Quantitative Biology

Many emerging pathogens infect multiple host species, and multi-host pathogens may have very different dynamics in different host species. This research addresses how pathogen replication rates and Immune System (IS) response times are constrained by host body size. An Ordinary Differential Equation (ODE) model is used to show that pathogen replication rates decline with host body size but IS response rates remain invariant with body size. An Agent-Based Model (ABM) is used to investigate two models of IS architecture that could explain scale invariance of IS response rates. A stage structured hybrid model is proposed that strikes a balance between the detailed representation of an ABM and computational tractability of an ODE, by using them in the initial and latter stages of an infection, respectively.

Drought threatens food and water security around the world, and this threat is likely to become more severe under climate change. High resolution predictive information can help farmers, water managers, and others to manage the effects of drought. We have created an open source tool to produce short-term forecasts of vegetation health at high spatial resolution, using data that are global in coverage. The tool automates downloading and processing Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, and training gradient-boosted machine models on hundreds of millions of observations to predict future values of the Enhanced Vegetation Index. We compared the predictive power of different sets of variables (raw spectral MODIS data and Level-3 MODIS products) in two regions with distinct agro-ecological systems, climates, and cloud coverage: Sri Lanka and California. Our tool provides considerably greater predictive power on held-out datasets than simpler baseline models.

This work aims at showing the relevance and the applications possibilities of the Fibonacci sequence, and also its q-deformed or quantum extension, in the study of the genetic code(s). First, after the presentation of a new formula, an indexed double Fibonacci sequence, comprising the first six Fibonacci numbers, is shown to describe the 20 amino acids multiplets and their degeneracy as well as a characteristic pattern for the 61 meaningful codons. Next, the twenty amino acids, classified according to their increasing atom-number (carbon, nitrogen, oxygen and sulfur), exhibit several Fibonacci sequence patterns. Several mathematical relations are given, describing various atom-number patterns. Finally, a q-Fibonacci simple phenomenological model, with q a real deformation parameter, is used to describe, in a unified way, not only the standard genetic code, when q=1, but also all known slight variations of this latter, when q~1, as well as the case of the 21st amino acid (Selenocysteine) and the 22nd one (Pyrrolysine), also when q~1. As a by-product of this elementary model, we also show that, in the limit q=0, the number of amino acids reaches the value 6, in good agreement with old and still persistent claims stating that life, in its early development, could have used only a small number of amino acids.

This note describes a simple score to indicate the effectiveness of mitigation against infections of COVID-19 as observed by new case counts. The score includes normalization, making comparisons across jurisdictions possible. The smoothing employed provides robustness in the face of reporting vagaries while retaining salient features of evolution, enabling a clearer picture for decision makers and the public.

The results of a long-established investigation into teneral transpiration are used as a rudimentary data set. These data are not complete in that all are at 25 ∘ C and the temperature-dependence cannot, therefore be resolved. An allowance is, nonetheless, made for the outstanding temperature-dependent data. The data are generalised to all humidities, levels of activity and, in theory, temperatures, by invoking the property of multiplicative separability. In this way a formulation, which is a very simple, first order, ordinary differential equation, is devised. The model is extended to include a variety of Glossina species by resorting to their relative, resting water loss rates in dry air. The calculated, total water loss is converted to the relevant humidity, at 24 ∘ C , that which produced an equivalent water loss in the pupa, in order to exploit an adaption of an established survival relationship. The resulting computational model calculates total, teneral water loss, consequent mortality and adult recruitment. Surprisingly, the postulated race against time, to feed, applies more to the mesophilic and xerophilic species, in that increasing order. So much so that it is reasonable to conclude that, should Glossina brevipalpis survive the pupal phase, it will almost certainly survive to locate a host, without there being any significant prospect of death from dehydration. With the conclusion of this work comes the revelation that the classification of species as hygrophilic, mesophilic and xerophilic is largely true only in so much as their third and fourth instars are and, possibly, the hours shortly before eclosion.

A model of genome evolution is proposed. Based on three assumptions the evolutionary theory of a genome is formulated. The general law on the direction of genome evolution is given. Both the deterministic classical equation and the stochastic quantum equation are proposed. It is proved that the classical equation can be put in a form of the least action principle and the latter can be used for obtaining the quantum generalization of the evolutionary law. The wave equation and uncertainty relation for the quantum evolution are deduced logically. It is shown that the classical trajectory is a limiting case of the general quantum evolution depicted in the coarse-grained time. The observed smooth/sudden evolution is interpreted by the alternating occurrence of the classical and quantum phases. The speciation event is explained by the quantum transition in quantum phase. Fundamental constants of time dimension, the quantization constant and the evolutionary inertia, are introduced for characterizing the genome evolution. The size of minimum genome is deduced from the quantum uncertainty lower bound. The present work shows the quantum law may be more general than thought, since it plays key roles not only in atomic physics, but also in genome evolution.

The structure of the genetic code is discussed in formal terms. A rectangular table of the code ("the code matrix"), whose properties reveal its arithmetical content tagged with the information symbols in several notations. New parameters used to analyze of the code matrix, the serial numbers of the encoded products and coding elements, ordered by molecular mass. The structural similarity of the amino acid sequences corresponding to two aminoacyl tRNA synthetases classes is found. The code matrix shows how can be organized the so-called second genetic code. The symmetrical pattern of the matrix is supported with the other parameters; it also serves as a basis to construct a 3D model of the genetic code which follows the structure of the simplest Plato solid, tetrahedron. The reasons for this unusual structure of the genetic code remains unclear.

Rift Valley fever virus (RVFV) has been expanding its geographical distribution with important implications for both human and animal health. The emergence of Rift Valley fever (RVF) in the Middle East, and its continuing presence in many areas of Africa, has negatively impacted both medical and veterinary infrastructures and human health. Furthermore, worldwide attention should be directed towards the broader infection dynamics of RVFV. We propose a new compartmentalized model of RVF and the related ordinary differential equations to assess disease spread in both time and space; with the latter driven as a function of contact networks. The model is based on weighted contact networks, where nodes of the networks represent geographical regions and the weights represent the level of contact between regional pairings for each set of species. The inclusion of human, animal, and vector movements among regions is new to RVF modeling. The movement of the infected individuals is not only treated as a possibility, but also an actuality that can be incorporated into the model. We have tested, calibrated, and evaluated the model using data from the recent 2010 RVF outbreak in South Africa as a case study; mapping the epidemic spread within and among three South African provinces. An extensive set of simulation results shows the potential of the proposed approach for accurately modeling the RVF spreading process in additional regions of the world. The benefits of the proposed model are twofold: not only can the model differentiate the maximum number of infected individuals among different provinces, but also it can reproduce the different starting times of the outbreak in multiple locations. Finally, the exact value of the reproduction number is numerically computed and upper and lower bounds for the reproduction number are analytically derived in the case of homogeneous populations.

This paper provides a framework in order to statistically model sequences from human genome, which is allowing a formulation to synthesize gene sequences. We start by converting the alphabetic sequence of genome to decimal sequence by Huffman coding. Then, this decimal sequence is decomposed by HP filter into two components, trend and cyclic. Next, a statistical modeling, ARIMA-GARCH, is implemented on trend component exhibiting heteroskedasticity, autoregressive integrated moving average (ARIMA) to capture the linear characteristics of the sequence and later, generalized autoregressive conditional heteroskedasticity (GARCH) is then appropriated for the statistical nonlinearity of genome sequence. This modeling approach synthesizes a given genome sequence regarding to its statistical features. Finally, the PDF of a given sequence is estimated using Gaussian mixture model and based on estimated PDF, we determine a new PDF presenting sequences that counteract statistically the original sequence. Our strategy is performed on several genes as well as HIV nucleotide sequence and corresponding results is presented.

Free recall consists of two separate stages: the emptying of working memory and reactivation [1]. The Tarnow Unchunkable Test (TUT, [2]) uses double integer items to separate out only the first stage by making it difficult to reactivate items due to the lack of intra-item relationships. 193 Russian college students were tested via the internet version of the TUT. The average number of items remembered in the 3 item test was 2.54 items. In the 4 item test, the average number of items decreased to 2.38. This, and a number of other qualitative distribution differences between the 3 and 4 item tests, indicates that the average capacity limit of working memory has been reached at 3 items. This provides the first direct measurement of the unchunkable capacity limit of language based items. That the average number of items remembered decreased as the number of items increased from 3 to 4 indicates that most subjects were unable to manage their working memories as the number of items increased just beyond the average capacity. Further evidence for the difficulty in managing the capacity limit is that 25% of subjects could not remember any items correctly at least in one of three 4 item tests and that the Pearson correlation between the 3 item and 4 item subject recalls was a relatively small 38%. This failure of managing a basic memory resource should have important consequences for pedagogy including instruction, text book design and test design. Because working memory scores are important for academic achievement, it also suggests that an individual can gain academically by learning how to manage her or his capacity limit.