Local Application of Ibandronate/Gelatin Sponge Improves Osteotomy Healing in Rabbits

Delayed healing or non-union of skeletal fractures are common clinical complications. Ibandronate is a highly potent anti-catabolic reagent used for treatment of osteopenia and fracture prevention. We hypothesized that local application of ibandronate after fracture fixation may improve and sustain callus formation and therefore prevent delayed healing or non-union. This study tested the effect of local application of an ibandronate/gelatin sponge composite on osteotomy healing. A right-side distal-femoral osteotomy was created surgically, with fixation using a k-wire, in forty adult male rabbits. The animals were divided into four groups of ten animals and treated by: (i) intravenous injection of normal saline (Control); (ii) local implantation of absorbable gelatin sponge (GS); (iii) local implantation of absorbable GS containing ibandronate (IB+GS), and (iv) intravenous injection of ibandronate (IB i.v.). At two and four weeks the affected femora were harvested for X-ray photography, computed tomography (CT), biomechanical testing and histopathology. At both time-points the results showed that the calluses in both the ibandronate-treated groups, but especially in the IB+GS group, were significantly larger than in the control and GS groups. At four weeks the cross sectional area (CSA) and mechanical test results of ultimate load and energy in the IB+GS group were significantly higher than in other groups. Histological procedures showed a significant reduction in osteoclast numbers in the IB+GS and IB i.v. groups at day 14. The results indicate that local application of an ibandronate/gelatin sponge biomaterial improved early osteotomy healing after surgical fixation and suggest that such treatment may be a valuable local therapy to enhance fracture repair and potentially prevent delayed or non-union.     

Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States

Increasing drought and extreme rainfall are major threats to maize production in the United States. However, compared to drought impact, the impact of excessive rainfall on crop yield remains unresolved. Here, we present observational evidence from crop yield and insurance data that excessive rainfall can reduce maize yield up to ?34% (?17 ± 3% on average) in the United States relative to the expected yield from the long‐term trend, comparable to the up to ?37% loss by extreme drought (?32 ± 2% on average) from 1981 to 2016. Drought consistently decreases maize yield due to water deficiency and concurrent heat, with greater yield loss for rainfed maize in wetter areas. Excessive rainfall can have either negative or positive impact on crop yield, and its sign varies regionally. Excessive rainfall decreases maize yield significantly in cooler areas in conjunction with poorly drained soils, and such yield loss gets exacerbated under the condition of high preseason soil water storage. Current process‐based crop models cannot capture the yield loss from excessive rainfall and overestimate yield under wet conditions. Our results highlight the need for improved understanding and modeling of the excessive rainfall impact on crop yield.     

Bioelectricity generation using two chamber microbial fuel cell treating wastewater from food processing

Electricity generation from microbial fuel cells which treat food processing wastewater was investigated in this study. Anaerobic anode and aerobic cathode chambers were separated by a proton exchange membrane in a two-compartment MFC reactor. Buffer solutions and food industry wastewater were used as electrolytes in the anode and cathode chambers, respectively. The produced voltage and current intensity were measured using a digital multimeter. Effluents from the anode compartment were tested for COD, BOD₅, NH₃, P, TSS, VSS, SO₄ and alkalinity. The maximum current density and power production were measured 527 mA/m² and 230 mW/m² in the anode area, respectively, at operation organic loading (OLR) of 0.364 g COD/l.d. At OLR of 0.182 g COD/l.d, maximum voltage and columbic efficiency production were recorded 0.475 V and 21%, respectively. Maximum removal efficiency of COD, BOD₅, NH₃, P, TSS, VSS, SO₄ and alkalinity were 86, 79, 73, 18, 68, 62, 30 and 58%, respectively. The results indicated that catalysts and mediator-less microbial fuel cells (CAML-MFC) can be considered as a better choice for simple and complete energy conversion from the wastewater of such industries and also this could be considered as a new method to offset wastewater treatment plant operating costs.     

Modeling and analysis of soybean (Glycine max. L) Cu/Zn,Mn and Fe superoxide dismutases

Superoxide dismutase (SOD, EC 1.15.1.1) is an important metal-containing antioxidant enzyme that provides the first line of defense against toxic superoxide radicals by catalyzing their dismutation to oxygen and hydrogen peroxide. SOD is classified into four metalloprotein isoforms, namely, Cu/Zn SOD, Mn SOD, Ni SOD and Fe SOD. The structural models of soybean SOD isoforms have not yet been solved. In this study, we describe structural models for soybean Cu/Zn SOD, Mn SOD and Fe SOD and provide insights into the molecular function of this metal-binding enzyme in improving tolerance to oxidative stress in plants.     

基于稀疏表示算法的蛋白质质谱数据特征选择

高维、小样本数据的特征选择方法在蛋白质质谱数据处理分析领域有着广泛应用。本文针对蛋白质质谱特征选择问题,结合稀疏表示这一新理论框架,提出了一种基于稀疏表示的特征选择算法(sparse representation based feature selection,SRFS)。该方法将稀疏表示分类的结果作为评定某一个特征子空间特征相对重要性的度量,然后通过对大量随机采样子空间计算结果的统计,得到特征空间中每个特征的排序,并进一步分析提炼出与肿瘤疾病相关的若干谱峰。通过在卵巢癌公共数据集OC-WCX2a和浙江省肿瘤医院乳腺癌数据集BC-WCX2a上的实验结果表明,SRFS算法可以有效应用于本文所使用的SELDI-TOF蛋白质质谱数据的分析。     

A novel xylanase,XynA4-2, from thermoacidophilic <Emphasis Type="Italic">Alicyclobacillus</Emphasis> sp. A4 with potential applications in the brewing industry

A xylanase gene, xynA4-2, was obtained from the genome sequence of thermoacidophilic Alicyclobacillus sp. A4 and expressed in Escherichia coli BL21 (DE3). xynA4-2 encodes a mature protein of 411 residues with a calculated molecular weight of 46.8 kDa. Based on the amino acid sequence similarities (highest identity of 61%), the enzyme was confined into glycoside hydrolase family 10. The purified recombinant XynA4-2 exhibited maximum activity at pH 6.2 and 55°C. The enzyme was stable over a broad pH range, retaining more than 90% of the original activity at pH 5.8–12.0, 37°C for 1 h. The substrate specificity of XynA4-2 was relatively narrow, exhibiting 100, 93, and 35% of the relative activity towards birchwood xylan, oat spelt xylan, and wheat arabinoxylan, respectively. Supplementation of XynA4-2 to mash caused the reduction of mash filtration rate (5.6%) and viscosity (4.0%). When combined with the commercial glucanase from Sunson, higher reduction was detected in the filtration rate (12.0%) and viscosity (17.2%). These favorable properties make XynA4-2 a good candidate in the brewing industry.     

Solution structure and dynamics of human metallothionein-3 (MT-3)

Alzheimer's disease is characterized by progressive loss of neurons accompanied by the formation of intraneural neurofibrillary tangles and extracellular amyloid plaques. Human neuronal growth inhibitory factor, classified as metallothionein-3 (MT-3), was found to be related to the neurotrophic activity promoting cortical neuron survival and dendrite outgrowth in the cell culture studies. We have determined the solution structure of the alpha-domain of human MT-3 (residues 32-68) by multinuclear and multidimensional NMR spectroscopy in combination with the molecular dynamic simulated annealing approach. The human MT-3 shows two metal-thiolate clusters, one in the N-terminus (beta-domain) and one in the C-terminus (alpha-domain). The overall fold of the alpha-domain is similar to that of mouse MT-3. However, human MT-3 has a longer loop in the acidic hexapeptide insertion than that of mouse MT-3. Surprisingly, the backbone dynamics of the protein revealed that the beta-domain exhibits similar internal motion to the alpha-domain, although the N-terminal residues are more flexible. Our results may provide useful information for understanding the structure-function relationship of human MT-3.     

Three new taxanes with the 10-alkoxy group from the heartwood of Taxus cuspidata

Three novel taxanes with the 10-alkoxy group were isolated from heartwood of Taxus cuspidata. The structures were identified as 2alpha,14beta-diacetoxy-10beta-ethoxytaxa-11,4(20)-dien-5alpha-ol (1), 2alpha,14beta-diacetoxy-10beta-methoxytaxa-11,4(20)-dien-5alpha-ol (2), and 2alpha-acetoxy-10beta-ethoxytaxa-11,4(20)-diene-5alpha,14beta-diol (3) on the basis of spectroscopic analysis. These are the first taxanes with an alkoxy moiety on the skeleton.     

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%±0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups). Using support vector machine classifiers, FG-Nup sequences are distinguished from structured protein sequences found in Protein Data Bank (PDB) with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined. Importantly, this model needs to be trained only once and can then be applied to extract a comprehensive set of information regarding proteins of interest. Moreover, this representation can be considered as pre-training for various applications of deep learning in bioinformatics. The related data is available at Life Language Processing Website: http://llp.berkeley.edu and Harvard Dataverse: http://dx.doi.org/10.7910/DVN/JMFHTN.