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1. Buffered or impaired: Maternal anemia, inflammation and breast milk macronutrients in northern Kenya

   https://doi.org/10.1002/ajpa.23752  

   Fujita, Masako; Paredes Ruvalcaba, Nerli; Wander, Katherine; Corbitt, Mary; Brindle, Eleanor (December 2018, American Journal of Physical Anthropology)

   Abstract BackgroundMaternal anemia has adverse consequences for the mother‐infant dyad. To evaluate whether and how milk nutrient content may change in ways that could “buffer” infants against the conditions underlying maternal anemia, this study assessed associations between milk macronutrients and maternal iron‐deficiency anemia (IDA), non‐iron‐deficiency anemia (NIDA), and inflammation. MethodsA secondary analysis of cross‐sectional data and milk from northern Kenya was conducted (n = 204). The combination of hemoglobin and transferrin receptor defined IDA/NIDA. Elevated serum C‐reactive protein defined acute inflammation. The effects of IDA, NIDA, and inflammation on milk macronutrients were evaluated in regression models. ResultsIDA (β = 0.077,p =.022) and NIDA (β = 0.083,p =.100) predicted higher total protein (ln). IDA (β = −0.293,p =.002), NIDA (β = −0.313,p =.047), and inflammation (β = −0.269,p =.007) each predicted lower fat (ln); however, anemia accompanying inflammation predictedhigherfat (β = 0.655,p =.007 for IDA and β = 0.468,p =.092 for NIDA). NIDA predicted higher lactose (β = 1.020,p =.003). ConclusionsMilk macronutrient content both increases and decreases in the presence of maternal anemia and inflammation, suggesting a more complicated and dynamic change than simple impairment of nutrient delivery during maternal stress. Maternal fat delivery to milk may be impaired under anemia. Mothers may buffer infant nutrition against adverse conditions or poor maternal health by elevating milk protein (mothers with IDA/NIDA), lactose (mothers with NIDA), or fat (mothers with anemiaandinflammation). This study demonstrates the foundational importance of maternal micronutrient health and inflammation or infection for advancing the ecological understanding of human milk nutrient variation. 

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2. Automated Assessment of Student Self-explanation During Source Code Comprehension

   https://doi.org/10.32473/flairs.v35i.130540  

   Jeevan Chapagain, Lasang Tamang (May 2022, The International FLAIRS Conference Proceedings)

   Roman Bartak and Fazel Keshtkar and Michael Franklin (Ed.)

   This paper presents a novel method to automatically assess self-explanations generated by students during code comprehension activities. The self-explanations are produced in the context of an online learning environment that asks students to freely explain Java code examples line-by-line. We explored a number of models consisting of textual features in conjunction with machine learning algorithms such as Support Vector Regression (SVR), Decision Trees (DT), and Random Forests (RF). Support Vector Regression (SVR) performed best having a correlation score with human judgments of 0.7088. The best model used a combination of features such as semantic measures obtained using a Sentence BERT pre-trained model and from previously developed semantic algorithms used in a state-of-the-art intelligent tutoring system. 

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3. Variation in breast milk macronutrient contents by maternal anemia and hemoglobin concentration in northern Kenya

   https://doi.org/10.1002/ajhb.23238  

   Corbitt, Mary; Paredes Ruvalcaba, Nerli; Fujita, Masako (March 2019, American Journal of Human Biology)

   Abstract ObjectivesThis study explored differing levels of macronutrients in breast milk in relation to maternal anemia and hemoglobin. MethodsArchived milk specimens and data from a cross‐sectional sample of 208 breastfeeding mothers in northern Kenya, originally collected in 2006, were analyzed; data included milk fat, maternal hemoglobin concentration, and anemia status (anemia defined as hemoglobin <12 g/dL). Total protein and lactose were measured and energy was calculated. To explore the association between milk outcomes (fat, protein, lactose, and energy) and anemia, regression models were constructed with and without adjustment for maternal age, parity, and time (days) postpartum. The same models were constructed using hemoglobin as a continuous predictor in lieu of dichotomous anemia to explore the role of hemoglobin levels and anemia severity in predicting milk outcomes. ResultsThe group comparison indicated significantly higher milk protein and lower milk fat for anemic mothers relative to nonanemic counterparts. After adjustment for maternal age, parity, and time postpartum, maternal anemia was associated with significantly higher milk protein (P = 0.001) and significantly lower milk fat (P = 0.025). Hemoglobin had a significant inverse relationship with milk protein (P = 0.017) and a marginally significant positive relationship with milk fat (P = 0.060) after adjusting for the maternal variables. Neither anemia nor hemoglobin was significant in predicting lactose or milk energy. ConclusionsMaternal anemia and hemoglobin concentration may be associated with complex changes in milk macronutrients. Future research should clarify the impact of maternal anemia on a range of breast milk components while accounting for other maternal characteristics. 

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4. An STL-Based Formulation of Resilience in Cyber-Physical Systems

   https://doi.org/10.1007/978-3-031-15839-1_7  

   Chen, Hongkai; Lin, Shan; Smolka, Scott A.; Paoletti, Nicola (August 2022, Formal Modeling and Analysis of Timed Systems)

   Bogomolov, Sergiy; Parker, David (Ed.)

   Resiliency is the ability to quickly recover from a violation and avoid future violations for as long as possible. Such a property is of fundamental importance for Cyber-Physical Systems (CPS), and yet, to date, there is no widely agreed-upon formal treatment of CPS resiliency. We present an STL-based framework for reasoning about resiliency in CPS in which resiliency has a syntactic characterization in the form of an STL-based Resiliency Specification (SRS). Given an arbitrary STL formula φ, time bounds α and β, the SRS of φ, Rα,β (φ), is the STL formula ¬φU[0,α]G[0,β)φ, specifying that recovery from a violation of φ occur within time α (recoverability), and subsequently that φ be maintained for duration β (durability). These R-expressions, which are atoms in our SRS logic, can be combined using STL operators, allowing one to express composite resiliency specifications, e.g., multiple SRSs must hold simultaneously, or the system must eventually be resilient. We define a quantitative semantics for SRSs in the form of a Resilience Satisfaction Value (ReSV) function r and prove its soundness and completeness w.r.t. STL’s Boolean semantics. The r-value for Rα,β (φ) atoms is a singleton set containing a pair quantifying recoverability and durability. The r-value for a composite SRS formula results in a set of non-dominated recoverability-durability pairs, given that the ReSVs of subformulas might not be directly comparable (e.g., one subformula has superior durability but worse recoverability than another). To the best of our knowledge, this is the first multi-dimensional quantitative semantics for an STL-based logic. Two case studies demonstrate the practical utility of our approach. https://doi.org/10.1007/978-3-031-15839-1_7 

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5. Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

   https://doi.org/10.1093/gigascience/giaa133  

   Rokem, Ariel; Kay, Kendrick (November 2020, GigaScience)

   null (Ed.)

   Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets. 

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