ZBTB40 is a telomere-associated protein and protects telomeres in human ALT cells.

Alternative lengthening of telomeres (ALTs) mechanism is activated in some somatic, germ cells, and human cancer cells. However, the key regulators and mechanisms of the ALT pathway remain elusive. Here we demonstrated that ZBTB40 is a novel telomere-associated protein and binds to telomeric dsDNA through its N-terminal BTB (BR-C, ttk and bab) or POZ (Pox virus and Zinc finger) domain in ALT cells. Notably, the knockout or knockdown of ZBTB40 resulted in the telomere dysfunction-induced foci and telomere lengthening in the ALT cells. The results also show that ZBTB40 is associated with ALT-associated promyelocytic leukemia nuclear bodies, and the loss of ZBTB40 induces the accumulation of the ALT-associated promyelocytic leukemia nuclear bodies in U2OS cells. Taken together, our results implicate that ZBTB40 is a key player of telomere protection and telomere lengthening regulation in human ALT cells.

A Quick Method to Synthesize Extrachromosomal Circular DNA In Vitro.

Extrachromosomal circular DNA (eccDNA) is a special class of circular DNA in eukaryotes. Recent studies have suggested that eccDNA is the product of genomic instability and has important biological functions to regulate many downstream biological processes. While NGS (Next-Generation Sequencing)-based eccDNA sequencing has led to the identification of many eccDNAs in both healthy and diseased tissues, the specific biological functions of individual eccDNAs have yet to be clearly elucidated. Synthesizing eccDNAs longer than 1 kb with specific sequences remains a major challenge in the field, which has hindered our ability to fully understand their functions. Current methods for synthesizing eccDNAs primarily rely on chemical oligo synthesis, ligation, or the use of a specific gene editing and recombination systems. Therefore, these methods are often limited by the length of eccDNAs and are complex, expensive, as well as time-consuming. In this study, we introduce a novel method named QuickLAMA (Ligase-Assisted Minicircle Accumulation) for rapidly synthesizing eccDNAs up to 2.6 kb using a simple PCR and ligation approach. To validate the efficacy of our method, we synthesized three eccDNAs of varying lengths from cancer tissue and PC3 cells and confirmed successful circularization through sequencing and restriction enzyme digestion. Additional analyses have demonstrated that this method is highly efficient, cost-effective, and time-efficient, with good reproducibility. Using the method, a well-trained molecular biologist can synthesize and purify multiple eccDNAs within a single day, and it can be easily standardized and processed in a high-throughput manner, indicating the potential of the method to produce a wide range of desired eccDNAs and promote the translation of eccDNA research into clinical applications.

Deep convolutional neural networks using an active learning strategy for cervical cancer screening and diagnosis.

Cervical cancer (CC) is the fourth most common malignant tumor among women worldwide. Constructing a high-accuracy deep convolutional neural network (DCNN) for cervical cancer screening and diagnosis is important for the successful prevention of cervical cancer. In this work, we proposed a robust DCNN for cervical cancer screening using whole-slide images (WSI) of ThinPrep cytologic test (TCT) slides from 211 cervical cancer and 189 normal patients. We used an active learning strategy to improve the efficiency and accuracy of image labeling. The sensitivity, specificity, and accuracy of the best model were 96.21%, 98.95%, and 97.5% for CC patient identification respectively. Our results also demonstrated that the active learning strategy was superior to the traditional supervised learning strategy in cost reduction and improvement of image labeling quality. The related data and source code are freely available at https://github.com/hqyone/cancer_rcnn.

Construction of Mental Health Knowledge Service Model Based on Online Medical Community.

This paper discusses a common mental disorder prevention mode to improve residents' mental health quality and achieve the "comprehensive and fine-grained" mental health education knowledge service. The construction of mental health knowledge service model is conducive to accurately grasp the group cognition and psychological changes and take the initiative to make decisions. This paper analyzes the needs of mental health education knowledge service system and combs the research status of applying information technology and artificial intelligence to mental health education at home and abroad, the resource data of five major online medical communities at home and abroad were screened and mined, and the mental health feature tags of college students were extracted. Based on the existing mental health diagnosis experience database and multisource text, reuse and optimize ontology integration method to systematically construct mental health education ontology. Taking mental illness as an example, the rule base is constructed and the personalized recommendation service of mental health is realized. The service model can infer and output all kinds of diagnosis and treatment knowledge to provide users with intelligent mental health education knowledge services.

Deformation Analysis of Continuous Milling of Inconel718 Nickel-Based Superalloy.

As a difficult-to-process material, Inconel718 nickel-based superalloy is more and more widely used in aerospace, ocean navigation, and large-scale machinery manufacturing. Based on ABAQUS simulation software, this paper takes the milling force and temperature in the milling process of the nickel-based superalloy as the research object, and establishes the empirical formula for the prediction model of cutting force and cutting temperature based on the method of multiple linear regression. The significance of the prediction model was verified by the residual analysis method. Through data analysis, it is obtained: within a certain experimental range, the influence degrees of each milling parameter on the cutting force and cutting temperature are fz>ap>n and fz>ap≈n, respectively. The actual orthogonal cutting test was carried out on the machine tool, and the reliability and accuracy of the prediction model of cutting force, cutting temperature and tool wear amount were verified. The model formulas of the shear velocity field, shear strain field and shear strain rate field of the main shear deformation zone are constructed by using mathematical analysis methods. The influence law of cutting speed and tool rake angle on the variables of main shear zone is calculated and analyzed. Through the combination of theory and experiment, the relationship between cutting force, chip shape and machined surface quality in milling process was analyzed. Finally, with the increase in the cutting force, the serration of the chip becomes more and more serious, and the roughness of the machined surface becomes greater and greater.

SOC Estimation of Lithium-Ion Battery for Electric Vehicle Based on Deep Multilayer Perceptron.

The state of charge (SOC) is one of the main indexes of the lithium-ion battery, which affects the practice range of new energy vehicles and the safety of the battery. Nevertheless, the value of SOC cannot be measured directly. At present, the algorithm for estimating the state of charge is not very satisfactory. The multilayer perceptron algorithm designed during this paper encompasses a sensible impact on state estimation. During this paper, the multilayer network is designed to estimate the charged state of lithium batteries from the three-layer artificial neural network to the eleven-layer artificial neural network. After preprocessing the dataset and comparing several activation functions, the ten-layer fully connected neural network is the most efficient to estimate the SOC. In order to prevent over-fitting of the multilayer perceptron algorithm, the two techniques of the BatchNormalization layer and Dropout layer work together to inhibit over-fitting. At the same time, the accuracy of extended Kalman filter, long and short memory network, and recurrent neural network are compared. The multilayer perceptron network designed during this paper has the highest accuracy. Finally, in the open dataset, both the training and test errors achieve good results. The algorithm developed in this paper has made some progress in SOC estimation.

Pyrolysis of vegetable oil soapstock in fluidized bed: Characteristics of thermal decomposition and analysis of pyrolysis products.

This study investigated the effect of temperature on pyrolysis of soapstock in a fluidized bed reactor, and the characterization of soapstock chars (SCs) and pyrolysis oils (POs) were analyzed. TGA, TG-FTIR, TG-MS, and Py-GCMS were employed to investigate characteristics of SS pyrolysis. Experimental results indicated that the yield of SC decreased with increasing temperature. Pyrolysis oil (PO) yield reached the maximum of 21.05 wt% at 600 °C and the yield of non-condensable gas varied with temperatures. The content of carbon, hydrogen and nitrogen distributed in the SC decreased with the increasing temperature, and sulfur tended to be retained in SC during pyrolysis with the distribution ratio of 0.55-0.62. Ketones, alcohols and hydrocarbons were the dominate substances in PO, and higher temperature promoted the production of short-chain alkanes and the conversion of alkenes to benzene derivatives. SS pyrolysis can be divided into three stages. Stage I was mainly the evaporation of free water and light organics in the raw material. Decomposition and conversion of organics mainly occurred at stage II. Stage III was the decomposition of CaCO3 and secondary cracking of residual organics. Ca2+ delayed the pyrolysis reaction of fatty acids and promoted decarboxylation which was the main deoxygenation pathway, and alkene production. This study provided a theoretical basis for the application of soapstock thermochemical treatment. It is of great significance for the quality improvement of PO and pollution control for pyrolysis processes.

Influence of corn straw on distribution and migration of nitrogen and heavy metals during microwave-assisted pyrolysis of municipal sewage sludge.

This study explored pyrolysis characteristics, nitrogen transformation and migration of heavy metals during microwave-assisted pyrolysis of municipal sewage sludge in a continuously operated auger pyrolyser at different temperatures and corn straw ratios. The results showed higher temperatures and more corn straw resulted in more gas yield (e.g., CO2, CO, CH4 and H2) and less char yield. 5 wt% corn straw addition at 750 °C achieved high-quality bio-oil with less O-containing compounds, which was more favorable for upgrading to transportation fuels. Sludge chars prepared at higher corn straw ratios had lower ratios of H/C and N/C, and higher carbon content. Nitrogen transformation pathways and mechanisms were investigated. The residual ratio of heavy metals (except Cd) in sludge char was 67.74-100%. However, the residual ratio of Cd decreased significantly to 6.46% at 750 °C. Concentrations of all heavy metals in sludge char conformed to national standard (CJ/T 362-2011, China), and the potential ecological risk was slight. Sludge chars prepared in the presence of corn straw had lower ecological risk and higher retention capacity of heavy metals (e.g., Pb, Cr, Mn, Cu, Zn, and Ni) compared with pyrolysis of sewage sludge.

Automatic polyp image segmentation and cancer prediction based on deep learning.

The similar shape and texture of colonic polyps and normal mucosal tissues lead to low accuracy of medical image segmentation algorithms. To solve these problems, we proposed a polyp image segmentation algorithm based on deep learning technology, which combines a HarDNet module, attention module, and multi-scale coding module with the U-Net network as the basic framework, including two stages of coding and decoding. In the encoder stage, HarDNet68 is used as the main backbone network to extract features using four null space convolutional pooling pyramids while improving the inference speed and computational efficiency; the attention mechanism module is added to the encoding and decoding network; then the model can learn the global and local feature information of the polyp image, thus having the ability to process information in both spatial and channel dimensions, to solve the problem of information loss in the encoding stage of the network and improving the performance of the segmentation network. Through comparative analysis with other algorithms, we can find that the network of this paper has a certain degree of improvement in segmentation accuracy and operation speed, which can effectively assist physicians in removing abnormal colorectal tissues and thus reduce the probability of polyp cancer, and improve the survival rate and quality of life of patients. Also, it has good generalization ability, which can provide technical support and prevention for colon cancer.

Nuclear Factor Related to KappaB Binding Protein (NFRKB) Is a Telomere-Associated Protein and Involved in Liver Cancer Development.

Alternative lengthening of telomeres (ALT) is a homologous recombination-based telomere maintenance mechanism activated in 10-15% of human cancers. Although significant progress has been made, the key regulators of the ALT pathway and its role in cancer development remain elusive. Bioinformatics methods were used to predict novel telomere-associated proteins (TAPs) by analysis of large-scale ChIP-Seq data. Immunostaining and fluorescence in situ hybridization experiments were applied to detect the subcellular location of target genes and telomeres. Western blot and reverse transcription-polymerase chain reaction (RT-PCR) were used to examine the expression of targeting genes. Overall survival (OS) analyses were used to evaluate the relationship between gene expression and survival time; immunohistochemistry was used to detect the distribution of target genes in liver cancer tissues. We found that nuclear factor related to kappaB binding protein (NFRKB), a metazoan-specific subunit of the INO80 complex, can associate with telomeres in human ALT cells. Loss of NFRKB induces dysfunction of telomeres and less PML bodies in U2OS cells. In addition, NFRKB is low/moderately expressed in cytoplasm of normal hepatocytes but heavily accumulating in the nucleus of liver cancer cells. Finally, the high expression of NFRKB is associated with short OS time and poor prognosis. NFRKB is a TAP and protects telomeres from DNA damage in ALT cells. It is highly expressed in hepatocellular carcinoma (HCC) cells and predicts a poor prognosis. NFRKB may be a promising prognostic biomarker for the treatment of HCC in the future.

Integrated genomic analysis reveals regulatory pathways and dynamic landscapes of the tRNA transcriptome.

tRNAs and tRNA-derived RNA fragments (tRFs) play various roles in many cellular processes outside of protein synthesis. However, comprehensive investigations of tRNA/tRF regulation are rare. In this study, we used new algorithms to extensively analyze the publicly available data from 1332 ChIP-Seq and 42 small-RNA-Seq experiments in human cell lines and tissues to investigate the transcriptional and posttranscriptional regulatory mechanisms of tRNAs. We found that histone acetylation, cAMP, and pluripotency pathways play important roles in the regulation of the tRNA gene transcription in a cell-specific manner. Analysis of RNA-Seq data identified 950 high-confidence tRFs, and the results suggested that tRNA pools are dramatically distinct across the samples in terms of expression profiles and tRF composition. The mismatch analysis identified new potential modification sites and specific modification patterns in tRNA families. The results also show that RNA library preparation technologies have a considerable impact on tRNA profiling and need to be optimized in the future.

Extrachromosomal Circular DNA (eccDNA): From Chaos to Function.

Extrachromosomal circular DNA (eccDNA) is a type of double-stranded circular DNA that is derived and free from chromosomes. It has a strong heterogeneity in sequence, length, and origin and has been identified in both normal and cancer cells. Although many studies suggested its potential roles in various physiological and pathological procedures including aging, telomere and rDNA maintenance, drug resistance, and tumorigenesis, the functional relevance of eccDNA remains to be elucidated. Recently, due to technological advancements, accumulated evidence highlighted that eccDNA plays an important role in cancers by regulating the expression of oncogenes, chromosome accessibility, genome replication, immune response, and cellular communications. Here, we review the features, biogenesis, physiological functions, potential functions in cancer, and research methods of eccDNAs with a focus on some open problems in the field and provide a perspective on how eccDNAs evolve specific functions out of the chaos in cells.