CAD enabled trajectory optimization and accurate motion control for repetitive tasks

As machine users generally only define the start and end point of the movement, a large trajectory optimization potential rises for single axis mechanisms performing repetitive tasks. However, a descriptive mathematical model of the mecha- nism needs to be defined in order to apply existing optimization techniques. This is usually done with complex methods like virtual work or Lagrange equations. In this paper, a generic technique is presented to optimize the design of point-to-point trajectories by extracting position dependent properties with CAD motion simulations. The optimization problem is solved by a genetic algorithm. Nevertheless, the potential savings will only be achieved if the machine is capable of accurately following the optimized trajectory. Therefore, a feedforward motion controller is derived from the generic model allowing to use the controller for various settings and position profiles. Moreover, the theoretical savings are compared with experimental data from a physical set-up. The results quantitatively show that the savings potential is effectively achieved thanks to advanced torque feedforward with a reduction of the maximum torque by 12.6% compared with a standard 1/3-profil

Optimal load angle learning algorithm for sensorless closed-loop stepping motor control

Stepping motors are well suited for open-loop positioning tasks at low-power. The rotor position of the machine is simply controlled by the user. Every time the user sends a next pulse, the stepping motor driver excites the correct stator phases to rotate the rotor over a pre-defined discrete angular position. In this way, counting the step command pulses enables open-loop positioning. However, when the motor is overloaded or stuck, the relation between the expected rotor position based on the number of step command pulses and the actual rotor position is lost. To avoid this, the bulk of the widely used full-step open-loop stepping motor drive algorithms are driven at maximum current. This non-optimal way of control leads to low efficiency. To use stepping motors more optimally, closed-loop control is needed. A previously described sensorless load angle estimation algorithm, solely based on voltage and current measurements, is used to provide sensorless feedback. A closed-loop load angle controller adapts the current level to reach the setpoint load angle to obtain the optimal torque/current ratio. The difficulty is that the optimal load angle depends on the mechanical dynamics. To avoid the requirement of knowledge of the mechanical parameters, a practical learning algorithm to determine the optimal load angle is presented in this paper. Measurements validate the proposed approach

Determinants of Vaccination and Willingness to Vaccinate against COVID-19 among Pregnant and Postpartum Women during the Third Wave of the Pandemic: A European Multinational Cross-Sectional Survey.

With COVID-19 vaccination hesitancy at around 50% in the obstetric population, it is critical to identify which women should be addressed and how. Our study aimed to assess COVID-19 vaccination willingness among pregnant and postpartum women in Europe and to investigate associated determinants. This study was a cross-sectional, web-based survey conducted in Belgium, Norway, Switzerland, The Netherlands, and United Kingdom (UK) in June-August 2021. Among 3194 pregnant women, the proportions of women vaccinated or willing to be vaccinated ranged from 80.5% in Belgium to 21.5% in Norway. The associated characteristics were country of residence, chronic illness, history of flu vaccine, trimester of pregnancy, belief that COVID-19 is more severe during pregnancy, and belief that the COVID-19 vaccine is effective and safe during pregnancy. Among 1659 postpartum women, the proportions of women vaccinated or willing to be vaccinated ranged from 86.0% in the UK to 58.6% in Switzerland. The associated determinants were country of residence, chronic illness, history of flu vaccine, breastfeeding, and belief that the COVID-19 vaccine is safe during breastfeeding. Vaccine hesitancy in the obstetric population depends on medical history and especially on the opinion that the vaccine is safe and on the country of residence

Antidiabetic Medication Utilisation before and during Pregnancy in Switzerland between 2012 and 2019: An Administrative Claim Database from the MAMA Cohort.

BACKGROUND The incidence of diabetes mellitus (both pregestational and gestational) is increasing worldwide, and hyperglycemia during pregnancy is associated with adverse pregnancy outcomes. Evidence on the safety and efficacy of metformin during pregnancy has accumulated resulting in an increase in its prescription in many reports. AIMS We aimed to determine the prevalence of antidiabetic drug use (insulins and blood glucose-lowering drugs) before and during pregnancy in Switzerland and the changes therein during pregnancy and over time. METHODS We conducted a descriptive study using Swiss health insurance claims (2012-2019). We established the MAMA cohort by identifying deliveries and estimating the last menstrual period. We identified claims for any antidiabetic medication (ADM), insulins, blood glucose-lowering drugs, and individual substances within each class. We defined three groups of pattern use based on timing of dispensation: (1) dispensation of at least one ADM in the prepregnancy period and in or after trimester 2 (T2) (pregestational diabetes); (2) dispensation for the first time in or after T2 (GDM); and (3) dispensation in the prepregnancy period and no dispensation in or after T2 (discontinuers). Within the pregestational diabetes group, we further defined continuers (dispensation for the same group of ADM) and switchers (different ADM group dispensed in the prepregnancy period and in or after T2). RESULTS MAMA included 104,098 deliveries with a mean maternal age at delivery of 31.7. Antidiabetic dispensations among pregnancies with pregestational and gestational diabetes increased over time. Insulin was the most dispensed medication for both diseases. Between 2017 and 2019, less than 10% of pregnancies treated for pregestational diabetes continued metformin rather than switching to insulin. Metformin was offered to less than 2% of pregnancies to treat gestational diabetes (2017-2019). CONCLUSION Despite its position in the guidelines and the attractive alternative that metformin represents to patients who may encounter barriers with insulin therapy, there was reluctance to prescribe it

Equilibrium responses of global net primary production and carbon storage to doubled atmospheric carbon dioxide: sensitivity to changes in vegetation nitrogen concentration

We ran the terrestrial ecosystem model (TEM) for the globe at 0.5° resolution for atmospheric CO2 concentrations of 340 and 680 parts per million by volume (ppmv) to evaluate global and regional responses of net primary production (NPP) and carbon storage to elevated CO2 for their sensitivity to changes in vegetation nitrogen concentration. At 340 ppmv, TEM estimated global NPP of 49.0 1015 g (Pg) C yr−1 and global total carbon storage of 1701.8 Pg C; the estimate of total carbon storage does not include the carbon content of inert soil organic matter. For the reference simulation in which doubled atmospheric CO2 was accompanied with no change in vegetation nitrogen concentration, global NPP increased 4.1 Pg C yr−1 (8.3%), and global total carbon storage increased 114.2 Pg C. To examine sensitivity in the global responses of NPP and carbon storage to decreases in the nitrogen concentration of vegetation, we compared doubled CO2 responses of the reference TEM to simulations in which the vegetation nitrogen concentration was reduced without influencing decomposition dynamics (“lower N” simulations) and to simulations in which reductions in vegetation nitrogen concentration influence decomposition dynamics (“lower N+D” simulations). We conducted three lower N simulations and three lower N+D simulations in which we reduced the nitrogen concentration of vegetation by 7.5, 15.0, and 22.5%. In the lower N simulations, the response of global NPP to doubled atmospheric CO2 increased approximately 2 Pg C yr−1 for each incremental 7.5% reduction in vegetation nitrogen concentration, and vegetation carbon increased approximately an additional 40 Pg C, and soil carbon increased an additional 30 Pg C, for a total carbon storage increase of approximately 70 Pg C. In the lower N+D simulations, the responses of NPP and vegetation carbon storage were relatively insensitive to differences in the reduction of nitrogen concentration, but soil carbon storage showed a large change. The insensitivity of NPP in the N+D simulations occurred because potential enhancements in NPP associated with reduced vegetation nitrogen concentration were approximately offset by lower nitrogen availability associated with the decomposition dynamics of reduced litter nitrogen concentration. For each 7.5% reduction in vegetation nitrogen concentration, soil carbon increased approximately an additional 60 Pg C, while vegetation carbon storage increased by only approximately 5 Pg C. As the reduction in vegetation nitrogen concentration gets greater in the lower N+D simulations, more of the additional carbon storage tends to become concentrated in the north temperate-boreal region in comparison to the tropics. Other studies with TEM show that elevated CO2 more than offsets the effects of climate change to cause increased carbon storage. The results of this study indicate that carbon storage would be enhanced by the influence of changes in plant nitrogen concentration on carbon assimilation and decomposition rates. Thus changes in vegetation nitrogen concentration may have important implications for the ability of the terrestrial biosphere to mitigate increases in the atmospheric concentration of CO2 and climate changes associated with the increases

Evaluation of Three Amorphous Drug Delivery Technologies to Improve the Oral Absorption of Flubendazole

AbstractThis study investigates 3 amorphous technologies to improve the dissolution rate and oral bioavailability of flubendazole (FLU). The selected approaches are (1) a standard spray-dried dispersion with hydroxypropylmethylcellulose (HPMC) E5 or polyvinylpyrrolidone-vinyl acetate 64, both with Vitamin E d-α-tocopheryl polyethylene glycol succinate; (2) a modified process spray-dried dispersion (MPSDD) with either HPMC E3 or hydroxypropylmethylcellulose acetate succinate (HPMCAS-M); and (3) confining FLU in ordered mesoporous silica (OMS). The physicochemical stability and in vitro release of optimized formulations were evaluated following 2 weeks of open conditions at 25°C/60% relative humidity (RH) and 40°C/75% RH. All formulations remained amorphous at 25°C/60% RH. Only the MPSDD formulation containing HPMCAS-M and 3/7 (wt./wt.) FLU/OMS did not crystallize following 40°C/75% RH exposure. The OMS and MPSDD formulations contained the lowest and highest amount of hydrolyzed degradant, respectively. All formulations were dosed to rats at 20 mg/kg in suspension. One FLU/OMS formulation was also dosed as a capsule blend. Plasma concentration profiles were determined following a single dose. In vivo findings show that the OMS capsule and suspension resulted in the overall highest area under the curve and Cmax values, respectively. These results cross-evaluate various amorphous formulations and provide a link to enhanced biopharmaceutical performance

Datamama, bringing pregnancy research into the future: design, development, and evaluation of a citizen science pregnancy mobile application

Background: Pregnancy mobile applications (apps) have grown in popularity over the past decade, with some being used to promote study recruitment or health behaviors. However, no app serves as an all-in-one solution for collecting general data for research purposes and providing women with useful and desirable features. Aim: To create and develop a Swiss pregnancy mobile app as an innovative means to collect research data and provide users with reliable information. Methods: Determining the key features of the app involved a review of the literature and assessment of popular apps in the Swiss AppStore. A team of engineers developed the app, which includes a pregnancy timeline, questionnaires for data collection, medical and psychological articles and a checklist with appointment reminders. The content was written and reviewed by healthcare providers considered experts in the topics adressed. The questionnaires are distributed based on the user’s gestational age, by a chatbot. The project was authorized by the ethics commission in the canton of Vaud. An online survey of ten questions, advertised on Datamama’s home screen, was conducted to assess the users’ use of the app (27.11- 19.12.2022). Results: A review of 84 articles and 25 popular apps showed the need for a comprehensive pregnancy app. The development of Datamama took 2 years and included the creation of 70 medical and psychological articles and 29 questionnaires covering 300 unique variables. Six months after the launch, there were 800 users with a 73% average participation rate in the questionnaires. Sixty-five women completed the survey, with 70.8% using the app once to multiple times per week. The primary reason for using the app was to help research by answering the questionnaires, followed by access to reliable medical information. The reason most frequently ranked first for using the app was to help research by answering the questionnaires (42/65, 67% of women rated it first), followed by access to reliable medical information (34/65, 54% women rated it second). Women rated the information as clear, understandable, and interesting with a trust rating in data handling at 98.5%. The average grade for recommending the app was 8/10, with suggestions for increasing the amount of medical content and tailoring it based on gestational age. Conclusion: Datamama is the first pregnancy app to address the needs of both patients and researchers. Initial feedback from users was positive, highlighting future challenges for success. Future work will consist in improving the app, validating the data and use it to answer specific pregnancy-related research questions

Industry-Scale Orchestrated Federated Learning for Drug Discovery

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.Comment: 9 pages, 4 figures, to appear in AAAI-23 ([IAAI-23 track] Deployed Highly Innovative Applications of AI

Optimizing structural modeling for a specific protein scaffold: knottins or inhibitor cystine knots

<p>Abstract</p> <p>Background</p> <p>Knottins are small, diverse and stable proteins with important drug design potential. They can be classified in 30 families which cover a wide range of sequences (1621 sequenced), three-dimensional structures (155 solved) and functions (> 10). Inter knottin similarity lies mainly between 15% and 40% sequence identity and 1.5 to 4.5 Å backbone deviations although they all share a tightly knotted disulfide core. This important variability is likely to arise from the highly diverse loops which connect the successive knotted cysteines. The prediction of structural models for all knottin sequences would open new directions for the analysis of interaction sites and to provide a better understanding of the structural and functional organization of proteins sharing this scaffold.</p> <p>Results</p> <p>We have designed an automated modeling procedure for predicting the three-dimensionnal structure of knottins. The different steps of the homology modeling pipeline were carefully optimized relatively to a test set of knottins with known structures: template selection and alignment, extraction of structural constraints and model building, model evaluation and refinement. After optimization, the accuracy of predicted models was shown to lie between 1.50 and 1.96 Å from native structures at 50% and 10% maximum sequence identity levels, respectively. These average model deviations represent an improvement varying between 0.74 and 1.17 Å over a basic homology modeling derived from a unique template. A database of 1621 structural models for all known knottin sequences was generated and is freely accessible from our web server at <url>http://knottin.cbs.cnrs.fr</url>. Models can also be interactively constructed from any knottin sequence using the structure prediction module Knoter1D3D available from our protein analysis toolkit PAT at <url>http://pat.cbs.cnrs.fr</url>.</p> <p>Conclusions</p> <p>This work explores different directions for a systematic homology modeling of a diverse family of protein sequences. In particular, we have shown that the accuracy of the models constructed at a low level of sequence identity can be improved by 1) a careful optimization of the modeling procedure, 2) the combination of multiple structural templates and 3) the use of conserved structural features as modeling restraints.</p