Production of Bio-Energy from Pig Manure: A Focus on the Dynamics Change of Four Parameters under Sunlight-Dark Conditions

This study investigated the effect of sunlight-dark conditions on volatile fatty acids (VFAs), total ammonium nitrogen (TAN), total alkalinity (TA) and pH during pig manure (PM) digestion and then the subsequent influence on biogas yield of PM. PM₁ and PM₂ were performed in a transparent reactor and a non-transparent reactor, respectively. Two sets of experiments were conducted with a temperature of 35.0±2.0 °C and a total solid concentration of 8.0% to the digestion material. The dynamic change of the four parameters in response to sunlight-dark conditions resulted in variations of the physiological properties in the digester and affected the cumulative biogas production (CBP). PM₁ obtained higher CBP (15020.0 mL) with a more stable pH and a lower TAN concentration (1414.5 mg/L) compared to PM₂ (2675.0 mL and 1670.0 mg/L, respectively). The direct path coefficients and indirect path coefficients between the four parameters and CBP were also analyzed.     

基于指标自动筛选的新疆开孔河流域生态健康评价

生态健康评价对了解区域生态健康状况和促进区域可持续发展具有重要意义,如何自动筛选出能反映生态系统特性的重要指标,是生态健康定量评估的关键问题。基于压力-状态-响应(PSR,Press-State-Response)框架和生态等级网络框架(EHN,Ecological Hierarchy Network),通过文献调研和因果分析建立要素层与指标层之间的交叉联系,构建了生态健康评价"网状"指标体系;在保证指标体系完备性基础上,通过结合主成分分析和熵权法的候选指标权重的客观计算,基于目标优化理论构建了评价指标的自动筛选模型,并基于中选指标计算了新疆开孔河流域2001—2017年生态健康指数(EHCI,Ecological Health Comprehensive Indexes),分析其空间分异和时间变化特征。结果表明:利用所建立的评价指标自动筛选模型,开孔河流域生态健康评价指标由31个候选指标自动筛选出了17个中选指标,用54.8%的指标表达了85.98%的信息,中选的17个指标在干旱/半干旱区域有关文献中应用较多,使用频次比例都在20%以上,其中归一化植被指数(NDVI,Normalized Difference Vegetation Index)、年降水量和植被覆盖度(FVC,Fractional Vegetation Coverage)3个指标的使用频次百分比均超过了50%,说明指标自动筛选模型的合理性;开孔河流域空间分布差异显著,总体上西北高、东南低,东南部和中部绿洲区外围生态健康状况较差,西北部河谷地带和中部两大绿洲区生态健康状况较好;17年来,流域生态质量整体趋于改善,显著改善区域占10.26%,远高于显著退化的1.61%,显著改善区域以孔雀河绿洲最为明显。开孔河流域生态健康的总体好转趋势说明区域生态综合治理取得一定成效。     

Phosphorylation and ubiquitination of dynamin-related proteins (AtDRP3A/3B) synergically regulate mitochondrial proliferation during mitosis

The balance between mitochondrial fission and fusion is disrupted during mitosis, but the mechanism governing this phenomenon in plant cells remains enigmatic. Here, we used mitochondrial matrix‐localized Kaede protein (mt‐Kaede) to analyze the dynamics of mitochondrial fission in BY‐2 suspension cells. Analysis of the photoactivatable fluorescence of mt‐Kaede suggested that the fission process is dominant during mitosis. This finding was confirmed by an electron microscopic analysis of the size distribution of mitochondria in BY‐2 suspension cells at various stages. Cellular proteins interacting with Myc‐tagged dynamin‐related protein 3A/3B (AtDRP3A and AtDRP3B) were immunoprecipitated with anti‐Myc antibody‐conjugated beads and subsequently identified by microcapillary liquid chromatography–quadrupole time‐of‐flight mass spectrometry (CapLC Q‐TOF) MS/MS. The identified proteins were broadly associated with cytoskeletal (microtubular), phosphorylation, or ubiquitination functions. Mitotic phosphorylation of AtDRP3A/AtDRP3B and mitochondrial fission at metaphase were inhibited by treatment of the cells with a CdkB/cyclin B inhibitor or a serine/threonine protein kinase inhibitor. The fate of AtDRP3A/3B during the cell cycle was followed by time‐lapse imaging of the fluorescence of Dendra2‐tagged AtDRP3A/3B after green‐to‐red photoconversion; this experiment showed that AtDRP3A/3B is partially degraded during interphase. Additionally, we found that microtubules are involved in mitochondrial fission during mitosis, and that mitochondria movement to daughter cell was limited as early as metaphase. Taken together, these findings suggest that mitotic phosphorylation of AtDRP3A/3B promotes mitochondrial fission during plant cell mitosis, and that AtDRP3A/3B is partially degraded at interphase, providing mechanistic insight into the mitochondrial morphological changes associated with cell‐cycle transitions in BY‐2 suspension cells.     

A Mathematical Framework for Combining Decisions of Multiple Experts toward Accurate and Remote Diagnosis of Malaria Using Tele-Microscopy

We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing ‘slide-level’ diagnosis by using individual ‘cell-level’ diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform.