Biased Sampling Research Articles

A cooperative EV charging scheduling strategy based on double deep Q-network and Prioritized experience replay

The rapid growth of the number of electric vehicles (EVs) has significantly increased the demand for electricity of residents, which may lead to transformer overload of distribution networks if there is no reasonable power consumption planning. In view of the coordinated charging of EVs under the photovoltaic (PV) energy connected to the power grid, a collaborative charging control strategy is proposed based on a double deep q-network with Prioritized experience replay (DDQN-PER). Specifically, Long–Short-Term Memory (LSTM) neural network is utilized to capture the uncertainties caused by power requirement, PV power, and real-time electricity price. Then, a modified DDQN approach and the corresponding reward function are applied to solve the cooperative charging problem. In addition, the introduction of a PER mechanism based on TD bias sampling in reinforcement learning alleviates the problem of reward sparsity in learning scenarios and improves the training stability and efficiency. The proposed approach can achieve collaborative scheduling of EVs, which helps reduce the charging cost, promote the consumption of PV energy and avoid transformer overload. Simulation case studies based on real data demonstrate that the proposed algorithm can reduce the charging cost by 30% and increase the PV power utilization by 10% compared to the DDQN algorithm.

Abstract PR002: Genomic predictor can discriminate between high- and low-risk DCIS

Abstract Introduction: Ductal carcinoma in situ (DCIS) is considered a non-obligate precursor of invasive ductal carcinoma. With the aim of preventing a subsequent invasive cancer, all DCIS lesions are currently treated with surgical excision often supplemented with radiotherapy (RT). To prevent DCIS over- or undertreatment, a reliable marker of DCIS invasiveness risk is urgently needed. Methods: We studied two large DCIS cohorts: the Sloane cohort, a prospective breast screening cohort from the UK (median follow-up of 11 years), and a Dutch population-based cohort (NKI, median follow-up of 13 years). FFPE tissue specimens from patients with pure primary DCIS after breast-conserving surgery (BCS) +/- RT that did develop a subsequent ipsilateral event (DCIS or invasive) were considered as cases, whereas patients that did not develop any form of recurrence up to the last follow-up or death were considered as controls. We performed copy number analysis (CNA) and RNAseq analysis on 229 cases (80 DCIS only recurrences) and 344 controls. Results: DCIS was classified into the PAM50 subtypes using RNAseq data which revealed an enrichment of luminal A phenotype in DCIS that did not recur (P = 0.01, Fisher Exact test). No single copy number aberration was more common in cases compared to controls. RNAseq data did not reveal any genes significantly over/under-expressed in cases versus controls after FDR correction. However, by limiting the analysis to samples that had not had RT and excluding pure DCIS recurrences, we could develop a penalized Cox model from RNAseq data. The model was trained on weighted samples (to correct for the biased sampling of the case-control dataset) from the NKI series with double loop cross-validation. The genes were selected using the Elastic net framework of penalization. Using this predicted hazard ratio, the samples were split into high, medium, and low-risk quantiles, with a recurrence risk of 23%, 7% and 2%, respectively at 5 years (p = 10-10, Wald test). The NKI-trained predictor was independently validated in the Sloane No RT no DCIS recurrence cohort (p = 0.02, Wald test). GSEA analysis revealed proliferation hallmarks enriched in the recurrence predictor (FDR = 0.058). The RNAseq predictor was more predictive of recurrence than PAM50, clinical features (Grade, Her2 and ER) and the 12-gene Oncotype DCIS score (p < 0.001, permutation test using the Wald statistic) in both the NKI and Sloane series. Conclusion: Genomic profiling of two independent series of DCIS with outcome data did not reveal any clear associations with recurrence until analysis was limited to a set of samples who had not had radiotherapy and DCIS recurrences were excluded. We then identified an RNAseq-based classifier that could differentiate primary DCIS in low-, medium-, and high-risk groups, and validated it in an independent cohort. This classifier, if validated in other datasets, will allow us to identify women who do not need intensive treatment for their DCIS. Citation Format: Maria Roman Escorza, Michael Sheinman, Tycho Bismeijer, Ahmed A. Ahmed, Vandna Shah, Jeffrey R. Marks, Lorraine M. King, Anargyros Megalios, Lindy L. Visser, Marlous Hoogstrat, Helen R. Davies, Tapsi Kumar, Deborah Collyar, Hilary Stobart, Sarah Pinder, Nicholas N. Navin, Andrew Futreal, Serena Nik-Zainal, E. Shelley Hwang, Esther H. Lips, Alastair Thompson, Lodewyk F.A. Wessels, Jelle Wesseling, Elinor J. Sawyer. Genomic predictor can discriminate between high- and low-risk DCIS [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr PR002.

Protein Structure Validation Derives a Smart Conformational Search in a Physically Relevant Configurational Subspace.

Since proteins perform biological functions through their dynamic properties, molecular dynamics (MD) simulation is a sophisticated strategy for investigating their functions. Analyses of trajectories provide statistical information about a specific protein as a free-energy landscape (FEL). However, the timescale of normal MD is shorter than that of biological functions, resulting in statistically insufficient conformational sampling, finally leading to unreliable FEL calculation. To search for a broad configurational subspace, an external bias is imposed on a target protein as biased sampling. However, its regulation is challenging because the optimal strength of the perturbation is unknown. Furthermore, a physically irrelevant configurational subspace was searched when imposing an inappropriate external bias. To address this issue, we newly proposed an external biased regulation scheme known as the G-factor external bias limiter (GERBIL). In GERBIL, protein configurations generated by external bias are structurally validated by an indicator (G-factor), enabling the search for a physically relevant subspace. In addition to biased sampling, nonbiased sampling might search for a physically irrelevant configurational subspace because repeating multiple MD simulations from several initial structures tends to search for an overly broad configurational subspace. For this issue, the structural qualities of configurations generated by nonbiased sampling have not been investigated. Therefore, we confirmed whether the G-factor screened the collapsed (low-quality) configurations generated by nonbiased sampling. To address this issue, the outlier flooding method (OFLOOD) was adopted in GERBIL as a nonbiased sampling method, which is referred to as OFLOOD-GERBIL. OFLOOD rapidly expands a configurational subspace by resampling the rarely occurring states of a given protein and tends to search an overly broad subspace. Thus, we considered that GERBIL might improve the excessive conformational search of OFLOOD for a physically irrelevant configurational subspace. As a demonstration, OFLOOD and OFLOOD-GERBIL were applied to a globular protein (T4 lysozyme) and their conformational search qualities were assessed. Based on our assessment, normal OFLOOD without the outlier validation frequently sampled low-quality configurations, whereas OFLOOD-GERBIL with the outlier validation intensively sampled high-quality configurations. In conclusion, OFLOOD-GERBIL derives a smart conformational search in a physically relevant configurational subspace, indicating that protein structure validation works in both nonbiased and biased sampling methods.

Developing nondestructive species‐specific tree allometry with terrestrial laser scanning

Abstract Allometric equations predict organism attributes from simple measurements and underlie many global‐scale estimates, from plant productivity to ecosystem carbon stocks. In forests, destructive harvesting of trees in common groups (e.g. plant functional type) or at the species level is necessary to develop allometry but, since sampling is extremely difficult, predictions from these equations have high uncertainty due to low sample size, spatial bias and unrepresentative sampling of tree size. Terrestrial laser scanning (TLS) is a promising remote sensing technology that enables efficient and nondestructive estimates of tree‐level structure for developing allometric equations. Here, we nondestructively estimated component biomass of three coniferous tree species (Pinus ponderosa, Pinus contorta and Pseudotsuga menziesii) in Colorado, USA using TLS. We evaluated the suitability for this nondestructive data to be supplanted for destructive data in the development of species‐specific allometric equations and compared the prediction accuracy against a commonly used national‐scale allometry. We found TLS biomass estimates were consistently more precise across species (RMSE = ~19%) than nation‐scale allometry (RMSE = ~39%). Nondestructive biomass estimates from TLS are a suitable addition to or replacement for traditional sampling methods, with indistinguishable biomass predictions across most of the tested diameter range. We further show how TLS can be used to develop allometric equations compatible with airborne LiDAR and other remote sensing variables (e.g. height and crown area), developing generalized biomass predictions from crown area and tree height (R2 = 0.87). The ability for TLS to support the development of nondestructive allometry at a global scale will enable a more nuanced understanding of the drivers of individual tree architecture, while supporting the next generation of biomass remote sensing.

New First-Order Algorithms for Stochastic Variational Inequalities

In this paper, we propose two new solution schemes to solve the stochastic strongly monotone variational inequality (VI) problems: the stochastic extra-point solution scheme and the stochastic extra-momentum solution scheme. The first one is a general scheme based on updating the iterative sequence and an auxiliary extra-point sequence. In the case of a deterministic VI model, this approach includes several state-of-the-art first-order methods as its special cases. The second scheme combines two momentum-based directions: the so-called heavy-ball direction and the optimism direction, where only one projection per iteration is required in its updating process. We show that if the variance of the stochastic oracle is appropriately controlled, then both schemes can be made to achieve optimal iteration complexity of $\mathcal{O}\left(\kappa\ln\left(\frac{1}{\epsilon}\right)\right)$ to reach an $\epsilon$-solution for a strongly monotone VI problem with condition number $\kappa$. As a specific application to stochastic VI, we demonstrate how to incorporate a zeroth-order approach for solving stochastic minimax saddle-point problems in our schemes, where only noisy and biased samples of the objective can be obtained, with a total sample complexity of $\mathcal{O}\left(\frac{\kappa}{\epsilon}\right)$.

Marketing Mix Strategy and Business Growth In 5 Star Hotels in Nairobi City County, Kenya: A Case of Hilton Hotel

Marketing strategy is the fundamental theme of the entire company, be it a small business or a well-known multinational corporation. The task and task of the organization is to formulate, establish and implement a coherent and elegant marketing strategy to promote its business development to attract the right types of customers and allocate resources for business growth. It does this by using profitability as a measure of business growth. Largely assessed financial efficiency. This search has led to increased competition in the Kenyan hotel industry. This has resulted in hotels developing lean marketing strategies to maintain their relevance in the extremely aggressive Kenyan hotel market. The1main1goals1for1this1findings1is1to1find1out the impacts of marketing mix on business growth in 51star1hotels1in1Nairobi1City1County1Kenya.1A1case1of1Hilton hotel. This study aims to test the degree of product differentiation in marketing strategy: the impact of price marketing strategy, niche marketing strategy, and advertising marketing strategy on profitability, market share, Hilton Kenya consumer service and sales quantity, effects of product differentiation strategy, pricing strategy, placement strategy and advertising strategy on the earnings, market values, number of customers, customer service and volume. Nairobi sales department. Research will rely on the Promotion Mix Theory, Marketing Mix Model, and Marketing Impact and Continuity Model in order to provide a more proper knowledge of the marketing mix and business growth. The study population includes 5-star hotels in Nairobi City County, Kenya, including Sankara Nairobi, Dusit D2 Nairobi, Villa Rosa Kempinski, Sarova Stanley, and Hilton Nairobi. 5 Star Hotels in Nairobi, Kenya The target group will be 84 employees outside of the Hilton hotel headquarters in Nairobi. A stratified random sample is utilized to sample 42 employees at the headquarters, who make up 50% of the total population be to choose a delegate and unbiased sample in order to improve the reliability and validity of the survey results. The questionnaire is used as a data collection tool. A questionnaire will be sent to the research director to check its effectiveness and a pilot study will be undertaken to improve the reliability of the instrument. The quantitative evaluation of the data is carried out with the software SPSS Version 21 and is represented by applying tables of statistical distribution, charts, and pie charts for easy comprehensive. On the other hand, analyzing the qualitative data through content analysis, the research will help various stakeholders of the Hilton hotels understand the importance of the marketing mix and its impact on the business growth of the organization and provide references for forthcoming researchers and academics.

Optic flow in the natural habitats of zebrafish supports spatial biases in visual self-motion estimation

Animals benefit from knowing if and how they are moving. Across the animal kingdom, sensory information in the form of optic flow over the visual field is used to estimate self-motion. However, different species exhibit strong spatial biases in how they use optic flow. Here, we show computationally that noisy natural environments favor visual systems that extract spatially biased samples of optic flow when estimating self-motion. The performance associated with these biases, however, depends on interactions between the environment and the animal's brain and behavior. Using the larval zebrafish as a model, we recorded natural optic flow associated with swimming trajectories in the animal's habitat with an omnidirectional camera mounted on a mechanical arm. An analysis of these flow fields suggests that lateral regions of the lower visual field are most informative about swimming speed. This pattern is consistent with the recent findings that zebrafish optomotor responses are preferentially driven by optic flow in the lateral lower visual field, which we extend with behavioral results from a high-resolution spherical arena. Spatial biases in optic-flow sampling are likely pervasive because they are an effective strategy for determining self-motion in noisy natural environments.

Evaluation of impacts of vaginal implant transmitter use in moose

AbstractVaginal implant transmitters (VITs) are a popular tool in ungulate parturition studies and have been used in moose (Alces alces), but their potential impact on animal performance has not been thoroughly assessed. We looked at potential short‐term impacts of VIT use on maternal condition through assessments of neonate calf mass, early calf survival, and parturition date. We also assessed long‐term impacts of VIT use on future reproduction and the inflammatory response after prolonged VIT retention through qualifying discharge at VIT removal and measures of acute phase proteins haptoglobin and ceruloplasmin. We captured 54 neonates and assessed over 900 potential parturition events of 278 adult moose over multiple years and we did not detect any evidence of a negative impact of VIT use on neonate calf mass, parturition date, or future reproduction. We did find elevated haptoglobin and ceruloplasmin levels in non‐pregnant individuals that had unintentional VIT retention past parturition, but this was not universal across all cases. We found a potential negative impact of VIT use on early calf survival which was likely driven by biased sampling. A bias can exist (i.e., left truncation) if sampling is mixed between cows that had a VIT (and, therefore, a known parturition time) and those without a VIT that were sampled opportunistically. Future evaluations need to overcome this bias when assessing early calf survival. With that caveat, we did not detect clear negative impacts of VIT use on moose and recommend late winter to early spring VIT insertion in pregnant females to minimize retention time.

DPT-based seismic liquefaction triggering assessment in gravelly soils based on expanded case history dataset

The gravelly soil liquefaction caused by earthquake motions has been observed and reported in different regions in the world. However, few studies have been conducted to develop models for its prediction and risk assessment. Furthermore, the single available model was developed merely based on one case history earthquake. Thus, to bridge this gap in research, this study first created a comprehensive database of dynamic penetration test (DPT) using the documentation of eight global earthquakes, which includes a considerable variation of earthquake magnitudes, the frequencies of motions, epi-central distance, geometry type, deposit layering, gravelly soil depth, geometry type, and soil properties. Then, a deterministic model was developed to estimate the cyclic strength ratio (CRR) to estimate the safety factor (SF) against liquefaction triggering. Then, to account for uncertainties in the estimations of parameters and model prediction, Bayesian mapping function and logistic regression were applied to develop probabilistic models to classify liquefied and non-liquefied regions for liquefaction occurrence prediction. The maximizing likelihood function was used to calculate the model's parameters. The effect of the bias sampling factor was surveyed via supposing a range of variation for its value to discover its decent value. To validate the presented models, they were compared to the existed model. The results were studied via two issues, as a capability that shows the accuracy of the model's prediction and a risk issue via a recall index.

Quashing a suspected selection bias in galaxy samples having dynamically measured supermassive black holes

ABSTRACT Local early-type galaxies (ETGs) with directly measured black hole masses, Mbh, have been reported to represent a biased sample relative to the ETG population at large. Such galaxies with Spitzer Space Telescope imaging have been purported to possess velocity dispersions, σ, at least ∼0.1 dex larger for a given galaxy stellar mass, M*,gal, than is typically observed among thousands of ETGs imaged by the Sloan Digital Sky Survey (SDSS). This apparent offset led Shankar et al. to reduce the normalisation of the observed Mbh ∝ σ5 relation by at least ∼0.5 dex to give their ‘intrinsic relations’, including σ-based modifications to the observed Mbh–M*,gal relation. These modifications were based on the untested assumption that the stellar masses had been derived consistently between the two samples. Here, we provide the necessary check using galaxies common to the Spitzer Survey of Stellar Structure in Galaxies (S4G) and the SDSS. We find that the stellar masses of galaxies with and without directly measured black hole masses had appeared offset from each other due to the use of inconsistent stellar mass-to-light ratios, Υ*, for the optical and infrared data. We briefly discuss the ‘intrinsic relations’ and why some of these will at times appear to have had partial success when applied to data based on similarly inconsistent values ofΥ*. Finally, we reiterate the importance of the υ (lower-case Υ) term, which we previously introduced into the Mbh–M* relations to help avoid Υ*-related mismatches.

Generalization properties of restricted Boltzmann machine for short-range order

A biased sampling algorithm for the restricted Boltzmann machine (RBM) is proposed, which allows generating configurations with a conserved quantity. To validate the method, a study of the short-range order in binary alloys with positive and negative exchange interactions is carried out. The network is trained on the data collected by Monte–Carlo simulations for a simple Ising-like binary alloy model and used to calculate the Warren–Cowley short-range order parameter and other thermodynamic properties. We demonstrate that the proposed method allows us not only to correctly reproduce the order parameters for the alloy concentration at which the network was trained, but can also predict them for any other concentrations.

Sediment concentrations and transport in icebergs, Scoresby Sound, East Greenland

AbstractGlaciers erode their beds and the adjacent landscape by abrasion and plucking and entraining sediment on their way downwards driven by gravity. Ice becomes a sediment transport agent. To determine glacial transport of sediment, measurements of both the ice flux and its concentration of sediment are needed. Once glaciers reach the ocean, ice and its entrained sediment is released into the ocean by calving. Further transport takes place by icebergs. Quantification of IRD (ice‐rafted debris) fluxes, which upon ultimate deposition on the ocean floor is an important climate indicator, becomes even more complicated as icebergs topple and differentially melt while being in transport. While the volume of tidewater glacier ice released by recent calving is quite well constrained by satellite measurements, there is a lack of measurements of the concentration of sediment within the moving ice. Here, we describe a method to collect samples of ice from icebergs systematically for the first time in Greenland together with a strategy to obtain representative samples. Our method is tested in Scoresby Sound, East Greenland in order to describe the transport of sediment into the fjord system related to calving from known glacial source areas. Our data clearly demonstrate that biased and sparse sampling potentially produces unrealistic values of sediment concentrations. Seventy‐two samples from 24 icebergs had an average concentration of sediment of 35.5 g/L of ice with a standard deviation of 97%, between the 24 individual icebergs. The origin of the sediment is related to specific source areas. Based on the samples, we present an estimate of the annual transport of sediment out of Scoresby Sound related to calving ~100 (0.3–200) million t year−1. Finally, we discuss the uncertainties of our estimate.

Are the host galaxies of long gamma-ray bursts more compact than star-forming galaxies of the field?

Context. Long gamma-ray bursts (GRBs) offer a promising tool for tracing the cosmic history of star formation, especially at high redshift, where conventional methods are known to suffer from intrinsic biases. Previous studies of GRB host galaxies at low redshift showed that high surface density of stellar mass and high surface density of star formation rate (SFR) can potentially enhance the GRB production. Evaluating the effect of such stellar densities at high redshift is therefore crucial to fully control the ability of long GRBs for probing the activity of star formation in the distant Universe. Aims. We assess how the size, stellar mass, and star formation rate surface densities of distant galaxies affect the probability of their hosting a long GRB, using a sample of GRB hosts at z > 1 and a control sample of star-forming sources from the field. Methods. We gathered a sample of 45 GRB host galaxies at 1 < z < 3.1 observed with the Hubble Space Telescope WFC3 camera in the near-infrared. Our subsample at 1 < z < 2 has cumulative distributions of redshift and stellar mass consistent with the host galaxies of known unbiased GRB samples, while our GRB host selection at 2 < z < 3.1 has lower statistics and is probably biased toward the high end of the stellar mass function. Using the GALFIT parametric approach, we modeled the GRB host light profile with a Sérsic component and derived the half-light radius for 35 GRB hosts, which we used to estimate the star formation rate and stellar mass surface densities of each object. We compared the distribution of these physical quantities to the SFR-weighted properties of a complete sample of star-forming galaxies from the 3D-HST deep survey at a comparable redshift and stellar mass. Results. We show that similarly to z < 1, GRB hosts are smaller in size and they have higher stellar mass and star formation rate surface densities than field galaxies at 1 < z < 2. Interestingly, this result is robust even when separately considering the hosts of GRBs with optically bright afterglows and the hosts of dark GRBs, as the two subsamples share similar size distributions. At z > 2, however, GRB hosts appear to have sizes and stellar mass surface densities more consistent with those characterizing the field galaxies. This may reveal an evolution with redshift of the bias between GRB hosts and the overall population of star-forming sources, although we cannot exclude that our result at z > 2 is also affected by the prevalence of dark GRBs in our selection. Conclusions. In addition to a possible trend toward a low-metallicity environment, other environmental properties such as stellar density appear to play a role in the formation of long GRBs, at least up to z ∼ 2. This might suggest that GRBs require special environments to enhance their production.

TOPoS

Context. The TOPoS project has the goal to find and analyse Turn-Off (TO) stars of extremely low metallicity. To select the targets for spectroscopic follow-up at high spectral resolution, we have relied on low-resolution spectra from the Sloan Digital Sky Survey. Aims. In this paper we use the metallicity estimates we have obtained from our analysis of the SDSS spectra to construct the metallicity distribution function (MDF) of the Milky Way, with special emphasis on its metal-weak tail. The goal is to provide the underlying distribution out of which the TOPoS sample was extracted. Methods. We make use of SDSS photometry, Gaia photometry and distance estimates derived from the Gaia parallaxes to derive a metallicity estimate for a large sample of over 24 million TO stars. This sample is used to derive the metallicity bias of the sample for which SDSS spectra are available. Results. We determined that the spectroscopic sample is strongly biased in favour of metal-poor stars, as intended. A comparison with the unbiased photometric sample allows to correct for the selection bias. We select a sub-sample of stars with reliable parallaxes for which we combine the SDSS radial velocities with Gaia proper motions and parallaxes to compute actions and orbital parameters in the Galactic potential. This allows us to characterize the stars dynamically, and in particular to select a sub-sample that belongs to the Gaia-Sausage-Enceladus (GSE) accretion event. We are thus able to provide also the MDF of GSE. Conclusions. The metal-weak tail derived in our study is very similar to that derived in the H3 survey and in the Hamburg/ESO Survey. This allows us to average the three MDFs and provide an error bar for each metallicity bin. Inasmuch the GSE structure is representative of the progenitor galaxy that collided with the Milky Way, that galaxy appears to be strongly deficient in metal-poor stars compared to the Milky Way, suggesting that the metal-weak tail of the latter has been largely formed by accretion of low mass galaxies rather than massive galaxies, such as the GSE progenitor.

The impact of sampling bias on viral phylogeographic reconstruction.

Genomic epidemiology plays an ever-increasing role in our understanding of and response to the spread of infectious pathogens. Phylogeography, the reconstruction of the historical location and movement of pathogens from the evolutionary relationships among sampled pathogen sequences, can inform policy decisions related to viral movement among jurisdictions. However, phylogeographic reconstruction is impacted by the fact that the sampling and virus sequencing policies differ among jurisdictions, and these differences can cause bias in phylogeographic reconstructions. Here we assess the potential impacts of geographic-based sampling bias on estimated viral locations in the past, and on whether key viral movements can be detected. We quantify the effect of bias using simulated phylogenies with known geographic histories, and determine the impact of the biased sampling and of the underlying migration rate on the accuracy of estimated past viral locations. We find that overall, the accuracy of phylogeographic reconstruction is high, particularly when the migration rate is low. However, results depend on sampling, and sampling bias can have a large impact on the numbers and nature of estimated migration events. We apply these insights to the geographic spread of Ebolavirus in the 2014-2016 West Africa epidemic. This work highlights how sampling policy can both impact geographic inference and be optimized to best ensure the accuracy of specific features of geographic spread.

How can augmented care experience (ACE) improve ACLR patient's participation to web questionnaires

BackgroundDigital surveys are commonly used to collect PROMS but could lead to a biased sample due to low response rate. Augmented care experience (ACE) is a process of offering higher quality of care for patients undergoing an ACLR via a Web-based platform. HypothesisACE improve participation to follow-up questionnaires using a Web-based platform (Orthense ®) Study designCohort Study. Methods101 patients scheduled for ACLR were divided in two groups: Control group (n = 50) in which patients were offered an Orthense® account to respond to 11 questionnaires post-operatively and a paper prescription of a cold knee brace (CKB) for post-operative cryotherapy; ACE group (n = 51), in addition to offering Orthense® account, the patients received in automated and digital process through their account a connection with a supplier who take in charge to send the CKB directly to their home without any additional charge. Age and sex, responses on total questionnaires, level of education, and satisfaction from the surgery at D+45 were collected. Results31.7% female and 68.3% male with an average age of 29 years old participated. Patients in ACE group responded significantly better with mean response rate of 80.75% vs 51.64% in control group (p < 0.001). Mean response rate to 11 questionnaires was 66.34% in all patients. Female and older patients were factors associated with higher response rate. There was no significant variation in response rate over the time in both groups. ConclusionACE showed to be an effective strategy to increase patient's participation to a Web-based follow-up platform.

Balanced Sex Ratios and the Autism Continuum

SEE CORRESPONDING ARTICLE ON PAGE 654 A Data-Driven Approach in an Unbiased Sample Reveals Equivalent Sex Ratio of Autism Spectrum Disorder–Associated Impairment in Early ChildhoodBiological PsychiatryVol. 92Issue 8PreviewSex differences in the prevalence of neurodevelopmental disorders are particularly evident in autism spectrum disorder (ASD). Heterogeneous symptom presentation and the potential of measurement bias hinder early ASD detection in females and may contribute to discrepant prevalence estimates. We examined trajectories of social communication (SC) and restricted and repetitive behaviors (RRBs) in a sample of infant siblings of children with ASD, adjusting for age- and sex-based measurement bias. We hypothesized that leveraging a prospective elevated familial likelihood sample, deriving data-driven behavioral constructs, and accounting for measurement bias would reveal less discrepant sex ratios than are typically seen in ASD. Full-Text PDF

Healthy-like CD4+ Regulatory and CD4+ Conventional T-Cell Receptor Repertoires Predict Protection from GVHD Following Donor Lymphocyte Infusion.

Donor lymphocyte infusion (DLI) can (re-)induce durable remission in relapsing patients after allogeneic hematopoietic stem-cell transplantation (alloHSCT). However, DLI harbors the risk of increased non-relapse mortality due to the co-occurrence of graft-versus-host disease (GVHD). GVHD onset may be caused or accompanied by changes in the clonal T-cell receptor (TCR) repertoire. To investigate this, we analyzed T cells in a cohort of 21 patients receiving DLI after alloHSCT. We performed deep T-cell receptor β (TRB) sequencing of sorted CD4+CD25+CD127low regulatory T cells (Treg cells) and CD4+ conventional T cells (Tcon cells) in order to track longitudinal changes in the TCR repertoire. GVHD following DLI was associated with less diverse but clonally expanded CD4+CD25+CD127low Treg and CD4+ Tcon TCR repertoires, while patients without GVHD exhibited healthy-like repertoire properties. Moreover, the diversification of the repertoires upon GVHD treatment was linked to steroid-sensitive GVHD, whereas decreased diversity was observed in steroid-refractory GVHD. Finally, the unbiased sample analysis revealed that the healthy-like attributes of the CD4+CD25+CD127low Treg TCR repertoire were associated with reduced GVHD incidence. In conclusion, CD4+CD25+CD127low Treg and CD4+ Tcon TRB repertoire dynamics may provide a helpful real-time tool to improve the diagnosis and monitoring of treatment in GVHD following DLI.

Ice thickness monitoring for cryo-EM grids by interferometry imaging

While recent technological developments contributed to breakthrough advances in single particle cryo-electron microscopy (cryo-EM), sample preparation remains a significant bottleneck for the structure determination of macromolecular complexes. A critical time factor is sample optimization that requires the use of an electron microscope to screen grids prepared under different conditions to achieve the ideal vitreous ice thickness containing the particles. Evaluating sample quality requires access to cryo-electron microscopes and a strong expertise in EM. To facilitate and accelerate the selection procedure of probes suitable for high-resolution cryo-EM, we devised a method to assess the vitreous ice layer thickness of sample coated grids. The experimental setup comprises an optical interferometric microscope equipped with a cryogenic stage and image analysis software based on artificial neural networks (ANN) for an unbiased sample selection. We present and validate this approach for different protein complexes and grid types, and demonstrate its performance for the assessment of ice quality. This technique is moderate in cost and can be easily performed on a laboratory bench. We expect that its throughput and its versatility will contribute to facilitate the sample optimization process for structural biologists.

Enrollment and Retention of Participants in Remote Digital Health Studies: Scoping Review and Framework Proposal.

BackgroundDigital technologies are increasingly used in health research to collect real-world data from wider populations. A new wave of digital health studies relies primarily on digital technologies to conduct research entirely remotely. Remote digital health studies hold promise to significant cost and time advantages over traditional, in-person studies. However, such studies have been reported to typically suffer from participant attrition, the sources for which are still largely understudied.ObjectiveTo contribute to future remote digital health study planning, we present a conceptual framework and hypotheses for study enrollment and completion. The framework introduces 3 participation criteria that impact remote digital health study outcomes: (1) participant motivation profile and incentives or nudges, (2) participant task complexity, and (3) scientific requirements. The goal of this study is to inform the planning and implementation of remote digital health studies from a person-centered perspective.MethodsWe conducted a scoping review to collect information on participation in remote digital health studies, focusing on methodological aspects that impact participant enrollment and retention. Comprehensive searches were conducted on the PubMed, CINAHL, and Web of Science databases, and additional sources were included in our study from citation searching. We included digital health studies that were fully conducted remotely, included information on at least one of the framework criteria during recruitment, onboarding or retention phases of the studies, and included study enrollment or completion outcomes. Qualitative analyses were performed to synthesize the findings from the included studies.ResultsWe report qualitative findings from 37 included studies that reveal high values of achieved median participant enrollment based on target sample size calculations, 128% (IQR 100%-234%), and median study completion, 48% (IQR 35%-76%). Increased median study completion is observed for studies that provided incentives or nudges to extrinsically motivated participants (62%, IQR 43%-78%). Reducing task complexity for participants in the absence of incentives or nudges did not improve median study enrollment (103%, IQR 102%-370%) or completion (43%, IQR 22%-60%) in observational studies, in comparison to interventional studies that provided more incentives or nudges (median study completion rate of 55%, IQR 38%-79%). Furthermore, there were inconsistencies in measures of completion across the assessed remote digital health studies, where only around half of the studies with completion measures (14/27, 52%) were based on participant retention throughout the study period.ConclusionsFew studies reported on participatory factors and study outcomes in a consistent manner, which may have limited the evidence base for our study. Our assessment may also have suffered from publication bias or unrepresentative study samples due to an observed preference for participants with digital literacy skills in digital health studies. Nevertheless, we find that future remote digital health study planning can benefit from targeting specific participant profiles, providing incentives and nudges, and reducing study complexity to improve study outcomes.