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The least absolute shrinkage and selection operator (Lasso) is a popular method for high-dimensional statistics. However, it is known that the Lasso often has estimation bias and prediction error. To address such disadvantages, many alternatives and refitting strategies have been proposed and studied. This work introduces a novel Lasso--Ridge method. Our analysis indicates that the proposed estimator achieves improved prediction performance in a range of settings, including cases where the Lasso is tuned at its theoretical optimal rate \\(\\sqrt{\\log(p)/n}\\). Moreover, the proposed method retains several key advantages of the Lasso, such as prediction consistency and reliable variable selection under mild conditions. Through extensive simulations, we further demonstrate that our estimator outperforms the Lasso in both prediction and estimation accuracy, highlighting its potential as a powerful tool for high-dimensional linear regression.

Background The collective of somatic mutations in a genome represents a record of mutational processes that have been operative in a cell. These processes can be investigated by extracting relevant mutational patterns from sequencing data. Results Here, we present the next version of MutationalPatterns, an R/Bioconductor package, which allows in-depth mutational analysis of catalogues of single and double base substitutions as well as small insertions and deletions. Major features of the package include the possibility to perform regional mutation spectra analyses and the possibility to detect strand asymmetry phenomena, such as lesion segregation. On top of this, the package also contains functions to determine how likely it is that a signature can cause damaging mutations (i.e., mutations that affect protein function). This updated package supports stricter signature refitting on known signatures in order to prevent overfitting. Using simulated mutation matrices containing varied signature contributions, we showed that reliable refitting can be achieved even when only 50 mutations are present per signature. Additionally, we incorporated bootstrapped signature refitting to assess the robustness of the signature analyses. Finally, we applied the package on genome mutation data of cell lines in which we deleted specific DNA repair processes and on large cancer datasets, to show how the package can be used to generate novel biological insights. Conclusions This novel version of MutationalPatterns allows for more comprehensive analyses and visualization of mutational patterns in order to study the underlying processes. Ultimately, in-depth mutational analyses may contribute to improved biological insights in mechanisms of mutation accumulation as well as aid cancer diagnostics. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns .

Herpes simplex virus type 2 (HSV-2) infection causes significant disease globally. Adolescent and adult infection may present as painful genital ulcers. Neonatal infection has high morbidity and mortality. Additionally, HSV-2 likely contributes substantially to the spread of HIV infection. The global burden of HSV-2 infection was last estimated for 2003. Here we present new global estimates for 2012 of the burden of prevalent (existing) and incident (new) HSV-2 infection among females and males aged 15-49 years, using updated methodology to adjust for test performance and estimate by World Health Organization (WHO) region. We conducted a literature review of HSV-2 prevalence studies world-wide since 2000. We then fitted a model with constant HSV-2 incidence by age to pooled HSV-2 prevalence values by age and sex. Prevalence values were adjusted for test sensitivity and specificity. The model estimated prevalence and incidence by sex for each WHO region to obtain global burden estimates. Uncertainty bounds were computed by refitting the model to reflect the variation in the underlying prevalence data. In 2012, we estimate that there were 417 million people aged 15-49 years (range: 274-678 million) living with HSV-2 infection world-wide (11.3% global prevalence), of whom 267 million were women. We also estimate that in 2012, 19.2 million (range: 13.0-28.6 million) individuals aged 15-49 years were newly-infected (0.5% of all individuals globally). The highest burden was in Africa. However, despite lower prevalence, South-East Asia and Western Pacific regions also contributed large numbers to the global totals because of large population sizes. The global burden of HSV-2 infection is large, leaving over 400 million people at increased risk of genital ulcer disease, HIV acquisition, and transmission of HSV-2 to partners or neonates. These estimates highlight the critical need for development of vaccines, microbicides, and other new HSV prevention strategies.

We consider the linear regression problem under semi-supervised settings wherein the available data typically consists of: (i) a small or moderate sized “labeled” data, and (ii) a much larger sized “unlabeled” data. Such data arises naturally from settings where the outcome, unlike the covariates, is expensive to obtain, a frequent scenario in modern studies involving large databases like electronic medical records (EMR). Supervised estimators like the ordinary least squares (OLS) estimator utilize only the labeled data. It is often of interest to investigate if and when the unlabeled data can be exploited to improve estimation of the regression parameter in the adopted linear model. In this paper, we propose a class of “Efficient and Adaptive Semi-Supervised Estimators” (EASE) to improve estimation efficiency. The EASE are two-step estimators adaptive to model mis-specification, leading to improved (optimal in some cases) efficiency under model mis-specification, and equal (optimal) efficiency under a linear model. This adaptive property, often unaddressed in the existing literature, is crucial for advocating “safe” use of the unlabeled data. The construction of EASE primarily involves a flexible “semi-nonparametric” imputation, including a smoothing step that works well even when the number of covariates is not small; and a follow up “refitting” step along with a cross-validation (CV) strategy both of which have useful practical as well as theoretical implications towards addressing two important issues: under-smoothing and over-fitting. We establish asymptotic results including consistency, asymptotic normality and the adaptive properties of EASE. We also provide influence function expansions and a “double” CV strategy for inference. The results are further validated through extensive simulations, followed by application to an EMR study on auto-immunity.

We propose new ensemble approaches to estimate the population mean for missing response data with fully observed auxiliary variables. We first compress the working models according to their categories through a weighted average, where the weights are proportional to the square of the least-squares coefficients of model refitting. Based on the compressed values, we develop two ensemble frameworks, under which one is to adjust weights in the inverse probability weighting procedure and the other is built upon an additive structure by reformulating the augmented inverse probability weighting function. The asymptotic normality property is established for the proposed estimators through the theory of estimating functions with plugged-in nuisance parameter estimates. Simulation studies show that the new proposals have substantial advantages over existing ones for small sample sizes, and an acquired immune deficiency syndrome data example is used for illustration.

This paper takes a platform supply vessel as the parent vessel and introduces its power system configuration and working conditions; to meet the requirements of carbon emissions reduction and improve economic efficiency, a preliminary program of marine hybrid power retrofitting of diesel generator sets assisted with Lithium battery module is proposed on the base of analyzing its working conditions, the fuel consumptions and carbon emissions are compared between before and after the retrofitting, etc. According to the calculation results, the paper concludes that the investment payback period of the refitting is about 6.38 years, and this refitting will have good economic benefits and social benefits.

This paper presents a rare example of the multi-proxy investigation results on the prehistoric settlement from vast areas of the Mongolian Gobi Desert, where, during favorable climatic conditions, postglacial hunter-gatherer groups occupied a seasonal lake district. The geoarchaeological research conducted at site FV92, located at the Luulityn Toirom Paleolake, provides insight into the problem of human relations with the changing environment of the Early Holocene, as well as the problem of the site formation process in the Gobi area. Sedimentological studies and luminescence dating of the Luulityn Toirom Lake sediments indicate the presence of the lake and favorable environmental conditions for human settlement in the Early Holocene in the period before 8130 ± 83 BP. Spatial analyses of the artifact distribution, as well as refitting studies of the discovered lithic assemblage, enabled the determination of the site’s formation process. Initially, the site was influenced by fluvial processes, but as the climate dried, it was subsequently affected by aeolian processes. The techno-typological analysis, refitting studies, and microscopic analyses carried out provide the first such detailed insight into the technological behavior and identification of the chaîne opératoire used by the Early Holocene hunter-gatherer communities of the Gobi area. The results confirmed that the lithic technology was mainly based on microblade technology. Microscopic analyses of traces created during tool use indicate butchery activity and the use of plant resources. The studies indicate a high degree of mobility of hunter-gatherer communities living by the lakes, as evidenced by the medium-range transport of raw material brought to the campsite from the surrounding mountainous Altai area.

A well-known drawback of ℓ₁-penalized estimators is the systematic shrinkage of the large coefficients towards zero. A simple remedy is to treat Lasso as a model-selection procedure and to perform a second refitting step on the selected support. In this work, we formalize the notion of refitting and provide oracle bounds for arbitrary refitting procedures of the Lasso solution. One of the most widely used refitting techniques which is based on Least-Squares may bring a problem of interpretability, since the signs of the refitted estimator might be flipped with respect to the original estimator. This problem arises from the fact that the Least-Squares refitting considers only the support of the Lasso solution, avoiding any information about signs or amplitudes. To this end, we define a sign consistent refitting as an arbitrary refitting procedure, preserving the signs of the first step Lasso solution and provide Oracle inequalities for such estimators. Finally, we consider special refitting strategies: Bregman Lasso and Boosted Lasso. Bregman Lasso has a fruitful property to converge to the Sign-Least-Squares refitting (Least-Squares with sign constraints), which provides with greater interpretability. We additionally study the Bregman Lasso refitting in the case of orthogonal design, providing with simple intuition behind the proposed method. Boosted Lasso, in contrast, considers information about magnitudes of the first Lasso step and allows to develop better oracle rates for prediction. Finally, we conduct an extensive numerical study to show advantages of one approach over others in different synthetic and semi-real scenarios.

The supporting force distribution is very important for the vehicle to maintain safety and stability, especially for the heavy-duty refitting vehicle with eccentric load. A simple and generic model to compute the supporting force distribution is essential in the design stage of refitting vehicle, whereas there is little research involving in solving this problem by now. This article presents an efficient and simple method to predict the supporting force of tire (supporting point). Firstly, the model for the vehicle with three or four axles is simplified. Then, the mathematical matrix is established by force equilibrium equation, moment equilibrium equation and deformation compatibility equation. With the known parameters of load, position and dimension, the force of every supporting point can be calculated, and so the supporting force distribution is acquired. Finally, the method of predicting force distribution is utilized to computing the supporting force of a vehicle, and a corresponding experiment is also implemented. The results indicate that the general prediction method to calculate supporting force distribution presented in this paper is accurate and validated in the pre-design stage of the refitting vehicle.

Forensic anthropologists are often confronted with human remains that have been damaged due to trauma, fire, or postmortem taphonomic alteration, frequently resulting in the fracture and fragmentation of skeletal elements. The augmented reality (AR) technology introduced in this paper builds on familiar 3D visualization methods and utilizes them to make three dimensional holographic meshes of skeletal fragments that can be manipulated, tagged, and examined by the user. Here, CT scans, neural radiance fields (NeRF) artificial intelligence software, and Unreal Engine production software are utilized to construct a three-dimensional holographic image that can be manipulated with HoloLens™ technology to analyze the fracture margin and reconstruct craniofacial elements without causing damage to fragile remains via excessive handling. This allows forensic anthropologists a means of assessing aspects of the biological profile and traumatic injuries without risking further damage to the skeleton. It can also be utilized by students and professional anthropologists to practice refitting before reconstructing craniofacial fragments if refitting is necessary. Additionally, the holographic images can be used to explain complicated concepts in a courtroom without the emotional response related to using bony elements as courtroom exhibits. •Craniofacial reconstruction aids with the biological profile and signs of trauma.•CT-based AR craniofacial reconstruction can reduce bone damage from overhandling.•Neural resonance fields (NeRF) can rapidly convert CT scans into AR visual content.•AR visualization minimizes damage from manual manipulation.•AR allows the viewer to assess the skeleton in three rather than two dimensions.