Arsenic Reduces Methane Emissions from Paddy Soils: Insights from Continental Investigation and Laboratory Incubations.

Arsenic (As) contamination and methane (CH4) emissions co-occur in rice paddies. However, how As impacts CH4 production, oxidation, and emission dynamics is unknown. Here, we investigated the abundances and activities of CH4-cycling microbes from 132 paddy soils with different As concentrations across continental China using metagenomics and the reverse transcription polymerase chain reaction. Our results revealed that As was a crucial factor affecting the abundance and distribution patterns of the mcrA gene, which is responsible for CH4 production and anaerobic CH4 oxidation. Laboratory incubation experiments showed that adding 30 mg kg-1 arsenate increased 13CO2 production by 10-fold, ultimately decreasing CH4 emissions by 68.5%. The inhibition of CH4 emissions by As was induced through three aspects: (1) the toxicity of As decreased the abundance and activity of the methanogens; (2) the adaptability and response of methanotrophs to As is beneficial for CH4 oxidation under As stress; and (3) the more robust arsenate reduction would anaerobically consume more CH4 in paddies. Additionally, significant positive correlations were observed between arsC and pmoA gene abundance in both the observational study and incubation experiment. These findings enhance our understanding of the mechanisms underlying the interactions between As and CH4 cycling in soils.

Organic fertilization co-selects genetically linked antibiotic and metal(loid) resistance genes in global soil microbiome.

Antibiotic resistance genes (ARGs) and metal(loid) resistance genes (MRGs) coexist in organic fertilized agroecosystems based on their correlations in abundance, yet evidence for the genetic linkage of ARG-MRGs co-selected by organic fertilization remains elusive. Here, an analysis of 511 global agricultural soil metagenomes reveals that organic fertilization correlates with a threefold increase in the number of diverse types of ARG-MRG-carrying contigs (AMCCs) in the microbiome (63 types) compared to non-organic fertilized soils (22 types). Metatranscriptomic data indicates increased expression of AMCCs under higher arsenic stress, with co-regulation of the ARG-MRG pairs. Organic fertilization heightens the coexistence of ARG-MRG in genomic elements through impacting soil properties and ARG and MRG abundances. Accordingly, a comprehensive global map was constructed to delineate the distribution of coexistent ARG-MRGs with virulence factors and mobile genes in metagenome-assembled genomes from agricultural lands. The map unveils a heightened relative abundance and potential pathogenicity risks (range of 4-6) for the spread of coexistent ARG-MRGs in Central North America, Eastern Europe, Western Asia, and Northeast China compared to other regions, which acquire a risk range of 1-3. Our findings highlight that organic fertilization co-selects genetically linked ARGs and MRGs in the global soil microbiome, and underscore the need to mitigate the spread of these co-resistant genes to safeguard public health.

Study on Structural Design and Motion Characteristics of Magnetic Helical Soft Microrobots with Drug-Carrying Function.

Magnetic soft microrobots have a wide range of applications in targeted drug therapy, cell manipulation, and other aspects. Currently, the research on magnetic soft microrobots is still in the exploratory stage, and most of the research focuses on a single helical structure, which has limited space to perform drug-carrying tasks efficiently and cannot satisfy specific medical goals in terms of propulsion speed. Therefore, balancing the motion speed and drug-carrying performance is a current challenge to overcome. In this paper, a magnetically controlled cone-helix soft microrobot structure with a drug-carrying function is proposed, its helical propulsion mechanism is deduced, a dynamical model is constructed, and the microrobot structure is prepared using femtosecond laser two-photon polymerization three-dimensional printing technology for magnetic drive control experiments. The results show that under the premise of ensuring sufficient drug-carrying space, the microrobot structure proposed in this paper can realize helical propulsion quickly and stably, and the speed of motion increases with increases in the frequency of the rotating magnetic field. The microrobot with a larger cavity diameter and a larger helical pitch exhibits faster rotary advancement speed, while the microrobot with a smaller helical height and a smaller helical cone angle outperforms other structures with the same feature sizes. The microrobot with a cone angle of 0.2 rad, a helical pitch of 100 µm, a helical height of 220 µm, and a cavity diameter of 80 µm achieves a maximum longitudinal motion speed of 390 µm/s.

Development of a Highly Adaptive Miniature Piezoelectric Robot Inspired by Earthworms.

Miniature resonant piezoelectric robots have the advantages of compact structure, fast response, high speed, and easy control, which have attracted the interest of many scholars in recent years. However, piezoelectric robots usually suffer from the problem of poor adaptability due to the micron-level amplitude at the feet. Inspired by the fact that earthworms have actuation trajectories all around their bodies to move flexibly under the ground, a miniature piezoelectric robot with circumferentially arranged driving feet to improve adaptability is proposed. Notably, a longitudinal-vibration-compound actuation principle with multilegged collaboration is designed to achieve the actuation trajectories around the robot, similar to the earthworms. The structure and operating principle are simulated by the finite element method, and the prototype is fabricated. The robot weighs 22.7 g and has dimensions of 35.5 × 36.5 × 47 mm3. The robot is tethered to an ultrasonic power supply, and the experimental results show that the speed reaches 179.35 mm s-1  under an exciting signal with a frequency of 58.5 kHz and a voltage of 200 Vp-p. High adaptability is achieved by the proposed robot, it can move on flat, fold, concave, and convex surfaces, and even in an inclined or rotating tube.

Protocol for building a user-friendly temperature control system to support microscopes, microfluidic chambers, and custom incubators.

Biological experiments require precise temperature control, necessitating an integrated adjustable temperature system for instruments such as microscopes, microfluidic chambers, or custom incubators. We present a protocol for building a user-friendly temperature control system suitable for both in vitro and in vivo assays. We describe steps for preparing materials, assembling the printed circuit board and enclosure, and fine-tuning the heating algorithm for accuracy. This system can maintain a stable temperature of up to 60°C with stabilities under 0.06°C.

EIF4A3-induced circZFAND6 promotes breast cancer proliferation and metastasis through the miR-647/FASN axis.

AIMS: Circular RNAs (circRNAs) are important regulators in breast cancer progression. However, the underlying mechanism of circRNAs functions in breast cancer remain largely unclear. MAIN METHODS: To investigate the circRNAs expression pattern in breast cancer, high-throughput circRNA microarray assay was used. The top up-regulated circRNA, circZFAND6, was submitted to further experiments, including cell counting kit-8 (CCK-8) assay, colony formation assay, transwell assay and mouse xenograft assay. To investigate the underlying mechanism of circZFAND6 function in breast cancer progression, luciferase reporter assay and RNA immunoprecipitation (RIP) assay were conducted. KEY FINDINGS: We found a novel circRNA, circZFAND6, was up-regulated in breast cancer tissues and cell lines. Inhibition of circZFAND6 reduced proliferation and metastasis of breast cancer. Mechanically, circZFAND6 acted as a competing endogenous RNA (ceRNA) to sponge miR-647 and increase fatty acid synthase (FASN) expression. And eukaryotic translation initiation factor 4A3 (EIF4A3) was found to bind to circZFAND6 pre-mRNA transcript upstream region, leading to the high expression of circZFAND6 in breast cancer. Inhibition of EIF4A3 also suppressed proliferation and metastasis of breast cancer. SIGNIFICANCE: EIF4A3-induced circZFAND6 up-regulation promoted proliferation and metastasis of breast cancer through the miR-647/FASN axis. Our results uncovered a possible mechanism underlying breast cancer progression and might provide a breast cancer treatment target.

Rapid and sensitive determination of Piroxicam by N-doped carbon dots prepared by plant soot.

Piroxicam (PX) as a nonsteroidal anti-inflammatory drug (NSAID) can be effectively used for anti-inflammatory and analgesia. However, overdoses may induce side effects such as gastrointestinal ulcers and headaches. Therefore, the assay of piroxicam has considerable significance. In this work, nitrogen-doped carbon dots (N-CDs) was synthesized for PX detection. The fluorescence sensor was fabricated by hydrothermal method with plant soot and ethylenediamine. The strategy exhibited a detection range of 6-200 µg/mL and 250-700 µg/mL with the limited detection of 2 µg/mL. The mechanism of the PX assay base on the fluorescence sensor was the process of electron transfer between the PX and N-CDs. The assay subsequently demonstrated could be successfully used in actual sample. The results indicated that the N-CDs could be a superior candidate nanomaterial for piroxicam monitoring in the healthcare product industry.

Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation.

A common problem with segmentation of medical images using neural networks is the difficulty to obtain a significant number of pixel-level annotated data for training. To address this issue, we proposed a semi-supervised segmentation network based on contrastive learning. In contrast to the previous state-of-the-art, we introduce Min-Max Similarity (MMS), a contrastive learning form of dual-view training by employing classifiers and projectors to build all-negative, and positive and negative feature pairs, respectively, to formulate the learning as solving a MMS problem. The all-negative pairs are used to supervise the networks learning from different views and to capture general features, and the consistency of unlabeled predictions is measured by pixel-wise contrastive loss between positive and negative pairs. To quantitatively and qualitatively evaluate our proposed method, we test it on four public endoscopy surgical tool segmentation datasets and one cochlear implant surgery dataset, which we manually annotated. Results indicate that our proposed method consistently outperforms state-of-the-art semi-supervised and fully supervised segmentation algorithms. And our semi-supervised segmentation algorithm can successfully recognize unknown surgical tools and provide good predictions. Also, our MMS approach could achieve inference speeds of about 40 frames per second (fps) and is suitable to deal with the real-time video segmentation.

High stretchable and self-healing nanocellulose-poly(acrylic acid) composite hydrogels for sustainable CO2 shutoff.

The injection of CO2 into oil reservoirs to enhance oil recovery (EOR) has become a widely accepted and effective technical method, which, however, remains subject to the gas channeling caused by the reservoir fractures. Herein, this work developed a novel plugging gel combining excellent mechanical properties, fatigue resistance, elastic and self-healing properties for the CO2 shutoff purpose. This gel consisting of grafted nanocellulose and polymer network was synthesized via a free-radical polymerization, and reinforced by using Fe3+ to cross-link the two networks. The as-prepared PAA-TOCNF-Fe3+ gel has a stress of 1.03 MPa and a high strain of 1491 %, and self-heals to its original 98 % in stress and 96 % in strain after rupture, respectively. The introduce of TOCNF/Fe3+ improves the excellent energy dissipation and self-healing via the synergy effect of dynamical coordination bonds and hydrogen bonds. Further, the PAA-TOCNF-Fe3+ gel is both flexible and high-strength in plugging the multi-round CO2 injection, during which the CO2 breakthrough pressure is above 9.9 MPa/m, the plugging efficiency exceeds 96 %, and the self-healing rate is larger than 90 %. Given that above, this gel shows a great potential to plug the high-pressure CO2 flow, which could offer a new method for CO2-EOR and carbon storage.

A Bifunctional Fluorescence Probe Based on AIE-ICT Strategy for Visual Detection of Cu2+/Co2+ in Complex Matrix.

A novel fluorescence chemical sensor-based probe 1-{[(E)-(2-aminophenyl)azanylidene]methyl}naphthalen-2-ol (AMN) was designed and synthesized, which performed a "naked eye" detection ability toward Cu2+ and Co2+ based on aggregation-induced emission (AIE) fluorescence strategy. It has sensitive detection ability for Cu2+ and Co2+. In addition, the color changed from yellow-green to orange under the sunlight, realizing the rapid identification of Cu2+/Co2+, which has the potential of on-site visual detection under the "naked eye". Moreover, different "on" and "off" fluorescence expressions were exhibited under excessive glutathione (GSH) in AMN-Cu2+ and AMN-Co2+ systems, which could be employed to distinguish Cu2+ from Co2+. The detection limits for Cu2+ and Co2+ were measured to be 8.29 × 10-8 M and 9.13 × 10-8 M, respectively. The binding mode of AMN was calculated to be 2:1 by Jobs' plot method analysis. Ultimately, the new fluorescence sensor was applied to detect Cu2+ and Co2+ in real samples (tap water, river water, and yellow croaker), and the results were satisfying. Therefore, this high-efficiency bifunctional chemical sensor platform based on "on-off" fluorescence detection will provide significant guidance for the advance development of single-molecule sensors for multi-ion detection.

Occurrence of OCPs & PCBs and their effects on multitrophic biological communities in riparian groundwater of the Beiluo River, China.

Persistent Organic Pollutants (POPs) may exert adverse effects on human and ecosystem health. However, as an ecologically fragile zone with strong interaction between river and groundwater, the POPs pollution in the riparian zone has received little attention. The goal of this research is to examine the concentrations, spatial distribution, potential ecological risks, and biological effects of organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in the riparian groundwater of the Beiluo River, China. The results showed that the pollution level and ecological risk of OCPs in riparian groundwater of the Beiluo River were higher than PCBs. The presence of PCBs (Penta-CBs, Hexa-CBs) and CHLs, respectively, may have reduced the richness of bacteria (Firmicutes) and fungi (Ascomycota). Furthermore, the richness and Shannon's diversity index of algae (Chrysophyceae and Bacillariophyta) decreased, which could be linked to the presence of OCPs (DDTs, CHLs, DRINs), and PCBs (Penta-CBs, Hepta-CBs), while for metazoans (Arthropoda) the tendency was reversed, presumably as a result of SULPHs pollution. In the network analysis, core species belonging to bacteria (Proteobacteria), fungi (Ascomycota), and algae (Bacillariophyta) played essential roles in maintaining community function. Burkholderiaceae and Bradyrhizobium can be considered biological indicators of PCBs pollution in the Beiluo River. Note that the core species of interaction network, playing a fundamental role in community interactions, are strongly affected by POPs pollutants. This work provides insights into the functions of multitrophic biological communities in maintaining the stability of riparian ecosystems through the response of core species to riparian groundwater POPs contamination.

Surface deformation tracking in monocular laparoscopic video.

Image-guided surgery has been proven to enhance the accuracy and safety of minimally invasive surgery (MIS). Nonrigid deformation tracking of soft tissue is one of the main challenges in image-guided MIS owing to the existence of tissue deformation, homogeneous texture, smoke and instrument occlusion, etc. In this paper, we proposed a piecewise affine deformation model-based nonrigid deformation tracking method. A Markov random field based mask generation method is developed to eliminate tracking anomalies. The deformation information vanishes when the regular constraint is invalid, which further deteriorates the tracking accuracy. Atime-series deformation solidification mechanism is introduced to reduce the degradation of the deformation field of the model. For the quantitative evaluation of the proposed method, we synthesized nine laparoscopic videos mimicking instrument occlusion and tissue deformation. Quantitative tracking robustness was evaluated on the synthetic videos. Three real videos of MIS containing challenges of large-scale deformation, large-range smoke, instrument occlusion, and permanent changes in soft tissue texture were also used to evaluate the performance of the proposed method. Experimental results indicate the proposed method outperforms state-of-the-art methods in terms of accuracy and robustness, which shows good performance in image-guided MIS.

Super-resolution segmentation network for inner-ear tissue segmentation.

Cochlear implants (CIs) are considered the standard-of-care treatment for profound sensory-based hearing loss. Several groups have proposed computational models of the cochlea in order to study the neural activation patterns in response to CI stimulation. However, most of the current implementations either rely on high-resolution histological images that cannot be customized for CI users or CT images that lack the spatial resolution to show cochlear structures. In this work, we propose to use a deep learning-based method to obtain µCT level tissue labels using patient CT images. Experiments showed that the proposed super-resolution segmentation architecture achieved very good performance on the inner-ear tissue segmentation. Our best-performing model (0.871) outperformed the UNet (0.746), VNet (0.853), nnUNet (0.861), TransUNet (0.848), and SRGAN (0.780) in terms of mean dice score.

Fast Prediction of Lipophilicity of Organofluorine Molecules: Deep Learning-Derived Polarity Characters and Experimental Tests.

Fast and accurate estimation of lipophilicity for organofluorine molecules is in great demand for accelerating drug and materials discovery. A lipophilicity data set of organofluorine molecules (OFL data set), containing 1907 samples, is constructed through density functional theory (DFT) calculations and experimental measurements. An efficient and interpretable model, called PoLogP, is developed to predict the n-octanol/water partition coefficient, log Po/w, of organofluorine molecules on the basis of the descriptors of polarization, which is a combination of polarity descriptors, including the molecular polarity index and molecular polarizability (α), and hydrogen bond (HBs) index, consisting of the number of donors (NHBD) and acceptors (NHBA and NHB-FA). The present PoLogP with a combination of polarity descriptors is demonstrated to perform better than the dipole moment (µ) alone for the F-contained molecules. With the aid of a multilevel attention graph convolutional neural network model, the fast generation of polarity descriptors of organofluorine molecules could be achieved with the DFT accuracy based only on a topological molecular graph structure. The performance of PoLogP is further validated on synthesized organofluorine molecules and 2626 non-fluorinated molecules with satisfactory accuracy, highlighting the potential usage of PoLogP in high-throughput screening of the functional molecules with the desired solubility in various solvent media.

Molecular partition coefficient from machine learning with polarization and entropy embedded atom-centered symmetry functions.

Efficient prediction of the partition coefficient (log P) between polar and non-polar phases could shorten the cycle of drug and materials design. In this work, a descriptor, named 〈q - ACSFs〉conf, is proposed to take the explicit polarization effects in the polar phase and the conformation ensemble of energetic and entropic significance in the non-polar phase into consideration. The polarization effects are involved by embedding the partial charge directly derived from force fields or quantum chemistry calculations into the atom-centered symmetry functions (ACSFs), together with the entropy effects, which are averaged according to the Boltzmann distribution of different conformations taken from the similarity matrix. The model was trained with high-dimensional neural networks (HDNNs) on a public dataset PhysProp (with 41 039 samples). Satisfactory log P prediction performance was achieved on three other datasets, namely, Martel (707 molecules), Star & Non-Star (266) and Huuskonen (1870). The present 〈q - ACSFs〉conf model was also applicable to n-carboxylic acids with the number of carbons ranging from 2 to 14 and 54 kinds of organic solvent. It is easy to apply the present method to arbitrary sized systems and give a transferable atom-based partition coefficient.

Real-time biomechanical modelling of the liver using LightGBM model.

BACKGROUND: Accurate and real-time biomechanical modelling of the liver is a major challenge in computer-assisted surgery. Finite element method is often used to predict the deformation of organs for its high modelling accuracy. However, its high computation cost hinders its application in real time, such as virtual surgery simulations. METHOD: A liver model with biomechanical properties similar to real one is created using finite element method and a data set of the liver deformation with different forces (whose magnitude ranges from 0.1 to 0.5 N in omni-direction) acting on different surface points is generated. The mechanical behaviour of liver is simulated in real time by a tree-based LightGBM regression model trained with the generated data set. RESULTS: In comparison with the Random Forest and XGBoost, the LightGBM model achieves the best accuracy with 0.0774 mm, 0.0786 mm, 0.0801 mm in the mean absolute error (MAE) and 0.0591 mm, 0.0609 and 0.0622 mm in the root mean square error (RMSE) along x, y and z axis, respectively. In addition, it only takes 33 ms for the LightGBM model to estimate the deformation of the liver, which is much faster than finite element model (29.91 s). CONCLUSION: These results lay a foundation for the future development of real-time virtual surgery systems of simulating liver deformation during minimally invasive surgeries using our method.

Coacervate microdroplets incorporating J-aggregates toward photoactive membraneless protocells.

Cooperative coacervation of a porphyrin and a polycation electrolyte gives birth to photoactive membraneless protocells via liquid-liquid phase separation, where J-aggregates are formed to offer energy transduction pathways, rendering an adaptive platform for confining photocatalytic reactions within protocell compartments.