A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training

Extreme learning machine (ELM) is a novel and fast learning method to train single layer feed-forward networks. However due to the demand for larger number of hidden neurons, the prediction speed of ELM is not fast enough. An evolutionary based ELM with differential evolution (DE) has been proposed to reduce the prediction time of original ELM. But it may still get stuck at local optima. In this paper, a novel algorithm hybridizing DE and metaheuristic coral reef optimization (CRO), which is called differential evolution coral reef optimization (DECRO), is proposed to balance the explorative power and exploitive power to reach better performance. The thought and the implement of DECRO algorithm are discussed in this article with detail. DE, CRO and DECRO are applied to ELM training respectively. Experimental results show that DECRO-ELM can reduce the prediction time of original ELM, and obtain better performance for training ELM than both DE and CRO.     

曼地亚红豆杉RAPD反应体系与程序的优化

以曼地亚红豆杉为研究对象,采用L16(45)正交组合实验和单因素梯度实验对MgCl2、dNTP、随机引物、Taq酶、模板DNA浓度和退火温度、循环次数等影响RAPD扩增的重要因素进行优化,以期建立最优的RAPD反应体系与程序。实验结果表明,各因素最适条件为:25μLPCR反应体系中10×Buffer2.5μL,MgCl21.5mmol/L,dNTP0.2mmol/L,随机引物0.6μmol/L,Taq酶1.0U,模板DNA80ng;退火温度为37℃,循环次数为45次。     

5-酮基-D-葡萄糖酸发酵生产的工艺条件优化

葡萄糖酸氧化杆菌可将葡萄糖转化为5-酮基-D-葡萄糖酸(5-KGA),而5-KGA是重要食品添加剂L(+)-酒石酸的合成前体。为提高5-KGA产量及其对葡萄糖的转化率,对5-KGA发酵生产的工艺条件进行优化。在摇瓶水平最适的培养基和培养条件下,5-KGA最高产量为19.7 g/L,较优化前提高43.8%。在5 L发酵罐上控制恒定pH值5.5、溶氧浓度15%条件下,5-KGA产量达到46.0 g/L,较摇瓶最高产量提高1.3倍,应用葡萄糖流加工艺,5-KGA最高产量达到75.5 g/L,转化率超过70%,与已见报道的最高水平相比提高了32.0%,为实现微生物发酵生产5-KGA、进而合成L(+)-酒石酸的工业化提供了切实有效的途径。     

环境因子对小球藻生长的影响及高产油培养条件的优化

探讨了不同环境条件对小球藻(Chlorella sp.)叶绿素荧光动力学参数以及净光合放氧速率的影响,确定了以L₁海水培养基为基础,以8.8 mmol/L浓度的(NH₂)₂CO为氮源、0.145 mmol/L NaH₂PO₄ · H₂O浓度为磷源,在150 μmol · m^-2 · s^-1光照强度、培养温度为18 ℃的小球藻最优培养条件。在此条件下,明显加快了小球藻细胞的生长速度,促进了油脂和脂肪酸的积累,细胞密度增加24%,油脂和脂肪酸含量分别增加了16.8%和66.6%。在培养液中添加外源柠檬酸(最适浓度以0.06 mmol · L^-1 · d^-1为宜)可以明显提高小球藻的生长速度,促进其脂肪酸的积累。同时也可看出,筛选的小球藻藻种具有生长快、易培养、产油高的优点,可作为生物能源研究的良好材料,为海洋微藻的开发利用奠定了基础。     

Crystallization of silver through reduction process using Elaeis guineensis biosolid extract

This study presents a special, economically valuable, unprecedented eco-friendly green process for the synthesis of silver nanoparticles. The silver nanoparticles were obtained from a waste material with oil palm biosolid extract as the reducing agent. The use of the oil palm biosolid extract for the nanoparticle synthesis offers the benefit of amenability for large-scale production. An aqueous solution of silver (Ag(+) ) ions was treated with the oil palm biosolid extract for the formation of Ag nanoparticles. The nanometallic dispersion was characterized by surface plasmon absorbance measuring 428 nm. Transmission electron microscopy showed the formation of silver nanoparticles in the range of 5-50 nm. Scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) and X-ray diffraction analysis of the freeze-dried powder confirmed the formation of metallic silver nanoparticles. Moreover, Fourier Transform Infrared Spectroscopy provided evidence of phenolics or proteins as the biomolecules that were likely responsible for the reduction and capping agent, which helps to increase the stability of the synthesized silver nanoparticles. In addition, we have optimized the production with various parameters.     

甜叶菊RAPD反应体系的优化

以甜叶菊为试材,对影响其RAPD反应体系的7个因子进行优化.结果表明,20 μL的优化体系包括:双蒸水13.6 μL,10×Buffer(含15 mmol/L MgCI2)溶液3μL,2.5 mmol/L的dNTPS 1.2 μL,10 μmol/L的引物1μL,20 ng的模板DNA 1μL,1UTaq聚合酶;热循环...     

海洋真菌Xylaria sp.(No.2508)生产xyloketal B的发酵优化

对海洋真菌Xylariasp．（No．2508）生产xyloketa]B的摇瓶发酵过程进行了初步优化。结果表明,Xylariasp．（No．2508）在培养120h时xyloketalB产量最高,当培养168h后菌体进入衰亡期且xyloketalB迅速降解,因此Xylariasp．（No．2508）生产xyloketalB的最适发酵周期为120h;Xylariasp．（No．2508）合成xyloketalB的过程及用乙酸乙酯萃取xyloketalB的过程对pH非常敏感,其中最适xyloketalB生产的发酵液初始pH为5．7,利用乙酸乙酯萃取xyloketalB时的发酵液最适pH为3。0;最适接种量为10％（v／v）;接种前发酵液中加入10g／L的大孔树脂XAD-16可以降低某些代谢产物对xyloketalB合成的抑制作用,从而使产量提高2倍左右,而菌体进入稳定期以后加入大孔树脂XAD-16对产物产量没有影响。     

A colorimetric assay optimization for high-throughput screening of dihydroorotase by detecting ureido groups

Dihydroorotase (DHOase) is the third enzyme in the de novo pyrimidine biosynthesis pathway and is a potential new antibacterial drug target. No target-based high-throughput screening (HTS) assay for this enzyme has been reported to date. Here, we optimized two colorimetric-based enzymatic assays that detect the ureido moiety of the DHOase substrate, carbamyl-aspartate (Ca-asp). Each assay was developed in a 40-μl assay volume using 384-well plates with a different color mix, diacetylmonoxime (DAMO)–thiosemicarbazide (TSC) or DAMO–antipyrine. The sensitivity and color interference of both color mixes were compared in the presence of common HTS buffer additives, including dimethyl sulfoxide, reducing agents, detergents, and bovine serum albumin. DAMO–TSC (Z′-factors 0.7–0.8) was determined to be superior to DAMO–antipyrine (Z′-factors 0.5–0.6) with significantly less variability within replicates. An HTS pilot screening with 29,552 compounds from four structurally diverse libraries confirmed the quality of our newly optimized colorimetric assay with DAMO–TSC. This robust method has no heating requirement, which was the main obstacle to applying previous assays to HTS. More important, this well-optimized HTS assay for DHOase, the first of its kind, should make it possible to screen large-scale compound libraries to develop new inhibitors against any enzymes that produce ureido functional groups.     

Reconstructing parameters of the FitzHugh–Nagumo system from boundary potential measurements

We consider distributed parameter identification problems for the FitzHugh–Nagumo model of electrocardiology. The model describes the evolution of electrical potentials in heart tissues. The mathematical problem is to reconstruct physical parameters of the system through partial knowledge of its solutions on the boundary of the domain. We present a parallel algorithm of Newton–Krylov type that combines Newton’s method for numerical optimization with Krylov subspace solvers for the resulting Karush–Kuhn–Tucker system. We show by numerical simulations that parameter reconstruction can be performed from measurements taken on the boundary of the domain only. We discuss the effects of various model parameters on the quality of reconstructions. Action Editor: David Terman     

Multiobjective optimization of the antioxidant activities of horse mackerel hydrolysates produced with protease mixtures

Fish protein hydrolysates (FPH) from horse mackerel were produced by employing an enzyme mixture of subtilisin and trypsin. The antioxidant activity of fish hydrolysates (DPPH scavenging activity, Fe²⁺ chelating activity and Fe³⁺ reducing power) was modelled as a function of the operating conditions for the hydrolysis (i.e. protein concentration, temperature and composition of the enzyme mixture). The antioxidant activities showed different behavior depending on whether their controlling pathway was the transference of electrons/protons (i.e. DPPH scavenging activity and Fe³⁺ reducing power) or metal chelation. In the first case, the antioxidant activities increased with the decrease of substrate concentration and temperature when pure trypsin (DPPH scavenging activity) or a mixture of enzymes (Fe³⁺ reducing capacity) was employed. Contrarily, hydrolysates showed higher Fe²⁺ chelating activities at moderate concentration and high temperature (i.e. 5 g/L and 55 °C) employing solely subtilisin. The conflictive behavior among the antioxidant properties suggested using a multiobjective optimization technique. The ε-constraint method was chosen for this purpose. This approach allows determining the most adequate operational conditions for producing hydrolysate with a specific antioxidant profile which is the first approximation to the production of taylor-made antioxidant hydrolysates.