Modeling microbial populations with the original and modified versions of the continuous and discrete logistic equations

The life histories of microbial populations, under favorable and adverse conditions, exhibit a variety of growth, decay, and fluctuation patterns. They have been described by numerous mathematical models that varies considerably in structure and number of constants. The continuous logistic equation alone and combined with itself or with its mirror image, the Fermi function, can produce many of the observed growth patterns. They include those that are traditionally described by the Gompertz equation and peaked curves, with the peak being symmetric or asymmetric narrow or wide. The shape of survival and dose response curves appears to be determined by the distribution of the resistance's to the lethal agent among the individual organisms. Thus, exponential decay and Fermian or Gompertz‐type curves can be considered manifestations of skewed to the right, symmetric, and skewed to the left distributions, respectively.

Because of the mathematical constraints and determinism, the original discrete logistic equation can rarely be an adequate model of real microbial populations. However, by making its proportionality constant a normal‐random variate it can simulate realistic histories of fluctuating microbial populations, including scenarios of aperiodic population explosions of varying intensities of the kind found in food‐poisoning episodes.     

A descriptive study of the survival of Holstein-Friesian heifers through to third calving on English dairy farms

A short herd lifespan severely limits opportunities for on-farm selection of breeding cows in addition to causing financial losses on dairy farms and presenting welfare issues for individual animals. This prospective study monitored survival up to third calving and reasons for culling of a cohort of 468 Holstein-Friesian heifers on 18 dairy farms across southern England. Heifers born during 2003 and 2004 were monitored from 1 mo of age through to third calving. A longevity index was calculated as the proportion of days alive spent in milk production, a good measure of lifetime performance. On average, 11% of heifers recruited at 1 mo did not survive until first calving (0% longevity index). Of those that did calve, 19% were culled in lactation 1 (total average lifetime days in milk of 322 with a longevity index of 24%) and 24% were culled during lactation 2 (total average lifetime days in milk of 623 with a longevity index of 40%). The primary cause for culling was infertility. Only 55% of replacement heifers calved successfully for a third time, ranging from 80 to 32% across individual farms. These results show that on a selection of UK farms, a large number of heifers never become productive or are culled before they reach their full lactation potential. Increasing the productive lifetime of dairy cows would improve the efficiency of dairy production by lowering replacement costs and capturing a greater proportion of potential lactation milk yield from mature cows.     

酸奶用乳酸菌的人工消化耐受性研究

对酸奶尤其益生菌酸奶加工常用的嗜热链球菌、保加利亚乳杆菌、嗜酸乳杆菌和双歧杆菌4支菌种,在人工模拟胃肠消化条件下进行了耐受性研究.结果表明,嗜热链球菌和保加利亚乳杆菌在经过胃肠消化后活菌数几乎消失殆尽,嗜酸乳杆菌和双歧杆菌耐受消化的能力较强.本研究初步证实了嗜酸乳杆菌和双歧杆菌的益生菌功效.     

2011年我国磷肥企业生存和发展的手段

目前,多变的市场环境使一部分磷肥企业的生存、发展遇到了困难,应采取相应的措施,将紧随政策、科技领先、品牌竞争、追求效益、科学管理、差异化、竞合共赢、质量诚信、资本运作、专业化模式作为生存和发展的手段。论述了以上十大生存与发展手段,以促进2011年磷肥企业摆脱困境、健康发展。     

救生舱用超氧化钾药板供氧性能试验研究

在救生舱等待救援的过程中,由于避难人员代谢导致的舱内温湿度及各项气体浓度复杂波动的情况会对超氧化钾氧板的产氧性能产生显著影响。为了使氧板能够满足救生舱内供氧及二氧化碳控制的关键救援指标,建立了密闭舱室实验平台,并设计了辅助氧气再生装置等,通过模拟救援状态下真人呼吸情况,对超氧化钾氧板的性能进行了实验研究。通过实验精确测定了超氧化钾氧板在不同环境湿度、表面湿度、二氧化碳浓度条件下的产氧量、供氧速率、二氧化碳吸收速率等关键指标,并通过密闭舱内的真人生存试验对其进行了验证,得到了氧板在真人使用情况下的用法及用量。结果表明,每隔10 min向超氧化钾氧板喷30 mL水,2块氧板即可维持4人2 h的呼吸量;4块氧板配比2 kg二氧化碳吸收剂即可维持4人4 h的生存需求。     

Impact of inulin and okara on Lactobacillus acidophilus La-5 and Bifidobacterium animalis Bb-12 viability in a fermented soy product and probiotic survival under in vitro simulated gastrointestinal conditions

The effect of inulin and/or okara flour on Lactobacillus acidophilus La-5 and Bifidobacterium animalis Bb-12 viability in a fermented soy product (FSP) and on probiotic survival under in vitro simulated gastrointestinal conditions were investigated throughout 28 days of storage at 4 °C. Employing a 2² design, four FSP trials were produced from soymilk fermented with ABT-4 culture (La-5, Bb-12, and Streptococcus thermophilus): FSP (control); FSP-I (with inulin, 3 g/100 mL of soymilk); FSP-O (with okara, 5 g/100 mL); FSP-IO (with inulin + okara, ratio 3:5 g/100 mL). Probiotic viabilities ranged from 8 to 9 log cfu/g during the 28 days of storage, and inulin and/or okara flour did not affect the viability of La-5 and Bb-12. Bb-12 resistance to the artificial gastrointestinal juices was higher than for La-5, since the Bb-12 and La-5 populations decreased approximately 0.6 log cfu/g and 3.8 log cfu/g, respectively, throughout storage period. Even though the protective effect of inulin and/or okara flour on probiotic microorganisms was not significant, when compared to a fresh culture, the FSP matrix improved Bb-12 survival on day 1 of storage and may be considered a good vehicle for Bb-12 and could play an important role in probiotic protection against gastrointestinal juices.     

The red flour beetle Tribolium castaneum allows for the convenient determination of fitness and survival as a measure of toxic effects of the food contaminant acrylamide

Acrylamide is a toxic ingredient generated as a by-product of the Maillard reaction in starchy foods processed at temperatures above 120°C. Here we used the red flour beetle Tribolium castaneum as a model organism to test the effects of acrylamide on fitness and survival. Beetles were fed on flour spiked with acrylamide between 0.5% and 10% at 32°C over 2 weeks. Fitness of the beetles was tested by measuring the running distance and survival was recorded after 2 weeks of feeding at increased temperatures at 42°C. Both parameters were dose-dependent reduced by acrylamide. Knockdown of gene homologues of ahr, the arylhydrocarbon receptor, and of nrf-2, the nuclear factor erythroid 2-related factor 2, both reduced fitness and survival. Application of 0.5% acrylamide under knockdown of each factor further reduced fitness and survival, suggesting that ahr and nrf-2 are important for an adequate response to the toxicant. RNA-interference for ahr blocked completely the increase in nrf-2 mRNA levels, suggesting that the actions of ahr on acrylamide detoxification are mediated via Nrf-2.     

Personalised Medicine for Colorectal Cancer Using Mechanism-Based Machine Learning Models

Gaining insight into the mechanisms of signal transduction networks (STNs) by using critical features from patient-specific mathematical models can improve patient stratification and help to identify potential drug targets. To achieve this, these models should focus on the critical STNs for each cancer, include prognostic genes and proteins, and correctly predict patient-specific differences in STN activity. Focussing on colorectal cancer and the WNT STN, we used mechanism-based machine learning models to identify genes and proteins with significant associations to event-free patient survival and predictive power for explaining patient-specific differences of STN activity. First, we identified the WNT pathway as the most significant pathway associated with event-free survival. Second, we built linear-regression models that incorporated both genes and proteins from established mechanistic models in the literature and novel genes with significant associations to event-free patient survival. Data from The Cancer Genome Atlas and Clinical Proteomic Tumour Analysis Consortium were used, and patient-specific STN activity scores were computed using PROGENy. Three linear regression models were built, based on; (1) the gene-set of a state-of-the-art mechanistic model in the literature, (2) novel genes identified, and (3) novel proteins identified. The novel genes and proteins were genes and proteins of the extant WNT pathway whose expression was significantly associated with event-free survival. The results show that the predictive power of a model that incorporated novel event-free associated genes is better compared to a model focussing on the genes of a current state-of-the-art mechanistic model. Several significant genes that should be integrated into future mechanistic models of the WNT pathway are DVL3, FZD5, RAC1, ROCK2, GSK3B, CTB2, CBT1, and PRKCA. Thus, the study demonstrates that using mechanistic information in combination with machine learning can identify novel features (genes and proteins) that are important for explaining the STN heterogeneity between patients and their association to clinical outcomes.