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The calculation of the height of fractured water-conducting zone (FWCZ) is of great significance for mine optimization design, water disaster prevention and safety production of the coal mines. In this paper, a height-prediction model of FWCZ based on extreme learning machine (ELM) is proposed. Aiming at its disadvantages of low prediction accuracy and relatively difficult parameter optimization, the ELM prediction model is optimized by the grey-wolf optimization algorithm (GOA), whale optimization algorithm (WOA) and salp optimization algorithm (SOA) respectively. These optimization algorithms overcome the problems of slow convergence, poor stability, and tendency to fall into local optimality of traditional neural networks. The mining depth, mining height, overburden strata structure, working face length and coal seam dip angle are selected as the main controlling factors for the height of FWCZ. A total of 42 fields measured samples are collected and divided into two subsets for training and validating with a ratio of 36/6. The prediction capability of GOA-ELM, WOA-ELM and SOA-ELM models are evaluated and compared, and the results show that the calculation results of the three models are optimized compared with the ELM model. The prediction capability of GOA and WOA are similar, while the prediction results of SOA-ELM are better than the other two models, and the relative errors of the test sets are all less than 10%. Therefore, the SOA-ELM model is finally applied to predict the height of FWCZ formed after the mining of No.15 coal seam in Xinjian Coal Mine. Finally, prediction results are verified by the measured data of the borehole television detection instrument, which shows good consistency. It further proves the effectiveness of the swarm intelligence optimization algorithm in the prediction of the height of FWCZ.

I-R3 DNAzyme is a small, highly active catalytic DNA for DNA hydrolysis. In here, we designed two cis-structure DNAzymes (I-R3N and I-R3S) based on the different locates of the joint linker between I-R3 and its substrate. Data demonstrated that both DNAzymes were highly dependent on Zn2+, and worked at a narrow range around pH 7.0. They exhibited strong anti-interference with Mg2+ and Ca2+, but inhibited by Na+ and K+. Moreover, single and multiple-site mutations were generated within the catalytic core to carry out a comprehensive mutational study of I-R3 motif, in which most nucleotides were highly conserved and the nucleotides A5, T11 and T8 were identified as the mutational hotspots. Furthermore, an efficient variant A5G was obtained and its reaction condition was optimized. Finally, we constructed A5G to the 3’ end of a single-stranded DNA (ssDNA) and applied it for asymmetrical PCR amplification to produce a single and double-stranded DNA mixture, in which A5G within ssDNA can self-cleave to generate a shorter desired ssDNA by denaturing gel separation. This would provide a new non-chemical modification approach for preparation of the expected ssDNA for in vitro selection of DNAzymes.

The functions of mammalian Atg8 proteins (mATG8s) expand beyond canonical autophagy and include processes collectively referred to as Atg8ylation. Global modulation of protein synthesis under stress conditions is governed by MTOR and liquid-liquid phase separated condensates containing ribonucleoprotein particles known as stress granules (SGs). We report that lysosomal damage induces SGs acting as a hitherto unappreciated inhibitor of protein translation via EIF2A/eIF2α phosphorylation while favoring an ATF4-dependent integrated stress response. SGs are induced by lysosome-damaging agents, SARS-CoV-2 open reading frame 3a protein (ORF3a) expression, Mycobacterium tuberculosis infection, and exposure to proteopathic MAPT/tau. Proteomic studies revealed recruitment to damaged lysosomes of the core SG proteins NUFIP2 and G3BP1 along with the GABARAPs of the mATG8 family. The recruitment of these proteins is independent of SG condensates or canonical autophagy. GABARAPs interact directly with NUFIP2 and G3BP1 whereas Atg8ylation is needed for their recruitment to damaged lysosomes. At the lysosome, NUFIP2 contributes to MTOR inactivation together with LGALS8 (galectin 8) via the Ragulator-RRAGA-RRAGB complex. The separable functions of NUFIP2 and G3BP1 in SG formation vis-a-vis their role in MTOR inactivation are governed by GABARAP and Atg8ylation. Thus, cells employ membrane Atg8ylation to control and coordinate SG and MTOR responses to lysosomal damage.

We report that lysosomal damage is a hitherto unknown inducer of stress granule (SG) formation and that the process termed membrane atg8ylation coordinates SG formation with mTOR inactivation during lysosomal stress. SGs were induced by lysosome-damaging agents including SARS-CoV-2ORF3a, Mycobacterium tuberculosis, and proteopathic tau. During damage, mammalian ATG8s directly interacted with the core SG proteins NUFIP2 and G3BP1. Atg8ylation was needed for their recruitment to damaged lysosomes independently of SG condensates whereupon NUFIP2 contributed to mTOR inactivation via the Ragulator–RagA/B complex. Thus, cells employ membrane atg8ylation to control and coordinate SG and mTOR responses to lysosomal damage.

Background: The MSI/MSS status does not fully explain cancer immunotherapy response in colorectal cancer. Thus, we developed a colorectal cancer-specific method that predicts cancer immunotherapy response.Methods: We used gene expression data of 454 samples (MSI = 131, MSI-L = 23, MSS = 284, and Unknown = 16) and developed a TMEPRE method that models signatures of CD8+ T-cell infiltration and CD8+ T-cell exhaustion states in the tumor microenvironment of colorectal cancer. TMEPRE model was validated on three RNAseq datasets of melanoma patients who received pembrolizumab or nivolumab and one RNAseq dataset of purified CD8+ T cells in different exhaustion states.Results: TMEPRE showed predictive power in three datasets of anti-PD1-treated patients (p = 0.056, 0.115, 0.003). CD8+ T-cell exhaustion component of TMEPRE model correlates with anti-PD1 responding progenitor exhausted CD8+ T cells in both tumor and viral infection (p = 0.048, 0.001). The global pattern of TMEPRE on 454 colorectal cancer samples indicated that 10.6% of MSS patients and 67.2% of MSI patients show biological characteristics that can potentially benefit from anti-PD1 treatment. Within MSI nonresponders, approximately 50% showed insufficient tumor-infiltrating CD8+ T cells and 50% showed terminal exhaustion of CD8+ T cells. These terminally exhausted CD8+ T cells coexisted with signatures of myeloid-derived suppressor cells in colorectal cancer.Conclusion: TMEPRE is a colorectal cancer-specific method. It captures characteristics of CD8+ T-cell infiltration and CD8+ T-cell exhaustion state and predicts cancer immunotherapy response. A subset of MSS patients could potentially benefit from anti-PD1 treatment. Anti-PD1 resistance MSI patients with insufficient infiltration of CD8+ T cells or terminal exhaustion of CD8+ T cells need different treatment strategies.

With the advent of 5th generation (5G) mobile communication era, higher requirements are put forward for ultra-reliable and low-latency transmission compared with 4G, especially in the field of short packet communication. In this paper, we propose a segmented cyclic redundancy check aided order statistical decoding algorithm with multiple biases (BIAS- SCRC-OSD) for short polar codes. This algorithm constructs multiple information sets by repeatedly adding bias value, and selects more effective information sets to improve the decoding performance. For further improving the decoding performance, we change the original cyclic redundancy check (CRC) into segment check. The simulation results show that a suitable bias value can significantly improve the decoding performance with a small increase in computational complexity. Compared with the original CRC-aided order statistical decoding (OSD) algorithm, the proposed algorithm has a gain of about 0.8 dB at target bit error rate (BER) 10–4 with code rate R = 0.5 and code length N = 64.

Background Precise methods for postoperative risk stratification to guide the administration of adjuvant chemotherapy (ACT) in localized colorectal cancer (CRC) are still lacking. Here, we conducted a prospective, observational, and multicenter study to investigate the utility of circulating tumor DNA (ctDNA) in predicting the recurrence risk. Methods From September 2017 to March 2020, 276 patients with stage II/III CRC were prospectively recruited in this study and 240 evaluable patients were retained for analysis, of which 1290 serial plasma samples were collected. Somatic variants in both the primary tumor and plasma were detected via a targeted sequencing panel of 425 cancer-related genes. Patients were treated and followed up per standard of care. Results Preoperatively, ctDNA was detectable in 154 of 240 patients (64.2%). At day 3–7 postoperation, ctDNA positivity was associated with remarkably high recurrence risk (hazard ratio [HR], 10.98; 95%CI, 5.31–22.72; P P P Conclusions Postoperative serial ctDNA detection predicted high relapse risk and identified disease recurrence ahead of radiological imaging in patients with stage II/III CRC. ctDNA may be used to guide the decision-making in postsurgical management.

Background High tumor mutational burden (TMB-H) is correlated with enhanced objective response rate (ORR) and progression-free survival (PFS) for certain cancers receiving immunotherapy. This study aimed to investigate the safety and efficacy of toripalimab, a humanized programmed death-1 (PD-1) antibody, in advanced gastric cancer (AGC), and the predictive survival benefit of TMB and PD-L1. Patients and methods We reported on the AGC cohort of phase Ib/II trial evaluating the safety and activity of toripalimab in patients with AGC, oesophageal squamous cell carcinoma, nasopharyngeal carcinoma and head and neck squamous cell carcinoma. In cohort 1, 58 chemo-refractory AGC patients received toripalimab (3 mg/kg d1, Q2W) as a monotherapy. In cohort 2, 18 chemotherapy-naive AGC patients received toripalimab (360 mg d1, Q3W) with oxaliplatin 130 mg/m2 qd, d1, capecitabine 1000 mg/m2 b.i.d., d1–d14, Q3W as first-line treatment. Primary end point was ORR. Biomarkers such as PD-L1 and TMB were evaluated for correlation with clinical efficacy. Results In cohort 1, the ORR was 12.1% and the disease control rate (DCR) was 39.7%. Median PFS was 1.9 months and median OS was 4.8 months. The TMB-H group showed significant superior OS than the TMB-L group [14.6 versus 4.0 months, HR = 0.48 (96% CI 0.24–0.96), P = 0.038], while PD-L1 overexpression did not correlate with significant survival benefit. A 77.6% of patients experienced at least one treatment-related adverse event (TRAE), and 22.4% of patients experienced a grade 3 or higher TRAE. In cohort 2, the ORR was 66.7% and the DCR was 88.9%. A 94.4% of patients experienced at least one TRAE and 38.9% of patients experienced grade 3 or higher TRAEs. Conclusions Toripalimab has demonstrated a manageable safety profile and promising antitumor activity in AGC patients, especially in combination with XELOX. High TMB may be a predictive marker for OS of AGC patients receiving toripalimab as a single agent. Trial registration ClinicalTrials.gov NCT02915432.

Ultrafine Pt nanocrystals with an average particle size of 2.2 ± 1 nm coupled over the petaloid Fe2P surface are proposed as a novel, efficient, and robust catalyst for alcohol fuel electro-oxidation. The strong coupling effect of metal-support imparts a strong electronic interaction between the Fe2P and Pt interface that can weaken the adsorption of poisoning CO species according to the d-band theory. Defects and increased surface area of the petaloid Fe2P are beneficial to the Pt nanoparticle anchoring and dispersion as well as the charge transfer and reactant transportation during the electrochemical reaction. These features make the Pt-Fe2P catalyst system exhibit excellent catalytic activity, antipoisoning ability, and catalytic stability for alcohol fuel of methanol and ethanol electro-oxidation compared with a controlled Pt/C catalyst. The high catalytic efficiency is proposed to come from the strong coupling effect of Pt and petaloid Fe2P interface that can maintain the mechanical and chemical stability of the catalyst system. This kind of phosphide-supported ultrafine Pt nanocrystals will be a promising catalyst in fuel cells.

Carbohydrate antigen 19-9 (CA19-9) is one of the most widely used tumor markers in gastrointestinal cancer. However, serum CA19-9 is not a recommended routine measurement in colon cancer. In this study, we evaluated the value of the preoperative serum CA19-9 level for the prediction of postoperative prognosis in stage III colon cancer. The medical records of 367 consecutive patients with stage III colon cancer who underwent curative resection followed by adjuvant chemotherapy with oxaliplatin and capecitabine between December 2007 and April 2015 were retrospectively reviewed. We determined the optimal cutoff value of CA19-9 for 3-year recurrence using the receiver operating characteristic (ROC) method. Differences in disease-free survival (DFS) and overall survival (OS) rates stratified by CA19-9 level were compared by using Kaplan-Meier and log-rank tests. A Cox proportional hazards model was used to identify prognostic variables for DFS and OS. The statistically determined best cutoff value for CA19-9 was 24 U/ml. High CA19-9 levels (> 24 U/ml) were significantly associated with poorly differentiated tumors, abnormal carcinoembryonic antigen (CEA) levels, and a high cumulative incidence of lung metastasis. Additionally, compared with low CA19-9 levels, high preoperative CA19-9 levels were associated with inferior 3-year DFS and OS rates, especially for high-risk patients (T4Nany or TanyN2). Multivariate analyses revealed that CA19-9 was an independent factor associated with both DFS (hazard ratio [HR], 2.248; 95% confidence interval [CI], 1.393-3.628; P = 0.001) and OS (HR: 2.081; 95% CI: 1.137-3.808; P = 0.017). The results of this study showed that high levels of preoperative serum CA19-9 indicated a worse prognostic outcome for stage III colon cancer patients. An early follow-up protocol to assess lung metastasis and a full course of adjuvant chemotherapy should be used for these patients.

In this study, a bottleneck was overcome for direct alcohol fuel cells using state-of-the-art PtRu catalysts for alcohol fuel oxidation. Herein, a core-shell structured PtRu catalyst system based on the emerging promoter FeP was developed that showed excellent catalytic performance for the oxidation of alcohol fuels. The surface spectrometric analysis and morphology observation confirmed the formation of a nanointerface of the PtRu shell and FeP core hybrid catalyst (PtRu@FeP), and efficient ligand effects and electronic effects were found to result from the noble metal active sites and adjacent promoter in the core-shell structure. The facile formation of oxygen-containing species and the strong electronic effects could activate the Pt active sites, leading to high catalytic performance. High anti-CO poisoning ability was found for this catalyst system when compared with the case of the benchmark commercial PtRu/C catalyst (110 mV less and 60 mV less as evaluated by the peak and onset potentials for CO oxidation, respectively). The PtRu@FeP catalysts also exhibited much higher catalytic activity and stability when compared with commercial and home-made PtRu/C catalysts; specifically, the peak current density of the PtRu@FeP 1 : 1 catalyst was about 2 and 3 times higher than those of the commercial PtRu/C catalyst and home-made PtRu/C for the oxidation of the alcohol fuels methanol and ethanol; moreover, high catalytic efficiency, improved by 2 times, was found, as expressed by the specific activity. Excellent catalytic stability as evaluated by 1000 cycles of cyclic voltammetry measurements was also demonstrated for the PtRu@FeP catalysts. The high catalytic performance could be attributed to the intimate nanointerface contact of the core-shell structured PtRu shell over the FeP core via a bi-functional catalytic mechanism and electronic effects based on the ligand effect in this catalyst system. The current study is a significant step to increase the PtRu catalytic performance via nanointerface construction by a core-shell structure on a novel promoter for direct alcohol fuel cells.

The technique of interwell tracer testing is considered as one of the most effective method to identify the thief zone (TZ) in reservoirs. However, in heavy oil reservoirs, tracer breakthrough curves are mostly parabolic and unimodal, thus resulting in slight differences between curves. It is inefficient and inaccurate to identify different types of curves with traditional methods applied to characterize the levels of TZs. In this paper, convolutional neural network (CNN) is applied to construct a classification model for the automatic identification of the levers of TZs. According to the TZs criteria specified on the field, the analytical tracer transport model was applied to generate 3000 curves as the sample, which can meet the requirements of model training accuracy. In the meantime, One-hot encoding, Xavier initialization, Adam optimizer, and mini-batch normalization were used to construct the model, and the key parameters are optimized to improve the performance of the model. The results show that the appropriate activation function is ReLU and the optimal dropout rate is 0.5. Moreover, the construction of CNN with discrete data points (DDP-CNN) as input contributed to a further improvement of classification accuracy of tracer curves. The accuracy of DDP-CNN in training set is 0.96, which is 14% and 23% higher than random forest (RF) and k-means, respectively. In practical applications, DDP-CNN proves capable to correctly classify 88 of the 100 curves.

The integral membrane protein ATG9A plays a key role in autophagy. It displays a broad intracellular distribution and is present in numerous compartments, including the plasma membrane (PM). The reasons for the distribution of ATG9A to the PM and its role at the PM are not understood. Here, we show that ATG9A organizes, in concert with IQGAP1, components of the ESCRT system and uncover cooperation between ATG9A, IQGAP1 and ESCRTs in protection from PM damage. ESCRTs and ATG9A phenocopied each other in protection against PM injury. ATG9A knockouts sensitized the PM to permeabilization by a broad spectrum of microbial and endogenous agents, including gasdermin, MLKL and the MLKL-like action of coronavirus ORF3a. Thus, ATG9A engages IQGAP1 and the ESCRT system to maintain PM integrity., Claude-Taupin et al. show that ATG9A mediates protection against plasma membrane damage in diverse biological contexts through a mechanism involving IQGAP1 and ESCRTs.

Additional file 11: Table S4. Patients’ clinical features stratified by preoperative ctDNA status.

A well control optimization method is a key technology to adjust the flow direction of waterflooding and improve the effect of oilfield development. The existing well control optimization method is mainly based on optimization algorithms and numerical simulators. In the face of larger models, longer optimization periods, or reservoir models with a large number of optimized wells, there are many optimization variables, which will cause algorithm convergence difficulties and optimization costs. The application effect is not good because of the problems of time length, few comparison schemes, and only fixed control frequency. This paper proposes a new method of a well control optimization method based on a multi-input deep neural network. This method takes the production history data of the reservoir as the main input and the saturation field as the auxiliary input and establishes a multi-input deep neural network for learning, forming a production dynamic prediction model instead of conventional numerical simulators. Based on the production dynamic prediction model, a series of model generation, production prediction, comparison, and optimization are carried out to find the best production plan of the reservoir. The calculation results of the examples show that (1) compared with the single-input production dynamic prediction model, the production dynamic prediction model based on multiple inputs has better prediction accuracy, and the results are close to the calculation results of the conventional numerical simulator; (2) the well control optimization method based on the multiple-input deep neural network has a fast optimization speed, with many comparison schemes and good optimization effect.

Additional file 3: Methods.

Additional file 5: Figure S1. Heatmap demonstrating the mutational landscape of the primary tumors from 240 evaluable patients, with the upper third depicting the mutation counts of each patient, the middle third presenting the top 20 recurrent-mutated genes in this cohort, and the lower third showing the corresponding clinicopathological features.

Additional file 2: Table S2. Gene list of the Geneseeq Prime™ 425-gene panel.