Comparison of Different Fusion Radiomics for Predicting Benign and Malignant Sacral Tumors: A Pilot Study.

Differentiating between benign and malignant sacral tumors is crucial for determining appropriate treatment options. This study aims to develop two benchmark fusion models and a deep learning radiomic nomogram (DLRN) capable of distinguishing between benign and malignant sacral tumors using multiple imaging modalities. We reviewed axial T2-weighted imaging (T2WI) and non-contrast computed tomography (NCCT) of 134 patients pathologically confirmed as sacral tumors. The two benchmark fusion models were developed using fusion deep learning (DL) features and fusion classical machine learning (CML) features from multiple imaging modalities, employing logistic regression, K-nearest neighbor classification, and extremely randomized trees. The two benchmark models exhibiting the most robust predictive performance were merged with clinical data to formulate the DLRN. Performance assessment involved computing the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV). The DL benchmark fusion model demonstrated superior performance compared to the CML fusion model. The DLRN, identified as the optimal model, exhibited the highest predictive performance, achieving an accuracy of 0.889 and an AUC of 0.961 in the test sets. Calibration curves were utilized to evaluate the predictive capability of the models, and decision curve analysis (DCA) was conducted to assess the clinical net benefit of the DLR model. The DLRN could serve as a practical predictive tool, capable of distinguishing between benign and malignant sacral tumors, offering valuable information for risk counseling, and aiding in clinical treatment decisions.

Research on Tire Surface Damage Detection Method Based on Image Processing.

The performance of the tire has a very important impact on the safe driving of the car, and in the actual use of the tire, due to complex road conditions or use conditions, it will inevitably cause immeasurable wear, scratches and other damage. In order to effectively detect the damage existing in the key parts of the tire, a tire surface damage detection method based on image processing was proposed. In this method, the image of tire side is captured by camera first. Then, the collected images are preprocessed by optimizing the multi-scale bilateral filtering algorithm to enhance the detailed information of the damaged area, and the optimization effect is obvious. Thirdly, the image segmentation based on clustering algorithm is carried out. Finally, the Harris corner detection method is used to capture the "salt and pepper" corner of the target region, and the segmsegmed binary image is screened and matched based on histogram correlation, and the target region is finally obtained. The experimental results show that the similarity detection is accurate, and the damage area can meet the requirements of accurate identification.

A Novel Network Pharmacology Strategy Based on the Universal Effectiveness-Common Mechanism of Medical Herbs Uncovers Therapeutic Targets in Traumatic Brain Injury.

Purpose: Many herbs can promote neurological recovery following traumatic brain injury (TBI). There must lie a shared mechanism behind the common effectiveness. We aimed to explore the key therapeutic targets for TBI based on the common effectiveness of the medicinal plants. Material and methods: The TBI-effective herbs were retrieved from the literature as imputes of network pharmacology. Then, the active ingredients in at least two herbs were screened out as common components. The hub targets of all active compounds were identified through Cytohubba. Next, AutoDock vina was used to rank the common compound-hub target interactions by molecular docking. A highly scored compound-target pair was selected for in vivo validation. Results: We enrolled sixteen TBI-effective medicinal herbs and screened out twenty-one common compounds, such as luteolin. Ten hub targets were recognized according to the topology of the protein-protein interaction network of targets, including epidermal growth factor receptor (EGFR). Molecular docking analysis suggested that luteolin could bind strongly to the active pocket of EGFR. Administration of luteolin or the selective EGFR inhibitor AZD3759 to TBI mice promoted the recovery of body weight and neurological function, reduced astrocyte activation and EGFR expression, decreased chondroitin sulfate proteoglycans deposition, and upregulated GAP43 levels in the cortex. The effects were similar to those when treated with the selective EGFR inhibitor. Conclusion: The common effectiveness-based, common target screening strategy suggests that inhibition of EGFR can be an effective therapy for TBI. This strategy can be applied to discover core targets and therapeutic compounds in other diseases.

Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences.

Using radiomics to predict O6-methylguanine-DNA methyltransferase promoter methylation status in patients with newly diagnosed glioblastoma and compare the performances of different MRI sequences. Preoperative MRI scans from 215 patients were included in this retrospective study. After image preprocessing and feature extraction, two kinds of machine-learning models were established and compared for their performances. One kind was established using all MRI sequences (T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient), and the other kind was based on single MRI sequence as listed above. For the machine-learning model based on all sequences, a total of seven radiomic features were selected with the Maximum Relevance and Minimum Redundancy algorithm. The predictive accuracy was 0.993 and 0.750 in the training and validation sets, respectively, and the area under curves were 1.000 and 0.754 in the two sets, respectively. For the machine-learning model based on single sequence, the numbers of selected features were 8, 10, 10, 13, 9, 7 and 6 for T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient, respectively, with predictive accuracies of 0.797-1.000 and 0.583-0.694 in the training and validation sets, respectively, and the area under curves of 0.874-1.000 and 0.538-0.697 in the two sets, respectively. Specifically, T1-weighted image-based model performed best, while contrast enhancement-based model performed worst in the independent validation set. The machine-learning models based on seven different single MRI sequences performed differently in predicting O6-methylguanine-DNA methyltransferase status in glioblastoma, while the machine-learning model based on the combination of all sequences performed best.

Unsupervised OCT image despeckling with ground-truth- and repeated-scanning-free features.

Optical coherence tomography (OCT) can resolve biological three-dimensional tissue structures, but it is inevitably plagued by speckle noise that degrades image quality and obscures biological structure. Recently unsupervised deep learning methods are becoming more popular in OCT despeckling but they still have to use unpaired noisy-clean images or paired noisy-noisy images. To address the above problem, we propose what we believe to be a novel unsupervised deep learning method for OCT despeckling, termed Double-free Net, which eliminates the need for ground truth data and repeated scanning by sub-sampling noisy images and synthesizing noisier images. In comparison to existing unsupervised methods, Double-free Net obtains superior denoising performance when trained on datasets comprising retinal and human tissue images without clean images. The efficacy of Double-free Net in denoising holds significant promise for diagnostic applications in retinal pathologies and enhances the accuracy of retinal layer segmentation. Results demonstrate that Double-free Net outperforms state-of-the-art methods and exhibits strong convenience and adaptability across different OCT images.

Xuefu Zhuyu decoction promotes synaptic plasticity by targeting miR-191a-5p/BDNF-TrkB axis in severe traumatic brain injury.

BACKGROUND: Xuefu Zhuyu decoction (XFZYD) is a traditional Chinese herbal formula known for its ability to eliminate blood stasis and improve blood circulation, providing neuroprotection against severe traumatic brain injury (sTBI). However, the underlying mechanism is still unclear. PURPOSE: We aim to investigate the neuroprotective effects of XFZYD in sTBI from a novel mechanistic perspective of miRNA-mRNA. Additionally, we sought to elucidate a potential specific mechanism by integrating transcriptomics, bioinformatics, and conducting both in vitro and in vivo experiments. METHODS: The sTBI rat model was established, and the rats were treated with XFZYD for 14 days. The neuroprotective effects of XFZYD were evaluated using a modified neurological severity score, hematoxylin and eosin staining, as well as Nissl staining. The anti-inflammatory effects of XFZYD were explored using quantitative real-time PCR (qRT-PCR), Western blot analysis, and immunofluorescence. Next, miRNA sequencing of the hippocampus was performed to determine which miRNAs were differentially expressed. Subsequently, qRT-PCR was used to validate the differentially expressed miRNAs. Target core mRNAs were determined using various methods, including miRNA prediction targets, mRNA sequencing, miRNA-mRNA network, and protein-protein interaction (PPI) analysis. The miRNA/mRNA regulatory axis were verified through qRT-PCR or Western blot analysis. Finally, morphological changes in the neural synapses were observed using transmission electron microscopy and immunofluorescence. RESULTS: XFZYD exhibited significant neuroprotective and anti-inflammatory effects on subacute sTBI rats' hippocampus. The analyses of miRNA/mRNA sequences combined with the PPI network revealed that the therapeutic effects of XFZYD on sTBI were associated with the regulation of the rno-miR-191a-5p/BDNF axis. Subsequently, qRT-PCR and Western blot analysis confirmed XFZYD reversed the decrease of BDNF and TrkB in the hippocampus caused by sTBI. Additionally, XFZYD treatment potentially increased the number of synaptic connections, and the expression of the synapse-related protein PSD95, axon-related protein GAP43 and neuron-specific protein TUBB3. CONCLUSIONS: XFZYD exerts neuroprotective effects by promoting hippocampal synaptic remodeling and improving cognition during the subacute phase of sTBI through downregulating of rno-miR-191a-5p/BDNF axis, further activating BDNF-TrkB signaling.

Single-incision plus one-port laparoscopy surgery versus conventional multi-port laparoscopy surgery for colorectal cancer: a systematic review and meta-analysis.

OBJECTIVE: The efficacy of single-incision plus one-port laparoscopic surgery (SILS + 1) versus conventional laparoscopic surgery (CLS) for colorectal cancer treatment remains unclear. This study compares the short-term and long-term outcomes of SILS + 1 and CLS using a high-quality systematic review and meta-analysis. METHOD: Literature search followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, drawing from PubMed, Embase, Web of Science, and the Cochrane Library until December 10, 2023. Statistical analysis was conducted using RevMan and Stata. RESULT: The review and meta-analysis included seven studies with 1740 colorectal cancer patients. Compared to CLS, SILS + 1 showed significant improvements in operation time (WMD = - 18.33, P < 0.00001), blood loss (WMD = - 21.31, P < 0.00001), incision length (WMD = - 2.07, P < 0.00001), time to first defecation (WMD = - 14.91, P = 0.009), time to oral intake (WMD = - 11.46, P = 0.04), and time to ambulation (WMD = - 11.52, P = 0.01). There were no significant differences in lymph node harvest, resection margins, complications, anastomotic leakage, hospital stay, disease-free survival, overall survival, and postoperative recurrence. CONCLUSIONS: Compared to CLS, SILS + 1 demonstrates superiority in shortening the surgical incision and promoting postoperative recovery. SILS + 1 can provide a safe and feasible alternative to CLS.

Guided Lamb Wave Array Time-Delay-Based MUSIC Algorithm for Impact Imaging.

Composite materials, valued in aerospace for their stiffness, strength and lightness, require impact monitoring for structural health, especially against low-velocity impacts. The MUSIC algorithm, known for efficient directional scanning and easy sensor deployment, is gaining prominence in this area. However, in practical engineering applications, the broadband characteristics of impact response signals and the time delay errors in array elements' signal reception lead to inconsistencies between the steering vector and the actual signal subspace, affecting the precision of the MUSIC impact localization method. Furthermore, the anisotropy of composite materials results in time delay differences between array elements in different directions. If the MUSIC algorithm uses a fixed velocity value, this also introduces time delay errors, further reducing the accuracy of localization. Addressing these challenges, this paper proposes an innovative MUSIC algorithm for impact imaging using a guided Lamb wave array, with an emphasis on time delay management. This approach focuses on the extraction of high-energy, single-frequency components from impact response signals, ensuring accurate time delay measurement across array elements and enhancing noise resistance. It also calculates the average velocity of single-frequency components in varying directions for an initial impact angle estimation. This estimated angle then guides the selection of a specific single-frequency velocity, culminating in precise impact position localization. The experimental evaluation, employing equidistantly spaced array elements to capture impact response signals, assessed the effectiveness of the proposed method in accurately determining array time delays. Furthermore, impact localization tests on reinforced composite structures were conducted, with the results indicating high precision in pinpointing impact locations.

Buyang huanwu decoction promotes remyelination via miR-760-3p/GPR17 axis after intracerebral hemorrhage.

ETHNOPHARMACOLOGICAL RELEVANCE: The repairment of myelin sheaths is crucial for mitigating neurological impairments of intracerebral hemorrhage (ICH). However, the current research on remyelination processes in ICH remains limited. A representative traditional Chinese medicine, Buyang Huanwu decoction (BYHWD), shows a promising therapeutic strategy for ICH treatment. AIM OF THE STUDY: To investigate the pro-remyelination effects of BYHWD on ICH and explore the underlying mechanisms. MATERIALS AND METHODS: The collagenase-induced mice ICH model was created for investigation. BYHWD's protective effects were assessed by behavioral tests and histological staining. Transmission electron microscopy was used for displaying the structure of myelin sheaths. The remyelination and oligodendrocyte differentiation were evaluated by the expressions of myelin proteolipid protein (PLP), myelin basic protein (MBP), MBP/TAU, Olig2/CC1, and PDGFRα/proliferating cell nuclear antigen (PCNA) through RT-qPCR and immunofluorescence. Transcriptomics integrated with disease database analysis and experiments in vivo and in vitro revealed the microRNA-related underlying mechanisms. RESULTS: Here, we reported that BYHWD promoted the neurological function of ICH mice and improved remyelination by increasing PLP, MBP, and TAU, as well as restoring myelin structure. Besides, we showed that BYHWD promoted remyelination by boosting the differentiation of PDGFRα+ oligodendrocyte precursor cells into olig2+/CC1+ oligodendrocytes. Additionally, we demonstrated that the remyelination effects of BYHWD worked by inhibiting G protein-coupled receptor 17 (GPR17). miRNA sequencing integrated with miRNA database prediction screened potential miRNAs targeting GPR17. By applying immunofluorescence, RNA in situ hybridization and dual luciferase reporter gene assay, we confirmed that BYHWD suppressed GPR17 and improved remyelination by increasing miR-760-3p. CONCLUSIONS: BYHWD improves remyelination and neurological function in ICH mice by targeting miR-760-3p to inhibit GPR17. This study may shed light on the orchestration of remyelination mechanisms after ICH, thus providing novel insights for developing innovative prescriptions with brain-protective properties.

Ginseng fermentation solution affects the gut microbiota in zebrafish with alcoholic liver disease via PI3K/Akt pathway.

BACKGROUND: Ginsenosides have received increased amounts of attention due to their ability to modulate the intestinal flora, which may subsequently alleviate alcoholic liver disease (ALD). The effects of ginseng fermentation solution (GFS) on the gut microbiota and metabolism in ALD patients have not been explored. PURPOSE: This research aimed to explore the regulatory effect of GFS on ALD both in vitro and in vivo. METHOD: This study assessed the anti-ALD efficacy of GFS using an LO2 cell model and a zebrafish model. Untargeted metabolomics was used for differentially abundant metabolite analysis, and high-throughput 16S rRNA sequencing was used to examine the effect of GFS on ALD. RESULTS: The LO2 cell line experiments demonstrated that GFS effectively mitigated alcohol-induced oxidative stress and reduced apoptosis by upregulating PI3K and Bcl-2 expression and decreasing the levels of malondialdehyde, total cholesterol, and triglycerides. In zebrafish, GFS improved morphological and physiological parameters and diminished oxidative stress-induced ALD. Meanwhile, the results from Western blotting indicated that GFS enhanced the expression of PI3K, Akt, and Bcl-2 proteins while reducing Bax protein expression, thereby ameliorating the ALD model in zebrafish. Metabolomics data revealed significant changes in a total of 46 potential biomarkers. Among them, metabolites such as prostaglandin F2 alpha belong to arachidonic acid metabolism. In addition, GFS also partly reversed the imbalance of gut microbiota composition caused by alcohol. At the genus level, alcohol consumption elevated the presence of Flectobacillus, Curvibacter, among others, and diminished Elizabethkingia within the intestinal microbes of zebrafish. Conversely, GFS reversed these effects, notably enhancing the abundance of Proteobacteria and Archaea. Correlation analyses further indicated a significant negative correlation between prostaglandin F2 alpha, 11,14,15-THETA, Taurocholic acid and Curvibacter. CONCLUSION: This study highlights a novel mechanism by which GFS modulates anti-ALD activity through the PI3K/Akt signalling pathway by influencing the intestinal flora-metabolite axis. These results indicate the potential of GFS as a functional food for ALD treatment via modulation of the gut flora.

Thrombin receptor activating peptide-6 decreases acute graft-versus-host disease through activating GPR15.

G-protein coupled receptor 15 (GPR15) is expressed on T-cells. We previously reported knockout of GPR15 increased acute graft-versus-host disease (GvHD) in mice. In this study, we identified thrombin receptor activating peptide-6 (TRAP-6, peptide sequence: SFLLRN) as an activator of GPR15. GRP15 and ß-arrestin2 were needed for TRAP-6-mediated inhibition of mixed lymphocyte reactions. TRAP-6 decreased acute GvHD in allotransplant models in mice, an effect dependent on GPR15-expression in donor T-cells. RNA-seq and protein analyses indicated TRAP-6 increased binding of ß-arrestin2/TAB1 and inhibited phosphorylation of TAK1 and NF-κB-P65. GPR15 is expressed differently on CD4+ T-cells and CD8+ T-cells. TRAP-6 inhibited phosphorylation of NF-κB-P65 in CD4+ T-cells but increased granzyme B expression in CD8+ T-cells. TRAP-6 decreased acute GvHD without inhibiting graft-versus-tumor (GvT) efficacy against A20 lymphoma cells. SALLRN, a mutant of TRAP-6, preserved the anti-acute GvHD effect but avoided the adverse effects of TRAP-6. TRAP-6 and SALLRN also decreased allogeneic and xenogeneic reactions induced by human blood mononuclear cells. In conclusion, TRAP-6 activated GPR15 on T-cells and decreased acute GvHD in mice without impairing GvT efficacy.

MRI-Based Machine Learning Fusion Models to Distinguish Encephalitis and Gliomas.

This paper aims to compare the performance of the classical machine learning (CML) model and the deep learning (DL) model, and to assess the effectiveness of utilizing fusion radiomics from both CML and DL in distinguishing encephalitis from glioma in atypical cases. We analysed the axial FLAIR images of preoperative MRI in 116 patients pathologically confirmed as gliomas and clinically diagnosed with encephalitis. The 3 CML models (logistic regression (LR), support vector machine (SVM) and multi-layer perceptron (MLP)), 3 DL models (DenseNet 121, ResNet 50 and ResNet 18) and a deep learning radiomic (DLR) model were established, respectively. The area under the receiver operating curve (AUC) and sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and validation sets. In addition, a deep learning radiomic nomogram (DLRN) and a web calculator were designed as a tool to aid clinical decision-making. The best DL model (ResNet50) consistently outperformed the best CML model (LR). The DLR model had the best predictive performance, with AUC, sensitivity, specificity, accuracy, NPV and PPV of 0.879, 0.929, 0.800, 0.875, 0.867 and 0.889 in the validation sets, respectively. Calibration curve of DLR model shows good agreement between prediction and observation, and the decision curve analysis (DCA) indicated that the DLR model had higher overall net benefit than the other two models (ResNet50 and LR). Meanwhile, the DLRN and web calculator can provide dynamic assessments. Machine learning (ML) models have the potential to non-invasively differentiate between encephalitis and glioma in atypical cases. Furthermore, combining DL and CML techniques could enhance the performance of the ML models.

Ultraflexible, High-Performance Transistors and Logic Circuits on Biocompatible Polylactic Acid (PLA) Substrate.

Wearable and implantable devices have gained significant popularity, playing a crucial role in smart healthcare and human-machine interfaces, which necessitates the development of more complex electronic devices and circuits on biocompatible flexible materials. Polylactic acid (PLA) stands out due to its biodegradability, cost-effectiveness, and low immunogenicity. In this study, we utilize a solution-based spin-coating method to produce high-quality PLA thin films, serving as substrates for the fabrication of thin-film transistors (TFTs) in which the dielectric layer material is silicon dioxide, the channel layer material is IGZO, and the gate, drain, and source material is ITO at low temperatures (<40 °C) through a shadow masking technique. The resulting PLA-TFT devices exhibited remarkable flexibility, biocompatibility, and impressive electrical characteristics, including a charge carrier mobility of 27.81 cm2/(V s), a subthreshold swing of 162.8 mV/decade, and an ON/OFF current ratio of up to 1 × 106, and maintained performance under various deformations. We successfully constructed fundamental logic gate circuits using PLA-TFTs, including AND, OR, and NOT gates, which effectively performed logical functions and demonstrated stability under diverse bending conditions. These research findings provide valuable support for future endeavors in fabricating intricate logic circuits and realizing advanced functionalities on biocompatible flexible materials.

High-accuracy 3D segmentation of wet age-related macular degeneration via multi-scale and cross-channel feature extraction and channel attention.

Wet age-related macular degeneration (AMD) is the leading cause of visual impairment and vision loss in the elderly, and optical coherence tomography (OCT) enables revolving biotissue three-dimensional micro-structure widely used to diagnose and monitor wet AMD lesions. Many wet AMD segmentation methods based on deep learning have achieved good results, but these segmentation results are two-dimensional, and cannot take full advantage of OCT's three-dimensional (3D) imaging characteristics. Here we propose a novel deep-learning network characterizing multi-scale and cross-channel feature extraction and channel attention to obtain high-accuracy 3D segmentation results of wet AMD lesions and show the 3D specific morphology, a task unattainable with traditional two-dimensional segmentation. This probably helps to understand the ophthalmologic disease and provides great convenience for the clinical diagnosis and treatment of wet AMD.

Engineering of glycoside hydrolase family 7 cellobiohydrolases directed by natural diversity screening.

Protein engineering and screening of processive fungal cellobiohydrolases (CBHs) remain challenging due to limited expression hosts, synergy-dependency, and recalcitrant substrates. In particular, glycoside hydrolase family 7 (GH7) CBHs are critically important for the bioeconomy and typically difficult to engineer. Here, we target the discovery of highly active natural GH7 CBHs and engineering of variants with improved activity. Using experimentally assayed activities of genome mined CBHs, we applied sequence and structural alignments to top performers to identify key point mutations linked to improved activity. From ∼1500 known GH7 sequences, an evolutionarily diverse subset of 57 GH7 CBH genes was expressed in Trichoderma reesei and screened using a multiplexed activity screening assay. Ten catalytically enhanced natural variants were identified, produced, purified, and tested for efficacy using industrially relevant conditions and substrates. Three key amino acids in CBHs with performance comparable or superior to Penicillium funiculosum Cel7A were identified and combinatorially engineered into P. funiculosum cel7a, expressed in T. reesei, and assayed on lignocellulosic biomass. The top performer generated using this combined approach of natural diversity genome mining, experimental assays, and computational modeling produced a 41% increase in conversion extent over native P. funiculosum Cel7A, a 55% increase over the current industrial standard T. reesei Cel7A, and 10% improvement over Aspergillus oryzae Cel7C, the best natural GH7 CBH previously identified in our laboratory.

Toward ground-truth optical coherence tomography via three-dimensional unsupervised deep learning processing and data.

Optical coherence tomography (OCT) can perform non-invasive high-resolution three-dimensional (3D) imaging and has been widely used in biomedical fields, while it is inevitably affected by coherence speckle noise which degrades OCT imaging performance and restricts its applications. Here we present a novel speckle-free OCT imaging strategy, named toward-ground-truth OCT (tGT-OCT), that utilizes unsupervised 3D deep-learning processing and leverages OCT 3D imaging features to achieve speckle-free OCT imaging. Specifically, our proposed tGT-OCT utilizes an unsupervised 3D-convolution deep-learning network trained using random 3D volumetric data to distinguish and separate speckle from real structures in 3D imaging volumetric space; moreover, tGT-OCT effectively further reduces speckle noise and reveals structures that would otherwise be obscured by speckle noise while preserving spatial resolution. Results derived from different samples demonstrated the high-quality speckle-free 3D imaging performance of tGT-OCT and its advancement beyond the previous state-of-the-art. The code is available online: https://github.com/Voluntino/tGT-OCT.

Chronic cadmium exposure impairs flight behavior by dampening flight muscle carbon metabolism in bumblebees.

Cadmium pollution affects the global ecosystem because cadmium can be transferred up the food chain. The bumblebee, Bombus terrestris, is an important insect pollinator. Their foraging activity on flowers exposes them to harmful heavy metals, which damages their health and leads to massive population declines. However, the effects of chronic exposure to heavy metals on the flight performance of bumblebees have not yet been characterized. Here, we studied variation in the flight capacity of bumblebees induced by chronic cadmium exposure at field-realistic concentrations using behavioral, physiological, and molecular approaches. Chronic cadmium exposure caused a significant reduction in the duration, distance, and mean velocity of bumblebee flight. Transcriptome analysis showed that the impairment of carbon metabolism and mitochondrial dysfunction in the flight muscle were the primary causes. Physiological, biochemical, and metabolomic analyses validated disruptions in energy metabolism, and impairments in mitochondrial respiratory chain complexes activities. Histological analysis revealed muscle fiber damage and mitochondrial loss. Exogenous decanoic acid or citric acid partially restored sustained flight ability of bumblebees by mitigating muscle fiber damage and increasing energy generation. These findings provide insights into how long-term cadmium stress affects the flight ability of insects and will aid human muscle or exercise-related disease research.

Multifunctional two-dimensional perovskite based solar cells for photodetectors and resistive switching.

The escalating interest in low-dimensional perovskites stems from their tunable optoelectronic traits and robust stability. The pursuit of multifaceted optoelectronic devices holds substantial importance for energy-efficient and space-constrained systems. This investigation showcases the realization of multifunctional two-dimensional perovskite solar cells, incorporating transient light detection and resistive switching functions within a single device, achievable by facile external bias adjustments. Serving as a photodetector, the device exhibits commendable self-powered photodetection attributes, including an exceptionally low dark current density of 1 nA mm-2, a remarkable specific detectivity of 7.67 × 1012 Jones, a swift response time of 0.60 µs, and an expansive linear dynamic range of 72 dB. As a memristor, it showcases enduring performance across 4 × 102 cycles, a substantial on/off ratio of 106, and a rapid operation time of less than 1 µs. This endeavor unveils a pioneering avenue for advancing high-performance, air-stable multifunctional two-dimensional perovskite electronics.

Complete degradation of PET waste using a thermophilic microbe-enzyme system.

Enzymatic degradation has been proposed as a suitable solution for addressing PET pollution, but approximately 10 % of PET is left as nonbiodegradable. Microbes can completely degrade PET at the gram level per year. Based on the complementary benefits of microbes and enzymes, a microbe-enzyme system was created to completely degrade PET. Here, a thermophilic microbe-enzyme (TME) system composed of Bacillus thermoamylovorans JQ3 and leaf-branch compost cutinase variant (ICCG) was used to demonstrate the synergistic degradation of PET, enabling 100 % degradation of PET waste at a high PET loading level (360 g/L). Six endogenous PET hydrolases of strain JQ3 were discovered by employing an ester bond hydrolysis function-first genome mining (EGM) strategy and first successfully expressed in E. coli. These hydrolases could release TPA as the final product from PET and preferentially degraded BHET instead of MHET. Of these, carboxylesterase 39_5 and ICCG could degrade PET in a synergistic manner to generate 50 µM of TPA, which was greater than the sum of the individual treatments. Finally, the degradation pathway of the TME system was speculated to include biofilm formation, PET degradation and utilization. The successful implementation of this study rendered a scale-up degradation feasible of PET at a lower cost.