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The guess‐and‐determine technique find wide applications in the recovery of unknown variables within given system of relations. The fundamental concept behind this technique involves guessing certain unknown variables and deducing the remaining ones based on the relational system. In the context of symmetric cryptography analysis, the guess‐and‐determine technique is employed to deduce partial subkey information to recover the master key. The set of variables that need to be guessed is called the guess basis.The crux of the guess‐and‐determine technique lies in identifying the minimal guess basis. By introducing new equal relations and initial constraints, this paper enhances the minimal guess basis mixed integer linear programming (MILP) model. The new model offers a more comprehensive depiction of key schedule, thereby enabling a more accurate and efficient derivation of the minimal guess basis.The novel model is applied to the eight‐sided fortress (ESF) block cipher algorithm. By extending forward by three rounds and backward by three rounds based on a nine‐round impossible differential distinguisher, a 15‐round impossible differential attack is conducted. Utilizing the new model, the minimal guess basis required for key recovery is determined to be 54 bits. This represents a significant improvement compared to the existing result of 58 bits.The results indicate that for a 15‐round impossible differential attack on the ESF algorithm, the data complexity is 231.18 CP, the time complexity involves 261.67 iterations of 15‐round encryption, and the memory complexity is 266.18 bytes. Furthermore, this paper introduces, for the first time, a principle for designing key scheduling algorithms based on the guessing basis. This principle is applied to the ESF algorithm, where the minimal guess basis is employed to determine the positions of S‐boxes and the parameters for cyclic shifts within the key scheduling algorithm. Without altering the consumption of software or hardware resources, a global optimal search is conducted among various key scheduling candidate approaches. By employing an equivalence class partitioning approach derived from 2108 instances of nine‐round impossible differential distinguishers, the search space is reduced. Eventually, a selection process identifies a set of eight novel key schedule algorithms that achieve the maximum value of 77 bits for the minimal guess basis. These new key scheduling algorithms exhibit enhanced resistance against impossible differential attacks.

Weight loss is a firmly established negative survival factor for individuals with cancer, yet effective biomarkers and targeted therapies remain elusive. In this study, we collected skeletal muscle, noncancerous, and cancerous tissues using the Illumina EPIC array to identify conserved DNA methylation probes associated with weight loss following bariatric surgery. Next, the consistency of the probes is evaluated and then the probes are integrated into a generalizable pathway enrichment score. Our results emphasize the gene-centered design, identifying KCNB1 , PEAK1 , SCG5 , and TNIK as key targets of DNA methylation, as confirmed by mouse phenotype data and druggability resources. Moreover, an illustrative test of protein abundance in cell lines is conducted. Utilizing the Clinical Proteomic Tumor Analysis Consortium data, a positive correlation is established between the chromosomal instability scores and our generated score in tumor tissues. In addition, considering these correlation findings, the presence of identifiable methylation blocks in the co-occurring gain samples. Our findings also suggest that upstream molecular drivers may influence this pathway enrichment score, potentially leading to dysregulated methylation associated with weight loss. In summary, DNA methylation analysis not only identifies functional targets but also uncovers new gene-disease connections.

BackgroundParenting health literacy is essential for effective caregiving, yet its impact on nurturing care ability remains insufficiently studied. This study aimed to investigate the association between parenting health literacy and nurturing care ability, with a focus on the mediating role of parenting stress.MethodsA cross-sectional survey was conducted among 3623 caregivers of children aged 0–6 years in Yiwu, Zhejiang Province, China. Standardised instruments were used to assess parenting health literacy, parenting stress and nurturing care ability. Mediation analysis quantified the contribution of parenting stress to the association between health literacy and nurturing care ability, and domain-specific mediation effects were examined.ResultsHigher parenting health literacy was significantly associated with lower parenting stress (β=−0.12, p<0.001) and greater nurturing care ability (β=0.10, p<0.001). Parenting stress mediated 33.68% of the association between parenting health literacy and nurturing care ability. Domain-specific analyses revealed stronger mediation for parenting style (42.31%), play experience ability (37.81%) and responsive care (37.26%) compared with other domains.ConclusionsParenting health literacy improves nurturing care ability both directly and indirectly by reducing parenting stress, with particularly pronounced effects in emotionally demanding domains. Interventions to enhance parenting outcomes should integrate health literacy promotion with stress management strategies.

Background and objectives Immune checkpoint inhibitors (ICIs) bring cancer patients tumor control and survival benefits, yet they also trigger immune-related adverse effects (irAEs), notably checkpoint inhibitor-related pneumonitis (CIP), affecting about 5% of patients among whom 1–2% experiencing severe grade 3 or higher pneumonitis. Current research points to potential links with T cell subset dysfunction and autoantibody increase, but the specific mechanisms underlying different grades of CIP are understudied. Methods Herein, we employed single-cell RNA sequencing (scRNA-seq) on bronchoalveolar lavage fluid (BALF) from CIP patients across varying severity levels, aiming to elucidate underlying immune environment and mechanisms of CIP progression at cellular and molecular levels. Findings Totally, 121,409 high qualified cells from BALF of 11 patients were annotated and categorized into five major cell types. Severe CIP (CIP-S) cases have a significant increase in the percentage of unreported epithelial cells in their bronchoalveolar lavage fluid compared with mild CIP (CIP-M) cases. These cells were defined as aberrant basaloid cells. They upregulated SOX9, increased the expression of CXCL3/5, recruited neutrophils, and activated the immune system. Additionally, macrophages in the CIP-S group had stronger antigen-presenting abilities and resulted in more CD8 + effective T cells infiltrated. Conclusions Utilizing single-cell sequencing of BALF, we discovered an enriched population of aberrant basaloid cells in CIP-S patients, which had not been previously reported. Aberrant basaloid cells may upregulate SOX9 via CXCL3/5-CXCR2 to recruit and activate neutrophils, and further activate the immune system, resulting in CIP-S. This finding could identify new targets for stratified treatment of CIP patients, holding promise of a novel approach for clinical guidance.

Soil drying cracking is the most common natural phenomenon affecting soil stability. Due to the complexity of the geometric shapes of soil cracks during the cracking process, it has become a major problem in engineering science. The extremely irregular and complex crack networks formed in civil engineering materials can be quantitatively investigated using fractal theory. In this paper, fractal dimension is proposed to characterize the drying cracking characteristics of composite soil by adding recycled waste brick micro-powder. At the same time, the concept of the probability entropy of cracking is introduced to quantify the ordered state of crack development. Correspondingly, the endpoint value of probability entropy was solved mathematically, and the meaning of the probability entropy of cracking was clarified. In this study, the fracture fractal characteristics of composite soil mixed with different materials were first investigated. Then, five groups of composite soil-saturated muds with added recycled waste brick micro-powder of different contents were prepared in the laboratory. Using the evaporation test under constant temperature and humidity, the change rules of the fractal dimensions, probability entropy, crack ratios, and water contents of cracks during the cracking process of the soil samples were obtained. The results show that: (1) on the whole, the fractal dimensions of the soil samples added with recycled waste brick micro-powder of different contents increased over time, and the fractal dimensions of the soil samples without recycled waste brick micro-powder were obviously larger than those of the soil samples with recycled waste brick micro-powder. With the increase in the content of recycled waste brick micro-powder, the maximum fractal dimension decreased in turn. The maximum fractal dimensions of the five groups of soil samples were 1.74, 1.68, 1.62, 1.57, and 1.45. (2) The change trends of the probability entropy and fractal dimensions were similar; both of them showed an upward trend over time, and the probability entropy of the soil samples without recycled waste brick micro-powder was greater than that of the soil samples with recycled waste brick micro-powder. With the increase in the contents of recycled waste brick micro-powder, the probability entropy decreased in turn. The maximum values of the crack probability entropy of the five groups of soil samples were 0.99, 0.92, 0.87, 0.83, and 0.80. (3) Under the action of continuous evaporation, the moisture contents of the soil samples gradually decreased over time, while the crack ratios increased over time. To sum up, both from the perspective of the development process of the cracks of the soil samples and from the perspective of the final stable crack networks of the soil samples, the geometric shapes of the cracks of the soil samples without recycled waste brick micro-powder were the most complex. With the increase in the content of recycled waste brick micro-powder, the fractal characteristics of the cracks gradually changed from complex to simple.

Background The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. Methods A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients. Results Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets. Conclusion We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given.

Recent breakthrough therapies have improved survival rates in non-small cell lung cancer (NSCLC), but a paradigm for prospective confirmation is still lacking. Patientdatasets were mainly downloaded from TCGA, CPTAC and GEO. We conducted downstream analysis by collecting metagenes and generated 42-gene subtype classifiers to elucidate biological pathways. Subsequently, scRNA, eRNA, methylation, mutation, and copy number variation were depicted from a phenotype perspective. Enhancing the clinical translatability of molecular subtypes, preclinical models including CMAP, CCLE, and GDSC were utilized for drug repositioning. Importantly, we verified the presence of previously described three phenotypes including bronchioid, neuroendocrine, and squamoid. Poor prognosis was seen in squamoid and neuroendocrine clusters for treatment-naive and immunotherapy populations. The neuroendocrine cluster was dominated by STK11 mutations and 14q13.3 amplifications, whose related methylated loci are predictive of immunotherapy. And the greatest therapeutic potential lies in the bronchioid cluster. We further estimated the relative cell abundance of the tumor microenvironment (TME), specific cell types could be reflected among three clusters. Meanwhile, the higher portion of immune cell infiltration belonged to bronchioid and squamoid, not the neuroendocrine cluster. In drug repositioning, MEK inhibitors resisted bronchioid but were squamoid-sensitive. To conceptually validate compounds/targets, we employed RNA-seq and CCK-8/western blot assays. Our results indicated that dinaciclib and alvocidib exhibited similar activity and sensitivity in the neuroendocrine cluster. Also, a lineage factor named KLF5 recognized by inferred transcriptional factors activity could be suppressed by verteporfin.

Background Coronavirus disease 2019 (COVID-19) has spread around the world. This retrospective study aims to analyze the clinical features of COVID-19 patients with cancer and identify death outcome related risk factors. Methods From February 10th to April 15th, 2020, 103 COVID-19 patients with cancer were enrolled. Difference analyses were performed between severe and non-severe patients. A propensity score matching (PSM) analysis was performed, including 103 COVID-19 patients with cancer and 206 matched non-cancer COVID-19 patients. Next, we identified death related risk factors and developed a nomogram for predicting the probability. Results In 103 COVID-19 patients with cancer, the main cancer categories were breast cancer, lung cancer and bladder cancer. Compared to non-severe patients, severe patients had a higher median age, and a higher proportion of smokers, diabetes, heart disease and dyspnea. In addition, most of the laboratory results between two groups were significantly different. PSM analysis found that the proportion of dyspnea was much higher in COVID-19 patients with cancer. The severity incidence in two groups were similar, while a much higher mortality was found in COVID-19 patients with cancer compared to that in COVID-19 patients without cancer (11.7% vs. 4.4%, P = 0.028). Furthermore, we found that neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were related to death outcome. And a nomogram based on the factors was developed. Conclusion In COVID-19 patients with cancer, the clinical features and laboratory results between severe group and non-severe group were significantly different. NLR and CRP were the risk factors that could predict death outcome.

In lung squamous cell carcinoma (LUSC), current cancer vaccines show promising effects, despite a lack of benefit for a large number of patients. We first identified the tumor antigens into shared and private antigens, and determined the population by clustering analysis in public datasets. For vaccine development, The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were collected. WGCNA method was furthermore applied to construct a consensus gene co-expression network based on TCGA and CPTAC datasets. The main analyses in bulk sequencing included survival, clinical features, tumor microenvironment (TME), and pathways enrichment. In addition, single-cell RNA (scRNA) analysis of cancer epithelium dissected consensus subtype. We identified the ideal population for cancer vaccines, and candidate neoantigens including AOC1, COL5A2, LGI2, and POSTN. According to subtype analysis, Lung squamous 1 (LSQ1) type exhibited a higher tumor mutational load (TMB) and copy number but no immune infiltration, whereas lung squamous 2 (LSQ2) tumors had a higher global methylation level and more fibroblasts but had less stemness. Meanwhile, trajectory analysis further revealed that the evolution of TME influenced prognosis. We emphasized specific pathways or targets with the potential for combination immunotherapy by consensus network and single-cell RNA analyses. Anti-androgen therapy has been validated in vitro experiments of LUSC as proof of concept. In conclusion, LSQ1 was linked to immune exclusion and might be utilized for vaccination, while LSQ2 was linked to immune dysfunction and could be used for programmed cell death protein 1 (PD1) blocking therapy.