Artificial neural network and response surface methodology: a comparative analysis for optimizing rice straw pretreatment and saccharification

Abstract

The present study demonstrates a comparative analysis between the artificial neural network (ANN) and response surface methodology (RSM) as optimization tools for pretreatment and enzymatic hydrolysis of lignocellulosic rice straw. The efficacy for both the processes, that is, pretreatment and enzymatic hydrolysis was evaluated using correlation coefficient (R²) & mean squared error (MSE). The values of R² obtained by ANN after training, validation, and testing were 1, 0.9005, and 0.997 for pretreatment and 0.962, 0.923, and 0.9941 for enzymatic saccharification, respectively. On the other hand, the R² values obtained with RSM were 0.9965 for cellulose recovery and 0.9994 for saccharification efficiency. Thus, ANN and RSM together successfully identify the substantial process conditions for rice straw pretreatment and enzymatic saccharification. The percentage of error for ANN and RSM were 0.009 and 0.01 for cellulose recovery and for 0.004 and 0.005 for saccharification efficiency, respectively, which showed the authority of ANN in exemplifying the non-linear behavior of the system.     

Rapid reversal of a potentially constraining genetic covariance between leaf and flower traits in Silene latifolia

Genetic covariance between two traits generates correlated responses to selection, and may either enhance or constrain adaptation. Silene latifolia exhibits potentially constraining genetic covariance between specific leaf area (SLA) and flower number in males. Flower number is likely to increase via fecundity selection but the correlated increase in SLA increases mortality, and SLA is under selection to decrease in dry habitats. We selected on trait combinations in two selection lines for four generations to test whether genetic covariance could be reduced without significantly altering trait means. In one selection line, the genetic covariance changed sign and eigenstructure changed significantly, while in the other selection line eigenstructure remained similar to the control line. Changes in genetic variance–covariance structure are therefore possible without the introduction of new alleles, and the responses we observed suggest that founder effects and changes in frequency of alleles of major effect may be acting to produce the changes.     

草莓种苗壮苗指数模型的构建与质量评价

随着我国草莓栽培面积逐年增加,草莓种苗需求量越来越大,为了确保种苗育苗质量,亟需开展壮苗评价的研究。本研究以生长40 d的‘红颜’穴盘苗为对象,在测定地上部和地下部生长、鲜重、干重等16项指标的基础上,分别构建单项指标隶属函数,使用加权模糊评判法计算种苗综合评价指数;利用主成分分析筛选的关键指标组成多个壮苗指数模型,与种苗综合评价指数进行相关性分析后,确定最佳草莓壮苗指数模型并进行验证。结果表明: 随机选取的320株草莓种苗16项指标存在显著差异,综合评价指数为0.165～0.817,可作为壮苗指数模型构建和种苗质量评价的依据。主成分分析将16项指标划分为地上部相关指标、地下部相关指标和色素指标3个主成分,累计贡献率达到79.7%;从每个主成分中选择贡献值最大的3个指标随机组成27种壮苗指数模型,通过相关性分析筛选出与综合评价指数相关性最大的5个壮苗指数模型,其中“地上干重×根系表面积×叶绿素a”的相关性最高,用‘红颜’、‘香野’和‘甜查理’种苗验证相关系数均最大,分别为0.879、0.924和0.975,确定可作为草莓壮苗指数计算模型。以综合评价指数为种苗质量分级依据,可将种苗健壮程度分为3个等级:等级Ⅰ(综合评价指数≥0.5,壮苗指数≥4.0)为优质苗,等级Ⅱ(综合评价指数0.3～0.5,壮苗指数0.5～4.0)为合格苗,等级Ⅲ(综合评价指数≤0.3,壮苗指数≤0.5)为弱苗。研究结果可为草莓或其他种苗壮苗指数计算和种苗健壮程度评价提供理论依据与科学方法。     

基于灰色综合关联分析法的粤港澳大湾区生物医药产业效益研究*

生物医药产业是中国医药制造业的第三大产业,也是粤港澳大湾区重点扶持的新兴高技术产业之一。透视产业主营业务收入与各类指标的关联情况是进一步指导粤港澳大湾区生物医药产业建设规划的必要之举。而灰色综合关联分析法是探索指标间关联程度的重要工具。通过多种权威途径获得粤港澳大湾区生物医药产业规模以上企业数量、企业孵化器数量、新增专利数、自然科学基金立项投入总额、非自然科学基金立项数量、产学研合作项数量六大指标数据。采用灰色关联分析法,将所得数据进行建模并求解,得出这六大指标与粤港澳大湾区生物医药产业主营业务收入的关联度。结果显示,规模以上企业数量与粤港澳大湾区生物医药产业主营业务收入的关联度最高,其次为自然科学基金立项投入总额,再次为产学研合作项数量,非自然科学基金立项数量位列第四,企业孵化器数量、新增专利数分别位列第五、第六。因此,谋求粤港澳大湾区生物医药产业的进一步发展应着重从提高规模以上企业数量、重视自然科学基金投入、加强政产学研合作3个层面入手。     

Genotype × region and genotype × production level interactions in Holstein cows

The identification of the presence of genotype by environment interaction effects on important traits in Holstein cattle allows for the use of international genetic evaluations and a more efficient design of regional genetic evaluation programmes. The aim of this study was to determine the genotype × environment interaction effects in Chilean Holstein dairy cattle through the analysis of records corresponding to calvings between 1998 and 2015. Herds were classified in the central and southern regions of Chile based on herd location as well as by high and low levels of production environments based on the fat plus protein yield averages per herd within each region. The central region has a Mediterranean climate and a confined production system while the southern region has a humid temperate climate and a production system based on grazing with supplementation. Traits studied were milk yield (MY), fat yield (FY), protein yield (PY), fat content (FC) and protein content (PC) by lactation, age at first calving (AFC) and calving interval (CI). Several four-trait mixed animal models were applied to environmental category data as different traits, which included herd-year-calving season (herd-year-birth season for AFC) and lactation number as fixed effects, and animal additive genetic, sire-herd, permanent environment and residual effects as random effects. Genetic correlations (r_g) for MY, FY, FC, PC and CI were found to decrease as differences between environmental categories increased. The r_g between the most extreme environmental categories considered in this study for AFC (0.26) was the only one found statistically lower than 0.60. Genetic correlation values statistically lower than 0.80 (P < 0.05) were observed for AFC, CI, MY, FY and PY between some environmental categories. If separate genetic evaluations are adopted as practical criteria when the value of r_g is lower than 0.60, the consequence of improving a multi-trait economic breeding objective in this population is likely to be small unless extreme environmental categories are considered. However, a moderate decrease in selection response and re-ranking of selection candidates is expected for AFC, CI and yield traits when selection is performed in different environmental conditions. Genotype × environment interaction effects involving production systems in a Mediterranean climate and confinement vs. Temperate Oceanic climate and grazing with supplementation, and between two fat plus protein yield level categories within each environment, were at most moderate for the studied traits.     

Individual‐level leaf trait variation and correlation across biological and spatial scales

Even with increasing interest in the ecological importance of intraspecific trait variation (ITV) for better understanding ecological processes, few studies have quantified ITV in seedlings and assessed constraints imposed by trade‐offs and correlations among individual‐level leaf traits. Estimating the amount and role of ITV in seedlings is important to understand tree recruitment and long‐term forest dynamics. We measured ten different size, economics, and whole leaf traits (lamina and petiole) for more than 2,800 seedlings (height ≥ 10 cm and diameter at breast height < 1 cm) in 283 seedling plots and then quantified the amount of ITV and trait correlations across two biological (intraspecific and interspecific) and spatial (within and among plots) scales. Finally, we explored the effects of trait variance and sample size on the strength of trait correlations. We found about 40% (6%–63%) variation in leaf‐level traits was explained by ITV across all traits. Lamina and petiole traits were correlated across biological and spatial scales, whereas leaf size traits (e.g., lamina area) were weakly correlated with economics traits (e.g., specific lamina area); lamina mass ratio was strongly related to the petiole length. Trait correlations varied among species, plots, and different scales but there was no evidence that the strength of trait relationships was stronger at broader than finer biological and spatial scales. While larger trait variance increased the strength of correlations, the sample size was the most important factor that was negatively related to the strength of trait correlations. Our results showed that a large amount of trait variation was explained by ITV, which highlighted the importance of considering ITV when using trait‐based approaches in seedling ecology. In addition, sample size was an important factor that influenced the strength of trait correlations, which suggests that comparing trait correlations across studies should consider the differences in sample size.     

Water availability and population origin affect the expression of the tradeoff between reproduction and growth in Plantago coronopus

Investment in reproduction and growth represent a classic tradeoff with implication for life history evolution. The local environment can play a major role in the magnitude and evolutionary consequences of such a tradeoff. Here, we examined the investment in reproductive and vegetative tissue in 40 maternal half‐sib families from four different populations of the herb Plantago coronopus growing in either a dry or wet greenhouse environment. Plants originated from populations with an annual or a perennial life form, with annuals prevailing in drier habitats with greater seasonal variation in both temperature and precipitation. We found that water availability affected the expression of the tradeoff (both phenotypic and genetic) between reproduction and growth, being most accentuated under dry condition. However, populations responded very differently to water treatments. Plants from annual populations showed a similar response to drought condition with little variation among maternal families, suggesting a history of selection favouring genotypes with high allocation to reproduction when water availability is low. Plants from annual populations also expressed the highest level of plasticity. For the perennial populations, one showed a large variation among maternal families in resource allocation and expressed significant negative genetic correlations between reproductive and vegetative biomass under drought. The other perennial population showed less variation in response to treatment and had trait values similar to those of the annuals, although it was significantly less plastic. We stress the importance of considering intraspecific variation in response to environmental change such as drought, as conspecific plants exhibited very different abilities and strategies to respond to high versus low water availability even among geographically close populations.     

Habitat variation and wing coloration affect wing shape evolution in dragonflies

Habitats are spatially and temporally variable, and organisms must be able to track these changes. One potential mechanism for this is dispersal by flight. Therefore, we would expect flying animals to show adaptations in wing shape related to habitat variation. In this work, we explored variation in wing shape in relation to preferred water body (flowing water or standing water with tolerance for temporary conditions) and landscape (forested to open) using 32 species of dragonflies of the genus Trithemis (80% of the known species). We included a potential source of variation linked to sexual selection: the extent of wing coloration on hindwings. We used geometric morphometric methods for studying wing shape. We also explored the phenotypic correlation of wing shape between the sexes. We found that wing shape showed a phylogenetic structure and therefore also ran phylogenetic independent contrasts. After correcting for the phylogenetic effects, we found (i) no significant effect of water body on wing shape; (ii) male forewings and female hindwings differed with regard to landscape, being progressively broader from forested to open habitats; (iii) hindwings showed a wider base in wings with more coloration, especially in males; and (iv) evidence for phenotypic correlation of wing shape between the sexes across species. Hence, our results suggest that natural and sexual selection are acting partially independently on fore‐ and hindwings and with differences between the sexes, despite evidence for phenotypic correlation of wing shape between males and females.