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Abstract Objective To develop and validate a machine learning model for predicting mortality-associated prognostic factors in order to reduce in-hospital mortality rates among HIV/AIDS patients with Cryptococcus infection in Guangxi, China. Methods This retrospective prognostic study included HIV/AIDS patients with cryptococcosis in the Fourth People’s Hospital of Nanning from October 2011 to June 2019. Clinical features were extracted and used to train ten machine learning models, including Logistic Regression, KNN, DT, RF, Adaboost, Xgboost, LightGBM, Catboost, SVM, and NBM, to predict the outcome of HIV patients with cryptococcosis infection. The sensitivity, specificity, AUC, and F1 value were applied to assess model performance in both the testing and training sets. The optimal model was selected and interpreted. Results A total of 396 patients were included in the study. The average in-hospital mortality of HIV/AIDS patients with cryptococcosis was 12.9% from 2012 to 2019. After feature screening, 20 clinical features were selected for model construction, accounting for 93.8%, including ART, Electrolyte disorder, Anemia, and 17 laboratory tests. The RF model (AUC 0.9787, Sensitivity 0.9535, Specificity 0.8889, F1 0.7455) and the SVM model (AUC 0.9286, Sensitivity 0.7907, Specificity 0.9786, F1 0.8293) had excellent performance. The SHAP analysis showed that the primary risk factors for prognosis prediction were identified as BUN/CREA, Electrolyte disorder, NEUT%, Urea, and IBIL. Conclusions RF and SVM machine learning models have shown promising predictive abilities for the prognosis of hospitalized HIV/AIDS patients with cryptococcosis, which can aid clinical assessment and treatment decisions for patient prognosis.

The use of hydrogen as a source of fuel for marine applications is relatively nascent. As the maritime industry pivots to the use of alternate low and zero-emission fuels to adapt to a changing regulatory landscape, hydrogen energy needs to present and substantiate a technical and commercially viable use case to secure its value proposition in the future fuel mix. This paper leverages the technoeconomic and environmental assessment previously performed on HyForce, a hydrogen-fuelled harbour tug which has shown encouraging results for both technical and commercial aspects. This study aims to create a digital twin of HyForce to accurately predict her operability in real-world scenarios. The results from this study identify the strengths and drawbacks of the proposed use case. This is achieved by embedding the detailed design of HyForce in a virtual environment to further evaluate its operational performance through Computational Fluid Dynamics (CFD) simulations of realistic environmental conditions such as wind, wave, sea currents, and friction attributed to the properties of seawater. The results from this study indicate a base case power requirement of 93 kW to 1892 kW to achieve speeds of 5 to 12 knots in the absence of external environmental influences. Consequently, the speed of HyForce has a profound impact on total resistance peaking at 97.3 kN at 12 knots. Seawater properties such as low seawater temperature of 0 °C, and a high salinity of 50 g/kg increased friction. Additionally, wind speeds of 10 m/s acting on HyForce, delivered a resistance of 3 kN. However, these will be well mitigated through the design of the propulsion system which will be able to deliver a thrust power of 1892 kW and with assistance from the energy storage systems produce 2 MW of power to overcome the resistance experienced. The findings presented in this paper can serve as a foundation for constructing a robust model for the development of a predictive controller for future work. This controller has the potential to optimize the configuration of hydrogen and battery energy storage, aligning with desired cost functions.

The paper analyzes the mechanism of real estate prices on economic development with panel quantile regression model. It is found that real estate prices can significantly promote economic development. Generally speaking, the contribution of real estate prices to economic development in regions with higher level of economic development is higher than that in regions with lower level. With the continuous improvement of the quantile, the impact of real estate prices has generally increased gradually, and the impact of urbanization level basically shows the law of diminishing marginal effect.

The evolution of immune landscape in lung adenocarcinoma (ADC) is largely unknown. Here the authors use a cohort of resected invasive lung ADC and its precursors and show a gradual increase of immunosuppression and decrease of anti-tumor response associated with specific genomic and epigenetic features.

For wireless sensor networks (WSN) with connection failure uncertainties, traditional minimum spanning trees are no longer a feasible option for selecting routes. Reliability should come first before cost since no one wants a network that cannot work most of the time. First, reliable route selection for WSNs with connection failure uncertainties is formulated by considering the top-k most reliable spanning trees (RST) from graphs with structural uncertainties. The reliable spanning trees are defined as a set of spanning trees with top reliabilities and limited tree weights based on the possible world model. Second, two tree-filtering algorithms are proposed: the k minimum spanning tree (KMST) based tree-filtering algorithm and the depth-first search (DFS) based tree-filtering algorithm. Tree-filtering strategy filters the candidate RSTs generated by tree enumeration with explicit weight thresholds and implicit reliability thresholds. Third, an innovative edge-filtering method is presented in which edge combinations that act as upper bounds for RST reliabilities are utilized to filter the RST candidates and to prune search spaces. Optimization strategies are also proposed for improving pruning capabilities further and for enhancing computations. Extensive experiments are conducted to show the effectiveness and efficiency of the proposed algorithms.

Genotype-to-phenotype correlation studies in myotonic dystrophy type 1 (DM1) have been confounded by the age-dependent, tissue-specific and expansion-biased features of somatic mosaicism of the expanded CTG repeat. Previously, we showed that by controlling for the confounding effects of somatic instability to estimate the progenitor allele CTG length in blood DNA, age at onset correlations could be significantly improved. To determine the suitability of saliva DNA as a source for genotyping, we used small pool-PCR to perform a detailed quantitative study of the somatic mutational dynamics of the CTG repeat in saliva and blood DNA from 40 DM1 patients. Notably, the modal allele length in saliva was only moderately higher in saliva and not as large as previously observed in most other tissues. The lower boundary of the allele distribution was also slightly higher in saliva than it was in blood DNA. However, the progenitor allele length estimated in blood explained more of the variation in age at onset than that estimated from saliva. Interestingly, although the modal allele length was slightly higher in saliva, the overall degree of somatic variation was typically lower than in blood DNA, revealing new insights into the tissue-specific dynamics of somatic mosaicism. These data indicate that saliva constitutes an accessible, non-invasive and suitable DNA sample source for performing genetic studies in DM1.

This paper presents a comparison analysis of carrier statistics on numerical simulations of MOSFET and tunneling FET (TFET). While the MOSFET current characteristics are not sensitive to the carrier statistic utilized in the simulation, a detailed analysis is presented by inspections of the channel charge density and electric field profile. On the other hand, numerical simulations of TFETs with the Fermi-Dirac (F-D) statistics give larger current even in the sub-threshold region. It is attributed to the larger electric field across the tunnel junction between degenerated channel and heavily doped source which governs the interband tunneling process. As a result, F-D statistics should be utilized when modeling the tunneling current in TFETs.

Previous studies have shown that MDM2 SNP309 and p53 codon 72 have modifier effects on germline P53 mutations, but those studies relied on case-only studies with small sample sizes. The impact of MDM4 polymorphism on tumor onset in germline mutation carriers has not previously been studied.We analyzed 213 p53 germline mutation carriers including 168(78.9%) affected with cancer and 174 who had genotypic data. We analyzed time to first cancer using Kaplan-Meier and Cox proportional hazards methods, comparing risks according to polymorphism genotypes. For MDM2 SNP309, a significant difference of 9.0 years in the average age of cancer diagnosis was observed between GG/GT and TT carriers (18.6 versus 27.6 years, P = 0.0087). The hazards ratio was 1.58 (P = 0.03) comparing risks among individuals with GG/GT to risk among TT, but this effect was only significant in females (HR = 1.60, P = 0.02). Compared to other genotypes, P53 codon 72 PP homozygotes had a 2.24 times (P = 0.03) higher rate for time to develop cancer. We observed a multiplicative joint effect of MDM2 and p53 codon72 polymorphism on risk. The MDM4 polymorphism had no significant effects.Our results suggest that the MDM2 SNP309 G allele is associated with cancer risk in p53 germline mutation carriers and accelerates time to cancer onset with a pronounced effect in females. A multiplicative joint effect exists between the MDM2 SNP309 G allele and the p53 codon 72 G allele in the risk of cancer development. Our results further define cancer risk in carriers of germline p53 mutations.

BackgroundBreast cancer in young women tends to have a natural history of aggressive disease for which rates of recurrence are higher than in breast cancers detected later in life. Little is known about the genetic pathways that underlie early-onset breast cancer. Here we report the discovery of DEAR1 (ductal epithelium-associated RING Chromosome 1), a novel gene encoding a member of the TRIM (tripartite motif) subfamily of RING finger proteins, and provide evidence for its role as a dominant regulator of acinar morphogenesis in the mammary gland and as an independent predictor of local recurrence-free survival in early-onset breast cancer.Methods and findingsSuppression subtractive hybridization identified DEAR1 as a novel gene mapping to a region of high-frequency loss of heterozygosity (LOH) in a number of histologically diverse human cancers within Chromosome 1p35.1. In the breast epithelium, DEAR1 expression is limited to the ductal and glandular epithelium and is down-regulated in transition to ductal carcinoma in situ (DCIS), an early histologic stage in breast tumorigenesis. DEAR1 missense mutations and homozygous deletion (HD) were discovered in breast cancer cell lines and tumor samples. Introduction of the DEAR1 wild type and not the missense mutant alleles to complement a mutation in a breast cancer cell line, derived from a 36-year-old female with invasive breast cancer, initiated acinar morphogenesis in three-dimensional (3D) basement membrane culture and restored tissue architecture reminiscent of normal acinar structures in the mammary gland in vivo. Stable knockdown of DEAR1 in immortalized human mammary epithelial cells (HMECs) recapitulated the growth in 3D culture of breast cancer cell lines containing mutated DEAR1, in that shDEAR1 clones demonstrated disruption of tissue architecture, loss of apical basal polarity, diffuse apoptosis, and failure of lumen formation. Furthermore, immunohistochemical staining of a tissue microarray from a cohort of 123 young female breast cancer patients with a 20-year follow-up indicated that in early-onset breast cancer, DEAR1 expression serves as an independent predictor of local recurrence-free survival and correlates significantly with strong family history of breast cancer and the triple-negative phenotype (ER(-), PR(-), HER-2(-)) of breast cancers with poor prognosis.ConclusionsOur data provide compelling evidence for the genetic alteration and loss of expression of DEAR1 in breast cancer, for the functional role of DEAR1 in the dominant regulation of acinar morphogenesis in 3D culture, and for the potential utility of an immunohistochemical assay for DEAR1 expression as an independent prognostic marker for stratification of early-onset disease.

Loss of genome-wide methylation is a common feature of cancer, and the degree of hypomethylation has been correlated with genomic instability. Global methylation of repetitive elements possibly arose as a defense mechanism against parasitic DNA elements, including retrotransposons and viral pathogens. Given the alterations of global methylation in both viral infection and cancer, we examined genome-wide methylation levels in head and neck squamous cell carcinoma (HNSCC), a cancer causally associated with human papilloma virus (HPV). We assayed global hypomethylation levels in 26 HNSCC samples, compared with their matched normal adjacent tissue, using Pyrosequencing-based methylation assays for LINE repeats. In addition, we examined cell lines derived from a variety of solid tumors for LINE and SINE (Alu) repeats. The degree of LINE and Alu hypomethylation varied among different cancer cell lines. There was only moderate correlation between LINE and Alu methylation levels, with the range of variation in methylation levels being greater for the LINE elements. LINE hypomethylation was more pronounced in HPV-negative than in HPV-positive tumors. Moreover, genomic instability, as measured by genome-wide loss-of-heterozygosity (LOH) single nucleotide polymorphism (SNP) analysis, was greater in HNSCC samples with more pronounced LINE hypomethylation. Global hypomethylation was variable in HNSCC. Its correlation with both HPV status and degree of LOH as a surrogate for genomic instability may reflect alternative oncogenic pathways in HPV-positive versus HPV-negative tumors.