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Criminals could manipulate own DNA to avoid detection

Daily Mail - Science & tech

Criminals could manipulate their own DNA to avoid detection on police databases using a kit available from chemists - leading experts have warned. Crispr kits, used to prevent hereditary diseases, are available from online stores for just £150 ($288) and can be used to change a person's genetic makeup. The revolutionary kits are being developed to help fight diseases such as sickle cell anaemia, cystic fibrosis and muscular dystrophy. But Professor George Church, of Harvard University, who pioneered the use of the Crispr technique, said it could also be used by criminals to disappear from forensic databases or evade detection. Asked if Crispr could alter DNA to the extent it would make forensic evidence unusable, Church said: 'We could do that today, easily.


Mosquito detection with low-cost smartphones: data acquisition for malaria research

arXiv.org Machine Learning

Mosquitoes are a major vector for malaria, causing hundreds of thousands of deaths in the developing world each year. Not only is the prevention of mosquito bites of paramount importance to the reduction of malaria transmission cases, but understanding in more forensic detail the interplay between malaria, mosquito vectors, vegetation, standing water and human populations is crucial to the deployment of more effective interventions. Typically the presence and detection of malaria-vectoring mosquitoes is only quantified by hand-operated insect traps or signified by the diagnosis of malaria. If we are to gather timely, large-scale data to improve this situation, we need to automate the process of mosquito detection and classification as much as possible. In this paper, we present a candidate mobile sensing system that acts as both a portable early warning device and an automatic acoustic data acquisition pipeline to help fuel scientific inquiry and policy. The machine learning algorithm that powers the mobile system achieves excellent off-line multi-species detection performance while remaining computationally efficient. Further, we have conducted preliminary live mosquito detection tests using low-cost mobile phones and achieved promising results. The deployment of this system for field usage in Southeast Asia and Africa is planned in the near future. In order to accelerate processing of field recordings and labelling of collected data, we employ a citizen science platform in conjunction with automated methods, the former implemented using the Zooniverse platform, allowing crowdsourcing on a grand scale.


Disease Detection and Symptom Tracking by Retrieving Information from the Web

AAAI Conferences

This paper proposes techniques for preliminary disease detection and personal symptom tracking adopting concepts and methods of web information retrieval. The proposed approaches are inspired by web users’ behavior. People look for information of symptoms from Internet. Therefore, considering information in Web pages, the developed system proposes possible diseases related to one or more queried symptoms. Moreover, these queried symptoms would be recorded in the query log so that the user could utilize these records to trace the history of symptoms, further to manage their own health or provide them to doctors as reference. As ranking detected diseases needs professional knowledge, we instead evaluate relevancy of retrieved sentences containing detected diseases in both strict and lenient metrics. Experimental results support the proposed ranking approach. The techniques described in this paper are also implemented to develop an Android application called “Health Generation”. In this application, the detected disease is further linked to its Wikipedia introduction and the nearby clinics are listed. Users can utilize the GPS function provided by cell phones to plan the route for them. Through the proposed approaches and the application to provide medical information and solutions according to users’ need and further to help users manage their health is the aim of this research.


Artificial Intelligence Powers a Disease-Sniffing Device That Rivals a Dog's Nose

#artificialintelligence

Andreas Mershin visits with one of the trained disease-sniffing dogs in his office at MIT. The dogs are trained and handled in the UK by the organization Medical Detection Dogs. Trained dogs can detect cancer and other diseases by smell. A miniaturized detector can analyze trace molecules to mimic the process. Numerous studies have shown that trained dogs can detect many kinds of disease -- including lung, breast, ovarian, bladder, and prostate cancers, and possibly Covid-19 -- simply through smell.


Contralaterally Enhanced Networks for Thoracic Disease Detection

arXiv.org Artificial Intelligence

Identifying and locating diseases in chest X-rays are very challenging, due to the low visual contrast between normal and abnormal regions, and distortions caused by other overlapping tissues. An interesting phenomenon is that there exist many similar structures in the left and right parts of the chest, such as ribs, lung fields and bronchial tubes. This kind of similarities can be used to identify diseases in chest X-rays, according to the experience of broad-certificated radiologists. Aimed at improving the performance of existing detection methods, we propose a deep end-to-end module to exploit the contralateral context information for enhancing feature representations of disease proposals. First of all, under the guidance of the spine line, the spatial transformer network is employed to extract local contralateral patches, which can provide valuable context information for disease proposals. Then, we build up a specific module, based on both additive and subtractive operations, to fuse the features of the disease proposal and the contralateral patch. Our method can be integrated into both fully and weakly supervised disease detection frameworks. It achieves 33.17 AP50 on a carefully annotated private chest X-ray dataset which contains 31,000 images. Experiments on the NIH chest X-ray dataset indicate that our method achieves state-of-the-art performance in weakly-supervised disease localization.