Enhancing the Efficiency of Flexible, Large-Area, ITO-Free Organic Photovoltaic Devices.

The development of flexible ITO-free devices is crucial for the industrial advancement of organic photovoltaic (OPV) technology. Here, a novel ITO-free device architecture is proposed, and ITO-free OPV devices are realized on glass substrates with performance comparable to that of ITO-based devices. It is also demonstrated that the performance of ITO-free devices on polyethylene terephthalate (PET) substrates is limited due to the higher surface roughness of PET, leading to high voltage losses, low device quantum efficiency, and high device leakage current. To address the issue of high roughness on the PET surface, a polyimide (PI) modification strategy is developed and the PI-modified PET is employed as the substrate to construct flexible ITO-free OPV devices and large-area modules with an active area of up to 16.5 cm2. This approach leads to decreased trap-assisted recombination losses, enhanced exciton dissociation efficiency, and a reduced density of pinholes in flexible OPV devices, resulting in improved photovoltaic performance under both strong and weak illumination conditions. The outcomes of this work are expected to advance the industrial development of flexible organic photovoltaic technology.

Development and validation of a lung cancer polygenic risk score incorporating susceptibility variants for risk factors.

Incorporating susceptibility genetic variants of risk factors has been reported to enhance the risk prediction of polygenic risk score (PRS). However, it remains unclear whether this approach is effective for lung cancer. Hence, we aimed to construct a meta polygenic risk score (metaPRS) of lung cancer and assess its prediction of lung cancer risk and implication for risk stratification. Here, a total of 2180 genetic variants were used to develop nine PRSs for lung cancer, three PRSs for different histopathologic subtypes, and 17 PRSs for lung cancer-related risk factors, respectively. These PRSs were then integrated into a metaPRS for lung cancer using the elastic-net Cox regression model in the UK Biobank (N = 442,508). Furthermore, the predictive effects of the metaPRS were assessed in the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial (N = 108,665). The metaPRS was associated with lung cancer risk with a hazard ratio of 1.33 (95% confidence interval: 1.27-1.39) per standard deviation increased. The metaPRS showed the highest C-index (0.580) compared with the previous nine PRSs (C-index: 0.513-0.564) in PLCO. Besides, smokers in the intermediate risk group predicted by the clinical risk model (1.34%-1.51%) with the intermediate-high genetic risk had a 6-year average absolute lung cancer risk that exceeded the clinical risk model threshold (≥1.51%). The addition of metaPRS to the clinical risk model showed continuous net reclassification improvement (continuous NRI = 6.50%) in PLCO. These findings suggest the metaPRS can improve the predictive efficiency of lung cancer compared with the previous PRSs and refine risk stratification for lung cancer.

The prevalence of developmental coordination disorder in children: a systematic review and meta-analysis.

Purpose: The aim of the study was to synthesize previous evidence and clarify the prevalence of developmental coordination disorder (DCD) in children by meta-analysis. Methods: A comprehensive computerized search of databases, including PubMed, Embase, Web of Science, The Cochrane Library, CINAHL, and PsycINFO databases, was conducted to identify relevant national and international articles published before 18 December 2023 on DCD prevalence in children. The meta-analysis of prevalence was conducted using Stata 18.0. Results: A total of 18 papers involving 31,203 patients were included. The prevalence of children with DCD was found to be 5%. A subgroup analysis showed that prevalence was 7% [95% confidence interval (CI) 4%-10%] and 4% (95% CI 3%-7%) for boys and girls, respectively; 4% (95% CI 2%-8%), 2% (95% CI 2%-2%), and 6% (95% CI 3%-10%) in Asia, Europe, and North America, respectively; and 18% (95% CI 8%-31%) and 6% (95% CI 4%-7%) for preterm (<37 weeks) and term infants (≥37 weeks), respectively. The prevalence of very low birth weight children (<1,250 g) with DCD was found to be 31%. Conclusion: In this study, we found that the prevalence of children with DCD in the general population was 5% and that preterm infants (<37 weeks) and very low birth weight infants (<1,250 g) have a higher prevalence of DCD and require early screening and regular follow-up. Systematic Review Registration: https://www.crd.york.ac.uk/, Identifier (CRD42024503320).

Configurational Isomerization-Induced Orientation Switching: Non-Fused Ring Dipodal Phosphonic Acids as Hole-Extraction Layers for Efficient Organic Solar Cells.

Phosphonic acid (PA) self-assembled molecules have recently emerged as efficient hole-extraction layers (HELs) for organic solar cells (OSCs). However, the structural effects of PAs on their self-assembly behaviors on indium tin oxide (ITO) and thus photovoltaic performance remain obscure. Herein, we present a novel class of PAs, namely "non-fused ring dipodal phosphonic acids" (NFR-DPAs), featuring simple and malleable non-fused ring backbones and dipodal phosphonic acid anchoring groups. The efficacy of configurational isomerism in modulating the photoelectronic properties and switching molecular orientation of PAs atop electrodes results in distinct substrate surface energy and electronic characteristics. The NFR-DPA with linear (C2h symmetry) and brominated backbone exhibits favorable face-on orientation and enhanced work function modification capability compared to its angular (C2v symmetry) and non-brominated counterparts. This makes it versatile HELs in mitigating interfacial resistance for energy barrier-free hole collection, and affording optimal active layer morphology, which results in an impressive efficiency of 19.11% with a low voltage loss of 0.52 V for binary OSC devices and an excellent efficiency of 19.66% for ternary OSC devices. This study presents a new dimension to design PA-based HELs for high-performance OSCs.

Source apportionment of organic carbon and nitrogen in sediments from river and lake in the highly urbanized Changjiang Delta.

While the impact of human activities on organic matter pollution is recognized, how these impacts vary seasonally in the Changjiang Delta needs further investigation. This study addresses this gap by investigating seasonal variations in organic matter sources and ecological responses to human activities in Changjiang Delta sediments. Total organic carbon (TOC), total nitrogen (TN), and carbon (δ13C) and nitrogen (δ15N) isotopic compositions of surface sediments collected from the Taipu River and Dalian Lake wetland were analyzed. Both water bodies exhibited similar seasonal trends for TOC and TN, with the Taipu River containing an average of 0.46% and 0.03% higher concentrations of TOC and TN, respectively, compared to Dalian Lake. Additionally, the organic index (OI) and organic nitrogen (ON) index were elevated in both water bodies during the wet season. Sediments from Dalian Lake remained uncontaminated to moderately contaminated, while those from the Taipu River were generally classified as moderately to heavily contaminated. Stable isotope analysis identified terrestrial C3 plants (averaging 25.5%), C4 plants (averaging 16.0%), and municipal wastewater (averaging 16.0%) as the main contributors to organic matter in the sediments. These findings suggest that terrestrial plant material and municipal wastewater are key targets for managing organic matter contamination in the Changjiang Delta. Implementing strategic land-use planning and targeted interventions to minimize these inputs can significantly improve water quality and ecosystem health.

Follow-up analysis and research of very low-risk and low-risk gastrointestinal stromal tumors after endoscopic resection.

Currently, due to limited long-term evidence, there remains some controversy surrounding the recommended postoperative monitoring strategy for primary low-risk gastrointestinal stromal tumors (GISTs). This study recruited a total of 532 patients diagnosed with very low-risk and low-risk GISTs who underwent endoscopic resection from 2015 to 2021, including 460 very low-risk patients and 72 low-risk patients. Descriptive statistical analysis was used to evaluate the clinical and pathological characteristics of GIST patients, and Kaplan-Meier methods were employed for survival analysis. The results showed that the 5-year recurrence-free survival rates for very low-risk and low-risk patients were 98.5% and 95.9%, respectively. The 5-year disease-specific survival rates for both groups were 100%. Additionally, the 5-year overall survival rates were 99.7% for very low-risk patients and 100% for low-risk patients (P = 0.69). Therefore, it is suggested that routine follow-up monitoring, including endoscopic surveillance and imaging, may not be necessary for very low-risk and low-risk GISTs after endoscopic resection.

Helical-sampled fiber Bragg grating inscribed by a femtosecond laser in a ring core fiber.

The inscription of a helical-sampled fiber Bragg grating (HSFBG) in a ring core fiber (RCF) using a low repetition rate femtosecond laser point-by-point technique is demonstrated. The reflection spectrum exhibits several peak groups attributed to the helical-sampled structure, with the wavelength interval between different groups determined by the helical pitch. Meanwhile, the number and spacing of the peaks within each group are dictated by the RCF. An investigation into the effects of helical pitch, helical radius, and grating length of the HSFBG on the reflection spectra is conducted. Furthermore, thermal annealing experiments demonstrate that this HSFBG can survive at the temperatures up to 800°C.

HucMSC extracellular vesicles increasing SATB 1 to activate the Wnt/ß-catenin pathway in 6-OHDA-induced Parkinson's disease model.

Parkinson's disease (PD) is a degenerative disorder of the nervous system characterized by the loss of dopaminergic neurons and damage of neurons in the substantia nigra (SN) and striatum, resulting in impaired motor functions. This study aims to investigate how extracellular vesicles (EVs) derived from human umbilical cord mesenchymal stem cells (HucMSC) regulate Special AT-rich sequence-binding protein-1 (SATB 1) and influence Wnt/ß-catenin pathway and autophagy in PD model. The PD model was induced by damaging SH-SY5Y cells and mice using 6-OHDA. According to the study, administering EVs every other day for 14 days improved the motor behavior of 6-OHDA-induced PD mice and reduced neuronal damage, including dopaminergic neurons. Treatment with EVs for 12 hours increased the viability of 6-OHDA-induced SH-SY5Y cells. The upregulation of SATB 1 expression with EV treatment resulted in the activation of the Wnt/ß-catenin pathway in PD model and led to overexpression of ß-catenin. Meanwhile, the expression of LC3 II was decreased, indicating alterations in autophagy. In conclusion, EVs could mitigate neuronal damage in the 6-OHDA-induced PD model by upregulating SATB 1 and activating Wnt/ß-catenin pathway while also regulating autophagy. Further studies on the potential therapeutic applications of EVs for PD could offer new insights and strategies.

Survey on Perkinsus species in two economic mussels (Mytilus coruscus and M. galloprovincialis) along the coast of the East China Sea and the Yellow Sea.

Perkinsus, a parasitic pathogen of marine bivalves, is widely distributed among various mollusks in numerous countries. However, the prevalence and diversity of Perkinsus species in the two economically important mussels, Mytilus coruscus and M. galloprovincialis, in China remain unknown. The presence of the Perkinsus species was identified in the two mussels sampled along the coast of the East China Sea and the Yellow Sea, using both the alternative Ray's fluid thioglycolate medium (ARFTM) and conventional polymerase chain reaction (PCR). The ARFTM test indicated the presence of Perkinsus-like hypnospores in the two mussels. The diameter of the hypnospores in M. coruscus was significantly smaller than that in M. galloprovincialis. The prevalence of Perkinsus in M. galloprovincialis and M. coruscus ranged from 0 to 37.5% and 0 to 25%, respectively. The mean intensity of Perkinsus in M. galloprovincialis and M. coruscus ranged from 0 to 5.14 and 0 to 4.92, respectively. The PCR assay showed that the prevalence of Perkinsus spp. in M. galloprovincialis and M. coruscus was 0 to 25.0% and 0 to 12.5%, respectively. The homology analysis of the newly obtained internal transcribed spacer (ITS) sequences of Perkinsus revealed the highest identity of 100% with P. beihaiensis. The phylogenetic analysis indicated that the Perkinsus isolates from the two mussels were clustered with P. beihaiensis. The results of the molecular biology indicated that only P. beihaiensis was detected in the two mussels. The highest prevalence of P. beihaiensis was observed in Liaoning province (Dalian, 20.83%), followed by Shandong province, Zhejiang province and Fujian province. Consequently, it is recommended that surveillance should be conducted in Dalian, where the prevalence and mean intensity of P. beihaiensis in M. galloprovincialis are the highest.

A Dual-Stream-Modulated Learning Framework for Illuminating and Super-Resolving Ultra-Dark Images.

Enhancement of image resolution for scenes captured under extremely dim conditions represents a practical yet challenging problem that has received little attention. In such low-light scenarios, the limited lighting and minimal signal clarity tend to intensify issues such as diminished detail visibility and altered color accuracy, which are often more severe during the image enhancement process than in scenarios with adequate lighting. Consequently, standard methods for enhancing low-light images or improving their resolution, whether implemented independently or through a combined approach, generally face challenges in effectively restoring luminance, preserving color integrity, and detailing intricate features. To conquer these issues, this article introduces an innovative dual-stream (DS) modulated learning framework designed to tackle the real-world coupled degradation issues in super-resolution (SR) under low-light conditions. Leveraging natural image color characteristics, we introduce a self-regularized luminance constraint to specifically target uneven illumination. We develop illumination-semantic dual modulator (ISDM), a refinement middleware embedded in the decoding stage to bridge illumination and semantic features concurrently, aimed at safeguarding the integrity of lighting and color details at the feature level. Our approach replaces simple upsampling methods with the resolution-sensitive merging upsampler (RSMU) module, which integrates diverse sampling techniques to effectively reduce artifacts and halo effects. Comprehensive experiments on three benchmarks showcase the applicability and generalizability of our approach to diverse and challenging ultra-poorly lit settings, outperforming state-of-the-art methods with a notable improvement. The code and benchmark are publicly available at https://github.com/moriyaya/UltraIS.

Children with developmental coordination disorders: a review of approaches to assessment and intervention.

Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder characterized by deficits in motor skills, with gross and fine motor dysfunction being the main symptom. This condition greatly impairs children's daily life, learning, and social interaction. Symptoms typically appear during preschool or school age, and if left untreated, they can persist into adulthood. Thus, early assessment and intervention are crucial to improve the prognosis. This study aims to review the existing literature on DCD, providing a comprehensive overview of the assessment for children with DCD in terms of body functions and structures, activities and participation, and environmental factors within the framework of the International Classification of Functioning, Disability, and Health - Children and Youth (ICF-CY). Additionally, specific rehabilitation interventions will be described, offering valuable insights for the clinical assessment and intervention of children with DCD.

The role of the "gut microbiota-mitochondria" crosstalk in the pathogenesis of multiple sclerosis.

Multiple Sclerosis (MS) is a neurologic autoimmune disease whose exact pathophysiologic mechanisms remain to be elucidated. Recent studies have shown that the onset and progression of MS are associated with dysbiosis of the gut microbiota. Similarly, a large body of evidence suggests that mitochondrial dysfunction may also have a significant impact on the development of MS. Endosymbiotic theory has found that human mitochondria are microbial in origin and share similar biological characteristics with the gut microbiota. Therefore, gut microbiota and mitochondrial function crosstalk are relevant in the development of MS. However, the relationship between gut microbiota and mitochondrial function in the development of MS is not fully understood. Therefore, by synthesizing previous relevant literature, this paper focuses on the changes in gut microbiota and metabolite composition in the development of MS and the possible mechanisms of the crosstalk between gut microbiota and mitochondrial function in the progression of MS, to provide new therapeutic approaches for the prevention or reduction of MS based on this crosstalk.

Local symmetry-driven interfacial magnetization and electronic states in (ZnO)n/(w-FeO)n superlattices.

Superlattices constructed with the wide-band-gap semiconductor ZnO and magnetic oxide FeO, both in the wurtzite structure, have been investigated using spin-polarized first-principles calculations. The structural, electronic and magnetic properties of the (ZnO)n/(w-FeO)n superlattices were studied in great detail. Two different interfaces in the (ZnO)n/(w-FeO)n superlattices were identified and they showed very different magnetic and electronic properties. Local symmetry-driven interfacial magnetization and electronic states can arise from different Fe/Zn distributions at different interfaces or spin ordering of Fe in the superlattice. The local symmetry-driven interfacial magnetization and electronic states, originating either from different Fe/Zn distribution across interfaces I and II, or by spin ordering of Fe in the superlattice, can be identified. It was also found that, in the case of the ferromagnetic phase, the electrons are more delocalized for the majority spin but strongly localized for the minority spin, which resulted in interesting spin-dependent transport properties. Our results will pave the way for designing novel spin-dependent electronic devices through the construction of superlattices from semiconductors and multiferroics.

MSR405: Inhibiting Neuroinflammation after Spinal Cord Injury in Rats.

The treatment of spinal cord injury (SCI) is often ineffective. Additionally, SCI-induced inflammation leads to secondary injury. Current anti-inflammatory hydrophilic drugs fail to reach the nerve injury site due to the blood-brain barrier. Here, we synthesized MSR405, a new lipophilic unsaturated fatty acid derivative of Radix Isatidis and investigated its therapeutic effect in SCI model rats. Furthermore, we systematically investigated its structure, toxicity, anti-inflammatory effect, and the underlying mechanism. MSR405 was injected into the abdominal cavity of the Sprague Dawley SCI model rats, and the effect on their behavioral scores and pathology was estimated to assess the status of neurological inflammation. Our data show that MSR405 treatment significantly improved the motor function of SCI rats, and markedly suppressed the associated neuroinflammation. Moreover, MSR405 could attenuate LPS-induced inflammatory response in BV2 cells (Mouse microglia cells) in vitro. Mechanistically, MSR405 inhibits proinflammatory cytokines, supporting the anti-inflammatory response. Additionally, MSR405 can significantly block the TLR4/NF-κB signaling pathway and nitric oxide production. In summary, MSR405 reduces inflammation in SCI rats through the TLR4/NF-κB signal cascade and can inhibit neuroinflammation after spinal cord injury.

LiDAR Dynamic Target Detection Based on Multidimensional Features.

To address the limitations of LiDAR dynamic target detection methods, which often require heuristic thresholding, indirect computational assistance, supplementary sensor data, or postdetection, we propose an innovative method based on multidimensional features. Using the differences between the positions and geometric structures of point cloud clusters scanned by the same target in adjacent frame point clouds, the motion states of the point cloud clusters are comprehensively evaluated. To enable the automatic precision pairing of point cloud clusters from adjacent frames of the same target, a double registration algorithm is proposed for point cloud cluster centroids. The iterative closest point (ICP) algorithm is employed for approximate interframe pose estimation during coarse registration. The random sample consensus (RANSAC) and four-parameter transformation algorithms are employed to obtain precise interframe pose relations during fine registration. These processes standardize the coordinate systems of adjacent point clouds and facilitate the association of point cloud clusters from the same target. Based on the paired point cloud cluster, a classification feature system is used to construct the XGBoost decision tree. To enhance the XGBoost training efficiency, a Spearman's rank correlation coefficient-bidirectional search for a dimensionality reduction algorithm is proposed to expedite the optimal classification feature subset construction. After preliminary outcomes are generated by XGBoost, a double Boyer-Moore voting-sliding window algorithm is proposed to refine the final LiDAR dynamic target detection accuracy. To validate the efficacy and efficiency of our method in LiDAR dynamic target detection, an experimental platform is established. Real-world data are collected and pertinent experiments are designed. The experimental results illustrate the soundness of our method. The LiDAR dynamic target correct detection rate is 92.41%, the static target error detection rate is 1.43%, and the detection efficiency is 0.0299 s. Our method exhibits notable advantages over open-source comparative methods, achieving highly efficient and precise LiDAR dynamic target detection.

Non-halogenated Solvent-Processed Organic Solar Cells with Approaching 20 % Efficiency and Improved Photostability.

The development of high-efficiency organic solar cells (OSCs) processed from non-halogenated solvents is crucially important for their scale-up industry production. However, owing to the difficulty of regulating molecular aggregation, there is a huge efficiency gap between non-halogenated and halogenated solvent processed OSCs. Herein, we fabricate o-xylene processed OSCs with approaching 20 % efficiency by incorporating a trimeric guest acceptor named Tri-V into the PM6:L8-BO-X host blend. The incorporation of Tri-V effectively restricts the excessive aggregation of L8-BO-X, regulates the molecular packing and optimizes the phase-separation morphology, which leads to mitigated trap density states, reduced energy loss and suppressed charge recombination. Consequently, the PM6:L8-BO-X:Tri-V-based device achieves an efficiency of 19.82 %, representing the highest efficiency for non-halogenated solvent-processed OSCs reported to date. Noticeably, with the addition of Tri-V, the ternary device shows an improved photostability than binary PM6:L8-BO-X-based device, and maintains 80 % of the initial efficiency after continuous illumination for 1380 h. This work provides a feasible approach for fabricating high-efficiency, stable, eco-friendly OSCs, and sheds new light on the large-scale industrial production of OSCs.

Original speech and its echo are segregated and separately processed in the human brain.

Speech recognition crucially relies on slow temporal modulations (<16 Hz) in speech. Recent studies, however, have demonstrated that the long-delay echoes, which are common during online conferencing, can eliminate crucial temporal modulations in speech but do not affect speech intelligibility. Here, we investigated the underlying neural mechanisms. MEG experiments demonstrated that cortical activity can effectively track the temporal modulations eliminated by an echo, which cannot be fully explained by basic neural adaptation mechanisms. Furthermore, cortical responses to echoic speech can be better explained by a model that segregates speech from its echo than by a model that encodes echoic speech as a whole. The speech segregation effect was observed even when attention was diverted but would disappear when segregation cues, i.e., speech fine structure, were removed. These results strongly suggested that, through mechanisms such as stream segregation, the auditory system can build an echo-insensitive representation of speech envelope, which can support reliable speech recognition.

Learning With Constraint Learning: New Perspective, Solution Strategy and Various Applications.

The complexity of learning problems, such as Generative Adversarial Network (GAN) and its variants, multi-task and meta-learning, hyper-parameter learning, and a variety of real-world vision applications, demands a deeper understanding of their underlying coupling mechanisms. Existing approaches often address these problems in isolation, lacking a unified perspective that can reveal commonalities and enable effective solutions. Therefore, in this work, we proposed a new framework, named Learning with Constraint Learning (LwCL), that can holistically examine challenges and provide a unified methodology to tackle all the above-mentioned complex learning and vision problems. Specifically, LwCL is designed as a general hierarchical optimization model that captures the essence of these diverse learning and vision problems. Furthermore, we develop a gradient-response based fast solution strategy to overcome optimization challenges of the LwCL framework. Our proposed framework efficiently addresses a wide range of applications in learning and vision, encompassing three categories and nine different problem types. Extensive experiments on synthetic tasks and real-world applications verify the effectiveness of our approach. The LwCL framework offers a comprehensive solution for tackling complex machine learning and computer vision problems, bridging the gap between theory and practice.

Development and validation of a preoperative risk nomogram prediction model for gastric gastrointestinal stromal tumors.

BACKGROUND AND STUDY AIMS: Gastrointestinal stromal tumors (GIST) carry a potential risk of malignancy, and the treatment of GIST varies for different risk levels. However, there is no systematic preoperative assessment protocol to predict the malignant potential of GIST. The aim of this study was to develop a reliable and clinically applicable preoperative nomogram prediction model to predict the malignant potential of gastric GIST. PATIENTS AND METHODS: Patients with a pathological diagnosis of gastric GIST from January 2015 to December 2021 were screened retrospectively. Univariate and multivariate logistic analyses were used to identify independent risk factors for gastric GIST with high malignancy potential. Based on these independent risk factors, a nomogram model predicting the malignant potential of gastric GIST was developed and the model was validated in the validation group. RESULTS: A total of 494 gastric GIST patients were included in this study and allocated to a development group (n = 345) and a validation group (n = 149). In the development group, multivariate logistic regression analysis revealed that tumor size, tumor ulceration, CT growth pattern and monocyte-to- lymphocyte ratio (MLR) were independent risk factors for gastric GIST with high malignancy potential. The AUC of the model were 0.932 (95% CI 0.890-0.974) and 0.922 (95% CI 0.868-0.977) in the development and validation groups, respectively. The best cutoff value for the development group was 0.184, and the sensitivity and specificity at this value were 0.895 and 0.875, respectively. The calibration curves indicated good agreement between predicted and actual observed outcomes, while the DCA indicated that the nomogram model had clinical application. CONCLUSIONS: Tumor size, tumor ulceration, CT growth pattern and MLR are independent risk factors for high malignancy potential gastric GIST, and a nomogram model developed based on these factors has a high ability to predict the malignant potential of gastric GIST.