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A 30-minute treatment can help reduce allergies from pet dander, dust, and more. Breakthroughs, discoveries, and DIY tips sent every weekday. While ultraviolet (UV) light is harmful for human skin, it could be a new tool in the fight against airborne allergies. A study recently published in the journal found that UV light can disarm common indoor allergens in only 30 minutes. "We have found that we can use a passive, generally safe ultraviolet light treatment to quickly inactivate airborne allergens," study-author Tess Eidem, a microbiologist at the University of Colorado Boulder, said in a statement .

Allergens, typically proteins capable of triggering adverse immune responses, represent a significant public health challenge. To accurately identify allergen proteins, we introduce Applm (Allergen Prediction with Protein Language Models), a computational framework that leverages the 100-billion parameter xTrimoPGLM protein language model. We show that Applm consistently outperforms seven state-of-the-art methods in a diverse set of tasks that closely resemble difficult real-world scenarios. These include identifying novel allergens that lack similar examples in the training set, differentiating between allergens and non-allergens among homologs with high sequence similarity, and assessing functional consequences of mutations that create few changes to the protein sequences. Our analysis confirms that xTrimoPGLM, originally trained on one trillion tokens to capture general protein sequence characteristics, is crucial for Applm's performance by detecting important differences among protein sequences. In addition to providing Applm as open-source software, we also provide our carefully curated benchmark datasets to facilitate future research.

With the exponential increase in information, it has become imperative to design mechanisms that allow users to access what matters to them as quickly as possible. The recommendation system ($RS$) with information technology development is the solution, it is an intelligent system. Various types of data can be collected on items of interest to users and presented as recommendations. $RS$ also play a very important role in e-commerce. The purpose of recommending a product is to designate the most appropriate designation for a specific product. The major challenges when recommending products are insufficient information about the products and the categories to which they belong. In this paper, we transform the product data using two methods of document representation: bag-of-words (BOW) and the neural network-based document combination known as vector-based (Doc2Vec). We propose three-criteria recommendation systems (product, package, and health) for each document representation method to foster online grocery, which depends on product characteristics such as (composition, packaging, nutrition table, allergen, etc.). For our evaluation, we conducted a user and expert survey. Finally, we have compared the performance of these three criteria for each document representation method, discovering that the neural network-based (Doc2Vec) performs better and completely alters the results.

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values 89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study.

My allergies are killing me. I can easily pop a quick pill and hope for the best, but there are a few tools that can keep my (and your) home freer from the allergens that plague so many people. We've found some great deals on some of our favorite robot vacuums, air purifiers, and surface cleaners to help you clear out dust, dirt, and dander in your home, giving you fewer sneeze attacks and more fresh air. Special offer for Gear readers: Get a 1-year subscription to WIRED for $5 ($25 off). This includes unlimited access to WIRED.com and our print magazine (if you'd like).

People with allergy symptoms can breathe a sigh of relief – thanks to the SkinLogic diagnostic solution co-created by a team from the Faculty of Electronics and Information Technology, it will be possible to conduct skin allergy tests more efficiently and obtain more reliable results, the Warsaw University of Technology (WUT) writes in a press release. A great number of those who are allergic or suspect that they might be allergic know this pattern all too well – a visit to a specialist, puncturing with special knife fragments of the forearm on which drops of allergen have been applied, twenty minutes of waiting for the result, and finally – measuring the bubbles with a ruler. Researchers from WUT, together with a team of Prof. Jacek Stępnień (Milton Essex) and researchers from the Military Medical Institute, have come up with a solution that is supposed to help improve this pattern. From an IT point of view, SkinLogic is a data processing system. It is based on a device consisting of a tripod and two cameras: video and thermal imaging.

The labelling of food products in the EU is regulated by the Food Information of Customers (FIC). Companies are required to provide the corresponding information regarding nutrients and allergens among others. With the rise of e-commerce more and more food products are sold online. There are often errors in the online product descriptions regarding the FIC-relevant information due to low data quality in the vendors' product data base. In this paper we propose a hybrid approach of both rule-based and machine learning to verify nutrient declaration and allergen labelling according to FIC requirements. Special focus is given to the problem of false negatives in allergen prediction since this poses a significant health risk to customers. Results show that a neural net trained on a subset of the ingredients of a product is capable of predicting the allergens contained with a high reliability.

These are the best Amazon deals of the day. Purchases you make through our links may earn us a commission. If the week was a thrilling roller coaster, Thursday would be located right before that super exciting, grin-inducing drop. With the weekend so close we can nearly taste it, we're kicking things off early with a little retail therapy to help with that final, end-of-week push. This Thursday, we've spotted some pretty electrifying deals at Amazon.

Purchases you make through our links may earn us a commission. Our colleague Marc Saltzman from USA TODAY is here to share some insight into how smart robots can help do your housework and make your life easier. Now that society is cautiously opening up ahead of the fall season, the last thing you want to do is more work around the house. Not to mention, you might be busy getting the kids ready for another school year – at home, in class, or a bit of both. Fortunately, technology can help, so you can focus on what matters.