Deciphering environmental factors influencing phytoplankton community structure in a polluted urban river.

Tuojiang River Basin is a first-class tributary of the upper reaches of the Yangtze River-which is the longest river in China. As phytoplankton are sensitive indicators of trophic changes in water bodies, characterizing phytoplankton communities and their growth influencing factors in polluted urban rivers can provide new ideas for pollution control. Here, we used direct microscopic count and environmental DNA (eDNA) metabarcoding methods to investigate phytoplankton community structure in Tuojiang River Basin (Chengdu, Sichuan Province, China). The association between phytoplankton community structure and water environmental factors was evaluated by Mantel analysis. Additional environmental monitoring data were used to pinpoint major factors that influenced phytoplankton growth based on structural equation modeling. At the phylum level, the dominant phytoplankton taxa identified by the conventional microscopic method mainly belonged to Bacillariophyta, Chlorophyta, and Cyanophyta, in contrast with Chlorophyta, Dinophyceae, and Bacillariophyta identified by eDNA metabarcoding. In α-diversity analysis, eDNA metabarcoding detected greater species diversity and achieved higher precision than the microscopic method. Phytoplankton growth was largely limited by phosphorus based on the nitrogen-to-phosphorus ratios > 16:1 in all water samples. Redundancy analysis and structural equation modeling also confirmed that the nitrogen-to-phosphorus ratio was the principal factor influencing phytoplankton growth. The results could be useful for implementing comprehensive management of the river basin environment. It is recommended to control the discharge of point- and surface-source pollutants and the concentration of dissolved oxygen in areas with excessive nutrients (e.g., Jianyang-Ziyang). Algae monitoring techniques and removal strategies should be improved in 201 Hospital, Hongrihe Bridge and Colmar Town areas.

Effects of polysaccharide from Pueraria lobata on osteoarthritis in rats.

The effects of Pueraria lobata polysaccharide (PPL-1) on osteoarthritis (OA) disease were comprehensively evaluated by using chondrocytes and synoviocytes extracted from the joints of SD rats based on in vitro cell experiments and by establishing pathological models of OA rats. The results showed that concentrations of 1.25-10 and 0.2-1.6 µg/mL, PPL-1 did not inhibit or promote chondrocytes and synoviocytes in vitro. However, at concentrations of 1.25-10 and 0.2-1.6 µg/mL, it can promote cartilage and synovial membrane cells after LPS stimulation of cell activity and inhibite LPS-induced apoptosis. The results of animal experiments showed that PPL-1 can reduce the symptoms of joint swelling in OA rats, decrease the production of serum inflammatory cytokines TNF-α, IL-1ß, and IL-6, and slow down the occurrence of inflammation. Therefore, from the perspective of symptoms, inflammatory factors and pathology, PPL-1 has therapeutic effects on OA rats and alleviates the development of inflammation. It indicated that PPL-1 has the potential to be developed into an OA therapeutic drug with anti-inflammatory properties that protects and activates chondrocytes.

Building endoplasmic reticulum stress-related LncRNAs signatures of lung adenocarcinoma.

BACKGROUND: Endoplasmic reticulum stress (ERS) could be a strategy for treating malignant tumors. Moreover, long noncoding RNAs (lncRNAs) can promote tumorigenesis and progression, and forecast the prognosis of cancers. Nevertheless, the prognostic value of ERS-related lncRNAs has not been reported in lung adenocarcinoma (LUAD). METHODS: The messenger RNA (mRNA), microRNA (miRNA) and lncRNA expression data related to LUAD were obtained in public databases (TCGA and GEO databases). Prognostic ERS-related differentially expressed lncRNAs (ERS-DELs) were obtained and used to build an ERS-related model by Cox regression analysis. Moreover, we further screened independent prognostic elements and built a nomogram. Furthermore, enrichment analysis of genes was conducted to investigate the functions. A lncRNA-miRNA-mRNA network was built to explore mechanism of lncRNAs. Finally, qRT-PCR was utilized to examine the expression levels of lncRNAs. RESULTS: 30 ERS-DELs were identified, and an ERS-related signature was built based on AF131215.2, LINC00472, LINC01352, RP1-78O14.1, RP11-253E3.3, RP11-98D18.9, and SNHG12. Gene set enrichment analysis indicated that genes in the high-risk group were chiefly focused on the regulation of mRNA binding, and genes in the low-risk group were significantly focused on protein localization to cilia. A lncRNA-miRNA-mRNA network, containing 7 signature lncRNAs, 23 miRNAs, and 128 mRNAs, was also established. Eventually, quantitative real-time polymerase chain reaction was used to confirm that seven prognostic lncRNAs had a consistent expression with the analysis. CONCLUSIONS: An ERS-related signature containing seven prognostic lncRNAs was built, which offered new thinking concerning the role of ERS-related lncRNAs in LUAD.

Multiple Enol-Keto Isomerization and Excited-State Unidirectional Intramolecular Proton Transfer Generate Intense, Narrowband Red OLEDs.

A novel series of excited-state intramolecular proton transfer (ESIPT) emitters, namely, DPNA, DPNA-F, and DPNA-tBu, endowed with dual intramolecular hydrogen bonds, were designed and synthesized. In the condensed phase, DPNAs exhibit unmatched absorption and emission spectral features, where the minor 0-0 absorption peak becomes a major one in the emission. Detailed spectroscopic and dynamic approaches conclude fast ground-state equilibrium among enol-enol (EE), enol-keto (EK), and keto-keto (KK) isomers. The equilibrium ratio can be fine-tuned by varying the substitutions in DPNAs. Independent of isomers and excitation wavelength, ultrafast ESIPT takes place for all DPNAs, giving solely KK tautomer emission maximized at >650 nm. The spectral temporal evolution of ESIPT was resolved by a state-of-the-art technique, namely, the transient grating photoluminescence (TGPL), where the rate of EK* → KK* is measured to be (157 fs)-1 for DPNA-tBu, while a stepwise process is resolved for EE* → EK* → KK*, with a rate of EE* → EK* of (72 fs)-1. For all DPNAs, the KK tautomer emission shows a narrowband emission with high photoluminescence quantum yields (PLQY, ∼62% for DPNA in toluene) in the red, offering advantages to fabricate deep-red organic light-emitting diodes (OLED). The resulting OLEDs give high external quantum efficiency with a spectral full width at half-maximum (FWHM) as narrow as ∼40 nm centered at 666-670 nm for DPNAs, fully satisfying the BT. 2020 standard. The unique ESIPT properties and highly intense tautomer emission with a small fwhm thus establish a benchmark for reaching red narrowband organic electroluminescence.

Electrical-Light Coordinately Modulated Synaptic Memristor Based on Ti3C2 MXene for Near-Infrared Artificial Vision Applications.

Emerging optoelectronic memristive devices with high parallelism and low-power consumption have made neuromorphic computing hardware a tangible reality. The coordination of conductivity regulation through both electrical and light signals is pivotal for advancing the development of synaptic memristors with brainlike functionalities. Here, an artificial visual synapse is presented with the Ti3C2 MXene memristor which demonstrates not only the nonvolatile memory effect (Set/Reset: 0.58/-0.55 V; Retention: >103 s) and sustained multistage conductivity, but also facile modulation of both electrical- and light-stimulated synaptic behaviors. By adjusting the stimulus parameters, the Ti3C2 MXene enables the realization of biosynaptic excitatory postsynaptic current, sustained conductivity, stable long-term facilitation/depression, paired pulse facilitation, spiking-timing-dependent plasticity, and experiential learning. Particularly, benefiting from the distinguishable photoconductive and memory effects of multiple near-infrared intensities (7-13 mW/cm2), potential applications in visual nociceptive perception ("threshold", "noadaption", "relaxation") and imaging (e.g., "Superman" cartoon character) in infrared environments are well achieved in such Ti3C2 MXene memristors. These results hold significant implications for the future advancement of integrated optoelectronic sensing, memory, nociception, and imaging systems.

Amine-Borane-Mediated, Nickel/Photoredox-Catalyzed Cross-Electrophile Coupling between Alkyl and Aryl Bromides.

Nickel/photoredox catalysis has emerged as a powerful platform for exploring nontraditional and challenging cross-couplings. Herein, a metallaphotoredox catalytic protocol has been developed on the basis of a tertiary amine-ligated boryl radical-induced halogen atom transfer process under blue-light irradiation. A wide variety of aryl and heteroaryl bromides featuring different functional groups and pharmaceutical moieties were facilely coupled to rapidly install C(sp3)-enriched aromatic scaffolds. The compatibility of Lewis base-ligated borane with nickel catalysis was well exemplified to extend the chemical space for Ni-catalyzed cross-electrophile coupling.

Multisite distribution of fibrillary inclusions in a patient with light chain proximal tubulopathy: A case report.

RATIONALE: Light chain proximal tubulopathy (LCPT) is a rare form of renal impairment associated with multiple myeloma (MM). LCPT is caused by inclusions formed of free light chains that are typically crystalline, but can also be noncrystalline structures. PATIENT CONCERNS: A 62-year-old man was hospitalized for the investigation of abnormal urine test results lasting for 1 year and kidney-function abnormalities persisting for more than 1 month. DIAGNOSES: Noncrystalline LCPT and MM. INTERVENTIONS: The patient was treated with the lenalidomide, bortezomib, and dexamethasone and pomalidomide, bortezomib, and dexamethasone chemotherapy regimens. OUTCOMES: Complete remission of MM was achieved, and the patient's renal function returned to normal. LESSONS: This case report highlights the importance of renal pathology in the diagnosis of patients with unexplained chronic kidney disease and proteinuria.

Spatiotemporal correlation enhanced real-time 4D-CBCT imaging using convolutional LSTM networks.

Purpose: To enhance the accuracy of real-time four-dimensional cone beam CT (4D-CBCT) imaging by incorporating spatiotemporal correlation from the sequential projection image into the single projection-based 4D-CBCT estimation process. Methods: We first derived 4D deformation vector fields (DVFs) from patient 4D-CT. Principal component analysis (PCA) was then employed to extract distinctive feature labels for each DVF, focusing on the first three PCA coefficients. To simulate a wide range of respiratory motion, we expanded the motion amplitude and used random sampling to generate approximately 900 sets of PCA labels. These labels were used to produce 900 simulated 4D-DVFs, which in turn deformed the 0% phase 4D-CT to obtain 900 CBCT volumes with continuous motion amplitudes. Following this, the forward projection was performed at one angle to get all of the digital reconstructed radiographs (DRRs). These DRRs and the PCA labels were used as the training data set. To capture the spatiotemporal correlation in the projections, we propose to use the convolutional LSTM (ConvLSTM) network for PCA coefficient estimation. For network testing, when several online CBCT projections (with different motion amplitudes that cover the full respiration range) are acquired and sent into the network, the corresponding 4D-PCA coefficients will be obtained and finally lead to a full online 4D-CBCT prediction. A phantom experiment is first performed with the XCAT phantom; then, a pilot clinical evaluation is further conducted. Results: Results on the XCAT phantom and the patient data show that the proposed approach outperformed other networks in terms of visual inspection and quantitative metrics. For the XCAT phantom experiment, ConvLSTM achieves the highest quantification accuracy with MAPE(Mean Absolute Percentage Error), PSNR (Peak Signal-to-Noise Ratio), and RMSE(Root Mean Squared Error) of 0.0459, 64.6742, and 0.0011, respectively. For the patient pilot clinical experiment, ConvLSTM also achieves the best quantification accuracy with that of 0.0934, 63.7294, and 0.0019, respectively. The quantification evaluation labels that we used are 1) the Mean Absolute Error (MAE), 2) the Normalized Cross Correlation (NCC), 3)the Structural Similarity Index Measurement(SSIM), 4)the Peak Signal-to-Noise Ratio (PSNR), 5)the Root Mean Squared Error(RMSE), and 6) the Absolute Percentage Error (MAPE). Conclusion: The spatiotemporal correlation-based respiration motion modeling supplied a potential solution for accurate real-time 4D-CBCT reconstruction.

Schottky Diode Leakage Current Fluctuations: Electrostatically Induced Flexoelectricity in Silicon.

Nearly four decades have passed since IBM scientists pioneered atomic force microscopy (AFM) by merging the principles of a scanning tunneling microscope with the features of a stylus profilometer. Today, electrical AFM modes are an indispensable asset within the semiconductor and nanotechnology industries, enabling the characterization and manipulation of electrical properties at the nanoscale. However, electrical AFM measurements suffer from reproducibility issues caused, for example, by surface contaminations, Joule heating, and hard-to-minimize tip drift and tilt. Using as experimental system nanoscale Schottky diodes assembled on oxide-free silicon crystals of precisely defined surface chemistry, it is revealed that voltage-dependent adhesion forces lead to significant rotation of the AFM platinum tip. The electrostatics-driven tip rotation causes a strain gradient on the silicon surface, which induces a flexoelectric reverse bias term. This directional flexoelectric internal-bias term adds to the external (instrumental) bias, causing both an increased diode leakage as well as a shift of the diode knee voltage to larger forward biases. These findings will aid the design and characterization of silicon-based devices, especially those that are deliberately operated under large strain or shear, such as in emerging energy harvesting technologies including Schottky-based triboelectric nanogenerators (TENGs).

Adaptive adjacent context negotiation network for object detection in remote sensing imagery.

Accurate localization of objects of interest in remote sensing images (RSIs) is of great significance for object identification, resource management, decision-making and disaster relief response. However, many difficulties, like complex backgrounds, dense target quantities, large-scale variations, and small-scale objects, which make the detection accuracy unsatisfactory. To improve the detection accuracy, we propose an Adaptive Adjacent Context Negotiation Network (A2CN-Net). Firstly, the composite fast Fourier convolution (CFFC) module is given to reduce the information loss of small objects, which is inserted into the backbone network to obtain spectral global context information. Then, the Global Context Information Enhancement (GCIE) module is given to capture and aggregate global spatial features, which is beneficial for locating objects of different scales. Furthermore, to alleviate the aliasing effect caused by the fusion of adjacent feature layers, a novel Adaptive Adjacent Context Negotiation network (A2CN) is given to adaptive integration of multi-level features, which consists of local and adjacent branches, with the local branch adaptively highlighting feature information and the adjacent branch introducing global information at the adjacent level to enhance feature representation. In the meantime, considering the variability in the focus of feature layers in different dimensions, learnable weights are applied to the local and adjacent branches for adaptive feature fusion. Finally, extensive experiments are performed in several available public datasets, including DIOR and DOTA-v1.0. Experimental studies show that A2CN-Net can significantly boost detection performance, with mAP increasing to 74.2% and 79.2%, respectively.

Promoted Stability and Reaction Kinetics in Ni-Rich Cathodes via Mechanical Fusing Multifunctional LiZr2(PO4)3 Nanocrystals for High Mass Loading All-Solid-State Lithium Batteries.

Sulfide all-solid-state lithium battery (ASSLB) with nickel-rich layered oxide as the cathode is promising for next-generation energy storage system. However, the Li+ transport dynamic and stability in ASSLB are hindered by the structural mismatches and the instabilities especially at the oxide cathode/sulfide solid electrolyte (SE) interface. In this work, we have demonstrated a simple and highly effective solid-state mechanofusion method (1500 rpm for 10 min) to combine lithium conductive NASICON-type LiZr2(PO4)3 nanocrystals (∼20 nm) uniformly and compactly onto the surface of the single crystallized LiNi0.8Co0.1Mn0.1O2, which can also attractively achieve Zr4+ doping in NCM811 and oxygen vacancies in the LZPO coating without solvent and annealing. Benefiting from the alleviated interface mismatches, sufficient Li+ ion flux through the LZPO coating, promoted structural stabilities for both NCM811 and sulfide SE, strong electronic coupling effect between the LZPO and NCM811, and enlarged (003) d-spacing with enriched Li+ migration channels in NCM811, the obtained LZPO-NCM811 exhibits superior stability (185 mAh/g at 0.1C for 200 cycles) and rate performance (105 mAh/g at 1C for 1300 cycles) with high mass loading of 27 mgNCM/cm2 in sulfide ASSLB. Even with a pronounced 54 mgNCM/cm2, LZPO-NCM811 manifests a high areal capacity of 9.85 mAh/cm2. The convenient and highly effective interface engineering strategy paves the way to large-scale production of various coated cathode materials with synergistic effects for high performance ASSLBs.

Characterization of the components in plasma EVs unveiling the link between EVs-derived complement C3 with the severity and initial treatment response of profound sudden sensorineural hearing loss.

BACKGROUND: Sudden sensorineural hearing loss (SSNHL) is characterized by rapid, unexplained loss of hearing within a 72-hour period and exhibits a high incidence globally. Despite this, the outcomes of therapeutic interventions remain largely unpredictable, especially for those with profound hearing loss. Extracellular vesicles (EVs), nano-sized entities containing biological materials, are implicated in the development of numerous diseases. The specific relationship between EVs and both the severity and treatment effectiveness of SSNHL, however, is not well understood. METHODS: This study involved the analysis of medical records from the Department of Otolaryngology (September 1, 2020 - December 31, 2022) of patients diagnosed with SSNHL according to the 2015 Guidelines for Diagnosis and Treatment of Sudden Deafness in China. Peripheral blood samples from patients with various types of SSNHL before and after treatment were collected, alongside samples from healthy volunteers serving as controls. Plasma EVs were isolated using gel rejection chromatography and analyzed for concentration, marker presence, and morphology using Nanosight, Western blot, and transmission electron microscopy (TEM), respectively. Proteomics and miRNA assessments were conducted to identify differentially expressed proteins and miRNAs in the plasma EVs of SSNHL patients and healthy volunteers. Key proteins were further validated through Western blot analysis. Enzyme-linked immunosorbent assay (ELISA) was utilized to determine the levels of complement C3 in plasma EVs, and correlation analyses were performed with audiological data pre- and post-treatment. RESULTS: Plasma from SSNHL patients of varying types was collected and their EVs were successfully isolated and characterized. Proteomic analysis revealed that complement C3 levels in the plasma EVs of patients with profound SSNHL were significantly higher compared to healthy controls. Differential expression of miRNAs in plasma EVs and their related functions were also identified. The study found that the level of complement C3 in plasma EVs, but not the total plasma complement C3, positively correlated with the severity of SSNHL in patients exhibiting positive therapeutic responses, particularly in those with initially lower levels of EV-associated complement C3. After treatment, complement C3 level was decreased in patients with initially higher levels of EV-associated complement C3. No significant correlation was observed between changes in plasma EV-derived complement C3 levels and the degree of hearing loss in either responders or non-responders among patients with profound SSNHL. CONCLUSION: Differential profiles of proteins and miRNAs were identified in patients with profound SSNHL. Notably, plasma EV-derived complement C3 was linked to both the severity and early treatment effectiveness of patients with profound SSNHL.

Structural basis for coupling of the WASH subunit FAM21 with the endosomal SNX27-Retromer complex.

Endosomal membrane trafficking is mediated by specific protein coats and formation of actin-rich membrane domains. The Retromer complex coordinates with sorting nexin (SNX) cargo adaptors including SNX27, and the SNX27-Retromer assembly interacts with the Wiskott-Aldrich syndrome protein and SCAR homolog (WASH) complex which nucleates actin filaments establishing the endosomal recycling domain. Crystal structures, modeling, biochemical, and cellular validation reveal how the FAM21 subunit of WASH interacts with both Retromer and SNX27. FAM21 binds the FERM domain of SNX27 using acidic-Asp-Leu-Phe (aDLF) motifs similar to those found in the SNX1 and SNX2 subunits of the ESCPE-1 complex. Overlapping FAM21 repeats and a specific Pro-Leu containing motif bind three distinct sites on Retromer involving both the VPS35 and VPS29 subunits. Mutation of the major VPS35-binding site does not prevent cargo recycling; however, it partially reduces endosomal WASH association indicating that a network of redundant interactions promote endosomal activity of the WASH complex. These studies establish the molecular basis for how SNX27-Retromer is coupled to the WASH complex via overlapping and multiplexed motif-based interactions required for the dynamic assembly of endosomal membrane recycling domains.

Protein-peptide binding residue prediction based on protein language models and cross-attention mechanism.

Accurate identifications of protein-peptide binding residues are essential for protein-peptide interactions and advancing drug discovery. To address this problem, extensive research efforts have been made to design more discriminative feature representations. However, extracting these explicit features usually depend on third-party tools, resulting in low computational efficacy and suffering from low predictive performance. In this study, we design an end-to-end deep learning-based method, E2EPep, for protein-peptide binding residue prediction using protein sequence only. E2EPep first employs and fine-tunes two state-of-the-art pre-trained protein language models that can extract two different high-latent feature representations from protein sequences relevant for protein structures and functions. A novel feature fusion module is then designed in E2EPep to fuse and optimize the above two feature representations of binding residues. In addition, we have also design E2EPep+, which integrates E2EPep and PepBCL models, to improve the prediction performance. Experimental results on two independent testing data sets demonstrate that E2EPep and E2EPep + could achieve the average AUC values of 0.846 and 0.842 while achieving an average Matthew's correlation coefficient value that is significantly higher than that of existing most of sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis shows that the primary strength of E2EPep lies in the effectiveness of feature representation using cross-attention mechanism to fuse the embeddings generated by two fine-tuned protein language models. The standalone package of E2EPep and E2EPep + can be obtained at https://github.com/ckx259/E2EPep.git for academic use only.

Multicolor Fully Light-Modulated Artificial Synapse Based on P-MoSe2/PxOy Heterostructured Memristor.

Developing brain-inspired neuromorphic paradigms is imperative to breaking through the von Neumann bottleneck. The emulation of synaptic functionality has motivated the exploration of optoelectronic memristive devices as high-performance artificial synapses, yet the realization of such a modulatory terminal capable of full light-modulation, especially near-infrared stimuli, remains a challenge. Here, a fully light-modulated synaptic memristor is reported on a P-MoSe2/PxOy heterostructure formed by a facile one-step selenization process. The results demonstrate successful achievement of multiwavelength (visible 470 nm to near-infrared 808 nm) modulated switching operations (reset in 0.21-0.97 V) and diverse synaptic behaviors, including postsynaptic current, paired-pulse facilitation, short- and long-term memory (STM and LTM), and learning-forgetting. Notably, the device can exhibit a 3.42 µA PSC increase under six identical 655 nm stimuli, a 11.90-46.24 µA PSC modulation by changing 808 nm light intensity from 6 to 14 mW/cm2, and a transition from STM to LTM lasting between 2.47 and 4.27 s by a prolonged 808 nm pulse from 1 to 30 s. A novel possible light-induced switching mechanism in such a heterostructure is proposed. Furthermore, brain-like light-stimulated memory behavior and Pavlov's classical conditioning demonstrate the device's capacity for processing complex inputs. The study presents a design toward a multiwavelength modulated artificial visual system for color recognition.

Restoration-mediated protein substances preferentially drive underlying bauxite residue macroaggregate formation during the simulated ecological reconstruction process.

Constructing a restoration strategy from bauxite residue to Technosols is a cost-effective and sustainable strategy for addressing the ecological and environmental issues caused by high alkalinity, salinity, and fine-grained bauxite residues. However, the quantitative contribution of restoration strategies on the upper bauxite residue-derived Technosols to the underlying untreated bauxite residue in the short term remains poorly understood. This study investigated the mediating mechanisms of vegetation and microbial metabolic effects on the alkalinity, nutrient content, and structure of the underlying bauxite residue (20-50 cm) through a simulated ecological reconstruction of the bauxite residue stockpile. Results indicated that implementing plant restoration strategies resulted in the content of polyphenolic compounds, lipids, tannins, and carbohydrates in bauxite residue dissolved organic matter (DOM) increased significantly from 52.5, 8.2, 3.3, and 2.0 % to 54.4, 10.4, 5.6, and 2.8 %, respectively, while the content of condensed aromatics, unsaturated hydrocarbons, and proteins/amino sugars decreased significantly from 15.5, 12.0, and 6.5 % to 12.1, 9.7, and 5.1 %, respectively. The newly produced molecules were concentrated in regions with low O/C and high H/C ratios, suggesting that short-term vegetation restoration strategies facilitate the transformation of substrate DOM towards easily decomposable and highly bioavailable substances. This led to the migration of the newly produced molecules to the underlying bauxite residue, and as a result, the protein and soluble microbial products of the underlying bauxite residue increased significantly, as well as the pH, exchangeable Na, and < 0.054 mm particles decreased from 10.2, 44.2 cmol kg-1, and 28.1 % to 9.7, 27.1 cmol kg-1, and 19.4 %, respectively, available nitrogen, urease, and 1-2 mm particles increased from 7.3 mg kg-1, 0.2 U mg-1, and 14.5 % to 7.6 mg kg-1, 0.3 U kg-1, and 21.7 %, respectively. Results of the structural equation model further confirmed that plant biomass, proteins/amino sugars, and condensed aromatics in the upper Technosol were the main factors controlling the aggregate formation of the underlying bauxite residue by mediating the protein-dominated biogenic organic matter produced by microbial metabolism.

Robotic-assisted versus video-assisted lobectomy for resectable non-small-cell lung cancer: the RVlob randomized controlled trial.

Background: The long-term survival and perioperative outcomes of robotic-assisted lobectomy (RAL) and video-assisted lobectomy (VAL) in resectable non-small-cell lung cancer (NSCLC) were found to be comparable in retrospective studies, but they have not been investigated in a randomized trial setting. We conducted the RVlob trial to investigate if RAL was non-inferior to VAL in patients with resectable NSCLC. Methods: In this single-center, open-label, and parallel-arm randomized controlled trial conducted in Ruijin Hospital (Shanghai, China) between May 2017 and May 2020, we randomly assigned patients with resectable NSCLC in a 1:1 ratio to receive either RAL or VAL. One of the primary endpoints was 3-year overall survival. Secondary endpoints included 3-year disease-free survival. The Kaplan-Meier approach was used to calculate overall survival and disease-free survival at 3 years. This study was registered with ClinicalTrials.gov, NCT03134534. Findings: A total of 320 patients were randomized to receive RAL (n = 157) or VAL (n = 163). The baseline characteristics of patients were well balanced between the two groups. After a median follow-up of 58.0 months, the 3-year overall survival was 94.6% (95% confidence interval [CI], 91.0-98.3) in the RAL group and 91.5% (95% CI, 87.2-96.0) in the VAL group (hazard ratio [HR] for death, 0.65; 95% CI, 0.33-1.28; P = 0.21); noninferiority of RAL was confirmed according to the predefined margin of -5% (absolute difference, 2.96%; a one-sided 90% CI, -1.39% to ∞; P = 0.0029 for noninferiority). The 3-year disease-free survival was 88.7% (95% CI, 83.6-94.1) in the RAL group and 85.4% (95% CI, 80.0-91.2) in the VAL group (HR for disease recurrence or death, 0.87; 95% CI, 0.50-1.52; P = 0.62). Interpretation: This study is the first randomized trial to show that RAL resulted in non-inferior overall survival compared with VAL in patients with resectable NSCLC. Based on our results, RAL is an equally oncologically effective treatment and can be considered as an alternative to VAL for resectable NSCLC. Funding: National Natural Science Foundation of China (82072557), National Key Research and Development Program of China (2021YFC2500900), Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant (20172005, the 2nd round of disbursement), program of Shanghai Academic Research Leader from Science and Technology Commission of Shanghai Municipality (20XD1402300), Novel Interdisciplinary Research Project from Shanghai Municipal Health Commission (2022JC023), and Interdisciplinary Program of Shanghai Jiao Tong University (YG2023ZD04).

Fabrication of conjugated polymers with aggregation-induced near-infrared-II emission for efficient phototheranostics.

Conjugated polymers (CPs), which emit in the second near-infrared window (NIR-II, 1000-1700 nm), are used as biomaterials for NIR-II fluorescence imaging because of their adjustable photophysical properties and high optical stability. However, the fluorescence signal of conventional CPs is quenched in an aggregated state due to strong π-π stacking, which results in the closure of the radiation attenuation pathway. To solve this problem, the aggregation-induced emission effect is considered a reasonable strategy for enhancing the aggregative fluorescence of IR-II emitters. We herein report NIR-II conjugated polymers with typical AIE characteristics (αAIE > 3) by changing the side chain structure of receptor units and the conjugation degree of donors. Conjugated polymer nanoparticles (PoBVT NPs) exhibit outstanding performance in NIR-II fluorescence imaging (QY = 1.94%) and highly effective photothermal therapy (η = 45%). In vivo studies have shown that the location of tumors can be accurately obtained by NIR-II FL/NIR-II PA imaging, and there is a significant anti-tumor effect after laser irradiation. This work offers prospects for the design of multifunctional conjugated polymers for NIR-II FL/PA imaging to guide NIR-II PTT applications.

Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencing.

Bathymodioline mussels dominate deep-sea methane seep and hydrothermal vent habitats and obtain nutrients and energy primarily through chemosynthetic endosymbiotic bacteria in the bacteriocytes of their gill. However, the molecular mechanisms that orchestrate mussel host-symbiont interactions remain unclear. Here, we constructed a comprehensive cell atlas of the gill in the mussel Gigantidas platifrons from the South China Sea methane seeps (1100 m depth) using single-nucleus RNA-sequencing (snRNA-seq) and whole-mount in situ hybridisation. We identified 13 types of cells, including three previously unknown ones, and uncovered unknown tissue heterogeneity. Every cell type has a designated function in supporting the gill's structure and function, creating an optimal environment for chemosynthesis, and effectively acquiring nutrients from the endosymbiotic bacteria. Analysis of snRNA-seq of in situ transplanted mussels clearly showed the shifts in cell state in response to environmental oscillations. Our findings provide insight into the principles of host-symbiont interaction and the bivalves' environmental adaption mechanisms.

Self-Supervised Cyclic Diffeomorphic Mapping for Soft Tissue Deformation Recovery in Robotic Surgery Scenes.

The ability to recover tissue deformation from visual features is fundamental for many robotic surgery applications. This has been a long-standing research topic in computer vision, however, is still unsolved due to complex dynamics of soft tissues when being manipulated by surgical instruments. The ambiguous pixel correspondence caused by homogeneous texture makes achieving dense and accurate tissue tracking even more challenging. In this paper, we propose a novel self-supervised framework to recover tissue deformations from stereo surgical videos. Our approach integrates semantics, cross-frame motion flow, and long-range temporal dependencies to enable the recovered deformations to represent actual tissue dynamics. Moreover, we incorporate diffeomorphic mapping to regularize the warping field to be physically realistic. To comprehensively evaluate our method, we collected stereo surgical video clips containing three types of tissue manipulation (i.e., pushing, dissection and retraction) from two different types of surgeries (i.e., hemicolectomy and mesorectal excision). Our method has achieved impressive results in capturing deformation in 3D mesh, and generalized well across manipulations and surgeries. It also outperforms current state-of-the-art methods on non-rigid registration and optical flow estimation. To the best of our knowledge, this is the first work on self-supervised learning for dense tissue deformation modeling from stereo surgical videos. Our code will be released.