Enhancing Babies’ Sleep Schedule Prediction through Machine Learning

In recent years, there has been a growing interest in improving sleep quality and understanding sleep patterns. This thesis will focus on enhancing sleeping schedules for babies through machine learning. Establishing a consistent bedtime routine at a young age is crucial, as it offers numerous health benefits and can prevent sleep-related issues later in life. Despite this, many parents still find it challenging to manage their babies’ sleep schedules effectively. This master’s thesis explores the integration of machine learning algorithms into babies’ sleep schedule predictions to provide more accurate and personalized recommendations. It focuses on the integration of advanced data analytics and processing techniques to improve sleep forecasts. Recognizing the importance of data cleanliness and processing efficiency, the research delves into different steps for preparing and analyzing the dataset on sleep and baby tracking information. With a strong focus also on feature analysis, it later dives into various machine learning models and assesses their effectiveness and performance. The regression task with machine learning models includes K-Nearest Neighbors (KNN), XGBoost, Random Forests (RF), Long Short-Term Memory networks (LSTM), and Recurrent Neural Networks (RNN). The project offers a methodical approach that includes background information, relevant literature, dataset specifics, suggested techniques, findings, and conclusions. The results demonstrate a clear potential to improve the current sleep schedules with machine learning to achieve the desired goals, with performance metrics showing proximity to baseline expectations. This thesis contributes to the field by advancing the methodology of baby sleep tracking, ultimately aiming to enhance the well-being of infants and ease the challenges faced by parents in managing their babies’ sleep routines

Naomi: a new modelling tool for estimating HIV epidemic indicators at the district level in sub-Saharan Africa.

INTRODUCTION: HIV planning requires granular estimates for the number of people living with HIV (PLHIV), antiretroviral treatment (ART) coverage and unmet need, and new HIV infections by district, or equivalent subnational administrative level. We developed a Bayesian small-area estimation model, called Naomi, to estimate these quantities stratified by subnational administrative units, sex, and five-year age groups. METHODS: Small-area regressions for HIV prevalence, ART coverage and HIV incidence were jointly calibrated using subnational household survey data on all three indicators, routine antenatal service delivery data on HIV prevalence and ART coverage among pregnant women, and service delivery data on the number of PLHIV receiving ART. Incidence was modelled by district-level HIV prevalence and ART coverage. Model outputs of counts and rates for each indicator were aggregated to multiple geographic and demographic stratifications of interest. The model was estimated in an empirical Bayes framework, furnishing probabilistic uncertainty ranges for all output indicators. Example results were presented using data from Malawi during 2016-2018. RESULTS: Adult HIV prevalence in September 2018 ranged from 3.2% to 17.1% across Malawi's districts and was higher in southern districts and in metropolitan areas. ART coverage was more homogenous, ranging from 75% to 82%. The largest number of PLHIV was among ages 35 to 39 for both women and men, while the most untreated PLHIV were among ages 25 to 29 for women and 30 to 34 for men. Relative uncertainty was larger for the untreated PLHIV than the number on ART or total PLHIV. Among clients receiving ART at facilities in Lilongwe city, an estimated 71% (95% CI, 61% to 79%) resided in Lilongwe city, 20% (14% to 27%) in Lilongwe district outside the metropolis, and 9% (6% to 12%) in neighbouring Dowa district. Thirty-eight percent (26% to 50%) of Lilongwe rural residents and 39% (27% to 50%) of Dowa residents received treatment at facilities in Lilongwe city. CONCLUSIONS: The Naomi model synthesizes multiple subnational data sources to furnish estimates of key indicators for HIV programme planning, resource allocation, and target setting. Further model development to meet evolving HIV policy priorities and programme need should be accompanied by continued strengthening and understanding of routine health system data

Two-site recognition of Staphylococcus aureus peptidoglycan by lysostaphin SH3b

Lysostaphin is a bacteriolytic enzyme targeting peptidoglycan, the essential component of the bacterial cell envelope. It displays a very potent and specific activity toward staphylococci, including methicillin-resistant Staphylococcus aureus. Lysostaphin causes rapid cell lysis and disrupts biofilms, and is therefore a therapeutic agent of choice to eradicate staphylococcal infections. The C-terminal SH3b domain of lysostaphin recognizes peptidoglycans containing a pentaglycine crossbridge and has been proposed to drive the preferential digestion of staphylococcal cell walls. Here we elucidate the molecular mechanism underpinning recognition of staphylococcal peptidoglycan by the lysostaphin SH3b domain. We show that the pentaglycine crossbridge and the peptide stem are recognized by two independent binding sites located on opposite sides of the SH3b domain, thereby inducing a clustering of SH3b domains. We propose that this unusual binding mechanism allows synergistic and structurally dynamic recognition of S. aureus peptidoglycan and underpins the potent bacteriolytic activity of this enzyme

Latent LytM at 1.3A resolution

LytM, an autolysin from Staphylococcus aureus, is a Zn2 dependent glycyl glycine endopeptidase with a characteristic HxH motif that belongs to the lysostaphin type MEROPS M23 37 of metallopeptidases. Here, we present the 1.3 crystal structure of LytM, the first structure of a lysostaphin type peptidase. In the LytM structure, the Zn2 is tetrahedrally coordinated by the side chains of N117, H210, D214 and H293, the second histidine of the HxH motif. Although close to the active site, H291, the first histidine of the HxH motif, is not directly involved in Zn2 coordination, and there is no water molecule in the coordination sphere of the Zn2 , suggesting that the crystal structure shows a latent form of the enzyme. Although LytM has not previously been considered as a proenzyme, we show that a truncated version of LytM that lacks the N terminal part with the poorly conserved Zn2 ligand N117 has much higher specific activity than full length enzyme. This observation is consistent with the known removal of profragments in other lysostaphin type proteins and with a prior observation of an active LytM degradation fragment in S. aureus supernatant. The asparagine switch in LytM is analogous to the cysteine switch in pro matrix metalloprotease