Confocal fluorescence microscopy is one of the most accessible and widely used imaging techniques for the study of biological processes at the cellular and subcellular levels. Scanning confocal microscopy allows the capture of high-quality images from thick three-dimensional (3D) samples, yet suffers from well-known limitations such as photobleaching and phototoxicity of specimens caused by intense light exposure, which limits its use in some applications, especially for living cells. Cellular damage can be alleviated by changing imaging parameters to reduce light exposure, often at the expense of image quality.Machine/deep learning methods for single-image super-resolution (SISR) can be applied to restore image quality by upscaling lower-resolution (LR) images to produce high-resolution images (HR). These SISR methods have been successfully applied to photo-realistic images due partly to the abundance of publicly available datasets. In contrast, the lack of publicly available data partly limits their application and success in scanning confocal microscopy.In this paper, we introduce a large scanning confocal microscopy dataset named SR-CACO-2 that is comprised of low-and high-resolution image pairs marked for three different fluorescent markers. It allows to evaluate the performance of SISR methods on three different upscaling levels (X2, X4, X8).

Sorry, no time policy will make winter days longer. Breakthroughs, discoveries, and DIY tips sent every weekday. It's a hot (yet also sleepy) debate that ignites twice a year in the United States: Why are we still changing the clocks? The "spring forward" every March can feel particularly volatile, with research linking that loss of a precious hour of sleep to more heart attacks and fatal car accidents . Now, a new study published today in the journal indicates that sticking with standard time may improve health.

Confocal fluorescence microscopy is one of the most accessible and widely used imaging techniques for the study of biological processes at the cellular and subcellular levels. Scanning confocal microscopy allows the capture of high-quality images from thick three-dimensional (3D) samples, yet suffers from well-known limitations such as photobleaching and phototoxicity of specimens caused by intense light exposure, which limits its use in some applications, especially for living cells. Cellular damage can be alleviated by changing imaging parameters to reduce light exposure, often at the expense of image quality.Machine/deep learning methods for single-image super-resolution (SISR) can be applied to restore image quality by upscaling lower-resolution (LR) images to produce high-resolution images (HR). These SISR methods have been successfully applied to photo-realistic images due partly to the abundance of publicly available datasets. In contrast, the lack of publicly available data partly limits their application and success in scanning confocal microscopy.In this paper, we introduce a large scanning confocal microscopy dataset named SR-CACO-2 that is comprised of low- and high-resolution image pairs marked for three different fluorescent markers. It allows to evaluate the performance of SISR methods on three different upscaling levels (X2, X4, X8).

Sinister, brain-controlling mushrooms are a staple in sci-fi shows and literature. While brainwashed humans doing the bidding of fungi remains fantasy, researchers have now learned how to control a robot's movement using electrical signals produced by the mycelium of the common King oyster mushroom. This part machine, part fungus robot could one day serve as a building block for more advanced "biohybrid" chimeras that can remotely analyze agricultural fields for potentially harmful changes in soil chemistry. Researchers from Cornell University and University of Florence in Italy wanted to see if electrical signals pulsing through the mycelium of fungi could be translated into a controlling input for robots. The findings were published last month in the journal Science Robotics.

Wherever you are reading this – on the couch or in bed – there is a good chance that you are doing it on some sort of screen. According to a 2022 review, almost everyone upped their screentime during the Covid pandemic, and there is little evidence that use has gone back down. While that may or may not be bad for all sorts of reasons, a concern for many people is blue light, and whether its haunting glow is affecting our bodies in ways sunshine doesn't. Could it somehow be bad light? To start with the basics: blue light sits on the short-wave, high-energy end of the visible spectrum, close to the UV rays that can lead to provably harmful effects on the skin and retinas.

Indoor thermal comfort in smart buildings has a significant impact on the health and performance of occupants. Consequently, machine learning (ML) is increasingly used to solve challenges related to indoor thermal comfort. Temporal variability of thermal comfort perception is an important problem that regulates occupant well-being and energy consumption. However, in most ML-based thermal comfort studies, temporal aspects such as the time of day, circadian rhythm, and outdoor temperature are not considered. This work addresses these problems. It investigates the impact of circadian rhythm and outdoor temperature on the prediction accuracy and classification performance of ML models. The data is gathered through month-long field experiments carried out in 14 classrooms of 5 schools, involving 512 primary school students. Four thermal comfort metrics are considered as the outputs of Deep Neural Networks and Support Vector Machine models for the dataset. The effect of temporal variability on school children's comfort is shown through a "time of day" analysis. Temporal variability in prediction accuracy is demonstrated (up to 80%). Furthermore, we show that outdoor temperature (varying over time) positively impacts the prediction performance of thermal comfort models by up to 30%. The importance of spatio-temporal context is demonstrated by contrasting micro-level (location specific) and macro-level (6 locations across a city) performance. The most important finding of this work is that a definitive improvement in prediction accuracy is shown with an increase in the time of day and sky illuminance, for multiple thermal comfort metrics.

Staring at your phone screen when you should be sleeping can make you depressed over time, new research suggests. Chinese experiments suggest harmful blue light emissions from your device at night trigger a mysterious neural mechanism, leading to behavioural changes. The research team found that mice exposed to blue light for two hours a night over a few weeks started showing depressive-like behaviour. But by blocking brain signals that are triggered by blue light at night, the mice no longer showed behavioural changes. The neural pathway responsible for this phenomenon may provide insight into how exposure to excessive light at night time affects humans.

Circadian rhythms govern most essential biological processes in the human body; they influence multiple biological activities including sleep, performance, mood, skin temperature, hormone production, and nutrient absorption. The dim light melatonin onset (DLMO) is the current gold standard for measuring human circadian phase (or timing). The collection of DLMO is expensive and time consuming since multiple saliva or blood samples are required from nighttime studies in specialized environmental conditions. In the past few years, several non-invasive approaches have been designed for estimating DLMO values. These methods collect daily sampled data (e.g., sleep onset/offset times) or frequently sampled data (e.g., light exposure, skin temperature, physical activity collected every minute) to train learning models for estimating DLMO, therefore previous studies only leveraged one time scale. In this paper, we propose a two-step framework for estimating DLMO using the data of both time scales. The first step summarizes the data prior to the current day, while the second step combines this summary with frequently sampled data of the current day. We evaluate several variants of moving average model which input sleep timing data as the first step and recurrent neural network models as the second step for estimating DLMO. The experimental results show that our two-step model with two-time-scale features has statistically significantly lower root-mean-square errors than the models that use either daily sampled data or frequently sampled data alone.

Machine learning can be the difference between life and death. The technology, which enables computers teach themselves, is about more than who has the world's biggest AI platform -- or how well a platform evaluates cookie recipes; it could be a tremendous boon for precision medicine, which tailors healthcare to the specifics of individual patients. An individual patient's genome is a massive DNA dataset that has already helped physicians tailor treatment to thousands of patients. Using precision medicine for cancer treatment, for example, involves identifying characteristics that could help predict a specific treatment's effectiveness for a specific patient, OncLive stated on Tuesday. Legacy methods might have based treatment on the cancer's stage, which is a relatively limited indicator of success.

Machine learning can be the difference between life and death. The technology, which enables computers teach themselves, is about more than who has the world's biggest AI platform -- or how well a platform evaluates cookie recipes; it could be a tremendous boon for precision medicine, which tailors healthcare to the specifics of individual patients. An individual patient's genome is a massive DNA dataset that has already helped physicians tailor treatment to thousands of patients. Using precision medicine for cancer treatment, for example, involves identifying characteristics that could help predict a specific treatment's effectiveness for a specific patient, OncLive stated on Tuesday. Legacy methods might have based treatment on the cancer's stage, which is a relatively limited indicator of success.