Dynamic multicolor emissions of multimodal phosphors by Mn trace doping in self-activated CaGaO.

The manipulation of excitation modes and resultant emission colors in luminescent materials holds pivotal importance for encrypting information in anti-counterfeiting applications. Despite considerable achievements in multimodal and multicolor luminescent materials, existing options generally suffer from static monocolor emission under fixed external stimulation, rendering them vulnerability to replication. Achieving dynamic multimodal luminescence within a single material presents a promising yet challenging solution.

Evaluation of the clinical efficacy of vaginal treatment options for persistent high-risk human papillomavirus infection after excisional treatment of cervical high-grade squamous intraepithelial lesions: a systematic review and Bayesian network meta-analysis.

Background: To evaluate the clinical efficacy of different vaginal administration on cervical persistent high-risk human papillomavirus (HR-HPV) infection after excisional treatment for high-grade squamous intraepithelial lesions (HSIL).

Methods: Six databases (PubMed, EmBase, Cochrane Central, China Knowledge Network database, China Biomedical Literature Service, and WanFang database) were searched to collect randomized controlled trials (RCTs) of various types of vaginal administration compared to no treatment on persistent HR-HPV infection after HSIL excisional treatment, and comprehensive analysis of the clearance of different drugs on HR-HPV was performed using Bayesian reticulation meta-analysis.

Results: The study analyzed the efficacy of eight interventions, including Interferon, Baofukang, Paiteling, Bletilla striata Sanhuang Powder, Lactobacilli vaginal capsules, Fuanning + Interferon, Interferon + Lactobacilli vaginal capsules, and Interferon + Baofukang, on the clearance of HR-HPV after excisional treatment through pooling and analyzing data from 52 RCTs.

Identifying and distinguishing of essential tremor and Parkinson's disease with grouped stability analysis based on searchlight-based MVPA.

Background: Since both essential tremor (ET) and Parkinson's disease (PD) are movement disorders and share similar clinical symptoms, it is very difficult to recognize the differences in the presentation, course, and treatment of ET and PD, which leads to misdiagnosed commonly.

Purpose: Although neuroimaging biomarker of ET and PD has been investigated based on statistical analysis, it is unable to assist the clinical diagnosis of ET and PD and ensure the efficiency of these biomarkers. The aim of the study was to identify the neuroimaging biomarkers of ET and PD based on structural magnetic resonance imaging (MRI).

Extended analysis on the global Mittag-Leffler synchronization problem for fractional-order octonion-valued BAM neural networks.

In this paper, a new case of neural networks called fractional-order octonion-valued bidirectional associative memory neural networks (FOOVBAMNNs) is established. First, the higher dimensional models are formulated for FOOVBAMNNs with general activation functions and the special linear threshold ones, respectively. On one hand, employing Cayley-Dichson construction in octonion multiplication which is essentially neither commutative nor associative, the system of FOOVBAMNNs is divided into four fractional-order complex-valued ones.

Event-triggering load frequency control for multi-area power system based on random dynamic triggering mechanism and two-side closed functional.

By taking into account sampled-data mechanism and transmission delay, the novel event-triggering load frequency control (LFC) strategy involving random dynamic triggering algorithm (RDTA) is developed for multi-area power systems in this paper. Firstly, an improved multi-area LFC model considering sampling and transmission delay (STD) simultaneously is addressed. Secondly, a modified event-triggering mechanism (ETM) with RDTA is proposed, considering parameter disturbances and a dynamic adjustment mechanism of the triggering threshold.

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning.

Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder(ASD), definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls(TDC).

Stability Evaluation of Brain Changes in Parkinson's Disease Based on Machine Learning.

Structural MRI (sMRI) has been widely used to examine the cerebral changes that occur in Parkinson's disease (PD). However, previous studies have aimed for brain changes at the group level rather than at the individual level. Additionally, previous studies have been inconsistent regarding the changes they identified.

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning.

Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. This study proposed an ML framework based on coarse-to-fine feature selection.