and NO3--N) using RPCA, performing the isometric log-ratio (ILR) transformation, and normalizing the results to environmental standards, thereby providing a more precise and accurate assessment of pollution. Validated against government criteria, the GPI has shown its potential as an alternative assessment tool, with its reliability confirmed by receiver operating characteristic curve analysis. This study highlights the essential role of CoDa, especially the ILR -transformation, in overcoming the limitations of traditional statistical methods that often neglect the relative nature of hydrochemical data. Our results emphasize the utility of the GPI in significantly advancing groundwater quality monitoring and management by addressing a methodological gap in the quantitative assessment of groundwater pollution.

The influence of spirituality on caregiver burden and quality of life in older adult informal caregivers.

BACKGROUND: Caregiver burden (CB) reduces quality of life (QOL) and causes poor health outcomes. Spirituality impacts this relationship. AIMS: To determine prevalence of CB and investigate relationships among CB, spirituality, and QOL in older U.S. adult informal caregivers (n = 754). METHODS: This was a cross-sectional, descriptive secondary analysis of data from the 2020 Health and Retirement Study using GLM and SEM. RESULTS: Caregiver mean age was 65.93 (SD=8.37). Caregivers were primarily female (n = 456, 54.0%), White (n = 500, 79.5%), and married (n = 469, 65.3%). Most caregivers had moderate CB (n = 369, 49.8%). Black caregivers who were spiritual (p=.031) and caregivers with a high school diploma/GED who were spiritual (p=.021) had lower CB. Lower CB was correlated with higher QOL (p=< 0.001). SEM depicting an influencing effect of spirituality revealed good model fit (NFI=0.988; IFI=0.993; TLI=0.983; PCFI=0.397, RMSEA=0.043; χ2=9.577, p=.048, DF=4) CONCLUSIONS: Fostering spirituality in older adult caregivers could reduce CB and improve QOL.

Ophthalmic Manifestations of COVID-19: A Retrospective Study on Prevalence, Characteristics, and Clinical Implications.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic, caused by the SARS-CoV-2 virus, has had far-reaching implications beyond the respiratory system. Mounting evidence suggests that COVID-19 can impact various organ systems, including the eyes. This study aimed to elucidate the prevalence, characteristics, and clinical implications of ophthalmic manifestations in patients diagnosed with COVID-19. METHODS: This retrospective study analyzed data from electronic medical records of adult patients hospitalized with COVID-19 between March 1, 2020, and December 31, 2020, at a large tertiary academic medical center. Ophthalmic manifestations, including conjunctivitis, dry eye symptoms, ocular pain, epiphora, ocular redness, and visual disturbances, were identified and examined for their prevalence, onset, duration, and associations with COVID-19 severity and systemic symptoms. RESULTS: Among 342 patients included in the study, 106 (31.0%) experienced at least one ophthalmic manifestation during their COVID-19 illness. Conjunctivitis was the most common manifestation in 62 patients (18.1%), followed by dry eye symptoms in 38 patients (11.1%), ocular pain/discomfort in 27 patients (7.9%), epiphora in 19 patients (5.6%), ocular redness in 14 patients (4.1%), and visual disturbances in nine patients (2.6%). Ophthalmic manifestations were significantly associated with severe COVID-19 illness (42.9% vs. 26.7%, p = 0.003) and the presence of systemic symptoms such as fever, cough, and dyspnea. The median time of onset was six days, and the median duration was 10 days. CONCLUSIONS: Ophthalmic manifestations are prevalent in COVID-19 patients, particularly in those with severe illness and systemic symptoms. The study highlights the importance of recognizing and managing ocular symptoms in affected individuals and underscores the multifaceted nature of COVID-19, necessitating interdisciplinary collaboration for comprehensive patient care.

Artificial intelligence approaches for tinnitus diagnosis: leveraging high-frequency audiometry data for enhanced clinical predictions.

This research investigates the application of machine learning to improve the diagnosis of tinnitus using high-frequency audiometry data. A Logistic Regression (LR) model was developed alongside an Artificial Neural Network (ANN) and various baseline classifiers to identify the most effective approach for classifying tinnitus presence. The methodology encompassed data preprocessing, feature extraction focused on point detection, and rigorous model evaluation through performance metrics including accuracy, Area Under the ROC Curve (AUC), precision, recall, and F1 scores. The main findings reveal that the LR model, supported by the ANN, significantly outperformed other machine learning models, achieving an accuracy of 94.06%, an AUC of 97.06%, and high precision and recall scores. These results demonstrate the efficacy of the LR model and ANN in accurately diagnosing tinnitus, surpassing traditional diagnostic methods that rely on subjective assessments. The implications of this research are substantial for clinical audiology, suggesting that machine learning, particularly advanced models like ANNs, can provide a more objective and quantifiable tool for tinnitus diagnosis, especially when utilizing high-frequency audiometry data not typically assessed in standard hearing tests. The study underscores the potential for machine learning to facilitate earlier and more accurate tinnitus detection, which could lead to improved patient outcomes. Future work should aim to expand the dataset diversity, explore a broader range of algorithms, and conduct clinical trials to validate the models' practical utility. The research highlights the transformative potential of machine learning, including the LR model and ANN, in audiology, paving the way for advancements in the diagnosis and treatment of tinnitus.

A Comparative Study of Gene Co-Expression Thresholding Algorithms.

The thresholding problem is studied in the context of graph theoretical analysis of gene co-expression data. A number of thresholding methodologies are described, implemented, and tested over a large collection of graphs derived from real high-throughput biological data. Comparative results are presented and discussed.

Sustainable Agrochemical Exploitation of Ligurian Aromatic Plant Ecotypes.

The characterization of local improved varieties as well as the reduction of synthetic chemical fertilizers are sustainable approaches in the vision of a new precision Farming. Aim of our study was to improve the geographical characterization of local ecotypes and to identify peculiar features of new crops in terms of bioactive compounds. NMR and LC-MS metabolite profiling approaches followed by multivariate data analysis were applied to characterize local rosemary and garlic ecotypes. With the aim of applying for a protected designation of origin, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify representative sensory quality indicators for Vessalico garlic and rosemary "Eretto Liguria" local ecotypes, Variable Influence on Projections (VIP) values of OPLS-DA indicated six metabolites as quality indicators for Vessalico garlic and sixteen metabolites as quality indicators for rosemary "Eretto Liguria". Finally, to discover and utilize new ecotypes in a sustainable way, Vessalico garlic extracts antiviral activity, previously evaluated against Tomato brown rugose fruit virus (ToBRFV), a Tobamovirus affecting tomato crops, was extended to Pepino mosaic virus (PepMV) with positive results.

Treatment Discontinuation in Patients with Psoriasis Treated with Biologics: A Retrospective Analysis of German Health Claims Data.

INTRODUCTION: Plaque psoriasis is a common, often debilitating, chronic autoimmune inflammatory skin disease. Moderate-to-severe forms of psoriasis can be treated with biologics such as anti-interleukin and anti-tumor necrosis factor antibodies. We aimed to investigate treatment discontinuation among patients with psoriasis who initiated biologic treatment. METHODS: We conducted a retrospective, non-interventional cohort study based on anonymized claims data from the German statutory health insurance which covered the years from 2016 to 2021. We included adult patients with psoriasis who initiated biologic treatment in drug-specific cohorts. Over a 365-day follow-up period, we assessed the frequencies and the time until treatment discontinuation for different biologics. Differences in discontinuation rates were compared using a multivariate Cox proportional hazards model. RESULTS: A total of 2565 patients with psoriasis who initiated treatment with secukinumab (n = 612), adalimumab (n = 454), guselkumab (n = 354), ixekizumab (n = 259), ustekinumab (n = 241), tildrakizumab (n = 205), brodalumab (n = 166), risankizumab (n = 145), etanercept (n = 91), certolizumab (n = 29), and infliximab (n = 9) were included. A total of 1290 patients (50.29%) discontinued treatment during the follow-up period, ranging from 30.34% (risankizumab) to 69.23% (etanercept). Median time until discontinuation of treatment ranged from 102 days (etanercept) to 208 days (risankizumab). Once the biologic treatment was discontinued, 45.05% of patients restarted the treatment with the same agent, 23.10% of patients switched to another biologic, and 31.86% received no further biologic agent. Compared to patients treated with risankizumab, the treatment discontinuation rate was significantly higher (p < 0.05) in patients treated with the other biologics except ustekinumab (p = 0.12). CONCLUSIONS: Further research should explore reasons leading to treatment discontinuation in order to support treatment choices for patients with moderate-to-severe psoriasis.

Dory: Computation of persistence diagrams up to dimension two for Vietoris-Rips filtrations of large data sets.

Persistent homology (PH) is an approach to topological data analysis (TDA) that computes multi-scale topologically invariant properties of high-dimensional data that are robust to noise. While PH has revealed useful patterns across various applications, computational requirements have limited applications to small data sets of a few thousand points. We present Dory, an efficient and scalable algorithm that can compute the persistent homology of sparse Vietoris-Rips complexes on larger data sets, up to and including dimension two and over the field Z2. As an application, we compute the PH of the human genome at high resolution as revealed by a genome-wide Hi-C data set containing approximately three million points. Extant algorithms were unable to process it, whereas Dory processed it within five minutes, using less than five GB of memory. Results show that the topology of the human genome changes significantly upon treatment with auxin, a molecule that degrades cohesin, corroborating the hypothesis that cohesin plays a crucial role in loop formation in DNA.

Persistent homology reveals strong phylogenetic signal in 3D protein structures.

Changes that occur in proteins over time provide a phylogenetic signal that can be used to decipher their evolutionary history and the relationships between organisms. Sequence comparison is the most common way to access this phylogenetic signal, while those based on 3D structure comparisons are still in their infancy. In this study, we propose an effective approach based on Persistent Homology Theory (PH) to extract the phylogenetic information contained in protein structures. PH provides efficient and robust algorithms for extracting and comparing geometric features from noisy datasets at different spatial resolutions. PH has a growing number of applications in the life sciences, including the study of proteins (e.g. classification, folding). However, it has never been used to study the phylogenetic signal they may contain. Here, using 518 protein families, representing 22,940 protein sequences and structures, from 10 major taxonomic groups, we show that distances calculated with PH from protein structures correlate strongly with phylogenetic distances calculated from protein sequences, at both small and large evolutionary scales. We test several methods for calculating PH distances and propose some refinements to improve their relevance for addressing evolutionary questions. This work opens up new perspectives in evolutionary biology by proposing an efficient way to access the phylogenetic signal contained in protein structures, as well as future developments of topological analysis in the life sciences.

Standardized multi-institutional data analysis of fixed and removable prosthesis: estimation of life expectancy with regards to variable risk factors.

PURPOSE: This study aims to assess and predict lifespan of dental prostheses using newly developed Korean Association of Prosthodontics (KAP) criteria through a large-scale, multi-institutional survey. MATERIALS AND METHODS: Survey was conducted including 16 institutions. Cox proportional hazards model and principal component analysis (PCA) were used to find out relevant factors and predict life expectancy. RESULTS: 1,703 fixed and 815 removable prostheses data were collected and evaluated. Statistically significant factors in fixed prosthesis failure were plaque index and material type, with a median survival of 10 to 18 years and 14 to 20 years each. In removable prosthesis, factors were national health insurance coverage, antagonist type, and prosthesis type (complete or partial denture), with median survival of 10 to 13 years, 11 to 14 years, and 10 to 15 years each. For still-usable prostheses, PCA analysis predicted an additional 3 years in fixed and 4.8 years in removable prosthesis. CONCLUSION: Life expectancy of a prosthesis differed significantly by factors mostly controllable either by dentist or a patient. Overall life expectancy was shown to be longer than previous research.

Erratum: NeuroDecodeR: a package for neural decoding in R.

[This corrects the article DOI: 10.3389/fninf.2023.1275903.].

Leveraging the potential of the German operating room benchmarking initiative for planning: A ready-to-use surgical process data set.

We present a freely available data set of surgical case mixes and surgery process duration distributions based on processed data from the German Operating Room Benchmarking initiative. This initiative collects surgical process data from over 320 German, Austrian, and Swiss hospitals. The data exhibits high levels of quantity, quality, standardization, and multi-dimensionality, making it especially valuable for operating room planning in Operations Research. We consider detailed steps of the perioperative process and group the data with respect to the hospital's level of care, the surgery specialty, and the type of surgery patient. We compare case mixes for different subgroups and conclude that they differ significantly, demonstrating that it is necessary to test operating room planning methods in different settings, e.g., using data sets like ours. Further, we discuss limitations and future research directions. Finally, we encourage the extension and foundation of new operating room benchmarking initiatives and their usage for operating room planning.