Efficient Modulation Research Articles

Exploration of the pathogenesis of nephrotic syndrome and traditional Chinese medicine intervention based on gut microbiota

Nephrotic syndrome (NS) represents a prevalent syndrome among various chronic kidney disease pathologies and is known for its higher severity and worse prognosis compared with chronic glomerulonephritis. Understanding its pathogenesis and identifying more effective treatment modalities have long been a concern of kidney specialists. With the introduction of the gut–kidney axis concept and the progress in omics technologies, alterations in the gut microbiota have been observed in primary and secondary NS. This link has been extensively researched in conditions such as diabetic nephropathy and immunoglobulin A (IgA) nephropathy. Thus, dysbiosis of the gut microbiota is seen as a crucial contributing factor in NS; however, there is a lack of comprehensive reviews that elucidate the changes in the gut microbiota across different NS conditions and that describe its mechanistic role in the disease. Moreover, serving as an innate regulator of the gut microbiota, traditional Chinese medicine (TCM) has the potential to exert a profound impact on the expression of inflammation-promoting agents, decreasing the levels of endotoxins and uremic toxins. In addition, it strengthens the stability of the intestinal barrier while controlling the metabolic function of the body through its efficient modulation of the gut microbiota. This intricate process yields far-reaching consequences for NS.

Revisiting Photovoltaic Module Antireflection Coatings: A Novel, Dense Sol–Gel Design to Address Long‐Standing Durability Limitations

ABSTRACTThe antireflection (AR) coating applied to solar glass in photovoltaic modules has remained largely unchanged for decades, despite its well‐documented lack of durability. Traditional porous structured single‐layer AR coatings last as little as 5 years in the field. In this paper, we propose a novel five‐layer dense AR coating design that offers improved durability and effectiveness compared to traditional coatings. This paper provides detailed insights into the development and characterization of the novel five‐layer AR coating, including simulation, optical measurements, and abrasion testing, providing guidance to the photovoltaic community seeking to improve the efficiency and longevity of solar modules.

Annual Energy Analysis of a Building Integrated Semi-Transparent Photovoltaic Thermal Facade

Abstract This study numerically examines the performance of a semi-transparent Building Integrated Photovoltaic (BiSPVT) façade in Srinagar, India's weather conditions. The primary objective is to explore the façade's potential in reducing building energy consumption while enhancing the electrical efficiency of PV modules by effectively cooling their surfaces through adjacent air circulation. A 1-D thermal model, implemented in MATLAB, analyses the façade's performance under distinct weather conditions: clear day, hazy day, hazy and cloudy day, and fully cloudy day. The assessment of climatic conditions considers their impact on both BIPV cell temperature and the annual energy output in a steady state, considering the fluctuations in thermal losses. Results reveal the highest electrical efficiency of 18.9% and an overall thermal efficiency of 57.58% in January. Annually, the BiSPVT façade system generated 121.22 kWh/m2 of electrical energy, 61.06 kWh/m2 of thermal energy, 366.23 kWh/m2 of overall thermal energy, and 122.36 kWh/m2 of useful exergy. These findings offer an insight regarding reduction in utilization of conventional source of energy due to sustainable building design and renewable energy utilization.

Enhancing non-viral DNA delivery systems: Recent advances in improving efficiency and target specificity.

DNA-based therapies are often limited by challenges such as stability, long-term integration, low transfection efficiency, and insufficient targeted DNA delivery. This review focuses on recent progress in the design of non-viral delivery systems for enhancing targeted DNA delivery and modulation of therapeutic efficiency. Cellular uptake and intracellular trafficking mechanisms play a crucial role in optimizing gene delivery efficiency. There are two main strategies employed to improve the efficiency of gene delivery vectors: (i) explore different administration routes (e.g., mucosal, intravenous, intramuscular, subcutaneous, intradermal, intratumoural, and intraocular) that best facilitates optimal uptake into the targeted cells and organs and (ii) modify the delivery vectors with cell-specific ligands (e.g., natural ligands, antibodies, peptides, carbohydrates, or aptamers) that enable targeted uptake to specific cells with higher specificity and improved biodistribution. We describe how recent progress in employing these DNA delivery strategies is advancing the field and increasing the clinical translation and ultimate clinical application of DNA therapies.

Reliable Prediction of Solar Photovoltaic Power and Module Efficiency using Bayesian Surrogate Assisted Explainable Data-Driven Model

This study proposes a Bayesian surrogate-driven explainable deep neural network model to predict and interpret the module efficiency and maximum output power of three commercially available photovoltaic modules: monocrystalline silicon, polycrystalline silicon, and amorphous silicon during the winter season. In addition, the influence of the photovoltaic material and ambient conditions on the predicted power and module efficiency is investigated. The model inputs include solar irradiance, module material (monocrystalline, polycrystalline, and amorphous silicon), season of the year (months), and time of day (morning to evening). Experiments were conducted on these three modules during the winter using data from Rawalpindi, Pakistan. These data were then fed into the deep neural network model to train and yield predictions. Several preprocessing techniques such as logarithmic transformation, square root, cube root, reciprocal, and exponential transformations are employed to improve the linearity of distributed data. For hyper-parameters optimization, Gaussian process, gradient boost regression trees, and random forest are used. The results show that the optimal deep neural network using random forest surrogate model along with square root and exponential transformation is able to predict the maximum power output and module efficiency of the considered photovoltaic modules for the entire investigated period with a correlation coefficient, R2 = 0.998. The least accurate model (R2=0.991) is a Gaussian process using simple data distribution. These results hold valuable insights for predicting and optimizing the performance of other solar photovoltaic modules in various climates and different seasons of the year.

CSegNet: a hybrid transformer-CNN network for road crack image segmentation

Detecting cracks from optical images plays a crucial role in road maintenance but its good realisation has many challenges. Road cracks exhibit significant diversity and complexity in terms of shape, size and texture and road images may contain various types of noise and interference, such as lighting variations, shadows and different appearances, due to varying perspectives and scales. To address these challenges, a comprehensive road crack dataset called CRCrack has been constructed, which encompasses various crack characteristics. This study proposes a road crack segmentation network called CSegNet, which combines convolutional neural networks (CNNs) and transformers. The network adopts an encoder-decoder framework, namely DeepLabV3+. In the encoder, leveraging the transformers' flexibility in modelling long-term dependencies and the CNNs' ability to capture local contextual information through local receptive fields, weight sharing and spatial subsampling, a ResNeXt-Transformer (ResNeXTR) feature extraction module is designed as the backbone network to enhance the feature extraction capability for road crack images. To reduce the computational cost in the self-attention (SA) computation of the transformer, an average pooling layer is introduced to downsample the dimensions of the encoded features. In the decoder, to focus on the key information of road cracks under diverse environmental conditions and interferences, an efficient channel attention module (ECAM) and a spatial attention module (SAM) are combined to design an efficient convolutional block attention module (ECBAM) to optimise feature representation. Through comparative experiments on the CRCrack dataset, the results demonstrate that the proposed method outperforms classic networks such as U-Net and DeepLabV3+ in terms of intersection over union (IoU), Dice coefficient and area under the receiver operating characteristic (AUROC) curve evaluation metrics. It exhibits good adaptability to ground crack images from different sources, providing a basis for estimating the degree of road damage.

Strain-gradient-induced modulation of photovoltaic efficiency

Strain-gradient-induced modulation of photovoltaic efficiency

WE-DeepLabV3+: A lightweight segmentation model for Panax notoginseng leaf diseases

Panax notoginseng plays an important role in traditional Chinese medicine. However, diseases pose a significant threat to the quality and yield of P. notoginseng. The main challenge related to the identification of P. notoginseng leaf diseases is how to achieve good performance in the case of small diseased spots on P. notoginseng leaf, overlapping edges of diseased leaf and the difficulty in mobile deployment. A lightweight semantic segmentation model Window Efficient-DeepLabv3+ is proposed for segmentation and quantification of P. notoginseng leaf diseases. We propose the Window Attention-ASPP module and present a hierarchical stacking of features, which improves model accuracy for minor target lesions while reducing parameters. In addition, a lightweight backbone network MobileNetV2 is utilized as a feature extraction module. The decoding stage introduces the Efficient Channel Attention module, which effectively improves the accuracy of the segmentation of the blade contour. Experimental results yielded the Mean Intersection Over Union, Mean Precision, and Mean Recall metrics of WE-DeepLabV3+ network to be 82.0 %, 87.6 %, and 92.4 % respectively, outperforming other segmentation models such as UNet, PSPNet, CaraNet, SegNet, and BiSeNetV2. Moreover, the number of parameters has been reduced by 90.6 % with only 5.1 M parameters. Finally, the method is used to quantify the disease of P. notoginseng leaf, the error is only 1.15 % and 0.82 %, which proves that it can quantify the disease severity accurately. Thus, the proposed method holds great significance for raising the yield and quality of P. notoginseng, also providing reliable guidance for precise fertilization and drug control.

Towards Efficient and Portable Software Modulator via Neural Networks for IoT Gateways

Towards Efficient and Portable Software Modulator via Neural Networks for IoT Gateways

EPMF: Efficient Perception-Aware Multi-Sensor Fusion for 3D Semantic Segmentation.

We study multi-sensor fusion for 3D semantic segmentation that is important to scene understanding for many applications, such as autonomous driving and robotics. Existing fusion-based methods, however, may not achieve promising performance due to the vast difference between the two modalities. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) to effectively exploit perceptual information from two modalities, namely, appearance information from RGB images and spatio-depth information from point clouds. To this end, we project point clouds to the camera coordinate using perspective projection, and process both inputs from LiDAR and cameras in 2D space while preventing the information loss of RGB images. Then, we propose a two-stream network to extract features from the two modalities, separately. The extracted features are fused by effective residual-based fusion modules. Moreover, we introduce additional perception-aware losses to measure the perceptual difference between the two modalities. Last, we propose an improved version of PMF, i.e., EPMF, which is more efficient and effective by optimizing data pre-processing and network architecture under perspective projection. Specifically, we propose cross-modal alignment and cropping to obtain tight inputs and reduce unnecessary computational costs. We then explore more efficient contextual modules under perspective projection and fuse the LiDAR features into the camera stream to boost the performance of the two-stream network. Extensive experiments on benchmark data sets show the superiority of our method. For example, on nuScenes test set, our EPMF outperforms the state-of-the-art method, i.e., RangeFormer, by 0.9% in mIoU.

Heterologous expression of a highly thermostable L-asparaginase from Thermococcus zilligii in Aspergillus niger for efficient reduction of acrylamide in French fries

L-asparaginase (L-ASNase) can hydrolyze L-asparagine, a precursor to acrylamide, thereby reducing toxic acrylamide formation in fried foods. Currently, commercial L-ASNases are primarily produced by wild-type (WT) filamentous fungi; however, these enzymes often exhibit rapid activity loss during high-temperature processing due to limited thermal stability. In this study, we screened a thermostable L-ASNase gene from thermophile bacteria and expressed it in Aspergillus niger to reduce acrylamide content in French fries. Initially, four genes encoding thermostable L-ASNases were selected and integrated into the A. niger genome via non-homologous end joining. Among these, the L-ASNase gene tzi from Thermococcus zilligii was successfully expressed in A. niger, yielding an extracellular activity of 114 U·mg−1. The recombinant enzyme (An-Tzi) displayed the same optimal temperature and pH as its WT counterpart but exhibited superior catalytic efficiency, likely due to the efficient post-translational modifications in A. niger. To further enhance expression, the tzi gene was integrated into the amylase (amyA) locus of the A. niger genome using the CRISPR-Cas9 system, resulting in increased activity of 128 U·mg−1. Additionally, various lengths of the highly expressed glucoamylase (glaA) protein from A. niger AG11 were fused to the N-terminus of the Tzi. Notably, fusing the 500-amino-acid catalytic domain of glaA led to a substantial 3.3-fold increase in enzyme activity. Despite the metabolic stress induced by high-level expression of glaA, supplementing the culture medium with metal ions and sophorose resulted in an extracellular activity of 486.74 U·mg−1, the highest reported yield of L-ASNase in shake flasks. Finally, applying the An-Tzi to French fries achieved a 32 % greater reduction in acrylamide compared to the commercial enzyme. Overall, the recombinant A. niger strain expressing thermostable An-Tzi demonstrates significant potential for industrial applications targeting acrylamide reduction in fried and baked foods.

Cashew phenol modified lignosulfonate strategy for the preparation of high-performance dye dispersants.

Lignin-based dye dispersants (LDD), as one of the most common lignin resource end-application product, is difficult to meet the rapidly developing needs of modern fiber dyeing due to their inherent structural defects. The efficient upgrading and modification of LDD is of great significance to the value-added application of lignin resources and the expansion of LDD sinking market. In this study, using industrial kraft lignin as raw material, cashew phenol was introduced into lignosulfonate in the form of CC bonds by ingenious utilizing the structural characteristics of lignin disordered condensation. Aromatic ring in cashew phenol could provide sufficient sulfonic acid group grafting sites, while C15 fatty chain could enhance steric hindrance effect. In the dispersibility test, the dispersing power showed 99.09 %, and the low/high temperature dispersion (LTD/HTD) were 5 s/4 and 6 s/4, respectively, higher than the dispersibility level of LDD in the market, has great commercial value and industrial potential. Combined with FTIR, 1H NMR, GPC and ICP, the structural properties of lignin were analyzed in detail, and the degree of sulfonation-condensation was balanced-optimized. The CC bond condensation process between cashew phenol and lignin was revealed, and the mechanism of action on dye particles was elucidated.

Chitosan and hyaluronic acid in colorectal cancer therapy: A review on EMT regulation, metastasis, and overcoming drug resistance.

Up to 90% of cancer-related fatalities could be attributed to metastasis. Therefore, understanding the mechanisms that facilitate tumor cell metastasis is beneficial for improving patient survival and results. EMT is considered the main process involved in the invasion and spread of CRC. Essential molecular components like Wnt, TGF-β, and PI3K/Akt play a role in controlling EMT in CRC, frequently triggered by various factors such as Snail, Twist, and ZEB1. These factors affect not only the spread of CRC but also determine the reaction to chemotherapy. The influence of non-coding RNAs, especially miRNAs and lncRNAs, on the regulation of EMT is clear in CRC. Exosomes, involved in cell-to-cell communication, can affect the TME and metastasis of CRC. Pharmacological substances and nanoparticles demonstrate promise as efficient modulators of EMT in CRC. Chitosan and HA are two major carbohydrate polymers with considerable potential in inhibiting CRC. Chitosan and HA can be employed to modify nanoparticles to enhance cargo transport for reducing CRC. Additionally, chitosan and HA-modified nanocarriers, which can be utilized as potential approaches in suppressing EMT and reversing drug resistance in CRC, can inhibit EMT and chemoresistance, crucial components in tumorigenesis.

Enhanced thermoelectric properties of bismuth telluride via Ultra-Low thermal conductivity BOSC compound addition

This study aimed to enhance the thermoelectric properties of bismuth telluride by adding Bi4O4SeCl2 (BOSC). Commercial N-type bismuth telluride was mixed with BOSC powder in varying concentrations. As the BOSC content increased, the carrier concentration also rose due to chlorine atoms acting as donor impurities. Despite this increase, the power factor values of the samples with BOSC additives did not significantly differ from those of the bare bismuth telluride sample. However, the total thermal conductivity decreased significantly with the addition of BOSC, reaching a minimum value of 0.54 W·m–1·K–1 at 150 °C for the sample with 1 wt% BOSC. Notably, the ZT value for the sample with 1 wt% BOSC was about 0.86, which is four times higher than that of the bare bismuth telluride sample. Our findings demonstrate superior thermoelectric performance, indicating a more efficient modification of thermoelectric properties through the addition of BOSC to the bismuth telluride matrix.

Novel electroporation microchip with field constriction enhances transfection efficiency and survival rates of feline embryos

This study introduces a low-voltage electroporation microchip designed for transfection in cat embryos, featuring real-time impedance monitoring. The microchip uses a field constriction strategy, which localises the electric field to the membrane region in contact with the micro-orifice, enhancing electroporation efficiency while minimising damage. Embryos were positioned on the orifice, and a series of voltage pulses (10, 15, and 20 V) were applied. Electroporation efficacy was assessed using fluorescent dyes, followed by real-time impedance measurements to monitor the membrane resealing time. It provided valuable insights into membrane recovery times, essential for optimizing gene editing conditions to ensure efficient delivery and maintain cell integrity. The results demonstrated that the microchip with 15 V achieved a 69.5% higher electroporation rate and 100% of survival compared to conventional devices (p < 0.05). Additionally, the microchip successfully facilitated the transfer of green fluorescent protein genes into embryos, achieving a 78.5% success rate significantly greater compared to 53.6% with the conventional device (p < 0.05). This innovative microchip provides transformative transfection technology for safer and more efficient genomic modifications in embryos. It holds promising applications across species and therapeutic interventions, paving the way for future studies in advanced genomic research.

Accurate Method for Solar Power Generation Estimation for Different PV (Photovoltaic Panels) Technologies

In 2023, solar photovoltaic energy alone accounted for 75% of the global increase in renewable capacity. Moreover, this natural energy resource is the one that requires the least investment, which makes it accessible to developing countries. Increasing return on investment in these regions requires a particular evaluation of environmental parameters influencing PV systems performance. Higher temperatures decrease PV module efficiency and, as a result, their power output. Additionally, fluctuations in solar irradiance directly impact the energy generated by these systems. Consequently, it is essential for investors to improve accurate predictive models that assess the power generation capacity of photovoltaic systems under local environmental conditions. Therefore, accurate estimation of maximum power generation is then crucial for optimizing photovoltaic (PV) system performances and selecting suitable PV modules for specific climates. In this context, this study presents an experimental comparison of three maximum power prediction methods for four PV module types (amorphous silicon, monocrystalline silicon, micromorphous silicon, and polycrystalline silicon) under real outdoor conditions. Experimental data gathered over the course of a year are analyzed and processed for the four PV technologies. Three different methods taking into account environmental parameters are presented and analyzed. The first estimation method utilizes irradiance as the primary input parameter, while two additional methods incorporate ambient temperature and PV module temperature for enhanced accuracy. The performance of each method is evaluated using standard statistical metrics, including the root mean square error (RMSE) and coefficient of determination (R2). The results demonstrate the effectiveness of all three methods, with RMSE values ranging from 1.6 W to 3.8 W and R2 values consistently above 0.95. The most appropriate method for estimating PV power output is determined by the specific type of photovoltaic module and the availability of meteorological parameters. This study provides valuable insights for selecting an appropriate maximum power prediction method and choosing the most suitable PV module for a given climate.

Edge-guided and hierarchical aggregation network for robust medical image segmentation

Medical image segmentation with the convolutional neural networks (CNNs), significantly enhances clinical analysis and disease diagnosis. However, medical images inherently exhibit large intra-class variability, minimal inter-class differences, and substantial noise levels. Extracting robust contextual information and aggregating discriminative features for fine-grained segmentation remains a formidable task. Additionally, existing methods often struggle with producing accurate mask edges, leading to blurred boundaries and reduced segmentation precision. This paper introduces a novel Edge-guided and Hierarchical Aggregation Network (EHANet) which excels at capturing rich contextual information and preserving fine spatial details, addressing the critical issues of inaccurate mask edges and detail loss prevalent in current segmentation models. The Inter-layer Edge-aware Module (IEM) enhances edge prediction accuracy by fusing early encoder layers, ensuring precise edge delineation. The Efficient Fusion Attention Module (EFA) adaptively emphasizes critical spatial and channel features while filtering out redundancies, enhancing the model’s perception and representation capabilities. The Adaptive Hierarchical Feature Aggregation Module (AHFA) module optimizes feature fusion within the decoder, maintaining essential information and improving reconstruction fidelity through hierarchical processing. Quantitative and qualitative experiments on four public datasets demonstrate the effectiveness of EHANet in achieving superior mIoU, mDice, and edge accuracy against eight other state-of-the-art segmentation methods, highlighting its robustness and precision in diverse clinical scenarios.

SNA·SMNP·CBE system: A novel integrative strategy for β-hemoglobinopathies gene therapy

Here, we developed and demonstrated a novel integrative system—Silica Nanorods (SNA) substrate cell capture combined with Supramolecular Nanoparticle (SMNP) delivery mediated CBE base editing (SNA·SMNP·CBE)—achieving the synchronization of CD34+HSPCs cell capture and gene editing for β-hemoglobinopathies. First, in vitro study shows it enables efficient and precise modification of BCL11A promoter in CD34+HSPCs, yielding the highly editing efficiency of 50.4 %, thus making an alternative strategy to conventional immunomagnetic cell separation and electroporation transfection system mediated CBE editing (IMS·EP·CBE). Then, we transplanted the edited human CD34+HSPCs into severe combined immunodeficiency (SCID) mice by using intraosseous injection strategy. When compared with conventional IMS·EP·CBE methods, our results showed that significantly higher human HBG expression in the bone marrow and peripheral blood of recipient mice, and long-term engraftment, evidenced from similar gene expression profiles to naïve CD34+HSPCs at 14 weeks. Conclusively, our integrative system—SNA·SMNP·CBE·intraosseous injection—offers an appealing novel way for the unique potential of gene therapy in the clinic application for β-hemoglobinopathies patients.

Advances in constructing Carbon Fiber‐MXene multilevel structures to reinforce interfacial properties of carbon fiber reinforced polymer composites

AbstractCarbon fiber reinforced polymers (CFRPs) have received widespread attention due to their excellent mechanical properties. However, the poor interfacial properties between CF and resin matrix urgently need feasible and efficient surface modification strategies of CF to regulate the interface. Due to its unique layered structure and abundant surface groups, MXene has been increasingly used in the field of CFRP interfacial modification in recent years. It possesses the advantages of good stability, large specific surface area, abundant adsorption sites and rich functional groups, which could effectively improve surface polarity, roughness and wettability of carbon fibers, thus enhancing effective interfacial bonding and enabling hierarchical regulation of interface in CFRPs. This paper reviews feasible strategies for constructing CF‐MXene multilevel structures, including electrophoretic deposition, chemical grafting, electrostatic self‐assembly, and sizing coating. Then, the strengthening mechanism involving surface morphology, mechanical properties, and interfacial strength of CFRPs are systematically evaluated centered on interfacial activity, interfacial adhesion, surface roughness and surface energy. Meanwhile, the problems in introducing MXene to CF surfaces and future research directions are systematically prospected. This paper could provide valuable guidance to construct CF‐MXene multilevel structures to reinforce interfacial properties of CFRPs composites, thus realizing hierarchical modulation of CFRPs interfaces.Highlights A feasible strategy for constructing CF‐MXene multilevel structures is built. The main methods for constructing CF‐MXene multilevel structures are provided. Treatment processes and reinforcing mechanisms of CFRPs are evaluated.

AFENet: An Attention-Focused Feature Enhancement Network for the Efficient Semantic Segmentation of Remote Sensing Images

The semantic segmentation of high-resolution remote sensing images (HRRSIs) faces persistent challenges in handling complex architectural structures and shadow occlusions, limiting the effectiveness of existing deep learning approaches. To address these limitations, we propose an attention-focused feature enhancement network (AFENet) with a novel encoder–decoder architecture. The encoder architecture combines ResNet50 with a parallel multistage feature enhancement group (PMFEG), enabling robust feature extraction through optimized channel reduction, scale expansion, and channel reassignment operations. Building upon this foundation, we develop a global multi-scale attention mechanism (GMAM) in the decoder that effectively synthesizes spatial information across multiple scales by learning comprehensive global–local relationships. The architecture is further enhanced by an efficient feature-weighted fusion module (FWFM) that systematically integrates remote spatial features with local semantic information to improve segmentation accuracy. Experimental results across diverse scenarios demonstrate that AFENet achieves superior performance in building structure detection, exhibiting enhanced segmentation connectivity and completeness compared to state-of-the-art methods.