BackgroundHandwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders.Materials and MethodsOne-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm.ResultsStroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and A...

Seaweed is very popular in East Asian cuisines, and it has enormous promise as a long-term food supply for the world's rising population. Seaweed, in addition to its nutritional value, protects the environment from a variety of hazards. It aids in the battle against climate change by absorbing extra carbon dioxide in the atmosphere and fertilizer run-off, keeping coasts clean. Seaweed, like so many other marine species, is endangered by the exact thing it serves to mitigate: climate change. Due to unregulated bacterial proliferation, entire seaweed farms are destroyed in days.

COVID-19 pandemic has profoundly influenced the health, financial, and social texture of countries. Recognizable proof of individual-level susceptibility factors may help individuals in distinguishing and dealing with their emotional, psychological, and social well-being. In March 2020, the episode of the Covid illness 2019 (COVID-19) arrived in all nations of the Western world. To decrease the speed of its spread, numerous nations hindered their economies and upheld articulated limitations on public life. After calamities, the vast majority are resilient and don't surrender to psychopathology.

These iterative optimization algorithms are computational expensive and difficult to converge. Unlike iterative optimization methods, supervised machine learning using two stage training-testing becomes a great advantage for fast real-time inference since the most expensive computations are performed during training. Deep Learning plays a very important role tackling these type of problems but requires large dataset to train the multi-layer model parameters of the network [1]. Here, we are interested in creating a molecular image dataset including shape images from real space and diffraction patterns from reciprocal space for machine learning practices. We call this dataset Molecular-MNIST because it consists 10 different size of molecules where each molecule has 2,000 structural variants - in an analogy of the famous 10-digit handwritten dataset MNIST [2]. 2. Molecular-MNIST Dataset 2.1.

New Products/Services: In rapidly changing industries, machine learning engagements should be conducted more frequently. For example, virtual reality, biotechnology, renewable energy and cyber security are all examples of industries with rapid growth and advances. As these industries shift, so do consumer expectations, and new customer needs emerge.