LOCALITY UNCERTAINTY AND THE DIFFERENTIAL PERFORMANCE OF FOUR COMMON NICHE-BASED MODELING TECHNIQUES

We address a poorly understood aspect of ecological niche modeling: its sensitivity to different levels of geographic uncertainty in organism occurrence data. Our primary interest was to assess how accuracy degrades under increasing uncertainty, with performance measured indirectly through model consistency. We used Monte Carlo simulations and a similarity measure to assess model sensitivity across three variables: locality accuracy, niche modeling method, and species. Randomly generated data sets with known levels of locality uncertainty were compared to an original prediction using Fuzzy Kappa. Data sets where locality uncertainty is low were expected to produce similar distribution maps to the original. In contrast, data sets where locality uncertainty is high were expected to produce less similar maps. BIOCLIM, DOMAIN, Maxent and GARP were used to predict the distributions for 1200 simulated datasets (3 species x 4 buffer sizes x 100 randomized data sets). Thus, our experimental design produced a total of 4800 similarity measures, with each of the simulated distributions compared to the prediction of the original data set and corresponding modeling method. A general linear model (GLM) analysis was performed which enables us to simultaneously measure the effect of buffer size, modeling method, and species, as well as interactions among all variables. Our results show that modeling method has the largest effect on similarity scores and uniquely accounts for 40% of the total variance in the model. The second most important factor was buffer size, but it uniquely accounts for only 3% of the variation in the model. The newer and currently more popular methods, GARP and Maxent, were shown to produce more inconsistent predictions than the earlier and simpler methods, BIOCLIM and DOMAIN. Understanding the performance of different niche modeling methods under varying levels of geographic uncertainty is an important step toward more productive applications of historical biodiversity collections

The War on Terror

Presents comments (from the EPIIC Symposium at Tufts University, February 2004) concerning the war on terror; concern on the problem about terrorism; elaboration on the claim that the world is not in a global war on terror; and problems of the use and abuse of the word terrorism

Comparison of two models for bridge-assisted charge transfer

Based on the reduced density matrix method, we compare two different approaches to calculate the dynamics of the electron transfer in systems with donor, bridge, and acceptor. In the first approach a vibrational substructure is taken into account for each electronic state and the corresponding states are displaced along a common reaction coordinate. In the second approach it is assumed that vibrational relaxation is much faster than the electron transfer and therefore the states are modeled by electronic levels only. In both approaches the system is coupled to a bath of harmonic oscillators but the way of relaxation is quite different. The theory is applied to the electron transfer in ${\rm H_2P}-{\rm ZnP}-{\rm Q}$ with free-base porphyrin (${\rm H_2P}$) being the donor, zinc porphyrin (${\rm ZnP}$) being the bridge and quinone (${\rm Q}$) the acceptor. The parameters are chosen as similar as possible for both approaches and the quality of the agreement is discussed.Comment: 12 pages including 4 figures, 1 table, 26 references. For more info see http://eee.tu-chemnitz.de/~kili

Structure and thermodynamics of multi-component/multi-Yukawa mixtures

New small angle scattering experiments reveal new peaks in colloidal systems (S.H. Chen et al) in the structure function S(k), in a region that was inaccessible with older instruments. We propose here general closure of the Ornstein Zernike equation, that is the sum of an arbitrary number of yukawas, and that that will go well beyond the MSA . For this closure we get for the Laplace transform of the pair correlation function . This function is easily transformed into S(k) by replacing the Laplace variable by the Fourier wariable. Although the method is general and valid for polydisperse systems, an explicit continued fraction solution is found for the monodisperse case.Comment: 16 page

2-(1,3-Thia­zol-4-yl)benzimidazolium nitrate monohydrate

In the title compound, C10H8N3S+·NO3 −·H2O, one of the N atoms of the benzimidazole unit is protonated, unlike than that in the thia­zole group. This protonation leads to equalization of the bond angles at the two N atoms of the benzimidazole group. The benzimidazole and thia­zole systems are almost coplanar, forming a dihedral angle of 0.5 (2)°. In the crystal, the nitrate anion and water mol­ecule bridge the thia­bendazolium cations through N—H⋯O and O—H⋯O hydrogen bonds, leading to a supra­molecular network based on an infinite one-dimensional chain using [001] as base vector

1-(2-Chloro-5-nitro­phen­yl)-3-(2,2-di­methyl­propion­yl)thio­urea

With the exception of the C atoms of two of the methyl groups of the tert-butyl substituent, all of the non-H atoms of the title compound, C12H14ClN3O3S, lie on a mirror plane. The 2-chloro-5-nitro­phenyl and 2,2-dimethyl­propionyl substituents are, respectively, cis and trans relative to the thio­carbonyl S atom across the two C—N bonds. Intra­molecular N—H⋯O and C—H⋯S hydrogen bonds form S(6) ring motifs, also in the mirror plane. Despite the presence of two N—H subsituents, no inter­molecular hydrogen bonds are observed in the crystal structure. Weak π–π contacts [centroid–centroid distances of 4.2903 (17) Å] involving adjacent aromatic rings link the mol­ecules in a head-to-tail fashion above and below the mol­ecular plane

Single Gene Deletions of Orexin, Leptin, Neuropeptide Y, and Ghrelin Do Not Appreciably Alter Food Anticipatory Activity in Mice

Timing activity to match resource availability is a widely conserved ability in nature. Scheduled feeding of a limited amount of food induces increased activity prior to feeding time in animals as diverse as fish and rodents. Typically, food anticipatory activity (FAA) involves temporally restricting unlimited food access (RF) to several hours in the middle of the light cycle, which is a time of day when rodents are not normally active. We compared this model to calorie restriction (CR), giving the mice 60% of their normal daily calorie intake at the same time each day. Measurement of body temperature and home cage behaviors suggests that the RF and CR models are very similar but CR has the advantage of a clearly defined food intake and more stable mean body temperature. Using the CR model, we then attempted to verify the published result that orexin deletion diminishes food anticipatory activity (FAA) but observed little to no diminution in the response to CR and, surprisingly, that orexin KO mice are refractory to body weight loss on a CR diet. Next we tested the orexigenic neuropeptide Y (NPY) and ghrelin and the anorexigenic hormone, leptin, using mouse mutants. NPY deletion did not alter the behavior or physiological response to CR. Leptin deletion impaired FAA in terms of some activity measures, such as walking and rearing, but did not substantially diminish hanging behavior preceding feeding time, suggesting that leptin knockout mice do anticipate daily meal time but do not manifest the full spectrum of activities that typify FAA. Ghrelin knockout mice do not have impaired FAA on a CR diet. Collectively, these results suggest that the individual hormones and neuropepetides tested do not regulate FAA by acting individually but this does not rule out the possibility of their concerted action in mediating FAA

On the Bounds of Function Approximations

Within machine learning, the subfield of Neural Architecture Search (NAS) has recently garnered research attention due to its ability to improve upon human-designed models. However, the computational requirements for finding an exact solution to this problem are often intractable, and the design of the search space still requires manual intervention. In this paper we attempt to establish a formalized framework from which we can better understand the computational bounds of NAS in relation to its search space. For this, we first reformulate the function approximation problem in terms of sequences of functions, and we call it the Function Approximation (FA) problem; then we show that it is computationally infeasible to devise a procedure that solves FA for all functions to zero error, regardless of the search space. We show also that such error will be minimal if a specific class of functions is present in the search space. Subsequently, we show that machine learning as a mathematical problem is a solution strategy for FA, albeit not an effective one, and further describe a stronger version of this approach: the Approximate Architectural Search Problem (a-ASP), which is the mathematical equivalent of NAS. We leverage the framework from this paper and results from the literature to describe the conditions under which a-ASP can potentially solve FA as well as an exhaustive search, but in polynomial time.Comment: Accepted as a full paper at ICANN 2019. The final, authenticated publication will be available at https://doi.org/10.1007/978-3-030-30487-4_3