In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?

It is often said that a deep learning model is "invariant" to some specific type of transformation. However, what is meant by this statement strongly depends on the context in which it is made. In this paper we explore the nature of invariance and equivariance of deep learning models with the goal of better understanding the ways in which they actually capture these concepts on a formal level. We introduce a family of invariance and equivariance metrics that allows us to quantify these properties in a way that disentangles them from other metrics such as loss or accuracy. We use our metrics to better understand the two most popular methods used to build invariance into networks: data augmentation and equivariant layers. We draw a range of conclusions about invariance and equivariance in deep learning models, ranging from whether initializing a model with pretrained weights has an effect on a trained model's invariance, to the extent to which invariance learned via training can generalize to out-of-distribution data.Comment: To appear at NeurIPS 202

Biochar-based fertilizer: Supercharging root membrane potential and biomass yield of rice

Biochar-based compound fertilizers (BCF) and amendments have proven to enhance crop yields and modify soil properties (pH, nutrients, organic matter, structure etc.) and are now in commercial production in China. While there is a good understanding of the changes in soil properties following biochar addition, the interactions within the rhizosphere remain largely unstudied, with benefits to yield observed beyond the changes in soil properties alone. We investigated the rhizosphere interactions following the addition of an activated wheat straw BCF at an application rates of 0.25% (g·g−1 soil), which could potentially explain the increase of plant biomass (by 67%), herbage N (by 40%) and P (by 46%) uptake in the rice plants grown in the BCF-treated soil, compared to the rice plants grown in the soil with conventional fertilizer alone. Examination of the roots revealed that micron and submicron-sized biochar were embedded in the plaque layer. BCF increased soil Eh by 85 mV and increased the potential difference between the rhizosphere soil and the root membrane by 65 mV. This increased potential difference lowered the free energy required for root nutrient accumulation, potentially explaining greater plant nutrient content and biomass. We also demonstrate an increased abundance of plant-growth promoting bacteria and fungi in the rhizosphere. We suggest that the redox properties of the biochar cause major changes in electron status of rhizosphere soils that drive the observed agronomic benefits

The mating-specific Gα interacts with a kinesin-14 and regulates pheromone-induced nuclear migration in budding yeast

As a budding yeast cell elongates toward its mating partner, cytoplasmic microtubules connect the nucleus to the cell cortex at the growth tip. The Kar3 kinesin-like motor protein is then thought to stimulate plus-end depolymerization of these microtubules, thus drawing the nucleus closer to the site where cell fusion and karyogamy will occur. Here, we show that pheromone stimulates a microtubule-independent interaction between Kar3 and the mating-specific Gα protein Gpa1 and that Gpa1 affects both microtubule orientation and cortical contact. The membrane localization of Gpa1 was found to polarize early in the mating response, at about the same time that the microtubules begin to attach to the incipient growth site. In the absence of Gpa1, microtubules lose contact with the cortex upon shrinking and Kar3 is improperly localized, suggesting that Gpa1 is a cortical anchor for Kar3. We infer that Gpa1 serves as a positional determinant for Kar3-bound microtubule plus ends during mating. © 2009 by The American Society for Cell Biology

Gene silencing in tick cell lines using small interfering or long double-stranded RNA

Gene silencing by RNA interference (RNAi) is an important research tool in many areas of biology. To effectively harness the power of this technique in order to explore tick functional genomics and tick-microorganism interactions, optimised parameters for RNAi-mediated gene silencing in tick cells need to be established. Ten cell lines from four economically important ixodid tick genera (Amblyomma, Hyalomma, Ixodes and Rhipicephalus including the sub-species Boophilus) were used to examine key parameters including small interfering RNA (siRNA), double stranded RNA (dsRNA), transfection reagent and incubation time for silencing virus reporter and endogenous tick genes. Transfection reagents were essential for the uptake of siRNA whereas long dsRNA alone was taken up by most tick cell lines. Significant virus reporter protein knockdown was achieved using either siRNA or dsRNA in all the cell lines tested. Optimum conditions varied according to the cell line. Consistency between replicates and duration of incubation with dsRNA were addressed for two Ixodes scapularis cell lines; IDE8 supported more consistent and effective silencing of the endogenous gene subolesin than ISE6, and highly significant knockdown of the endogenous gene 2I1F6 in IDE8 cells was achieved within 48 h incubation with dsRNA. In summary, this study shows that gene silencing by RNAi in tick cell lines is generally more efficient with dsRNA than with siRNA but results vary between cell lines and optimal parameters need to be determined for each experimental system