Despite the crucial function of the small intestine in nutrient uptake our understanding of the molecular events underlying the digestive function is still rudimentary. Recent studies demonstrated that enterocytes do not direct the entire dietary triacylglycerol toward immediate chylomicron synthesis. Especially after high-fat challenges, parts of the resynthesized triacylglycerol are packaged into cytosolic lipid droplets for transient storage in the endothelial layer of the small intestine. The reason for this temporary storage of triacylglycerol is not completely understood. To utilize lipids from cytosolic lipid droplets for chylomicron synthesis in the endoplasmic reticulum, stored triacylglycerol has to be hydrolyzed either by cytosolic lipolysis or lipophagy. Interestingly, triacylglycerol storage and chylomicron secretion rates are unevenly distributed along the small intestine, with the proximal jejunum exhibiting the highest intermittent storage capacity. We hypothesize that correlating hydrolytic enzyme activities with the reported distribution of triacylglycerol storage and chylomicron secretion in different sections of the small intestine is a promising strategy to determine key enzymes in triacylglycerol remobilization. We employed a serine hydrolase specific activity-based labeling approach in combination with quantitative proteomics to identify and rank hydrolases based on their relative activity in 11 sections of the small intestine. Moreover, we identified several clusters of enzymes showing similar activity distribution along the small intestine. Merging our activity-based results with substrate specificity and subcellular localization known from previous studies, carboxylesterase 2e and arylacetamide deacetylase emerge as promising candidates for triacylglycerol mobilization from cytosolic lipid droplets in enterocytes.

Quantification of ¹⁸F-FDG PET images is useful for accurate diagnosis and evaluation of various brain diseases, including brain tumors, epilepsy, dementia, and Parkinson disease. However, accurate quantification of ¹⁸F-FDG PET images requires matched 3-dimensional T₁ MRI scans of the same individuals to provide detailed information on brain anatomy. In this paper, we propose a transfer learning approach to adapt a pretrained deep neural network model from amyloid PET to spatially normalize ¹⁸F-FDG PET images without the need for 3-dimensional MRI. Methods: The proposed method is based on a deep learning model for automatic spatial normalization of ¹⁸F-FDG brain PET images, which was developed by fine-tuning a pretrained model for amyloid PET using only 103 ¹⁸F-FDG PET and MR images. After training, the algorithm was tested on 65 internal and 78 external test sets. All T₁ MR images with a 1-mm isotropic voxel size were processed with FreeSurfer software to provide cortical segmentation maps used to extract a ground-truth regional SUV ratio using cerebellar gray matter as a reference region. These values were compared with those from spatial normalization-based quantification methods using the proposed method and statistical parametric mapping software. Results: The proposed method showed superior spatial normalization compared with statistical parametric mapping, as evidenced by increased normalized mutual information and better size and shape matching in PET images. Quantitative evaluation revealed a consistently higher SUV ratio correlation and intraclass correlation coefficients for the proposed method across various brain regions in both internal and external datasets. The remarkably good correlation and intraclass correlation coefficient values of the proposed method for the external dataset are noteworthy, considering the dataset’s different ethnic distribution and the use of different PET scanners and image reconstruction algorithms. Conclusion: This study successfully applied transfer learning to a deep neural network for ¹⁸F-FDG PET spatial normalization, demonstrating its resource efficiency and improved performance. This highlights the efficacy of transfer learning, which requires a smaller number of datasets than does the original network training, thus increasing the potential for broader use of deep learning–based brain PET spatial normalization techniques for various clinical and research radiotracers.

Neural decoding is a tool for understanding how activities from a population of neurons inside the brain relate to the outside world and for engineering applications such as brain–machine interfaces. However, neural decoding studies mainly focused on different decoding algorithms rather than different neuron types which could use different coding strategies. In this study, we used two-photon calcium imaging to assess three auditory spatial decoders (space map, opponent channel, and population pattern) in excitatory and inhibitory neurons in the dorsal inferior colliculus of male and female mice. Our findings revealed a clustering of excitatory neurons that prefer similar interaural level difference (ILD), the primary spatial cues in mice, while inhibitory neurons showed random local ILD organization. We found that inhibitory neurons displayed lower decoding variability under the opponent channel decoder, while excitatory neurons achieved higher decoding accuracy under the space map and population pattern decoders. Further analysis revealed that the inhibitory neurons’ preference for ILD off the midline and the excitatory neurons’ heterogeneous ILD tuning account for their decoding differences. Additionally, we discovered a sharper ILD tuning in the inhibitory neurons. Our computational model, linking this to increased presynaptic inhibitory inputs, was corroborated using monaural and binaural stimuli. Overall, this study provides experimental and computational insight into how excitatory and inhibitory neurons uniquely contribute to the coding of sound locations.

Systemic study of pathogenic pathways and interrelationships underlying genes associated with Alzheimer's disease (AD) facilitates the identification of new targets for effective treatments. Recently available large-scale multiomics datasets provide opportunities to use computational approaches for such studies. Here, we devised a novel disease gene identification (digID) computational framework that consists of a semi-supervised deep learning classifier to predict AD-associated genes and a protein–protein interaction (PPI) network-based analysis to prioritize the importance of these predicted genes in AD. digID predicted 1,529 AD-associated genes and revealed potentially new AD molecular mechanisms and therapeutic targets including GNAI1 and GNB1, two G-protein subunits that regulate cell signaling, and KNG1, an upstream modulator of CDC42 small G-protein signaling and mediator of inflammation and candidate coregulator of amyloid precursor protein (APP). Analysis of mRNA expression validated their dysregulation in AD brains but further revealed the significant spatial patterns in different brain regions as well as among different subregions of the frontal cortex and hippocampi. Super-resolution STochastic Optical Reconstruction Microscopy (STORM) further demonstrated their subcellular colocalization and molecular interactions with APP in a transgenic mouse model of both sexes with AD-like mutations. These studies support the predictions made by digID while highlighting the importance of concurrent biological validation of computationally identified gene clusters as potential new AD therapeutic targets.

Advanced analytics programs can incorporate geospatial data. Learn how such data can be used to augment local marketing plans

Everything happens somewhere, and much of our customer data includes location information. Websites include x, y coordinates in semi-structured click streams, and the mobile apps your prospects depend on frequently support device location to provide a personalized, targeted experience. As my SAS peer Robby Powell said: "Human brains are hardwired [...]

SAS Customer Intelligence 360: Data visualization, location analytics and geospatial insights was published on Customer Intelligence Blog.

For many severe lung diseases, non-invasive biomarkers from imaging could improve early detection of lung injury or disease onset, establish a diagnosis, or help follow-up disease progression and treatment strategies. Imaging of the thorax and lung is challenging due to its size, respiration movement, transferred cardiac pulsation, vast density range and gravitation sensitivity. However, there is extensive ongoing research in this fast-evolving field. Recent improvements in spatial imaging have allowed us to study the three-dimensional structure of the lung, providing both spatial architecture and transcriptomic information at single-cell resolution. This fast progression, however, comes with several challenges, including significant image file storage and network capacity issues, increased costs, data processing and analysis, the role of artificial intelligence and machine learning, and mechanisms to combine several modalities. In this review, we provide an overview of advances and current issues in the field of spatial lung imaging.

This paper highlights the many enhancements to SAS/ETS software and demonstrates how these features can help your organization increase revenue and enhance productivity.

The premium gaming laptop supports glasses-free 3D gaming

The 14th edition of the Indo-Pacific Geo Intelligence Forum highlighted the critical role of geospatial technology in addressing security challenges in the region.

National Bureau of Economic Research

National Bureau of Economic Research

National Bureau of Economic Research

National Bureau of Economic Research

National Bureau of Economic Research

National Bureau of Economic Research

National Bureau of Economic Research

National Bureau of Economic Research

While the IMD has strived to improve its predictive models, global warming is the wild card factor

Apparao Galleries’ upcoming show is a curious take on space and architecture

Cambridge, Massachusetts : The MIT Press, [2024]

This paper highlights the many enhancements to SAS/ETS software and demonstrates how these features can help your organization increase revenue and enhance productivity.

The OECD Trento Centre organised five sessions within the 2018 Festival of Economics on Technology and Jobs on 1-3 June 2018. OECD and intenational experts along with high level government representatives discussed the impact of new technologies on jobs and its effect on local economies and policies and what regions and regional policy can do to narrow economic gaps and promote productivity catching-up.

Imagine you have an important decision to make. Do you carefully consider the long-term implications of each possible option or do you act impulsively? Would you approach the decision-making process differently if the consequences stretched out to 30 or even 50 years?

The OECD Trento Centre organised five sessions within the 2018 Festival of Economics on Technology and Jobs on 1-3 June 2018. OECD and intenational experts along with high level government representatives discussed the impact of new technologies on jobs and its effect on local economies and policies and what regions and regional policy can do to narrow economic gaps and promote productivity catching-up.