Upper-limb prostheses are either too expensive for many consumers or have a greatly simplified choice of actions. This research aims to enable an improvement to the quality of life for recipients of these devices by providing an understanding of how we use our hands in modern everyday life. To achieve this a taxonomy of grasps used in activities of daily living has been created. A portable motion capture system was developed to collect data. Thirteen participants’ hand movements were recorded (totalling 62 hours and 10 minutes of data). From these data, 38 and 22 grasps were observed from the left and right hands, respectively. The portable system effectively captured natural hand motions, which formed an updated taxonomy of grasps.

Quantifying finger kinematics can improve the authors' understanding of finger function and facilitate the design of efficient prosthetic devices while also identifying movement disorders and assessing the impact of rehabilitation interventions. Here, the authors present a study that quantifies grasps depicted in taxonomies during selected Activities of Daily Living (ADL). A single participant held a series of standard objects using specific grasps which were used to train Convolutional Neural Networks (CNN) for each of the four fingers individually. The experiment also recorded hand manipulation of objects during ADL. Each set of ADL finger kinematic data was tested using the trained CNN, which identified and quantified the grasps required to accomplish each task. Certain grasps appeared more often depending on the finger studied, meaning that even though there are physiological interdependencies, fingers have a certain degree of autonomy in performing dexterity tasks. The identified and most frequent grasps agreed with the previously reported findings, but also highlighted that an individual might have specific dexterity needs which may vary with profession and age. The proposed method can be used to identify and quantify key grasps for finger/hand prostheses, to provide a more efficient solution that is practical in their day-to-day tasks.

ABSTRACT We analyse 1602 microlensing events found in the VISTA Variables in the Via Lactea (VVV) near-infrared (NIR) survey data. We obtain spatially resolved, efficiency-corrected time-scale distributions across the Galactic bulge (|ℓ| < 10°, |b| < 5°), using a Bayesian hierarchical model. Spatially resolved peaks and means of the time-scale distributions, along with their marginal distributions in strips of longitude and latitude, are in agreement at a 1σ level with predictions based on the Besançon model of the Galaxy. We find that the event time-scales in the central bulge fields (|ℓ| < 5°) are on average shorter than the non-central (|ℓ| > 5°) fields, with the average peak of the lognormal time-scale distribution at 23.6 ± 1.9 d for the central fields and 29.0 ± 3.0 d for the non-central fields. Our ability to probe the structure of the bulge with this sample of NIR microlensing events is limited by the VVV survey’s sparse cadence and relatively small number of detected microlensing events compared to dedicated optical surveys. Looking forward to future surveys, we investigate the capability of the Roman telescope to detect spatially resolved asymmetries in the time-scale distributions. We propose two pairs of Roman fields, centred on (ℓ = ±9, 5°, b = −0.125°) and (ℓ = −5°, b = ±1.375°) as good targets to measure the asymmetry in longitude and latitude, respectively.

This paper presents parameter estimation for the passive moment and musculoskeletal scaling coefficients of a mathematical model describing the equations of motion of human finger segments. A novel musculoskeletal model was used as the actuator and parameter estimation was performed by least squares optimization of the differences between angular data from a motion capture system, and the solutions to the equations of motion of our model. The parameter estimates determined are unique to the individual and the dataset. Model fits show good agreement with filtered data R 2 = 0.98. The hope is that this model and approach can be used to determine the passive moment and musculoskeletal scaling coefficients for all types of grasps.

AbstractTyrosine kinase inhibitors (TKIs) are routinely prescribed for the treatment of non‐small cell lung cancer (NSCLC). As with all medications, patients can experience adverse events due to TKIs. Unfortunately, the relationship between many TKIs and the occurrence of certain adverse events remains unclear. There are limited in vivo studies which focus on TKIs and their effects on different regulation pathways. Many in vitro studies, however, that investigate the effects of TKIs observe additional changes, such as changes in gene activations or protein expressions. These studies could potentially help to gain greater understanding of the mechanisms for TKI induced adverse events. However, in order to utilize these pathways in a pharmacokinetic/pharmacodynamic (PK/PD) framework, an in vitro PK/PD model needs to be developed, in order to characterize the effects of TKIs in NSCLC cell lines. Through the use of ordinary differential equations, cell viability data and nonlinear mixed effects modeling, an in vitro TKI PK/PD model was developed with estimated PK and PD parameter values for the TKIs alectinib, crizotinib, erlotinib, and gefitinib. The relative standard errors for the population parameters are all less than 25%. The inclusion of random effects enabled the model to predict individual parameter values which provided a closer fit to the observed response. It is hoped that this model can be extended to include in vitro data of certain pathways that may potentially be linked with adverse events and provide a better understanding of TKI‐induced adverse events.

We use low-amplitude long period variable (LA-LPV) candidates in Gaia DR3 to trace the kinematics and dynamics of the Milky Way bar. LA-LPVs, like other LPVs, are intrinsically bright and follow a tight period-luminosity relation, but unlike e.g. Mira variables, their radial velocity measurements are reliable due to their smaller pulsation amplitudes. We supplement the Gaia astrometric and radial velocity measurements with distance moduli assigned using a period-luminosity relation to acquire full 6D phase space information. The assigned distances are validated by comparing to geometric distances and StarHorse distances, which shows biases less than similar to 5 per cent. Our sample provides an unprecedented panoramic picture of the inner Galaxy with minimal selection effects. We map the kinematics of the inner Milky Way and find a significant kinematic signature corresponding to the Galactic bar. We measure the pattern speed of the Galactic bar using the continuity equation and find Omega(b )= 34.1 +/- 2.4 km s(-1) kpc(-1). We develop a simple robust and potential-independent method to measure the dynamical length of the bar using only kinematics and find R-b similar to 4.0 kpc. We validate both measurements using N-body simulations. Assuming knowledge of the gravitational potential of the inner Milky Way, we analyse the orbital structure of the Galactic bar using orbital frequency ratios. The x(1) orbits are the dominant bar-supporting orbital family in our sample. Amongst the selected bar stars, the x(1)v(1) or 'banana' orbits constitute a larger fraction (similar to 15 per cent) than other orbital families in the bar, implying that they are the dominant family contributing to the Galactic X-shape, although contributions from other orbital families are also present.

The study of finger biomechanics requires special tools for accurately recording finger joint data. A marker set to evaluate finger postures during activities of daily living is needed to understand finger biomechanics in order to improve prosthesis design and clinical interventions. The purpose of this study was to evaluate the reliability of a proposed hand marker set (the Warwick marker set) to capture finger kinematics using motion capture. The marker set consisted of the application of two and three marker clusters to the fingers of twelve participants who participated in the tests across two sessions. Calibration markers were applied using a custom palpation technique. Each participant performed a series of range of motion movements and held a set of objects. Intra and inter-session reliability was calculated as well as Standard Error of Measurement (SEM) and Minimal Detectable Difference (MDD). The findings showed varying levels of intra- and inter-session reliability, ranging from poor to excellent. The SEM and MDD values were lower for the intra-session range of motion and grasp evaluation. The reduced reliability can potentially be attributed to skin artifacts, differences in marker placement, and the inherent kinematic variability of finger motion. The proposed marker set shows potential to assess finger postures and analyse activities of daily living, primarily within the context of single session tests.

The goal of this paper is twofold: firstly, to provide a novel mathematical model that describes the kinematic chain of motion of the human fingers based on Lagrangian mechanics with four degrees of freedom and secondly, to estimate the model parameters using data from able-bodied individuals. In the literature there are a variety of mathematical models that have been developed to describe the motion of the human finger. These models offer little to no information on the underlying mechanisms or corresponding equations of motion. Furthermore, these models do not provide information as to how they scale with different anthropometries. The data used here is generated using an experimental procedure that considers the free response motion of each finger segment with data captured via a motion capture system. The angular data collected are then filtered and fitted to a linear second-order differential approximation of the equations of motion. The results of the study show that the free response motion of the segments is underdamped across flexion/extension and ad/abduction.

Introduction: Across multiple studies, the most common serious adverse event groups that Small Cell Lung Cancer (SCLC) patients experience, whilst undergoing chemotherapy treatment, are: Blood and Lymphatic Disorders, Infections and Infestations together with Metabolism and Nutrition Disorders. The majority of the research that investigates the relationship between adverse events and SCLC patients, focuses on specific adverse events such as neutropenia and thrombocytopenia.Aim: This study aims to utilise machine learning in order to identify those patients who are at risk of developing common serious adverse event groups, as well as their specific adverse event classification grade.Methods: Data from five clinical trial studies were analysed and 12 analysis groups were formed based on the serious adverse event group and grade.Results: The best test runs for each of the models were able to produce an area under the curve (AUC) score of at least 0.714. The best model was the Blood and Lymphatic Disorder group, SAE grade 0 vs. grade 3 (best AUC = 1, sensitivity rate = 0.84, specificity rate = 0.96).Conclusion: The top features that contributed to this prediction were total bilirubin, alkaline phosphatase, and age. Future work should investigate the relationship between these features and common SAE groups.

In the last 15 years different ground-based spectroscopic surveys have been started (and completed) with the general aim of delivering stellar parameters and elemental abundances for large samples of Galactic stars, complementing Gaia astrometry. Among those surveys, the Gaia-ESO Public Spectroscopic Survey (GES), the only one performed on a 8m class telescope, was designed to target 100,000 stars using FLAMES on the ESO VLT (both Giraffe and UVES spectrographs), covering all the Milky Way populations, with a special focus on open star clusters. This article provides an overview of the survey implementation (observations, data quality, analysis and its success, data products, and releases), of the open cluster survey, of the science results and potential, and of the survey legacy. A companion article (Gilmore et al.) reviews the overall survey motivation, strategy, Giraffe pipeline data reduction, organisation, and workflow. The GES has determined homogeneous good-quality radial velocities and stellar parameters for a large fraction of its more than 110,000 unique target stars. Elemental abundances were derived for up to 31 elements for targets observed with UVES. Lithium abundances are delivered for about 1/3 of the sample. The analysis and homogenisation strategies have proven to be successful; several science topics have been addressed by the Gaia-ESO consortium and the community, with many highlight results achieved. The final catalogue has been released through the ESO archive at the end of May 2022, including the complete set of advanced data products. In addition to these results, the Gaia-ESO Survey will leave a very important legacy, for several aspects and for many years to come.

The Gaia-ESO Public Spectroscopic Survey is an ambitious project designed to obtain astrophysical parameters and elemental abundances for 100,000 stars, including large representative samples of the stellar populations in the Galaxy, and a well-defined sample of 60 (plus 20 archive) open clusters. We provide internally consistent results calibrated on benchmark stars and star clusters, extending across a very wide range of abundances and ages. This provides a legacy data set of intrinsic value, and equally a large wide-ranging dataset that is of value for homogenisation of other and future stellar surveys and Gaia's astrophysical parameters. This article provides an overview of the survey methodology, the scientific aims, and the implementation, including a description of the data processing for the GIRAFFE spectra. A companion paper (arXiv:2206.02901) introduces the survey results. Gaia-ESO aspires to quantify both random and systematic contributions to measurement uncertainties. Thus all available spectroscopic analysis techniques are utilised, each spectrum being analysed by up to several different analysis pipelines, with considerable effort being made to homogenise and calibrate the resulting parameters. We describe here the sequence of activities up to delivery of processed data products to the ESO Science Archive Facility for open use. The Gaia-ESO Survey obtained 202,000 spectra of 115,000 stars using 340 allocated VLT nights between December 2011 and January 2018 from GIRAFFE and UVES. The full consistently reduced final data set of spectra was released through the ESO Science Archive Facility in late 2020, with the full astrophysical parameters sets following in 2022.

We use Gaia photometry and astrometry to estimate the vertical spatial structure of the Milky Way at the Solar radius, formally accounting for sample incompleteness (the selection function) and parallax measurement uncertainty. Our results show impressive precision demonstrating the power of the Gaia data. However, systematic errors dominate the parameter value uncertainties. We thoroughly test and quantify the impacts of all systematic uncertainties. The vertical tracer density is modelled as a sum of two exponential profiles for the thin and thick discs, together with a spherically symmetric power-law for the stellar halo. We constrain the thin disc scale height as ${h_\mathrm{Tn}=260 \pm 3\, (\mathrm{stat}) \pm 26\,\mathrm{pc}\, (\mathrm{sys})}$ and thick disc ${h_\mathrm{Tk}=693 \pm 7 \,(\mathrm{stat}) \pm 121\,\mathrm{pc}\, (\mathrm{sys})}$. For the halo, we obtain a power law profile with $n_\mathrm{H}=3.543\pm0.023 \,(\mathrm{stat}) \pm0.259\, (\mathrm{sys})$. We infer a local stellar mass density for non-compact object stars of ${\rho_\mathrm{local}^* = 3.66\pm0.03\,(\mathrm{stat})\pm0.52 \times10^{-2}\,\mathrm{M}_\odot/\mathrm{pc}^3\,(\mathrm{sys})}$ and surface density of ${\Sigma_\mathrm{local}^* = 23.17\pm0.08\,(\mathrm{stat})\pm2.43\,\mathrm{M}_\odot/\mathrm{pc}^2\,(\mathrm{sys})}$. We find asymmetries above and below the disc with longer disc scale heights in the north but a flatter halo in the south at the $\lesssim 10$ per cent level.

Predicting the muscle excitations of the hand from kinematic data, exclusively, would enable the utilisation of motion capture data for the development of muscle controlled upper-limb prostheses. A method employing an existing musculoskeletal model and a selection of optimisation techniques is proposed for the prediction of muscle excitations of the hand from kinematic data. From 13 participants 62 hours and ten minutes of hand motions in activities of daily living (ADL) have been recorded, from which the functional hand shapes occurring within this time have been determined. A hybrid method utilising a gradient descent (GD) to determine the optimal initial conditions of a, then applied, particle swarm optimisation (PSO) technique is proposed as a means of predicting the muscle excitations of the hand from kinematic data. The proposed method has been applied to the found hand shapes to determine the muscle excitation of ADL. The resultant joint angles were within 16.1 degrees of that from the inputted hand shapes, with a mean correlation between outputted and desired of 0.77. The method has been shown to be applicable with real world recorded data and future modifications to the techniques utilised aims to further improve the accuracy of the output.

The S2 stream is a kinematically cold stream that is plunging downwards through the Galactic disc. It may be part of a hotter and more diffuse structure called the Helmi stream. We present a multi-instrument chemical analysis of the stars in the metal-poor S2 stream using both high- and low-resolution spectroscopy, complemented with a re-analysis of the archival data to give a total sample of 62 S2 members. Our high-resolution program provides alpha-elements (C, Mg, Si, Ca, and Ti), iron-peak elements (V, Cr, Mn, Fe, Ni), n-capture process elements (Sr, Ba), and other elements such as Li, Na, Al, and Sc for a subsample of S2 objects. We report coherent abundance patterns over a large metallicity spread (similar to 1 dex) confirming that the S2 stream was produced by a disrupted dwarf galaxy. The combination of S2's alpha-elements displays a mildly decreasing trend with increasing metallicity, which can be tentatively interpreted as a 'knee' at [Fe/H] < -2. At the low-metallicity end, the n-capture elements in S2 may be dominated by r-process production; however, several stars are Ba-enhanced but unusually poor in Sr. Moreover, some of the low-[Fe/H] stars appear to be carbon-enhanced. We interpret the observed abundance patterns with the help of chemical evolution models that demonstrate the need for modest star formation efficiency and low wind efficiency confirming that the progenitor of S2 was a primitive dwarf galaxy.

Missing data is a universal problem in analysing Real-World Evidence (RWE) datasets. In RWE datasets, there is a need to understand which features best correlate with clinical outcomes. In this context, the missing status of several biomarkers may appear as gaps in the dataset that hide meaningful values for analysis. Imputation methods are general strategies that replace missing values with plausible values. Using the Flatiron NSCLC dataset, including more than 35,000 subjects, we compare the imputation performance of six such methods on missing data: predictive mean matching, expectation-maximisation, factorial analysis, random forest, generative adversarial networks and multivariate imputations with tabular networks. We also conduct extensive synthetic data experiments with structural causal models. Statistical learning from incomplete datasets should select an appropriate imputation algorithm accounting for the nature of missingness, the impact of missing data, and the distribution shift induced by the imputation algorithm. For our synthetic data experiments, tabular networks had the best overall performance. Methods using neural networks are promising for complex datasets with non-linearities. However, conventional methods such as predictive mean matching work well for the Flatiron NSCLC biomarker dataset.

The Gaia Sausage (GS) and the Sequoia represent the major accretion events that formed the stellar halo of the Milky Way. A detailed chemical study of these main building blocks provides a pristine view of the early steps of the Galaxy’s assembly. We present the results of the analysis of the UVES high-resolution spectroscopic observations at the 8.2 m VLT of nine Sausage/Sequoia members selected kinematically using Gaia DR2. We season this set of measurements with archival data from Nissen & Schuster and GALAH DR3 (2020). Here, we focus on the neutron-capture process by analyzing Sr, Y, Ba, and Eu behavior. We detect clear enhancement in Eu abundance ([Eu/Fe] ∼ 0.6–0.7) indicative of large prevalence of the r-process in the stellar n-capture makeup. We are also able to trace the evolution of the heavy element production across a wide range of metallicity. The barium to europium changes from a tight, flat sequence with [Ba/Eu] = −0.7 reflecting dominant contribution from exploding massive stars, to a clear upturn at higher iron abundances, betraying the onset of contamination from asymptotic giant branch (AGB) ejecta. Additionally, we discover two clear sequences in the [Fe/H]−[Ba/Fe] plane likely caused by distinct levels of s -process pollution and mixing within the GS progenitor.

Penicillin binding proteins (PBPs) catalyzing transpeptidation reactions that stabilize the peptidoglycan component of the bacterial cell wall are the targets of β-lactams, the most clinically successful antibiotics to date. However, PBP-transpeptidation enzymology has evaded detailed analysis, because of the historical unavailability of kinetically competent assays with physiologically relevant substrates and the previously unappreciated contribution of protein cofactors to PBP activity. By re-engineering peptidoglycan synthesis, we have constructed a continuous spectrophotometric assay for transpeptidation of native or near native peptidoglycan precursors and fragments by Escherichia coli PBP1B, allowing us to (a) identify recognition elements of transpeptidase substrates, (b) reveal a novel mechanism of stereochemical editing within peptidoglycan transpeptidation, (c) assess the impact of peptidoglycan substrates on β-lactam targeting of transpeptidation, and (d) demonstrate that both substrates have to be bound before transpeptidation occurs. The results allow characterization of high molecular weight PBPs as enzymes and not merely the targets of β-lactam acylation.

A dynamic model is structurally identifiable if it is possible to infer its unknown parameters by observing its output. Structural identifiability depends on the system dynamics, output, and input, as well as on the specific values of initial conditions and parameters. Here we present a symbolic method that characterizes the input that a model requires to be structurally identifiable. It determines which derivatives must be non-zero in order to have a sufficiently exciting input. Our approach considers structural identifiability as a generalization of nonlinear observability and incorporates extended Lie derivatives. The methodology assesses structural identifiability for time-varying inputs and, additionally, it can be used to determine the input profile that is required to make the parameters structurally locally identifiable. Furthermore, it is sometimes possible to replace an experiment with time-varying input with multiple experiments with constant inputs. We implement the resulting method as a MATLAB toolbox named STRIKE-GOLDD2. This tool can assist in the design of new experiments for the purpose of parameter estimation.

Daratumumab (Dara) is a therapeutic human IgG1 monoclonal antibody indicated for multiple myeloma (MM). The pharmacokinetics of Dara demonstrate large differences in linear clearance between IgG patients and those with other M-protein isotypes. This could be explained by competition between Dara and endogenous IgG for binding neonatal Fc receptors (FcRn), effectively impacting IgG catabolic rates and half-life. We propose a model of coupled Dara and IgG kinetics accounting for recycling by FcRn and assess the impact on Dara usage in MM patients.

Salvage of endogenous immunoglobulin G (IgG) by the neonatal Fc receptor (FcRn) is implicated in many clinical areas, including therapeutic monoclonal antibody kinetics, patient monitoring in IgG multiple myeloma, and antibody-mediated transplant rejection. There is a clear clinical need for a fully parameterized model of FcRn-mediated recycling of endogenous IgG to allow for predictive modeling, with the potential for optimizing therapeutic regimens for better patient outcomes. In this paper we study a mechanism-based model incorporating nonlinear FcRn-IgG binding kinetics. The aim of this study is to determine whether parameter values can be estimated using the limited in vivo human data, available in the literature, from studies of the kinetics of radiolabeled IgG in humans. We derive mathematical descriptions of the experimental observations—timecourse data and fractional catabolic rate (FCR) data—based on the underlying physiological model. Structural identifiability analyses are performed to determine which, if any, of the parameters are unique with respect to the observations. Structurally identifiable parameters are then estimated from the data. It is found that parameter values estimated from timecourse data are not robust, suggesting that the model complexity is not supported by the available data. Based upon the structural identifiability analyses, a new expression for the FCR is derived. This expression is fitted to the FCR data to estimate unknown parameter values. Using these parameter estimates, the plasma IgG response is simulated under clinical conditions. Finally a suggestion is made for a reduced-order model based upon the newly derived expression for the FCR. The reduced-order model is used to predict the plasma IgG response, which is compared with the original four-compartment model, showing good agreement. This paper shows how techniques for compartmental model analysis—structural identifiability analysis, linearization, and reparameterization—can be used to ensure robust parameter identification.