DNA-Diffusion: Leveraging Generative Models for Controlling Chromatin Accessibility and Gene Expression via Synthetic Regulatory Elements.

The challenge of systematically modifying and optimizing regulatory elements for precise gene expression control is central to modern genomics and synthetic biology. Advancements in generative AI have paved the way for designing synthetic sequences with the aim of safely and accurately modulating gene expression. We leverage diffusion models to design context-specific DNA regulatory sequences, which hold significant potential toward enabling novel therapeutic applications requiring precise modulation of gene expression. Our framework uses a cell type-specific diffusion model to generate synthetic 200 bp regulatory elements based on chromatin accessibility across different cell types. We evaluate the generated sequences based on key metrics to ensure they retain properties of endogenous sequences: transcription factor binding site composition, potential for cell type-specific chromatin accessibility, and capacity for sequences generated by DNA diffusion to activate gene expression in different cell contexts using state-of-the-art prediction models. Our results demonstrate the ability to robustly generate DNA sequences with cell type-specific regulatory potential. DNA-Diffusion paves the way for revolutionizing a regulatory modulation approach to mammalian synthetic biology and precision gene therapy.

Research on Positioning Accuracy of Mobile Robot in Indoor Environment Based on Improved RTABMAP Algorithm.

Visual simultaneous localization and mapping is a widely used technology for mobile robots to carry out precise positioning in the environment of GNSS technology failure. However, as the robot moves around indoors, its position accuracy will gradually decrease over time due to common and unavoidable environmental factors. In this paper, we propose an improved method called RTABMAP-VIWO, which is based on RTABMAP. The basic idea is to use an Extended Kalman Filter (EKF) framework for fusion attitude estimates from the wheel odometry and IMU, and provide new prediction values. This helps to reduce the local cumulative error of RTABMAP and make it more accurate. We compare and evaluate three kinds of SLAM methods using both public datasets and real indoor scenes. In the dataset experiments, our proposed method reduces the Root-Mean-Square Error (RMSE) coefficient by 48.1% compared to the RTABMAP, and the coefficient is also reduced by at least 29.4% in the real environment experiments. The results demonstrate that the improved method is feasible. By incorporating the IMU into the RTABMAP method, the trajectory and posture errors of the mobile robot are significantly improved.

Mixed Receptive Fields Augmented YOLO with Multi-Path Spatial Pyramid Pooling for Steel Surface Defect Detection.

Aiming at the problems of low detection efficiency and poor detection accuracy caused by texture feature interference and dramatic changes in the scale of defect on steel surfaces, an improved YOLOv5s model is proposed. In this study, we propose a novel re-parameterized large kernel C3 module, which enables the model to obtain a larger effective receptive field and improve the ability of feature extraction under complex texture interference. Moreover, we construct a feature fusion structure with a multi-path spatial pyramid pooling module to adapt to the scale variation of steel surface defects. Finally, we propose a training strategy that applies different kernel sizes for feature maps of different scales so that the receptive field of the model can adapt to the scale changes of the feature maps to the greatest extent. The experiment on the NEU-DET dataset shows that our model improved the detection accuracy of crazing and rolled in-scale, which contain a large number of weak texture features and are densely distributed by 14.4% and 11.1%, respectively. Additionally, the detection accuracy of inclusion and scratched defects with prominent scale changes and significant shape features was improved by 10.5% and 6.6%, respectively. Meanwhile, the mean average precision value reaches 76.8%, compared with the YOLOv5s and YOLOv8s, which increased by 8.6% and 3.7%, respectively.

Green manure increases peanut production by shaping the rhizosphere bacterial community and regulating soil metabolites under continuous peanut production systems.

BACKGROUND: Green manure (GM) is a crop commonly grown during fallow periods, which has been applied in agriculture as a strategy to regulate nutrient cycling, improve organic matter, and enhance soil microbial biodiversity, but to date, few studies have examined the effects of GM treatments on rhizosphere soil bacterial community and soil metabolites from continuous cropping peanut field. RESULTS: In this study, we found that the abundances of several functionally significant bacterial groups containing Actinobacteria, Acidobacteria, and genus Sphingomonas, which are associated with nitrogen cycling, were dramatically increased in GM-applied soils. Consistent with the bacterial community results, metabolomics analysis revealed a strong perturbation of nitrogen- or carbon-related metabolisms in GM-applied soils. The substantially up-regulated beneficial metabolites including sucrose, adenine, lysophosphatidylcholine (LPC), malic acid, and betaines in GM-applied soils may contribute to overcome continuous cropping obstacle. In contrast to peanut continuous cropping, planting winter wheat and oilseed rape in winter fallow period under continuous spring peanut production systems evidently improved the soil quality, concomitantly with raised peanut pod yield by 32.93% and 25.20%, in the 2020 season, respectively. CONCLUSIONS: GMs application is an effective strategy to overcome continuous cropping obstacle under continuous peanut production systems by improving nutrient cycling, soil metabolites, and rhizobacterial properties.

Targeting of the COX-2/PGE2 axis enhances the antitumor activity of T7 peptide in vitro and in vivo.

T7 peptide is considered as an antiangiogenic polypeptide. The presents study aimed to further detect the antiangiogenic mechanisms of T7 peptide and determine whether combining T7 peptide and meloxicam (COX-2/PGE2 specific inhibitor) could offer a better therapy to combat hepatocellular carcinoma (HCC). T7 peptide suppressed the proliferation, migration, tube formation, and promoted the apoptosis of endothelial cells under both normoxic and hypoxic conditions via integrin α3ß1 and αvß3 pathways. Cell proliferation, migration, apoptosis, or tube formation ability were detected, and the expression of integrin-associated regulatory proteins was detected. The anti-tumor activity of T7 peptide, meloxicam, and their combination were evaluated in HCC tumor models established in mice. T7 peptide suppressed the proliferation, migration, tube formation, and promoted the apoptosis of endothelial cells under both normoxic and hypoxic conditions via integrin α3ß1 and αvß3 pathways. Meloxicam enhanced the activity of T7 peptide under hypoxic condition. T7 peptide partly inhibited COX-2 expression via integrin α3ß1 not αvß3-dependent pathways under hypoxic condition. T7 peptide regulated apoptosis associated protein through MAPK-dependent and -independent pathways under hypoxic condition. The MAPK pathway was activated by the COX-2/PGE2 axis under hypoxic condition. The combination of T7 and meloxicam showed a stronger anti-tumor effect against HCC tumors in mice. The data highlight that meloxicam enhanced the antiangiogenic activity of T7 peptide in vitro and in vivo.

Investigation of Electrode Electrochemical Reactions in CH3 NH3 PbBr3 Perovskite Single-Crystal Field-Effect Transistors.

Optoelectronic devices based on metal halide perovskites, including solar cells and light-emitting diodes, have attracted tremendous research attention globally in the last decade. Due to their potential to achieve high carrier mobilities, organic-inorganic hybrid perovskite materials can enable high-performance, solution-processed field-effect transistors (FETs) for next-generation, low-cost, flexible electronic circuits and displays. However, the performance of perovskite FETs is hampered predominantly by device instabilities, whose origin remains poorly understood. Here, perovskite single-crystal FETs based on methylammonium lead bromide are studied and device instabilities due to electrochemical reactions at the interface between the perovskite and gold source-drain top contacts are investigated. Despite forming the contacts by a gentle, soft lamination method, evidence is found that even at such "ideal" interfaces, a defective, intermixed layer is formed at the interface upon biasing of the device. Using a bottom-contact, bottom-gate architecture, it is shown that it is possible to minimize such a reaction through a chemical modification of the electrodes, and this enables fabrication of perovskite single-crystal FETs with high mobility of up to ≈15 cm2 V-1 s-1 at 80 K. This work addresses one of the key challenges toward the realization of high-performance solution-processed perovskite FETs.

Plasma Surface Functionalization of Carbon Nanofibres with Silver, Palladium and Platinum Nanoparticles for Cost-Effective and High-Performance Supercapacitors.

Due to their relatively low cost, large surface area and good chemical and physical properties, carbon nanofibers (CNFs) are attractive for the fabrication of electrodes for supercapacitors (SCs). However, their relatively low electrical conductivity has impeded their practical application. To this end, a novel active-screen plasma activation and deposition technology has been developed to deposit silver, platinum and palladium nanoparticles on activated CNFs surfaces to increase their specific surface area and electrical conductivity, thus improving the specific capacitance. The functionalised CNFs were fully characterised using scanning electron microscope (SEM), energy dispersive X-ray analysis (EDX) and X-ray diffraction (XRD) and their electrochemical properties were evaluated using cyclic voltammetry and electrochemical impedance spectroscopy. The results showed a significant improvement in specific capacitance, as well as electrochemical impedance over the untreated CNFs. The functionalisation of CNFs via environmental-friendly active-screen plasma technology provides a promising future for cost-effective supercapacitors with high power and energy density.

Point-and-Shoot Strategy for Identification of Alcoholic Beverages.

The lack of point-and-shoot detection methods of alcoholic beverages (ABs) available for ordinary people is a common cause of the overflow of various counterfeit ABs. Here, we, for the first time, provide a point-and-shoot identification for ABs via a smartphone. Using density functional theory, we find the binding ability of an ethylenediamine-functionalized polydiacetylene (P4) can reach a desirable trade-off among organic molecules in ABs. We therefore construct a versatile array consisting of P4 with different concentrations, which is able to generate unique color response patterns toward different ABs. The color response patterns are further analyzed by a custom-designed image processing algorithm based on machine learning. Finally, the identification of ABs can be achieved by capturing and analyzing the color pattern using an imaging recognition programmer on a smartphone, and the entire process is as fast as quick response (QR) code scanning. Our point-and-shoot strategy makes the identification of ABs accessible to every mobile phone user.

Synergistic Tailoring of Electrostatic and Hydrophobic Interactions for Rapid and Specific Recognition of Lysophosphatidic Acid, an Early-Stage Ovarian Cancer Biomarker.

Early detection of ovarian cancer, the most lethal type of gynecologic cancer, can dramatically improve the efficacy of available treatment strategies. However, few screening tools exist for rapidly and effectively diagnosing ovarian cancer in early stages. Here, we present a facile "lock-key" strategy, based on rapid, specific detection of plasma lysophosphatidic acid (LPA, an early stage biomarker) with polydiacetylenes (PDAs)-based probe, for the early diagnosis of ovarian cancer. This strategy relies on specifically inserting LPA "key" into the PDAs "lock" through the synergistic electrostatic and hydrophobic interactions between them, leading to conformation transition of the PDA backbone with a concomitant blue-to-red color change. The detailed mechanism underlying the high selectivity of PDAs toward LPA is revealed by comprehensive theoretical calculation and experiments. Moreover, the level of LPA can be quantified in plasma samples from both mouse xenograft tumor models and patients with ovarian cancer. Impressively, this approach can be introduced into a portable point-of-care device to successfully distinguish the blood samples of patients with ovarian cancer from those of healthy people, with 100% accuracy. This work provides a valuable portable tool for early diagnosis of ovarian cancer and thus holds a great promise to dramatically improve the overall survival.