Alleviation of stuck wine fermentations using salt-preconditioned yeast

The influence of salt (sodium chloride) on the cell physiology of wine yeast was investigated. Cellular viability and population growth of three wine-making yeast strains of Saccharomyces cerevisiae, and two non-Saccharomyces yeast strains associated with wine must microflora (Kluyveromyces thermotolerans and K. marxianus) were evaluated following salt pre-treatments. Yeast cells growing in glucose defined media exposed to different sodium chloride concentrations (4, 6 and 10% w/v) exhibited enhanced viabilities compared with nontreated cultures in subsequent trial fermentations. Salt ‘preconditioning’ of wine yeast seed cultures was also shown to alleviate stuck and sluggish fermentations at the winery scale, indicating potential benefits for industrial fermentation processes. It is hypothesized that salt induces specific osmostress response genes to enable yeast cells to better tolerate the rigours of fermentation, particularly in high sugar and alcohol concentrations

Visually Driven Activation in Macaque Areas V2 and V3 without Input from the Primary Visual Cortex

Creating focal lesions in primary visual cortex (V1) provides an opportunity to study the role of extra-geniculo-striate pathways for activating extrastriate visual cortex. Previous studies have shown that more than 95% of neurons in macaque area V2 and V3 stop firing after reversibly cooling V1 [1], [2], [3]. However, no studies on long term recovery in areas V2, V3 following permanent V1 lesions have been reported in the macaque. Here we use macaque fMRI to study area V2, V3 activity patterns from 1 to 22 months after lesioning area V1. We find that visually driven BOLD responses persist inside the V1-lesion projection zones (LPZ) of areas V2 and V3, but are reduced in strength by ∼70%, on average, compared to pre-lesion levels. Monitoring the LPZ activity over time starting one month following the V1 lesion did not reveal systematic changes in BOLD signal amplitude. Surprisingly, the retinotopic organization inside the LPZ of areas V2, V3 remained similar to that of the non-lesioned hemisphere, suggesting that LPZ activation in V2, V3 is not the result of input arising from nearby (non-lesioned) V1 cortex. Electrophysiology recordings of multi-unit activity corroborated the BOLD observations: visually driven multi-unit responses could be elicited inside the V2 LPZ, even when the visual stimulus was entirely contained within the scotoma induced by the V1 lesion. Restricting the stimulus to the intact visual hemi-field produced no significant BOLD modulation inside the V2, V3 LPZs. We conclude that the observed activity patterns are largely mediated by parallel, V1-bypassing, subcortical pathways that can activate areas V2 and V3 in the absence of V1 input. Such pathways may contribute to the behavioral phenomenon of blindsight

Effect of salt hyperosmotic stress on yeast cell viability

During fermentation for ethanol production, yeasts are subjected to different kinds of physico-chemical stresses such as: initially high sugar concentration and low temperature; and later, increased ethanol concentrations. Such conditions trigger a series of biological responses in an effort to maintain cell cycle progress and yeast cell viability. Regarding osmostress, many studies have been focused on transcriptional activation and gene expression in laboratory strains of Saccharomyces cerevisiae. The overall aim of this present work was to further our understanding of wine yeast performance during fermentations under osmotic stress conditions. Specifically, the research work focused on the evaluation of NaCl-induced stress responses of an industrial wine yeast strain S. cerevisiae (VIN 13), particularly with regard to yeast cell growth and viability. The hypothesis was that osmostress conditions energized specific genes to enable yeast cells to survive under stressful conditions. Experiments were designed by pretreating cells with different sodium chloride concentrations (NaCl: 4%, 6% and 10% w/v) growing in defined media containing D-glucose and evaluating the impact of this on yeast growth and viability. Subsequent fermentation cycles took place with increasing concentrations of D-glucose (20%, 30%, 40% w/v) using salt-adapted cells as inocula. We present evidence that osmostress induced by mild salt pre-treatments resulted in beneficial influences on both cell viability and fermentation performance of an industrial wine yeast strain

A new method for estimating population receptive field topography in visual cortex

We introduce a new method for measuring visual population receptive fields (pRF) with functional magnetic resonance imaging (fMRI). The pRF structure is modeled as a set of weights that can be estimated by solving a linear model that predicts the Blood Oxygen Level-Dependent (BOLD) signal using the stimulus protocol and the canonical hemodynamic response function. This method does not make a priori assumptions about the specific pRF shape and is therefore a useful tool for uncovering the underlying pRF structure at different spatial locations in an unbiased way. We show that our method is more accurate than a previously described method (Dumoulin and Wandell, 2008) which directly fits a 2-dimensional isotropic Gaussian pRF model to predict the fMRI time-series. We demonstrate that direct-fit models do not fully capture the actual pRF shape, and can be prone to pRF center mislocalization when the pRF is located near the border of the stimulus space. A quantitative comparison demonstrates that our method outperforms the direct-fit methods in the pRF center modeling by achieving higher explained variance of the BOLD signal. This was true for direct-fit isotropic Gaussian, anisotropic Gaussian, and difference of isotropic Gaussians model. Importantly, our model is also capable of exploring a variety of pRF properties such as surround suppression, receptive field center elongation, orientation, location and size. Additionally, the proposed method is particularly attractive for monitoring pRF properties in the visual areas of subjects with lesions of the visual pathways, where it is difficult to anticipate what shape the reorganized pRF might take. Finally, the method proposed here is more efficient in computation time than direct-fit methods, which need to search for a set of parameters in an extremely large searching space. Instead, this method uses the pRF topography to constrain the space that needs to be searched for the subsequent modeling

Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making

The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a 21.16% reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models’ outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient’s most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools

Estimating average single-neuron visual receptive field sizes by fMRI

The noninvasive estimation of neuronal receptive field (RF) properties in vivo allows a detailed understanding of brain organization as well as its plasticity by longitudinal following of potential changes. Visual RFs measured invasively by electrophysiology in animal models have traditionally provided a great extent of our current knowledge about the visual brain and its disorders. Voxel-based estimates of population RF (pRF) by functional magnetic resonance imaging (fMRI) in humans revolutionized the field and have been used extensively in numerous studies. However, current methods cannot estimate single-neuron RF sizes as they reflect large populations of neurons with individual RF scatter. Here, we introduce an approach to estimate RF size using spatial frequency selectivity to checkerboard patterns. This method allowed us to obtain noninvasive, average single-neuron RF estimates over a large portion of human early visual cortex. These estimates were significantly smaller compared with prior pRF methods. Furthermore, fMRI and electrophysiology experiments in nonhuman primates demonstrated an exceptionally good match, validating the approach

Motion Processing in the Macaque: Revisited with Functional Magnetic Resonance Imaging

this paper show that a distributed network of visual areas in the monkey contains information about the direction of motion of a stimulus, in agreement with previous single-unit electrophysiology studies. Specifically, the BOLD signal in areas V1, V2, V3, V3A, V4, and MT reflects the processing Figure 5. Information about the direction of motion in areas V2, V3, V3A, and V4. Lef t column shows the area of interest, marked in yellow,on a single horizontal slice of an individual monkey during one experimental session. Right column (same notations as in Fig. 4) shows the average activities of the different visual areas. This activity is the mean across all significant voxels in all slices, monkeys, and experimental sessions belonging to a particular visual area. Histogram insets have the same notation as in Figure 4. Voxels belonging to area V2 were identified within the posterior bank of the lunate sulcus (ls) excluding the f undus. The time course of the mean BOLD activity was computed from a total of 157 voxels, six slices, three monkeys, and five experimental sessions. Voxels belonging to area V2/V3 were typically identified within the inferior occipital sulcus (ios). The time course of the mean BOLD activity was computed from a total of 680 voxels, six slices, four monkeys, and four experimental sessions. Voxels belonging to area V3A were identified 4 within the anterior bank of the ls, excluding the f undus. The time course of the mean BOLD activity was computed from a total of 54 voxels, five slices, four monkeys, and four experimental sessions. Voxels belonging to V3/V3A were identified within the anterior bank of the ls, including the f undus. The time course of the mean BOLD activity was computed from a total of 170 voxels, five slices, four monkeys, and four e..