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Ultrafast laser welding technology for transparent materials has developed rapidly in recent years; however, high-strength non-optical contact transparent material welding has been a challenge. This work presents a welding method for silica glass using a double-pulse femtosecond (fs) laser and optimizes the laser processing parameters to enhance the welding performance. The welding characteristics of silica glass are analyzed under different time delays by controlling the pulse delay of double pulses. In addition to comprehensively study the influence of various experimental conditions on double-pulse fs laser welding, multi-level tests are designed for five factors, including average laser power, pulse delay, scanning interval, scanning speed, and repetition rate. Finally, by optimizing the parameters, a welding strength of 57.15 MPa is achieved at an average power of 3500 mW, repetition rate of 615 kHz, pulse delay of 66.7 ps, scanning interval of 10 µm, and scanning speed of 1000 µm/s. This work introduces a new approach to glass welding and presents optimal parameters for achieving higher welding strength, which can be widely used in aerospace, microelectronic packaging, microfluidics, and other fields. [ABSTRACT FROM AUTHOR]

Introduction: DNA ploidy (P), stroma fraction (S), and nucleotyping (N) collectively known as PSN, have proven prognostic accuracy in stage II colorectal cancer (CRC). However, few studies have reported on the prognostic value of the PSN panel in stage III colon cancer patients receiving capecitabine and oxaliplatin adjuvant chemotherapy. Objectives: This study aimed to validate PSN's prognostic impact on stage III colon cancer, identifying candidates for optimized adjuvant chemotherapy duration. Design: A retrospective analysis was conducted on a cohort of stage III colon cancer patients from April 2008 to June 2020. Methods: Postoperative pathological samples from stage III colon cancer patients who underwent radical surgery and postoperative adjuvant chemotherapy at Sun Yat-sen University Cancer Center were retrospectively collected. Automated digital imaging assessed PSN, categorizing risk groups. Kaplan–Meier, Cox regression, and time-dependent receiver operating characteristic analysis compared model validity. Results: Significant differences in 5-year disease-free survival (DFS) and overall survival (OS) were noted among PSN-based low-, moderate-, and high-risk groups (DFS: 92.10% versus 83.62% versus 79.80%, p = 0.029; OS: 96.69% versus 93.99% versus 90.12%, p = 0.016). PSN emerged as an independent prognostic factor for DFS [hazard ratio (HR) = 1.409, 95% confidence interval (CI): 1.002–1.981, p = 0.049] and OS (HR = 1.720, 95% CI: 1.127–2.624, p = 0.012). The PSN model, incorporating perineural invasion and tumor location, displayed superior area under the curve for 5-year (0.692 versus 0.553, p = 0.020) and 10-year (0.694 versus 0.532, p = 0.006) DFS than TNM stage. In the PSN high-risk group, completing eight cycles of adjuvant chemotherapy significantly improved 5-year DFS and OS compared to four to seven cycles (DFS: 89.43% versus 71.52%, p = 0.026; OS: 96.77% versus 85.46%, p = 0.007). Conclusion: The PSN panel effectively stratifies stage III colon cancer, aiding in optimized adjuvant chemotherapy duration determination. [ABSTRACT FROM AUTHOR]

Bevacizumab plays an important role in the first and second line treatment for metastatic colorectal cancer (CRC). And induction of hypoxia and the tumors response to it plays an important role in determining the efficacy of antiangiogenic therapy while the connection between them remains unclear. Here, we found that lactate accumulated in the tumor environment of CRC and acted as substrates for histone lactylation, and this process was further induced by cellular enhanced glycolysis in hypoxia. We determined that CRC patients resistant to bevacizumab treatment presented with elevated levels of histone lactylation and inhibition of histone lactylation efficiently suppressed CRC tumorigenesis, progression and survival in hypoxia. Histone lactylation promoted the transcription of RUBCNL/Pacer, facilitating autophagosome maturation through interacting with BECN1 (beclin 1) and mediating the recruitment and function of the class III phosphatidylinositol 3-kinase complex, which had a crucial role in hypoxic cancer cells proliferation and survival. Moreover, combining inhibition of histone lactylation and macroautophagy/autophagy with bevacizumab treatment demonstrated remarkable treatment efficacy in bevacizumab-resistance patients-derived pre-clinical models. These findings delivered a new exploration and important supplement of metabolic reprogramming-epigenetic regulation, and provided a new strategy for improving clinical efficacy of bevacizumab in CRC by inhibition of histone lactylation. Abbreviations: 2-DG: 2-deoxy-D-glucose; BECN1: beclin 1; CQ: chloroquine; CRC: colorectal cancer; DMOG: dimethyloxalylglycine; H3K18la: histone H3 lysine 18 lactylation; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; Nala: sodium lactate; PDO: patient-derived orgnoid; PDX: patient-derived xenograft; RUBCNL/Pacer: rubicon like autophagy enhancer; SQSTM1/p62: sequestosome 1. [ABSTRACT FROM AUTHOR]

The prediction of near field acoustic velocity is necessary when using equivalent source method and evaluating multi acoustic fields problem. According to the idea of acoustic analogy, this paper induces a vector wave equation of aeroacoustics by rearranging the conservation equations of mass and momentum with a permeable surface. Then, an analytical formulation of the acoustic velocity, called formulation V2A, is derived for sources in a quiescent medium by solving the vector wave equation with the free-space Green's function. The validity of the formulation is confirmed by numerical results for a stationary monopole, a stationary dipole source, and a rotating monopole source in a quiescent medium. The result indicates that the predication of formulation V2A is nearly identical to the exact solutions no matter in far-field or near-field. [ABSTRACT FROM AUTHOR]

Cryo-EM has emerged as the most important technique for structure determination of macromolecular complexes. However, raw cryo-EM maps often exhibit loss of contrast at high resolution and heterogeneity over the entire map. As such, various post-processing methods have been proposed to improve cryo-EM maps. Nevertheless, it is still challenging to improve both the quality and interpretability of EM maps. Addressing the challenge, we present a three-dimensional Swin-Conv-UNet-based deep learning framework to improve cryo-EM maps, named EMReady, by not only implementing both local and non-local modeling modules in a multiscale UNet architecture but also simultaneously minimizing the local smooth L1 distance and maximizing the non-local structural similarity between processed experimental and simulated target maps in the loss function. EMReady was extensively evaluated on diverse test sets of 110 primary cryo-EM maps and 25 pairs of half-maps at 3.0–6.0 Å resolutions, and compared with five state-of-the-art map post-processing methods. It is shown that EMReady can not only robustly enhance the quality of cryo-EM maps in terms of map-model correlations, but also improve the interpretability of the maps in automatic de novo model building. Map post-processing is crucial for cryo-EM modeling building. Here, the authors present a deep learning approach to improve both the quality and interpretability of cryo-EM maps by simultaneously considering local and non-local effects. [ABSTRACT FROM AUTHOR]

With the rapid development of modern industries, water pollution has become an urgent problem that endangers the health of human and wild animals. The photocatalysis technique is considered an environmentally friendly strategy for removing organic pollutants in wastewater. As an important member of Bi-series semiconductors, Bi2WO6 is widely used for fabricating high-performance photocatalysts. In this review, the recent advances of Bi2WO6-based photocatalysts are summarized. First, the controllable synthesis, surface modification and heteroatom doping of Bi2WO6 are introduced. In the respect of Bi2WO6-based composites, existing Bi2WO6-containing binary composites are classified into six types, including Bi2WO6/carbon or MOF composite, Bi2WO6/g-C3N4 composite, Bi2WO6/metal oxides composite, Bi2WO6/metal sulfides composite, Bi2WO6/Bi-series composite, and Bi2WO6/metal tungstates composite. Bi2WO6-based ternary composites are classified into four types, including Bi2WO6/g-C3N4/X, Bi2WO6/carbon/X, Bi2WO6/Au or Ag-based materials/X, and Bi2WO6/Bi-series semiconductors/X. The design, microstructure, and photocatalytic performance of Bi2WO6-based binary and ternary composites are highlighted. Finally, aimed at the existing problems in Bi2WO6-based photocatalysts, some solutions and promising research trends are proposed that would provide theoretical and practical guidelines for developing high-performance Bi2WO6-based photocatalysts. [ABSTRACT FROM AUTHOR]

The next generation of green electronic products will be flexible, eco‐friendly, have arbitrary patterning, and be produced using simple methods. Here, a silver nanoparticles (Ag NPs)–graphite carbon composite‐based electrochemical sensor is patterned on a flexible substrate by one‐step in situ femtosecond (fs) laser fabrication. Alkali lignin is used as a precursor and nontoxic natural reducing agent to reduce aqueous silver nitrate to produce Ag NPs. The fs laser carbonization process converts lignin into graphite carbon electrodes with diverse three‐dimensional (3D) micro/nano morphologies, causing less thermal damage to the Ag NPs and thin substrate. The 3D micro/nano morphologies combined with Ag NPs effectively increase the active surface area, conductivity, and catalytic activity of the electrode, which is further applied as an electrochemical sensor. This sensor has a wide linear detection range of nitrite from 1–4000 × 10−6 m, a detection limit of 0.117 × 10−6 m (S/N = 3), and long lifetime of 30 days. The sensor also has the potential to detect dopamine (DA). A detection limit of 0.098 × 10−6 m (S/N = 3) is obtained, with a linear detection range of 1–45 × 10−6 m. The sensor demonstrates a wide range of applications in food safety and human health‐related detection. [ABSTRACT FROM AUTHOR]

Motivation Protein–protein interactions (PPI) play important roles in cellular activities. Due to the technical difficulty and high cost of experimental methods, there are considerable interests towards the development of computational approaches, such as protein docking, to decipher PPI patterns. One of the important and difficult aspects in protein docking is recognizing near-native conformations from a set of decoys, but unfortunately, traditional scoring functions still suffer from limited accuracy. Therefore, new scoring methods are pressingly needed in methodological and/or practical implications. Results We present a new deep learning-based scoring method for ranking protein–protein docking models based on a 3D RepVGG network, named TRScore. To recognize near-native conformations from a set of decoys, TRScore voxelizes the protein–protein interface into a 3D grid labeled by the number of atoms in different physicochemical classes. Benefiting from the deep convolutional RepVGG architecture, TRScore can effectively capture the subtle differences between energetically favorable near-native models and unfavorable non-native decoys without needing extra information. TRScore was extensively evaluated on diverse test sets including protein–protein docking benchmark 5.0 update set, DockGround decoy set, as well as realistic CAPRI decoy set and overall obtained a significant improvement over existing methods in cross-validation and independent evaluations. Availability and implementation Codes available at: https://github.com/BioinformaticsCSU/TRScore [ABSTRACT FROM AUTHOR]

The effect of holmium (Ho) addition on the glass-forming ability (GFA) and crystallization behaviors of Zr54Cu29Al10Ni7 bulk metallic glass (BMGs) were studied by employing differential scanning calorimetry (DSC), X-ray diffraction (XRD), and transmission electron microscopy (TEM). The characteristic temperatures and activation energies of crystallization were obtained from DSC data. Classical kinetic modes were used to evaluate the crystallization processes of Zr54Cu29Al10Ni7 and Zr48Cu29Ni7Al10Ho6 BMGs. The results showed that Ho addition reduces the activation energy in the original crystallization period of Zr-based BMG and improves the nucleation, which is due to the formation of simpler compounds, such as CuZr2, Cu2Ho, and Al3Zr5. [ABSTRACT FROM AUTHOR]

Cryo-electron microscopy (cryo-EM) has become one of important experimental methods in structure determination. However, despite the rapid growth in the number of deposited cryo-EM maps motivated by advances in microscopy instruments and image processing algorithms, building accurate structure models for cryo-EM maps remains a challenge. Protein secondary structure information, which can be extracted from EM maps, is beneficial for cryo-EM structure modeling. Here, we present a novel secondary structure annotation framework for cryo-EM maps at both intermediate and high resolutions, named EMNUSS. EMNUSS adopts a three-dimensional (3D) nested U-net architecture to assign secondary structures for EM maps. Tested on three diverse datasets including simulated maps, middle resolution experimental maps, and high-resolution experimental maps, EMNUSS demonstrated its accuracy and robustness in identifying the secondary structures for cyro-EM maps of various resolutions. The EMNUSS program is freely available at http://huanglab.phys.hust.edu.cn/EMNUSS. [ABSTRACT FROM AUTHOR]

Motivation Advances in microscopy instruments and image processing algorithms have led to an increasing number of Cryo-electron microscopy (cryo-EM) maps. However, building accurate models for the EM maps at 3–5 Å resolution remains a challenging and time-consuming process. With the rapid growth of deposited EM maps, there is an increasing gap between the maps and reconstructed/modeled three-dimensional (3D) structures. Therefore, automatic reconstruction of atomic-accuracy full-atom structures from EM maps is pressingly needed. Results We present a semi-automatic de novo structure determination method using a deep learning-based framework, named as DeepMM, which builds atomic-accuracy all-atom models from cryo-EM maps at near-atomic resolution. In our method, the main-chain and C α positions as well as their amino acid and secondary structure types are predicted in the EM map using Densely Connected Convolutional Networks. DeepMM was extensively validated on 40 simulated maps at 5 Å resolution and 30 experimental maps at 2.6–4.8 Å resolution as well as an Electron Microscopy Data Bank-wide dataset of 2931 experimental maps at 2.6–4.9 Å resolution, and compared with state-of-the-art algorithms including RosettaES, MAINMAST and Phenix. Overall, our DeepMM algorithm obtained a significant improvement over existing methods in terms of both accuracy and coverage in building full-length protein structures on all test sets, demonstrating the efficacy and general applicability of DeepMM. Availability and implementation http://huanglab.phys.hust.edu.cn/DeepMM. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Type 2 diabetes mellitus (T2DM) is an independent risk factor of Alzheimer's disease (AD). Therefore, identifying periphery biomarkers correlated with mild cognitive impairment (MCI) is of importance for early diagnosis of AD. Here, we performed platelet proteomics in T2DM patients with MCI (T2DM‐MCI) and without MCI (T2DM‐nMCI). Pearson analysis of the omics data with MMSE (mini‐mental state examination), Aβ1‐42/Aβ1‐40 (β‐amyloid), and rGSK‐3β(T/S9) (total to Serine‐9‐phosphorylated glycogen synthase kinase‐3β) revealed that mitophagy/autophagy‐, insulin signaling‐, and glycolysis/gluconeogenesis pathways‐related proteins were most significantly involved. Among them, only the increase of optineurin, an autophagy‐related protein, was simultaneously correlated with the reduced MMSE score, and the increased Aβ1‐42/Aβ1‐40 and rGSK‐3β(T/S9), and the optineurin alone could discriminate T2DM‐MCI from T2DM‐nMCI. Combination of the elevated platelet optineurin and rGSK‐3β(T/S9) enhanced the MCI‐discriminating efficiency with AUC of 0.927, specificity of 86.7%, sensitivity of 85.3%, and accuracy of 0.859, which is promising for predicting cognitive decline in T2DM patients. [ABSTRACT FROM AUTHOR]

Memory loss is the most common clinical sign in Alzheimer's disease (AD); thus, searching for peripheral biomarkers to predict cognitive decline is promising for early diagnosis of AD. As platelets share similarities to neuron biology, it may serve as a peripheral matrix for biomarkers of neurological disorders. Here, we conducted a comprehensive and in‐depth platelet proteomic analysis using TMT‐LC‐MS/MS in the populations with mild cognitive impairment (MCI, MMSE = 18–23), severe cognitive impairments (AD, MMSE = 2–17), and the age‐/sex‐matched normal cognition controls (MMSE = 29–30). A total of 360 differential proteins were detected in MCI and AD patients compared with the controls. These differential proteins were involved in multiple KEGG pathways, including AD, AMP‐activated protein kinase (AMPK) pathway, telomerase RNA localization, platelet activation, and complement activation. By correlation analysis with MMSE score, three positively correlated pathways and two negatively correlated pathways were identified to be closely related to cognitive decline in MCI and AD patients. Partial least squares discriminant analysis (PLS‐DA) showed that changes of nine proteins, including PHB, UQCRH, CD63, GP1BA, FINC, RAP1A, ITPR1/2, and ADAM10 could effectively distinguish the cognitively impaired patients from the controls. Further machine learning analysis revealed that a combination of four decreased platelet proteins, that is, PHB, UQCRH, GP1BA, and FINC, was most promising for predicting cognitive decline in MCI and AD patients. Taken together, our data provide a set of platelet biomarkers for predicting cognitive decline which may be applied for the early screening of AD. [ABSTRACT FROM AUTHOR]

CHI3L2 (Chitinase-3-Like Protein 2) is a member of chitinase-like proteins (CLPs), which belong to the glycoside hydrolase 18 family. Its homologous gene, CHI3L1, has been extensively studied in various tumors and has been shown to be related to immune infiltration in breast cancer and glioblastoma. High CHI3L2 expression was reported to be associated with poor prognosis in breast cancer and renal cell carcinoma. However, the prognostic significance of CHI3L2 in glioma and its correlation between immune infiltration remains unclear. In this study, we examined 288 glioma samples by immunohistochemistry to find that CHI3L2 is expressed in tumor cells and macrophages in glioma tissues and highly expressed in glioblastoma and IDH wild-type gliomas. Relationships between CHI3L2 expression and clinical features (grade, age, Ki67 index, P53, PHH3 (mitotic figures), ATRX, TERTp, MGMTp, IDH, and 1p/19q co-deleted status) were evaluated. Kaplan-Meier survival was conducted to show high CHI3L2 expression in tumor cells (TC) and macrophage cells (MC) indicated poor prognosis in diffusely infiltrating glioma (DIG), lower-grade glioma (LGG), and IDH wild-type gliomas (IDH-wt). The overall survival time was higher in patients with dual-low CHI3L2 expression in TC and MC compared to those in patients with non-dual CHI3L2 expression and dual high expression in DIG and IDH wild-type gliomas. By univariate and multivariate analysis, we found that high CHI3L2 expression in tumor cells was an independent unfavorable prognostic factor in glioma patients. Moreover, we used two datasets (TCGA and CGGA) to verify the results of our study and explore the potential functional role of CHI3L2 by GO and KEGG analyses in gliomas. TIMER platform analysis indicated CHI3L2 expression was closely related to diverse marker genes of tumor immune infiltrating cells, including monocytes, TAMs, M1 macrophages, M2 macrophages, TGFβ1+ Treg and T cell exhaustion in GBM and LGG. Western Blot validated CHI3L2 is expressed in glioma cells and microglia cells. The results of flow cytometry showed that CHI3L2 induces the apoptosis of CD8+ T cells. In conclusion, these results demonstrate CHI3L2 is related to poor prognosis and immune infiltrates in gliomas, suggesting it may serve as a promising prognostic biomarker and represent a new target for glioma patients. [ABSTRACT FROM AUTHOR]

Protein‐protein docking plays an important role in the computational prediction of the complex structure between two proteins. For years, a variety of docking algorithms have been developed, as witnessed by the critical assessment of prediction interactions (CAPRI) experiments. However, despite their successes, many docking algorithms often require a series of manual operations like modeling structures from sequences, incorporating biological information, and selecting final models. The difficulties in these manual steps have significantly limited the applications of protein‐protein docking, as most of the users in the community are nonexperts in docking. Therefore, automated docking like a web server, which can give a comparable performance to human docking protocol, is pressingly needed. As such, we have participated in the blind CAPRI experiments for Rounds 38‐45 and CASP13‐CAPRI challenge for Round 46 with both our HDOCK automated docking web server and human docking protocol. It was shown that our HDOCK server achieved an "acceptable" or higher CAPRI‐rated model in the top 10 submitted predictions for 65.5% and 59.1% of the targets in the docking experiments of CAPRI and CASP13‐CAPRI, respectively, which are comparable to 66.7% and 54.5% for human docking protocol. Similar trends can also be observed in the scoring experiments. These results validated our HDOCK server as an efficient automated docking protocol for nonexpert users. Challenges and opportunities of automated docking are also discussed. [ABSTRACT FROM AUTHOR]

Motivation Protein structure alignment is one of the fundamental problems in computational structure biology. A variety of algorithms have been developed to address this important issue in the past decade. However, due to their heuristic nature, current structure alignment methods may suffer from suboptimal alignment and/or over-fragmentation and thus lead to a biologically wrong alignment in some cases. To overcome these limitations, we have developed an accurate topology-independent and global structure alignment method through an FFT-based exhaustive search algorithm, which is referred to as FTAlign. Results Our FTAlign algorithm was extensively tested on six commonly used datasets and compared with seven state-of-the-art structure alignment approaches, TMalign, DeepAlign, Kpax, 3DCOMB, MICAN, SPalignNS and CLICK. It was shown that FTAlign outperformed the other methods in reproducing manually curated alignments and obtained a high success rate of 96.7 and 90.0% on two gold-standard benchmarks, MALIDUP and MALISAM, respectively. Moreover, FTAlign also achieved the overall best performance in terms of biologically meaningful structure overlap (SO) and TMscore on both the sequential alignment test sets including MALIDUP, MALISAM and 64 difficult cases from HOMSTRAD, and the non-sequential sets including MALIDUP-NS, MALISAM-NS, 199 topology-different cases, where FTAlign especially showed more advantage for non-sequential alignment. Despite its global search feature, FTAlign is also computationally efficient and can normally complete a pairwise alignment within one second. Availability and implementation http://huanglab.phys.hust.edu.cn/ftalign/. [ABSTRACT FROM AUTHOR]

Motivation Given the importance of protein–ribonucleic acid (RNA) interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble–RNA docking strategy to explicitly consider the protein flexibility in protein–RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB). Results Our approach can not only avoid the reliability issue of structures from MD simulations but also circumvent the limited number of experimental structures for a target protein in the PDB. Tested on 68 unbound–bound and 18 unbound–unbound protein–RNA complexes, our MPRDock/DITScorePR considerably improved the docking performance and achieved a significantly higher success rate than single-protein rigid docking whether pseudo-unbound templates are included or not. Similar improvements were also observed when combining our ensemble docking strategy with other scoring functions. The present homology model-based ensemble docking approach will have a general application in molecular docking for other interactions. Availability and implementation http://huanglab.phys.hust.edu.cn/mprdock/ Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]