Baldness does not only come from your mother's side--and other myths debunked. There are plenty of myths about hair loss. While not always true, some reveal surprising truths. Breakthroughs, discoveries, and DIY tips sent every weekday. Bald men are more fertile.

Almost 80 million Americans suffer from hair loss due to aging, stress, medication, or genetic makeup. Hair and scalp-related diseases often go unnoticed in the beginning. Sometimes, a patient cannot differentiate between hair loss and regular hair fall. Diagnosing hair-related diseases is time-consuming as it requires professional dermatologists to perform visual and medical tests. Because of that, the overall diagnosis gets delayed, which worsens the severity of the illness. Due to the image-processing ability, neural network-based applications are used in various sectors, especially healthcare and health informatics, to predict deadly diseases like cancers and tumors. These applications assist clinicians and patients and provide an initial insight into early-stage symptoms. In this study, we used a deep learning approach that successfully predicts three main types of hair loss and scalp-related diseases: alopecia, psoriasis, and folliculitis. However, limited study in this area, unavailability of a proper dataset, and degree of variety among the images scattered over the internet made the task challenging. 150 images were obtained from various sources and then preprocessed by denoising, image equalization, enhancement, and data balancing, thereby minimizing the error rate. After feeding the processed data into the 2D convolutional neural network (CNN) model, we obtained overall training accuracy of 96.2%, with a validation accuracy of 91.1%. The precision and recall score of alopecia, psoriasis, and folliculitis are 0.895, 0.846, and 1.0, respectively. We also created a dataset of the scalp images for future prospective researchers.

Science and technology have a growing impact on our lives, from food and medicine to communication and entertainment. There's an argument to be made that we have already entered our cyborg era, having enhanced our abilities through computers and the internet. It seems almost inevitable that we'll eventually make our relationship with technology official by melding tech with our biological bodies. In it, Grey Trace -- played by Logan Marshall-Green -- is paralyzed during an automobile accident. Later, under pressure from a wealthy inventor, he accepts an AI implant which allows him to regain function in his limbs.

It's known as one of the most intelligent and fearless breeds of dog, but if you have a Doberman, a new study may prompt you to keep a close eye on it. Researchers from the Royal Veterinary College have revealed that the Doberman is the breed with the highest risk of hypothyroidism – a hormonal disorder that can cause lethargy, hair loss and a'tragic' facial expression. In contrast, French Bulldogs, Pugs and Yorkshire Terriers are among the breeds least likely to experience the condition. Bill Lambert, Health and Welfare Executive at The Kennel Club said: 'These findings are important to help us to identify which dogs may be at most risk of developing hypothyroidism. 'Ultimately, this should help owners to spot the initial signs, and vets to diagnose earlier to enable treatment, which is known to be effective in managing the disease.'

Knowledge-grounded dialog systems need to incorporate smooth transitions among knowledge selected for generating responses, to ensure that dialog flows naturally. For document-grounded dialog systems, the inter- and intra-document knowledge relations can be used to model such conversational flows. We develop a novel Multi-Document Co-Referential Graph (Coref-MDG) to effectively capture the inter-document relationships based on commonsense and similarity and the intra-document co-referential structures of knowledge segments within the grounding documents. We propose CorefDiffs, a Co-referential and Differential flow management method, to linearize the static Coref-MDG into conversational sequence logic. CorefDiffs performs knowledge selection by accounting for contextual graph structures and the knowledge difference sequences. CorefDiffs significantly outperforms the state-of-the-art by 9.5\%, 7.4\%, and 8.2\% on three public benchmarks. This demonstrates that the effective modeling of co-reference and knowledge difference for dialog flows are critical for transitions in document-grounded conversation