Learning a local-variable model of aromatic and conjugated systems

A collection of new approaches to building and training neural networks, collectively referred to as deep learning, are attracting attention in theoretical chemistry. Several groups aim to replace computationally expensive <i>ab initio</i> quantum mechanics calculations with learned estimators. This raises questions about the representability of complex quantum chemical systems with neural networks. Can local-variable models efficiently approximate nonlocal quantum chemical features? Here, we find that convolutional architectures, those that only aggregate information locally, cannot efficiently represent aromaticity and conjugation in large systems. They cannot represent long-range nonlocality known to be important in quantum chemistry. This study uses aromatic and conjugated systems computed from molecule graphs, though reproducing quantum simulations is the ultimate goal. This task, by definition, is both computable and known to be important to chemistry. The failure of convolutional architectures on this focused task calls into question their use in modeling quantum mechanics. To remedy this heretofore unrecognized deficiency, we introduce a new architecture that propagates information back and forth in waves of nonlinear computation. This architecture is still a local-variable model, and it is both computationally and representationally efficient, processing molecules in sublinear time with far fewer parameters than convolutional networks. Wave-like propagation models aromatic and conjugated systems with high accuracy, and even models the impact of small structural changes on large molecules. This new architecture demonstrates that some nonlocal features of quantum chemistry can be efficiently represented in local variable models

Modeling reactivity to biological macromolecules with a deep multitask network

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural networkthe XenoSite reactivity modelusing literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model’s performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity

The influence of instructional materials on mathematics achievement of senior secondary students in Akamkpa Local Government Area of Cross River State, Nigeria

Having observed the reckless abandonment of professional codes and ethics of teaching soon after certification, and specifically the subterfuge of the use of instructional materials in lesson delivery, the researchers set out to investigate the state-of-the-art in terms of availability and use of mathematical instructional materials, and the influence of these on students’ achievement in the subject. Three instruments, a 20-item Mathematics Teacher Instructional Materials Availability Questionnaire (TIMAQ), a 20-item Mathematics Teacher Instructional Material Use Questionnaire (TIMUQ) and a 30-item Multiple Choice Achievement Test (MCAT) were developed, validated (test-retest reliability coefficients of 0.87 and 0.97 respectively for TIMAQ and TIMUQ, and KR-20 coefficient of 0.89 with mean 40.8, S.D. 11.33) for MCAT. Two hundred (200) students (20 per school comprising 100 male and 100 female) were selected from the ten public secondary schools in Akamkpa Local Government Area by stratified random technique, and two (2) Mathematics teachers per school for the study. Results of the simple percentage and independent t-test analyses revealed the non-availability and non use of instructional materials in Mathematics instruction, as well as significant achievement differences between materials-available and non-available schools on one hand and achievement of students from material-used and non–used schools on the other. Useful recommendations were therefore made based on these findings.Keywords: mathematics instructional materials, students’ achievement, availability of instructional materials, use of instructional material

Impact of Aspirin Supplementation for Pre-Eclampsia Prevention on Neonatal Outcomes

Introduction. Preeclampsia affects approximately 4.6% of pregnancies worldwide. In 2018, ACOG updated their low dose aspirin (LDA) supplementation recommendation to include pregnant women at moderate risk for preeclampsia. In addition to the potential benefit of LDA supplementation for delaying or preventing preeclampsia, LDA supplementation can affect neonatal outcomes. We study the association of LDA supplementation with six neonatal outcomes: length of stay (LOS) in hospital, NICU admission, hospital readmission, birth weight (BW), one-minute Apgar score, and five-minute Apgar score in a sample of mostly minority pregnant women from Hispanic and Black race/ethnicities. Methods. This was a retrospective study of 634 patients from January 2018 through April 2021.Our main predictor variable was maternal LDA supplementation on 6 neonatal outcomes: NICU admission, neonatal readmission, one and five minute Apgar scores, neonatal BW and hospital LOS. We adjusted for demographics, comorbidities and maternal high- or moderate-risk designation per ACOG guidelines. Results. We found that high-risk designation was associated with neonatal increased rate of NICU admission (OR: 3.80, 95% CI: 2.02, 7.13, p&lt;0.001), LOS (B=0.15, SE=0.04, p&lt;0.001) and decreased birthweight (B=-442.10, SE=75.07, p &lt;0.001). We found no significant association with LDA supplementation and NICU admission, readmission, low one and five minute Apgar scores, BW and LOS. Conclusions. We did not find any association of LDA supplementation with NICU admission, hospital readmission, low one-minute or five-minute Apgar score, birthweight, and LOS. Clinicians recommending maternal LDA supplementation should be aware that LDA supplementation does not appear to provide any benefits for these neonatal outcomes

Impact of Aspirin Supplementation for Pre-Eclampsia Prevention on Neonatal Outcomes

Introduction. Preeclampsia affects approximately 4.6% of pregnancies worldwide. In 2018, ACOG updated their low dose aspirin (LDA) supplementation recommendation to include pregnant women at moderate risk for preeclampsia. In addition to the potential benefit of LDA supplementation for delaying or preventing preeclampsia, LDA supplementation can affect neonatal outcomes. We study the association of LDA supplementation with six neonatal outcomes: length of stay (LOS) in hospital, NICU admission, hospital readmission, birth weight (BW), one-minute Apgar score, and five-minute Apgar score in a sample of mostly minority pregnant women from Hispanic and Black race/ethnicities. Methods. This was a retrospective study of 634 patients from January 2018 through April 2021.Our main predictor variable was maternal LDA supplementation on 6 neonatal outcomes: NICU admission, neonatal readmission, one and five minute Apgar scores, neonatal BW and hospital LOS. We adjusted for demographics, comorbidities and maternal high- or moderate-risk designation per ACOG guidelines. Results. We found that high-risk designation was associated with neonatal increased rate of NICU admission (OR: 3.80, 95% CI: 2.02, 7.13, p&lt;0.001), LOS (B=0.15, SE=0.04, p&lt;0.001) and decreased birthweight (B=-442.10, SE=75.07, p &lt;0.001). We found no significant association with LDA supplementation and NICU admission, readmission, low one and five minute Apgar scores, BW and LOS. Conclusions. We did not find any association of LDA supplementation with NICU admission, hospital readmission, low one-minute or five-minute Apgar score, birthweight, and LOS. Clinicians recommending maternal LDA supplementation should be aware that LDA supplementation does not appear to provide any benefits for these neonatal outcomes

mHealth in China and the United States: How Mobile Technology is Transforming Healthcare in the World's Two Largest Economies

In this paper, we explore ways mobile technology can help with these difficulties. Specifically, we look at avenues through which mobile devices boost productivity, aid communications, and help providers improve affordability, access, and treatment. Using data drawn from China and the United States as well as global trends, we look at recent developments andemerging opportunities in mobile health, or mHealth. We argue that mobile technology assists patients, health providers, and policymakers in several different respects. It helps patients by giving them tools to monitor their health conditions and communicate those results to physicians. It enables health providers to connect with colleagues and offers alternative sources of information for patients. It is also an important tool to inform policymakers on health delivery and medical outcomes

Growing Season Air mass Equivalent Temperature (T\u3csub\u3eE\u3c/sub\u3e) in the East Central USA

Equivalent temperature (TE), which incorporates both dry (surface air temperature, T) and moist heat content associated with atmospheric moisture, is a better indicator of overall atmospheric heat content compared to T alone. This paper investigates the impacts of different types of air masses on TE during the growing season (April–September). The study used data from the Kentucky Mesonet for this purpose. The growing season was divided into early (April–May), mid (June–July), and late (August–September). Analysis suggests that TE for moist tropical (MT) air mass was as high as 61 and 81 °C for the early and mid-growing season, respectively. Further analysis suggests that TE for different parts of the growing seasons were statistically significantly different from each other. In addition, TE for different air masses was also statistically significantly different from each other. The difference between TE and T (i.e. TE-T) is smaller under dry atmospheric conditions but larger under moist conditions. For example, in Barren County, the lowest difference (20–10 °C) was 10 °C. It was reported on 18 April 2010, a dry weather day. On the other hand, the highest difference for this site was 48 °C and was reported on 11 August 2010, a humid day

Growing Season Air mass Equivalent Temperature (TE) in the East Central USA

Equivalent temperature (TE), which incorporates both dry (surface air temperature, T) and moist heat content associated with atmospheric moisture, is a better indicator of overall atmospheric heat content compared to T alone. This paper investigates the impacts of different types of air masses on TE during the growing season (April–September). The study used data from the Kentucky Mesonet for this purpose. The growing season was divided into early (April–May), mid (June–July), and late (August–September). Analysis suggests that TE for moist tropical (MT) air mass was as high as 61 and 81 C for the early and mid-growing season, respectively. Further analysis suggests that TE for different parts of the growing seasons were statistically significantly different from each other. In addition, TE for different air masses was also statistically significantly different from each other. The v between TE and T (i.e. TE-T) is smaller under dry atmospheric conditions but larger under moist conditions. For example, in Barren County, the lowest difference (20–10 C) was 10 C. It was reported on 18 April 2010, a dry weather day. On the other hand, the highest difference for this site was 48 C and was reported on 11 August 2010, a humid day

Inhibitory projections from the ventral nucleus of the lateral lemniscus and superior paraolivary nucleus create directional selectivity of frequency modulations in the inferior colliculus: A comparison of bats with other mammals

This review considers four auditory brainstem nuclear groups and shows how studies of both bats and other mammals have provided insights into their response properties and the impact of their convergence in the inferior colliculus (IC). The four groups are octopus cells in the cochlear nucleus, their connections with the ventral nucleus of the lateral lemniscus (VNLL) and the superior paraolivary nucleus (SPON), and the connections of the VNLL and SPON with the IC. The theme is that the response properties of neurons in the SPON and VNLL map closely onto the synaptic response features of a unique subpopulation of cells in the IC of bats whose inputs are dominated by inhibition. We propose that the convergence of VNLL and SPON inputs generates the tuning of these IC cells, their unique temporal responses to tones, and their directional selectivities for frequency modulated (FM) sweeps. Other IC neurons form directional properties in other ways, showing that selective response properties are formed in multiple ways. In the final section we discuss why multiple formations of common response properties could amplify differences in population activity patterns evoked by signals that have similar spectrotemporal features