Since cardiolipin, ATP synthase dimers, the MICOS complex, and dynamin-like Opa1/Mgm1 are known to be involved in shaping cristae, we examined their variation in the context of crista diversity. Moreover, we have identified both commonalities and differences that may collectively be manifested as diverse variations of crista form and function.Eukaryotic cells use a number of diverse mechanisms to swim through liquid or crawl across solid surfaces. The two most prevalent forms of eukaryotic cell motility are flagellar-dependent swimming and actin-dependent cell migration, both of which are used by animal cells and unicellular eukaryotes alike. Evolutionary cell biologists have used morphological and molecular phenotypes to trace the evolution of flagellar-based swimming. These efforts have resulted in a large body of evidence supporting a single evolutionary origin of the eukaryotic flagellum, an origin that dates back to before the diversification of modern eukaryotes. Actin-dependent crawling, in contrast, involves mutiple distinct molecular mechanisms, the evolution of which is just beginning to be explored.Comparative genomics reveals an unexpected diversity in the molecular mechanisms underlying conserved cellular functions, such as DNA replication and cytokinesis. However, the genetic bases and evolutionary processes underlying this ‘molecular diversity’ remain to be explained. Here, we review a tool to generate alternative mechanisms for conserved cellular functions and test hypotheses concerning the generation of molecular diversity – evolutionary repair experiments, in which laboratory microbial populations adapt in response to a genetic perturbation. We summarize the insights gained from evolutionary repair experiments, the spectrum and dynamics of compensatory mutations, and the alternative molecular mechanisms used to repair perturbed cellular functions. We relate these experiments to the modifications of conserved functions that have occurred outside the laboratory. We end by proposing strategies to improve evolutionary repair experiments as a tool to explore the molecular diversity of life.The first national-scale assessment of chromium (Cr) contamination in China’s agricultural soils was performed based on 1625 sites analysed with 1799 previously published papers. Spatial and temporal variations were assessed, and the ecological risk was estimated. The range of Cr concentrations in farmland soil is 1.48-820.24 mg/kg. At approximately 4.31% and 0.12% of the sampling sites, Cr concentrations exceeded the screening value (150 mg/kg) and the control value (800 mg/kg), respectively (GB15618-2018). Cr concentrations decreased in the following order Southwest > Northwest > East > South > Northeast > Central > North China. Moreover, the Cr accumulation rate in agricultural soils may have decreased during 2011-2016, possibly due to government-led changes in China’s industrial structure and policies limiting the discharge of polluted industrial wastes. Linear correlations were observed between the application amounts of fertilizers and Cr concentrations in the soil, indicating that the application of nitrogen, phosphorus and potassium fertilizers is an important contributor of Cr in agricultural soils. Additionally, geoaccumulation index (Igeo) values for Cr showed that more than 83.4% of the sampling sites were uncontaminated, with high Igeo values distributed in some areas, especially those with mining and electroplating industries. Overall, this study details the Cr contamination status of agricultural soils in China and provides insights for policymakers enacting measures to prevent pollution.Due to a convergence of the availability of large datasets, graphics-specific computer hardware, and important theoretical advancements, artificial intelligence (AI) has recently contributed to dramatic progress in medicine. One type of artificial intelligence known as deep learning (DL) has been particularly impactful for medical image analysis. DEG-77 Deep learning applications have shown promising results in dermatology and other specialties including radiology, cardiology and ophthalmology. The modern clinician will benefit from an understanding of the basic features of deep learning in order to effectively use new applications as well as to better gauge their utility and limitations. In this second article of a two part series, we review the existing and emerging clinical applications of deep learning in dermatology and discuss future opportunities and limitations. Part 1 of this series offered an introduction to the basic concepts of deep learning to facilitate effective communication between clinicians and technical experts.Microalgae exhibit extensive potential for counteracting imminent challenges in the nutraceuticals, pharmaceutical, and biomaterials sectors but lack economic viability. Biotechnological systems for contamination control could advance the economic viability of microalgal feedstock, but the selection of suitable strains that sustainably promote microalgal productivity remains challenging. In the present study, total diversity in phototrophic Chlorella vulgaris cultures was assessed by amplicon sequencing comparing cultures subjected to five different cultivation conditions. Overall, 12 eukaryotic and 53 prokaryotic taxa were identified; Alpha-proteobacteria (36.7%) dominated the prokaryotic, C. vulgaris (97.2%) the eukaryotic community. Despite altering cultivation conditions 2 eukaryotic and 40 prokaryotic taxa remained stably associated with C. vulgaris; diversity between systems did not significantly differ (p > 0.05). Among those, 20 cultivable taxa were isolated and identified by 16S rDNA sequencing. Subsequently, controlled co-cultures were investigated showing stable associations of C. vulgaris with Sphingopyxis sp. and Pseudomonas sp. Out-competition of C. vulgaris due to ammonium or phosphate limitation was not observed, despite significantly elevated growth of Sphingopyxis sp. and Tistrella sp. (p less then 0.05). Nevertheless, C. vulgaris growth was impaired by Tistrella sp. Hence, the study provides a selection of stable indigenous prokaryotes and eukaryotes for artificially tailoring microbial biocenoses. Following a bottom-up approach, it provides a base for controlled co-cultures and thus the establishment of even more complex biocenoses using inter-kingdom assemblages. Such assemblages can benefit from functional richness for improved nutrient utilization, as well as bacterial load control, which can enhance microalgal feedstock production through improved culture stability and productivity.