Nucleation Domains in Biomineralization: Biomolecular Sequence and Conformational Features.

Biomolecules play a vital role in the regulation of biomineralization. However, the characteristics of practical nucleation domains are still sketchy. Herein, the effects of the representative biomolecular sequence and conformations on calcium phosphate (Ca-P) nucleation and mineralization are investigated. The results of computer simulations and experiments prove that the line in the arrangement of dual acidic/essential amino acids with a single interval (Bc (Basic) -N (Neutral) -Bc-N-Ac (Acidic)- NN-Ac-N) is most conducive to the nucleation. 2α-helix conformation can best induce Ca-P ion cluster formation and nucleation. "Ac- × × × -Bc" sequences with α-helix are found to be the features of efficient nucleation domains, in which process, molecular recognition plays a non-negligible role. It further indicates that the sequence determines the potential of nucleation/mineralization of biomolecules, and conformation determines the ability of that during functional execution. The findings will guide the synthesis of biomimetic mineralized materials with improved performance for bone repair.

Preparation of O-g-C3N4 nanowires/Bi2O2CO3 porous plate composite photocatalysts for the efficient degradation of tetracycline hydrochloride in wastewater.

Both g-C3N4 and Bi2O2CO3 are good photocatalysts for the removal of antibiotic pollutants, but their morphological modulation and catalytic performance need to be further improved. In this study, the calcination-hydrothermal method is used to prepare a O-g-C3N4@Bi2O2CO3 (CN@BCO) composite photocatalyst from dicyandiamide and bismuth nitrate. The prepared catalyst is characterized through various methods, including X-ray diffraction (XRD) and transmission electron microscopy (TEM). Further, the effects of different parameters, such as catalyst concentration and initial pH of the reaction solution, on its photocatalytic activity are investigated. The results show that the CN@BCO sample achieves an optimal degradation rate of 98.1% for tetracycline hydrochloride (TCH) with a concentration of 20 mg/L and a removal rate of 69.4% for total organic carbon (TOC) at 40 min. The quenching experiments show that ·O2-, h+, and ·OH participate in the photocatalytic process, with ·O2- being the most dominant active species. The toxicity of the predicted TCH degradation intermediates is analyzed using Toxicity Estimation Software Tool (TEST). Overall, the CN@BCO composite exhibits excellent photocatalytic performance, making it a promising candidate for environmental purification and wastewater treatment.

Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT.

BACKGROUND: To assess the improvement of image quality and diagnostic acceptance of thinner slice iodine maps enabled by deep learning image reconstruction (DLIR) in abdominal dual-energy CT (DECT). METHODS: This study prospectively included 104 participants with 136 lesions. Four series of iodine maps were generated based on portal-venous scans of contrast-enhanced abdominal DECT: 5-mm and 1.25-mm using adaptive statistical iterative reconstruction-V (Asir-V) with 50% blending (AV-50), and 1.25-mm using DLIR with medium (DLIR-M), and high strength (DLIR-H). The iodine concentrations (IC) and their standard deviations of nine anatomical sites were measured, and the corresponding coefficient of variations (CV) were calculated. Noise-power-spectrum (NPS) and edge-rise-slope (ERS) were measured. Five radiologists rated image quality in terms of image noise, contrast, sharpness, texture, and small structure visibility, and evaluated overall diagnostic acceptability of images and lesion conspicuity. RESULTS: The four reconstructions maintained the IC values unchanged in nine anatomical sites (all p > 0.999). Compared to 1.25-mm AV-50, 1.25-mm DLIR-M and DLIR-H significantly reduced CV values (all p < 0.001) and presented lower noise and noise peak (both p < 0.001). Compared to 5-mm AV-50, 1.25-mm images had higher ERS (all p < 0.001). The difference of the peak and average spatial frequency among the four reconstructions was relatively small but statistically significant (both p < 0.001). The 1.25-mm DLIR-M images were rated higher than the 5-mm and 1.25-mm AV-50 images for diagnostic acceptability and lesion conspicuity (all P < 0.001). CONCLUSIONS: DLIR may facilitate the thinner slice thickness iodine maps in abdominal DECT for improvement of image quality, diagnostic acceptability, and lesion conspicuity.

Robustness of Quantitative Diffusion Metrics from Four Models: A Prospective Study on the Influence of Scan-Rescans, Voxel Size, Coils, and Observers.

BACKGROUND: Quantitative diffusion metrics provide additional microstructural information of diseases. The robustness of quantitative diffusion metrics should be established before clinical application. PURPOSE: To evaluate the variability and reproducibility of quantitative diffusion MRI metrics. STUDY TYPE: Prospective. POPULATION: 14 volunteers (7 men; median age, range, 28, 26-59 years). FIELD STRENGTH/SEQUENCE: 3.0-T/Diffusion spectrum imaging. ASSESSMENT: Brain MRI studies were performed four times per subject: involving different combinations of coil types and voxel sizes. Regions of interest of 13 brain anatomical sites were drawn by one observer twice and another observer once to allow interobserver and intraobserver reproducibility assessment. Twenty-five quantitative metrics were calculated using four diffusion models. STATISTICAL TESTS: The variability was evaluated with coefficients of variation (CV), and quartile coefficient of dispersion (QCD). The reproducibility was assessed with intraclass correlation coefficient (ICC), and concordance correlation coefficient (CCC). Wilcoxon signed rank test was used to compare the influence of factors on robustness of quantitative diffusion metrics. A two-tailed P < 0.05 was considered statistically significant. RESULTS: The variability of quantitative diffusion metrics showed CV of 2.4%-68.2%, and QCD of 0.6%-48.2%, respectively. The reproducibility of scans using 20-channel coils with voxels of 2 × 2 × 2 mm3 and 3 × 3 × 3 mm3 , respectively (ICC 0.03-0.84, CCC 0.03-0.84) was significantly worse than that of repeated scans using a 20-channel coil with a voxel size of 2 × 2 × 2 mm3 (ICC of 0.74-0.97, CCC 0.74-0.97) and that of scans using 20- and 64-channel coils, respectively, with a voxel size of 2 × 2 × 2 mm3 (ICC 0.59-0.95, CCC 0.59-0.95). The intraobserver reproducibility (ICC 0.49-0.94, CCC 0.49-0.94) was significantly better than the interobserver reproducibility (ICC 0.28-0.91, CCC 0.28-0.91). DATA CONCLUSION: Our study indicated that the voxel size has a greater influence on the reproducibility of quantitative diffusion metrics than scan-rescans and coils. The reproducibility within one observer was higher than that between two observers. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

A systematic review of radiomics in chondrosarcoma: assessment of study quality and clinical value needs handy tools.

OBJECTIVE: To evaluate the study quality and clinical value of radiomics studies on chondrosarcoma. METHODS: PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data were searched for articles on radiomics for evaluating chondrosarcoma as of January 31, 2022. The study quality was assessed according to Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, Image Biomarker Standardization Initiative (IBSI) guideline, and modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The level of evidence supporting clinical use of radiomics on chondrosarcoma differential diagnosis was determined based on meta-analyses. RESULTS: Twelve articles were included. The median RQS was 10.5 (range, -3 to 15), with an adherence rate of 36%. The adherence rate was extremely low in domains of high-level evidence (0%), open science and data (17%), and imaging and segmentation (35%). The adherence rate of the TRIPOD checklist was 61%, and low for section of title and abstract (13%), introduction (42%), and results (56%). The reporting rate of pre-processing steps according to the IBSI guideline was 60%. The risk of bias and concern of application were mainly related to the index test. The meta-analysis on differential diagnosis of enchondromas vs. chondrosarcomas showed a diagnostic odds ratio of 43.90 (95% confidential interval, 25.33-76.10), which was rated as weak evidence. CONCLUSIONS: The current scientific and reporting quality of radiomics studies on chondrosarcoma was insufficient. Radiomics has potential in facilitating the optimization of operation decision-making in chondrosarcoma. KEY POINTS: • Among radiomics studies on chondrosarcoma, although differential diagnostic models showed promising performance, only pieces of weak level of evidence were reached with insufficient study quality. • Since the RQS rating, the TRIPOD checklist, and the IBSI guideline have largely overlapped with each other, it is necessary to establish one widely acceptable methodological and reporting guideline for radiomics research. • The TRIPOD model typing, the phase classification of image mining studies, and the level of evidence category are useful tools to assess the gap between academic research and clinical application, although their modifications for radiomics studies are needed.

Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers.

OBJECTIVES: To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V) at 50% blending (AV-50), and to identify potential factors impacting lesion conspicuity. METHODS: The portal-venous phase scans in abdominal DECT of 47 participants with 84 lesions were prospectively included. The raw data were reconstructed to virtual monoenergetic image (VMI) at 50 keV using filtered back-projection (FBP), AV-50, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). A noise power spectrum (NPS) was generated. CT number and standard deviation values of eight anatomical sites were measured. Signal-to-noise (SNR), and contrast-to-noise ratio (CNR) values were calculated. Five radiologists assessed image quality in terms of image contrast, image noise, image sharpness, artificial sensation, and diagnostic acceptability, and evaluated the lesion conspicuity. RESULTS: DLIR further reduced image noise (p < 0.001) compared to AV-50 while better preserved the average NPS frequency (p < 0.001). DLIR maintained CT number values (p > 0.99) and improved SNR and CNR values compared to AV-50 (p < 0.001). DLIR-H and DLIR-M showed higher ratings in all image quality analyses than AV-50 (p < 0.001). DLIR-H provided significantly better lesion conspicuity than AV-50 and DLIR-M regardless of lesion size, relative CT attenuation to surrounding tissue, or clinical purpose (p < 0.05). CONCLUSIONS: DLIR-H could be safely recommended for routine low-keV VMI reconstruction in daily contrast-enhanced abdominal DECT to improve image quality, diagnostic acceptability, and lesion conspicuity. KEY POINTS: • DLIR is superior to AV-50 in noise reduction, with less shifts of the average spatial frequency of NPS towards low frequency, and larger improvements of NPS noise, noise peak, SNR, and CNR values. • DLIR-M and DLIR-H generate better image quality in terms of image contrast, noise, sharpness, artificial sensation, and diagnostic acceptability than AV-50, while DLIR-H provides better lesion conspicuity than AV-50 and DLIR-M. • DLIR-H could be safely recommended as a new standard for routine low-keV VMI reconstruction in contrast-enhanced abdominal DECT to provide better lesion conspicuity and better image quality than the standard AV-50.

The endorsement of general and artificial intelligence reporting guidelines in radiological journals: a meta-research study.

BACKGROUND: Complete reporting is essential for clinical research. However, the endorsement of reporting guidelines in radiological journals is still unclear. Further, as a field extensively utilizing artificial intelligence (AI), the adoption of both general and AI reporting guidelines would be necessary for enhancing quality and transparency of radiological research. This study aims to investigate the endorsement of general reporting guidelines and those for AI applications in medical imaging in radiological journals, and explore associated journal characteristic variables. METHODS: This meta-research study screened journals from the Radiology, Nuclear Medicine & Medical Imaging category, Science Citation Index Expanded of the 2022 Journal Citation Reports, and excluded journals not publishing original research, in non-English languages, and instructions for authors unavailable. The endorsement of fifteen general reporting guidelines and ten AI reporting guidelines was rated using a five-level tool: "active strong", "active weak", "passive moderate", "passive weak", and "none". The association between endorsement and journal characteristic variables was evaluated by logistic regression analysis. RESULTS: We included 117 journals. The top-five endorsed reporting guidelines were CONSORT (Consolidated Standards of Reporting Trials, 58.1%, 68/117), PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses, 54.7%, 64/117), STROBE (STrengthening the Reporting of Observational Studies in Epidemiology, 51.3%, 60/117), STARD (Standards for Reporting of Diagnostic Accuracy, 50.4%, 59/117), and ARRIVE (Animal Research Reporting of In Vivo Experiments, 35.9%, 42/117). The most implemented AI reporting guideline was CLAIM (Checklist for Artificial Intelligence in Medical Imaging, 1.7%, 2/117), while other nine AI reporting guidelines were not mentioned. The Journal Impact Factor quartile and publisher were associated with endorsement of reporting guidelines in radiological journals. CONCLUSIONS: The general reporting guideline endorsement was suboptimal in radiological journals. The implementation of reporting guidelines for AI applications in medical imaging was extremely low. Their adoption should be strengthened to facilitate quality and transparency of radiological study reporting.

Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT.

This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V (ASIR-V). The Gammex 464 phantom was scanned in SECT and DECT modes at three dose levels (5, 10, and 20 mGy). Raw data were reconstructed using six algorithms: filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) strength, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H), to generate SECT 120kVp images and DECT 120kVp-like images. Objective image quality metrics were computed, including noise power spectrum (NPS), task transfer function (TTF), and detectability index (d'). Subjective image quality evaluation, including image noise, texture, sharpness, overall quality, and low- and high-contrast detectability, was performed by six readers. DLIR-H reduced overall noise magnitudes from FBP by 55.2% in a more balanced way of low and high frequency ranges comparing to AV-40, and improved the TTF values at 50% for acrylic inserts by average percentages of 18.32%. Comparing to SECT 20 mGy AV-40 images, the DECT 10 mGy DLIR-H images showed 20.90% and 7.75% improvement in d' for the small-object high-contrast and large-object low-contrast tasks, respectively. Subjective evaluation showed higher image quality and better detectability. At 50% of the radiation dose level, DECT with DLIR-H yields a gain in objective detectability index compared to full-dose AV-40 SECT images used in daily practice.

[LC-MS analysis of 2-(2-phenylethyl) chromones in sodium chloride-treated suspension cells of Aquilaria sinensis].

Qualitative and quantitative analysis of 2-(2-phenylethyl) chromones in sodium chloride(NaCl)-treated suspension cells of Aquilaria sinensis was conducted by UPLC-Q-Exactive-MS and UPLC-QQQ-MS/MS. Both analyses were performed on a Waters T3 column(2.1 mm×50 mm, 1.8 µm) with 0.1% formic acid aqueous solution(A)-acetonitrile(B) as mobile phases at gradient elution. MS data were collected by electrospray ionization in positive ion mode. Forty-seven phenylethylchromones was identified from NaCl-treated suspension cell samples of A. sinensis using UPLC-Q-Exactive-MS, including 22 flindersia-type 2-(2-phenylethyl) chromones and their glycosides, 10 5,6,7,8-tetrahydro-2-(2-phenylethyl) chromones and 15 mono-epoxy or diepoxy-5,6,7,8-tetrahydro-2-(2-phenylethyl) chromones. Additionally, 25 phenylethylchromones were quantitated by UPLC-QQQ-MS/MS. Overall, the rapid and efficient qualitative and quantitative analysis of phenylethylchromones in NaCl-treated suspension cells of A. sinensis by two LC-MS techniques, provides an important reference for the yield of phenylethylchromones in Aquilariae Lignum Resinatum using in vitro culture and other biotechnologies.

Mechanically Reinforced Localized Structure Design to Stabilize Solid-Electrolyte Interface of the Composited Electrode of Si Nanoparticles and TiO2 Nanotubes.

Silicon anode with extremely high theoretical specific capacity (≈4200 mAh g-1 ), experiences huge volume changes during Li-ion insertion and extraction, causing mechanical fracture of Si particles and the growth of a solid-electrolyte interface (SEI), which results in a rapid capacity fading of Si electrodes. Herein, a mechanically reinforced localized structure is designed for carbon-coated Si nanoparticles (C@Si) via elongated TiO2 nanotubes networks toward stabilizing Si electrode via alleviating mechanical strain and stabilizing the SEI layer. Benefited from the rational localized structure design, the carbon-coated Si nanoparticles/TiO2 nanotubes composited electrode (C@Si/TiNT) exhibits an ideal electrode thickness swelling, which is lower than 1% after the first cycle and increases to about 6.6% even after 1600 cycles. While for traditional C@Si/carbon nanotube composited electrode, the initial swelling ratio is about 16.7% and reaches ≈190% after 1600 cycles. As a result, the C@Si/TiNT electrode exhibits an outstanding capacity of 1510 mAh g-1 at 0.1 A g-1 with high rate capability and long-time cycling performance with 95% capacity retention after 1600 cycles. The rational design on mechanically reinforced localized structure for silicon electrode will provide a versatile platform to solve the current bottlenecks for other alloyed-type electrode materials with large volume expansion toward practical applications.

Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images.

BACKGROUND: The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy. METHODS: Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist's assessment were also compared. RESULTS: Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1 CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists' assessment. CONCLUSIONS: Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

Lowering Charge Transfer Barrier of LiMn2O4 via Nickel Surface Doping To Enhance Li+ Intercalation Kinetics at Subzero Temperatures.

Sluggish interfacial kinetics leading to considerable loss of energy and power capabilities at subzero temperatures is still a big challenge to overcome for Li-ion batteries operating under extreme environmental conditions. Herein, using LiMn2O4 as the model system, we demonstrated that nickel surface doping to construct a new interface owning lower charge transfer energy barrier, could effectively facilitate the interfacial process and inhibit the capacity loss with decreased temperature. Detailed investigations on the charge transfer process via electrochemical impedance spectroscopy and density functional theory calculation, indicate that the interfacial chemistry tuning could effectively lower the activation energy of charge transfer process by nearly 20%, endowing the cells with ∼75.4% capacity at -30 °C, far surpassing the hardly discharged unmodified counterpart. This control of surface chemistry to tune interfacial dynamics proposes insights and design ideas for batteries to well survive under thermal extremes.

Particle Self-Aligning, Focusing, and Electric Impedance Microcytometer Device for Label-Free Single Cell Morphology Discrimination and Yeast Budding Analysis.

Microfluidic electric impedance flow cytometry (IFC) devices have been applied in single cell analysis, such as cell counting, volume discrimination, cell viability, etc. A cell's shape provides specific information about cellular physiological and pathological conditions, especially in microorganisms such as yeast. In this study, the particle orientation focusing was theoretically analyzed and realized by hydrodynamics. The pulse width (passing time for the particles) of the conductance signal was used to discriminate particle shapes. Spherical and rod-shaped particles with similar volumes/lengths were differentiated by the IFC device, using the impedance pulse parameters of the events. Then, typical late-budding, early budding, and unbudded yeast cells were distinguished by the width, amplitude, and ratio of width to amplitude (R) of the impedance pulse. The pulse amplitude and the R combination gate for identifying the late-budding yeast was estimated through the statistic results. Using the gate, the late-budding rates under different conditions were calculated. Late-budding rates obtained using our method showed a high correlation (R2 = 0.83) with the manual cell counting result and represented the budding status of yeast cells under different conditions proficiently. Thus, the late-budding rate calculated using the above method can be used as a qualitative parameter to assess the reproductive performance of yeast and whether a yeast culturing environment is optimal. This IFC device and cell shape discrimination method is very simple and could be applied in the fermentation industry and other microorganisms' discrimination as a rapid analysis technique in the future.

Approaching the Lithiation Limit of MoS2 While Maintaining Its Layered Crystalline Structure to Improve Lithium Storage.

MoS2 holds great promise as high-rate electrode for lithium-ion batteries since its large interlayer can allow fast lithium diffusion in 3.0-1.0 V. However, the low theoretical capacity (167 mAh g-1 ) limits its wide application. Here, by fine tuning the lithiation depth of MoS2 , we demonstrate that its parent layered structure can be preserved with expanded interlayers while cycling in 3.0-0.6 V. The deeper lithiation and maintained crystalline structure endows commercially micrometer-sized MoS2 with a capacity of 232 mAh g-1 at 0.05 A g-1 and circa 92 % capacity retention after 1000 cycles at 1.0 A g-1 . Moreover, the enlarged interlayers enable MoS2 to release a capacity of 165 mAh g-1 at 5.0 A g-1 , which is double the capacity obtained under 3.0-1.0 V at the same rate. Our strategy of controlling the lithiation depth of MoS2 to avoid fracture ushers in new possibilities to enhance the lithium storage of layered transition-metal dichalcogenides.

The effects of upaB deletion and the double/triple deletion of upaB, aatA, and aatB genes on pathogenicity of avian pathogenic Escherichia coli.

Autotransporters (ATs) are associated with pathogenesis of Avian Pathogenic Escherichia coli (APEC). The molecular characterization of APEC ATs can provide insights about their relevance to APEC pathogenesis. Here, we characterized a conventional autotransporter UpaB in APEC DE205B genome. The upaB existed in 41.9 % of 236 APEC isolates and was predominantly associated with ECOR B2 and D. Our studies showed that UpaB mediates the DE205B adhesion in DF-1 cells, and enhances autoaggregation and biofilm formation of fimbria-negative E. coli AAEC189 (MG1655Δfim) in vitro. Deletion of upaB of DE205B attenuates the virulence in duck model and early colonization in the duck lungs during APEC systemic infection. Furthermore, double and triple deletion of upaB, aatA, and aatB genes cumulatively attenuated DE205B adhesion in DF-1 cells, accompanying with decreased 50 % lethal dose (LD50) in duck model and the early colonization in the duck lungs. However, DE205BΔupaB/ΔaatA/ΔaatB might "compensate" the influence of gene deletion by upregulating the expression of fimbrial adhesin genes yqiL, yadN, and vacuolating autotransporter vat during early colonization of APEC. Finally, we demonstrated that vaccination with recombinant UpaB, AatA, and AatB proteins conferred protection against colisepticemia caused by DE205B infection in duck model.

Bioinformatics and Experimental Study Revealed LINC00982/ miR-183-5p/ABCA8 Axis Suppresses LUAD Progression.

BACKGROUND: Lung adenocarcinoma (LUAD) is a major health challenge worldwide with an undesirable prognosis. LINC00982 has been implicated as a tumor suppressor in diverse human cancers; however, its role in LUAD has not been fully characterized. METHODS: Expression level and prognostic value of LINC00982 were investigated in pan-cancer and lung cancer from The Cancer Genome Atlas (TCGA) project. Differential expression analysis based on the LINC00982 expression level was performed in LUAD followed by gene set enrichment analysis (GSEA) and functional enrichment analyses. The association between LINC00982 expression and tumor immune microenvironment characteristics was evaluated. A potential ceRNA regulatory axis was identified and experimentally validated. RESULTS: We found that LINC00982 expression was downregulated and correlated with poor prognosis in LUAD. Enrichment analyses revealed that LINC00982 could inhibit DNA damage repair and cell proliferation, but enhance tumor metabolic reprogramming. We identified a competing endogenous RNA network involving LINC00982, miR-183-5p, and ATP-binding cassette subfamily A member 8 (ABCA8). Luciferase assays confirmed that miR-183-5p can interact with LINC00982 and ABCA8. Forced miR-183-5p expression reduced LINC00982 transcript levels and suppressed ABCA8 expression. CONCLUSIONS: Our findings revealed the LINC00982/miR-183-5p/ABCA8 axis as a potential therapeutic target in LUAD.

Realizing Lean-Leachate Recycling of Spent Lithium Nickel Oxides by Dynamically Stabilizing the Hole-Mediated Diffusion Kinetics.

While hydrometallurgy is the primary technology for recycling spent lithium-ion batteries due to various advantages, it still involves substantial consumption of chemical reagents and poses challenges in wastewater emission. Herein, we report the realization of cathode recycling under lean-leachate conditions by dynamically stabilizing hole-mediated diffusion kinetics, which is enabled by synchronizing the extraction step during the leaching stage, thus continuously removing the dissolved ions out of the leachate. Theoretical molecular dynamics simulations predict that preventing the accumulation of the dissolved ions is efficient for keeping the leaching process proceeding. Experimentally, even with a small dosage of leachate (0.5 mL), a 94.51% leaching efficiency can be achieved (90 °C, 40 min) for spent LNO materials. Considering that our strategy is not limited to a specific materials system, it could be extended to recycle other valuable materials (including LCO or NCM 811) with minimal leachate usage.

Ovonic threshold switching-based artificial afferent neurons for thermal in-sensor computing.

Artificial afferent neurons in the sensory nervous system inspired by biology have enormous potential for efficiently perceiving and processing environmental information. However, the previously reported artificial afferent neurons suffer from two prominent challenges: considerable power consumption and limited scalability efficiency. Herein, addressing these challenges, a bioinspired artificial thermal afferent neuron based on a N-doped SiTe ovonic threshold switching (OTS) device is presented for the first time. The engineered OTS device shows remarkable uniformity and robust endurance, ensuring the reliability and efficacy of the artificial afferent neurons. A substantially decreased leakage current of the SiTe OTS device by nitrogen doping results in ultra-low power consumption less than 0.3 nJ per spike for artificial afferent neurons. The inherent temperature response exhibited by N-doped SiTe OTS materials allows us to construct a highly compact artificial thermal afferent neuron over a wide temperature range. An edge detection task is performed to further verify its thermal perceptual computing function. Our work provides an insight into OTS-based artificial afferent neurons for electronic skin and sensory neurorobotics.

Identification and seasonal variation of specific particulate bound (halogenated) polycyclic aromatic hydrocarbons in air from different metal industrial parks in Northwest China.

During the process of industrial heating, a large amount of polycyclic aromatic hydrocarbons (PAHs) and their halogenated compounds (Cl/Br-PAHs) can be formed. However, there is still limited understanding of the chemicals from different metal smelting industrial parks. This study evaluated the seasonal variations, composition profiles, and source allocations of the atmospheric particulate-bound PAHs and Cl/Br-PAHs in different metal industrial parks in a typical industrial city in northwest China. The results showed that the main PAHs produced by metal smelting were low molecular weight isomers, and the concentrations of Cl-PAHs were lower compared to Br-PAHs. The main Br-PAHs were 1-Br-Pyr and 4-Br-Pyr, while 9-Cl-Fle, 1-Cl-Pyr, and 6-Cl-BaP were the dominated Cl-PAH isomers. No significant difference was found in the concentrations among the sites, whereas the levels of the target chemicals were higher during cold months compared to warm months. The main source of PAHs was coal combustion and gasoline vehicle emission during metal smelting, and that of Cl/Br-PAHs was also industrial coal burning. In addition to the primary source, the secondary chlorination of parent PAHs was also a significant source of Cl-PAHs in the production of high purity aluminum. This study suggests that Cl-PAHs and Br-PAHs may behave differently in the atmosphere.

Elastic Polyurethane as Stress-Redistribution-Adhesive-Layer (SRAL) for Directly Integrated High-Energy-Density Flexible Batteries.

The low mechanical reliability and integration failure are key challenges hindering the commercialization of geometrically flexible batteries. This work proposes that the failure of directly integrating flexible batteries using traditional rigid adhesives is primarily due to the mismatch between the generated stress at the adhesive/substrate interface, and the maximum allowable stress. Accordingly, a stress redistribution adhesive layer (SRAL) strategy is conceived by using elastic adhesive to redistribute the generated stress. The function mechanism of the SRAL strategy is confirmed by theoretical finite element analysis. Experimentally, a polyurethane (PU) type elastic adhesive (with maximum strain of 1425%) is synthesized and used as the SRAL to integrate rigid cells on different flexible substrates to fabricate directly integrated flexible battery with robust output under various harsh environments, such as stretching, twisting, and even bending in water. The SRAL strategy is expected to be generally applicable in various flexible devices that involve the integration of rigid components onto flexible substrates.