Growth mixture modeling as an exploratory analysis tool in longitudinal quantitative trait loci analysis

We examined the properties of growth mixture modeling in finding longitudinal quantitative trait loci in a genome-wide association study. Two software packages are commonly used in these analyses: Mplus and the SAS TRAJ procedure. We analyzed the 200 replicates of the simulated data with these programs using three tests: the likelihood-ratio test statistic, a direct test of genetic model coefficients, and the chi-square test classifying subjects based on the trajectory model's posterior Bayesian probability. The Mplus program was not effective in this application due to its computational demands. The distributions of these tests applied to genes not related to the trait were sensitive to departures from Hardy-Weinberg equilibrium. The likelihood-ratio test statistic was not usable in this application because its distribution was far from the expected asymptotic distributions when applied to markers with no genetic relation to the quantitative trait. The other two tests were satisfactory. Power was still substantial when we used markers near the gene rather than the gene itself. That is, growth mixture modeling may be useful in genome-wide association studies. For markers near the actual gene, there was somewhat greater power for the direct test of the coefficients and lesser power for the posterior Bayesian probability chi-square test

A worldwide bibliometric analysis of malignant peripheral nerve sheath tumors from 2000 to 2022

BackgroundCurrently, malignant peripheral nerve sheath tumors (MPNST) are the subject of intense research interest. However, bibliometric studies have not been conducted in this field. The purpose of the study was to identify historical trends and presents a bibliometric analysis of the MPNST literature from 2000 to 2022.MethodsFor the bibliometric analysis, publications were retrieved from the Web of Science database based on the following search terms: [TI = (MPNST) OR TI= (malignant peripheral nerve sheath tumors) AND PY = (2000–2022)]. The following information was collected for each document: the publication trends and geographical distribution, important authors and collaboration, keyword distribution and evaluation, most popular journals, and most influential articles.ResultsWe included 1400 documents for bibliometric analysis, covering five categories: 824 articles, 17 proceedings papers, 68 letters, 402 meeting abstracts, and 89 reviews. Corrections, editorials, book chapters, data papers, publications with expressed concerns, and retractions were excluded from our research.ConclusionSince 2000, the number of publications on MPNST has continuously increased. Among all countries that contributed to the MPNST research, the USA, Japan, and China were the three most productive countries. The journal Modern Pathology has the most publications on MPNST, while those in the Cancer Research journal were the most frequently cited. The University of Texas MD Anderson Cancer Center may be a good partner to collaborate with. Recent research trends in MPNST have focused on tumorigenesis, clinical management, and predictive biomarkers

Performance of fast-ion loss diagnostic on EAST

The scintillator-based detector for fast-ion loss measurements has been installed on EAST. To obtain high temporal resolution for fast-ion loss diagnostics, fast photomultiplier tube systems have been developed which can supply the complementary measurements to the previous image system with good energy and pitch resolution by using a CCD camera. By applying the rotatable platform, the prompt losses of beam-ions can be measured in normal and reverse magnetic field. The thick-target bremsstrahlung occurring in the stainless steel shield with energetic electrons can produce X-rays, which will strike on the scintillator based detector. To understand this interference on fast-ion loss signals, the effects of energetic electrons on the scintillator-based detector are studied, including runaway electrons in the plasma ramping-up phase and fast electrons accelerated by the lower hybrid wave

Characterizing the Structural Pattern Predicting Medication Response in Herpes Zoster Patients Using Multivoxel Pattern Analysis

Herpes zoster (HZ) can cause a blistering skin rash with severe neuropathic pain. Pharmacotherapy is the most common treatment for HZ patients. However, most patients are usually the elderly or those that are immunocompromised, and thus often suffer from side effects or easily get intractable post-herpetic neuralgia (PHN) if medication fails. It is challenging for clinicians to tailor treatment to patients, due to the lack of prognosis information on the neurological pathogenesis that underlies HZ. In the current study, we aimed at characterizing the brain structural pattern of HZ before treatment with medication that could help predict medication responses. High-resolution structural magnetic resonance imaging (MRI) scans of 14 right-handed HZ patients (aged 61.0 ± 7.0, 8 males) with poor response and 15 (aged 62.6 ± 8.3, 5 males) age- (p = 0.58), gender-matched (p = 0.20) patients responding well, were acquired and analyzed. Multivoxel pattern analysis (MVPA) with a searchlight algorithm and support vector machine (SVM), was applied to identify the spatial pattern of the gray matter (GM) volume, with high predicting accuracy. The predictive regions, with an accuracy higher than 79%, were located within the cerebellum, posterior insular cortex (pIC), middle and orbital frontal lobes (mFC and OFC), anterior and middle cingulum (ACC and MCC), precuneus (PCu) and cuneus. Among these regions, mFC, pIC and MCC displayed significant increases of GM volumes in patients with poor response, compared to those with a good response. The combination of sMRI and MVPA might be a useful tool to explore the neuroanatomical imaging biomarkers of HZ-related pain associated with medication responses

Multiple Instance Learning Convolutional Neural Networks for Fine-Grained Aircraft Recognition

The key to fine-grained aircraft recognition is discovering the subtle traits that can distinguish different subcategories. Early approaches leverage part annotations of fine-grained objects to derive rich representations. However, manual labeling part information is cumbersome. In response to this issue, previous CNN-based methods reuse the backbone network to extract part-discrimination features, the inference process of which consumes much time. Therefore, we introduce generalized multiple instance learning (MIL) into fine-grained recognition. In generalized MIL, an aircraft is assumed to consist of multiple instances (such as head, tail, and body). Firstly, instance-level representations are obtained by the feature extractor and instance conversion component. Secondly, the obtained instance features are scored by an MIL classifier, which can yield high-level part semantics. Finally, a fine-grained object label is inferred by a MIL pooling function that aggregates multiple instance scores. The proposed approach is trained end-to-end without part annotations and complex location networks. Experimental evidence is conducted to prove the feasibility and effectiveness of our approach on combined aircraft images (CAIs)

Multiple Instance Learning Convolutional Neural Networks for Fine-Grained Aircraft Recognition

The key to fine-grained aircraft recognition is discovering the subtle traits that can distinguish different subcategories. Early approaches leverage part annotations of fine-grained objects to derive rich representations. However, manual labeling part information is cumbersome. In response to this issue, previous CNN-based methods reuse the backbone network to extract part-discrimination features, the inference process of which consumes much time. Therefore, we introduce generalized multiple instance learning (MIL) into fine-grained recognition. In generalized MIL, an aircraft is assumed to consist of multiple instances (such as head, tail, and body). Firstly, instance-level representations are obtained by the feature extractor and instance conversion component. Secondly, the obtained instance features are scored by an MIL classifier, which can yield high-level part semantics. Finally, a fine-grained object label is inferred by a MIL pooling function that aggregates multiple instance scores. The proposed approach is trained end-to-end without part annotations and complex location networks. Experimental evidence is conducted to prove the feasibility and effectiveness of our approach on combined aircraft images (CAIs)