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Background To estimate crude mortality, excess mortality, and standardized mortality rates (SMR) among people living with HIV (PLHIV) initiating highly active antiretroviral therapy (HAART) in Luzhou, China 2006–2020, and assess associated factors. Methods PLHIV initiating HAART in the HIV/AIDS Comprehensive Response Information Management System (CRIMS) in Luzhou, China 2006–2020 were included in the retrospective cohort study. The crude mortality, excess mortality, and SMR were estimated. Multivariable Poisson regression model was used for analyzing risk factors associated with excess mortality rates. Results The median age among 11,468 PLHIV initiating HAART was 54.5 years (IQR:43.1–65.2). The excess mortality rate decreased from 1.8 deaths/100 person-years (95% confidence interval [CI]:1.4–2.4) in 2006–2011 to 0.8 deaths/100 person-years (95%CI:0.7–0.9) in 2016–2020. SMR decreased from 5.4 deaths/100 person-years (95%CI:4.3–6.8) to 1.7 deaths/100 person-years (95%CI:1.5–1.8). Males had greater excess mortality with the eHR of 1.6 (95%CI:1.2–2.1) than females. PLHIV with CD4 counts ≥ 500 cells/μL had the eHR of 0.3 (95%CI:0.2–0.5) in comparison to those with CD4 counts Conclusions The excess mortality and SMR among PLHIV initiating HAART in Luzhou, China decreased substantially from 2006 to 2020, but the mortality rate among PLHIV was still higher than general population. PLHIV who were male, with baseline CD4 counts less than 200 cells/μL, WHO clinical stages III/IV, time from diagnosis to HAART initiation ≥ 12 months, initial HAART regimens unchanged, and virological failure had a greater risk of excess deaths. Early and efficient HAART would be significant in reducing excess mortality among PLHIV.

BackgroundOsteosarcoma is the most common primary malignant bone tumor. The existing treatment regimens remained essentially unchanged over the past 30 years; hence the prognosis has plateaued at a poor level. Precise and personalized therapy is yet to be exploited.MethodsOne discovery cohort (n=98) and two validation cohorts (n=53 & n=48) were collected from public data sources. We performed a non-negative matrix factorization (NMF) method on the discovery cohort to stratify osteosarcoma. Survival analysis and transcriptomic profiling characterized each subtype. Then, a drug target was screened based on subtypes’ features and hazard ratios. We also used specific siRNAs and added a cholesterol pathway inhibitor to osteosarcoma cell lines (U2OS and Saos-2) to verify the target. Moreover, PermFIT and ProMS, two support vector machine (SVM) tools, and the least absolute shrinkage and selection operator (LASSO) method, were employed to establish predictive models.ResultsWe herein divided osteosarcoma patients into four subtypes (S-I ~ S-IV). Patients of S- I were found probable to live longer. S-II was characterized by the highest immune infiltration. Cancer cells proliferated most in S-III. Notably, S-IV held the most unfavorable outcome and active cholesterol metabolism. SQLE, a rate-limiting enzyme for cholesterol biosynthesis, was identified as a potential drug target for S-IV patients. This finding was further validated in two external independent osteosarcoma cohorts. The function of SQLE to promote proliferation and migration was confirmed by cell phenotypic assays after the specific gene knockdown or addition of terbinafine, an inhibitor of SQLE. We further employed two machine learning tools based on SVM algorithms to develop a subtype diagnostic model and used the LASSO method to establish a 4-gene model for predicting prognosis. These two models were also verified in a validation cohort.ConclusionThe molecular classification enhanced our understanding of osteosarcoma; the novel predicting models served as robust prognostic biomarkers; the therapeutic target SQLE opened a new way for treatment. Our results served as valuable hints for future biological studies and clinical trials of osteosarcoma.

Objective Reducing the prevalence of treatment failure among people living with HIV (PLHIV) on highly active antiretroviral therapy (HAART) is crucial for improving individual health and reducing disease burden. This study aimed to assess existing evidence on treatment failure and its associated factors among PLHIV in mainland China. Methods We conducted a comprehensive search of PubMed, Web of Science, Cochrane Library, WanFang, China National Knowledge Infrastructure, and SinoMed databases. Relevant studies on treatment failure among PLHIV in mainland China until September 2022 were searched, including cross-sectional, case-control, and cohort studies. The primary outcome was treatment failure, and secondary outcomes were the potential influencing factors of treatment failure. We performed a meta-analysis to pool each outcome of interest, including meta-regression, subgroup, publication bias, and sensitivity analyses. Results A total of 81 studies were deemed eligible and included in the final meta-analysis. The pooled treatment failure prevalence among PLHIV in mainland China was 14.40% (95% confidence interval [CI]:12.30–16.63), of which the virological and immunological failure prevalence was 10.53% (95%CI:8.51–12.74) and 18.75% (95%CI:15.44–22.06), respectively. The treatment failure prevalence before and after 2016 was 18.96% (95%CI:13.84–24.67) and 13.19% (95%CI:10.91–15.64). Factors associated with treatment failure included good treatment adherence (odds ratio [OR] = 0.36, 95%CI:0.26–0.51), baseline CD4 counts>200 cells/μL (OR = 0.39, 95%CI:0.21–0.75), HAART regimens containing Tenofovir Disoproxil Fumarate (TDF) (OR = 0.70, 95%CI:0.54–0.92), WHO clinical stage III/IV (OR = 2.02, 95%CI:1.14–3.59) and age≥40 years (OR = 1.56, 95%CI:1.23–1.97). Conclusion The prevalence of treatment failure among PLHIV receiving HAART in mainland China was low and tended to decline. Poor adherence, low baseline CD4 count, HAART regimens without TDF, advanced clinical stage, and old age were contributing factors for treatment failure. Relevant intervention programs are needed with increasing treatment adherence through behavioral intervention or precise intervention targeting older adults.

Tumorigenesis and the subsequent development and metastasis of tumors are continuous evolutionary processes that are a result of several molecular evolutionary events. During these processes, the tumor cells gradually gain advantages of survival and proliferation and promote tumor heterogeneity. An in-depth understanding of the mechanism underlying the molecular evolution of tumor cells will help us understand the intrinsic mechanism of tumorigenesis, tumor development, and metastasis. This in turn will aid in the development of targeted treatment strategies for blocking tumor development and metastasis as well as improve the therapeutic effects of these strategies. This review systematically summarizes the molecular evolutionary events, such as gene mutation, gene copy number variation, the generation of de novo genes, and chromosome abnormality, during the process of tumorigenesis, tumor development, and metastasis as well as their relationship with tumor development. This review summarizes the main theoretical models of molecular evolution in tumor cells and discusses future research trends and prospects. Furthermore, it provides valuable reference information for additional in-depth theoretical and applied research in this field in the future.

With the rapid development of Internet of Things, hand gesture recognition has drawn wide attention in the field of ubiquitous computing because it provides us with simple and natural human-computer interaction mode. Among these various implementations, hand gesture recognition using ultrasonic signals of smartphone has become a hot research topic due to its various advantages. In this paper, we consider the smartphone as an active sonar sensing system to identify hand movements. Specifically, the speakers emit ultrasonic signal and the microphone on the same phone receives the changed echo affected by hand movements. This paper investigates the state-of-the-art hand gesture applications and presents a comprehensive survey on the characteristics of studies using the active sonar sensing system. Firstly, we review the existing research of hand gesture recognition based on acoustic signals. After that, we introduce the characteristics of ultrasonic signal and describe the fundamental principle of hand gesture recognition. Then, we focus on the typical methods used in these studies and present a detailed analysis on signal generation, feature extraction, preprocessing, and recognition methods. Next, we investigate the state-of-the-art ultrasonic-based applications of hand gesture recognition using smartphone and analyze them in detail from dynamic gesture recognition and hand tracking. Afterwards, we make a discussion about these systems from signal acquisition, signal processing, and performance evaluation to obtain some insight into development of the ultrasonic hand gesture recognition system. Finally, we conclude by discussing the challenges, insight, and open issues involved in hand gesture recognition based on ultrasonic signal of the smartphone.

Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including pattern-based, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions.

Additional file 1: Figure S1. The schematic diagram of the domain architecture of KZFP and the key amino acids in zinc finger binding to DNA. Figure S2. The SDR values of KRAB domains with different gene age grades in 7 mammals. Figure S3. The expression pattern of KZFP genes with different zinc finger divergence time grades. Figure S4. The expression pattern of KZFP genes in 7 mammals. Figure S5. The highly expressed KZFP genes in human. Figure S6. The expressional and functional characteristics of the co-expression modules containing old-zinc-finger-containing KZFPs. Figure S7. The PPIs of young- or old-zinc-finger-containing KZFPs. Figure S8. The SDR values and interactors of ZKSCAN3, ZNF287 and ZNF90. Figure S9. The WGCNA parameter for early development dataset (A) and brain development dataset (B).

Phasor measurement units (PMUs) have been widely and successfully used in power transmission grids. PMU technologies could be exploited in distribution grids to manage the impact of distributed generation and the electrification of the transportation sector. However, distribution grids are quite different from transmission ones resulting in more stringent accuracy requirements for the distribution PMUs (D-PMUs)). Currently, no standards for D-PMUs are available. However, there are numerous requirements proposed for D-PMUs that need further understanding and assessment. In this paper, a comparison between the IEEE Std C37.118 that is applicable to T-PMUs and requirements for D-PMUs is documented. A classification based on accuracy is proposed. Finally, the factors influencing the accuracy of D-PMUs are discussed in detail.

Quantitative analysis of Cr-adsorption from a gas phase on CeO 2 , pure and doped BaCeO 3 was performed in combination with surface techniques and electrochemical impedance spectroscopy. The Cr-adsorption on BaCeO 3 is 3 times higher than on CeO 2 and it increases by 12–25% on In doping in Ba(Ce 1−x In x )O 3 (x = 0.1–0.3) in the temperature range of 600–700 °C. These perovskites adsorb chromium with the formation of a multi-phase surface layer containing BaCr 6+ O 4 and cerium oxide. Coexistence of small fractions of Cr 3+ and Cr 5+ cations in the surface layer was identified by X-ray photoelectron spectroscopy. The thickness of the surface layer and its morphology vary with temperature, affecting the conductivity of Ce containing perovskites below 700 °C. BaCrO 4 exhibits under air noticeably larger anisotropic thermal expansion than BaCeO 3 . The results indicate that high tolerance to Cr-gaseous species should be considered as an important criterion for further development of materials for proton-conducting SOFCs.