Extending the Cultivation Area of Pecan (Carya illinoinensis) Toward the South in Southeastern Subtropical China May Cause Increased Cold Damage

Pecan (Carya illinoinensis) is an important nut tree species in its native areas in temperate and subtropical North America, and as an introduced crop in subtropical southeastern China as well. We used process-based modeling to assess the effects of climatic warming in southeastern China on the leaf-out phenology of pecan seedlings and the subsequent risk of “false springs,” i.e., damage caused by low temperatures occurring as a result of prematurely leafing out. In order to maximize the biological realism of the model used in scenario simulations, we developed the model on the basis of experiments explicitly designed for determining the responses modeled. The model showed reasonable internal accuracy when calibrated against leaf-out observations in a whole-tree chamber (WTC) experiment with nine different natural-like fluctuating temperature treatments. The model was used to project the timing of leaf-out in the period 2022–2099 under the warming scenarios RCP4.5 and RCP8.5 in southeastern China. Two locations in the main pecan cultivation area in the northern subtropical zone and one location south of the main cultivation area were addressed. Generally, an advancing trend of leaf-out was projected for all the three locations under both warming scenarios, but in the southern location, a delay was projected under RCP8.5 in many years during the first decades of the 21st century. In the two northern locations, cold damage caused by false springs was projected to occur once in 15–26 years at most, suggesting that pecan cultivation can be continued relatively safely in these two locations. Paradoxically, more frequent cold damage was projected for the southern location than for the two northern locations. The results for the southern location also differed from those for the northern locations in that more frequent cold damage was projected under the RCP4.5 warming scenario (once in 6 years) than under the RCP8.5 scenario (once in 11 years) in the southern location. Due to the uncertainties of the model applied, our conclusions need to be re-examined in an additional experimental study and further model development based on it; but on the basis of our present results, we do not recommend starting large-scale pecan cultivation in locations south of the present main pecan cultivation area in southeastern subtropical China.

Hierarchical Control of Visually-Guided Movements in a 3D-Printed Robot Arm

The control architecture guiding simple movements such as reaching toward a visual target remains an open problem. The nervous system needs to integrate different sensory modalities and coordinate multiple degrees of freedom in the human arm to achieve that goal. The challenge increases due to noise and transport delays in neural signals, non-linear and fatigable muscles as actuators, and unpredictable environmental disturbances. Here we examined the capabilities of hierarchical feedback control models proposed by W. T. Powers, so far only tested in silico. We built a robot arm system with four degrees of freedom, including a visual system for locating the planar position of the hand, joint angle proprioception, and pressure sensing in one point of contact. We subjected the robot to various human-inspired reaching and tracking tasks and found features of biological movement, such as isochrony and bell-shaped velocity profiles in straight-line movements, and the speed-curvature power law in curved movements. These behavioral properties emerge without trajectory planning or explicit optimization algorithms. We then applied static structural perturbations to the robot: we blocked the wrist joint, tilted the writing surface, extended the hand with a tool, and rotated the visual system. For all of them, we found that the arm in machina adapts its behavior without being reprogrammed. In sum, while limited in speed and precision (by the nature of the do-it-yourself inexpensive components we used to build the robot from scratch), when faced with the noise, delays, non-linearities, and unpredictable disturbances of the real world, the embodied control architecture shown here balances biological realism with design simplicity.

Hierarchical control of visually-guided movements in a 3D-printed robot arm

The control architecture guiding simple movements such as reaching toward a visual target remains an open problem. The nervous system needs to integrate different sensory modalities and coordinate multiple degrees of freedom in the human arm to achieve that goal. The challenge increases due to noise and transport delays in neural signals, nonlinear and fatigable muscles as actuators, and unpredictable environmental disturbances. Here we examined the capabilities of a previously proposed hierarchical feedback control model (Powers 1999, 2008), so far only tested in silico. We built a robot arm system with four degrees of freedom, including a visual system for locating the planar position of the hand, joint angle proprioception, and pressure sensing in one point of contact. We subjected the robot to various human-inspired reaching and tracking tasks and found features of biological movement, such as isochrony and bell-shaped velocity profiles in straight-line movements, and the speed-curvature power law in curved movements. These behavioral properties emerge without trajectory planning or explicit optimization algorithms. We then applied static structural perturbations to the robot: we blocked the wrist joint, tilted the writing surface, extended the hand with a tool, and rotated the visual system. For all of them, we found that the arm in machina adapts its behavior without being reprogrammed. In sum, while limited in speed and precision (by the nature of the do-it-yourself inexpensive components we used to build the robot from scratch), when faced with the noise, delays, nonlinearities, and unpredictable disturbances of the real world, the embodied control architecture shown here balances biological realism with design simplicity.

A spatially explicit model for estimating risks of pesticide exposure to bird populations

Pesticides are used widely in agriculture and have the potential to affect non-target organisms, including birds. We developed an integrated modeling system to allow for spatially-explicit evaluation of potential impacts to bird populations following exposures to pesticides. Our novel methodology builds upon three existing models: the Terrestrial Investigation Model (TIM), the Markov Chain Nest Productivity Model (MCnest), and HexSim to simulate population dynamics. We parameterized the integrated modeling system using information required under the Federal Insecticide, Fungicide, and Rodenticide Act, together with species habitat and life history data available from the scientific literature as well as landcover data representing agricultural areas and species habitat. Our case study of the federally threatened California Gnatcatcher (Polioptila californica) illustrates how the integrated modeling system can estimate the population-scale consequences of pesticide applications. We simulated impacts from two insecticides applied to wheat: one causing mortality (survival stressor), and the other causing reproductive failure (reproductive stressor). We observed declines in simulated gnatcatcher abundance and changes in the species’ distribution following applications of each pesticide; however, the impacts of the two pesticides were different. Our methodology attempts to strike a balance between biological realism and model complexity and should be applicable to a wide array of species, systems, and stressors.

Effects of climatic warming on spring phenology in subtropical trees: process-based modelling with experiments designed for model development

To project the effects of climatic warming on the timing of spring leafout and flowering in trees, process-based tree phenology models are often used nowadays. Unfortunately, the biological realism of the models is often compromised because the model development has often been based on various assumptions and indirect methods. We developed process-based tree phenology models for four subtropical tree species, and for the first time for these trees, we based the model development on explicit experimental work particularly designed to address the processes being modelled. For all the four species, a model of seedling leafout was developed, and for Torreya grandis, a model for female flowering in adult trees was additionally developed. The models generally showed reasonable accuracy when tested against two sources of independent data: observational phenological records and leafout data from a whole-tree chamber warming experiment. In scenario simulations, the models projected an advanced spring phenology under climatic warming for 2020 – 2100. For the leafout of seedlings, the advancing rates varied between 4.7 and 5.9 days per one °C warming, with no major differences found between the climatic scenarios RCP4.5 and RCP8.5. For Torreya flowering, less advancing was projected, and the projected advancing per one °C warming was less for RCP8.5 (0.9 days / °C) than for RCP4.5 (2.3 days / °C). The low advancing rates of Torreya flowering were caused by reduced chilling under the warming climate and by the particular temperature responses found for Torreya flowering. For instance, our results show that in Torreya flower buds, no rest break (endodormancy release) is seen at +15 °C, whereas in the seedlings of all four species, +15 °C has a clear rest-breaking effect. These findings highlight the need to base the model development on explicit experiments particularly designed to address the process being modelled.

Medicine, anti-realism and ideology: Variation in medical genetics does not show that race is biologically real

Lee McIntyre’s Respecting Truth chronicles the contemporary challenges regarding the relationship amongst evidence, belief formation and ideology. The discussion in his book focusses on the ‘politicisation of knowledge’ and the purportedly growing public (and sometimes academic) tendency to choose to believe what is determined by prior ideological commitments rather than what is determined by evidence-based reasoning. In considering these issues, McIntyre posits that the claim “race is a myth” is founded on a political ideology rather than on support from scientific evidence. He contrasts this view with the argument that racially correlated biomedical outcomes for self-identified racial groups suggest that biological races are real. I explore how McIntyre’s framing of the claim “race is a myth” as fundamentally ideological results in him failing to engage with the arguments and evidence many constructionists and biological anti-realists put forward in support of their views. I also show how the biomedical evidence he thinks supports biological realism is unconvincing.

Application of hierarchical clustering to identify high risk pests to Sitka spruce: Ireland as a case study

Invertebrate forest pests and pathogens can cause considerable economic losses and modern patterns of trade have facilitated the international movement of pest species on an unprecedented level. This upsurge in trade has increased the pathways available to high risk species, facilitating entry and potential establishment in nations where they were previously absent. To support policy and pest prioritization, pest risk analyses are conducted to decide ‘if’ and ‘how’ pests should be regulated in order to prevent entry or establishment; however, they cannot be carried out for every potential pest. This paper utilizes a hierarchical clustering (HC) approach to analyse distribution data for pests of Sitka spruce (Picea sitchensis (Bong.) Carr.) in order to identify species of high risk to Ireland, as well as potential source regions of these pests. The presence and absence of almost a 1000 pests across 386 regions globally are clustered based on their similarity of pest assemblages, to provide an objective examination of the highest risk pests to Irish forestry. Regional clusters were produced for each taxon analysed including the Coleoptera, Diptera, Hemiptera, Hymenoptera, Nematoda, Lepidoptera and the Fungi. The results produced by the HC analysis were interpreted with regard to biological realism and climate. Biologically meaningful clusters were produced for each of the groups, except for the Diptera and Nematoda, and each of the species analysed were ranked within their group by a quantitative risk index specific to the island of Ireland. The impact of uncertainty in the distribution data is also examined, in order to assess its influence over the final groupings produced. The outputs from this analysis suggest that the highest risk pests for Ireland’s Sitka spruce plantations will originate from within Europe. Ultimately, Ireland could benefit from seeking regulation for some of the higher ranking pests identified in this analysis. This analysis provides the first of its type for Sitka spruce, as well as its application in Ireland. It also serves to highlight the potential utility of HC as a ‘first approach’ to assessing the risk posed by alien species to hitherto novel regions.

Finer Metagenomic Reconstruction via Biodiversity Optimization

When analyzing communities of microorganisms from their sequenced DNA, an important task is taxonomic profiling: enumerating the presence and relative abundance of all organisms, or merely of all taxa, contained in the sample. This task can be tackled via compressive-sensing-based approaches, which favor communities featuring the fewest organisms among those consistent with the observed DNA data. Despite their successes, these parsimonious approaches sometimes conflict with biological realism by overlooking organism similarities. Here, we leverage a recently developed notion of biological diversity that simultaneously accounts for organism similarities and retains the optimization strategy underlying compressive-sensing-based approaches. We demonstrate that minimizing biological diversity still produces sparse taxonomic profiles and we experimentally validate superiority to existing compressive-sensing-based approaches. Despite showing that the objective function is almost never convex and often concave, generally yielding NP-hard problems, we exhibit ways of representing organism similarities for which minimizing diversity can be performed via a sequence of linear programs guaranteed to decrease diversity. Better yet, when biological similarity is quantified by k-mer co-occurrence (a popular notion in bioinformatics), minimizing diversity actually reduces to one linear program that can utilize multiple k-mer sizes to enhance performance. In proof-of-concept experiments, we verify that the latter procedure can lead to significant gains when taxonomically profiling a metagenomic sample, both in terms of reconstruction accuracy and computational performance. Reproducible code is available at https://github.com/dkoslicki/MinimizeBiologicalDiversity.

Dendritic normalisation improves learning in sparsely connected artificial neural networks

Inspired by the physiology of neuronal systems in the brain, artificial neural networks have become an invaluable tool for machine learning applications. However, their biological realism and theoretical tractability are limited, resulting in poorly understood parameters. We have recently shown that biological neuronal firing rates in response to distributed inputs are largely independent of size, meaning that neurons are typically responsive to the proportion, not the absolute number, of their inputs that are active. Here we introduce such a normalisation, where the strength of a neuron’s afferents is divided by their number, to various sparsely-connected artificial networks. The learning performance is dramatically increased, providing an improvement over other widely-used normalisations in sparse networks. The resulting machine learning tools are universally applicable and biologically inspired, rendering them better understood and more stable in our tests.

Robust banded protoxylem pattern formation through microtubule-based directional ROP diffusion restriction

In plant vascular tissue development, different cell wall patterns are formed, offering different mechanical properties optimised for different growth stages. Critical in these patterning processes are Rho of Plants (ROP) proteins, a class of evolutionarily conserved small GTPase proteins responsible for local membrane domain formation in many organisms. While the spotted metaxylem pattern can easily be understood as a result of a Turing-style reaction-diffusion mechanism, it remains an open question how the consistent orientation of evenly spaced bands and spirals as found in protoxylem is achieved. We hypothesise that this orientation results from an interaction between ROPs and an array of transversely oriented cortical microtubules that acts as a directional diffusion barrier. Here, we explore this hypothesis using partial differential equation models with anisotropic ROP diffusion and show that a horizontal microtubule array acting as a vertical diffusion barrier to active ROP can yield a horizontally banded ROP pattern. We then study the underlying mechanism in more detail, finding that it can only orient curved pattern features but not straight lines. This implies that, once formed, banded and spiral patterns cannot be reoriented by this mechanism. Finally, we observe that ROPs and microtubules together only form ultimately static patterns if the interaction is implemented with sufficient biological realism.