Fence methods for mixed model selection

Many model search strategies involve trading off model fit with model complexity in a penalized goodness of fit measure. Asymptotic properties for these types of procedures in settings like linear regression and ARMA time series have been studied, but these do not naturally extend to nonstandard situations such as mixed effects models, where simple definition of the sample size is not meaningful. This paper introduces a new class of strategies, known as fence methods, for mixed model selection, which includes linear and generalized linear mixed models. The idea involves a procedure to isolate a subgroup of what are known as correct models (of which the optimal model is a member). This is accomplished by constructing a statistical fence, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model is selected from among those within the fence according to a criterion which can be made flexible. In addition, we propose two variations of the fence. The first is a stepwise procedure to handle situations of many predictors; the second is an adaptive approach for choosing a tuning constant. We give sufficient conditions for consistency of fence and its variations, a desirable property for a good model selection procedure. The methods are illustrated through simulation studies and real data analysis.Comment: Published in at http://dx.doi.org/10.1214/07-AOS517 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Diagnostic value of multislice computed tomography angiography in coronary artery disease: A meta-analysis

Purpose: To perform a meta-analysis of the diagnostic value of multislice CT (MSCT) angiography in the detection of coronary artery disease (CAD) when compared to conventional coronary angiography.Materials and Methods: A search of PubMed and MEDLINE databases for English literature was performed. Only studies with at least 10 patients comparing MSCT angiography with conventional coronary angiography in the detection of CAD were included. Diagnostic value of MSCT angiography compared to coronary angiography was compared and analyzed at segment-, vessel- and patient-based assessment.Results: 47 studies (67 comparisons) met the criteria and were included in our study. Pooled overall sensitivity, specificity and 95% confidence interval for MSCT angiography in the detection of CAD were 83% (79%, 89%), 93% (91%, 96%) at segment-based analysis; 90% (87%, 94%), 87% (80%, 93%) at vessel-based analysis; and 91% (88%, 95%), 86% (81%, 92%) at patient-based analysis, respectively. Diagnostic accuracy of MSCT angiography in evaluating assessable segments was significantly improved with 64-slice scanners when compared to that with 4- and 16-slice scanners (p<0.05).Conclusion: Our meta-analysis showed that MSCT angiography has potential diagnostic accuracy in the detection of CAD. Diagnostic performance of MSCT angiography has been significantly improved with the latest 64-slice CT, with resultant high qualitative and quantitative diagnostic accuracy. 16-slice CT was limited in spatial resolution which makes it difficult to perform quantitative assessment of coronary artery stenoses

Sustainability Trends in Textile and Clothing Industry of Bangladesh Before and Post Pandemic Era

The textile and clothing industry of Bangladesh, a vital player in the global supply chain, has undergone a transformative phase accentuated by the challenges posed by the COVID-19 pandemic. As the industry adapts to the "new normal," sustainability has emerged as a central theme, reshaping the trajectory of business operations and strategies. This abstract explores key sustainability trends influencing the textile and clothing sector in Bangladesh amid the post-pandemic era. The analysis encompasses environmental, social, and economic dimensions, shedding light on the industry's commitment to responsible practices. The post-pandemic era has accentuated the significance of social responsibility and ethical labor practices within the textile and clothing industry. The disruptions caused by the pandemic have prompted a reevaluation of sustainable textile trends. It emphasizes the need for regulatory frameworks that incentivize sustainable practices, enforce environmental standards, and ensure fair labor conditions. The abstract highlights the collaboration between the government, industry stakeholders, international organizations to foster a conducive environment for sustainable growth. In this abstract a comprehensive overview of the sustainability trends shaping the textile and clothing industry in Bangladesh post-pandemic. It underscores the industry's commitment to responsible practices, outlines key areas of development, and proposes a roadmap for a more sustainable and resilient future.&nbsp

Performance Evaluation of Coordinated Intersections with GPS Devices

This paper presents methods for applying GPS devices to evaluate the performance of coordinated intersections in terms of traffic delays. It illustrates that GPS-recorded data provides detailed information on traffic delays of individual intersections as well as traffic delays of coordinated intersections as a whole system, including travel-time delay, stopped-time delay, time-in-queue delay, approach delay, and total delay. Most of these intersection delays are difficult to measure manually; however, with GPS-collected vehicle-positioning data, they can be either directly identified or indirectly derived. Methods for obtaining different types of intersection delays with GPS devices are introduced

Prediction of protein-protein binding site by using core interface residue and support vector machine

<p>Abstract</p> <p>Background</p> <p>The prediction of protein-protein binding site can provide structural annotation to the protein interaction data from proteomics studies. This is very important for the biological application of the protein interaction data that is increasing rapidly. Moreover, methods for predicting protein interaction sites can also provide crucial information for improving the speed and accuracy of protein docking methods.</p> <p>Results</p> <p>In this work, we describe a binding site prediction method by designing a new residue neighbour profile and by selecting only the core-interface residues for SVM training. The residue neighbour profile includes both the sequential and the spatial neighbour residues of an interface residue, which is a more complete description of the physical and chemical characteristics surrounding the interface residue. The concept of core interface is applied in selecting the interface residues for training the SVM models, which is shown to result in better discrimination between the core interface and other residues.</p> <p>The best SVM model trained was tested on a test set of 50 randomly selected proteins. The sensitivity, specificity, and MCC for the prediction of the core interface residues were 60.6%, 53.4%, and 0.243, respectively. Our prediction results on this test set were compared with other three binding site prediction methods and found to perform better. Furthermore, our method was tested on the 101 unbound proteins from the protein-protein interaction benchmark v2.0. The sensitivity, specificity, and MCC of this test were 57.5%, 32.5%, and 0.168, respectively.</p> <p>Conclusion</p> <p>By improving both the descriptions of the interface residues and their surrounding environment and the training strategy, better SVM models were obtained and shown to outperform previous methods. Our tests on the unbound protein structures suggest further improvement is possible.</p

Segregation and expression of transgenes in the progenies of Bt transgenic rice crossed to conventional rice varieties

β-Glucuronidase (GUS) activity bioassay, western blotting and polymerase chain reaction (PCR) analysis demonstrated that the cry1Ab gene was closely inherited and expressed with reporter gene gus in the progenies of Bacillus thuringiensis (Bt) transgenic rice (Oryza sativa L.) crossed to conventional rice varieties. Therefore, it is feasible using GUS-assisted-selection to preliminarily identify the Bt gene and study the inheritance of transgenes in breeding program. Mendelian segregation was observed in BC1F1, BC1F2 and F2 populations derived from Bt rice crossed to japonica rice respectively which indicated that the cry1Ab gene was inherited as a single dominant locus. PCR, Southern blotting and Western dot blotting analysis confirmed that cry1Ab gene was transferred to the genome of conventional rice varieties and it was highly expressed in the different progenies of Bt rice crossed to conventional rice varieties. Among these lines, the highest Bt toxin protein content reached 2.88% of total soluble proteins, even though the amount of Bt toxin protein in leaves of some GUS positive plants was higher than that detected in the original Bt rice. Meanwhile, the variances in Bt toxin protein between crosses and its parents were significant at 0.05 or 0.01 levels, respectively. Therefore, foreign cry1Ab gene with native insect resistant trait can be easily transferred to other rice varieties with higher yield potential and good quality through classical breeding.Keywords: Oryza sativa L., transgenes, inheritance, expressio

Multislice CT virtual endoscopy in pre-aortic stent grafting: optimization of scanning protocals

The purpose of this study was to investigate the optimal scanning protocols of multislice CT (MSCT) angiography in pre-aortic stent grafting, visualized on virtual endoscopy (VE). A series of scans were performed on a human aorta phantom with a 16-slice multislice CT scanner with the scanning protocols as follows: section thickness of 1.0/1.5/2.0/3.0 mm, pitch value of 1.0/1.25/1.5, and reconstruction interval of 50% overlap. Signal to noise ratio and standard deviation (SD) of the signal intensity on VE images were measured to determine the image quality in relation to MSCT scanning protocols. Subjective assessment was performed by two observers evaluating the degree of artefacts and the configuration of the renal ostium visualized on VE images. Our results showed that the scanning protocol with a section thickness of 2.0 mm resulted in the highest SNR and lowest SD compared to other protocols (p<0.05). Subjective assessment demonstrated that VE image quality was determined by section thickness, but independent of pitch values. We recommended the scanning protocol of section thickness 2.0 mm, pitch 1.5 with a reconstruction interval of 1.0 mm as the optimal one since it allows optimal visualization of VE images of aortic ostia, fewer artefacts and less radiation dose

LDPC-Coded CAP with Spatial Diversity for UVLC Systems over Generalized-Gamma Fading Channel

In this paper, low-density parity-check (LDPC)-coded carrierless amplitude and phase (CAP) modulation with spatial diversity is proposed to mitigate turbulence-induced fading in an underwater visible-light communication (UVLC) channel. Generalized-gamma (GG) distribution was used to model the fading, as this model is valid for weak- and strong-turbulence regimes. On the basis of the characteristic function (CHF) of GG random variables, we derived an approximated bit-error rate (BER) for the CAP modulation scheme with spatial diversity and equal-gain combining (EGC). Furthermore, we simulated the performance of the CAP system with diversity and LDPC for various turbulence conditions and validated the analysis. Obtained results showed that the combination of LDPC and spatial diversity is effective in mitigating turbulence-induced fading, especially when turbulence strength is strong

FocusFlow: Boosting Key-Points Optical Flow Estimation for Autonomous Driving

Key-point-based scene understanding is fundamental for autonomous driving applications. At the same time, optical flow plays an important role in many vision tasks. However, due to the implicit bias of equal attention on all points, classic data-driven optical flow estimation methods yield less satisfactory performance on key points, limiting their implementations in key-point-critical safety-relevant scenarios. To address these issues, we introduce a points-based modeling method that requires the model to learn key-point-related priors explicitly. Based on the modeling method, we present FocusFlow, a framework consisting of 1) a mix loss function combined with a classic photometric loss function and our proposed Conditional Point Control Loss (CPCL) function for diverse point-wise supervision; 2) a conditioned controlling model which substitutes the conventional feature encoder by our proposed Condition Control Encoder (CCE). CCE incorporates a Frame Feature Encoder (FFE) that extracts features from frames, a Condition Feature Encoder (CFE) that learns to control the feature extraction behavior of FFE from input masks containing information of key points, and fusion modules that transfer the controlling information between FFE and CFE. Our FocusFlow framework shows outstanding performance with up to +44.5% precision improvement on various key points such as ORB, SIFT, and even learning-based SiLK, along with exceptional scalability for most existing data-driven optical flow methods like PWC-Net, RAFT, and FlowFormer. Notably, FocusFlow yields competitive or superior performances rivaling the original models on the whole frame. The source code will be available at https://github.com/ZhonghuaYi/FocusFlow_official.Comment: The source code of FocusFlow will be available at https://github.com/ZhonghuaYi/FocusFlow_officia