Subtype-specific 3D genome alteration in acute myeloid leukaemia.

Acute myeloid leukaemia (AML) represents a set of heterogeneous myeloid malignancies, and hallmarks include mutations in epigenetic modifiers, transcription factors and kinases1-5. The extent to which mutations in AML drive alterations in chromatin 3D structure and contribute to myeloid transformation is unclear. Here we use Hi-C and whole-genome sequencing to analyse 25 samples from patients with AML and 7 samples from healthy donors. Recurrent and subtype-specific alterations in A/B compartments, topologically associating domains and chromatin loops were identified. RNA sequencing, ATAC with sequencing and CUT&Tag for CTCF, H3K27ac and H3K27me3 in the same AML samples also revealed extensive and recurrent AML-specific promoter-enhancer and promoter-silencer loops. We validated the role of repressive loops on their target genes by CRISPR deletion and interference. Structural variation-induced enhancer-hijacking and silencer-hijacking events were further identified in AML samples. Hijacked enhancers play a part in AML cell growth, as demonstrated by CRISPR screening, whereas hijacked silencers have a downregulating role, as evidenced by CRISPR-interference-mediated de-repression. Finally, whole-genome bisulfite sequencing of 20 AML and normal samples revealed the delicate relationship between DNA methylation, CTCF binding and 3D genome structure. Treatment of AML cells with a DNA hypomethylating agent and triple knockdown of DNMT1, DNMT3A and DNMT3B enabled the manipulation of DNA methylation to revert 3D genome organization and gene expression. Overall, this study provides a resource for leukaemia studies and highlights the role of repressive loops and hijacked cis elements in human diseases.

Producing fast and active Rubisco in tobacco to enhance photosynthesis.

Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) performs most of the carbon fixation on Earth. However, plant Rubisco is an intrinsically inefficient enzyme given its low carboxylation rate, representing a major limitation to photosynthesis. Replacing endogenous plant Rubisco with a faster Rubisco is anticipated to enhance crop photosynthesis and productivity. However, the requirement of chaperones for Rubisco expression and assembly has obstructed the efficient production of functional foreign Rubisco in chloroplasts. Here, we report the engineering of a Form 1A Rubisco from the proteobacterium Halothiobacillus neapolitanus in Escherichia coli and tobacco (Nicotiana tabacum) chloroplasts without any cognate chaperones. The native tobacco gene encoding Rubisco large subunit was genetically replaced with H. neapolitanus Rubisco (HnRubisco) large and small subunit genes. We show that HnRubisco subunits can form functional L8S8 hexadecamers in tobacco chloroplasts at high efficiency, accounting for ∼40% of the wild-type tobacco Rubisco content. The chloroplast-expressed HnRubisco displayed a ∼2-fold greater carboxylation rate and supported a similar autotrophic growth rate of transgenic plants to that of wild-type in air supplemented with 1% CO2. This study represents a step toward the engineering of a fast and highly active Rubisco in chloroplasts to improve crop photosynthesis and growth.

Bioproduction of cerium-bearing magnetite and application to improve carbon-black supported platinum catalysts.

BACKGROUND: Biogeochemical processing of metals including the fabrication of novel nanomaterials from metal contaminated waste streams by microbial cells is an area of intense interest in the environmental sciences. RESULTS: Here we focus on the fate of Ce during the microbial reduction of a suite of Ce-bearing ferrihydrites with between 0.2 and 4.2 mol% Ce. Cerium K-edge X-ray absorption near edge structure (XANES) analyses showed that trivalent and tetravalent cerium co-existed, with a higher proportion of tetravalent cerium observed with increasing Ce-bearing of the ferrihydrite. The subsurface metal-reducing bacterium Geobacter sulfurreducens was used to bioreduce Ce-bearing ferrihydrite, and with 0.2 mol% and 0.5 mol% Ce, an Fe(II)-bearing mineral, magnetite (Fe(II)(III)2O4), formed alongside a small amount of goethite (FeOOH). At higher Ce-doping (1.4 mol% and 4.2 mol%) Fe(III) bioreduction was inhibited and goethite dominated the final products. During microbial Fe(III) reduction Ce was not released to solution, suggesting Ce remained associated with the Fe minerals during redox cycling, even at high Ce loadings. In addition, Fe L2,3 X-ray magnetic circular dichroism (XMCD) analyses suggested that Ce partially incorporated into the Fe(III) crystallographic sites in the magnetite. The use of Ce-bearing biomagnetite prepared in this study was tested for hydrogen fuel cell catalyst applications. Platinum/carbon black electrodes were fabricated, containing 10% biomagnetite with 0.2 mol% Ce in the catalyst. The addition of bioreduced Ce-magnetite improved the electrode durability when compared to a normal Pt/CB catalyst. CONCLUSION: Different concentrations of Ce can inhibit the bioreduction of Fe(III) minerals, resulting in the formation of different bioreduction products. Bioprocessing of Fe-minerals to form Ce-containing magnetite (potentially from waste sources) offers a sustainable route to the production of fuel cell catalysts with improved performance.

A Review of Dynamic Object Filtering in SLAM Based on 3D LiDAR.

SLAM (Simultaneous Localization and Mapping) based on 3D LiDAR (Laser Detection and Ranging) is an expanding field of research with numerous applications in the areas of autonomous driving, mobile robotics, and UAVs (Unmanned Aerial Vehicles). However, in most real-world scenarios, dynamic objects can negatively impact the accuracy and robustness of SLAM. In recent years, the challenge of achieving optimal SLAM performance in dynamic environments has led to the emergence of various research efforts, but there has been relatively little relevant review. This work delves into the development process and current state of SLAM based on 3D LiDAR in dynamic environments. After analyzing the necessity and importance of filtering dynamic objects in SLAM, this paper is developed from two dimensions. At the solution-oriented level, mainstream methods of filtering dynamic targets in 3D point cloud are introduced in detail, such as the ray-tracing-based approach, the visibility-based approach, the segmentation-based approach, and others. Then, at the problem-oriented level, this paper classifies dynamic objects and summarizes the corresponding processing strategies for different categories in the SLAM framework, such as online real-time filtering, post-processing after the mapping, and Long-term SLAM. Finally, the development trends and research directions of dynamic object filtering in SLAM based on 3D LiDAR are discussed and predicted.

Constructing an Ultra-Rapid Nanoconfinement-Enhanced Fluorescence Clinical Detection Platform by Using Machine Learning and Tunable DNA Xerogel "Probe".

Low mass transfer efficiency and unavoidable matrix effects seriously limit the development of rapid and accurate determination of biosensing systems. Herein, we have successfully constructed an ultra-rapid nanoconfinement-enhanced fluorescence clinical detection platform based on machine learning (ML) and DNA xerogel "probe", which was performed by detecting neutrophil gelatinase-associated lipocalin (NGAL, protein biomarker of acute kidney injury). By regulating pore sizes of the xerogels, the transfer of NGAL in xerogels can approximate that in homogeneous solution. Due to electrostatic attraction of the pore entrances, NGAL rapidly enriches on the surface and inside the xerogels. The reaction rate of NGAL and aptamer cross-linked in xerogels is also accelerated because of the nanoconfinement effect-induced increasing reactant concentration and the enhanced affinity constant KD between reactants, which can be promoted by ∼667-fold than that in bulk solution, thus achieving ultra-rapid detection (ca. 5 min) of human urine. The platform could realize one-step detection without sample pretreatments due to the antiligand exchange effect on the surface of N-doped carbon quantum dots (N-CQDs) in xerogels, in which ligand exchange between -COOH and underlying interfering ions in urine will be inhibited due to higher adsorption energy of -COOH on the N-CQD surface relative to the interfering ions. Based on the ML-extended program, the real-time analysis of the urine fluorescence spectra can be completed within 2 s. Interestingly, by changing DNA, aptamer sequences, or xerogel fluorescence intensities, the detection platform can be customized for targeted diseases.

Thermalbibacter longus gen. nov., sp. nov., isolated from a hot spring.

An isolate, designated CFH 74404T, was recovered from a hot spring in Tengchong, Yunnan province, PR China. Phylogenetic analysis indicated that the isolate belongs to the family Thermomicrobiaceae and showed the highest 16S rRNA gene sequence similarity to Thermorudis peleae KI4T (93.6 %), Thermorudis pharmacophila WKT50.2T (93.1 %), Thermomicrobium roseum DSM 5159T (92.0 %) and Thermomicrobium carboxidum KI3T (91.7 %). The average amino acid identity and average nucleotide identity values between strain CFH 74404T and the closest relatives were 42.0-75.9 % and 67.0-77.3 %, respectively. Cells of strain CFH 74404T stained Gram-positive and were aerobic, non-motile and short rod-shaped. Growth occurred at 20-65 °C (optimum, 55 °C), pH 6.0-8.0 (optimum, pH 7.0) and with up to 2.0 % (w/v) NaCl (optimum 0-1.0 %, w/v). The predominant respiratory quinone was MK-8. The major fatty acids (>10 %) were C18 : 0 (50.8 %) and C20 : 0 (16.8 %). The polar lipid profile of strain CFH 74404T included diphosphatidylglycerol, four unidentified phosphoglycolipids, phosphatidylinositol and three unidentified glycolipids. The G+C content of the genomic DNA was determined to be 67.1 mol% based on the draft genome sequence. On the basis of phenotypic, phylogenetic and genotypic analyses, it is concluded that strain CFH 74404T represents a new species of a novel genus Thermalbibacter of the family Thermomicrobiaceae, for which the name Thermalbibacter longus gen. nov., sp. nov. is proposed. The type strain is CFH 74404T (=KCTC 62930T=CGMCC 1.61585T).

Hydrothermal synthesis of sewage sludge biochar for activation of persulfate for antibiotic removal: Efficiency, stability and mechanism.

The use of biochar materials as catalysts to activate persulfate (PS) for the degradation of antibiotics has attracted much attention. In this study, a carbonaceous material (Cu/Zn-SBC) was prepared from sewage sludge by hydrothermal modification. The efficiency of PS activation by Cu/Zn-SBC was investigated using tetracycline (TC) as the model antibiotic. In the Cu/Zn-SBC + PS system, the TC removal rate reached 90.13% at 10 min and exceeded 99% within 4 h. This not only met the requirement of removing large amounts of pollutants in a short time but also achieved the complete removal of pollutants in the subsequent time. Additionally, the Cu/Zn-SBC + PS system was found to be dominated by radical and nonradical pathways. Cu, hydroxyl and carboxyl groups on the surface of Cu/Zn-SBC promoted the production of free radicals and non-free radicals. Under several changes in reaction conditions and water environment factors, the TC removal rate remained above 85% within 10 min. Furthermore, the removal rate of TC was still 85.79% when Cu/Zn-SBC combined with PS was reused twice and 77.14% when reused four times. This study provides an ideal solution for the treatment of sewage sludge, and offers a stable and efficient material for removing antibiotics from wastewater.

Dual peptides-modified cationic liposomes for enhanced Lung cancer gene therapy by a gap junction regulating strategy.

BACKGROUND: Gene therapy for lung cancer has emerged as a novel tumor-combating strategy for its superior tumor specificity, low systematical toxicity and huge clinical translation potential. Especially, the applications of microRNA shed led on effective tumor ablation by directly interfering with the crucial gene expression, making it one of the most promising gene therapy agents. However, for lung cancer therapy, the microRNA treatment confronted three bottlenecks, the poor tumor tissue penetration effect, the insufficient lung drug accumulation and unsatisfied gene transfection efficiency. To address these issues, an inhalable RGD-TAT dual peptides-modified cationic liposomes loaded with microRNA miR-34a and gap junction (GJ) regulation agent all-trans retinoic acid (ATRA) was proposed, which was further engineered into dry powder inhalers (DPIs). RESULTS: Equipped with a rough particle surface and appropriate aerodynamic size, the proposed RGD-TAT-CLPs/ARTA@miR-34a DPIs were expected to deposit into the deep lung and reach lung tumor lesions guided by targeting peptide RGD. Assisted by cellular transmembrane peptides TAT, the RGD-TAT-CLPs/ARTA@miR-34a was proven to be effectively internalized by cancer cells, enhancing gene transfection efficiency. Then, the GJ between tumor cells was upregulated by ARTA, facilitating the intercellular transport of miR-34a and boosting the gene expression in the deep tumor. CONCLUSION: Overall, the proposed RGD-TAT-CLPs/ARTA@miR-34a DPIs could enhance tumor tissue penetration, elevate lung drug accumulation and boost gene transfection efficiency, breaking the three bottlenecks to enhancing tumor elimination in vitro and in vivo. We believe that the proposed RGD-TAT-CLPs/ARTA@miR-34a DPIs could serve as a promising pulmonary gene delivery platform for multiple lung local disease treatments.

Selective optogenetic modulation of the PBN terminals in the lateral hypothalamic area and basal forebrain regulates emergence from isoflurane anesthesia in mice.

While the mechanism of general anesthesia has been extensively studied, the underlying neural circuitry has yet to be fully understood. The parabrachial nucleus (PBN) plays a crucial role in modulating wakefulness and promoting arousal from general anesthesia. However, the specific role of PBN projections in the process of general anesthesia remains unclear. In this study, we bilaterally injected AAV-associated viruses encoding excitatory or inhibitory optogenetic probes into the PBN and implanted optical fibers in the LH or BF area. After four weeks, we optogenetically activated or inhibited the PBN-LH and PBN-BF pathways under 1.5 vol% isoflurane. We calculated the time it took for anesthesia induction and emergence, simultaneously monitoring changes in the burst-suppression ratio using electroencephalogram recording. Our findings indicate that optogenetic activation of the PBN-LH and PBN-BF projections plays a significant role in promoting both cortical and behavioral emergence from isoflurane inhalation, without significantly affecting the induction time. Conversely, photoinhibition of these pathways prolonged the recovery time, with no notable difference observed during the induction phase.In summary, our results demonstrate that the PBN-LH and PBN-BF pathways are crucial for promoting arousal from isoflurane general anesthesia, but do not have a pronounced impact on the induction phase.

Real-Time Bucket Pose Estimation Based on Deep Neural Network and Registration Using Onboard 3D Sensor.

Real-time and accurate bucket pose estimation plays a vital role in improving the intelligence level of mining excavators, as the bucket is a crucial component of the excavator. Existing methods for bucket pose estimation are realized by installing multiple non-visual sensors. However, these sensors suffer from cumulative errors caused by loose connections and short service lives caused by strong vibrations. In this paper, we propose a method for bucket pose estimation based on deep neural network and registration to solve the large registration error problem caused by occlusion. Specifically, we optimize the Point Transformer network for bucket point cloud semantic segmentation, significantly improving the segmentation accuracy. We employ point cloud preprocessing and continuous frame registration to reduce the registration distance and accelerate the Fast Iterative Closest Point algorithm, enabling real-time pose estimation. By achieving precise semantic segmentation and faster registration, we effectively address the problem of intermittent pose estimation caused by occlusion. We collected our own dataset for training and testing, and the experimental results are compared with other relevant studies, validating the accuracy and effectiveness of the proposed method.

Microbiome and metabolome reveal the metabolic and microbial variations induced by depression and constipation.

BACKGROUND: Depressed patients are often accompanied with constipation symptoms, and vice versa. However, the underlying mechanisms of such a bidirectional correlation have remained elusive. We aim to reveal the possible correlations between depression and constipation from the perspectives of gut microbiome and plasma metabolome. METHODS: We constructed the depressed model and the constipated model of rats, respectively. First, we measured the locomotor activity status and the gastrointestinal functions of rats. And then, nuclear magnetic resonance plasma metabolomics was applied to reveal the shared and the unique metabolites of depression and constipation. In addition, 16 S ribosomal RNA gene sequencing was used to detect the impacts of constipation and depression on gut microbiota of rats. Finally, a multiscale and multifactorial network, that is, the 'phenotypes - differential metabolites - microbial biomarkers' integrated network, was constructed to visualise the mechanisms of connections between depression and constipation. RESULTS: We found that spontaneous locomotor activity and gastrointestinal functions of both depressed rats and constipated rats significantly decreased. Further, eight metabolites and 14 metabolites were associated depression and constipation, respectively. Among them, seven metabolites and four metabolic pathways were shared by constipation and depression, mainly perturbing energy metabolism and amino acid metabolism. Additionally, depression and constipation significantly disordered the functions and the compositions of gut microbiota of rats, and decreased the ratio of Firmicutes to Bacteroidetes. CONCLUSION: The current findings provide multiscale and multifactorial perspectives for understanding the correlations between depression and constipation, and demonstrate new mechanisms of comorbidity of depression and constipation.

Qipengyuania thermophila sp. nov., isolated from a Chinese hot spring.

A novel Gram-stain-negative, non-motile, short rod-shaped and aerobic bacterial strain, designated as CFH 74456 T, was isolated from sediment of a hot spring, Tengchong, Yunnan Province, south-western China. Growth occurred at 20-53 ºC (optimum 45 ºC), pH 7.0-9.0 (optimum pH 8.0) and up to 2.0% (w/v) NaCl (optimum 0-1.0%, w/v). The predominant respiratory quinone was ubiquinone 10 (Q-10). The major fatty acids (> 10%) were C17:1 ω6c (17.9%) and summed feature 8 (38.6%). The polar lipid profile of strain CFH 74456 T was identified as diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, phosphatidylcholine, sphingoglycolipid, three unidentified glycolipids and three unidentified polar lipids. Phylogenetic analysis based on 16S rRNA gene sequence indicated that strain CFH 74456 T belongs to the genus Qipengyuania, and was most closely related to Qipengyuania sediminis CGMCC 1.12928 T (95.7%). The draft genome size of the isolate was 2.29 Mb with G + C content of 68.5%. The amino acid identity, average nucleotide identity and the digital DNA-DNA hybridization values between strain CFH 74456 T and the closest relatives ranged from 67.0 to 67.9%, 73.0 to 74.2% and 18.2-19.3%, respectively. On the basis of phenotypic, phylogenetic and genotypic analyses, it is concluded that strain CFH 74456 T represents a new species of the genus Qipengyuania, for which the name Qipengyuania thermophila sp. nov. is proposed. The type strain is CFH 74456 T (= KCTC 62921 T = CCTCC AB 2018237 T).

Agromyces agglutinans sp. nov., isolated from saline lake sediment.

A novel actinobacterium, designated strain CFH 90414T, was isolated from sediment sampled at a saline lake in Yuncheng, Shanxi, PR China. The taxonomic position of the strain was investigated by using a polyphasic approach. Cells of strain CFH 90414T were Gram-reaction-positive, aerobic and non-motile. Growth occured at 4-40 °C, pH 5.0-9.0 and in the presence of up to 0-3.0 % (w/v) NaCl. Phylogenetic analysis based on 16S rRNA gene sequences revealed that strain CFH 90414T was a member of the genus Agromyces. The 16S rRNA gene sequence similarity analysis indicated that strain CFH 90414T was most closely related to Agromyces italicus JCM 14320T (98.07 %) and Agromyces lapidis JCM 14321T (97.18 %). The whole genome of CFH 90414T was 3.64 Mb, and showed a G+C content of 71.5 mol%. The average nucleotide identity (ANI) values and digital DNA-DNA hybridization (dDDH) values between CFH 90414T and the other species of the genus Agromyces were found to be low (ANI <78.99 % and dDDH <22.9 %). The whole-cell sugars were rhamnose, mannose, ribose, glucose and galactose. The isolate contained l-2,4-diaminobutyric acid, d-alanine, d-glutamic acid and glycine in the cell-wall peptidoglycan. The predominant menaquinone was MK-12. The major cellular fatty acids were anteiso-C15 : 0, anteiso-C17 : 0 and iso-C16 : 0. The polar lipid profile contained diphosphatidylglycerol, phosphatidylglycerol and an unidentified glycolipid. On the basis of phenotypic, genotypic and phylogenetic data, strain CFH 90414T is considered to represent a novel species of the genus Agromyces, for which the name Agromyces agglutinans sp. nov. is proposed. The type strain is CFH 90414T (=DSM 105966T=KCTC 49062T).

LncRNA PCAT19 Regulates Neuropathic Pain via Regulation of miR-182-5p/JMJD1A in a Rat Model of Chronic Constriction Injury.

INTRODUCTION: Neuropathic pain (NP) is one of the most severe chronic pain types. In recent years, more and more studies have shown that long noncoding RNA (LncRNA) plays a key role in a variety of human diseases, including NP. However, the role of LncRNA prostate cancer-associated transcript 19 (PCAT19) in NP and its specific mechanism remain unclear. METHODS: A chronic constrictive injury (CCI) rat model was established. Rat paw withdrawal threshold and paw withdrawal latency were used to evaluate the neuronal pain behavior of rats in this model. mRNA expression of PCAT19, neuroinflammatory factor, microRNA (miR)-182-5p, and Jumonji domain containing 1A (JMJD1A) were detected by quantitative real-time PCR. ELISA analysis was used to detect inflammatory factor protein expression. Dual-luciferase reporter assay was used to evaluate the targeting relationship between genes. RESULTS: PCAT19 was continuously upregulated in CCI rats. miR-182-5p was the target of PCAT19, and miR-182-5p was increased after PCAT19 knockdown. NP behaviors such as mechanical ectopic pain and thermal hyperalgesia as well as neuroinflammation can be reduced by knocking down PCAT19. However, the injection of miR-182-5p antagomir significantly reversed the level of the NP behaviors and neuroinflammation caused by PCAT19 knockdown. Besides, dual-luciferase reporter assay showed that JMJD1A was the target gene of miR-182-5p. The level of JMJD1A in CCI rats increased with time. After PCAT19 knockdown, JMJD1A was significantly decreased, but inhibition of miR-182-5p can reverse its levels. CONCLUSION: This study shows that PCAT19 plays a role in NP by targeting the miR-182-5p/JMJD1A axis, and PCAT19 can be used as a new therapeutic target for NP.

Prevalence of Giardia duodenalis Among Dogs in China from 2001 to 2021: A Systematic Review and Meta-Analysis.

Giardia duodenalis has a wide range of host species and is a common causative agent of diarrheal disease in humans and animals. This study conducted a systematic review and meta-analysis to evaluate the pooled prevalence of Giardia among dogs in China. We extracted 33 studies related to the prevalence of G. duodenalis in dogs, with samples taken from 2001 to 2021. The random-effect model was used to calculate pooled prevalence estimates with 95% confidence intervals, and the analyzed data were from 14 provinces in China. The estimated overall prevalence of G. duodenalis among dogs in China was 11.2%. The prevalence of Giardia was significantly higher in Northwestern China (35.7%) than in other regions. The prevalence in 2010 or later (11.8%) was significantly higher than in 2010 or before (6.9%). The estimated prevalence detected by microscopy (9.3%) was lower than molecular (12.3%) and serological (14.3%) ones. The prevalence was higher in dogs <1 year of age (12.2%) than that >1 year (5.4%). Among the genotype groups, the positive rate of assemblage A (5.2%) was significantly higher than that of other assemblages. Depending on the dog' type, the prevalence of G. duodenalis in stray dogs (3.5%) was lower than that in pet dogs (6.7%) and intensively breeding dogs (11.8%). In addition, no correlation was found between Giardia positive rate and the dogs' gender (p > 0.05). We also analyzed the effects of different geographic factor subgroups (longitude, latitude, precipitation, temperature, humidity, and altitude) on the prevalence of G. duodenalis in dogs in China. The results showed that giardiasis was widespread in dogs in China. It is suggested that corresponding control scheme and effective management measures should be formulated and applied to reduce the transmission of G. duodenalis according to the difference in geographical conditions in different areas.

A Novel Approach for UAV Image Crack Detection.

Cracks are the most significant pre-disaster of a road, and are also important indicators for evaluating the damage level of a road. At present, road crack detection mainly depends on manual detection and road detection vehicles, with which the safety of detection workers is not guaranteed and the detection efficiency is low. A road detection vehicle can speed up the efficiency to a certain extent, but the automation level is low and it is easy to block the traffic. Unmanned Aerial Vehicles (UAV) have the characteristics of low energy consumption and easy control. If UAV technology can be applied to road crack detection, it will greatly improve the detection efficiency and produce huge economic benefits. In order to find a way to apply UAV to road crack detection, we developed a new technique for road crack detection based on UAV pictures, called DenxiDeepCrack, which is a trainable deep convolutional neural network for automatic crack detection which utilises learning high-level features for crack representation. In addition, we create a new dataset based on drone images called UCrack 11 to enrich the crack database of drone images for future crack detection research.

MAFF-Net: Multi-Attention Guided Feature Fusion Network for Change Detection in Remote Sensing Images.

One of the most important tasks in remote sensing image analysis is remote sensing image Change Detection (CD), and CD is the key to helping people obtain more accurate information about changes on the Earth's surface. A Multi-Attention Guided Feature Fusion Network (MAFF-Net) for CD tasks has been designed. The network enhances feature extraction and feature fusion by building different blocks. First, a Feature Enhancement Module (FEM) is proposed. The FEM introduces Coordinate Attention (CA). The CA block embeds the position information into the channel attention to obtain the accurate position information and channel relationships of the remote sensing images. An updated feature map is obtained by using an element-wise summation of the input of the FEM and the output of the CA. The FEM enhances the feature representation in the network. Then, an attention-based Feature Fusion Module (FFM) is designed. It changes the previous idea of layer-by-layer fusion and chooses cross-layer aggregation. The FFM is to compensate for some semantic information missing as the number of layers increases. FFM plays an important role in the communication of feature maps at different scales. To further refine the feature representation, a Refinement Residual Block (RRB) is proposed. The RRB changes the number of channels of the aggregated features and uses convolutional blocks to further refine the feature representation. Compared with all compared methods, MAFF-Net improves the F1-Score scores by 4.9%, 3.2%, and 1.7% on three publicly available benchmark datasets, the CDD, LEVIR-CD, and WHU-CD datasets, respectively. The experimental results show that MAFF-Net achieves state-of-the-art (SOTA) CD performance on these three challenging datasets.

A Particleboard Surface Defect Detection Method Research Based on the Deep Learning Algorithm.

Particleboard surface defects have a significant impact on product quality. A surface defect detection method is essential to enhancing the quality of particleboard because the conventional defect detection method has low accuracy and efficiency. This paper proposes a YOLO v5-Seg-Lab-4 (You Only Look Once v5 Segmentation-Lab-4) model based on deep learning. The model integrates object detection and semantic segmentation, which ensures real-time performance and improves the detection accuracy of the model. Firstly, YOLO v5s is used as the object detection network, and it is added into the SELayer module to improve the adaptability of the model to receptive field. Then, the Seg-Lab v3+ model is designed on the basis of DeepLab v3+. In this model, the object detection network is utilized as the backbone network of feature extraction, and the expansion rate of atrus convolution is reduced to the computational complexity of the model. The channel attention mechanism is added onto the feature fusion module, for the purpose of enhancing the feature characterization capabilities of the network algorithm as well as realizing the rapid and accurate detection of lightweight networks and small objects. Experimental results indicate that the proposed YOLO v5-Seg-Lab-4 model has mAP (Mean Average Precision) and mIoU (Mean Intersection over Union) of 93.20% and 76.63%, with a recognition efficiency of 56.02 fps. Finally, a case study of the Huizhou particleboard factory inspection is carried out to demonstrate the tiny detection accuracy and real-time performance of this proposed method, and the missed detection rate of surface defects of particleboard is less than 1.8%.

TMT-Based Quantitative Proteomic Analysis of Intestinal Organoids Infected by Listeria monocytogenes Strains with Different Virulence.

L. monocytogenes, consisting of 13 serotypes, is an opportunistic food-borne pathogen that causes different host reactions depending on its serotypes. In this study, highly toxic L. monocytogenes 10403s resulted in more severe infections and lower survival rates. Additionally, to investigate the remodeling of the host proteome by strains exhibiting differential toxicity, the cellular protein responses of intestinal organoids were analyzed using tandem mass tag (TMT) labeling and high-performance liquid chromatography−mass spectrometry. The virulent strain 10403s caused 102 up-regulated and 52 down-regulated proteins, while the low virulent strain M7 caused 188 up-regulated and 25 down-regulated proteins. Based on the analysis of gene ontology (GO) and KEGG databases, the expressions of differential proteins in organoids infected by L. monocytogenes 10403s (virulent strain) or M7 (low virulent strain) were involved in regulating essential processes such as the biological metabolism, the energy metabolism, and immune system processes. The results showed that the immune system process, as the primary host defense response to L. monocytogenes, comprised five pathways, including ECM−receptor interaction, the complement and coagulation cascades, HIF-1, ferroptosis, and NOD-like receptor signaling pathways. As for the L. monocytogenes 10403s vs. M7 group, the expression of differential proteins was involved in two pathways: systemic lupus erythematosus and transcriptional mis-regulation in cancer. All in all, these results revealed that L. monocytogenes strains with different toxicity induced similar biological functions and immune responses while having different regulations on differential proteins in the pathway.

Dry Powder Inhalers Based on Chitosan-Mannitol Binary Carriers: Effect of the Powder Properties on the Aerosolization Performance.

Carriers play an important role in improving the aerosolization performance of dry powder inhalers (DPIs). Despite that intensive attention had been paid to the establishment of the advanced carriers with controllable physicochemical properties in recent years, the design and optimization of carrier-based DPIs remain an empiricism-based process. DPIs are a powder system of complex multiphase, and thus their physicochemical properties cannot fully explain the powder behavior. A comprehensive exposition of powder properties is demanded to build a bridge between the physicochemical properties of carriers and the aerosolization performance of DPIs. In this study, an FT-4 powder rheometer was employed to explore the powder properties, including dynamic flow energy, aeration, and permeability of the chitosan-mannitol binary carriers (CMBCs). CMBCs were self-designed as an advanced carrier with controllable surface roughness to obtain enhanced aerosolization performance. The specific mechanism of CMBCs to enhance the aerosolization performance of DPIs was elaborated based on the theory of pulmonary delivery processes by introducing powder properties. The results exhibited that CMBCs with appropriate surface roughness had lower special energy, lower aeration energy, and higher permeability. It could be predicted that CMBC-based DPIs had greater tendency to fluidize and disperse in airflow, and the lower adhesion force between particles enabled drugs to be detached from the carrier to achieve higher fine particle fractions. The specific mechanism on how physicochemical properties influenced the aerosolization performance during the pulmonary delivery processes could be figured out with the introduction of powder properties.