Evaluation on photocatalytic activity of bismuth nitrate derived materials at different calcination temperatures using paper microzones method.

Our work reveals for the first time that directly calcined bismuth nitrate derivatives (BNDs) possess significant photocatalytic activity towards rhodamine B (RhB). As the calcination temperature increased, the Bi(NO3)3·5H2O powder gradually ruptured and transformed into different bismuth nitrate products and their mixtures, finally into stable α-Bi2O3 at 500 °C. Among them, BNDs-100 could achieve 100 % photocatalytic degradation of 10 mg/L RhB solution under UV irradiation for 6 min. The ImageJ-led paper microzones (PMZs) method is introduced for the first time into the performance evaluation process of photocatalysts, which can achieve the green chemistry pathway and the rapid evaluation of different catalysts. The accuracy of the results of the PMZs method relative to the spectrophotometric method was up to 91.14 %, which has a better reliability and is suitable for qualitative analysis, and a certain ability when used for quantitative analysis. The results showed that the PMZs method was used to assess the photocatalytic degradation of rhodamine B by bismuth nitrate-derived materials at different calcination temperatures with well reliability, and the preparation of BNDs by direct calcination was a simple and effective strategy.

Impact of dietary inflammatory index on gestational diabetes mellitus in normal and overweight women: a systematic review and meta-analysis of observational studies.

BACKGROUND AND OBJECTIVES: To systematically investigate the association between the dietary inflammatory index (DII) and gestational diabetes mellitus (GDM), with a focus on the role of BMI in this relationship. METHODS AND STUDY DESIGN: A comprehensive search was conducted in PubMed, Embase, Web of Science, The Cochrane Library, Medline, CINAHL Complete, Chinese Periodical Full-text Database, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, and China Wanfang Database for rele-vant observational studies published up to August 2023. The quality of the included studies was assessed using the Newcastle-Ottawa Scale. The pooled effect size was calculated using a random-effects model. Sub-group and meta-regression analyses were performed to explore potential sources of heterogeneity. RESULTS: The study included 54,058 participants from 10 studies. Pregnant women with a higher DII, indicating a pro-inflammatory diet, had a significantly increased risk of GDM compared to those with a lower DII, indicating an anti-inflammatory diet (pooled OR: 1.17, 95% CI: 1.01-1.36; I²=70%, p <0.001). Subgroup analyses revealed a stronger association in normal weight stratification (OR: 1.25, 95%CI: 1.04-1.51), case-control studies (OR: 1.45, 95%CI: 1.03-2.05), Asia (OR: 1.26, 95%CI: 1.10-1.43), Europe (OR: 1.27, 95%CI: 1.09-1.48), 3-day dietary record as a dietary assessment tool (OR: 1.30, 95%CI: 1.16-1.46), physical activity adjustment (OR: 1.28, 95%CI: 1.13-1.46), and energy intake adjustment (OR: 1.33, 95%CI: 1.19-1.48). Meta-regression analysis confirmed that geographical region significantly influenced heterogeneity between studies (p <0.05). CONCLUSIONS: An elevated DII is independently linked to a higher risk of GDM, especially in women of normal weight.

Flow Analysis of Central Venous Outflow Tract: A New Approach to Understanding Pulse-Synchronous Tinnitus.

OBJECTIVE: Pulse-synchronous tinnitus (PST) has been linked to multiple anatomical variants of the central venous outflow tract (CVOT) including sigmoid sinus (SS) dehiscence and diverticulum. This study investigates flow turbulence, pressure, and wall shear stress along the CVOT and proposes a mechanism that results in SS dehiscence and PST. STUDY DESIGN: Case series. SETTING: Tertiary Academic Center. METHODS: Venous models were reconstructed from computed tomography scans of 3 patients with unilateral PST. Two models for each patient are obtained: a symptomatic and contralateral asymptomatic side. A turbulent model-enabled commercial flow solver was used to simulate the pulsatile blood flow over the cardiac cycle through the models. Fluid flow through the transverse and SS junction was analyzed to observe the velocity, pressure, turbulent kinetic energy (TKE), and shear stress over a simulated cardiac cycle. RESULTS: Fluid flow on the symptomatic side showed increased vorticity in the presence of an SS diverticulum. Higher TKE with periodicity following the cardiac cycle was observed on the symptomatic side, and a sharp increase was observed if SS diverticulum was present. Shear stress was highest near the narrowest segments of the vessel. Pressure was observed to be lower on the symptomatic side at the transverse-SS junction for all 3 patients. CONCLUSION: Computational fluid dynamics modeling of blood flow through the CVOT in PST suggests that low pressure may be the cause of dehiscence, and tinnitus may result from periodic increases in TKE.

High-purity La3-xTe4 Material with Superior Thermoelectric Performance Synthesized via Te-vapor Transport and Solid-State Diffusion.

La3-xTe4 is a very promising high-temperature candidate applied in next-generation Radioisotope Thermoelectric Generators (RTGs). Conventional synthesis of such materials is based on the mechanochemical method, which makes the sample difficult to purify due to the high-energy ball milling. In this report, a novel synthetic method is developed, which utilizes Te-vapor transport and solid-phase diffusion to efficiently produce the RE3-xTe4 phases (RE = La, Ce, Pr, Nd). Notably, this method obviates the requirement for high-energy ball-milling instruments, conventionally indispensable in the mechanochemical syntheses. For as-synthesized La2.74Te4 material, a high figure of merit of 1.5 is achieved at 1073 K, owning to the reduced electronic thermal conductivity with metal impurities well eliminated.

Assessment of Pelvic Tilt in Anteroposterior Radiographs by Area Ratio Based on Deep Learning.

STUDY DESIGN: Diagnostics. OBJECTIVES: Based on deep learning semantic segmentation model, we sought to assess pelvic tilt by area ratio of the lesser pelvic and the obturator foramen in anteroposterior (AP) radiographs. BACKGROUND: Pelvic tilt is a critical factor in hip and spinal surgery, commonly evaluated by medical professionals through sagittal pelvic radiographs. The inherent pelvic asymmetry, as well as potential obstructions from clothing and musculature in roentgenography, may result in ghosting and blurring artifacts, thereby complicating precise measurement. METHODS: PT directly affects the area ratio of the lesser pelvis to the obturator foramen in AP radiographs. An exponential regression analysis of simulated radiographs from ten male and ten female pelvises in specific tilt positions derived a formula correlating this area ratio with PT. Two blinded investigators evaluated this formula using 161 simulated AP pelvic radiographs. A deep learning semantic segmentation model was then fine-tuned to automatically calculate the area ratio, enabling intelligent PT evaluation. This model and the regression function were integrated for automated PT measurement and tested on a dataset of 231 clinical cases. RESULTS: We observed no disparity between males and females in the aforementioned area ratio. The test results from two blinded investigators analyzing 161 simulated radiographs revealed a mean absolute error of 0.19° (SD±4.71°), with a correlation coefficient between them reaching 0.96. Additionally, the mean absolute error obtained from testing 231 clinical AP radiographs using the fine-tuned semantic segmentation model mentioned earlier is -0.58° (SD±5.97°). CONCLUSION: We found that using deep learning neural networks enabled a more accurate and robust automatic measurement of PT through the area ratio of the lesser pelvis and obturator foramen.

Elevated expression of ECT2 as a diagnostic marker and prognostic indicator in endometrial cancer.

OBJECTIVES: The study aims to investigate genes associated with endometrial cancer (EC) progression to identify new biomarkers for early detection. METHODS: Differentially expressed genes (DEGs), Series test of cluster (STC) and protein-protein interaction analyses identified hub genes in EC. Clinical samples were utilized to examine the expression pattern of ECT2, assess its prognostic value, and evaluate its diagnostic potential. RESULTS: Upregulated DEGs were significantly enriched in cancer-related processes and pathways. Validations across databases identified ASPM, ATAD2, BUB1B, ECT2, KIF14, NUF2, NCAPG, and SPAG5 as potential hub genes, with ECT2 exhibiting the highest diagnostic efficacy. The expression levels of ECT2 varied significantly across different clinical stages, pathological grades, and metastasis statuses in UCEC. Furthermore, ECT2 mRNA was upregulated in the p53abn group, indicating a poorer prognosis, and downregulated in the MMRd and NSMP groups, suggesting a moderate prognosis. In clinical samples, ECT2 expression increased from normal endometria and endometrial hyperplasia without atypia (EH) to atypical endometrial hyperplasia (AH) and EC, effectively distinguishing between benign and malignant endometria. High ECT2 expression was associated with an unfavourable prognosis. CONCLUSIONS: ECT2 expression significantly rises in AH and EC, showing high accuracy in distinguishing between benign and malignant endometria. ECT2 emerges as a promising biomarker for diagnosing endometrial neoplasia and as a prognostic indicator in EC.

The m6A reader SlYTH2 negatively regulates tomato fruit aroma by impeding the translation process.

N6-methyladenosine (m6A) is a fundamentally important RNA modification for gene regulation, whose function is achieved through m6A readers. However, whether and how m6A readers play regulatory roles during fruit ripening and quality formation remains unclear. Here, we characterized SlYTH2 as a tomato m6A reader protein and profiled the binding sites of SlYTH2 at the transcriptome-wide level. SlYTH2 undergoes liquid-liquid phase separation and promotes RNA-protein condensate formation. The target mRNAs of SlYTH2, namely m6A-modified SlHPL and SlCCD1B associated with volatile synthesis, are enriched in SlYTH2-induced condensates. Through polysome profiling assays and proteomic analysis, we demonstrate that knockout of SlYTH2 expedites the translation process of SlHPL and SlCCD1B, resulting in augmented production of aroma-associated volatiles. This aroma enrichment significantly increased consumer preferences for CRISPR-edited fruit over wild type. These findings shed light on the underlying mechanisms of m6A in plant RNA metabolism and provided a promising strategy to generate fruits that are more attractive to consumers.

Computed tomography-based radiomics combined with machine learning allows differentiation between primary intestinal lymphoma and Crohn's disease.

BACKGROUND: Due to similar clinical manifestations and imaging signs, differential diagnosis of primary intestinal lymphoma (PIL) and Crohn's disease (CD) is a challenge in clinical practice. AIM: To investigate the ability of radiomics combined with machine learning methods to differentiate PIL from CD. METHODS: We collected contrast-enhanced computed tomography (CECT) and clinical data from 120 patients form center 1. A total of 944 features were extracted single-phase images of CECT scans. Using the last absolute shrinkage and selection operator model, the best predictive radiographic features and clinical indications were screened. Data from 54 patients were collected at center 2 as an external validation set to verify the robustness of the model. The area under the receiver operating characteristic curve, accuracy, sensitivity and specificity were used for evaluation. RESULTS: A total of five machine learning models were built to distinguish PIL from CD. Based on the results from the test group, most models performed well with a large area under the curve (AUC) (> 0.850) and high accuracy (> 0.900). The combined clinical and radiomics model (AUC = 1.000, accuracy = 1.000) was the best model among all models. CONCLUSION: Based on machine learning, a model combining clinical data with radiologic features was constructed that can effectively differentiate PIL from CD.

Role of sRNAs protein molecules in extracellular vesicles derived from Lactobacillus plantarum rejuvenate against ultraviolet B-induced photoaging in human keratinocytes.

Ultraviolet B (UVB) radiation accelerates the aging process of skin cells by triggering oxidative stress and inflammatory responses. The aim of this study was to investigate the mechanism of action of sRNAs and protein molecules in the regenerative extracellular vesicles of Lactobacillus plantarum against the UVB-induced photoaging process of human keratinocytes. The extracellular vesicles regenerated by Lactobacillus plantarum were isolated and purified to identify sRNAs and protein components. Human keratinocytes were treated with UVB radiation to simulate the photoaging model. The effects of different concentrations of vesicle extract on cell survival rate, oxidative stress index and inflammatory marker expression were evaluated in control group and treatment group. The results showed that the regenerated extracellular vesicles of L. plantarum significantly improved the survival rate of keratinocytes after UVB radiation, and delayed the aging process of skin cells by reducing oxidative stress and inhibiting inflammatory response.

Nodal T follicular helper cell lymphoma with aberrant CD20 expression and monoclonal TCR, IG rearrangements secondary to Classical Hodgkin Lymphoma: a case report.

Classical Hodgkin Lymphoma (CHL) is a rare malignant neoplasm of the lymphatic system. While CHL typically responds well to conventional treatments, some cases may experience relapse to other subtypes, with the development of secondary peripheral T-cell lymphoma (PTCL) being relatively uncommon. Herein, we report a rare case of nodal T follicular helper cell lymphomas,nos (nTFHL-NOS) secondary to CHL, accompanied by aberrant CD20 expression and clonal rearrangements of T-cell receptor (TCR) and immunoglobulin (IG). A 74-year-old male, was diagnosed with CHL, leaning toward the mixed cell type, 6 years ago. He received six cycles of the Adriamycin, Bleomycin, Vinblastine, Dacarbazine (ABVD) regimen, achieving complete clinical remission. The patient was admitted to our hospital due to the appearance of multiple skin nodules 66 months later. Histopathological analysis revealed nTFHL-NOS, with aberrant CD20 expression and clonal rearrangements of TCR and IG. The patient underwent two cycles of chemotherapy with brentuximab vedotin and the Gemcitabine-Oxaliplatin (G-mox) regimen, resulting in a reduction of the skin lesions to 2 cm × 1 cm. We discuss this rare case and review related literature.

Human papillomavirus prevalence, genotype distribution, and prognostic factors of vaginal cancer.

We aimed to investigate human papillomavirus (HPV) prevalence and genotype distribution and prognostic factors in vaginal cancer (VC). VC patients who received treatment between 1989 and 2020 were retrospectively reviewed. L1 general polymerase chain reaction (PCR) followed by HPV Blot (King Car, I-Lan, Taiwan) and E6 type-specific-PCR were performed for genotyping firstly. P16 and p53 immunohistochemistry staining was performed. Univariate and multivariate analyses identified predictors of clinical outcomes.79 VC patients were eligible for analysis. 73 patients (92.4%) were squamous cell carcinoma (SCC) and 6 (7.6%) as non-SCC. The median follow-up time was 134.3 months (range 0.9-273.4). Among nine initially HPV-negative cases, seven were identified as being positive through HPV16/18/45/52/58 whole-genome amplification followed by Sanger sequencing (WGASS). HPV DNA sequences were detected in 98.6% of SCC and 83.3% of non-SCC, respectively, with HPV16 (49.4%), HPV52 (15.2%) and HPV58 (8.9%) being predominant. Patients with paraaortic lymph node (LN) metastasis had a 5-year cancer-specific survival (CSS) rate of 0%. Multivariate analysis revealed that only p16 and stage were significantly correlated with prognosis. Variables with strong correlations (p16- and HPV-positivity, LN metastasis and stage), were included in models 2-5 alternatively. Stage III/IV (hazard ratio [HR] = 3.64-4.56) and LN metastasis (HR = 2.81-3.44) were significant negative predictors of CSS, whereas p16-positivity (HR = 0.29-0.32) and HPV-positivity (HR = 0.14) were related to better prognosis. In conclusion, 97.5% of VCs were HPV-positive with WGASS. Stage III/IV and LN metastasis were significant negative predictors, whereas p16- and HPV-positivity were significantly associated with better prognosis.

A Hydrophilic Polyethylene Glycol-Blended Anion Exchange Membrane to Facilitate the Migration of Hydroxide Ions.

As one of the most important sources for green hydrogen, anion exchange membrane water electrolyzers (AEMWEs) have been developing rapidly in recent decades. Among these components, anion exchange membranes (AEMs) with high ionic conductivity and good stability play an important role in the performance of AEMWEs. In this study, we have developed a simple blending method to fabricate the blended membrane ImPSF-PEGx via the introduction of a hydrophilic PEG into the PSF-based ionic polymer. Given their hydrophilicity and coordination properties, the introduced PEGs are beneficial in assembling the ionic groups to form the ion-conducting channels. Moreover, an asymmetric structure is observed in ImPSF-PEGx membranes with a layer of finger-like cracks at the upper surface because PEGs can act as pore-forming agents. During the study, the ImPSF-PEGx membranes exhibited higher water uptake and ionic conductivity with lower swelling ratios and much better mechanical properties in comparison to the pristine ImPSF membrane. The ImPSF-PEG1000 membrane showed the best overall performance among the membranes with higher ionic conductivity (82.6 mS cm-1 at 80 °C), which was approximately two times higher than the conductivity of ImPSF, and demonstrated better mechanical and alkaline stability. The alkaline water electrolyzer assembled by ImPSF-PEG1000 achieved a current density of 606 mA cm-2 at 80 °C under conditions of 1 M KOH and 2.06 V, and maintained an essentially unchanged performance after 48 h running.

Calixarene-Based Supramolecular Sensor Array for Pesticide Discrimination.

The identification and detection of pesticides is crucial to protecting both the environment and human health. However, it can be challenging to conveniently and rapidly differentiate between different types of pesticides. We developed a supramolecular fluorescent sensor array, in which calixarenes with broad-spectrum encapsulation capacity served as recognition receptors. The sensor array exhibits distinct fluorescence change patterns for seven tested pesticides, encompassing herbicides, insecticides, and fungicides. With a reaction time of just three minutes, the sensor array proves to be a rapid and efficient tool for the discrimination of pesticides. Furthermore, this supramolecular sensing approach can be easily extended to enable real-time and on-site visual detection of varying concentrations of imazalil using a smartphone with a color scanning application. This work not only provides a simple and effective method for pesticide identification and quantification, but also offers a versatile and advantageous platform for the recognition of other analytes in relevant fields.

Effective Connectivity of Default Mode Network Subsystems in Parkinson's Disease with Mild Cognitive Impairment Based on Spectral Dynamic Causal Modeling.

OBJECTIVE: The objective of this study is to compare the differences in effective connectivity within the default mode network (DMN) subsystems between patients with Parkinson's disease with mild cognitive impairment (PD-MCI) and patients with Parkinson's disease with normal cognition (PD-CN). The mechanisms underlying DMN dysfunction in PD-MCI patients and its association with clinical cognitive function in PD-MCI are aimed to be investigated. METHODS: The spectral dynamic causal model (spDCM) was employed to analyze the effective connectivity of functional magnetic resonance imaging (fMRI) data in the resting state for the DMN subsystems, which include the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), left and right angular gyrus (LAG, RAG) in 23 PD-MCI and 22 PD-CN patients, respectively. The effective connectivity values of DMN subsystems in the two groups were statistically analyzed using a two-sample t-test. The Spearman correlation analysis was used to test the correlation between the effective connectivity values of the subsystems with significant differences between the two groups and the clinical cognitive function (as measured by Montreal Cognitive Assessment Scale (MoCA) score). RESULTS: Statistical analysis revealed significant differences in the effective connections of MPFC-LAG and LAG-PCC between the two patient groups (MPFC-LAG: t = -2.993, p < 0.05; LAG-PCC: t = 2.174, p < 0.05). CONCLUSIONS: The study findings suggest that abnormal strength and direction of effective connections between DMN subsystems are found in PD-MCI patients.

Anesthetic Effect on the Subthalamic Nucleus in Microelectrode Recording and Local Field Potential of Parkinson's Disease.

OBJECTIVES: Anesthetic agents used during deep brain stimulation (DBS) surgery might interfere with microelectrode recording (MER) and local field potential (LFP) and thus affect the accuracy of surgical target localization. This review aimed to identify the effects of different anesthetic agents on neuronal activity of the subthalamic nucleus (STN) during the MER procedure. MATERIALS AND METHODS: We used Medical Subject Heading terms to search the PubMed, EMBASE, EBSCO, and ScienceDirect data bases. MER characteristics were sorted into quantitative and qualitative data types. Quantitative data included the burst index, pause index, firing rate (FR), and interspike interval. Qualitative data included background activity, burst discharge (BD), and anesthetic agent effect. We also categorized the reviewed manuscripts into those describing local anesthesia with sedation (LAWS) and those describing general anesthesia (GA) and compiled the effects of anesthetic agents on MER and LFP characteristics. RESULTS: In total, 26 studies on MER were identified, of which 12 used LAWS and 14 used GA. Three studies on LFP also were identified. We found that the FR was preserved under LAWS but tended to be lower under GA, and BD was reduced in both groups. Individually, propofol enhanced BD but was better used for sedation, or the dosage should be minimized in GA. Similarly, low-dose dexmedetomidine sedation did not disturb MER. Opioids could be used as adjunctive anesthetic agents. Volatile anesthesia had the least adverse effect on MER under GA, with minimal alveolar concentration at 0.5. Dexmedetomidine anesthesia did not affect LFP, whereas propofol interfered with the power of LFP. CONCLUSIONS: The effects of the tested anesthetics on the STN in MER and LFP of Parkinson's disease varied; however, identifying the STN and achieving a good clinical outcome are possible under controlled anesthetic conditions. For patient comfort, anesthesia should be considered in STN-DBS.

PTEN status determines therapeutic vulnerability to celastrol in cholangiocarcinoma.

BACKGROUND: A balanced protein homeostasis network helps cholangiocarcinoma (CCA) maintain their oncogenic growth, and disrupting proteostasis therapeutically will induce proteotoxic stress. Phosphatase and tensin homolog (PTEN) have been reported to be involved in proteostasis, and PTEN-associated pathways are commonly altered in CCA. Celastrol, a triterpene from plants, exhibits cytotoxic effects in various types of cancer. However, the underlying mechanisms remain unclear. PURPOSE: We investigated the therapeutic effect of celastrol in CCA and identified the molecular characteristics of tumors that were sensitive to celastrol. The target of celastrol was explored. We then evaluated the candidate combination therapeutic strategy to increase the effectiveness of celastrol in celastrol-insensitive CCA tumors. METHODS: Various CCA cells were categorized as either celastrol-sensitive or celastrol-insensitive based on their response to celastrol. The molecular characteristics of cells from different groups were determined by RNA-seq. PTEN status and its role in proteasome activity in CCA cells were investigated. The CMAP analysis, molecular docking, and functional assay were performed to explore the effect of celastrol on proteasome activities. The correlation between PTEN status and clinical outcomes, as well as proteasomal activity, were measured in CCA patients. The synergistic therapeutic effect of autophagy inhibitors on celastrol-insensitive CCA cells were measured. RESULTS: Diverse responses to celastrol were observed in CCA cells. PTEN expression varied among different CCA cells, and its status could impact cell sensitivity to celastrol: PTENhigh tumor cells were resistant to celastrol, while PTENlow cells were more sensitive. Celastrol induced proteasomal dysregulation in CCA cells by directly targeting PSMB5. Cells with low PTEN status transcriptionally promoted proteasome subunit expression in an AKT-dependent manner, making these cells more reliant on proteasomal activities to maintain proteostasis. This caused the PTENlow CCA cells sensitive to celastrol. A negative correlation was found between PTEN levels and the proteasome signature in CCA patients. Moreover, celastrol treatment could induce autophagy in PTENhigh CCA cells. Disrupting the autophagic pathway in PTENhigh CCA cells enhanced the cytotoxic effect of celastrol. CONCLUSION: PTEN status in CCA cells determines their sensitivity to celastrol, and autophagy inhibitors could enhance the anti-tumor effect in PTENhigh CCA.

Temporal Dynamics and Physical Priori Multimodal Network for Rehabilitation Physical Training Evaluation.

Sensor-based rehabilitation physical training assessment methods have attracted significant attention in refined evaluation scenarios. A refined rehabilitation evaluation method combines the expertise of clinicians with advanced sensor-based technology to capture and analyze subtle movement variations often unobserved by traditional subjective methods. Current approaches center on either body postures or muscle strength, which lack more sophisticated analysis features of muscle activation and coordination, thereby hindering analysis efficacy in deep rehabilitation feature exploration. To address this issue, we present a multimodal network algorithm that integrates surface electromyography (sEMG) and stress distribution signals. The algorithm considers the physical knowledge a priori to interpret the current rehabilitation stage and efficiently handles temporal dynamics arising from diverse user profiles in an online setting. Besides, we verified the performance of this model using a learned-nonuse phenomenon assessment task in 24 subjects, achieving an accuracy of 94.7%. Our results surpass those of conventional feature-based, distance-based, and ensemble baseline models, highlighting the advantages of incorporating multimodal information rather than relying solely on unimodal data. Moreover, the proposed model presents a network design solution for rehabilitation physical training that requires deep bioinformatic features and can potentially assist real-time and home-based physical training work.

Altered nucleocytoplasmic export of adenosine-rich circRNAs by PABPC1 contributes to neuronal function.

Circular RNAs (circRNAs) are upregulated during neurogenesis. Where and how circRNAs are localized and what roles they play during this process have remained elusive. Comparing the nuclear and cytoplasmic circRNAs between H9 cells and H9-derived forebrain (FB) neurons, we identify that a subset of adenosine (A)-rich circRNAs are restricted in H9 nuclei but exported to cytosols upon differentiation. Such a subcellular relocation of circRNAs is modulated by the poly(A)-binding protein PABPC1. In the H9 nucleus, newly produced (A)-rich circRNAs are bound by PABPC1 and trapped by the nuclear basket protein TPR to prevent their export. Modulating (A)-rich motifs in circRNAs alters their subcellular localization, and introducing (A)-rich circRNAs in H9 cytosols results in mRNA translation suppression. Moreover, decreased nuclear PABPC1 upon neuronal differentiation enables the export of (A)-rich circRNAs, including circRTN4(2,3), which is required for neurite outgrowth. These findings uncover subcellular localization features of circRNAs, linking their processing and function during neurogenesis.

Classification of field wheat varieties based on a lightweight G-PPW-VGG11 model.

Introduction: In agriculture, especially wheat cultivation, farmers often use multi-variety planting strategies to reduce monoculture-related harvest risks. However, the subtle morphological differences among wheat varieties make accurate discrimination technically challenging. Traditional variety classification methods, reliant on expert knowledge, are inefficient for modern intelligent agricultural management. Numerous existing classification models are computationally complex, memory-intensive, and difficult to deploy on mobile devices effectively. This study introduces G-PPW-VGG11, an innovative lightweight convolutional neural network model, to address these issues. Methods: G-PPW-VGG11 ingeniously combines partial convolution (PConv) and partially mixed depthwise separable convolution (PMConv), reducing computational complexity and feature redundancy. Simultaneously, incorporating ECANet, an efficient channel attention mechanism, enables precise leaf information capture and effective background noise suppression. Additionally, G-PPW-VGG11 replaces traditional VGG11's fully connected layers with two pointwise convolutional layers and a global average pooling layer, significantly reducing memory footprint and enhancing nonlinear expressiveness and training efficiency. Results: Rigorous testing showed G-PPW-VGG11's superior performance, with an impressive 93.52% classification accuracy and only 1.79MB memory usage. Compared to VGG11, G-PPW-VGG11 showed a 5.89% increase in accuracy, 35.44% faster inference, and a 99.64% reduction in memory usage. G-PPW-VGG11 also surpasses traditional lightweight networks in classification accuracy and inference speed. Notably, G-PPW-VGG11 was successfully deployed on Android and its performance evaluated in real-world settings. The results showed an 84.67% classification accuracy with an average time of 291.04ms per image. Discussion: This validates the model's feasibility for practical agricultural wheat variety classification, establishing a foundation for intelligent management. For future research, the trained model and complete dataset are made publicly available.