Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.     

The controlling factors and distribution prediction of H2S formation in marine carbonate gas reservoir, China

Generally, there are some anhydrites in carbonate reservoir, as H2S is also familiar in carbonate oil and gas reservoirs. Nowadays, natural gas with high H2S concentration is usually considered as TSR origin, so there is close relationship between H2S and anhydrite. On the contrary, some carbonate rocks with anhydrite do not contain H2S. Recently, researches show that H2S is only a necessary condition of H2S formation. The reservoir porosity, sulfate ion content within formation water, reservoir temperature, oil/gas and water interface, hydrocarbon and some elements of reservoir rock have great controlling effects on the TSR occurrence. TSR deoxidizes hydrocarbon into the acidic gas such as H2S and CO2, and the H2S formation is controlled by TSR occurrence, so the relationship among reaction room, the contact chance of sulfate ion and hydrocarbon, the reservoir temperature has great influence on the TSR reaction. H2S has relatively active chemical quality, so it is still controlled by the content of heavy metal ion. Good conditions of TSR reaction and H2S preservation are the prerequisite of H2S distribu- tion prediction. This paper builds a predictive model based on the characteristic of natural gas reservoir with high H2S-bearing. In the porosity reservoir with anhydrite, the formation water is rich in sulfate and poor in heavy metal ion. Oil and gas fill and accumulate in the gas reservoir with good preservation conditions, and they suffered high temperature later, which indicates the profitable area of natural gas with high H2S-bearing.     

项目教学法在PLC课程中的运用

分析了项目教学法在PLC课程中运用的重要性,探讨了项目教学法在PLC课程中的运用。     

小波分析在1-10TeV宇宙线气象效应研究中的应用

利用小波变换,结合折叠周期分析方法,对羊八井宇宙线观测站TibetASγ阵列1997年11月到1998年6月的实验记录数据进行周期分析,发现了气象参量,包括观测面处大气压和室外温度的日变化和半日变化.接下来的关联分析表明:宇宙线流强的周期变化与气压和温度的周期变化显著相关.     

ZrO_2纳米微粒红外振动特征

测定了单斜ＺｒＯ_２纳米材料在各种粒度下的付里叶变换红外光声光谱（ＦＴ—ＩＲ—ＰＡＳ），发现随粒度的减小，大部分的谱线位置蓝移，而有些谱线的位置红移，同时在不同频段，有的强度增强，有的强度减弱甚至消失。研究了量子尺寸效应和介电限城效应对其光学特征的影响，对观察到的物理现象进行了物理机制探讨     

量子阱中类氢杂质束缚能的压力效应

本文采用变分法研究了量子阱中类氢杂质的束缚能的压力效应，尤其在阱较窄的情形下，其压力效应更为显著．     

炭黑／结晶性含氟树脂合金在自控温加热电缆中应用

选择了结晶性含氟树脂添加改性剂为树脂基体，与炭黑制居复合导电体系。通过这个对体系不同条件下ＰＴＣ特性的研究，对其配方与加工工艺进行优化。并利用扫描电镜，Ｘ射线衍射，ＤＳＣ等分析测试手段，对体系的各种开矿结构进行了研究，阐明了该导电复合体系的形态结构与性能的关系。     

绝缘栅PMOS的SUPREM—SEDAN联机模拟研究

本文分析及研究了作者对绝缘栅ＰＭＯＳ场效应结构所进行的ＳＵＰＲＥＭ—ＳＥＤＡＮ联机模拟．重点在衬底电阻率（掺杂浓度）与ＭＯＳ结构阈值电压之间的定量关系上得到了与实验完全一致的结果．同时，定量地描述了栅氧层中电离电荷的分布及其行为．揭示了上述效应的机理．     

掺杂离子导体的粒子交叠广义晶格模型及其电导的粒子尺度效应

提出了一种掺杂离子导体（ＤＩＣ）的粒子交叠广义晶格模型，由此模型，推导随机－电阻－网络（ＲＲＮ）理论中３种导电键各自组分的几率，并采用有效介质近似（ＥＭＡ）计算了ＤＩＣ系统的电导及其粒子尺度效应。     

神经网络在可靠性分析中的应用

应用人工神经网络对舰炮发生故障的发射炮弹数进行了预测。借助前馈神经网络对非线性函数的逼近能力，对发生故障的发射炮弹数进行了单步预测，并与使用灰色数列预测的结果进行了对比，验证了人工神经网络进行可靠性预测的可行性。