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Turbo codes play a crucial role in wireless communication systems, and their compiled code structures are key factors affecting the performance of the entire communication system. As a result, the study of turbo compiled code structures has been a focal point for researchers. The iterative decoding of turbo code structures has multiple limitations and large storage resource consumption, leading to poor system anti‐interference ability and a rapid increase in BER. To address these issues, this paper proposes the mimic turbo compiled code structure (MTCCS) for wireless communication systems. MTCCS is based on the DHR idea, incorporating dynamic, heterogeneous, and redundancy characteristics. Dynamicity is achieved through a dynamic scheduling algorithm based on abnormal feedback information. Heterogeneity is achieved through a codec component collection design method based on intrinsic and extrinsic heterogeneity. Redundancy is achieved through a majority voting algorithm. At the beginning of information transmission, MTCCS randomly selects heterogeneous codecs from the heterogeneous codec collection to enter the runtime pool. After the information transmission is complete, the majority voting algorithm is used to adjudicate the multi‐mode output of the codecs, resulting in a relatively accurate decoding outcome. Meanwhile, the dynamic scheduling module calculates the abnormal feedback information of each codec and accordingly dynamically schedules the mimic turbo codecs to replace the abnormal ones. Through the above process, MTCCS realizes the adaptive compilation code and improves the anti‐interference ability of turbo code. Simulation experiments are conducted on MTCCS in both non‐interference and interference scenarios. Simulation experiments show that MTCCS introducing the DHR idea achieves a balance between anti‐interference and decoding performance. It effectively addresses the issue of poor anti‐interference ability in turbo codes, and the decoding performance of MTCCS is superior to that of the previous single conventional turbo codes. [ABSTRACT FROM AUTHOR]

DNA nanostructures have been regarded as promising platforms for molecular information coding for their high programmability and nanoscale addressability. However, steganography based on DNA nanostructures still needs further investigation. Here, we designed and synthesized a coumarin derivative structure with selective photo responsiveness in the visible light spectrum and developed a DNA origami steganography system that can only be decrypted through specific light exposure conditions. Under right light treatment, the effective cleavage of photoresponsive groups would cause some of the streptavidin binding sites to detach from the origami, thereby allowing the steganographic information to be read correctly. [ABSTRACT FROM AUTHOR]

Abstract Background There are no reports on the incidence of chronic mercury poisoning in a large population in China. This study investigated the epidemiology, clinical manifestations, treatment, and follow-up of Chinese patients with chronic mercury poisoning. Methods Data for 288 mercury poisoning patients were collected at our hospital from July 2014 to September 2019, including sex, age, admission time, blood mercury content, urine mercury content, creatinine, urinary mercury/creatinine ratio, 24-h urinary protein levels, electromyography (EMG) findings, renal biopsy, and follow-up. Patient characteristics were evaluated by statistical and correlation analyses. Results First, mercury poisoning in China mainly occurred through occupational exposure and the inappropriate use of mercury-containing cosmetics and Chinese folk remedies (CFRs). Second, the most common symptoms were nervous system (50.3 %), kidney (16.4 %) and breathing (8.0 %). Mercury poisoning-induced Nephrotic syndrome (NS) and peripheral neuropathy are common long-term complications. The complications of occupational and cosmetics-induced mercury poisoning are consistent with international belief. However, the NS caused by CFRs is mainly membranous nephropathy and the probability of peripheral neuropathy caused by CFRs is higher than other pathogens. Third, follow-up data shows that 13 patients with EMG-confirmed neurological injury, 10 showed full recovery after 38.50 ± 8.03 months. Furthermore, among 18 patients with NS, 15 had normal urine protein and serum albumin levels after 22.67 ± 10.26 months. Conclusions Regulation of skin-lightening cosmetic products, safety surveillance of CFRs, and prevention and control of occupational exposure must be improved to decrease the incidence of mercury poisoning in China.

Bovine coronavirus (BCoV) is a causative agent of enteric and respiratory disease in cattle. BCoV has been reported to cause a variety of animal diseases and is closely related to human coronaviruses; moreover, it has attracted extensive attention from both cattle farmers and researchers. With the rise of BCoV, a vaccine that is prophylactic and immunotherapeutic has to be utilized for a preemptive and adroit therapeutic approach. The aim of this study was to develop a novel multiepitope‐based BCoV vaccine that can induce an immune response using a silicon reverse vaccinology approach. In this study, an immunoinformatics approach was employed to identify potential vaccine targets against BCoV, and four candidate antigen proteins were selected to predict B‐cell and T‐cell epitopes. To identify dominant epitopes, we employed a variety of bioinformatics techniques, including antigenicity prediction, immunogenicity assessment, allergenicity analysis, conservative analysis, and toxicity assessment. Finally, six multiepitope vaccines were developed using dominant epitopes, suitable adjuvants, Pan HLADR—binding epitope (PADRE), and linkers. Then based on the antigenicity score, solubility analysis, allergenicity evaluation, physicochemical property assessment, and tertiary structure verification score, construct 6 was selected as the best candidate vaccine; it was named CY. Molecular modeling and structural validation ensured the high‐quality 3D structure of construct CY. The immunogenicity and complex stability of the vaccine were evaluated by molecular docking and kinetic simulation. In silicon clones, the BCoV vaccine had high levels of gene expression in the insect expression system. These results may contribute to the development of experimental BCoV vaccines with higher potency and safety. [ABSTRACT FROM AUTHOR]

Objective: The objective of the study was to assess the prognostic value of muscle invasion (MI), a key histopathological feature of tumor aggressiveness, and construct a superior prognostic prediction model combining the current TNM staging system. Materials and Methods: MI was analyzed in the whole‐slide images from a total of 301 patients with primary buccal mucosa squamous cell carcinoma (BMSCC). Survival times of patients with/without MI were evaluated by Kaplan–Meier analysis. MI was further combined with the TNM staging system to explore its predictive value for prognosis. Moreover, 204 cases of head and neck carcinoma from the TCGA database were included. Results: MI positive rate reached to 76% (229/301) in patients with BMSCC. MI was associated with poor overall survival (p = 0.012) and disease‐free survival (p = 0.022). The novel system (TNM staging combined with MI) revealed strong predictive performance, with the largest area under the curve (OS: p < 0.001, DFS: p < 0.004). MI and the established classification system were also had good predictive ability in the TCGA cohort. Conclusions: MI is an independent predictor of poor prognosis of BMSCC. The inclusion of MI in prediction system can augment the risk stratification of patients with oral squamous cell carcinoma and may assist in the clinical decision‐making process. [ABSTRACT FROM AUTHOR]

Objective: Disease metabolomes have been studied for identifying diagnostic and predictive biomarkers of pathology. Oral tongue squamous cell carcinoma (OTSCC) is one of the most prevalent subtypes of head and neck squamous cell carcinoma, yet the profile and diagnostic value of its tissue metabolite are unclear. Subjects and Methods: Tumor tissue samples and matched normal mucosal tissue samples were collected from 40 OTSCC patients. Untargeted metabolic analysis by liquid chromatography–mass spectrometry/mass spectrometry, in positive and negative ion modes, was used to identify dysregulated metabolites in OTSCC. Further, utilizing LASSO regression and receiver operating characteristic analyses, biomarker metabolites were selected and validated, and a diagnostic model was established. Results: One hundred and ninety metabolites were detected. The OTSCC had a total of 89 dysregulated metabolites, of which 73 were elevated. A diagnostic panel of nine metabolites was subsequently created that could accurately identify OTSCC with 100% sensitivity of 100%, 100% specificity and an AUC of 1.00. Conclusions: This study identified distinct metabolic characteristics of OTSCC and established a diagnostic model. Our research also contributes to the investigation of the pathogenesis of OTSCC. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel self-supervised framework for Sentiment Cue Extraction (SCE) aimed at enhancing the interpretability of text sentiment analysis models. Our approach leverages self-supervised learning to identify and highlight key textual elements that significantly influence sentiment classification decisions. Central to our framework is the development of an innovative Mask Sequence Interpretation Score (MSIS), a bespoke metric designed to assess the relevance and coherence of identified sentiment cues within binary text classification tasks. By employing Monte Carlo Sampling techniques optimized for computational efficiency, our framework demonstrates exceptional effectiveness in processing large-scale text data across diverse datasets, including English and Chinese, thus proving its versatility and scalability. The effectiveness of our approach is validated through extensive experiments on several benchmark datasets, including SST-2, IMDb, Yelp, and ChnSentiCorp. The results indicate a substantial improvement in the interpretability of the sentiment analysis models without compromising their predictive accuracy. Furthermore, our method stands out for its global interpretability, offering an efficient solution for analyzing new data compared to traditional techniques focused on local explanations. [ABSTRACT FROM AUTHOR]