Strongly separated pairs of core electrons in computed ground states of small molecules

We have performed full configuration interaction computations of the ground states of the molecules Be, BeH_2, Li, LiH, B, and BH and verified that the core electrons constitute "separated electron pairs." These separated pairs of core electrons have nontrivial structure; the core pair does not simply occupy a single spatial orbital. Our method of establishing the presence of separated electron pairs is direct and conclusive. We do not fit a separated pair model; we work with the wavefunctions of interest directly. To establish that a given group of spin-orbitals contains a quasi-separated pair, we verify by direct computation that the quantum state of the electrons that occupy those spin-orbitals is nearly a pure 2-electron state.Comment: To appear in Computational and Theoretical Chemistr

Effect of hydrolyzed milk on the adhesion of Lactobacilli to intestinal cells

Milk is an essential part of the human diet and is undoubtedly a major calcium source in human nutrition, accepted well by most individuals. Knowledge on how the components from dairy products support or reduce the adherence of probiotics to the intestinal epithelium is limited. The purpose of this study was to investigate the effect of acid-hydrolyzed milk on the adhesion ability of two potentially probiotic strains (Lactobacillus plantarum S2, Lactobacillus gasseri R) to in vitro human intestinal epithelial model consisting of Caco-2 and mucus-secreting HT29-MTX co-culture. The adhesion of our tested strains L. gasseri and L. plantarum was 4.74 and 7.16%, respectively, when using inoculum of 2 × 108 CFU ml–1. Addition of acid-hydrolyzed milk to co-culture decreased the adherence by 53.7% for L. gasseri R and by 62.2% for L. plantarum S2. The results of this study evidently indicate the potential importance of the food matrix as a factor influencing probiotic colonization of the gut

An Object-Oriented Approach to Knowledge Representation in a Biomedical Domain

An object-oriented approach has been applied to the different stages involved in developing a knowledge base about insulin metabolism. At an early stage the separation of terminological and assertional knowledge was made. The terminological component was developed by medical experts and represented in CORE. An object-oriented knowledge acquisition process was applied to the assertional knowledge. A frame description is proposed which includes features like states and events, inheritance and collaboration. States and events are formalized with qualitative calculus. The terminological knowledge was very useful in the development of the assertional component. It assisteed in understanding the problem domain, and in the implementation stage, it assisted in building good inheritance hierarchies

Laboratory-scale anaerobic sequencing batch reactor for treatment of stillage from fruit distillation

This work describes batch anaerobic digestion tests carried out on stillages, the residue of the distillation process on fruit, in order to contribute to the setting of design parameters for a planned plant. The experimental apparatus was characterized by 3 reactors, each with a useful volume of 5 L. The different phases of the work carried out were: determining the basic components of the Chemical Oxygen Demand (COD) of the stillages; determining the specific production of biogas; and estimating the rapidly biodegradable COD contained in the stillages. In particular, the main goal of the anaerobic digestion tests on stillages was to measure the parameters of Specific Gas Production (SGP) and Gas Production Rate (GPR) in reactors in which stillages were being digested using ASBR (Anaerobic Sequencing Batch Reactor) technology. Runs were developed with increasing concentrations of the feed. The optimal loads for obtaining the maximum SGP and GPR values were 8\u20139 gCOD L-1 and 0.9 gCOD g-1VS

500+ Times Faster Than Deep Learning (A Case Study Exploring Faster Methods for Text Mining StackOverflow)

Deep learning methods are useful for high-dimensional data and are becoming widely used in many areas of software engineering. Deep learners utilizes extensive computational power and can take a long time to train-- making it difficult to widely validate and repeat and improve their results. Further, they are not the best solution in all domains. For example, recent results show that for finding related Stack Overflow posts, a tuned SVM performs similarly to a deep learner, but is significantly faster to train. This paper extends that recent result by clustering the dataset, then tuning very learners within each cluster. This approach is over 500 times faster than deep learning (and over 900 times faster if we use all the cores on a standard laptop computer). Significantly, this faster approach generates classifiers nearly as good (within 2\% F1 Score) as the much slower deep learning method. Hence we recommend this faster methods since it is much easier to reproduce and utilizes far fewer CPU resources. More generally, we recommend that before researchers release research results, that they compare their supposedly sophisticated methods against simpler alternatives (e.g applying simpler learners to build local models)

Mechanistic Modeling of Mycobacterium tuberculosis Infection in Murine Models for Drug and Vaccine Efficacy Studies.

Tuberculosis (TB) drug, regimen, and vaccine development rely heavily on preclinical animal experiments, and quantification of bacterial and immune response dynamics is essential for understanding drug and vaccine efficacy. A mechanism-based model was built to describe Mycobacterium tuberculosis H37Rv infection over time in BALB/c and athymic nude mice, which consisted of bacterial replication, bacterial death, and adaptive immune effects. The adaptive immune effect was best described by a sigmoidal function on both bacterial load and incubation time. Applications to demonstrate the utility of this baseline model showed (i) the important influence of the adaptive immune response on pyrazinamide (PZA) drug efficacy, (ii) a persistent adaptive immune effect in mice relapsing after chemotherapy cessation, and (iii) the protective effect of vaccines after M. tuberculosis challenge. These findings demonstrate the utility of our model for describing M. tuberculosis infection and corresponding adaptive immune dynamics for evaluating the efficacy of TB drugs, regimens, and vaccines

Hasil Belajar Kognitif Peserta Didik Melalui Penerapan Model Pembelajaran Inkuiri Berbantuan Media Simulasi PhET Kelas XI IPA SMA Negeri 1 Anggana Materi Fluida Statis

Model pembelajaran inkuiri berbantuan media simulasi PhET adalah suatu tahapan pembelajaran mengikuti langkah-langkah model pembelajaran inkuiri dengan dibantu aplikasi simulasi PhET sebagai laboratorium virtual. Penelitian ini bertujuan untuk mengetahui rata-rata dan peningkatan hasil belajar kognitif peserta didik melalui model pembelajaran inkuiri berbantuan media simulasi PhET pada materi fluida statik. Penelitian ini dilakukan di SMA Negeri 1 Anggana dengan jumlah sampel sebanyak 30 peserta didik pada kelas XI IPA 1. Pengumpulan data dalam penelitian ini menggunakan teknik tes. Berdasarkan hasil penelitian diperoleh nilai rata-rata hasil belajar kognitif peserta didik sebesar 75. Hal ini menunjukkan bahwa rata-rata peserta didik mendapatkan nilai dengan kategori baik. Hasil N-Gain diperoleh rata-rata sebesar 69,7 dengan perolehan 47 % untuk kriteria N-Gain sedang dan perolehan 53 % untuk kriteria N-Gain sedang. Hal ini menunjukan bahwa terdapat  peningkatan pada hasil belajar kognitif peserta didik kelas XI IPA 1 materi fluida statik dengan kategori N-Gain sedang