Suite Of Programs Research Articles

Digital Health Innovations, Tools, and Resources to Support Helping Babies Survive Programs

The Helping Babies Survive (HBS) initiative features a suite of evidence-based curricula and simulation-based training programs designed to provide health workers in low- and middle-income countries (LMICs) with the knowledge, skills, and competencies to prevent, recognize, and manage leading causes of newborn morbidity and mortality. Global scale-up of HBS initiatives has been rapid. As HBS initiatives rolled out across LMIC settings, numerous bottlenecks, gaps, and barriers to the effective, consistent dissemination and implementation of the programs, across both the pre- and in-service continuums, emerged. Within the first decade of expansive scale-up of HBS programs, mobile phone ownership and access to cellular networks have also concomitantly surged in LMICs. In this article, we describe a number of HBS digital health innovations and resources that have been developed from 2010 to 2020 to support education and training, data collection for monitoring and evaluation, clinical decision support, and quality improvement. Helping Babies Survive partners and stakeholders can potentially integrate the described digital tools with HBS dissemination and implementation efforts in a myriad of ways to support low-dose high-frequency skills practice, in-person refresher courses, continuing medical and nursing education, on-the-job training, or peer-to-peer learning, and strengthen data collection for key newborn care and quality improvement indicators and outcomes. Thoughtful integration of purpose-built digital health tools, innovations, and resources may assist HBS practitioners to more effectively disseminate and implement newborn care programs in LMICs, and facilitate progress toward the achievement of Sustainable Development Goal health goals, targets, and objectives.

Achieving Country-Wide Scale for Helping Babies Breathe and Helping Babies Survive.

Helping Babies Breathe (HBB) was piloted in 2009 as a program targeted to reduce neonatal mortality (NM). The program has morphed into a suite of programs termed Helping Babies Survive that includes Essential Care for Every Baby. Since 2010, the HBB and Helping Babies Survive training programs have been taught to >850 000 providers in 80 countries. Initial HBB training is associated with a significant improvement in knowledge and skills. However, at refresher training, there is a knowledge-skill gap evident, with a falloff in skills. Accumulating evidence supports the role for frequent refresher resuscitation training in facilitating skills retention. Beyond skill acquisition, HBB has been associated with a significant reduction in early NM (<24 hours) and fresh stillbirth rates. To evaluate the large-scale impact of the growth of skilled birth attendants, we analyzed NM rates in sub-Saharan Africa (n = 11) and Nepal (as areas of growing HBB implementation). All have revealed a consistent reduction in NM at 28 days between 2009 and 2018; a mean reduction of 5.34%. The number of skilled birth attendants, an indirect measure of HBB sustained rollout, reveals significant correlation with NM, fresh stillbirth, and perinatal mortality rates, highlighting HBB's success and the need for continued efforts to train frontline providers. A novel live newborn resuscitation trainer as well as a novel app (HBB Prompt) have been developed, increasing knowledge and skills while providing simulation-based repeated practice. Ongoing challenges in sustaining resources (financial and other) for newborn programming emphasize the need for innovative implementation strategies and training tools.

Can addressing food literacy across the life cycle improve the health of vulnerable populations? A case study approach.

Food literacy programs aim to improve an individual's knowledge and skills in the planning, management, selection, preparation and eating of healthy foods. Unhealthy dietary patterns across the life cycle are associated with an increased risk of chronic disease. Foodbank WA's Healthy Food for All® (HFFA) team has made addressing health inequity a priority, by enhancing food literacy skills of vulnerable people across the lifespan. A case study approach was utilised to explore HFFA's suite of evidence-based food literacy programs: Food Sensations® (FS) for Parents (of 0-5year olds), FS for Schools (kindergarten to Year 12), Fuel Your Future (adolescents 12-18years), and FS for Adults (FSA) (18years and over). These programs are contextualised to meet the needs of vulnerable groups at all life stages. In the last decade the HFFA team have delivered 5047 food literacy sessions to over 62000 vulnerable Western Australians. Evaluation results demonstrate the FS programs are successful at improving vulnerable people's food literacy skills and dietary behaviours. For example, over 70% of participants make at least one positive food behaviour change after attending FSA. By targeting vulnerable people of all ages, HFFA's food literacy programs provide multiple opportunities for intervention, to enhance health behaviours, and therefore reduce risk of chronic disease. SO WHAT?: Food literacy programs are one effective strategy that is complementary in helping to address the health inequities in Australia. Government and broader community investment in food literacy initiatives is vital to improving the health outcomes of vulnerable populations.

The aqueous solution thermodynamics of niobium under conditions of environmental and biological interest

The available equilibrium constant data for reactions of niobium in aqueous solution have been critically evaluated and entered into a thermodynamic database. The relevant literature is sparse and, where comparable, often inconsistent. Problems include poor characterisation of solid phases and limitations with older analytical techniques. Nevertheless, the JESS suite of computer programs has been applied where possible to achieve thermodynamic consistency and provide a critically-selected set of equilibrium constants that could be useful for modelling purposes. However, the utility of the resulting set is evidently limited by a lack of adequate data. This is unfortunate given the significance of niobium in radioactive waste management and certain catalytic applications.

Analytic Gradients of Approximate Coupled Cluster Methods with Quadruple Excitations.

The analytic gradient theory for both iterative and noniterative coupled-cluster approximations that include connected quadruple excitations is presented. These methods include, in particular, CCSDT(Q), which is an analog of the well-known CCSD(T) method which starts from the full CCSDT method rather than CCSD. The resulting methods are implemented in the CFOUR program suite, and pilot applications are presented for the equilibrium geometries and harmonic vibrational frequencies of the simplest Criegee intermediate, CH2OO, as well as to the isomerization pathway between dimethylcarbene and propene. While all methods are seen to approximate the full CCSDTQ results well for "well-behaved" systems, the more difficult case of the Criegee intermediate shows that CCSDT(Q), as well as certain iterative approximations, display problematic behavior.

SHFuzz: Selective Hybrid Fuzzing with Branch Scheduling Based on Binary Instrumentation

Hybrid fuzzing is a popular software testing technique that combines random fuzzing with concolic execution. It is widely used in the security domain known for its ability to find deeply hidden vulnerabilities and reach high code coverage. Hybrid fuzzing is based on negating branches in the execution path of a specific input to generate new test cases. However, due to numerous inputs and related branches, it does not show the best of its effectiveness without input and branch selection methods. In this paper, we systematically analyze the branch scheduling problem in the internal attributes of hybrid fuzzing, focusing on the synchronization mechanism. To solve the problems, we propose the Selective Hybrid Fuzzing (SHF) approach with branch scheduling based on binary instrumentation. There are two major parts to the SHF approach: (1) we propose a critical branch selection algorithm to select critical branches by three metrics: hit accuracy, solvability, and complexity; (2) we propose a priority score calculation algorithm to select inputs by the number of critical branches. With the SHF approach, we choose only the branches that can be negated to generate new coverage, instead of repeatedly executing the same branches and generating duplicates of inputs. We implement a hybrid fuzzer called SHFuzz with our SHF approach and compare it with the state-of-the-art hybrid fuzzer QSYM. In the evaluation, SHFuzz outperforms QSYM in 20 real-world applications from the Google Fuzzer Test Suite and other program suites in a 12 h test. On average, SHFuzz achieves 8.40% more code coverage and 100 more unique crashes in each application. Our work also finds existing vulnerabilities 7.85× faster than QSYM. We also find new bugs by SHFuzz, which QSYM fails to find. Our evaluation shows that the selective hybrid fuzzing approach can reduce the number of branches executed in concolic execution, enhancing hybrid fuzzing on code coverage and bug finding capabilities.

Fragment-based determination of a proteinase K structure from MicroED data using ARCIMBOLDO_SHREDDER

Structure determination of novel biological macromolecules by X-ray crystallography can be facilitated by the use of small structural fragments, some of only a few residues in length, as effective search models for molecular replacement to overcome the phase problem. Independence from the need for a complete pre-existing model with sequence similarity to the crystallized molecule is the primary appeal of ARCIMBOLDO, a suite of programs which employs this ab initio algorithm for phase determination. Here, the use of ARCIMBOLDO is investigated to overcome the phase problem with the electron cryomicroscopy (cryoEM) method known as microcrystal electron diffraction (MicroED). The results support the use of the ARCIMBOLDO_SHREDDER pipeline to provide phasing solutions for a structure of proteinase K from 1.6 Å resolution data using model fragments derived from the structures of proteins sharing a sequence identity of as low as 20%. ARCIMBOLDO_SHREDDER identified the most accurate polyalanine fragments from a set of distantly related sequence homologues. Alternatively, such templates were extracted in spherical volumes and given internal degrees of freedom to refine towards the target structure. Both modes relied on the rotation function in Phaser to identify or refine fragment models and its translation function to place them. Model completion from the placed fragments proceeded through phase combination of partial solutions and/or density modification and main-chain autotracing using SHELXE. The combined set of fragments was sufficient to arrive at a solution that resembled that determined by conventional molecular replacement using the known target structure as a search model. This approach obviates the need for a single, complete and highly accurate search model when phasing MicroED data, and permits the evaluation of large fragment libraries for this purpose.

Extrapolation of hydroxytyrosol and its analogues as potential anti-inflammatory agents

Discovery of potential lead molecule is a challenging, and complex process which require lots of money, patience, and manpower. Human beings are using natural products, predominantly secondary metabolites, for this purpose since ancient time and they are still working on them as a potent source for drug discovery due to their wide structural diversity. Phenolic phytochemicals such as hydroxytyrosol and tyrosol are natural antioxidant and involved in many biological disease cure. Herein, we have carried out the quantum chemical calculations for conformational analysis, geometry optimization and computation of electronic as well as optical properties of hydroxytyrosol and its analogues (1a–1k) in terms of density functional theory by using Gaussian 09 program suite. The eventual charge transfer within the molecules has been confirmed by the analysis of frontier molecular orbitals. The molecular docking studies of 1a–1k with cyclooxygenase-2 showed the noticeable binding affinity as compared to other nonsteroidal anti-inflammatory drugs viz. aspirin, naproxen and celecoxib. Computation of pharmacokinetics and pharmacological properties confirmed the lead/drug like potential of these screened molecules. Furthermore, the molecular dynamics simulation of best three docked ligands (1f, 1h and 1k)-receptor complex and their binding free energy calculations reveals that these molecules bind in the catalytic cavity of cyclooxygenase-2 and found stable during the 100 ns of simulation. Communicated by Ramaswamy H. Sarma

A Learning-Based Approach to Synthesizing Invariants for Incomplete Verification Engines

We propose a framework for synthesizing inductive invariants for incomplete verification engines, which soundly reduce logical problems in undecidable theories to decidable theories. Our framework is based on the counterexample guided inductive synthesis principle and allows verification engines to communicate non-provability information to guide invariant synthesis. We show precisely how the verification engine can compute such non-provability information and how to build effective learning algorithms when invariants are expressed as Boolean combinations of a fixed set of predicates. Moreover, we evaluate our framework in two verification settings, one in which verification engines need to handle quantified formulas and one in which verification engines have to reason about heap properties expressed in an expressive but undecidable separation logic. Our experiments show that our invariant synthesis framework based on non-provability information can both effectively synthesize inductive invariants and adequately strengthen contracts across a large suite of programs. This work is an extended version of a conference paper titled “Invariant Synthesis for Incomplete Verification Engines”.

Thermal Decomposition of CxF2x+1C(O)OONO2 (x = 2, 3, 4).

The atmospheric degradation of molecules containing the CxF2x+1C(O) moiety, such as perfluoroaldehydes CxF2x+1C(O)H (x = 2-4) formed in the degradation of telomeric alcohols, could lead to the formation of perfluoroacyl peroxynitrates CxF2x+1C(O)OONO2. The thermal decomposition of the CxF2x+1C(O)OONO2 family (x = 2, 3, 4) was investigated by infrared spectroscopy and computational models. Each peroxynitrate synthesis was performed through the photolysis of gas mixtures of the corresponding perfluoroaldehyde, chlorine, nitrogen dioxide, and oxygen. Kinetic analysis for the thermal decomposition of peroxynitrates were performed in the range from 297.0 to 313.7 K at a total pressure of 1000 mbar and the activation energy was experimentally determined. Experimental data were complemented with theoretical data using the Gaussian09 Program Suite. The structures of peroxynitrates were optimized using DFT methods. The activation energies were calculated and investigated taking into account the stereoelectronic effects and using theoretical calculations as well as NBO analysis. The influence of anomeric interaction over the O-N bond was evaluated for all the molecules. Analysis of the results shows that CxF2x+1C(O)OONO2 stability is independent of CxF2x+1 chain length, in contrast to the behavior for perfluoroalkyl peroxynitrates (CxF2x+1OONO2).

How does PEDOT grow like nanoropes? A theoretical investigation in pace with experimental insights

Poly 3–4 ethylene dioxythiophene (PEDOT) is a highly conducting polymer insoluble and intractable when synthesized under normal synthesis conditions. This polymer was found to assume spiral or ropelike nanostructures with some modification in physical properties when grown in presence of a liquid crystal 5CB. The present work focuses on finding out the underlying reason behind the unique morphology of PEDOT theoretically, using density functional theory (DFT) as implemented in the Gaussian 03 suite of programs. From the energy minimized structures of 5CB clusters as well as PEDOT-5CB combinations it was observed that in the clusters of 6 or 10 molecules, 5CB is self-assembled in spiral orientation which might have guided the similar growth of PEDOT. It was also established from molecular orbital studies that the neighboring 5CB environment undergoes some sort of nonbonding interaction with the growing PEDOT chains and has resulted in the modification of solubility and few other properties of PEDOT. Therefore the PEDOT nanostructure obtained under the present synthesis condition is supposed to be a hybrid material having optical and electrical properties dominated by the PEDOT counterpart.

MFIT 1.0.0: Multi-Flow Inversion of Tracer breakthrough curves in fractured and karst aquifers

Abstract. More than half of the Earth's population depends largely or entirely on fractured or karst aquifers for their drinking water supply. Both the characterization and modeling of these groundwater reservoirs are therefore of worldwide concern. Artificial tracer testing is a widely used method for the characterization of solute (including contaminant) transport in groundwater. Tracer experiments consist of a two-step procedure: (1) introducing a conservative tracer-labeled solution into an aquifer, usually through a sinkhole or a well, and (2) measuring the concentration breakthrough curve (BTC) response(s) at one or several downstream monitoring locations, usually spring(s) or pumping well(s). However, the modeling and interpretation of tracer test responses can be a challenging task in some cases, notably when the BTCs exhibit multiple local peaks and/or extensive backward tailing. MFIT (Multi-Flow Inversion of Tracer breakthrough curves) is a new open-source Windows-based computer package for the analytical modeling of tracer BTCs. This software integrates four transport models that are all capable of simulating single- or multiple-peak and/or heavy-tailed BTCs. The four transport models are encapsulated in a general multiflow modeling framework, which assumes that the spatial heterogeneity of an aquifer can be approximated by a combination of independent one-dimensional channels. Two of the MFIT transport models are believed to be new, as they combine the multiflow approach and the double-porosity concept, which is applied at the scale of the individual channels. Another salient feature of MFIT is its compatibility and interface with the advanced optimization tools of the PEST suite of programs. Hence, MFIT is the first BTC fitting tool that allows for regularized inversion and nonlinear analysis of the postcalibration uncertainty of model parameters.

Efficient and Accurate Approach To Estimate Hybrid Functional and Large Basis-Set Contributions to Condensed-Phase Systems and Molecule-Surface Interactions.

We present a graph theoretic approach to adaptively compute contributions from many-body approximations in an efficient manner and perform accurate hybrid density functional theory (DFT) electronic structure calculations for condensed-phase systems. The salient features of the approach are ONIOM-like. (a) The full-system calculation is performed at a lower level of theory (pure DFT) by enforcing periodic boundary conditions. (b) This treatment is then improved through a correction term that captures many-body interactions up to any given order within a higher (in this case, hybrid DFT) level of theory. (c) In the spirit of ONIOM, contributions from the many-body approximations that arise from the higher level of theory [i.e., from (b) above] are included through extrapolation from the lower level calculation. The approach is implemented in a general, system-independent, fashion using the graph-theoretic functionalities available within Python. For example, the individual one-body components within the unit cell are designated as "nodes" within a graph. The interactions between these nodes are captured through edges, faces, tetrahedrons, and so forth, thus building a many-body interaction hierarchy. Electronic energy extrapolation contributions from all of these geometric entities are included within the above-mentioned ONIOM paradigm. The implementation of the method simultaneously uses multiple electronic structure packages. Here, for example, we present results which use both the Gaussian suite of electronic structure programs and the Quantum ESPRESSO program within a single calculation. Thus, the method integrates both plane-wave basis functions and atom-centered basis functions within a single structure calculation. The method is demonstrated for a range of condensed-phase problems for computing (i) hybrid DFT energies for condensed-phase systems at pure DFT cost and (ii) large, triple-zeta, multiply polarized, and diffuse atom-centered basis-set energies at computational costs commensurate with much smaller sets of basis functions. The methods are demonstrated through calculations performed on (a) homogeneous water surfaces as well as heterogeneous surfaces that contain organic solutes studied using two-dimensional periodic boundary conditions and (b) bulk simulations of water enforced through three-dimensional periodic boundary conditions. A range of structures are considered, and in all cases, the results are in good agreement with those obtained using large atom-centered basis and hybrid DFT calculations on the full periodic systems at significantly lower cost.

Pair Potentials as Machine Learning Features.

Atom pairwise potential functions make up an essential part of many scoring functions for protein decoy detection. With the development of machine learning (ML) tools, there are multiple ways to combine potential functions to create novel ML models and methods. Potential function parameters can be easily extracted; however, it is usually hard to directly obtain the calculated atom pairwise energies from scoring functions. Amber, as one of the most popular suites of modeling programs, has an extensive history and library of force field potential functions. In this work, we directly used the force field parameters in ff94 and ff14SB from Amber and encoded them to calculate atom pairwise energies for different interactions. Two sets of structures (single amino acid set and a dipeptide set) were used to evaluate the performance of our encoded Amber potentials. From the comparison results between energy terms obtained from our encoding and Amber, we find energy difference within ±0.06 kcal/mol for all tested structures. Previously we have shown that the Random Forest (RF) model can help to emphasize more important atom pairwise interactions and ignore insignificant ones [Pei, J.; Zheng, Z.; Merz, K. M. J. Chem. Inf. Model. 2019, 59, 1919-1929]. Here, as an example of combining ML methods with traditional potential functions, we followed the same work flow to combine the RF models with force field potential functions from Amber. To determine the performance of our RF models with force field potential functions, 224 different protein native-decoy systems were used as our training and testing sets We find that the RF models with ff94 and ff14SB force field parameters outperformed all other scoring functions (RF models with KECSA2, RWplus, DFIRE, dDFIRE, and GOAP) considered in this work for native structure detection, and they performed similarly in detecting the best decoy. Through inclusion of best decoy to decoy comparisons in building our RF models, we were able to generate models that outperformed the score functions tested herein both on accuracy and best decoy detection, again showing the performance and flexibility of our RF models to tackle this problem. Finally, the importance of the RF algorithm and force field parameters were also tested and the comparison results suggest that both the RF algorithm and force field potentials are important with the ML scoring function achieving its best performance only by combining them together. All code and data used in this work are available at https://github.com/JunPei000/FFENCODER_for_Protein_Folding_Pose_Selection.

X-Seed 4: updates to a program for small-molecule supramolecular crystallography

X-Seed is a native Microsoft Windows program with three primary functions: (i) to serve as a graphical user interface to the SHELX suite of programs, (ii) to facilitate exploration of crystal packing and intermolecular interactions, and (iii) to generate high-quality molecular graphics artwork suitable for publication and presentation. Development of X-Seed Version 1.0 began in 1998, when point-and-click crystallographic software was still limited in scope and power. Considerable enhancements have been implemented within X-Seed over the past two decades. Of particular importance are support for the SHELX2019 programs (SHELXS, SHELXD, SHELXT and SHELXL) for structure solution and refinement, and MSRoll for rendering void spaces in crystal structures. The current version (i.e. Version 4) of X-Seed has a new interface designed to be more interactive and user friendly, and the software can be downloaded and used free of charge.

AMBER-DYES in AMBER: Implementation of fluorophore and linker parameters into AmberTools.

Molecular dynamics (MD) simulations of explicit representations of fluorescent dyes attached via a linker to a protein allow, e.g., probing commonly used approximations for dye localization and/or orientation or modeling Förster resonance energy transfer. However, setting up and performing such MD simulations with the AMBER suite of biomolecular simulation programs has remained challenging due to the unavailability of an easy-to-use set of parameters within AMBER. Here, we adapted the AMBER-DYES parameter set derived by Graen et al. [J. Chem. Theory Comput. 10, 5505 (2014)] into "AMBER-DYES in AMBER" to generate a force field applicable within AMBER for commonly used fluorescent dyes and linkers attached to a protein. In particular, the computationally efficient graphics processing unit (GPU) implementation of the AMBER MD engine can now be exploited to overcome sampling issues of dye movements. The implementation is compatible with state-of-the-art force fields such as GAFF, GAFF2, ff99SB, ff14SB, lipid17, and GLYCAM_06j, which allows simulating post-translationally modified proteins and/or protein-ligand complexes and/or proteins in membrane environments. It is applicable with frequently used water models such as TIP3P, TIP4P, TIP4P-Ew, and OPC. For ease of use, a LEaP-based workflow was created, which allows attaching (multiple) dye/linker combinations to a protein prior to further system preparation steps. Following the parameter development described by Graen et al. [J. Chem. Theory Comput. 10, 5505 (2014)] and the adaptation steps described here, AMBER-DYES in AMBER can be extended by additional linkers and fluorescent molecules.

Density functional theory calculations of the effect (CH2, CH3, NH3, NH2, OH, CN, NO2) subgroups on the electronic structure of biphenyl molecule

Theoretical study of the effect (CH2, CH3, NH3, NH2, OH, CN, NO2) subgroups on the electronic properties of the biphenyl molecule was completed using density functional theory (DFT) with B3LYP/6-31G [d, p] basis set employing Gaussian 09 suite of programs. The optimized structures, total energies, the energy of HOMO and LUMO, energy gaps, electrochemical hardness, softness, Fermi level, molecular symmetry, dipole moment, were calculated. The results showed that the total energy of studied molecules gradually decreasing compared with the original molecule. The addition of subgroups for biphenyl compound have effect on the energy gap and thus affects on the (LUMO) and (HOMO) energy, were causes decreasing the (HOMO) and (LUMO) energy compared with the original molecule. This decrease is very good because it develop the conductivities and solubilities of these molecules.

Total pattern analyses for non-crystalline materials

A total pattern analysis suite of programs has been developed and incorporated into the ICDD®PDF-4 database. While the suite of programs is intended for the analysis of any diffraction pattern, particular attention was focused on the analysis of common amorphous, non-crystalline, or partially crystalline materials found in minerals, polymers, and pharmaceuticals. The suite of programs directly interfaces to the ICDD database and libraries of non-crystalline references.

Using a linear mixed-effect model framework to estimate multivariate generalizability theory parameters in R.

Multivariate generalizability theory (mG-theory) is an important framework in many behavioral and educational studies, as it describes useful psychometric properties of multidimensional assessments. Nevertheless, the use of mG-theory estimation is limited due to the lack of available software for carrying out the necessary calculations: users rely heavily on independent software programs such as mGENOVA and the BUGS/JAGS suite of programs. Considering the prevalence of R software, this paper presents a solution using the glmmTMB package to accomplish the estimation task. Users adopting the proposed method may find it more convenient for conducting both applied investigation and simulation studies without the need to switch between different software programs.

Isolation, identification, thermal analysis, DFT calculations and antioxidant activity studies of lichen metabolites norstictic acid and evernic acid Abstract

This manuscript deals with successful trials to isolate two lichen acids (Noristicic and Everinic acids) from natural sources. The isolated compounds were purified, crystallized from suitable solvent, and their melting points were measured. Their structures were identified by elemental analyses, spectroscopic tools (Electronic spectra, FT-IR, Mass), and finally using thermal analyses (TG, DTG and DTA). Their thermal fragmentation pathways were compared with mass fragmentation. These fragmentation pathways were compared with the theoretical molecular orbital calculations (DFT- MOCs). The molecular structures of the title compounds in the ground state were optimized by a DFT method using B3LYP functional combined with 6-311G** (d) basis set. Calculations were carried out using GAUSSIAN 09 suite of programs. In addition, antioxidant activity of norstictic acid and everinic acid were determined. The results showed that both compounds exhibited antioxidant activity, but that the norstictic acid showed several times higher activity than evernic acid. This study refers to successful obtaining of these biologically active acids from natural sources and of wide biomedical applications.