Unlock the potential of vaccines in food-producing animals.

Broader coverage can have economic, climate-related, animal welfare, and human health benefits.

Accurate prediction of neurologic changes in critically ill infants using pose AI.

Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose AI, could predict neurologic changes in the neonatal intensive care unit (NICU). We collected 4,705 hours of video linked to electroencephalograms (EEG) from 115 infants. We trained a deep learning pose algorithm that accurately predicted anatomic landmarks in three evaluation sets (ROC-AUCs 0.83-0.94), showing feasibility of applying pose AI in an ICU. We then trained classifiers on landmarks from pose AI and observed high performance for sedation (ROC-AUCs 0.87-0.91) and cerebral dysfunction (ROC-AUCs 0.76-0.91), demonstrating that an EEG diagnosis can be predicted from video data alone. Taken together, deep learning with pose AI may offer a scalable, minimally invasive method for neuro-telemetry in the NICU.

Burden of bacterial antimicrobial resistance in low-income and middle-income countries avertible by existing interventions: an evidence review and modelling analysis.

National action plans enumerate many interventions as potential strategies to reduce the burden of bacterial antimicrobial resistance (AMR). However, knowledge of the benefits achievable by specific approaches is needed to inform policy making, especially in low-income and middle-income countries (LMICs) with substantial AMR burden and low health-care system capacity. In a modelling analysis, we estimated that improving infection prevention and control programmes in LMIC health-care settings could prevent at least 337 000 (95% CI 250 200-465 200) AMR-associated deaths annually. Ensuring universal access to high-quality water, sanitation, and hygiene services would prevent 247 800 (160 000-337 800) AMR-associated deaths and paediatric vaccines 181 500 (153 400-206 800) AMR-associated deaths, from both direct prevention of resistant infections and reductions in antibiotic consumption. These estimates translate to prevention of 7·8% (5·6-11·0) of all AMR-associated mortality in LMICs by infection prevention and control, 5·7% (3·7-8·0) by water, sanitation, and hygiene, and 4·2% (3·4-5·1) by vaccination interventions. Despite the continuing need for research and innovation to overcome limitations of existing approaches, our findings indicate that reducing global AMR burden by 10% by the year 2030 is achievable with existing interventions. Our results should guide investments in public health interventions with the greatest potential to reduce AMR burden.

Kozak Similarity Score Algorithm Identifies Alternative Translation Initiation Codons Implicated in Cancers.

Ribosome profiling and mass spectroscopy have identified canonical and noncanonical translation initiation codons (TICs) that are upstream of the main translation initiation site and used to translate oncogenic proteins. There have previously been conflicting reports about the patterns of nucleotides that surround noncanonical TICs. Here, we use a Kozak Similarity Score algorithm to find that nearly all of these TICs have flanking nucleotides closely matching the Kozak sequence. Remarkably, the nucleotides flanking alternative noncanonical TICs are frequently closer to the Kozak sequence than the nucleotides flanking TICs used to translate the gene's main protein. Of note, the 5' untranslated region (5'UTR) of cancer-associated genes with an upstream TIC tend to be significantly longer than the same region in genes not associated with cancer. The presence of a longer-than-typical 5'UTR increases the likelihood of ribosome binding to upstream noncanonical TICs, and may be a distinguishing feature of a number of genes overexpressed in cancer. Noncanonical TICs that are located in the 5'UTR, although thought by some to be disadvantageous and suppressed by evolution, may translate oncogenic proteins because of their flanking nucleotides.

Machine learning predicts translation initiation sites in neurologic diseases with nucleotide repeat expansions.

A number of neurologic diseases associated with expanded nucleotide repeats, including an inherited form of amyotrophic lateral sclerosis, have an unconventional form of translation called repeat-associated non-AUG (RAN) translation. It has been speculated that the repeat regions in the RNA fold into secondary structures in a length-dependent manner, promoting RAN translation. Repeat protein products are translated, accumulate, and may contribute to disease pathogenesis. Nucleotides that flank the repeat region, especially ones closest to the initiation site, are believed to enhance translation initiation. A machine learning model has been published to help identify ATG and near-cognate translation initiation sites; however, this model has diminished predictive power due to its extensive feature selection and limited training data. Here, we overcome this limitation and increase prediction accuracy by the following: a) capture the effect of nucleotides most critical for translation initiation via feature reduction, b) implement an alternative machine learning algorithm better suited for limited data, c) build comprehensive and balanced training data (via sampling without replacement) that includes previously unavailable sequences, and d) split ATG and near-cognate translation initiation codon data to train two separate models. We also design a supplementary scoring system to provide an additional prognostic assessment of model predictions. The resultant models have high performance, with ~85-88% accuracy, exceeding that of the previously published model by >18%. The models presented here are used to identify translation initiation sites in genes associated with a number of neurologic repeat expansion disorders. The results confirm a number of sites of translation initiation upstream of the expanded repeats that have been found experimentally, and predict sites that are not yet established.