Mechanism of lethal action of 2,450-MHz radiation on microorganisms.

Various bacteria, actinomycetes, fungi, and bacteriophages were exposed to microwaves of 2,450 +/- 20 MHz in the presence and in the absence of water. It was found that microorganisms were inactivated only when in the presence of water and that dry or lyophilized organisms were not affected even by extended exposures. The data presented here prove that microorganisms are killed by "thermal effect" only and that, most likely, there is no "nonthermal effect"; cell constituents other than water do not absorb sufficient energy to kill microbial cells.     

On the statistical significance of primary structural features found in DNA-protein interaction sites

Probabilities of occurrence for a number of the symmetries and other sequence regularities found in DNA-protein interaction site sequences have been calculated for segments of random DNA sequence. Results show that many of the symmetrical and repetitive features seen in these interaction sites are likely to have occurred by chance. Other features are so unlikely to have occurred by chance that they are probably involved in the DNA-protein interaction processes.     

Guidelines for model adaptation: A study of the transferability of a general seagrass ecosystem Dynamic Bayesian Networks model

In general, it is not feasible to collect enough empirical data to capture the entire range of processes that define a complex system, either intrinsically or when viewing the system from a different geographical or temporal perspective. In this context, an alternative approach is to consider model transferability, which is the act of translating a model built for one environment to another less well‐known situation. Model transferability and adaptability may be extremely beneficial—approaches that aid in the reuse and adaption of models, particularly for sites with limited data, would benefit from widespread model uptake. Besides the reduced effort required to develop a model, data collection can be simplified when transferring a model to a different application context. The research presented in this paper focused on a case study to identify and implement guidelines for model adaptation. Our study adapted a general Dynamic Bayesian Networks (DBN) of a seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of seagrass (Zostera noltei and Zostera marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer‐reviewed literature to identify which components needed adjustment including parameterization and quantification of the model and desired outcomes. We adopted both linguistic labels and scenario‐based elicitation to elicit from experts the conditional probabilities used to quantify the DBN. Following the proposed guidelines, the model structure of the general DBN was retained, but the conditional probability tables were adapted for nodes that characterized the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses. Our guidelines provide a way to adapt a general DBN to specific ecosystems to maximize model reuse and minimize re‐development effort. Especially important from a transferability perspective are guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts.     

Chromosome-level genome assembly and characterization of Sophora Japonica

Sophora japonica is a medium-size deciduous tree belonging to Leguminosae family and famous for its high ecological, economic and medicinal value. Here, we reveal a draft genome of S. japonica, which was ∼511.49 Mb long (contig N50 size of 17.34 Mb) based on Illumina, Nanopore and Hi-C data. We reliably assembled 110 contigs into 14 chromosomes, representing 91.62% of the total genome, with an improved N50 size of 31.32 Mb based on Hi-C data. Further investigation identified 271.76 Mb (53.13%) of repetitive sequences and 31,000 protein-coding genes, of which 30,721 (99.1%) were functionally annotated. Phylogenetic analysis indicates that S. japonica separated from Arabidopsis thaliana and Glycine max ∼107.53 and 61.24 million years ago, respectively. We detected evidence of species-specific and common-legume whole-genome duplication events in S. japonica. We further found that multiple TF families (e.g. BBX and PAL) have expanded in S. japonica, which might have led to its enhanced tolerance to abiotic stress. In addition, S. japonica harbours more genes involved in the lignin and cellulose biosynthesis pathways than the other two species. Finally, population genomic analyses revealed no obvious differentiation among geographical groups and the effective population size continuously declined since 2 Ma. Our genomic data provide a powerful comparative framework to study the adaptation, evolution and active ingredients biosynthesis in S. japonica. More importantly, our high-quality S. japonica genome is important for elucidating the biosynthesis of its main bioactive components, and improving its production and/or processing.     

Effects of Luteolin on Human Breast Cancer Using Gene Expression Array: Inferring Novel Genes

Taraxacum officinale (dandelion) is often used in traditional Chinese medicine for the treatment of cancer; however, the downstream regulatory genes and signaling pathways mediating its effects on breast cancer remain unclear. The present study aimed to explore the effects of luteolin, the main biologically active compound of T. officinale, on gene expression profiles in MDA-MB-231 and MCF-7 breast cancer cells. The results revealed that luteolin effectively inhibited the proliferation and motility of the MDA-MB-231 and MCF-7 cells. The mRNA expression profiles were determined using gene expression array analysis and analyzed using a bioinformatics approach. A total of 41 differentially expressed genes (DEGs) were found in the luteolin-treated MDA-MB-231 and MCF-7 cells. A Gene Ontology analysis revealed that the DEGs, including AP2B1, APP, GPNMB and DLST, mainly functioned as oncogenes. The human protein atlas database also found that AP2B1, APP, GPNMB and DLST were highly expressed in breast cancer and that AP2B1 (cut-off value, 75%) was significantly associated with survival rate (p = 0.044). In addition, a Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that the DEGs were involved in T-cell leukemia virus 1 infection and differentiation. On the whole, the findings of the present study provide a scientific basis that may be used to evaluate the potential benefits of luteolin in human breast cancer. Further studies are required, however, to fully elucidate the role of the related molecular pathways.     

合成己酸乙酯脂肪酶产生菌的筛选及产酶条件

从２７株脂肪酶产生菌中筛选到能由乙醇和己酸合成己酸乙酯的菌株８株。其中Ｒｈｉｚｏｐｕｓｓｐ．Ｈ－３菌株脂肪酶活力为５０－６０ｕ／ｍｌ,全细胞在有机溶剂中的酯化率可达己酸的９１％。Ｈ３产酶的最适碳源为淀粉或葡萄糖。６％黄豆饼粉加４％蛋白陈复合氮源有利于酶活力的增加。     

Exercise increases the release of NAMPT in extracellular vesicles and alters NAD + activity in recipient cells

Aging is associated with a loss of metabolic homeostasis, with cofactors such as nicotinamide adenine dinucleotide (NAD⁺) declining over time. The decrease in NAD⁺ production has been linked to the age‐related loss of circulating extracellular nicotinamide phosphoribosyltransferase (eNAMPT), the rate‐limiting enzyme in the NAD⁺ biosynthetic pathway. eNAMPT is found almost exclusively in extracellular vesicles (EVs), providing a mechanism for the distribution of the enzyme in different tissues. Currently, the physiological cause for the release of eNAMPT is unknown, and how it may be affected by age and physical exercise. Here, we show that release of small EVs into the bloodstream is stimulated following moderate intensity exercise in humans. Exercise also increased the eNAMPT content in EVs, most prominently in young individuals with higher aerobic fitness. Both mature fit and young unfit individuals exhibited a limited increase in EV‐eNAMPT release following exercise, indicating that this mechanism is related to both the age and physical fitness of a person. Notably, unfit mature individuals were unable to increase the release of eNAMPT in EVs after exercise, suggesting that lower fitness levels and aging attenuate this important signalling mechanism in the body. EVs isolated from exercising humans containing eNAMPT were able to alter the abundance of NAD⁺ and SIRT1 activity in recipient cells compared to pre‐exercise EVs, indicating a pathway for inter‐tissue signalling promoted through exercise. Our results suggest a mechanism to limit age‐related NAD⁺ decline, through the systemic delivery of eNAMPT via EVs released during exercise.     

Epidemic management and control through risk-dependent individual contact interventions

Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.