Current and future directions in network biology.

Summary: Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation: Not applicable.

An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits.

Modern nanotechnology has generated numerous datasets from in vitro and in vivo studies on nanomaterials, with some available on nanoinformatics portals. However, these existing databases lack the digital data and tools suitable for machine learning studies. Here, we report a nanoinformatics platform that accurately annotates nanostructures into machine-readable data files and provides modeling toolkits. This platform, accessible to the public at https://vinas-toolbox.com/, has annotated nanostructures of 14 material types. The associated nanodescriptor data and assay test results are appropriate for modeling purposes. The modeling toolkits enable data standardization, data visualization, and machine learning model development to predict properties and bioactivities of new nanomaterials. Moreover, a library of virtual nanostructures with their predicted properties and bioactivities is available, directing the synthesis of new nanomaterials. This platform provides a data-driven computational modeling platform for the nanoscience community, significantly aiding in the development of safe and effective nanomaterials.

Machine learning model predicts airway stenosis requiring clinical intervention in patients after lung transplantation: a retrospective case-controlled study.

BACKGROUND: Patients with airway stenosis (AS) are associated with considerable morbidity and mortality after lung transplantation (LTx). This study aims to develop and validate machine learning (ML) models to predict AS requiring clinical intervention in patients after LTx. METHODS: Patients who underwent LTx between January 2017 and December 2019 were reviewed. The conventional logistic regression (LR) model was fitted by the independent risk factors which were determined by multivariate LR. The optimal ML model was determined based on 7 feature selection methods and 8 ML algorithms. Model performance was assessed by the area under the curve (AUC) and brier score, which were internally validated by the bootstrap method. RESULTS: A total of 381 LTx patients were included, and 40 (10.5%) patients developed AS. Multivariate analysis indicated that male, pulmonary arterial hypertension, and postoperative 6-min walking test were significantly associated with AS (all P < 0.001). The conventional LR model showed performance with an AUC of 0.689 and brier score of 0.091. In total, 56 ML models were developed and the optimal ML model was the model fitted using a random forest algorithm with a determination coefficient feature selection method. The optimal model exhibited the highest AUC and brier score values of 0.760 (95% confidence interval [CI], 0.666-0.864) and 0.085 (95% CI, 0.058-0.117) among all ML models, which was superior to the conventional LR model. CONCLUSIONS: The optimal ML model, which was developed by clinical characteristics, allows for the satisfactory prediction of AS in patients after LTx.

Prognostic impact of postoperative adjuvant chemotherapy on elderly patients with colorectal adenocarcinoma: A propensity score matching study.

BACKGROUND AND AIM: Currently, the primary treatment modality for patients with colorectal adenocarcinoma (CRA) is radical surgery combined with postoperative adjuvant chemotherapy (PAC). However, many elderly patients decline PAC due to concerns about their underlying physiological condition, and the impact of PAC on the prognosis of elderly patients remains uncertain. METHODS: We extracted data from the SEER database for CRA patients aged 75 and above between 2010 and 2019. Utilizing propensity score matching (PSM), we stratified the patients into a PAC group and a non-PAC group, enabling us to compare the differences in Kaplan-Meier survival curves between these two groups. Furthermore, through univariate and multivariate Cox regression analyses, we identified the clinical factors that influence the survival of elderly CRA patients and compared the prognostic disparities between the two patient groups within specific subgroups of these clinical factors. RESULTS: Following PSM, a total of 3668 patients were included and divided into the PAC group and the non-PAC group, with no statistically significant differences observed in crucial clinical characteristics between the two groups. Kaplan-Meier analysis revealed a significantly better prognosis for patients in the PAC group compared with those in the non-PAC group. In addition, age, chemotherapy, TNM staging, gender, and preoperative CEA levels were all identified as important factors affecting patient prognosis. Moreover, PAC provided survival benefits across the majority of levels within the aforementioned subgroups. However, in specific subgroups (age > 90, Grade IV stage, median household income < $40 000), PAC did not confer any survival benefits. CONCLUSION: PAC can significantly improve the prognosis of elderly CRA patients. Nonetheless, in certain population subsets characterized by specific clinical features, PAC does not provide any survival benefits.

Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity.

Zeroth-order (a.k.a, derivative-free) methods are a class of effective optimization methods for solving complex machine learning problems, where gradients of the objective functions are not available or computationally prohibitive. Recently, although many zeroth-order methods have been developed, these approaches still have two main drawbacks: 1) high function query complexity; 2) not being well suitable for solving the problems with complex penalties and constraints. To address these challenging drawbacks, in this paper, we propose a class of faster zeroth-order stochastic alternating direction method of multipliers (ADMM) methods (ZO-SPIDER-ADMM) to solve the nonconvex finite-sum problems with multiple nonsmooth penalties. Moreover, we prove that the ZO-SPIDER-ADMM methods can achieve a lower function query complexity of [Formula: see text] for finding an ϵ-stationary point, which improves the existing best nonconvex zeroth-order ADMM methods by a factor of [Formula: see text], where n and d denote the sample size and data dimension, respectively. At the same time, we propose a class of faster zeroth-order online ADMM methods (ZOO-ADMM+) to solve the nonconvex online problems with multiple nonsmooth penalties. We also prove that the proposed ZOO-ADMM+ methods achieve a lower function query complexity of [Formula: see text], which improves the existing best result by a factor of [Formula: see text]. Extensive experimental results on the structure adversarial attack on black-box deep neural networks demonstrate the efficiency of our new algorithms.

Transcriptomic profiling of a late recurrent nuclear protein in testis carcinoma of the lung 14 years after the initial operation: a case report.

Background: Nuclear protein in testis (NUT) carcinoma (NC) of the lung is a rare cancer that occurs mainly in young adolescents and adults. NC is genetically characterized by NUTM1 rearrangements, which usually take the form of BRD4-NUT fusions. The prognosis for NC is dismal, and treatment with conventional chemotherapeutic regimens is ineffective. Case Description: We herein describe the case of a 53-year-old woman with recurrent NC of the lung 14 years after surgery for nasal cavity cancer. Chest computed tomography revealed a 5.5-cm tumor in the lower lobe of the left lung. We completely resected the recurrent lung NC via thoracotomy. Immunohistochemistry (IHC) of the lung and nasal cavity cancers showed diffuse strong expression of NUT. RNA-seq of the lung NC revealed NUTM1 rearrangement, with a fusion of BRD4 exon 10 to NUTM1 exon 4. This breakpoint has never been reported before. In addition, IHC revealed elevated expression of parathyroid hormone-like hormone in the lung NC but not in the nasal cavity NC, indicating that the lung and nasal cavity NCs were metachronous multiple primary cancers. Conclusions: We experienced a rare recurrence of lung NC 14 years after the initial surgery. The BRD4-NUT fusion consisted of a new breakpoint. Furthermore, the expression pattern of parathyroid hormone-like hormone (PTHLH) suggested that the NCs in the nasal cavity and lung may be metachronous multiple lung cancers. This extremely rare case highlighted the possibility of identifying less malignant NCs in patients with poorly differentiated tumors via fusion gene analysis and the need to develop more effective treatment strategies for this malignancy.

Brain Cell-based Genetic Subtyping and Drug Repositioning for Alzheimer Disease.

Alzheimer's Disease (AD) is characterized by its complex and heterogeneous etiology and gradual progression, leading to high drug failure rates in late-stage clinical trials. In order to better stratify individuals at risk for AD and discern potential therapeutic targets we employed a novel procedure utilizing cell-based co-regulated gene networks and polygenic risk scores (cbPRSs). After defining genetic subtypes using extremes of cbPRS distributions, we evaluated correlations of the genetic subtypes with previously defined AD subtypes defined on the basis of domain-specific cognitive functioning and neuroimaging biomarkers. Employing a PageRank algorithm, we identified priority gene targets for the genetic subtypes. Pathway analysis of priority genes demonstrated associations with neurodegeneration and suggested candidate drugs currently utilized in diabetes, hypertension, and epilepsy for repositioning in AD. Experimental validation utilizing human induced pluripotent stem cell (hiPSC)-derived astrocytes demonstrated the modifying effects of estradiol, levetiracetam, and pioglitazone on expression of APOE and complement C4 genes, suggesting potential repositioning for AD.

A unified model-based framework for doublet or multiplet detection in single-cell multiomics data.

Droplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erroneous cell type annotations. Here, we propose a compound Poisson model-based framework for multiplet detection in single-cell multiomics data. Leveraging experimental cell hashing results as the ground truth for multiplet status, we conducted trimodal DOGMA-seq experiments and generated 17 benchmarking datasets from two tissues, involving a total of 280,123 droplets. We demonstrated that the proposed method is an essential tool for integrating cross-modality multiplet signals, effectively eliminating multiplet clusters in single-cell multiomics data-a task at which the benchmarked single-omics methods proved inadequate.

Efficacy of budesonide/formoterol inhalation powder in treating viral pneumonia in children.

BACKGROUND: Respiratory viruses are increasingly detected in children with community-acquired pneumonia. Further strategies to limit antibiotic use in children with viral pneumonia are warranted. AIM: To explore clinical efficacy of budesonide/formoterol inhalation powder for viral pneumonia in children and its impact on cellular immunity and inflammatory factor production. METHODS: A total of 60 children with viral pneumonia were recruited: 30 receiving budesonide/formoterol inhalation powder and 30 conventional symptomatic treatment. Outcome measures included peripheral blood levels of inflammatory cytokines, CD4+, CD8+, Th1, Th2, Th17 and Treg, clinical efficacy, and incidence of adverse reactions. RESULTS: Compared with the control group, the observation group showed a significant reduction in interleukin-6 and high-sensitivity C-reactive protein levels after treatment. Compared with the control group, the observation group showed a significant increase in CD4+/CD8+ and Th1/Th2 levels, and a decrease in Th17/Treg levels after treatment. The total effective rates in the observation group and the control group were 93.75% and 85.00%, respectively, which was a significant difference (P = 0.003). CONCLUSION: Budesonide/formoterol inhalation powder significantly improved therapeutic efficacy for viral pneumonia in children. The mechanism of action may be related to downregulation of the inflammatory response and improved cellular immune function.

Astrocyte-TREM2 alleviates brain injury by regulating reactive astrocyte states following ischemic stroke.

Triggering receptor expressed on myeloid cells 2 (TREM2) has been shown to confer strong neuroprotective effects in acute ischemic stroke (AIS). However, as the vast majority of research findings to date are based on its functions in microglia, the precise role of TREM2 in astrocytes after AIS is unknown. Here, both loss- and gain-of-function experiments were employed to investigate how astrocytic TREM2 influences the pathogenesis of AIS in vivo and in vitro. Our results demonstrated that cerebral ischemia triggered induction of TREM2 expression on reactive astrocytes following AIS. In addition, astrocyte-specific TREM2 knockout mice exhibited much greater brain injury than TREM2 flox/flox controls following AIS, as evidenced by increased cerebral infarct volume, neuronal apoptosis and neurological deficit, which was associated with an increased expression of pro-inflammatory molecule complement component 3 (C3) on reactive astrocytes and activation of microglia/macrophages but decreased expression of S100 calcium binding protein A10 (S100A10) and arginase1 (Arg1) on reactive astrocytes. Mechanistic analyses revealed that astrocytic TREM2 alleviated brain injury by inhibiting detrimental actions of reactive astrocytes but promoting their neuro- and glioprotective actions via the kruppel-like transcription factor-4-nuclear factor-κB axis. Together, this study provides novel evidence for a critical protective role of astrocyte-derived TREM2 in AIS and highlights a potential therapeutic target for the treatment of AIS.

Interpretable deep clustering survival machines for Alzheimer's disease subtype discovery.

Alzheimer's disease (AD) is a complex neurodegenerative disorder that has impacted millions of people worldwide. The neuroanatomical heterogeneity of AD has made it challenging to fully understand the disease mechanism. Identifying AD subtypes during the prodromal stage and determining their genetic basis would be immensely valuable for drug discovery and subsequent clinical treatment. Previous studies that clustered subgroups typically used unsupervised learning techniques, neglecting the survival information and potentially limiting the insights gained. To address this problem, we propose an interpretable survival analysis method called Deep Clustering Survival Machines (DCSM), which combines both discriminative and generative mechanisms. Similar to mixture models, we assume that the timing information of survival data can be generatively described by a mixture of parametric distributions, referred to as expert distributions. We learn the weights of these expert distributions for individual instances in a discriminative manner by leveraging their features. This allows us to characterize the survival information of each instance through a weighted combination of the learned expert distributions. We demonstrate the superiority of the DCSM method by applying this approach to cluster patients with mild cognitive impairment (MCI) into subgroups with different risks of converting to AD. Conventional clustering measurements for survival analysis along with genetic association studies successfully validate the effectiveness of the proposed method and characterize our clustering findings.

Clinical evaluation of tooth angle and peripheral bone thickness considering sex and age for implant placement in the maxillary anterior region.

STATEMENT OF PROBLEM: Consideration of the thickness of alveolar bone on both the palatal and labial sides and the inclination angle of teeth is important for immediate implant placement in the maxillary anterior region. However, comprehensive research exploring how sex and age influence the thickness of bone and tooth angle in the maxillary anterior region is lacking. PURPOSE: The purpose of this clinical study was to investigate the effect of sex and age on the thickness of labial and palatal bone and on the tooth inclination angle of maxillary central and lateral incisors using cone beam computed tomography (CBCT) images in an Asian population. MATERIAL AND METHODS: The labial and palatal bone thickness and the inclination angle of maxillary central and lateral incisors were measured from the CBCT images of 149 participants aged between 20 and 65 years. The correlation between the angles of the maxillary central and lateral incisors and sex and age on the tooth angles and thicknesses of labial and palatal bone was determined statistically. RESULTS: A high correlation (R=0.73) was found between the angle of maxillary central and lateral incisors. Men had higher angles of the maxillary central and lateral incisors and thicker palatal bone at the middle and root of the teeth compared with women. In both sexes, the angle and palatal bone thickness of maxillary central incisors were significantly higher than those of maxillary lateral incisors. The angle of both maxillary central and lateral incisors decreased gradually with age, with a difference in angle of over 10 degrees. CONCLUSIONS: The study revealed that women exhibited relatively thinner palatal bone compared with men. Additionally, participants aged between 40 and 65 years exhibited significantly thicker palatal bone compared with younger age groups, potentially providing a larger site for implant placement. Furthermore, tooth angle gradually decreases with age in the maxillary anterior region.

The features of male breast cancer in China: A real-world study.

BACKGROUND: Male breast cancer (MBC) is a rare disease. Although several large-scale studies have investigated MBC patients in other countries, the features of MBC patients in China have not been fully explored. This study aims to explore the features of Chinese MBC patients comprehensively. METHODS: We retrospectively collected data of MBC patients from 36 centers in China. Overall survival (OS) was evaluated by the Kaplan-Meier method, log-rank test, and Cox regression analyses. Multivariate Cox analyses were used to identify independent prognostic factors of the patients. RESULTS: In total, 1119 patients were included. The mean age at diagnosis was 60.9 years, and a significant extension over time was observed (P < 0.001). The majority of the patients (89.1 %) received mastectomy. Sentinel lymph node biopsy was performed in 7.8 % of the patients diagnosed in 2009 or earlier, and this percentage increased significantly to 38.8 % in 2020 or later (P < 0.001). The five-year OS rate for the population was 85.5 % [95 % confidence interval (CI), 82.8 %-88.4 %]. Multivariate Cox analysis identified taxane-based [T-based, hazard ratio (HR) = 0.32, 95 % CI, 0.13 to 0.78, P = 0.012] and anthracycline plus taxane-based (A + T-based, HR = 0.47, 95 % CI, 0.23 to 0.96, P = 0.037) regimens as independent protective factors for OS. However, the anthracycline-based regimen showed no significance in outcome (P = 0.175). CONCLUSION: As the most extensive MBC study in China, we described the characteristics, treatment and prognosis of Chinese MBC population comprehensively. T-based and A + T-based regimens were protective factors for OS in these patients. More research is required for this population.

Preoperative assessment of bone density for dental implantation: a comparative study of three different ROI methods.

BACKGROUND: Dental cone beam computed tomography (CBCT) is commonly used to evaluate cancellous bone density before dental implant surgery. However, to our knowledge, no measurement approach has been standardized yet. This study aimed to evaluate the relationship between three different regions of interest (ROI) methods on cancellous bone density at the dental implant site using dental CBCT images. METHODS: Patients' dental CBCT images (n = 300) obtained before dental implant surgery were processed using Mimics (Materialise, Leuven, Belgium). At the potential implant sites, the rectangle, cylinder, and surrounding cylinder ROI methods were used to measure bone density. Repeated measures one-way analysis of variance was performed to compare the three ROI methods in terms of measurement results. Pearson correlation analysis was performed to identify the likely pair-wise correlations between the three ROI methods. RESULTS: The density value obtained using the surrounding cylinder approach (grayscale value [GV],523.56 ± 228.03) was significantly higher than the values obtained using the rectangle (GV, 497.04 ± 236.69) and cylinder (GV,493 ± 231.19) ROI methods in terms of results. Furthermore, significant correlations were noted between the ROI methods (r > 0.965; p < 0.001). CONCLUSIONS: The density measured using the surrounding cylinder method was the highest. The choice of method may not influence the trends of measurement results. TRIAL REGISTRATION: This study was approved by the Institutional Review Board of China Medical University Hospital, No. CMUH111-REC3-205. Informed consent was waived by the Institutional Review Board of China Medical University Hospital, CMUH111-REC3-205, owing to the retrospective nature of the study.

Design and Biomechanical Analysis of Customized Angled Abutment Based on Tooth Inclination Angle for Immediate Implant Placement on Maxillary Anterior Region.

PURPOSE: Finite element analysis and an in vitro experiment were employed to investigate the loading effects of angled abutments, comparing various customized angled abutments derived from the average angle of incisors in patients with a commercial 15°∆ angled abutment, on both the implant and surrounding bone. METHODS: Four customized angled abutment models (21.9°∆, 24.15°∆, 20.22°∆, 33°∆) were developed using cone-beam computed tomography (CBCT) images of incisor inclination from various age groups of patients. 3D maxillary bone models were created from CBCT images of four individual patients. Finite element analysis and in-vitro strain gauge experiments were conducted, applying 100N or 50N of axial or oblique force, to assess the differences in stress/strain between the customized and the commercial 15°∆ angled abutments in both the implants and surrounding bone. RESULTS: Under axial loading, the stress values in the dental implant and surrounding bone were elevated due to the relatively higher angles of the customized angled abutments (21.9°∆, 24.15°∆, 20.22°∆, 33°∆) when compared to the commercial 15°∆ angled abutment; however, under oblique loading the commercial 15°∆angled abutment exhibited higher stress values in both the implant and surrounding bone. For in vitro experiment, there is no statically difference in bone strain between the customized (21.9°∆) and the commercial 15°∆ angled abutments in axial loading. Nevertheless, in oblique loading using a commercial 15°∆ angled abutment induced the higher bone strains. CONCLUSION: Customized angled abutments offer lower stress/strain under oblique loads but higher stress/strain under axial loads compared to commercial ones. Therefore, in the design and application of angled abutments, careful consideration of the occlusal load direction is paramount for achieving biomechanical success of dental implant.

Stagewise Training With Exponentially Growing Training Sets.

In the world of big data, training large-scale machine learning problems has gained considerable attention. Numerous innovative optimization strategies have been presented in recent years to accelerate the large-scale training process. However, the possibility of further accelerating the training process of various optimization algorithms remains an unresolved subject. To begin addressing this difficult problem, we exploit the researched findings that when training data are independent and identically distributed, the learning problem on a smaller dataset is not significantly different from the original one. Upon that, we propose a stagewise training technique that grows the size of the training set exponentially while solving nonsmooth subproblem. We demonstrate that our stagewise training via exponentially growing the size of the training sets (STEGSs) are compatible with a large number of proximal gradient descent and gradient hard thresholding (GHT) techniques. Interestingly, we demonstrate that STEGS can greatly reduce overall complexity while maintaining statistical accuracy or even surpassing the intrinsic error introduced by GHT approaches. In addition, we analyze the effect of the training data growth rate on the overall complexity. The practical results of applying l2,1 -and l0 -norms to a variety of large-scale real-world datasets not only corroborate our theories but also demonstrate the benefits of our STEGS framework.

Does the Dose of Standard Adjuvant Chemotherapy Affect the Triple-negative Breast Cancer Benefit from Extended Capecitabine Metronomic Therapy? An Exploratory Analysis of the SYSUCC-001 Trial.

Purpose: Results from studies of extended capecitabine after the standard adjuvant chemotherapy in early stage triple-negative breast cancer (TNBC) were inconsistent, and only low-dose capecitabine from the SYSUCC-001 trial improved disease-free survival (DFS). Adjustment of the conventional adjuvant chemotherapy doses affect the prognosis and may affect the efficacy of subsequent treatments. This study investigated whether the survival benefit of the SYSUCC-001 trial was affected by dose adjustment of the standard adjuvant chemotherapy or not. Patients and Methods: We reviewed the adjuvant chemotherapy regimens before the extended capecitabine in the SYSUCC-001 trial. Patients were classified into "consistent" (standard acceptable dose) and "inconsistent" (doses lower than acceptable dose) dose based on the minimum acceptable dose range in the landmark clinical trials. Cox proportional hazards model was used to investigate the impact of dose on the survival outcomes. Results: All 434 patients in SYSUCC-001 trial were enrolled in this study. Most of patients administered the anthracycline-taxane regimen accounted for 88.94%. Among patients in the "inconsistent" dose, 60.8% and 47% received lower doses of anthracycline and taxane separately. In the observation group, the "inconsistent" dose of anthracycline and taxane did not affect DFS compared with the "consistent" dose. Moreover, in the capecitabine group, the "inconsistent" anthracycline dose did not affect DFS compared with the "consistent" dose. However, patients with "consistent" taxane doses benefited significantly from extended capecitabine (P=0.014). The sufficient dose of adjuvant taxane had a positive effect of extended capecitabine (hazard ratio [HR] 2.04; 95% confidence interval [CI] 1.02 to 4.06). Conclusion: This study found the dose reduction of adjuvant taxane might negatively impact the efficacy of capecitabine. Therefore, the reduction of anthracycline dose over paclitaxel should be given priority during conventional adjuvant chemotherapy, if patients need dose reduction and plan for extended capecitabine.

Effects of diameters of implant and abutment screw on stress distribution within dental implant and alveolar bone: A three-dimensional finite element analysis.

Background/purpose: Few studies have investigated the effects of abutment screw diameter in the stress of dental implants and alveolar bones under occlusal forces. In this study, we investigated how variations in implant diameter, abutment screw diameter, and bone condition affect stresses in the abutment screw, implant, and surrounding bone. Materials and methods: Three-dimensional finite element (FE) models were fabricated for dental implants with external hex-type abutments measuring 4 and 5 mm in diameter. The models also included abutment screws measuring 2.0 and 2.5 mm in diameter. Each implant model was integrated with the mandibular bone comprising the cortical bone and four types of cancellous bone. In total, 12 finite element models were generated, subjected to three different occlusal forces, and analyzed using FE software to investigate the stress distribution of dental implant and alveolar bone. Results: Wider implants demonstrated lower stresses in implant and bone compared with standard-diameter implants. The quality of cancellous bone has a minimal impact on the stress values of the implant, abutment screw, and cortical bone. Regardless of occlusal arrangement or quality of cancellous bone, a consistent pattern emerged: larger abutment screw diameters led to increased stress levels on the screws, while the stress levels in both cortical and cancellous bone showed comparatively minor fluctuations. Conclusion: Wider implants tend to have better stress distribution than standard-diameter implants. The potential advantage of augmenting the abutment screw diameter is unfavorable. It may result in elevated stresses in the implant system.

Asynchronous Parallel Large-Scale Gaussian Process Regression.

Gaussian process regression (GPR) is an important nonparametric learning method in machine learning research with many real-world applications. It is well known that training large-scale GPR is a challenging task due to the required heavy computational cost and large volume memory. To address this challenging problem, in this article, we propose an asynchronous doubly stochastic gradient algorithm to handle the large-scale training of GPR. We formulate the GPR to a convex optimization problem, i.e., kernel ridge regression. After that, in order to efficiently solve this convex kernel problem, we first use the random feature mapping method to approximate the kernel model and then utilize two unbiased stochastic approximations, i.e., stochastic variance reduced gradient and stochastic coordinate descent, to update the solution asynchronously and in parallel. In this way, our algorithm scales well in both sample size and dimensionality, and speeds up the training computation. More importantly, we prove that our algorithm has a global linear convergence rate. Our experimental results on eight large-scale benchmark datasets with both regression and classification tasks show that the proposed algorithm outperforms the existing state-of-the-art GPR methods.

Identifying Shared Neuroanatomic Architecture between Cognitive Traits through Multiscale Morphometric Correlation Analysis.

We introduce an informative metric, called morphometric correlation, as a measure of shared neuroanatomic similarity between two cognitive traits. Traditional estimates of trait correlations can be confounded by factors beyond brain morphology. To exclude these confounding factors, we adopt a Gaussian kernel to measure the morphological similarity between individuals and compare pure neuroanatomic correlations among cognitive traits. In our empirical study, we employ a multiscale strategy. Given a set of cognitive traits, we first perform morphometric correlation analysis for each pair of traits to reveal their shared neuroanatomic correlation at the whole brain (or global) level. After that, we extend our whole brain concept to regional morphometric correlation and estimate shared neuroanatomic similarity between two cognitive traits at the regional (or local) level. Our results demonstrate that morphometric correlation can provide insights into shared neuroanatomic architecture between cognitive traits. Furthermore, we also estimate the morphometricity of each cognitive trait at both global and local levels, which can be used to better understand how neuroanatomic changes influence individuals' cognitive status.