Although free-floating thrombus (FFT) poses a significant risk of stroke or transient ischemic attack (TIA), optimal management strategies are uncertain. To determine the state-of-the-art of medical interventions for FFT, we conducted a systematic review of the efficacy of various medical interventions and factors influencing FFT resolution and recurrence. A comprehensive search of Embase, PubMed, and ScienceDirect identified 61 studies encompassing 179 patients with FFT-related stroke or TIA treated with anticoagulants, antiplatelets, or their combinations. Primary outcomes assessed were stroke recurrence and thrombus resolution. Statistical analyses (Fisher\'s exact test, chi-square test, Mann-Whitney test, and Kruskal-Wallis test) utilized significance set at p < 0.05. Over a median follow-up of 7 months, thrombus resolution occurred in 65% of patients, while 11.2% experienced recurrence, primarily as TIAs. Cardioembolism was significantly less common in resolved cases (p = 0.025). Combination therapy (antiplatelets, anticoagulants, and statins) significantly enhanced clot resolution (OR 11.4; 95% CI 1.436-91.91; p = 0.021) compared to monotherapies. Ulcerated plaque was a significant predictor of recurrence (OR 8.2; 95% CI 1.02-66.07; p = 0.048). These findings underscore the superiority of combination therapy in FFT management and highlight the need for targeted interventions in patients with ulcerated plaques to mitigate recurrence risk.

In this paper we develop an online statistical inference approach for high-dimensional generalized linear models with streaming data for realtime estimation and inference. We propose an online debiased lasso method that aligns with the data collection scheme of streaming data. Online debiased lasso differs from offline debiased lasso in two important aspects. First, it updates component-wise confidence intervals of regression coefficients with only summary statistics of the historical data. Second, online debiased lasso adds an additional term to correct approximation errors accumulated throughout the online updating procedure. We show that our proposed online debiased estimators in generalized linear models are asymptotically normal. This result provides a theoretical basis for carrying out real-time interim statistical inference with streaming data. Extensive numerical experiments are conducted to evaluate the performance of our proposed online debiased lasso method. These experiments demonstrate the effectiveness of our algorithm and support the theoretical results. Furthermore, we illustrate the application of our method with a high-dimensional text dataset.

The problem of extreme phenomena with a more precise estimation of their return periods for early warnings, notably to preserve the safety of populations and properties, arises all over the world. This work develops another aspect in the estimation of Return Levels (RLs) and Return Periods (RPs) of extreme precipitation in particular and natural risk in general. In particular, it gives the Return Dates (RDs) with their Confidence Intervals (CIs). The RPs, the RLs and their CIs for extreme rainfall were also investigated. These estimates were made by approaching the peak over a threshold chosen by the Generalized Pareto Distribution (GPD). The CIs of RPs and RLs were determined by the Delta method. The daily rainfall data used were obtained from the data of the synoptic report for the period 2011 to 2021 for the Douala weather station (more details can be found on http://www.ogimet.com/guia.phtml.en). To validate the methods used, real cases of floods occurred in Douala city were considered: for example, a local press compiled flood dates and mentioned that a flood occurred on the April 16, 2013 in this city. Following the data of synoptic report, the corresponding amount of rainfall was around 150 mm. The results obtained have shown a RD on the August 12, 2014. The confidence intervals of return levels and return dates determined by the Delta method were [131.66, 168.456] and [June 23, 2014, January 02, 2015], respectively. These results are in agreement with the data of synoptic report since the rainfall amounts was 132.2 mm (belonging to the confidence interval of return levels), on the August 11, 2014 (belonging to the confidence interval of return dates). These predictions of RDs and RLs with their CIs, at reasonable time scales, can help for efficient management of floods and thus, improve early warnings for safety of populations and goods.

OBJECTIVE: To provide comprehensive epidemiological information about the distribution and occurrence of rabies during 2022 in the US, Canada, and Mexico.
METHODS: The US National Rabies Surveillance System collected 2022 animal rabies data from US state and territorial public health departments and USDA Wildlife Services. Temporal and geographic analyses were conducted to evaluate trends in animal rabies cases.
RESULTS: During 2022, 54 US jurisdictions reported 3,579 animal rabies cases, reflecting a 2.3% decline from 3,663 cases reported in 2021. Six states collectively reported > 50% of animal rabies cases: Texas (395 [11.0%]), Virginia (337 [9.4%]), Pennsylvania (329 [9.2%]), New York (267 [7.5%]), North Carolina (264 [7.4%]), and California (241 [6.7%]). Out of the total reported rabies animal cases, 3,234 (90.4%) were attributed to wildlife, with bats (1,218 [34.0%]), raccoons (1,014 [28.3%]), skunks (660 [18.4%]), and foxes (269 [7.5%]) representing the primary hosts confirmed with rabies. Rabid cats (222 [6.2%]), cattle (42 [1.2%]), and dogs (50 [1.4%]) constituted > 90% of reported domestic animal rabies cases.
CONCLUSIONS: In 2022, there was an increase in the number of animal samples submitted for rabies testing in the US and Canada. A notable geographic expansion of gray fox rabies virus variant was detected in the US. Three human rabies deaths due to vampire bat rabies infection occurred in Mexico; none were reported from the US and Canada.
CONCLUSIONS: Laboratory diagnosis of rabies in animals is critical to ensure judicious use of human rabies postexposure prophylaxis.

Powdery mildew (PM), caused by Blumeria graminis f. sp. tritici, poses a significant threat to wheat production, necessitating the development of genetically resistant varieties for long-term control. Therefore, exploring genetic architecture of PM in wheat to uncover important genomic regions is an important area of wheat research. In recent years, the utilization of meta-QTL (MQTL) analysis has gained prominence as an essential tool for unraveling the complex genetic architecture underlying complex quantitative traits. The aim of this research was to conduct a QTL meta-analysis to pinpoint the specific genomic regions in wheat responsible for governing PM resistance. This study integrated 222 QTLs from 33 linkage-based studies using a consensus map with 54,672 markers. The analysis revealed 39 MQTLs, refined to 9 high-confidence MQTLs (hcMQTLs) with confidence intervals of 0.49 to 12.94 cM. The MQTLs had an average physical interval of 41.00 Mb, ranging from 0.000048 Mb to 380.71 Mb per MQTL. Importantly, 18 MQTLs co-localized with known resistance genes like Pm2, Pm3, Pm8, Pm21, Pm38, and Pm41. The study identified 256 gene models within hcMQTLs, providing potential targets for marker-assisted breeding and genomic prediction programs to enhance PM resistance. These MQTLs would serve as a foundation for fine mapping, gene isolation, and functional genomics studies, facilitating a deeper understanding of molecular mechanisms. The identification of candidate genes opens up exciting possibilities for the development of PM-resistant wheat varieties after validation.

Introduction The mandible constitutes one of the skull\'s largest and strongest bones. Growth spurts can influence it, and it has a variety of dimorphic traits that can be used to identify sex. In addition to observing, comparing, and evaluating the potential for mandibular ramus flexure and bigonial breadth to discriminate between sexes using digital orthopantomograms (OPG), a retrospective study was conducted to examine the validity of this method for sex estimation in the Indian population. Aim and objective This study aims to quantify sexual dimorphism by analyzing two mandibular parameters, the ramus flexure and the bigonial width, using orthopantomography (OPG). The objective is to determine the accuracy of sex determination using the ramus flexure and bigonial width. Materials and methods A total of 500 OPG images (250 males and 250 females) were analyzed using the Planmeca software (Helsinki, Finland). The ramus flexure was measured as the angle formed between the tangent to the inferior border of the mandible and the tangent to the posterior border of the ramus. The bigonial width was measured as the distance between the left and right gonion points. A statistical analysis was performed to assess sexual dimorphism and determine the accuracy of sex determination using these parameters. The study employed descriptive statistics, such as means and standard deviations, and an independent t-test to determine the significance of the characteristics in relation to males and females. Results The mean bigonial width for females was 193.3068 mm (SD = 13.51669 mm) and for males was 217.6308 mm (SD = 10.87453 mm), with a statistically significant difference (p = 0.000). The 95% confidence interval for the difference in the bigonial width between males and females was between -49.97173 mm and -43.93787 mm. For the ramus flexure, the mean was 0.0000 for both males and females (SD = 0.00000), with a significant difference between males and females (p = 0.003). The 95% confidence interval for the difference in the ramus flexure between males and females was between -0.59543 and -0.12457. Conclusion The results indicated significant sexual dimorphism in both the ramus flexure and bigonial width. This study demonstrated that the ramus flexure and bigonial width, measured using orthopantomography (OPG), exhibited significant sexual dimorphism. The analysis of these mandibular parameters provided valuable information for sex determination in forensic and anthropological contexts.

We introduce and study two new inferential challenges associated with the sequential detection of change in a high-dimensional mean vector. First, we seek a confidence interval for the changepoint, and second, we estimate the set of indices of coordinates in which the mean changes. We propose an online algorithm that produces an interval with guaranteed nominal coverage, and whose length is, with high probability, of the same order as the average detection delay, up to a logarithmic factor. The corresponding support estimate enjoys control of both false negatives and false positives. Simulations confirm the effectiveness of our methodology, and we also illustrate its applicability on the U.S. excess deaths data from 2017 to 2020. The supplementary material, which contains the proofs of our theoretical results, is available online.

In practice, we often encounter binary classification problems where both main classes consist of multiple subclasses. For example, in an ovarian cancer study where biomarkers were evaluated for their accuracy of distinguishing noncancer cases from cancer cases, the noncancer class consists of healthy subjects and benign cases, while the cancer class consists of subjects at both early and late stages. This article aims to provide a large number of optimal cut-point selection methods for such setting. Furthermore, we also study confidence interval estimation of the optimal cut-points. Simulation studies are carried out to explore the performance of the proposed cut-point selection methods as well as confidence interval estimation methods. A real ovarian cancer data set is analyzed using the proposed methods.

In any meta-analysis it is important to report not only the mean effect size but also how the effect size varies across studies. A treatment that has a moderate clinical impact in all studies is very different than a treatment where the impact is moderate on average, but in some studies is large and in others is trivial (or even harmful). A treatment that has no impact in any studies is very different than a treatment that has no impact on average because it is helpful in some studies but harmful in others. The majority of meta-analyses use the I-squared index to quantify heterogeneity. While this practice is common it is nevertheless incorrect. I-squared does not tell us how much the effect size varies (except when I-squared is zero percent). The statistic that does convey this information is the prediction interval. It allows us to report, for example, that a treatment has a clinically trivial or moderate effect in roughly 10 % of studies, a large effect in roughly 50 %, and a very large effect in roughly 40 %. This is the information that researchers or clinicians have in mind when they ask about heterogeneity. It is the information that researchers believe (incorrectly) is provided by I-squared.

UNASSIGNED: Positive and negative likelihood ratios (PLR and NLR) are important metrics of accuracy for diagnostic devices with a binary output. However, the properties of Bayesian and frequentist interval estimators of PLR/NLR have not been extensively studied and compared. In this study, we explore the potential use of the Bayesian method for interval estimation of PLR/NLR, and, more broadly, for interval estimation of the ratio of two independent proportions.
UNASSIGNED: We develop a Bayesian-based approach for interval estimation of PLR/NLR for use as a part of a diagnostic device performance evaluation. Our approach is applicable to a broader setting for interval estimation of any ratio of two independent proportions. We compare score and Bayesian interval estimators for the ratio of two proportions in terms of the coverage probability (CP) and expected interval width (EW) via extensive experiments and applications to two case studies. A supplementary experiment was also conducted to assess the performance of the proposed exact Bayesian method under different priors.
UNASSIGNED: Our experimental results show that the overall mean CP for Bayesian interval estimation is consistent with that for the score method (0.950 vs. 0.952), and the overall mean EW for Bayesian is shorter than that for score method (15.929 vs. 19.724). Application to two case studies showed that the intervals estimated using the Bayesian and frequentist approaches are very similar.
UNASSIGNED: Our numerical results indicate that the proposed Bayesian approach has a comparable CP performance with the score method while yielding higher precision (i.e. a shorter EW).