Características e controle da podridão "olho de boi" nas maçãs do sul do Brasil.

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Standardized and reproducible measurement of decision-making in mice

Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We designed a task for head-fixed mice that combines established assays of perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be successfully reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path towards achieving reproducibility in neuroscience through collaborative open-science approaches

Risk factors of one year increment of coronary calcifications and survival in hemodialysis patients

<p>Abstract</p> <p>Background</p> <p>Heart and coronary calcifications in hemodialysis patients are of very common occurrence and linked to cardiovascular events and mortality. Several studies have been published with similar results. Most of them were mainly cross-sectional and some of the prospective protocols were aimed to evaluate the results of the control of altered biochemical parameters of mineral disturbances with special regard to serum calcium, phosphate and CaxP with the use of calcium containing and calcium free phosphate chelating agents. The aim of the present study was to evaluate in hemodialysis patients classic and some non classic risk factors as predictors of calcification changes after one year and to evaluate the impact of progression on survival.</p> <p>Methods</p> <p>81 patients on hemodialysis were studied, with a wide age range and HD vintage. Several classic parameters and some less classic risk factors were studied like fetuin-A, CRP, 25-OHD and leptin. Calcifications, as Agatston scores, were evaluated with Multislice CT basally and after 12-18 months.</p> <p>Results</p> <p>Coronary artery calcifications were observed in 71 of 81 patients. Non parametric correlations between Agatston scores and Age, HD Age, PTH and CRP were significant. Delta increments of Agatston scores correlated also with serum calcium, CaxP, Fetuin-A, triglycerides and serum albumin. Logistic regression analysis showed Age, PTH and serum calcium as important predictors of Delta Agatston scores. LN transformation of the not normally distributed variables restricted the significant correlations to Age, BMI and CRP. Considering the Delta Agatston scores as dependent, significant predictors were Age, PTH and HDL. A strong association was found between basal calcification scores and Delta increment at one year. By logistic analysis, the one year increments in Agatston scores were found to be predictors of mortality. Diabetic and hypertensive patients have significantly higher Delta scores.</p> <p>Conclusions</p> <p>Progression of calcification is of common occurrence, with special regard to elevated basal scores, and is predictive of survival. Higher predictive value of survival is linked to the one year increment of calcification scores. Some classic and non classic risk factors play an important role in progression. Some of them could be controlled with appropriate management with possible improvement of mortality.</p

Réponse cardiaque et perception de l’effort lors de deux stratégies de relais en contre-la-montre cycliste par équipe

Introduction : Le but de cette étude était de comparer deux stratégies de relais en termes de performance, de réponses physiologiques et de perception de l’effort lors d’un contre-la-montre (CLM) cycliste par équipe. Synthèse des faits : Une meilleure performance est réalisée lors du CLM en ligne (CLM-L) que lors du CLM en double ligne (CLM-DL) (42,0 vs 40,5 km/h). La puissance moyenne (78,5 ± 2,2 vs 74,5 ± 2,5 % PMA) ainsi que la FC moyenne (88,5 ± 2,2 vs 86,0 ± 1,9 % FCmax) sont significativement plus élevées lors du CLM-L comparé au CLM-DL (p < 0,05). Le CLM-L est perçu significativement plus difficile que le CLM-DL (p < 0,05). Conclusion : En termes de performance, le CLM-L est plus efficace mais semble aussi plus éprouvant que le CLM-DL. Ainsi, ces deux modalités sont intéressantes en compétition mais nourrissent des ambitions différentes

Encoding models reveal brain-wide signaling of motor activity and reward delivery

Mixed selectivity in neural codes is well documented in multiple brain regions, with individual neurons exhibiting tuning to several variables that are explicit or implicit in behavior. While this mixed selectivity has been observed in multiple brain regions, the scope of such selectivity, and the variables selected for, have never been documented on the scale of the entire brain itself.We examine single neuron firing using neural activity recorded by the international brain lab (IBL) in its brain-wide map: 583 neuropixel penetrations covering 361 brain regions defined by the Allen atlas. The recordings were made in mice performing a task in which mice maximize rewards by exploiting a blockwise stimulus probability governing the appearance of stimuli. The task features auditory inputs, visual inputs, and a variety of behavioral signals which we can examine in the context of single-unit activity.We fit generalized linear models to express single-unit firing as a function of task and behavioral regressors. For each neuron, a model is fit which describes spike counts in bins as a function of stimulus, feedback, wheel speed (absolute value of velocity), block stimulus probability, and first movement onset. The resulting weights governing the predicted response of the model are compared against a statistical null distribution, and the per-region proportions of significantly modulated neurons are reported.Preliminary results show brain-wide sensitivity to wheel speed and reward, and to a lesser extent, the block probability of trial stimulus side. Notably there is very little sensitivity to directional wheel velocity (i.e., signed speed). Global sensitivity to reward delivery is more unexpected, and to our knowledge not previously observed in the literature.The broad sensitivity to block probability within the trial after stimulus is also a surprising result. Because we have overlapping regressors during the within-trial period for both stimulus side and movement direction, it seems unlikely that this result is simply attributable to correlations with those variables. In future work we aim to further investigate the effect of expectation and the prior on neural activity using behaviorally-informed estimates of the animal’s internal prior. We also aim to investigate the basis of widespread responses to reward, and whether those responses can be explained by motor activity like licking not included as model regressors

International Brain Laboratory brainwide analysis: decoding of task and behavioral variables from populations of neurons

Decoding is a popular approach for assessing the information the activity of a neural population contains about externally accessible variables. Decoding analyses are typically limited to examining a small fraction of the brain at high temporal resolution (e.g. using electrophysiology), or a large fraction of the brain at low temporal resolution (e.g. fMRI). Here we use data from hundreds of neuropixel penetrations covering hundreds of brain regions in the Allen atlas to decode task and behavioral variables at unprecedented spatial and temporal resolutions in mice performing a perceptual decision-making task. In the International Brain Lab (IBL) task, mice are presented with a visual grating stimulus on one side of a screen, and report whether this was left or right by turning a steering wheel; this results in a reward if the chosen side matches the stimulus side. Mice maximize rewards by exploiting a blockwise prior probability governing stimulus side. We decode task variables (blockwise prior probability; stimulus identity; reward) and behavioral variables (choice; wheel speed; whisker movements) from neural activity using maximum likelihood linear and logistic regression. We report decoding measures (R2 or accuracy) on held-out test trials using multi-fold cross validation, assessing statistical significance by comparison with bespoke null distributions. Preliminary results indicate substantial variability in decoding performance of all variables across sessions and brain regions. Despite this, we find several broad trends in the data. The reward signal, motion energy of the whisker pad, and wheel movements are represented across many brain regions. Notably, wheel speed is better decoded than wheel velocity across all brain regions considered. This result indicates that much of the movement-related information in brainwide neural activity is not specific to the exact kinematics of the movement. We can also decode blockwise prior probability, stimulus, and choice across a number of cortical and subcortical regions. Decoding of pre-stimulus blockwise prior probability, however, is more modest than for stimulus and choice decoding. We find, for example, strong stimulus decoding in VISp, VISpm, and ZI and strong choice decoding in MOp, MOs, CP, and MRN. In future work, we aim to compare our results with encoding and dimensionality reduction analyses. We also hope to build on our decoding of the blockwise prior probability to better understand how prior probabilities are represented across brain regions

Distributed neural representations of prior information in mouse decision-making

Despite numerous studies, the neural basis of approximate Bayesian inference, and, in particular, how prior information impacts decisions, remains unclear. A dominant hypothesis is that prior information is incorporated in decision-making at a late stage of processing, in high-order areas such as OFC, ACC or LIP, right before motor commands are issued. Alternatively, information may be broadcast throughout the brain with top-down influences all the way to sensory areas. To address this question, we examined brainwide neuropixel recordings collected by the International Brain Lab (IBL). In the IBL task, mice are trained to indicate the location of a visual grating stimulus (left or right). Crucially, the prior probability that the stimulus appears on the left flips between 20% and 80% between blocks of variable length. We found that mice leverage the prior probability over the block to improve their decision accuracy. In particular, they perform better than chance (using this prior) when the grating contrast is set to zero. As a crude approximation to their computation, we therefore designed a Bayes optimal algorithm for estimating the block probability on a trial by trial basis given the specific set of trials experienced by each animal in each session. We then decoded this Bayes optimal estimate from 361 brain regions in the Allen atlas, using carefully quality-controlled recordings of the activity of over 200 000 putative single neurons. For each brain region, we used cross-validated Lasso linear decoders. Statistical significance was assessed by comparing our result to a null distribution designed to account for potential spurious correlations between blocks and neural drift or other slow changes. In both inter-trial and within-trial periods, we observed that the prior is widely represented throughout the mouse brain. Consistent with previous work, it is present in particular in high level cortical areas such as ACC or OFC. However, it is also seen throughout substantial portions of the rest of the brain, including early sensory cortical and subcortical regions, such as primary visual cortex or superior colliculus. Overall, we find that around 24% of regions reflect the prior, both in cortical and subcortical regions. This widespread representation of the prior argues for a neural model of Bayesian inference involving loops between areas, as opposed to a model in which the prior is incorporated only in decision making areas. This study offers the first brain-wide perspective on prior encoding, underscoring the importance of using large scale recordings on a single standardized task

Reproducibility of in-vivo electrophysiological measurements in mice

Understanding whole-brain-scale electrophysiological recordings will rely on the collective work of multiple labs. Because two labs recording from the same brain area often reach different conclusions, it is critical to quantify and control for features that decrease reproducibility. To address these issues, we formed a multi-lab collaboration using a shared, open-source behavioral task and experimental apparatus. We repeatedly inserted Neuropixels multi-electrode probes targeting the same brain locations (including posterior parietal cortex, hippocampus, and thalamus) in mice performing the behavioral task. We gathered data across 9 labs and developed a common histological and data processing pipeline to analyze the resulting large datasets. After applying stringent behavioral, histological, and electrophysiological quality-control criteria, we found that neuronal yield, firing rates, spike amplitudes, and task-modulated neuronal activity were reproducible across laboratories. To quantify variance in neural activity explained by task variables (e.g., stimulus onset time), behavioral variables (timing of licks/paw movements), and other variables (e.g., spatial location in the brain or the lab ID), we developed a multi-task neural network encoding model that extends common, simpler regression approaches by allowing nonlinear interactions between variables. We found that within-lab random effects captured by this model were comparable to between-lab random effects. Taken together, these results demonstrate that across-lab standardization of electrophysiological procedures can lead to reproducible results across labs. Moreover, our protocols to achieve reproducibility, along with our analyses to evaluate it are openly accessible to the scientific community, along with our extensive electrophysiological dataset with corresponding behavior and open-source analysis code