Implementing a Bundle for Evidence-based Cesarean Delivery May Not Be As Beneficial as Expected: A Multi-center, Pre- and Post- Study.

BACKGROUND: Standardization of procedures improves outcomes. Though systematic reviews have summarized the evidence-based steps (EBS) of cesarean delivery (CD), their bundled implementation has not been investigated. OBJECTIVE: In this pre- and post-implementation trial, we sought to ascertain if bundled EBS of CD, compared to surgeon's preference, improves outcomes. STUDY DESIGN: A StaRI (Standards for Reporting Implementation Studies) compliant, multi-center pre- and post-implementation trial at 4 teaching hospitals was conducted. The pre-implementation period consisted of CD done based on the physicians' preferences for 3 months; educational intervention (e.g., didactics, badge cards, posters, video) occurred at the 4th month. CDs in post-implementation period employed the bundled EBS. A pre-planned 10% randomized audit of both groups assessed adherence and uptake of EBS. The primary outcome was a composite maternal morbidity (CMM), which included estimated blood loss > 1,000 mL, blood transfusion, endometritis, post-partum fever, wound complications, sepsis, thrombosis, ICU admission, hysterectomy, or death. The secondary outcome was a composite neonatal morbidity (CNM) and some of its components were 5-min Apgar score < 7, positive pressure oxygen use, hypoglycemia, or sepsis. A priori Bayesian sample size calculation indicated 700 CD in each group was needed to demonstrate 20% relative reduction (from 15% to 12%) of CMM with 75% certainty. Bayesian logistic regression with neutral priors was used to calculate likelihood of net-improvement in adjusted relative risk (aRR) with 95% credible intervals (CrI). RESULTS: A total of 1,425 consecutive CD (721 in pre- and 704 in post-implementation group) were examined. Audited data indicated that the baseline EBS utilization rate during the pre-implementation period was 79%; after the implementation bundled EBS of CD the audited adherence was 89%-an uptake of 10.0% of the EBS. In four aspects, the maternal characteristics differed significantly in the pre- and post-implementation periods: race/ethnicity, hypertensive disorder, and the relative contribution of the 4 centers to the cohorts and the gestational age at delivery, but the indications for CD and whether its duration was < versus > 60 min did not. The rates of CMM in the pre- and post-implementation groups were 26% and 22%, respectively (aRR, 0.88; 95% CrI, 0.73-1.04), with a 94 % Bayesian probability of a reduction in CMM. The CNM occurred in 37% of the pre- and in 41% of the post-implementation group (aRR, 1.12; 95% CrI 0.98-1.39), with a 95% Bayesian probability of worsening in CNM. When CMM were segregated by preterm (<37 wks) and term (> 37 weeks) CD, the improvement in maternal outcomes persisted; when CNM were segregated by gestational age subgroupsthe potential for worsening neonatal outcomes persisted as well. CONCLUSIONS: Standardization of the evidence-based bundled steps of cesarean delivery resulted in a modest reduction of the composite maternal outcome; however, a paradoxical increase in neonatal composite morbidity was noted. Although individual evidence-based steps may be of value, while awaiting additional intervention trials a formal bundling of such steps is currently not recommended.

The Role of Religiosity and Personal Faith in Young People's Sexual Debut and Sexual Risk Taking: A Bayesian Approach.

Using a Bayesian statistical approach, this study aimed to provide a robust assessment of associations between religiosity and personal faith, timing of sexual initiation and sexual risk taking in young people. To produce posterior probability distribution of the estimation of associations, this study combined two population-based surveys of emerging Croatian adults and meta-analytical estimates pertaining to international studies published between 2000 and 2020. Regardless of the prior used, age at sexual debut was delayed by both religiosity (correlations ranged from 0.10 to 0.13) and personal faith (r = 0.09-0.13). Apart from delaying sexual debut, the findings suggest a very limited role of religiosity and personal faith in the protection of sexual and reproductive health among young people.

Enhancing Genomic Prediction Accuracy for Body Conformation Traits in Korean Holstein Cattle.

The Holstein breed is the mainstay of dairy production in Korea. In this study, we evaluated the genomic prediction accuracy for body conformation traits in Korean Holstein cattle, using a range of π levels (0.75, 0.90, 0.99, and 0.995) in Bayesian methods (BayesB and BayesC). Focusing on 24 traits, we analyzed the impact of different π levels on prediction accuracy. We observed a general increase in accuracy at higher levels for specific traits, with variations depending on the Bayesian method applied. Notably, the highest accuracy was achieved for rear teat angle when using deregressed estimated breeding values including parent average as a response variable. We further demonstrated that incorporating parent average into deregressed estimated breeding values enhances genomic prediction accuracy, showcasing the effectiveness of the model in integrating both offspring and parental genetic information. Additionally, we identified 18 significant window regions through genome-wide association studies, which are crucial for future fine mapping and discovery of causal mutations. These findings provide valuable insights into the efficiency of genomic selection for body conformation traits in Korean Holstein cattle and highlight the potential for advancements in the prediction accuracy using larger datasets and more sophisticated genomic models.

Assessing green manure impact on wheat productivity through Bayesian analysis of yield monitor data.

Agronomy research traditionally relies on small, controlled trial plots, which may not accurately represent the complexities and variabilities found in larger, real-world settings. To address this gap, we introduce a Bayesian methodology for the analysis of yield monitor data, systematically collected across extensive agricultural landscapes during the 2020/21 and 2021/22 growing seasons. Utilizing advanced yield monitoring equipment, our method provides a detailed examination of the effects of green manure on wheat yields in a real-world context. The results from this comprehensive analysis reveal significant insights into the impact of green manure application on wheat production, demonstrating enhanced yield outcomes across varied landscapes. This evidence suggests that the Bayesian approach to analyzing yield monitor data can offer more precise and contextually relevant information than traditional experimental designs. This research underscores the value of integrating large-scale data analysis techniques in agronomy, moving beyond small-scale trials to offer a broader, more accurate perspective on agricultural practices. The adoption of such methodologies promises to refine farming strategies and policies, ultimately leading to more effective and sustainable agricultural outcomes. The inclusion of a Python script in the appendix illustrates our analytical process, providing a tangible resource for replicating and extending this research within the agronomic community.

Principal component analysis of phenotypic and breeding value data for semen traits in Egyptian buffalo bulls.

Buffalo bull semen traits are economically important traits that influence farm fertility and profitability. Genetic improvement of semen characteristics is an important detail of the genetic improvement. This study was conducted to assess the relationship between the breeding values as well as the phenotypic values for semen traits (VOL, MM, LS, AS and CONC) of the Egyptian buffalo bulls. A total of 7761 normal semen ejaculates were collected and characterized at ILMTC laboratory from 26 bulls from 2009 to 2019. For VOL, MM, LS, AS, and CONC, the actual means were 3.89 mL, 62.37%, 60.64%, 3.94%, and 0.67 × 109 sperm/mL, respectively. The prediction of breeding values for semen traits was estimated using a Bayesian procedure. The estimated standardized EBVs and phenotypic values were used in the principal component analysis (PCA). Of five PCs, one PC (PC1) had > 1 eigenvalues that was responsible for 87.19% of the total variation of SEBV, and two PCs had > 1 eigenvalues that were responsible for 59.61% and 21.35% of the total variation of the phenotypic values. Together, PC1 and PC2 accounted for 97.97% of the total variance of SEBV and 80.96% of the total variance of phenotypic values. A graphs of the first two components showed the traits separated into two different directions by group. This indicates each group was under similar genetic influence. Therefore, selection can be done separately for each group without influencing the other. Principal component analysis reduced variables to describe the key information in buffalo semen data.

Indicated versus universal third trimester ultrasound examination in low-risk pregnancies: a pre- and post-intervention study.

BACKGROUND: In low-risk pregnancies, third trimester ultrasound examination is indicated if fundal height measurement and gestational age discrepancy is observed. Despite potential improvement in detection of ultrasound abnormality, prior trials to date on universal third trimester ultrasound examination in low-risk pregnancies, compared to indicated ultrasound examination, have not demonstrated improvement in neonatal or maternal adverse outcomes. OBJECTIVE: The primary objective was to determine if universal third trimester ultrasound examination in low-risk pregnancies could attenuate composite neonatal adverse outcome. The secondary objectives were to compare change in composite maternal adverse outcomes and detection of abnormalities of fetal growth (fetal growth restriction or large for gestational age) or amniotic fluid (oligo- or poly-hydramnios). STUDY DESIGN: Our pre- and post-intervention study at 9 locations included low-risk pregnancies; those without indication for ultrasound examination in the third trimester. Compared to indicated ultrasound in the pre-implementation period, in the post-implementation period all patients were scheduled for ultrasound examination at 36.0 - 37.6 weeks. In both periods clinicians intervened based on abnormalities identified. Composite neonatal adverse outcomes included any of: Apgar score ≤ 5 at 5 min, cord pH < 7.00, birth trauma (bone fracture or brachial plexus palsy), intubation for > 24 hours, hypoxic ischemic encephalopathy, seizure, sepsis (bacteremia proven with blood culture), meconium aspiration syndrome, intraventricular hemorrhage grade III or IV, periventricular leukomalacia, necrotizing enterocolitis, stillbirth after 36 weeks, or neonatal death within 28 days of birth. Composite maternal adverse outcomes included any of: chorioamnionitis, wound infection, estimated blood loss >1000mL, blood transfusion, deep venous thrombus or pulmonary embolism, admission to intensive care unit, or death. Using Bayesian statistics, we calculated a sample size of 600 individuals in each arm to detect >75% probability of any reduction in primary outcome, (80% power; 50% hypothesized risk reduction). RESULTS: During the pre-intervention phase, 747 individuals were identified during the initial ultrasound examination and among them 568 (76.0%) met the inclusion criteria at 36.0 to 37.6 weeks; during the post-intervention period, the corresponding numbers were 770 and 661 (85.8%). The rate of identified abnormalities of fetal growth or of amniotic fluid increased from between pre- and post-intervention period (7.1% versus 22.2%, p< 0.0001; number needed to diagnose, 7; 95% confidence interval, 5-9). The primary outcome occurred in 15 of 568 (2.6%) individuals in the pre- and 12 of 661 (1.8%) in the post-intervention group (83% probability of risk reduction, posterior relative risk, 0.69; 95% credible interval, 0.34-1.42). The composite maternal adverse outcomes occurred in 8.6% in the pre- and 6.5% in post-intervention group (90% probability of risk (posterior relative risk, 0.74; 95% credible interval, 0.49-1.15). The number needed to treat to reduce composite neonatal adverse outcomes was 121 (95% confidence interval, 40-200), to reduce composite maternal adverse outcomes was 46 (95% confidence interval, 19-74), and to prevent cesarean delivery was 18 (95% confidence interval, 9-31). CONCLUSION: Among low-risk pregnancies, compared to routine care with indicated ultrasound examination, implementation of a universal third-trimester ultrasound examination at 36.0-37.6 weeks attenuated composite neonatal and maternal adverse outcomes.

Climate change anxiety, fear, and intention to act.

Climate change anxiety (CCA) is an emerging yet not clearly defined construct. Here, we examine the relationship between CCA and climate change-related fear in response to messages differently framing uncertainty and anticipation concerning climate change, exploring how the former differs from general anxiety measures. To this purpose, young and healthy volunteers were assigned to three different framing conditions. Their emotional responses as well as eco-emotions and beliefs about climate change were collected. By employing a Bayesian approach, we found that framing the consequences of climate change effectively induces heightened fear and that CCA strongly predicted fear levels, while general anxiety measures did not. Overall, these results reflect CCA's unique and specific nature in influencing climate change-related fear. Interestingly, we found fear to predict intention scores only following the framings that did not effectively induce action intentions, consistent with prior findings on fear without efficacy framing. Instead, reading about the negative consequences motivated action the most. Following this framing, we found that eco-anger, instead of fear, consistently predicted intentions to engage in climate action. These results emphasize the complex interplay between CCA, eco-emotions, efficacy, and behavioral engagement.

Assessing the use of GEE methods for analyzing binary outcomes in family studies: the Strong Heart Family Study.

The generalized estimating equations method (GEE) is commonly applied to analyze data obtained from family studies. GEE is well known for its robustness on misspecification of correlation structure. However, the unbalanced distribution of family sizes and complicated genetic relatedness structure within each family may challenge GEE performance. We focused our research on binary outcomes. To evaluate the performance of GEE, we conducted a series of simulations, on data generated adopting the kinship matrix (correlation structure within each family) from the Strong Heart Family Study (SHFS). We performed a fivefold cross-validation to further evaluate the GEE predictive power on data from the SHFS. A Bayesian modeling approach, with direct integration of the kinship matrix, was also included to contrast with GEE. Our simulation studies revealed that GEE performs well on a binary outcome from families having a relatively simple kinship structure. However, data with a binary outcome generated from families with complex kinship structures, especially with a large genetic variance, can challenge the performance of GEE.

rBMA: A robust Bayesian Model Averaging Method for phase II basket trials based on informative mixture priors.

Oncology drug research in the last few decades has been driven by the development of targeted agents. In the era of targeted therapies, basket trials are often used to test the antitumor activity of a novel treatment in multiple indications sharing the same genomic alteration. As patient population are further fragmented into biomarker-defined subgroups in basket trials, novel statistical methods are needed to facilitate cross-indication learning to improve the statistical power in basket trial design. Here we propose a robust Bayesian model averaging (rBMA) technique for the design and analysis of phase II basket trials. We consider the posterior distribution of each indication (basket) as the weighted average of three different models which only differ in their priors (enthusiastic, pessimistic and non-informative). The posterior weights of these models are determined based on the effect of the experimental treatment in all the indications tested. In early phase oncology trials, different binary endpoints might be chosen for different indications (objective response, disease control or PFS at landmark times), which makes it even more challenging to borrow information across indications. Compared to previous approaches, the proposed method has the flexibility to support cross-indication learning in the presence of mixed endpoints. We evaluate and compare the performance of the proposed rBMA approach to competing approaches in simulation studies. R scripts to implement the proposed method are available at https://github.com/xwang317/rBMA.

A latent class linear mixed model for monotonic continuous processes measured with error.

Motivated by measurement errors in radiographic diagnosis of osteoarthritis, we propose a Bayesian approach to identify latent classes in a model with continuous response subject to a monotonic, that is, non-decreasing or non-increasing, process with measurement error. A latent class linear mixed model has been introduced to consider measurement error while the monotonic process is accounted for via truncated normal distributions. The main purpose is to classify the response trajectories through the latent classes to better describe the disease progression within homogeneous subpopulations.

Symptom-specific improvement across therapies and their putative mediators: A mediation network intervention analysis.

OBJECTIVE: We evaluated differential treatment effects on specific symptoms and their mediators for Cognitive Behavioral Analysis System of Psychotherapy (CBASP) and Supportive Psychotherapy (SP) in persistently depressed patients. METHOD: We conducted a Bayesian mediation network intervention analysis with data from a randomized controlled trial comparing CBASP and SP. Three networks were calculated to investigate (1) differential treatment effects on specific symptoms, (2) differential treatment effects on the potential mediators interpersonal problems and social functioning, and (3) associations between change in symptoms and change in the potential mediators. RESULTS: First, we found no evidence that CBASP more strongly improves most depressive symptoms specifically, except minimal evidence of symptom-specific effects on sleeping problems and self-esteem. Second, no and minimal evidence for differential treatment effects on interpersonal problems and social functioning was shown, respectively. Third, interpersonal problems and social functioning were strongly related to depressive symptoms. CONCLUSION: While CBASP showed superior treatment effects for overall symptom severity, this treatment might not be superior in improving specific symptoms and the potential mediators interpersonal problems and social functioning. Still, interpersonal problems and social functioning seem to play an important role for depression symptoms. Future research needs to further investigate potential working mechanisms of CBASP.Trial registration: ClinicalTrials.gov identifier: NCT00970437.

Decadal demographic shifts and size-dependent disturbance responses of corals in a subtropical warming hotspot.

Long-term demographic studies at biogeographic transition zones can elucidate how body size mediates disturbance responses. Focusing on subtropical reefs in eastern Australia, we examine trends in the size-structure of corals with contrasting life-histories and zoogeographies surrounding the 2016 coral bleaching event (2010-2019) to determine their resilience and recovery capacity. We document demographic shifts, with disproportionate declines in the number of small corals and long-term persistence of larger corals. The incidence of bleaching (Pocillopora, Turbinaria) and partial mortality (Acropora, Pocillopora) increased with coral size, and bleached corals had greater risk of partial mortality. While endemic Pocillopora experienced marked declines, decadal stability of Turbinaria despite bleaching, coupled with abundance increase and bleaching resistance in Acropora indicate remarkable resilience of these taxa in the subtropics. Declines in the number of small corals and variable associations with environmental drivers indicate bottlenecks to recovery mediated by inhibitory effects of thermal extremes for Pocillopora (heat stress) and Acropora (heat and cold stress), and stimulatory effects of chlorophyll-a for Turbinaria. Although our study reveals signs of resilience, it foreshadows the vulnerability of subtropical corals to changing disturbance regimes that include marine heatwaves. Disparity in population dynamics suggest that subtropical reefs are ecologically distinct from tropical coral reefs.

Emergence and dissemination of equine-like G3P[8] rotavirus A in Brazil between 2015 and 2021.

Rotavirus A (RVA) is a major cause of acute gastroenteritis globally that is classically genotyped by its two immunodominant outer capsid proteins, VP7 (G-) and VP4 (P-). Recent evidence suggests that the reassortant equine-like G3P[8] strain played a substantial role in RVA transmission in Brazil since 2015. To understand its global emergence and dissemination in Brazilian territory, stool samples collected from 11 Brazilian states (n = 919) were genotyped by RT-qPCR and proceeded to sequence the VP7 gene (n = 102, 79 being newly generated) of the G3P[8] samples with pronounced viral loads. Our phylogenetic genotyping showed that G3P[8] became the dominant strain in Brazil between 2017 and 2020, with equine-like variants representing 75%-100% of VP7 samples in this period. A Bayesian discrete phylogeographic analysis strongly suggests that the equine-like G3P[8] strain originated in Asia during the early 2010s and subsequently spread to Europe, the Caribbean, and South America. Multiple introductions were detected in Brazil between 2014 and 2017, resulting in five national clusters. The reconstruction of the effective population size of the largest Brazilian cluster showed an expansion until 2017, followed by a plateau phase until 2019 and subsequent contraction. Our study also supports that most mutations fixed during equine-like G3P[8] evolution were synonymous, suggesting that adaptive evolution was not an important driving force during viral dissemination in humans, potentially increasing its susceptibility to acquired immunity. This research emphasizes the need for comprehensive rotavirus genomic surveillance that allows close monitoring of its ever-shifting composition and informs more effective public health policies.IMPORTANCEOur original article demonstrated the origin and spread in a short time of equine-like G3P[8] in Brazil and the world. Due to its segmented genome, it allows numerous mechanisms including genetic drift and reassortment contribute substantially to the genetic diversity of rotavirus. Although the effectiveness and increasing implementation of vaccination have not been questioned, a matter of concern is its impact on the emergence of escape mutants or even the spread of unusual strains of zoonotic transmission that could drive epidemic patterns worldwide. This research emphasizes the need for comprehensive rotavirus genomic surveillance, which could facilitate the formulation of public policies aimed at preventing and mitigating its transmission.

Identifying Trigger Cues for Hospital Blood Transfusions Based on Ensemble Learning Methods.

Background: Traumatic shock is the leading cause of preventable death with most patients dying within the first 6 hours. This underscores the importance of prehospital interventions, and growing evidence suggests prehospital transfusion improves survival. Optimizing transfusion triggers in the prehospital setting is key to improving outcomes for patients in hemorrhagic shock. Our objective was to identify factors associated with early in-hospital transfusion requirements available to prehospital clinicians in the field to develop a simple algorithm for prehospital transfusion, particularly for patients with occult shock. Methods: We included trauma patients transported by a single critical care transport service to a level I trauma center between 2012 and 2019. We used logistic regression, Fast and Frugal Trees (FFTs), and Bayesian analysis to identify factors associated with early in-hospital blood transfusion as a potential trigger for prehospital transfusion. Results: We included 2,157 patients transported from the scene or emergency department (ED) of whom 207 (9.60%) required blood transfusion within 4 hours of admission. The mean age was 47 (IQR = 28-62) and 1,480 (68.6%) patients were male. From 13 clinically relevant factors for early hospital transfusions, four were incorporated into the FFT in following order: 1) SBP, 2) prehospital lactate concentration, 3) Shock Index, 4) AIS of chest (sensitivity = 0.81, specificity = 0.71). The chosen thresholds were similar to conventional ones. Using conventional thresholds resulted in lower model sensitivity. Consistently, prehospital lactate was among most decisive factors of hospital transfusions identified by Bayesian analysis (OR = 2.31; 95% CI 1.55-3.37). Conclusions: Using an ensemble of frequentist statistics, Bayesian analysis and machine learning, we developed a simple, clinically relevant, prehospital algorithm to help identify patients requiring transfusion within 4 hours of hospital arrival.

Methodology for measuring photonuclear reaction cross sections with an electron accelerator based on Bayesian analysis.

Accurate measurements of photonuclear reaction cross sections are crucial for a number of applications, including radiation shielding design, absorbed dose calculations, reactor physics and engineering, nuclear safeguard and inspection, astrophysics, and nuclear medicine. Primarily motivated by the study of the production of selected radionuclides with high-energy photon beams (mainly 225Ac, 47Sc, and 67Cu), we have established a methodology for the measurement of photonuclear reaction cross sections with the microtron accelerator available at the Swiss Federal Institute of Metrology (METAS). The proposed methodology is based on the measurement of the produced activity with a High Purity Germanium (HPGe) spectrometer and on the knowledge of the photon fluence spectrum through Monte Carlo simulations. The data analysis is performed by applying a Bayesian fitting procedure to the experimental data and by assuming a functional trend of the cross section, in our case a Breit-Wigner function. We validated the entire methodology by measuring a well-established photonuclear cross section, namely the 197Au(γ, n)196Au reaction. The results are consistent with those reported in the literature.

Frailty, Seasonal Sensitivity and Health-related Quality of Life in Older People Living in High Southern Latitudes: a Bayesian Analysis.

Background: In older people, a notable research gap exists regarding the intricate dynamics between frailty, seasonal sensitivity, and health-related quality of life (HRQoL). This study aimed to determine the association between frailty, seasonal sensitivity, and HRQoL in older people from high southern latitudes. Methods: A cross-sectional observational study was conducted. Frailty, seasonal sensitivity, and HRQoL measurements were self-reported by participants through questionnaires. A total of 118 older people were recruited from a local community. The participants were selected through intentional non-probabilistic sampling. Results: The adjusted models showed a trend where lower education was associated with a higher risk of frailty (BF = 0.218). For frailty and HRQoL, we observed a trend suggesting that HRQoL decreases with increasing severity of frailty (BF = 1.76). In addition, we observed a linear effect based on the severity of seasonal sensitivity, meaning that older people with higher perceived severity report a proportional decrease in HRQoL (BF = 6.66). Conclusion: Sociodemographic factors, such as lower education levels, have increased the risk of frailty. At the same time, frailty and seasonal sensitivity perceived severity were associated with a lower HRQoL in older people.

Visible minority status and occupation were associated with increased COVID-19 infection in Greater Vancouver British Columbia between June and November 2020: an ecological study.

Background: The COVID-19 pandemic has highlighted health disparities, especially among specific population groups. This study examines the spatial relationship between the proportion of visible minorities (VM), occupation types and COVID-19 infection in the Greater Vancouver region of British Columbia, Canada. Methods: Provincial COVID-19 case data between June 24, 2020, and November 7, 2020, were aggregated by census dissemination area and linked with sociodemographic data from the Canadian 2016 census. Bayesian spatial Poisson regression models were used to examine the association between proportion of visible minorities, occupation types and COVID-19 infection. Models were adjusted for COVID-19 testing rates and other sociodemographic factors. Relative risk (RR) and 95% Credible Intervals (95% CrI) were calculated. Results: We found an inverse relationship between the proportion of the Chinese population and risk of COVID-19 infection (RR = 0.98 95% CrI = 0.96, 0.99), whereas an increased risk was observed for the proportions of the South Asian group (RR = 1.10, 95% CrI = 1.08, 1.12), and Other Visible Minority group (RR = 1.06, 95% CrI = 1.04, 1.08). Similarly, a higher proportion of frontline workers (RR = 1.05, 95% CrI = 1.04, 1.07) was associated with higher infection risk compared to non-frontline. Conclusion: Despite adjustments for testing, housing, occupation, and other social economic status variables, there is still a substantial association between the proportion of visible minorities, occupation types, and the risk of acquiring COVID-19 infection in British Columbia. This ecological analysis highlights the existing disparities in the burden of diseases among different visible minority populations and occupation types.

Comparative Analysis of the Mitochondrial Genomes of Chloropidae and Their Implications for the Phylogeny of the Family.

Chloropidae, commonly known as grass flies, represent the most taxonomically diverse family of Diptera Carnoidea, comprising over 3000 described species worldwide. Previous phylogenetic studies of this family have predominantly relied on morphological characters, with mitochondrial genomes being reported in a few species. This study presents 11 newly sequenced mitochondrial genomes (10 Chloropidae and 1 Milichiidae) and provides the first comprehensive comparative analysis of mitochondrial genomes for Chloropidae. Apart from 37 standard genes and the control region, three conserved intergenic sequences across Diptera Cyclorrhapha were identified in all available chloropid mitochondrial genomes. Evolutionary rates within Chloropidae exhibit significant variation across subfamilies, with Chloropinae displaying higher rates than the other three subfamilies. Phylogenetic relationships based on mitochondrial genomes were inferred using maximum likelihood and Bayesian methods. The monophyly of Chloropidae and all four subfamilies is consistently strongly supported, while subfamily relationships within Chloropidae remain poorly resolved, possibly due to rapid evolution.

Implementation of Adaptive-Bayesian DStoch technique for obtaining winds from MST radar covering higher altitudes.

It is challenging to estimate winds accurately from higher altitudes using VHF-MST radar. The current study introduces the Adaptive-Bayesian Deterministic Stochastics Technique (ADStoch), which implements an Empirical Bayesian 1D prediction method using stochastics to analyze radar signals. A new and robust estimator for empirical wavelet shrinkage with Gaussian prior of the nonzero mean for wavelet coefficients is presented, which makes the current prior different from other priors. The mean parameters and the prior covariance hyperparameters follow a pseudo maximum likelihood method for computation. Details on the implemented algorithm developed from scratch using C# are also presented. This technique outperforms contemporary techniques discussed in this context that can recover signals buried in noise established based on the analysis of moment and quality. The estimated Wind is cross-validated for accuracy with the observed wind from the GPS radiosonde operated simultaneously. This technique can consistently extract 3D wind that can reach the range of 25.5 km-28.2 km, improving the conventional maximum altitude of 21.2 km in real time for the MST radar. It is concluded that the ADStoch analysis technique can effectively obtain VHF-MST radar signals at significantly higher altitudes, which is helpful in various scientific investigations.

Determination of correlations in multivariate count data with informative observation times.

We consider there are various types of recurrent events and the total number of occurrences are collected at the random observation times. It has concerned that the observation process may not be independent to the multivariate event processes, hence the total counts and observation times may be correlated and the dependence may exist among different types of the event processes as well. Many methods have developed nonparametric models to accommodate such unknown structures; however, it is difficult to assess and directly quantify their correlation relationships. A multivariate frailty model is proposed to this study, in which the event and observation processes are linked by frailty variables whose joint distribution can be implicitly specified through the multivariate normal distribution with some unknown covariance matrix. The Bayesian inference method is conducted to obtain the estimates of the regression coefficients and correlation parameters. We use a form of trigonometric functions to represent the covariance matrix, so that it meets the positive-definiteness condition efficiently during the estimation schemes. The simulation studies demonstrate the utility of the proposed models. We apply the model to a skin cancer prevention study, and aim to determine the covariate and association effects. We found treatment is significant in determining the duration of examination times; prior-counts, age and gender are significant variables on the occurrence rates of tumor counts. Using the covariance matrix to access the underlying dependent structure, the mutual correlations among them are all positive, and the basal cell counts are more related to the examination times.