Logging damage and injured tree mortality in tropical forest management

Using insights from the forest ecology literature, we analyze the effect of injured trees on stand composition and carbon stored in above‐ground biomass and the implications for forest management decisions. Results from a Faustmann model with data for a tropical forest on Kalimantan show that up to 50% of the basal area of the stand before harvest can consist of injured trees. Considering injured trees leads to an increase in the amount of carbon in above‐ground biomass of up to 165%. These effects are larger under reduced impact logging than under conventional logging. The effects on land expectation value and cutting cycle are relatively small. The results suggest that considering injured trees in models for tropical forest management is important for the correct assessment of the potential of financial programs to store carbon and conserve forest ecosystem services in managed tropical forests, such as reducing emissions from deforestation and forest degradation and payment for ecosystem services. Recommendations for Resource Managers

• Considering the role of injured trees is important for managing tropical forests
• These trees can cover up to 50% of basal area and contain more than 50% of the carbon stored in above‐ground biomass
• Reduced impact logging leads to a larger basal area of injured trees and more carbon stored in injured trees than conventional logging
• Injured trees play an important role when assessing the potential for carbon storage in the context of payment for forest ecosystem services.

     

Glutamine: fructose-6-phosphate amidotransferase (GFAT): homology modelling and designing of new inhibitors using pharmacophore and docking based hierarchical virtual screening protocol

Glutamine: fructose-6-phosphate amidotransferase (GFAT), also termed GFPT1 and GFAT1, catalyzes the first committed step of the hexosamine biosynthesis pathway in mammals and consequently plays an important role in type 2 diabetes. In the present study, a combination of pharmacophore modelling, homology modelling, and molecular docking analysis was performed to design new glutamine competitive inhibitors of human GFAT, and to investigate important interaction details of inhibitor molecules. A pharmacophore model of GFAT inhibitors was developed, subsequently validated, and utilized for the screening by the PHASE database to identify new molecules. Afterwards, homology modelling was performed to construct the glutamine-binding site of the GFAT protein. The modelled active site was utilized to dock the studied molecules to investigate important receptor-ligand interactions and to scrutinize database-screened molecules on the basis of essential interactions. This systematic in silico protocol helped us to identify new molecules that would be explored for the treatment of type 2 diabetes and its complications.     

Método de elementos de borde jerárquico basado en el árbol de Barnes-Hut aplicado a flujo reptante exterior

In this work, a hierarchical variant of a boundary element method and its use in Stokes flow around three-dimensional rigid bodies in steady regime is presented. The proposal is based on the descending hierarchical low-order and self-adaptive algorithm of Barnes-Hut, and it is used in conjunction with an indirect boundary integral formulation of second class, whose source term is a function of the undisturbed velocity. The solution field is the double layer surface density, which is modified in order to complete the eigenvalue spectrum of the integral operator. In this way, the rigid modes are eliminated and both a non-zero force and a non-null torque on the body could be calculated. The elements are low order flat triangles, and an iterative solution by generalized minimal residual (GMRES) is used. Numerical examples include cases with analytical solutions, bodies with edges and vertices, or with intricate shapes. The main advantage of the presented technique is the possibility of considering a greater number of degrees of freedom regarding traditional collocation methods, due to the decreased demand of main memory and the reduction in the computation times.     

Protein Secondary Structure Prediction with Partially Recurrent Neural Networks

Abstract

Partially recurrent neural networks with different topologies are applied for secondary structure prediction of proteins. The state of some activations in the network is available after a pattern presentation via feedback connections as additional input during the processing of the next pattern in a sequence. A reference data set containing 91 proteins in the training set and 15 non-homologous proteins in the test set is used for training and testing a network with a modified, hierarchical Elman architecture. The network predicts the secondary structures α-helix, β-sheet, and “coil” for each amino acid. The percentage of correctly classified amino acids is 67.83% on the training set and 63.98% on the test set. The best performance of a three-layer feedforward network is 62.7% on the same test set. A cascaded network, where the outputs of the recurrent network are processed by a second net with 13 × 3 inputs, four hidden and three output units has a predictive performance of 64.49%. The best corresponding feedforward net has a performance of 64.3%.     

Engineering Hierarchical Nanostructures by Elastocapillary Self‐Assembly

Surfaces coated with nanoscale filaments such as silicon nanowires and carbon nanotubes are potentially compelling for high‐performance battery and capacitor electrodes, photovoltaics, electrical interconnects, substrates for engineered cell growth, dry adhesives, and other smart materials. However, many of these applications require a wet environment or involve wet processing during their synthesis. The capillary forces introduced by these wet environments can lead to undesirable aggregation of nanoscale filaments, but control of capillary forces can enable manipulation of the filaments into discrete aggregates and novel hierarchical structures. Recent studies suggest that the elastocapillary self‐assembly of nanofilaments can be a versatile and scalable means to build complex and robust surface architectures. To enable a wider understanding and use of elastocapillary self‐assembly as a fabrication technology, we give an overview of the underlying fundamentals and classify typical implementations and surface designs for nanowires, nanotubes, and nanopillars made from a wide variety of materials. Finally, we discuss exemplary applications and future opportunities to realize new engineered surfaces by the elastocapillary self‐assembly of nanofilaments.     

多级孔NiMo负载TS-1分子筛催化剂的制备及其加氢脱硫性能

钛硅(TS-1)分子筛的微孔孔道严重限制了其在复杂分子催化转化中的应用,为了克服这一问题,通过酸洗脱、碱刻蚀及二者相结合的方法制备了多级孔 TS-1 分子筛,并采用等体积共浸渍法制备了相应的 NiMo 负载型催化剂;使用 X 射线衍射(XRD)、N₂吸附-脱附、吡啶吸附红外光谱(Py-FTIR)、氢气程序升温还原(H₂-TPR)、X射线光电子能谱(XPS)和高分辨透射电子显微镜(HR-TEM)等方法对多级孔TS-1分子筛的理化性质进行了表征;以二苯并噻吩(DBT)为探针对催化剂的加氢脱硫(HDS)性能进行了评价。结果表明,和常规TS-1分子筛相比,多级孔TS-1分子筛保持了MFI拓扑结构,比表面积增大且具有介孔结构,分子筛表面形成了适量的Brønsted酸中心;相应催化剂上活性金属与载体间相互作用得以改善,MoS₂片晶长度和堆垛层数适宜,形成了更多的 NiMoS活性相;催化剂活性和选择性均有所提升,尤其是酸洗脱获得的 NiMo/AT-TS-1催化剂的活性相较未经处理的NiMo/TS-1催化剂提升了1.2倍,直接脱硫(DDS)路径选择性提升了22%。     

溶胶凝胶辅助水热可控合成ZnO不同形貌分级结构光催化剂

以Zn(NO₃)₂·6H₂O、柠檬酸和NaOH为原料,采用溶胶凝胶(sol-gel)辅助水热法,仅通过改变水热时间,就可得到片花、棒花及梭状3种不同形貌ZnO分级结构。利用XRD、SEM、UV-Vis DRS、光致发光光谱(PL)和氮低温吸附-脱附等手段对合成的ZnO样品进行了表征。推测了合成条件下不同形貌ZnO分级结构形成的机理。以活性翠兰(KGL)为模拟印染废水,考察了其光催化活性。结果表明,虽然所合成的不同形貌的ZnO样品在光照120 min后均能使KGL降解78%以上,但其中水热4 h得到的片花状ZnO光催化性能最好,可降解99%的KGL,这与其片花状的形貌、较大的比表面积、较多的表面氧缺陷和极性面有关。     

多孔碳负载镍纳米颗粒的制备及催化氨硼烷水解制氢

合成了蜂窝状的分级多孔碳,并以多孔碳为载体通过浸渍-化学还原法制备碳载镍(Ni/C)作为催化氨硼烷水解制氢的催化剂。采用XRD、BET、SEM、Raman、TEM等手段对样品进行了表征并研究了Ni/C室温催化性能。结果显示,多孔碳比表面积高达737 m²·g^-1,具有部分石墨化结构;负载的非晶态镍纳米颗粒平均粒径约为10 nm,均匀分布在碳基材。碳载镍对氨硼烷水解反应具有良好的催化活性,镍负载量为30wt%时催化性能最佳,298 K温度下放氢速率达到1 304.67 mL·min^-1·g^-1,活化能为29.1 kJ·mol^-1,并且具备一定的催化稳定性,表明Ni/C可作为一种廉价高效的催化剂应用于催化氨硼烷水解制氢。     

黄渤海近岸海域酚类内分泌干扰物分布特征及其来源解析

在黄渤海近岸海域采集了34个水体样品,利用HPLC-MS/MS分析了双酚A、辛基酚、壬基酚、2,4-二氯酚、对叔丁基苯酚和对特辛基苯酚等6种酚类内分泌干扰物的含量,并探讨了其分布特征及来源.结果表明,中国北部近岸海域6种酚类内分泌干扰物的含量范围在5.25～1351.20ng/mL之间.结合因子分析和层次聚类分析结果,说明渤海、黄海近岸海域中酚类化合物主要以辛基酚、壬基酚、2,4-二氯酚为主,局部海域伴有双酚A的高残留;从整个海域范围看,黄渤海近岸海域水体中酚类化合物污染状况具有区域特征,整体呈现出南高北低的特点,且酚类物质分布具有明显的地区特性,一定程度上具有聚集性;来源解析结果表明黄渤海近岸海域中酚类内分泌干扰物主要来源为生活污水和工业废水.