Quantum algorithm for programmed cell death of Caenorhabditis elegans

During the development of Caenorhabditis elegans, through cell divisions, a total of exactly 1090 cells are generated, 131 of which undergo programmed cell death (PCD) to result in an adult organism comprising 959 cells. Of those 131, exactly 113 undergo PCD during embryogenesis, subdivided across the cell lineages in the following fashion: 98 for AB lineage; 14 for MS lineage; and 1 for C lineage. Is there a law underlying these numbers, and if there is, what could it be? Here we wish to show that the count of the cells undergoing PCD complies with the cipher laws related to the algorithms of Shor and of Grover.     

Evidence for the presence of a threshold weight for entering diapause in the yellow-spotted longicorn beetle, Psacothea hilaris

Under long-day conditions larvae of Psacothea hilaris (Coleoptera: Cerambycidae) pupate after the 4th or 5th instar, while under short-day conditions they undergo 2-4 nonstationary supernumerary molts and eventually enter diapause. To explore the possibility of a threshold weight for entering diapause, P. hilaris larvae were deprived of food on days 0 (day of ecdysis), 4 or 8 of the 4th, 5th and 6th instars under short-day conditions. Within the first 40 days of starvation, 60% of the larvae starved starting on day 0 of the 4th instar died, but all the larvae starved at later stages survived. The incidence of diapause in these survivors was determined by the occurrence of pupation after a temporary chilling at 15 degrees C for 15 days. Diapause incidence increased as the onset of starvation was delayed; from 11% in the larvae starved on day 0 of the 5th instar to 100% in the larvae starved on day 4 and day 8 of the 6th instar. Analysis of the relationship between the initial weight of a respective larva at the onset of starvation and its pupation success revealed that none of the larvae weighing 690 mg did. This finding suggests the presence of a threshold weight (about 600 mg), below which larvae are incapable of entering diapause. We discuss these findings with reference to the life history of P. hilaris.     

Learning rules for optimal selection in a varying environment: mate choice revisited

The quality of a chosen partner can be one of the most significantfactors affecting an animal's long-term reproductive success.We investigate optimal mate choice rules in an environment wherethere is both local variation in the quality of potential mateswithin each local mating pool and spatial (or temporal) variationin the average quality of the pools themselves. In such a situation,a robust rule that works well across a variety of environmentswill confer a significant reproductive advantage. We formulatea full Bayesian model for updating information in such a varyingenvironment and derive the form of the rule that maximizes expectedreward in a spatially varying environment. We compare the theoreticalperformance of our optimal learning rule against both fixedthreshold rules and simpler near-optimal learning rules andshow that learning is most advantageous when both the localand environmental variances are large. We consider how optimalsimple learning rules might evolve and compare their evolutionwith that of fixed threshold rules using genetic algorithmsas minimal models of the relevant genetics. Our analysis pointsup the variety of ways in which a near-optimal rule can be expressed.Finally, we describe how our results extend to the case of temporallyvarying environments.     

基于系统安全性的死锁避免算法

死锁是多用户操作系统的一个重要事件,本文着重介绍了系统的安全状态,并对操作系统的避免死锁算法——Dijkstra银行家算法进行了研究分析。     

Numerical quantification of the significance of aquatic vegetation affecting spatial distribution of Japanese medaka (Oryzias latipes) in an agricultural canal

Aquatic vegetation plays a very important role in providing food, shelter, and nursery habitat and is also regarded as hydraulic resistance in the stream environment. To achieve better ecological restoration, this trade-off should be solved both hydraulically and ecologically. This study quantifies the effect of aquatic vegetation on the spatial distribution of Japanese medaka (Oryzias latipes) to evaluate its importance to fish habitat preference. The preference for aquatic vegetation index is calculated using a fuzzy preference intensity model (FPIM) with interactions among water depth, current velocity and cover ratio in an agricultural canal. In this model, simplified fuzzy reasoning is introduced to explicitly take the essential vagueness of fish behavior into consideration, and a simple genetic algorithm is used to search for an optimum model representation. Uncertainties in measurement errors and dispersions of the physical environment are positively taken into the model using symmetric triangular fuzzy numbers. To overcome the difficulty in model construction with insufficient data observed in an agricultural canal, this model was conjugated with a model developed in a laboratory experiment. The model obtained was then assessed using the AIC (Akaikes Information Criterion) to evaluate the significance of vegetation index with a statistical approach. The results suggest the significance of vegetation index to habitat selection by Japanese medaka and that utilization of the AIC enables us to grasp the validity of an additional factor contributing to habitat prediction with a view to a definite scale     

基于遗传算法和支持向量机的乳杆菌发酵过程建模

由于生化反应过程的复杂性和高度非线性,多数简单的数学模型不能准确描述。该文基于Matlab软件,利用改进的支持向量机(υ-SVR)对植物乳酸杆菌发酵这一典型生化过程进行研究,应用遗传算法估计模型最优参数,建立植物乳杆菌的菌体密度预测模型。同时建立传统的logistic动力学模型以进行比较。结果表明,采用结合遗传算法的υ-SVR预测模型拟合误差小,皮尔森相关系数(R)更高,可以较好地预测乳酸杆菌的发酵过程,为其优化控制及放大提供依据。     

Machine learning approaches for prediction of linear B-cell epitopes on proteins

Identification and characterization of antigenic determinants on proteins has received considerable attention utilizing both, experimental as well as computational methods. For computational routines mostly structural as well as physicochemical parameters have been utilized for predicting the antigenic propensity of protein sites. However, the performance of computational routines has been low when compared to experimental alternatives. Here we describe the construction of machine learning based classifiers to enhance the prediction quality for identifying linear B-cell epitopes on proteins. Our approach combines several parameters previously associated with antigenicity, and includes novel parameters based on frequencies of amino acids and amino acid neighborhood propensities. We utilized machine learning algorithms for deriving antigenicity classification functions assigning antigenic propensities to each amino acid of a given protein sequence. We compared the prediction quality of the novel classifiers with respect to established routines for epitope scoring, and tested prediction accuracy on experimental data available for HIV proteins. The major finding is that machine learning classifiers clearly outperform the reference classification systems on the HIV epitope validation set.     

Estimating biomarker-based HIV incidence using prevalence data in high risk groups with missing outcomes

The novel two-step serologic sensitive/less sensitive testing algorithm for detecting recent HIV seroconversion (STARHS) provides a simple and practical method to estimate HIV-1 incidence using cross-sectional HIV seroprevalence data. STARHS has been used increasingly in epidemiologic studies. However, the uncertainty of incidence estimates using this algorithm has not been well described, especially for high risk groups or when missing data is present because a fraction of sensitive enzyme immunoassay (EIA) positive specimens are not tested by the less sensitive EIA. Ad hoc methods used in practice provide incorrect confidence limits and thus may jeopardize statistical inference. In this report, we propose maximum likelihood and Bayesian methods for correctly estimating the uncertainty in incidence estimates obtained using prevalence data with a fraction missing, and extend the methods to regression settings. Using a study of injection drug users participating in a drug detoxification program in New York city as an example, we demonstrated the impact of underestimating the uncertainty in incidence estimates using ad hoc methods. Our methods can be applied to estimate the incidence of other diseases from prevalence data using similar testing algorithms when missing data is present.     

Performance of wheat crops with different chromosome ploidy: root-sourced signals, drought tolerance, and yield performance

Pot-culture experiments were carried out to estimate the role of non-hydraulic root signals (nHRS) and the relation of these signals to drought tolerance and grain yield formation under drought stress in six wheat varieties. These were two modern hexaploid wheat (Triticum aestivum L., AABBDD) Plateau602 and Longchun8139-2, two diploid wheat (Triticum monococcum L., AB) MO1 and MO4, and two tetraploid wheat (Triticum dicoccum Schuebl L., AABB) DM22 and DM31. In the two diploid relatives, the nHRS was switched on and off at a soil water content (SWC) of approximately 53–45% field water capacity (FWC). In contrast, in the modern hexaploid varieties, Longchun8139-2 and Plateau602 the nHRS occurred between a SWC of about 71 and 35% FWC, a much wider soil moisture range. The two tetraploid relatives, DM22 and DM31, were generally intermediate. The nHRS threshold range in SWC also narrowed as all six varieties went through successive developmental stages from shooting to grain filling. The two hexaploid wheat varieties had the longest duration of survival after the water supply ceased, and the best yield stability under drought stress, similar to with tetraploid wheat varieties; the diploid wheat varieties were least robust. These two parameters were both significantly correlated with the nHRS soil moisture threshold range (r=0.9456** and 0.8608*, respectively). Based on these patterns, we propose a ‘triple Z’ model to describe the features of non-hydraulic stomatal sensitivity versus soil drought in wheat growth.     

Analysis of the evolution of the thrombin-inhibiting DNA aptamers using a genetic algorithm

We previously identified a thrombin-inhibiting DNA aptamer that was presumed to form a G-quartet structure with a duplex. To investigate the importance of the sequences in the duplex region and to obtain aptamers with higher inhibitory activities, we randomized the sequences of the duplex region of this aptamer and carried out selection based on inhibitory activity using a genetic algorithm. This method consisted of selection via an inhibition assay, crossover, and mutation in silico. After two cycles, we obtained ligands with greater inhibitory activities than that of the original aptamer. In addition, the duplex sequences were found to contribute to the inhibitory activities of aptamers.