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The severe loss of body condition score (BCS) during the early lactation period has been associated with infertility in cows. However, the mechanisms are not fully understood. The aim of this study was to examine the effect of BCS loss on liver health, and ovarian functions in cows during early lactation. Retrospectively multiparous cows from two farms were categorized based on units of BCS (1–5 scale) loss as Moderate (MOD,

Abstract Deep neural networks (DNNs) optimized for visual tasks learn representations that align layer depth with the hierarchy of visual areas in the primate brain. One interpretation of this finding is that hierarchical representations are necessary to accurately predict brain activity in the primate visual system. To test this interpretation, we optimized DNNs to directly predict brain activity measured with fMRI in human visual areas V1-V4. We trained a single-branch DNN to predict activity in all four visual areas jointly, and a multi-branch DNN to predict each visual area independently. Although it was possible for the multi-branch DNN to learn hierarchical representations, only the single-branch DNN did so. This result shows that hierarchical representations are not necessary to accurately predict human brain activity in V1-V4, and that DNNs that encode brain-like visual representations may differ widely in their architecture, ranging from strict serial hierarchies to multiple independent branches.

Women with a history of gestational diabetes (GDM) have difficulty maintaining a healthy diet after delivery. The theory of planned behavior (TPB) is effective in identifying the determinants of adopting a healthy diet. The objectives were to identify the determinants of the intention to adopt a healthy diet among the TPB constructs in women with (GDM+) and without (GDM−) a history of GDM, and to identify the beliefs associated with these constructs. The study was conducted in Québec (Canada) between 2009 and 2017. Data from 213 GDM+ and 91 GDM− women were analyzed. Women completed a questionnaire on the determinants of intention to adopt a healthy diet, defined as adherence to 2007 Canada's Food Guide. The subjective norm and perceived behavioral control (PBC) constructs were associated with the intention to adopt a healthy diet among GDM+ women (β = 2.21 and β = 4.37, respectively, p

Ceftriaxone is a third-generation cephalosporin with broad-spectrum antibacterial activity. Here, we report a case of ceftriaxone-induced cholelithiasis in an adult patient after a short period of administering ceftriaxone. A 57-year-old female was admitted to our hospital for meningitis and treated empirically with ceftriaxone 2 g every 12 hours. Other medications given included vancomycin, ampicillin and acyclovir. Based on culture results and a sensitivity report, ceftriaxone was continued while other medications were discontinued on day three after admission. Her liver function test (LFT) demonstrated an elevation in hepatic transaminases, and alanine and aspartate transaminases peaked on the fifth day (339 and 153 IU/L, respectively). Computed tomography (CT) and ultrasound (US) confirmed the presence of uncomplicated cholelithiasis. Ceftriaxone was discontinued and switched to cefotaxime 2 g every four hours. Hepatic transaminases started declining after ceftriaxone discontinuation and dropped to normal levels on day nine. After the administration of cefotaxime on the 25th day, repeated US imaging revealed the persistence of biliary sludge. The patient was discharged in a good and stable clinical condition with follow-up planned at the outpatient clinic. When the concentration of ceftriaxone in the gallbladder exceeds the solubility of its calcium salt, precipitation occurs, forming a biliary sludge. Using the Naranjo Probability scale, the score was found to be 4, indicating a possible relationship between ceftriaxone and cholelithiasis. Multiple case reports of ceftriaxone-induced cholelithiasis have been documented previously, most of which focused on children or on the prolonged use of ceftriaxone. However, our case report highlights the development of cholelithiasis in adults after administering ceftriaxone for a short time.

Introduction Attachment to a primary care provider is an important component of primary care as it facilitates access. In Québec, Canada, attachment to a family physician is a concern. To address unattached patients’ barriers to accessing primary care, the Ministry of Health and Social Services mandated Québec’s 18 administrative regions to implement single points of access for unattached patients (Guichets d’accès première ligne (GAPs)) that aim to better orient patients towards the most appropriate services to meet their needs. The objectives of this study are to (1) analyse the implementation of GAPs, (2) measure the effects of GAPs on performance indicators and (3) assess unattached patients’ experiences of navigation, access and service utilisation.Methods and analysis A longitudinal mixed-methods case study design will be conducted. Objective 1. Implementation will be analysed through semistructured interviews with key stakeholders, observations of key meetings and document analysis. Objective 2. GAP effects on indicators will be measured using performance dashboards produced using clinical and administrative data. Objective 3. Unattached patients’ experiences will be assessed using a self-administered electronic questionnaire. Findings for each case will be interpreted and presented using a joint display, a visual tool for integrating qualitative and quantitative data. Intercase analyses will be conducted highlighting the similarities and differences across cases.Ethics and dissemination This study is funded by the Canadian Institutes of Health Research (# 475314) and the Fonds de Soutien à l’innovation en santé et en services sociaux (# 5-2-01) and was approved by the CISSS de la Montérégie-Centre Ethics Committee (MP-04-2023-716).

For security and justice professionals (e.g., police officers, lawyers, judges), the thousands of peer-reviewed articles on nonverbal communication represent important sources of knowledge. However, despite the scope of the scientific work carried out on this subject, professionals can turn to programs, methods, and approaches that fail to reflect the state of science. The objective of this article is to examine (i) concepts of nonverbal communication conveyed by these programs, methods, and approaches, but also (ii) the consequences of their use (e.g., on the life or liberty of individuals). To achieve this objective, we describe the scope of scientific research on nonverbal communication. A program (SPOT; Screening of Passengers by Observation Techniques), a method (the BAI; Behavior Analysis Interview) and an approach (synergology) that each run counter to the state of science are examined. Finally, we outline five hypotheses to explain why some organizations in the fields of security and justice are turning to pseudoscience and pseudoscientific techniques. We conclude the article by inviting these organizations to work with the international community of scholars who have scientific expertise in nonverbal communication and lie (and truth) detection to implement evidence-based practices.

Functional MRI (fMRI) is a powerful technique that has allowed us to characterize visual cortex responses to stimuli, yet such experiments are by nature constructed based on a priori hypotheses, limited to the set of images presented to the individual while they are in the scanner, are subject to noise in the observed brain responses, and may vary widely across individuals. In this work, we propose a novel computational strategy, which we call NeuroGen, to overcome these limitations and develop a powerful tool for human vision neuroscience discovery. NeuroGen combines an fMRI-trained neural encoding model of human vision with a deep generative network to synthesize images predicted to achieve a target pattern of macro-scale brain activation. We demonstrate that the reduction of noise that the encoding model provides, coupled with the generative network’s ability to produce images of high fidelity, results in a robust discovery architecture for visual neuroscience. By using only a small number of synthetic images created by NeuroGen, we demonstrate that we can detect and amplify differences in regional and individual human brain response patterns to visual stimuli. We then verify that these discoveries are reflected in the several thousand observed image responses measured with fMRI. We further demonstrate that NeuroGen can create synthetic images predicted to achieve regional response patterns not achievable by the best-matching natural images. The NeuroGen framework extends the utility of brain encoding models and opens up a new avenue for exploring, and possibly precisely controlling, the human visual system.

Encoding models based on deep convolutional neural networks (DCNN) predict BOLD responses to natural scenes in the human visual system more accurately than many other currently available models. However, DCNN-based encoding models fail to predict a significant amount of variance in the activity of most voxels in all visual areas. This failure could reflect limitations in the data (e.g., a noise ceiling), or could reflect limitations of the DCNN as a model of computation in the brain. Understanding the source and structure of the unexplained variance could therefore provide helpful clues for improving models of brain computation. Here, we characterize the structure of the variance that DCNN-based encoding models cannot explain. Using a publicly available dataset of BOLD responses to natural scenes, we determined if the source of unexplained variance was shared across voxels, individual brains, retinotopic locations, and hierarchically distant visual brain areas. We answered these questions using voxel-to-voxel (vox2vox) models that predict activity in a target voxel given activity in a population of source voxels. We found that simple linear vox2vox models increased within-subject prediction accuracy over DCNN-based models for any pair of source/target visual areas, clearly demonstrating that the source of unexplained variance is widely shared within and across visual brain areas. However, vox2vox models were not more accurate than DCNN-based encoding models when source and target voxels came from different brains, demonstrating that the source of unexplained variance was not shared across brains. Importantly, control analyses demonstrated that the source of unexplained variance was not encoded in the mean activity of source voxels, or the activity of voxels in white matter. Interestingly, the weights of vox2vox models revealed preferential connection of target voxel activity to source voxels with adjacent receptive fields, even when source and target voxels were in different functional brain areas. Finally, we found that the prediction accuracy of the vox2vox models decayed with hierarchical distance between the source and target voxels but showed detailed patterns of dependence on hierarchical relationships that we did not observe in DCNNs. Given these results, we argue that the structured variance unexplained by DCNN-based encoding models is unlikely to be entirely caused by non-neural artifacts (e.g., spatially correlated measurement noise) or a failure of DCNNs to approximate the features encoded in brain activity; rather, our results point to a need for brain models that provide both mechanistic and computational explanations for structured ongoing activity in the brain. Keywords: fMRI, encoding models, deep neural networks, functional connectivity