Life history and population dynamics of the Japanese Yam,Dioscorea japonica Thunb.

The adaptive significance of the emergence mode ofDioscorea japonica was studied with respect to initial plant size (seed, bulbil and tuber) and light intensity, using mathematical simulation based on Yokoi's (1976) model. Under 1.5% full sunlight conditions, plants emerging with only one leaf did not develop a shoot system throughout the growing period (Hori and Oshima, 1986). Simulation indicated that, for this species of plant under poor productive conditions, the optimal time for switch-over from the vegetative to reproductive growth phase to maximize the tuber weight at the end of the growing period, occurred immediately following the start of autotrophic growth. By means of shoot growth patterns, small and large size plants acquired the ability of shade tolerance and shade avoidance, respectively. Further, the life history ofD. japonica could be expressed as a flow chart based on plant size and light intensity data.     

Transmission from vaccinated individuals in a large SARS-CoV-2 Delta variant outbreak

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Assessment of alcohol percentage test for fungal surface hydrophobicity measurement

Aim: To determine whether assessing the penetration of solutions with different concentrations of ethanol (alcohol percentage test: APT) on fungal surfaces is effective in characterization of hydrophobicity on fungal surfaces. Methods and Results: APT and contact angle (CA) measurements were conducted on nine hydrophobic and two hydrophilic fungal strains from the phyla of Ascomycota, Basidiomycota and Zygomycota. There was a strong positive correlation (R² = 0·95) between the APT and CA measurements from eight of the nine hydrophobic stains (four pathogenic and mycotoxigenic Fusarium taxa, one melanosporaceous biotrophic taxon, Alternaria sp, Penicillium aurantiogriseum and Cladosporium cladosporioides). Hydrophilic control strains, Mortierella hyalina and Laccaria laccata, had CAs <90° and no measurable degree of hydrophobicity using the APT method. Conclusions: The APT method was effective in measuring the degree of hydrophobicity and can be conducted on different zones of fungal growth. Significance and Impact of the Study: Characterization of fungal surface hydrophobicity is important for understanding of its particular role and function in fungal morphogenesis and pathogenesis. APT is a simple method that can be utilized for fungal hydrophobicity measurements when CA cannot be measured because of obscured view from aerial mycelia growth.     

Ruthenium Triazine Composite: A Good Match for Increasing Hydrogen Evolution Activity through Contact Electrification

The development of Pt‐free catalysts for the alkaline hydrogen evolution reaction (HER), which is widely used in industrial scale water‐alkali electrolyzers, remains a contemporary and pressing challenge. Ruthenium (Ru) has excellent water‐dissociation abilities and could be an alternative water splitting catalyst. However, its large hydrogen binding energy limits HER activity. Here, a new approach is proposed to boost the HER activity of Ru through uniform loading of Ru nanoparticles on triazine‐ring (C₃N₃)‐doped carbon (triNC). The composite (Ru/triNC) exhibits outstanding HER activity with an ultralow overpotential of ≈2 mV at 10 mA cm^?2; thereby making it the best performing electrocatalyst hitherto reported for alkaline HER. The calculated metal mass activity of Ru/triNC is >10 and 15 times higher than that of Pt/C and Pt/triNC. Both theoretical and experimental studies reveal that the triazine‐ring is a good match for Ru to weaken the hydrogen binding on Ru through interfacial charge transfer via increased contact electrification. Therefore, Ru/triNC can provide the optimal hydrogen adsorption free energy (approaching zero), while maintaining the strong water‐dissociation activity. This study provides a new avenue for designing highly efficient and stable electrocatalysts for water splitting.     

Discordant patterns of introgression across a narrow hybrid zone between two cryptic lineages of an Iberian endemic newt

The study of natural hybrid zones can illuminate aspects of lineage divergence and speciation in morphologically cryptic taxa. We studied a hybrid zone between two highly divergent but morphologically similar lineages (south‐western and south‐eastern) of the Iberian endemic Bosca's newt (Lissotriton boscai) in SW Iberia with a multilocus dataset (microsatellites, nuclear and mitochondrial genes). STRUCTURE and NEWHYBRIDS analyses retrieved few admixed individuals, which classified as backcrosses involving parental individuals of the south‐western lineage. Our results show asymmetric introgression of mtDNA beyond the contact from this lineage into the south‐eastern lineage. Analysis of nongeographic introgression patterns revealed asymmetries in the direction of introgression, but except for mtDNA, we did not find evidence for nonconcordant introgression patterns across nuclear loci. Analysis of a 150‐km transect across the hybrid zone showed broadly coincident cline widths (ca. 3.2–27.9 km), and concordant cline centres across all markers, except for mtDNA that is displaced ca. 60 km northward. Results from ecological niche modelling show that the hybrid zone is in a climatically homogenous area with suitable habitat for the species, suggesting that contact between the two lineages is unlikely to occur further south as their distributions are currently separated by an extensive area of unfavourable habitat. Taken together, our findings suggest the genetic structure of this hybrid zone results from the interplay of historical (biogeographic) and population‐level processes. The narrowness and coincidence of genetic clines can be explained by weak selection against hybrids and reflect a degree of reproductive isolation that is consistent with cryptic speciation.     

Modeling of dynamical systems through deep learning

This review presents a modern perspective on dynamical systems in the context of current goals and open challenges. In particular, our review focuses on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. We explore various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are (1) nonlinear dynamics and (2) unknown or partially known dynamics. Machine learning is providing new and powerful techniques for both challenges. Dimensionality reduction methods are used for projecting dynamical methods in reduced form, and these methods perform computational efficiency on real-world data. Data-driven models drive to discover the governing equations and give laws of physics. The identification of dynamical systems through deep learning techniques succeeds in inferring physical systems. Machine learning provides advanced new and powerful algorithms for nonlinear dynamics. Advanced deep learning methods like autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are used in modeling of dynamical systems.     

Miro: A molecular switch at the center of mitochondrial regulation

The orchestration of mitochondria within the cell represents a critical aspect of cell biology. At the center of this process is the outer mitochondrial membrane protein, Miro. Miro coordinates diverse cellular processes by regulating connections between organelles and the cytoskeleton that range from mediating contacts between the endoplasmic reticulum and mitochondria to the regulation of both actin and microtubule motor proteins. Recently, a number of cell biological, biochemical, and protein structure studies have helped to characterize the myriad roles played by Miro. In addition to answering questions regarding Miro's function, these studies have opened the door to new avenues in the study of Miro in the cell. This review will focus on summarizing recent findings for Miro's structure, function, and activity while highlighting key questions that remain unanswered.