Physiological disorders, as well as mental stress induce an itching sensation that manifests as scratching behaviour in animals. Scratching is one of the most important behavioural traits.

For example, in experimental rodents, the basic condition is assessed by observing spontaneous locomotor activity 1, curiosity is assessed by observing rearing behaviours, and abdominal pain is assessed according to writhing behaviour 2, 3. Experimental animals exhibit many behaviours, and researchers focus on specific phenotypes according to their interests. Since the behaviour of experimental animals reflects their mental, physical, and cognitive status, it is often assessed in various fields of research. In conclusion, we established a novel scratching detection method using CRNN and showed that it can be used to study disease models. The trained CRNN could also successfully detect scratching in the hapten-induced atopic dermatitis mouse model (sensitivity, 94.8% positive predictive rate, 82.1%). The predicted number and durations of scratching events correlated with those of the human observation. Sensitivity and positive predictive rates reached 81.6% and 87.9%, respectively. The CRNN was trained using the manually labelled images and then evaluated for accuracy using a first-look dataset. Next, a convolutional recurrent neural network (CRNN), composed of sequential convolution, recurrent, and fully connected blocks, was constructed. Images showing differences between two consecutive frames in each video were generated, and each frame was manually labelled as showing scratching behaviour or not. Scratching was elicited by injecting a chemical pruritogen lysophosphatidic acid to the back of a mouse, and behaviour was recorded using a standard handy camera. Here, we aimed to establish a novel method to detect scratching using deep neural network.

NG: Require shaded chart boundaries to have the same 'type' (e.g., cannot use 'zero' function for lower boundary of a power register).Scratching is one of the most important behaviours in experimental animals because it can reflect itching and/or psychological stress.

Better protection for file corruption during firmware upgrades if power is removed.

Increase HTTP timeout from 10 to 30 seconds (mainly noticeable in CTid configuration where the password prompt would restart every 10 seconds).

After changing the proxy-server hostname, wait 10 seconds before activating the change.

Firmware upgrade will no longer download firmware file if meter is already on latest version.

Increase temp_internal() update frequency from 1-minute to 5-seconds.

Added function map420() for simplified 4-20mA sensor value conversions.

Add ECS20 (100A, 200A, 300A) CTid enabled CTs to legacy model meters CT selection options.Firmware Release Notes Firmware Release Notes.

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