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The yeast Saccharomyces cerevisiae has evolved to dominate grape juice fermentation. A suite of cellular properties, rapid nutrient depletion, production of inhibitory compounds and the metabolic narrowing of the niche, all enable a minor resident of the initial population to dramatically increase its relative biomass in the ecosystem. This dominance of the grape juice environment is fueled by a rapid launch of glycolysis and energy generation mediated by transport of hexoses and an efficient coupling of transport and catabolism. Fermentation occurs in the presence of molecular oxygen as the choice between respiratory or fermentative growth is regulated by the availability of sugar a phenomenon known as glucose or catabolite repression. Induction of the [GAR + ] prion alters the expression of the major hexose transporter active under these conditions, Hxt3, reducing glycolytic capacity. Bacteria present in the grape juice ecosystem were able to induce the [GAR + ] prion in wine strains of S. cerevisiae. This induction reduced fermentation capacity but did not block it entirely. However, dominance factors such as the rapid depletion of amino acids and other nitrogen sources from the environment were impeded enabling greater access to these substrates for the bacteria. Bacteria associated with arrested commercial wine fermentations were able to induce the prion state, and yeast cells isolated from arrested commercial fermentations were found to be [GAR + ] thus confirming the ecological relevance of prion induction. Subsequent analyses demonstrated that the presence of environmental acetic acid could lead to [GAR + ] induction in yeast strains under certain conditions. The induction of the prion enabled yeast growth on non-preferred substrates, oxidation and reduction products of glucose and fructose, present as a consequence of bacterial energy production. In native ecosystems prion induction never exceeded roughly 50-60% of the population of yeast cells suggesting that the population retains the capacity for maximal fermentation. Thus, the bacterial induction of the [GAR + ] prion represents a novel environmental response: the query of the environment for the presence of competing organisms and the biological decision to temper glucose repression and dominance and enter a metabolic state enabling coexistence.

Quantitative analysis of rock samples using laser-induced breakdown spectroscopy (LIBS) is a challenging task due to the significant differences in spectra between pressed pellet samples and rock samples, with pressed pellet samples usually exhibiting stronger spectral lines. The predictive ability of LIBS quantitative analysis models for rock samples is poorer compared to that for pressed pellet samples, and quantitative analysis models constructed using pressed pellet samples cannot be directly applied to predict the composition of rock samples. To address the issue, a quantitative analysis method for raw rocks based on feature transfer learning using transfer component analysis (TCA) is proposed in this paper. By establishing a feature mapping relationship between pressed pellet samples and rock samples, the differences between data features are reduced, thus enabling the training of a more quantitative analysis model. The transfer learning was introduced into a multivariate regression machine learning model for training using pressed pellet samples, which successfully addresses the problem of prediction accuracy of rock sample element contents. In this model, all data were mapped to a high-dimensional reproducing kernel Hilbert space, and a subset of the most similar features was selected to train the quantitative analysis model. Upon testing the model with independent rock samples, the difference between the predicted and true values was significantly reduced. The model yielded a root mean square error of prediction (RMSEP) of 3.7131, 1.0185, 0.2985, 13.0439, and 1.5450 for Si, Al, Fe, Ca, and Mg in rock samples, respectively. These results indicate that LIBS coupled with the transfer learning algorithm can effectively eliminate the differences between the spectra of the pressed pellet and the raw rock and provide another idea for in situ LIBS detection. [ABSTRACT FROM AUTHOR]

Uranium ore is one of the most important mineral resources in nuclear industry, and the nuclear industry is in urgent need of an analytical technology that can quickly and effectively explore uranium ore resources. Rapid and effective exploration of uranium ore resources could promote the stable and healthy development of the nuclear industry. Laser induced breakdown spectroscopy (LIBS) is a kind of emission spectral element analysis technology, which has the advantages of rapid detection of multi-target elements, and can realize the purpose of rapid and accurate field exploration and analysis of uranium resources. The quantitative analysis of U in uranium ore was carried out based on LIBS technology and machine learning. A total of 12 groups of experimental samples were prepared in this work, and 9 groups of samples were set as training sets for model establishment and hyper-parameter optimization. Three sets of samples were set for model validation. Partial least squares(PLS) and Random forest(RF) algorithms were used to establish a quantitative model, and the hyper-parameters of the two models were optimized by the ten-fold cross-validation method. Finally, the quantitative effects of the two models were verified and compared by three verification sets. After hyper-parameter optimization, both quantitative models had good linear correlation and model stability. The linear correlation coefficient of RF quantitative model was 0.996, while that of PLS quantitative model was 0.997. In terms of model validation, the relative errors of RF model for the three validation sets were 22.33%, 12.79% and 12.04%, respectively. The relative errors of PLS model for the three verification sets were 4.33%, 6.63% and 6.85 %, respectively. Compared with the verification results of the two quantitative models, the relative error of the PLS model on the three verification sets was lower than that of the RF model, which indicated that the PLS model had higher quantitative accuracy than the RF model. Therefore, compared with RF algorithm, PLS algorithm is more suitable for quantitative analysis of U LIBS in uranium ore. [ABSTRACT FROM AUTHOR]

To achieve high refresh rate 8K+ resolution display for virtual reality, we developed the sub‐area control scanning technology with the human eye visual perception characteristic, which can reduce the bandwidth by 80% while ensuring the clarity of the viewing area. [ABSTRACT FROM AUTHOR]

Laser spectroscopic imaging techniques have received tremendous attention in the field of cancer diagnosis due to their high sensitivity, high temporal resolution, and short acquisition time. However, the limited tissue penetration of the laser is still a challenge for the in vivo diagnosis of deep-seated lesions. Nanomaterials have been universally integrated with spectroscopic imaging techniques for deeper cancer diagnosis in vivo. The components, morphology, and sizes of nanomaterials are delicately designed, which could realize cancer diagnosis in vivo or in situ. Considering the enhanced signal emitting from the nanomaterials, we emphasized their combination with spectroscopic imaging techniques for cancer diagnosis, like the surface-enhanced Raman scattering (SERS), photoacoustic, fluorescence, and laser-induced breakdown spectroscopy (LIBS). Applications of the above spectroscopic techniques offer new prospects for cancer diagnosis. [ABSTRACT FROM AUTHOR]