带压堵漏作业防护探索与研究

本文分析了泄露现场的化学和物理危害因素及由此导致的各种后果,而且针对泄露事故对作业人员不同部位的伤害提出了相对应的个人防护用品,同时提供了与之相对应的国家行业标准.     

深圳市天然气安全应急储备及事故气源的研究

分析了深圳市建设天然气安全应急储备及事故气源的必要性,通过对深圳市天然气产业链中各环节之间相互关系的分析,提出了建设实施方案。     

双重时滞和非时滞耦合的复杂网络同步研究

为了更真实地仿真现实的网络世界和提高模型的适应性,研究一类具有双重时滞和非时滞耦合的复杂网络同步问题。不同于大多数研究中限定耦合矩阵满足耗散耦合条件,对耦合矩阵未添加任何限制。基于李雅普诺夫稳定性定理,结合线性矩阵不等式,利用广义模型中的等价转换和系数矩阵分解方法,引入自由矩阵,在驱动系统中设置非线性控制器,得到驱动系统与响应系统同步的充分条件。两个仿真例子选定了两个不同的拓扑结构,实验结果证明了定理的可行性和有效性。     

关于风险检测安全阀失效的分析系统

安全阀是保障石油化工设备正常运行的重要绀成部分,本文以目前广泛采用的AP1581标准为基础,建市安全阀的失效分析系统.该系统在计算机理和界向交互方式上改进原标准,可以根据工厂设备和安全阀管理数据,分析安伞阀的开启失效和泄漏频率,为提高石油化工牛产的安全性和可靠性提供重要的分析依据.     

具有多种特性的门限多代理多签名方案

为了提高多代理多签名的灵活性和实用性,在Schnorr签名的基础上,提出2个具有消息恢复特性的门限多代理多签名新方案,其中的不同类成员具有不等权限,只有指定验证者才能恢复消息并验证签名。分析结果表明,在离散对数假设和随机预言机模型下,该方案是安全的。     

施威德勒型单层球面网壳结构抗火的非线性有限元分析

根据ISO-834标准升温曲线与结构钢强度和刚度随温度的变化曲线,采用非线性有限元法对不同的矢跨比的施威德勒型单层球面网壳结构进行了抗火分析,得到了一些结果,可为经济、科学的结构综合防火设计提供理论依据.     

深部开采岩体变形分析的双曲函数法

在软岩地层深部厚矿体开采条件下,给出了用于预测分析地下开采岩体移动变形的双曲正切函数模型,采用该模型对国内某地下矿山深部开采岩体移动变形进行了具体的计算分析,并与概率积分法理论分析结果和实测数据进行了对比,结果令人满意.分析结果表明,本文所提出的理论方法适用于预测分析深部矿体开采引起的岩体移动变形问题.     

Therapeutic Vaccines for HPV-Associated Oropharyngeal and Cervical Cancer: The Next De-Intensification Strategy?

The rise in human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) has prompted a quest for further understanding of the role of high-risk HPV in tumor initiation and progression. Patients with HPV-positive OPSCC (HPV+ OPSCC) have better prognoses than their HPV-negative counterparts; however, current therapeutic strategies for HPV+ OPSCC are overly aggressive and leave patients with life-long sequalae and poor quality of life. This highlights a need for customized treatment. Several clinical trials of treatment de-intensification to reduce acute and late toxicity without compromising efficacy have been conducted. This article reviews the differences and similarities in the pathogenesis and progression of HPV-related OPSCC compared to cervical cancer, with emphasis on the role of prophylactic and therapeutic vaccines as a potential de-intensification treatment strategy. Overall, the future development of novel and effective therapeutic agents for HPV-associated head and neck tumors promises to meet the challenges posed by this growing epidemic.     

Reduced VDAC1, Maintained Mitochondrial Dynamics and Enhanced Mitochondrial Biogenesis in a Transgenic Tau Mouse Model of Alzheimer’s Disease

Alzheimer’s disease (AD) is one of the most common forms of neurodegeneration, defined by reduced cognitive function, which is caused by the gradual death of neurons in the brain. Recent studies have shown an age-dependent rise in the levels of voltage-dependent anion channel 1 (VDAC1) in AD. In addition, we discovered an aberrant interaction between VDAC1 and P-TAU in the brains of AD patients, which led to abnormalities in the structural and functional integrity of the mitochondria. The purpose of our study is to understand the protective effects of reduced VDAC1 against impaired mitochondrial dynamics and defective mitochondrial biogenesis in transgenic TAU mice. Recently, we crossed heterozygote VDAC1 knockout (VDAC1^+/−) mice with transgenic TAU mice to obtain double-mutant VDAC1^+/−/TAU mice. Our goal was to evaluate whether a partial decrease in VDAC1 lessens the amount of mitochondrial toxicity in transgenic Tau (P301L) mice. We found that mitochondrial fission proteins were significantly reduced, and mitochondrial fusion and biogenesis proteins were increased in double-mutant mice compared to TAU mice. On the basis of these discoveries, the current work may have significance for the development of reduced-VDAC1-based treatments for individuals suffering from AD as well as other tauopathies.     

CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention

Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug–target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities.