Lipid metabolism disorder in diabetic kidney disease.

Diabetic kidney disease (DKD), a significant complication associated with diabetes mellitus, presents limited treatment options. The progression of DKD is marked by substantial lipid disturbances, including alterations in triglycerides, cholesterol, sphingolipids, phospholipids, lipid droplets, and bile acids (BAs). Altered lipid metabolism serves as a crucial pathogenic mechanism in DKD, potentially intertwined with cellular ferroptosis, lipophagy, lipid metabolism reprogramming, and immune modulation of gut microbiota (thus impacting the liver-kidney axis). The elucidation of these mechanisms opens new potential therapeutic pathways for DKD management. This research explores the link between lipid metabolism disruptions and DKD onset.

Federated Learning With Only Positive Labels by Exploring Label Correlations.

Federated learning (FL) aims to collaboratively learn a model by using the data from multiple users under privacy constraints. In this article, we study the multilabel classification (MLC) problem under the FL setting, where trivial solution and extremely poor performance may be obtained, especially when only positive data with respect to a single class label is provided for each client. This issue can be addressed by adding a specially designed regularizer on the server side. Although effective sometimes, the label correlations are simply ignored and thus suboptimal performance may be obtained. Besides, it is expensive and unsafe to exchange user's private embeddings between server and clients frequently, especially when training model in the contrastive way. To remedy these drawbacks, we propose a novel and generic method termed federated averaging (FedAvg) by exploring label correlations (FedALCs). Specifically, FedALC estimates the label correlations in the class embedding learning for different label pairs and utilizes it to improve the model training. To further improve the safety and also reduce the communication overhead, we propose a variant to learn fixed class embedding for each client, so that the server and clients only need to exchange class embeddings once. Extensive experiments on multiple popular datasets demonstrate that our FedALC can significantly outperform the existing counterparts.

Evaluation of TikTok videos on acute pancreatitis: content quality and reliability analysis.

BACKGROUND: Acute pancreatitis (AP) is a common acute digestive system disorder, with patients often turning to TikTok for AP-related information. However, the platform's video quality on AP has not been thoroughly investigated. OBJECTIVE: The main purpose of this study is to evaluate the quality of videos about AP on TikTok, and the secondary purpose is to study the related factors of video quality. METHODS: This study involved retrieving AP-related videos from TikTok, determining, and analyzing them based on predefined inclusion and exclusion criteria. Relevant data were extracted and compiled for evaluation. Video quality was scored using the DISCERN instrument and the Health on the Net (HONcode) score, complemented by introducing the Acute Pancreatitis Content Score (APCS). Pearson correlation analysis was used to assess the correlation between video quality scores and user engagement metrics such as likes, comments, favorites, retweets, and video duration. RESULTS: A total of 111 TikTok videos were included for analysis, and video publishers were composed of physicians (89.18%), news media organizations (13.51%), individual users (5.41%), and medical institutions (0.9%). The majority of videos focused on AP-related educational content (64.87%), followed by physicians' diagnostic and treatment records (15.32%), and personal experiences (19.81%). The mean scores for DISCERN, HONcode, and APCS were 33.05 ± 7.87, 3.09 ± 0.93, and 1.86 ± 1.30, respectively. The highest video scores were those posted by physicians (35.17 ± 7.02 for DISCERN, 3.31 ± 0.56 for HONcode, and 1.94 ± 1.34 for APCS, respectively). According to the APCS, the main contents focused on etiology (n = 55, 49.5%) and clinical presentations (n = 36, 32.4%), followed by treatment (n = 24, 21.6%), severity (n = 20, 18.0%), prevention (n = 19, 17.1%), pathophysiology (n = 17, 15.3%), definitions (n = 13, 11.7%), examinations (n = 10, 9%), and other related content. There was no correlation between the scores of the three evaluation tools and the number of followers, likes, comments, favorites, and retweets of the video. However, DISCERN (r = 0.309) and APCS (r = 0.407) showed a significant positive correlation with video duration, while HONcode showed no correlation with the duration of the video. CONCLUSIONS: The general quality of TikTok videos related to AP is poor; however, the content posted by medical professionals shows relatively higher quality, predominantly focusing on clinical presentations and etiologies. There is a discernible correlation between video duration and quality ratings, indicating that a combined approach incorporating the guideline can comprehensively evaluate AP-related content on TikTok.

Multi-tailed vision transformer for efficient inference.

Recently, Vision Transformer (ViT) has achieved promising performance in image recognition and gradually serves as a powerful backbone in various vision tasks. To satisfy the sequential input of Transformer, the tail of ViT first splits each image into a sequence of visual tokens with a fixed length. Then, the following self-attention layers construct the global relationship between tokens to produce useful representation for the downstream tasks. Empirically, representing the image with more tokens leads to better performance, yet the quadratic computational complexity of self-attention layer to the number of tokens could seriously influence the efficiency of ViT's inference. For computational reduction, a few pruning methods progressively prune uninformative tokens in the Transformer encoder, while leaving the number of tokens before the Transformer untouched. In fact, fewer tokens as the input for the Transformer encoder can directly reduce the following computational cost. In this spirit, we propose a Multi-Tailed Vision Transformer (MT-ViT) in the paper. MT-ViT adopts multiple tails to produce visual sequences of different lengths for the following Transformer encoder. A tail predictor is introduced to decide which tail is the most efficient for the image to produce accurate prediction. Both modules are optimized in an end-to-end fashion, with the Gumbel-Softmax trick. Experiments on ImageNet-1K demonstrate that MT-ViT can achieve a significant reduction on FLOPs with no degradation of the accuracy and outperform compared methods in both accuracy and FLOPs.

Genome-wide association study and genomic prediction for resistance to brown planthopper in rice.

The brown planthopper (BPH) is the most destructive insect pest that threatens rice production globally. Developing rice varieties incorporating BPH-resistant genes has proven to be an effective control measure against BPH. In this study, we assessed the resistance of a core collection consisting of 502 rice germplasms by evaluating resistance scores, weight gain rates and honeydew excretions. A total of 117 rice varieties (23.31%) exhibited resistance to BPH. Genome-wide association studies (GWAS) were performed on both the entire panel of 502 rice varieties and its subspecies, and 6 loci were significantly associated with resistance scores (P value < 1.0e-8). Within these loci, we identified eight candidate genes encoding receptor-like protein kinase (RLK), nucleotide-binding and leucine-rich repeat (NB-LRR), or LRR proteins. Two loci had not been detected in previous study and were entirely novel. Furthermore, we evaluated the predictive ability of genomic selection for resistance to BPH. The results revealed that the highest prediction accuracy for BPH resistance reached 0.633. As expected, the prediction accuracy increased progressively with an increasing number of SNPs, and a total of 6.7K SNPs displayed comparable accuracy to 268K SNPs. Among various statistical models tested, the random forest model exhibited superior predictive accuracy. Moreover, increasing the size of training population improved prediction accuracy; however, there was no significant difference in prediction accuracy between a training population size of 737 and 1179. Additionally, when there existed close genetic relatedness between the training and validation populations, higher prediction accuracies were observed compared to scenarios when they were genetically distant. These findings provide valuable resistance candidate genes and germplasm resources and are crucial for the application of genomic selection for breeding durable BPH-resistant rice varieties.

A Modular Neural Motion Retargeting System Decoupling Skeleton and Shape Perception.

Motion mapping between characters with different structures but corresponding to homeomorphic graphs, meanwhile preserving motion semantics and perceiving shape geometries, poses significant challenges in skinned motion retargeting. We propose M-R2ET, a modular neural motion retargeting system to comprehensively address these challenges. The key insight driving M-R2ET is its capacity to learn residual motion modifications within a canonical skeleton space. Specifically, a cross-structure alignment module is designed to learn joint correspondences among diverse skeletons, enabling motion copy and forming a reliable initial motion for semantics and geometry perception. Besides, two residual modification modules, i.e., the skeleton-aware module and shape-aware module, preserving source motion semantics and perceiving target character geometries, effectively reduce interpenetration and contact-missing. Driven by our distance-based losses that explicitly model the semantics and geometry, these two modules learn residual motion modifications to the initial motion in a single inference without post-processing. To balance these two motion modifications, we further present a balancing gate to conduct linear interpolation between them. Extensive experiments on the public dataset Mixamo demonstrate that our M-R2ET achieves the state-of-the-art performance, enabling cross-structure motion retargeting, and providing a good balance among the preservation of motion semantics as well as the attenuation of interpenetration and contact-missing.

Clinical efficacy of tumor organoid-guided cancer therapy for locally advanced unresectable or metastatic breast cancer.

Patient-derived organoids (PDOs) may facilitate treatment selection. This retrospective cohort study evaluated the feasibility and clinical benefit of using PDOs to guide personalized treatment in metastatic breast cancer (MBC). Patients diagnosed with MBC were recruited between January 2019 and August 2022. PDOs were established and the efficacy of customized drug panels was determined by measuring cell mortality after drug exposure. Patients receiving organoid-guided treatment (OGT) were matched 1:2 by nearest neighbor propensity scores with patients receiving treatment of physician's choice (TPC). The primary outcome was progression-free survival. Secondary outcomes included objective response rate and disease control rate. Targeted gene sequencing and pathway enrichment analysis were performed. Forty-six PDOs (46 of 51, 90.2%) were generated from 45 MBC patients. PDO drug screening showed an accuracy of 78.4% (95% CI 64.9%-91.9%) in predicting clinical responses. Thirty-six OGT patients were matched to 69 TPC patients. OGT was associated with prolonged median progression-free survival (11.0 months vs. 5.0 months; hazard ratio 0.53 [95% CI 0.33-0.85]; p = .01) and improved disease control (88.9% vs. 63.8%; odd ratio 4.26 [1.44-18.62]) compared with TPC. The objective response rate of both groups was similar. Pathway enrichment analysis in hormone receptor-positive, human epidermal growth factor receptor 2-negative patients demonstrated differentially modulated pathways implicated in DNA repair and transcriptional regulation in those with reduced response to capecitabine/gemcitabine, and pathways associated with cell cycle regulation in those with reduced response to palbociclib. Our study shows that PDO-based functional precision medicine is a feasible and effective strategy for MBC treatment optimization and customization.

Correlation Between Anesthesia Methods and Adverse Short-Term Postoperative Outcomes Depending on Frailty: A Prospective Cohort Study.

Purpose: This study aims to investigate how the type of anesthesia used during major orthopedic surgery may impact adverse short-term postoperative outcomes depending on frailty. Methods: To conduct this investigation, we recruited individuals aged 65 years and older who underwent major orthopedic surgery between March 2022 and April 2023 at a single institution. We utilized the FRAIL scale to evaluate frailty. The primary focus was on occurrences of death or the inability to walk 60 days after the surgery. Secondary measures included death within 60 days; inability to walk without human assistance at 60 days; death or the inability to walk without human assistance at 30 days after surgery, the first time out of bed after surgery, postoperative blood transfusion, length of hospital stay, hospital costs, and the occurrence of surgical complications such as dislocation, periprosthetic fracture, infection, reoperation, wound complications/hematoma. Results: In a study of 387 old adult patients who had undergone major orthopedic surgery, 41.3% were found to be in a frail state. Among these patients, 262 had general anesthesia and 125 had neuraxial anesthesia. Multifactorial logistic regression analyses showed that anesthesia type was not linked to complications. Instead, frailty (OR 4.04, 95% CI 1.04 to 8.57, P< 0.001), age (OR 1.05, 95% CI 1.00-1.10, P= 0.017), and aCCI scores, age-adjusted Charlson Comorbidity Index, (OR 1.36, 95% CI 1.12 to 1.66, P= 0.002) were identified as independent risk factors for death or new walking disorders in these patients 60 days after surgery. After adjusting for frailty, anesthesia methods was not associated with the development of death or new walking disorders in these patients (P > 0.05). Conclusion: In different frail populations, neuraxial anesthesia is likely to be comparable to general anesthesia in terms of the incidence of short-term postoperative adverse outcomes.

Adversarial pair-wise distribution matching for remote sensing image cross-scene classification.

Remarkable achievements have been made in the field of remote sensing cross-scene classification in recent years. However, most methods directly align the entire image features for cross-scene knowledge transfer. They usually ignore the high background complexity and low category consistency of remote sensing images, which can significantly impair the performance of distribution alignment. Besides, shortcomings of the adversarial training paradigm and the inability to guarantee the prediction discriminability and diversity can also hinder cross-scene classification performance. To alleviate the above problems, we propose a novel cross-scene classification framework in a discriminator-free adversarial paradigm, called Adversarial Pair-wise Distribution Matching (APDM), to avoid irrelevant knowledge transfer and enable effective cross-domain modeling. Specifically, we propose the pair-wise cosine discrepancy for both inter-domain and intra-domain prediction measurements to fully leverage the prediction information, which can suppress negative semantic features and implicitly align the cross-scene distributions. Nuclear-norm maximization and minimization are introduced to enhance the target prediction quality and increase the applicability of the source knowledge, respectively. As a general cross-scene framework, APDM can be easily embedded with existing methods to boost the performance. Experimental results and analyses demonstrate that APDM can achieve competitive and effective performance on cross-scene classification tasks.

Exploring sparsity in graph transformers.

Graph Transformers (GTs) have achieved impressive results on various graph-related tasks. However, the huge computational cost of GTs hinders their deployment and application, especially in resource-constrained environments. Therefore, in this paper, we explore the feasibility of sparsifying GTs, a significant yet under-explored topic. We first discuss the redundancy of GTs based on the characteristics of existing GT models, and then propose a comprehensive Graph Transformer SParsification (GTSP) framework that helps to reduce the computational complexity of GTs from four dimensions: the input graph data, attention heads, model layers, and model weights. Specifically, GTSP designs differentiable masks for each individual compressible component, enabling effective end-to-end pruning. We examine our GTSP through extensive experiments on prominent GTs, including GraphTrans, Graphormer, and GraphGPS. The experimental results demonstrate that GTSP effectively reduces computational costs, with only marginal decreases in accuracy or, in some instances, even improvements. For example, GTSP results in a 30% reduction in Floating Point Operations while contributing to a 1.8% increase in Area Under the Curve accuracy on the OGBG-HIV dataset. Furthermore, we provide several insights on the characteristics of attention heads and the behavior of attention mechanisms, all of which have immense potential to inspire future research endeavors in this domain. Our code is available at https://github.com/LiuChuang0059/GTSP.

SAAN: Similarity-Aware Attention Flow Network for Change Detection With VHR Remote Sensing Images.

Change detection (CD) is a fundamental and important task for monitoring the land surface dynamics in the earth observation field. Existing deep learning-based CD methods typically extract bi-temporal image features using a weight-sharing Siamese encoder network and identify change regions using a decoder network. These CD methods, however, still perform far from satisfactorily as we observe that 1) deep encoder layers focus on irrelevant background regions; and 2) the models' confidence in the change regions is inconsistent at different decoder stages. The first problem is because deep encoder layers cannot effectively learn from imbalanced change categories using the sole output supervision, while the second problem is attributed to the lack of explicit semantic consistency preservation. To address these issues, we design a novel similarity-aware attention flow network (SAAN). SAAN incorporates a similarity-guided attention flow module with deeply supervised similarity optimization to achieve effective change detection. Specifically, we counter the first issue by explicitly guiding deep encoder layers to discover semantic relations from bi-temporal input images using deeply supervised similarity optimization. The extracted features are optimized to be semantically similar in the unchanged regions and dissimilar in the changing regions. The second drawback can be alleviated by the proposed similarity-guided attention flow module, which incorporates similarity-guided attention modules and attention flow mechanisms to guide the model to focus on discriminative channels and regions. We evaluated the effectiveness and generalization ability of the proposed method by conducting experiments on a wide range of CD tasks. The experimental results demonstrate that our method achieves excellent performance on several CD tasks, with discriminative features and semantic consistency preserved.

Label-Aware Calibration and Relation-Preserving in Visual Intention Understanding.

Visual intention understanding is a challenging task that explores the hidden intention behind the images of publishers in social media. Visual intention represents implicit semantics, whose ambiguous definition inevitably leads to label shifting and label blemish. The former indicates that the same image delivers intention discrepancies under different data augmentations, while the latter represents that the label of intention data is susceptible to errors or omissions during the annotation process. This paper proposes a novel method, called Label-aware Calibration and Relation-preserving (LabCR) to alleviate the above two problems from both intra-sample and inter-sample views. First, we disentangle the multiple intentions into a single intention for explicit distribution calibration in terms of the overall and the individual. Calibrating the class probability distributions in augmented instance pairs provides consistent inferred intention to address label shifting. Second, we utilize the intention similarity to establish correlations among samples, which offers additional supervision signals to form correlation alignments in instance pairs. This strategy alleviates the effect of label blemish. Extensive experiments have validated the superiority of the proposed method LabCR in visual intention understanding and pedestrian attribute recognition. Code is available at https://github.com/ShiQingHongYa/LabCR.

OPEN STOMATA1 phosphorylates CYCLIC NUCLEOTIDE-GATED CHANNELs to trigger Ca2+ signaling for ABA-induced stomatal closure in Arabidopsis.

Multiple cyclic nucleotide-gated channels (CNGCs) are abscisic acid (ABA)-activated Ca2+ channels in Arabidopsis (Arabidopsis thaliana) guard cells. In particular, CNGC5, CNGC6, CNGC9, and CNGC12 are essential for ABA-specific cytosolic Ca2+ signaling and stomatal movements. However, the mechanisms underlying ABA-mediated regulation of CNGCs and Ca2+ signaling are still unknown. In this study, we identified the Ca2+-independent protein kinase OPEN STOMATA1 (OST1) as a CNGC activator in Arabidopsis. OST1-targeted phosphorylation sites were identified in CNGC5, CNGC6, CNGC9 and CNGC12. These CNGCs were strongly inhibited by Ser-to-Ala mutations and fully activated by Ser-to-Asp mutations at the OST1-targeted sites. The overexpression of individual inactive CNGCs (iCNGCs) under the UBIQUITIN10 promoter in wild-type Arabidopsis conferred a strong dominant-negative-like ABA-insensitive stomatal closure phenotype. In contrast, expressing active CNGCs (aCNGCs) under their respective native promoters in the cngc5-1 cngc6-2 cngc9-1 cngc12-1 quadruple mutant fully restored ABA-activated cytosolic Ca2+ oscillations and Ca2+ currents in guard cells, and rescued the ABA-insensitive stomatal movement mutant phenotypes. Thus, we uncovered that ABA elicits cytosolic Ca2+ signaling via an OST1-CNGC module, in which OST1 functions as a convergence point of the Ca2+-dependent and -independent pathways in Arabidopsis guard cells.

Automatic Detection of 30 Fundus Diseases Using Ultra-Widefield Fluorescein Angiography with Deep Experts Aggregation.

INTRODUCTION: Inaccurate, untimely diagnoses of fundus diseases leads to vision-threatening complications and even blindness. We built a deep learning platform (DLP) for automatic detection of 30 fundus diseases using ultra-widefield fluorescein angiography (UWFFA) with deep experts aggregation. METHODS: This retrospective and cross-sectional database study included a total of 61,609 UWFFA images dating from 2016 to 2021, involving more than 3364 subjects in multiple centers across China. All subjects were divided into 30 different groups. The state-of-the-art convolutional neural network architecture, ConvNeXt, was chosen as the backbone to train and test the receiver operating characteristic curve (ROC) of the proposed system on test data and external test date. We compared the classification performance of the proposed system with that of ophthalmologists, including two retinal specialists. RESULTS: We built a DLP to analyze UWFFA, which can detect up to 30 fundus diseases, with a frequency-weighted average area under the receiver operating characteristic curve (AUC) of 0.940 in the primary test dataset and 0.954 in the external multi-hospital test dataset. The tool shows comparable accuracy with retina specialists in diagnosis and evaluation. CONCLUSIONS: This is the first study on a large-scale UWFFA dataset for multi-retina disease classification. We believe that our UWFFA DLP advances the diagnosis by artificial intelligence (AI) in various retinal diseases and would contribute to labor-saving and precision medicine especially in remote areas.

Towards a better negative sampling strategy for dynamic graphs.

As dynamic graphs have become indispensable in numerous fields due to their capacity to represent evolving relationships over time, there has been a concomitant increase in the development of Temporal Graph Neural Networks (TGNNs). When training TGNNs for dynamic graph link prediction, the commonly used negative sampling method often produces starkly contrasting samples, which can lead the model to overfit these pronounced differences and compromise its ability to generalize effectively to new data. To address this challenge, we introduce an innovative negative sampling approach named Enhanced Negative Sampling (ENS). This strategy takes into account two pervasive traits observed in dynamic graphs: (1) Historical dependence, indicating that nodes frequently reestablish connections they held in the past, and (2) Temporal proximity preference, which posits that nodes are more inclined to connect with those they have recently interacted with. Specifically, our technique employs a designed scheduling function to strategically control the progression of difficulty of the negative samples throughout the training. This ensures that the training progresses in a balanced manner, becoming incrementally challenging, and thereby enhancing TGNNs' proficiency in predicting links within dynamic graphs. In our empirical evaluation across multiple datasets, we discerned that our ENS, when integrated as a modular component, notably augments the performance of four SOTA baselines. Additionally, we further investigated the applicability of ENS in handling dynamic graphs of varied attributes. Our code is available at https://github.com/qqaazxddrr/ENS.

Organoid forming potential as complementary parameter for accurate evaluation of breast cancer neoadjuvant therapeutic efficacy.

BACKGROUND: 13-15% of breast cancer/BC patients diagnosed as pathological complete response/pCR after neoadjuvant systemic therapy/NST suffer from recurrence. This study aims to estimate the rationality of organoid forming potential/OFP for more accurate evaluation of NST efficacy. METHODS: OFPs of post-NST residual disease/RD were checked and compared with clinical approaches to estimate the recurrence risk. The phenotypes of organoids were classified via HE staining and ER, PR, HER2, Ki67 and CD133 immuno-labeling. The active growing organoids were subjected to drug sensitivity tests. RESULTS: Of 62 post-NST BC specimens, 24 were classified as OFP-I with long-term active organoid growth, 19 as OFP-II with stable organoid growth within 3 weeks, and 19 as OFP-III without organoid formation. Residual tumors were overall correlated with OFP grades (P < 0.001), while 3 of the 18 patients (16.67%) pathologically diagnosed as tumor-free (ypT0N0M0) showed tumor derived-organoid formation. The disease-free survival/DFS of OFP-I cases was worse than other two groups (Log-rank P < 0.05). Organoids of OFP-I/-II groups well maintained the biological features of their parental tumors and were resistant to the drugs used in NST. CONCLUSIONS: The OFP would be a complementary parameter to improve the evaluation accuracy of NST efficacy of breast cancers.

Endosymbiotic Fungal Diversity and Dynamics of the Brown Planthopper across Developmental Stages, Tissues, and Sexes Revealed Using Circular Consensus Sequencing.

Endosymbiotic fungi play an important role in the growth and development of insects. Understanding the endosymbiont communities hosted by the brown planthopper (BPH; Nilaparvata lugens Stål), the most destructive pest in rice, is a prerequisite for controlling BPH rice infestations. However, the endosymbiont diversity and dynamics of the BPH remain poorly studied. Here, we used circular consensus sequencing (CCS) to obtain 87,131 OTUs (operational taxonomic units), which annotated 730 species of endosymbiotic fungi in the various developmental stages and tissues. We found that three yeast-like symbionts (YLSs), Polycephalomyces prolificus, Ophiocordyceps heteropoda, and Hirsutella proturicola, were dominant in almost all samples, which was especially pronounced in instar nymphs 4-5, female adults, and the fat bodies of female and male adult BPH. Interestingly, honeydew as the only in vitro sample had a unique community structure. Various diversity indices might indicate the different activity of endosymbionts in these stages and tissues. The biomarkers analyzed using LEfSe suggested some special functions of samples at different developmental stages of growth and the active functions of specific tissues in different sexes. Finally, we found that the incidence of occurrence of three species of Malassezia and Fusarium sp. was higher in males than in females in all comparison groups. In summary, our study provides a comprehensive survey of symbiotic fungi in the BPH, which complements the previous research on YLSs. These results offer new theoretical insights and practical implications for novel pest management strategies to understand the BPH-microbe symbiosis and devise effective pest control strategies.

[Retracted] MicroRNA­509 acts as a tumor suppressor in tongue squamous cell carcinoma by targeting epidermal growth factor receptor.

Following the publication of this paper, it was drawn to the Editor's attention by a concerned reader that certain of the Transwell cell invasion assay data shown in Fig. 2C on p. 7248 and Fig. 3G on p. 7249 were strikingly similar to data in different form in other articles written by different authors at different research institutes, which had either already been published (some of which have now been retracted), or had been submitted for publication at around the same time. Owing to the fact that certain of the data in the above article had already been published prior to its submission to Molecular Medicine Reports, the Editor has decided that this paper should be retracted from the Journal. The authors were asked for an explanation to account for these concerns, but the Editorial Office did not receive a satisfactory reply. The Editor apologizes to the readership for any inconvenience caused. [Molecular Medicine Reports 16: 7245­7252, 2017; DOI: 10.3892/mmr.2017.7531].

High-Performance All-Small-Molecule Organic Solar Cells Fabricated via Halogen-Free Preparation Process.

Small-molecule organic photovoltaic materials attract more attention attributing to their precisely defined structure, ease of synthesis, and reduced batch-to-batch variations. The majority of all-small-molecule organic solar cells (ASM-OSCs) have traditionally relied on halogenated solvents for dissolving photovoltaic materials as well as used for the additives or solvent vapor annealing. However, these halogen-based processes pose risks to the environment and human health, potentially impeding future commercial production. Herein, we conducted an investigation into the impact of various nonhalogen solvents on the performance of the devices. By selecting the high boiling point solvent toluene, we achieved a desirable phase separation and stable morphology characterized by fibrous crystals within the blend film. Consequently, the power conversion efficiencies of 14.4 and 11.7% were obtained from H31:Y6-based small-area (0.04 cm2) and large-area (1 cm2) devices with steady performance, respectively. This study successfully demonstrated the fabrication of ASM-OSCs without employing halogenated solvent processes, thus offering promising prospects for the commercial production of ASM-OSCs.

Frequency Domain-Oriented Complex Graph Neural Networks for Graph Classification.

Graph neural networks (GNNs) could directly deal with the data of graph structure. Current GNNs are confined to the spatial domain and learn real low-dimensional embeddings in graph classification tasks. In this article, we explore frequency domain-oriented complex GNNs in which the node's embedding in each layer is a complex vector. The difficulty lies in the design of graph pooling and we propose a mirror-connected design with two crucial problems: parameter reduction problem and complex gradient backpropagation problem. To deal with the former problem, we propose the notion of squared singular value pooling (SSVP) and prove that the representation power of SSVP followed by a fully connected layer with nonnegative weights is exactly equivalent to that of a mirror-connected layer. To resolve the latter problem, we provide an alternative feasible method to solve singular values of complex embeddings with a theoretical guarantee. Finally, we propose a mixture of pooling strategies in which first-order statistics information is employed to enrich the last low-dimensional representation. Experiments on benchmarks demonstrate the effectiveness of the complex GNNs with mirror-connected layers.