Reproductive Tract Microbial Transitions from Late Gestation to Early Postpartum Using 16S rRNA Metagenetic Profiling in First-Pregnancy Heifers.

Studies in recent years indicate that reproductive tract microbial communities are crucial for shaping mammals' health and reproductive outcomes. Following parturition, uterine bacterial contamination often occurs due to the open cervix, which may lead to postpartum uterine inflammatory diseases, especially in primiparous individuals. However, investigations into spatio-temporal microbial transitions in the reproductive tract of primigravid females remain limited.

Impact of infection on fresh matter content and grain quality in maize.

, a ubiquitous soil-borne fungus found on plant roots and decaying residues, displays competitive traits and mycoparasitic behavior against diverse microorganisms. Selected strains of this fungus are known in agriculture for their beneficial effects on plant growth and as bio-fungicides. However, recent findings have pinpointed as the causal agent behind maize ear rot disease in Europe since 2018, notably impacting maize cobs in Germany, France, and Italy.

Question-asking as a mechanism of information seeking.

Ivancovsky et al. explore the relationship between curiosity and creativity, by suggesting they align through novelty-seeking mechanisms. We argue that a general mechanism linking both capacities together is question-asking: Curiosity drives question-asking that leads to creative problem solving.

The Importance of Understanding Deep Learning.

Some machine learning models, in particular deep neural networks (DNNs), are not very well understood; nevertheless, they are frequently used in science. Does this lack of understanding pose a problem for using DNNs to understand empirical phenomena? Emily Sullivan has recently argued that understanding with DNNs is not limited by our lack of understanding of DNNs themselves. In the present paper, we will argue, Sullivan, that our current lack of understanding of DNNs does limit our ability to understand with DNNs.

ML interpretability: Simple isn't easy.

The interpretability of ML models is important, but it is not clear what it amounts to. So far, most philosophers have discussed the lack of interpretability of black-box models such as neural networks, and methods such as explainable AI that aim to make these models more transparent. The goal of this paper is to clarify the nature of interpretability by focussing on the other end of the "interpretability spectrum".