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Abstract Ovarian cancer (OC) was the fifth leading cause of cancer death and the deadliest gynecological cancer in women. This was largely attributed to its late diagnosis, high therapeutic resistance, and a dearth of effective treatments. Clinical and preclinical studies have revealed that tumor-infiltrating CD8+T cells often lost their effector function, the dysfunctional state of CD8+T cells was known as exhaustion. Our objective was to identify genes associated with exhausted CD8+T cells (CD8TEXGs) and their prognostic significance in OC. We downloaded the RNA-seq and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD8TEXGs were initially identified from single-cell RNA-seq (scRNA-seq) datasets, then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were utilized to calculate risk score and to develop the CD8TEXGs risk signature. Kaplan–Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in the risk groups were used to figure out the closely correlated pathways with the risk group. The role of risk score has been further explored in the homologous recombination repair deficiency (HRD), BRAC1/2 gene mutations and tumor mutation burden (TMB). A risk signature with 4 CD8TEXGs in OC was finally built in the TCGA database and further validated in large GEO cohorts. The signature also demonstrated broad applicability across various types of cancer in the pan-cancer analysis. The high-risk score was significantly associated with a worse prognosis and the risk score was proven to be an independent prognostic biomarker. The 1-, 3-, and 5-years ROC values, nomogram, calibration, and comparison with the previously published models confirmed the excellent prediction power of this model. The low-risk group patients tended to exhibit a higher HRD score, BRCA1/2 gene mutation ratio and TMB. The low-risk group patients were more sensitive to Poly-ADP-ribose polymerase inhibitors (PARPi). Our findings of the prognostic value of CD8TEXGs in prognosis and drug response provided valuable insights into the molecular mechanisms and clinical management of OC.

A well-designed indoor thermal environment in winter not only meets the thermal comfort demands of the elderly, but also reduces their health risks. However, the thermal performance of residential buildings for the elderly in rural Xi’an, China, is poor, with no central heating system, and the quality of the thermal environment needs to be improved. Due to the lack of thermal comfort benchmarks that meet the thermal preferences of the elderly in rural areas, this study will address this issue through conducting a field investigation. 161 elderly people from 8 villages volunteered to participate in the survey. A clothing regulation model was established, which found that the sensitivity of the thermal resistance of their clothing to temperature changes was low. Additionally, a thermal sensation model was established, and the neutral temperature was calculated to be 11.3°C. The 80% and 90% acceptable lower limits were calculated to be 6.5°C and 8.2°C, respectively. When the indoor operative temperature was lower than 11.3°C (neutral temperature), an average of 78% of the elderly expected a slightly warmer environment, and when it was higher than 11.3°C, an average of 72% expected no change. The results of the study can be used as a basis for the design and improvement of the thermal environment of rural elderly living in cold regions of China.

IntroductionIt is believed that ovarian cancer (OC) is the most deadly form of gynecological cancer despite its infrequent occurrence, which makes it one of the most salient public health concerns. Clinical and preclinical studies have revealed that intratumoral CD4+ T cells possess cytotoxic capabilities and were capable of directly killing cancer cells. This study aimed to identify the CD4+ conventional T cells-related genes (CD4TGs) with respect to the prognosis in OC.MethodsWe obtained the transcriptome and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD4TGs were first identified from single-cell datasets, then univariate Cox regression was used to screen prognosis-related genes, LASSO was conducted to remove genes with coefficient zero, and multivariate Cox regression was used to calculate riskscore and to construct the CD4TGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), decision curve analysis (DCA), nomogram, and calibration were made to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in risk groups were conducted to explore the tightly correlated pathways with the risk group. The role of riskscore has been further explored in the tumor microenvironment (TME), immunotherapy, and chemotherapy. A risk signature with 11 CD4TGs in OC was finally established in the TCGA database and furtherly validated in several GEO cohorts.ResultsHigh riskscore was significantly associated with a poorer prognosis and proven to be an independent prognostic biomarker by multivariate Cox regression. The 1-, 3-, and 5-year ROC values, DCA curve, nomogram, and calibration results confirmed the excellent prediction power of this model. Compared with the reported risk models, our model showed better performance. The patients were grouped into high-risk and low-risk subgroups according to the riskscore by the median value. The low-risk group patients tended to exhibit a higher immune infiltration, immune-related gene expression and were more sensitive to immunotherapy and chemotherapy.DiscussionCollectively, our findings of the prognostic value of CD4TGs in prognosis and immune response, provided valuable insights into the molecular mechanisms and clinical management of OC.

Well-designed residential thermal environments that meet the comfort and health needs of elderly individuals can enhance their well-being and decrease associated health risks, which is one of the foundations of sustainable building development. However, limited evidence on thermal response patterns and thermal comfort needs of rural elderly leads to insufficient support for improving thermal environments. This study was conducted in seven villages in Xi'an, a cold region of China, and both subjective questionnaires of thermal comfort and objective physical environment tests were adopted. Correlations between nine kinds of human subjective responses and thermal environmental parameters were examined. The neutral values of operative temperature, relative humidity, and air velocity were calculated to be 23.9 °C, 63.3%, and 0.28 m/s, respectively. Comfort ranges for temperature, humidity, and air velocity were determined to be ≤29.3 °C (80% acceptability), 43.3–81.0% (80% acceptability), and 0.16–0.41 m/s (90% acceptability). Clothing insulation of rural elderly in summer was relatively higher and less sensitive to temperature shifts. The slope value of the thermal adaptative model was larger, with higher comfort temperatures in warmer environments. These results are the outcome of the prolonged adaptability to the regional climate and the poor indoor thermal environment, as evidenced by lower psychological expectations, higher behavior adjustment, and environmental decisions influenced by household low income. The findings in this study can be used as a basis for the design or improvement of residential thermal environments for rural older adults. [ABSTRACT FROM AUTHOR]

Implementing the "dual-carbon" objective has profoundly affected China's businesses, prompting a continuous process of transformation and innovation. Green, low-carbon transformation, upgrading, and sustainable development have emerged as the means to achieve high-quality company growth, with firms playing a crucial role in achieving the "dual-carbon" target. By prioritizing firms' green, low-carbon, and sustainable growth, not only can their economic efficiency be improved, but it also serves as a vital measure to advance high-quality development. To achieve this objective, it is crucial to analyze the elements that impact the energy-saving effectiveness of businesses to develop optimization tactics that can enhance their competitiveness. This study combines the three-stage Data Envelopment Analysis (DEA) and the Tobit model to assess the energy-saving efficiency of green and low-carbon firms from 2018 to 2022. The analysis focuses on selected samples from various areas and industries. The study investigates the relationship between energy-saving effectiveness and different regional and industrial parameters, considering the distinct attributes of each firm and the key elements that influence its energy-saving performance. The findings indicate that green, low-carbon firms typically demonstrate suboptimal energy-saving efficiency but have considerable room for improvement. Upon considering environmental concerns, it becomes clear that the primary limitation on energy-saving performance is the lower efficiency of the enterprise scale. The energy-saving efficiency of green, low-carbon firms varies significantly across different regions and industries. On average, northern firms in the energy-saving and carbon reduction industry, as well as the resource recycling industry, have higher energy-saving efficiency compared to southern enterprises. In contrast, the environmental protection industry in southern firms demonstrates a better average energy-saving efficiency compared to their northern counterparts. Employee quality, policy support, and automation are key factors that greatly enhance the energy-saving efficiency of firms in both regions. Enterprise size has a beneficial impact on the energy-saving efficiency of southern firms but a negative impact on northern enterprises. Moreover, the industry and financial structures exert a detrimental influence on enhancing the energy-saving efficiency of green and low-carbon firms. Thus, in order to improve the energy-saving effectiveness of businesses, it is essential to utilize the elements that have a positive influence on energy-saving performance and reduce the impact of those that have negative impacts. This strategy will enhance the energy-saving efficiency of businesses and promote the development of an environmentally friendly and low-carbon society. [ABSTRACT FROM AUTHOR]

Forward error correction (FEC) coding is a technique to cope with packet loss in network transmission.By adding redundant data to the transmission process,the receiver can recover the original data directly from the redundant data in packet loss scenarios.In the scenario of high packet loss and high latency,adding forward error correction coding appropriately can save a lot of waiting time for timeout retransmission to improve the quality of service of network transmission.Adding too much redundancy will result in bandwidth wasting,while insufficient redundancy will fail to recover lost data,so the difficulty of using forward error correction coding in practice is to properly control the proportion of redundant data.Nowadays,most of the forward error correction coding research is based on traditional network protocols,but with the rise of QUIC (quick UDP internet connections) protocol,more forward error correction researches start to incorporate QUIC protocol due to its characters like 0-RTT (round trip time) connectivity,multiplexing,seamless connection migration to further improve transmission performance.This paper gives an overview of forward error correction coding,introduces its application scenarios,basic policies and adaptive redundancy control strategies.Then it introduces the research status of forward error correcting coding in traditional protocols in unicast and multicast scenarios.Finally,this paper introduces the current research status and challenges of forward error correction coding in QUIC protocol.

Ovarian cancer (OC) leads to the most deaths among gynecological malignancies. The various epigenetic regulatory mechanisms of histone acetylation in cancer have attracted increasing attention from scientists. Long non-coding RNA (lncRNA) also plays an important role in multiple biology processes linked to OC. This study aimed to identify the histone acetylation-related lncRNAs (HARlncRNAs) with respect to the prognosis in OC. We obtained the transcriptome data from Genotype-Tissue Expression (GTEx) project and The Cancer Genome Atlas (TCGA); HARlncRNAs were first identified by co-expression and differential expression analyses, and then univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) were used to construct the HARlncRNAs risk signature. Kaplan–Meier analysis, time-dependent receiver operating characteristics (ROC), univariate Cox regression, multivariate Cox regression, nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analysis (GSEA) in risk groups were conducted to explore the tightly correlated pathways with the risk group. A risk signature with 14 HARlncRNAs in OC was finally established and further validated in the International Cancer Genome Consortium (ICGC) cohort; the 1-, 3-, and 5-year ROC value, nomogram, and calibration results confirmed the good prediction power of this model. The patients were grouped into high- and low-risk subgroups according to the risk score by the median value. The low-risk group patients exhibited a higher homologous recombination deficiency (HRD) score, LOH_frac_altered, and mutLoad_nonsilent. Furthermore, consensus clustering analysis was employed to divide OC patients into three clusters based on the expression of the 14 HARlncRNAs, which presented different survival probabilities. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were also performed to evaluate the three clusters. In conclusion, the risk signature composed of 14 HARlncRNAs might function as biomarkers and prognostic indicators with respect to predicting the response to the anti-cancer drugs in OC.

Currently, the construction and operation of buildings are responsible for 36% of global final energy usage and nearly 40% of energy-related carbon dioxide (CO2) emissions. From the perspective of sustainable development, and taking into account economy and thermal comfort, it is crucial to consider the influence of multi-objective realization on design parameters. In this paper, high-rise residential buildings in the cities of Xi'an and Yulin, which have differences in solar radiation, in the western solar enrichment area of China are taken as the research objects. The four objectives of building energy consumption, thermal comfort, life-cycle cost, and life-cycle carbon emissions are weighed using the SPEA-2 algorithm by adjusting eleven design variables, thereby obtaining the Pareto non-dominated solutions. The TOPSIS method is applied to obtain the suitable parameter combinations under different scenarios. The results show that the differences in climate and solar radiation influence the solution distribution, the range of objective function values, and the values of the design variables in Pareto non-dominated solutions. The obtained optimal scheme for the Xi'an area has an energy-saving rate of 61.7%, a TDHP improvement rate of 20.3%, an LCC of 254.8 CNY/m2, and an LCCO2 of 72.3 kgCO2/m2. The corresponding values in the Yulin area are 69.7%, 19.4%, 230.2 CNY/m2, and 0 kgCO2/m2. This reflects the potential of solar energy utilization to reduce buildings' energy consumption and carbon emissions. The methodology and findings can provide references for high-rise residential building design in Northwestern China. [ABSTRACT FROM AUTHOR]

The first-principles calculations have recently shown that implanting sufficient noble gas atoms into germanium (Ge) can expand its lattice to achieve the desired tensile strain for indirect-to-direct bandgap transition to develop the on-chip high-efficient light emitter. Here, to experimentally prove this strain-doping concept, we implant argon (Ar) ions into Ge and then recrystallize the Ar-doped amorphous Ge (a-Ge) layer using nanosecond laser annealing (NLA) and furnace thermal annealing (FTA), respectively. The NLA effectively recrystallizes the 12 nm thick a-Ge layer with minimal loss of Ar dopants, while FTA fails to fully recrystallize it and results in significant loss of Ar dopants. The regrown Ge layer with Ar concentration above the critical value (0.8%) for bandgap transition is 3.8 nm thick, making it a challenge to distinguish the photoluminescence signal of strain-doped layer from the substrate. To overcome this, increasing the implantation energy and adding a capping layer may be necessary to further prevent Ar loss and achieve a strain-doped layer with sufficient depth. These findings provide promising view of the strain-doping concept for direct-bandgap emission from Ge. [ABSTRACT FROM AUTHOR]

This study proposed a dynamic forming mechanism development of the negative Poisson’s ratio elastomer molds—plate to plate (P2P) forming process. To dynamically stretch molds and control the microstructural shape, the proposal is committed to using the NPR structure as a regulatory mechanism. The NPR structural and dynamic parallel NPR-molds to control microstructure mold-cores were simulated and analyzed. ANSYS and MATLAB were used to simulate and predict dynamic NPR embossing replication. The hot-embossing and UV-curing dynamic NPR P2P-forming systems are designed and developed for verification. The results illustrated that the dynamic forming mechanism of the negative Poisson’s ratio elastomer molds proposed by this study can effectively control microstructure molds. This can effectively predict and calculate the geometrical characteristics of the microstructures after embossing. The multi-directional dynamic NPR microstructural replication process can accurately transfer microstructures and provide high transfer rate-replication characteristics.

Rural houses in severe cold areas of China are mostly single-storey independent buildings with large shape coefficient. Compared with urban residential, it has larger contact area between envelope and outdoor environment of each household. Meanwhile, the heat transfer coefficient of window is usually greater than that of external wall and roof. The window-wall ratio is one of the important indicators affecting the energy consumption of rural house. This paper takes window-wall ratio as the main variable, building orientation, thermal performance of envelope and window heat transfer coefficient as the auxiliary variables, and applies DesignBuilder software to quantitatively analyse the mechanism of window-wall ratio on rural house’s heating energy consumption under the interactive influence of multiple factors. Results show that the influence rule of window-wall ratio with different orientations on heating energy consumption will change when the thermal performance of envelope or window heat transfer coefficient changed. The synthetic effect of various factors should be considered in the design to reasonably determine the windowwall ratio of rural house.

In urban scale, solar energy utilization potential is closely related to residential density. Taking Xining City as an example, this paper explored how density of urban residential area affects solar energy utilization potential of urban housing. By changing density related design variables , including building layout, density rate, floor-site area ratio and the number of floors, 36 general models of residential areas with low, medium and high density are Abstracted. The results show that solar energy utilization potential of buildings varies greatly with different density related design variables. Comparison of a number of different scenarios reveals how density related variables affect solar energy utilization potential, based on which suggestions for optimization of solar energy potential for urban residential areas in their initial planning and design stages are proposed.