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A Machine Learning framework for predicting enrollment is proposed. The framework consists of Amazon Web Services SageMaker together with standard Python tools for data analytics, including Pandas, NumPy, MatPlotLib, and ScikitLearn. The tools are deployed with Jupyter Notebooks running on AWS SageMaker. Based on three years of enrollment history, a model is built to compute--individually or in batch mode--probabilities of enrollments for given applicants. These probabilities can then be used during the admissions period to target undecided students. The audience for this paper is both SEM practitioners and technical practitioners in the area of data analytics. Through reading this paper, enrollment management professionals will be able to understand what goes into the preparation of a Machine Learning model to help with predicting admission rates. Technical experts, on the other hand, will gain a blueprint for what is required from them.

Dengue annually infects millions of people from a regionally and seasonally varying dengue virus population circulating as four distinct serotypes. Effective protection against dengue infection and disease requires tetravalent vaccine formulations to stimulate a balanced protective immune response to all four serotypes. However, this has been a challenge to achieve, and several clinical trials with different leading vaccine candidates have demonstrated unbalanced replication and interference of interindividual serotype components, leading to low efficacy and enhanced disease severity for dengue-naïve populations. Production of serotype-specific neutralizing antibodies is largely viewed as a correlate of protection against severe dengue disease. However, the underlying mechanisms that lead to these protective immune responses are not clearly elucidated. In this work, using a stochastic model of B cell affinity maturation, we tested different live-attenuated vaccine constructs with varied viral replication rates and contrasted the initiation and progress of adaptive immune responses during tetravalent vaccination and after dengue virus challenge. Comparison of our model simulations across different disease-severity levels suggested that individual production of high levels of serotype-specific antibodies together with a lower cross-reactive antibody are better correlates for protection. Furthermore, evolution of these serotype-specific antibodies was dependent on the percent of viral attenuation in the vaccine, and production of initial B cell and T cell populations pre- and post-secondary dengue infection was crucial in providing protective immunity for dengue-naïve populations. Furthermore, contrasting disease severity with respect to different dengue serotypes, our model simulations showed that tetravalent vaccines fare better against DENV-4 serotype when compared to other serotypes.

Meiosis produces haploid gametes through a succession of chromosomal events, including pairing, synapsis, and recombination. Mechanisms that orchestrate these events remain poorly understood. We found that the SUMO (small ubiquitin-like modifier)-modification and ubiquitin-proteasome systems regulate the major events of meiotic prophase in mouse. Interdependent localization of SUMO, ubiquitin, and proteasomes along chromosome axes was mediated largely by RNF212 and HEI10, two E3 ligases that are also essential for crossover recombination. RNF212-dependent SUMO conjugation effected a checkpointlike process that stalls recombination by rendering the turnover of a subset of recombination factors dependent on HEI10-mediated ubiquitylation. We propose that SUMO conjugation establishes a precondition for designating crossover sites via selective protein stabilization. Thus, meiotic chromosome axes are hubs for regulated proteolysis via SUMO-dependent control of the ubiquitin-proteasome system.

Tri-n-butyl phosphate (TBP), a representative of neutral organophosphorous ligands, is an important extractant used in the solvent extraction process for the recovery of uranium and plutonium from spent nuclear fuel. Microscopic pictures of TBP isomerism and its behavior in n-dodecane diluent were investigated utilizing MD simulations with previously optimized force field parameters for TBP and n-dodecane. Potential mean force (PMF) calculations on a single TBP molecule show seven probable TBP isomers. Radial distribution functions (RDFs) of TBP suggest the existence of TBP trimers at high TBP concentrations in addition to dimers. 2D PMF calculations were performed to determine the angle and distance criteria for TBP trimers. The dimerization and trimerization constants of TBP in n-dodecane were obtained and match our own experimental values using the FTIR technique. The new insights into the conformational behaviors of the TBP molecule as a monomer and as part of an aggregate could greatly aid in the understanding of the complexation between TBP and metal ions in a solvent extraction system.

Tri-n-butyl phosphate (TBP), a representative of neutral organophosphorous metal-ion-extracting reagents, is an important ligand used in solvent extraction processes for the recovery of uranium and plutonium from spent nuclear fuel, as well as other non-nuclear applications. Ligand-ligand and organic solvent-ligand interactions play an important role in these processes. The self-association behavior of TBP in various alkane diluents of different chain lengths (8, 12, and 16 carbons) and a branched alkane (iso-octane) was investigated by Fourier transform infrared spectroscopic measurements. By careful deconvolution of the spectra into multiple peaks, our results indicate that TBP self-associates to form not only dimers, as previous studies showed, but also trimers in the practical concentration range. Using a mathematical fitting procedure, the dimerization and trimerization constants were determined. As expected, these equilibrium constants are dependent on the solvent used. As the alkane chain for linear hydrocarbon solvents becomes longer, dimerization decreases whereas trimerization increases. For the more branched hydrocarbon, we observe a significantly higher dimerization constant. These effects are most likely due to the intermolecular van der Waals interactions between the butyl tails of each TBP molecule and the diluent hydrocarbon chain as all solvents in this study are relatively nonpolar.

Perylene-3,4,9,10-tetracarboxylic diimides (PTCDIs) are a well-known class of organic materials. Recently, these molecules have been incorporated within DNA as base surrogates, finding ready applications as probes of DNA structure and function. However, the assembly dynamics and kinetics of PTCDI DNA base surrogates have received little attention to date. Herein, we employ constant temperature molecular dynamics simulations to gain an improved understanding of the assembly of PTCDI dimers and trimers. We also use replica-exchange molecular dynamics simulations to elucidate the energetic landscape dictating the formation of stacked PTCDI structures. Our studies provide insight into the equilibrium configurations of multimeric PTCDIs and hold implications for the construction of DNA-inspired systems from perylene-derived organic semiconductor building blocks.

Three-dimensional networks of nanofibers, which are formed through self-assembly of peptide amphiphiles, serve as a biomimetic hydrogel scaffold for tissue engineering. With an emphasis to improve hydrogel properties for cell-specific behavior, a better understanding between structural characteristics and physical properties of the macroscopic gel is sought. Large-scale molecular dynamics simulations were performed on two PA sequences with identical composition (palmitoyl-V3A3E3 and palmitoyl-A3V3E3) showing different self-assembly kinetic mechanisms. While both sequences yielded cylindrical nanofibers, these structures have contrasting internal arrangement with respect to the hydrophobic core; the former is continuous with predominately alkyl tails, whereas the latter is disjointed with interconnecting micelles. Two additional sequences (palmitoyl-V6E3 and palmitoyl-A6E3) were examined to determine the effects of a homogeneous β-sheet forming segment that is either strongly or mildly hydrophobic on self-assembly. Results from this study indicate that internal structural arrangement of nanofibers can provide a correlation with structural stability and mechanical behavior of hydrogel nanostructures.

Stimuli-responsive, self-assembling nanomaterials hold a great promise to revolutionize medicine and technology. However, current discovery is slow and often serendipitous. Here we report a multiscale modeling study to elucidate the pH-controlled self-assembly of nanofibers from the peptide amphiphiles, palmitoyl-I-A3E4-NH2. The coarse-grained simulations revealed the formation of random-coil based spherical micelles at strong electrostatic repulsion. However, at weak or no electrostatic repulsion, the micelles merge into a nanofiber driven by the β-sheet formation between the peptide segments. The all-atom constant pH molecular dynamics revealed a cooperative transition between random coil and β-sheet in the pH range 6-7, matching the CD data. Interestingly, although the bulk pKa is more than one unit below the transition pH, consistent with the titration data, the highest pKa's coincide with the transition pH, suggesting that the latter may be tuned by modulating the pKa's of a few solvent-buried Glu side chains. Together, these data offer, to our best knowledge, the first multiresolution and quantitative view of the pH-dependent self-assembly of nanofibers. The novel protocols and insights gained are expected to advance the computer-aided design and discovery of pH-responsive nanomaterials.

Hanoi is the second largest city in Vietnam. The effects of climate change, coupled with rapid, unchecked urbanization and population increase are resulting in a severe rise in incidents of heat stress, which has serious ramifications for community health. In this study, a multivariable linear regression, heat index (HI) methods, and climate change scenarios for Vietnam in 2020 are used as input data to calculate the daily maximum heat index (HImax) for Hanoi during 2024-2028. Findings indicate that HImax at danger level has already occurred and will happen again in the future, with seasonal characteristics mainly distributed in June, July, August. For the years 2024-2028, HImax at the danger level increases in two scenarios compared to 2017-2021. Notably, the number of days with HImax at the extreme danger level, which may be the main cause of heat stroke, has 0.4 days/year occurring during 2017-2021. However, it will also appear in the years 2024-2028 in both RCP 4.5 and RCP 8.5 scenarios from June to August, at 0.6 days/year and 2.6 days/year, respectively. Knowing the daily maximum heat index for the future at specific times is crucial information for acting on the risk of heat stress in Hanoi given the current danger posed by climate change. [ABSTRACT FROM AUTHOR]

Even minor fluctuations in the carbon content in forest soil can significantly impact atmospheric carbon concentrations and future global climate patterns. Therefore, investigating the variation characteristics of soil organic carbon (SOC) in tropical forests is essential for comprehending the carbon sequestration potential of forest soil. This study delves into SOC dynamics within the natural forest types of Dongnai Culture and Nature Reserve in southeastern Vietnam. Nine 1000 m² plots were sampled across rich, medium, and poor forests. Soil samples were collected from depths ranging from 0 to 60 cm. The findings revealed increased SOC content from poor to rich forests, with a decrease in depth. The total carbon (C) stocks ranged from 92.20 Mg C ha-1 in poor forests to 124.30 Mg C ha-1 in rich forests within the top 60 cm. Over 81% of soil C was concentrated in the upper 40 cm. Factors such as soil water content and slope gradient emerged as crucial determinants affecting SOC distribution. These results underscore the potential of forest management practices to augment SOC levels, thereby offering valuable insights for estimating carbon stocks in the forest ecosystems of southeastern Vietnam. [ABSTRACT FROM AUTHOR]

The dengue virus circulates as four distinct serotypes, where a single serotype infection is typically asymptomatic and leads to acquired immunity against that serotype. However, the developed immunity to one serotype is thought to underlie the severe manifestation of the disease observed in subsequent infections from a different serotype. We developed a stochastic model of the adaptive immune response to dengue infections. We first delineated the mechanisms initiating and sustaining adaptive immune responses during primary infections. We then contrasted these immune responses during secondary infections of either a homotypic or heterotypic serotype to understand the role of pre-existing and reactivated immune pathways on disease severity. Comparison of non-symptomatic and severe cases from heterotypic infections demonstrated that overproduction of specific antibodies during primary infection induces an enhanced population of cross-reactive antibodies during secondary infection, ultimately leading to severe disease manifestations. In addition, the level of disease severity was found to correlate with immune response kinetics, which was dependent on beginning lymphocyte levels. Our results detail the contribution of specific lymphocytes and antibodies to immunity and memory recall that lead to either protective or pathological outcomes, allowing for the understanding and determination of mechanisms of protective immunity.