Importation and distribution of unregistered medicines in the public sector: similarities, differences and shared challenges among Southern African Development Community (SADC) countries

BackgroundThe purpose of the study was to assess the requirements for approval of the importation of unregistered medicines for use in the public sector in the Southern African Development Community (SADC) countries.MethodsThe study reviewed the legal provisions and requirements to be fulfilled when importing unregistered medicines for the public sector in SADC countries relative to two comparators drawn from countries with stringent regulatory systems through extant document analysis. The relative implementation index score was calculated and used to measure the level of implementing legal provisions and requirements to be fulfilled. Analysis was performed using the STATA software package. ResultsApproximately 13 out of 16 SADC countries had a relative implementation index below 50%. The aggregated implementation index across all SADC countries was 44%, ranging from 4% to 54%, while the two comparators had a relative implementation index of 81% and 85%, respectively.ConclusionImplementing the minimum requirements for importing unregistered medicines for the public sector was deficient compared to the jurisdictions with stringent regulatory systems, and wide implementation gaps also existed within the SADC region.

Demystifying the Differences between Structure-from-Motion Software Packages for Pre-Processing Drone Data

With the increased availability of low-cost, off-the-shelf drone platforms, drone data become easy to capture and are now a key component of environmental assessments and monitoring. Once the data are collected, there are many structure-from-motion (SfM) photogrammetry software options available to pre-process the data into digital elevation models (DEMs) and orthomosaics for further environmental analysis. However, not all software packages are created equal, nor are their outputs. Here, we evaluated the workflows and output products of four desktop SfM packages (AgiSoft Metashape, Correlator3D, Pix4Dmapper, WebODM), across five input datasets representing various ecosystems. We considered the processing times, output file characteristics, colour representation of orthomosaics, geographic shift, visual artefacts, and digital surface model (DSM) elevation values. No single software package was determined the “winner” across all metrics, but we hope our results help others demystify the differences between the options, allowing users to make an informed decision about which software and parameters to select for their specific application. Our comparisons highlight some of the challenges that may arise when comparing datasets that have been processed using different parameters and different software packages, thus demonstrating a need to provide metadata associated with processing workflows.

The Computational Thematic Analysis Toolkit

As online communities have grown, Computational Social Science has rapidly developed new techniques to study them. However, these techniques require researchers to become experts in a wide variety of tools in addition to qualitative and computational research methods. Studying online communities also requires researchers to constantly navigate highly contextual ethical and transparency considerations when engaging with data, such as respecting their members' privacy when discussing sensitive or stigmatized topics. To overcome these challenges, we developed the Computational Thematic Analysis Toolkit, a modular software package that supports analysis of online communities by combining aspects of reflexive thematic analysis with computational techniques. Our toolkit demonstrates how common analysis tasks like data collection, cleaning and filtering, modelling and sampling, and coding can be implemented within a single visual interface, and how that interface can encourage researchers to manage ethical and transparency considerations throughout their research process.

A Software Package for the Modeling of Electrophysiological Activity Data

A complex of programs has been developed for computer modeling of multichannel time series recorded in various experiments on electromagnetic fields created by the human body. Sets of coordinates and directions of sensors for magnetic encephalographs of several types, electroencephalographs and magnetic cardiographs are used as models of devices. To study the human brain, magnetic resonance tomograms are used as head models; to study the heart, a body model in the form of a half-space with a flat boundary is used. The sources are placed in the model space, for them the direct problem is solved in the physical model corresponding to the device used. For a magnetic encephalograph and an electroencephalograph, an equivalent current dipole model in a spherical conductor is used, for a magnetic cardiograph, an equivalent current dipole model in a flat conductor or a magnetic dipole model is used. For each source, a time dependence is set and a multichannel time series is calculated. Then the time series from all sources are summed and the noise component is added. The program consists of three modules: an input-output module, a calculation module and a visualization module. The input-output module is responsible for loading device models, brain models, and field source parameters. The calculation module is responsible for directly calculating the field and transforming coordinates between the index system and the head system. The visualization module is responsible for the image of the brain model, the position of the field sources, a graphical representation of the amplitude-time dependence of the field sources and the calculated values of the total field. The user interface has been developed. The software package provides: interactive placement of field sources in the head or body space and editing of the amplitude-time dependence; batch loading of a large number of sources; noise modeling; simulation of low-channel planar magnetometers of various orders, specifying the shape of the device, the number of sensors and their parameters. Magnetic and electric fields produced by sources in the brain areas responsible for processing speech stimuli are considered. The resulting multichannel signal can be used to test various data analysis methods and for the experiment planning.

Ultracold Neutron Storage Simulation Using the Kassiopeia Software Package

The Kassiopeia software package was originally developed to simulate electromagnetic fields and charged particle trajectories for neutrino mass measurement experiments. Recent additions to Kassiopeia also allow it to simulate neutral particle trajectories in magnetic fields based on their magnetic moments. Two different methods were implemented: an exact method that can work for arbitrary fields and an adiabatic method that is limited to slowly-varying fields but is much faster for large precession frequencies. Additional interactions to simulate reflection of ultracold neutrons from material walls and to allow spin-flip pulses were also added. These tools were used to simulate neutron precession in a room temperature neutron electric dipole moment experiment and predict the values of the longitudinal and transverse relaxation times as well as the trapping lifetime. All three parameters are found to closely match the experimentally determined values when simulated with both the exact and adiabatic methods, confirming that Kassiopeia is able to accurately simulate neutral particles. This opens the door for future uses of Kassiopeia to prototype the next generation of atomic traps and ultracold neutron experiments.

ESTIMATION OF THE POSSIBILITY OF TRANSITION OF THE FACILITY FOR METHANOL SYNTHESYS TO WORK WITH LOW-TEMPERATURE CATALYSTS

In the investigation, the modeling of the facility for the methanol synthesis was carried out under the conditions of the replacement of the catalyst. The process model was compiled using the DWSim software package. Based on the calculations, the possibility of working the high-pressure methanol plant with a low-temperature catalyst was evaluated

PARALLEL OPTIMIZATION TECHNOLOGY FOR ATOMIC-MOLECULAR MORSE CLUSTERS

В статье рассматриваются специализированные вычислительные технологии и алгоритмы, используемые для поиска низкопотенциальных атомно-молекулярных кластеров. Проведенные вычислительные эксперименты продемонстрировали достаточно высокую конкурентоспособность новых алгоритмов по сравнению с классическими для функций рассматриваемого типа. С использованием разработанного программного комплекса получены рекордные результаты оптимизации атомно-молекулярных кластеров Морса рекордных размерностей. The paper deals with specialized computing technology and algorithms used for finding low-potential atomic-molecular clusters. The performed computational experiments demonstrated a rather high competitiveness of the new algorithms in comparison with the classical methods for the considerable functions. Using the developed software, the applied problem of molecular docking was solved. Using the developed software package, record results for optimization of atomic-molecular Morse clusters of large dimensions have been obtained.

Photometric Classification of Early-time Supernova Light Curves with SCONE

In this work, we present classification results on early supernova light curves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using light-curve data. SCONE is able to identify SN types from light curves at any stage, from the night of initial alert to the end of their lifetimes. Simulated LSST SNe light curves were truncated at 0, 5, 15, 25, and 50 days after the trigger date and used to train Gaussian processes in wavelength and time space to produce wavelength–time heatmaps. SCONE uses these heatmaps to perform six-way classification between SN types Ia, II, Ibc, Ia-91bg, Iax, and SLSN-I. SCONE is able to perform classification with or without redshift, but we show that incorporating redshift information improves performance at each epoch. SCONE achieved 75% overall accuracy at the date of trigger (60% without redshift), and 89% accuracy 50 days after trigger (82% without redshift). SCONE was also tested on bright subsets of SNe (r < 20 mag) and produced 91% accuracy at the date of trigger (83% without redshift) and 95% five days after trigger (94.7% without redshift). SCONE is the first application of convolutional neural networks to the early-time photometric transient classification problem. All of the data processing and model code developed for this paper can be found in the SCONE software package 1 1 github.com/helenqu/scone located at github.com/helenqu/scone (Qu 2021).

skater: an R package for SNP-based kinship analysis, testing, and evaluation

Motivation: SNP-based kinship analysis with genome-wide relationship estimation and IBD segment analysis methods produces results that often require further downstream process- ing and manipulation. A dedicated software package that consistently and intuitively imple- ments this analysis functionality is needed. Results: Here we present the skater R package for SNP-based kinship analysis, testing, and evaluation with R. The skater package contains a suite of well-documented tools for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing IBD segment data. Availability: The skater package is implemented as an R package and is released under the MIT license at https://github.com/signaturescience/skater. Documentation is available at https://signaturescience.github.io/skater.