Scale Problems Research Articles

Relationship between stress and coping strategies of caregivers of elderly people with Alzheimer's Disease

This cross-sectional study aimed to identify the relationship between stress and coping strategies of 126 caregivers of elderly people with Alzheimer's Disease. A questionnaire was used to evaluate the sociodemographic, health and care profile of the elderly, the Perceived Stress Scale and the Ways of Coping with Problems Scale. Data analysis was performed using the Kolmogorov-Smirnov test, asymmetry and kurtosis coefficients, bootstrapping procedures, Pearson correlation, multiple linear regression, independent student's t-test, One-Way Anova and Tukey's Post-Hoc (p < 0.05). It was found that stress presented a significant (p < 0.05), moderate and negative correlation with the problem-focused coping strategy, in addition to a positive and moderate correlation with the emotion-focused strategy. Coping strategies focused on the problem (β = -0.54; p < 0.001) and emotion (β = 0.33; p < 0.001) presented a significant prediction on the caregivers' perception of stress. From the results obtained, it can be concluded that the problem-focused strategy appears to contribute to reducing caregivers' perception of stress, while the emotion-focused strategy appears to contribute positively to stress.

BE EPIC-VR: TRANSLATING AN IN-PERSON, PERSON-CENTERED COMMUNICATION TRAINING PROGRAM INTO VIRTUAL REALITY

Abstract Be EPIC is a dementia-specific, person-centered communication training program for frontline healthcare workers that uses simulations with trained actors, reflection, and feedback. Scaling Be EPIC is limited by resource and methodology demands of recruiting and training actors and consistency of delivery across sites. Virtual reality (VR) is a promising solution to the problem of scaling Be EPIC because it provides interactive, realistic, and consistent simulations. However, there is limited research on implementing VR to train frontline healthcare workers. Guided by the Consolidated Framework for Implementation Research, the current study used an effectiveness-implementation hybrid design to compare simulation experiences of Be EPIC-VR with Be EPIC-in person and real-world clinical encounters. The study also explored factors that can influence the future implementation of Be EPIC-VR in long-term care home settings during the pre-implementation period. Six frontline healthcare workers who completed the Be EPIC in-person version with trained actors also completed the same assessment simulation in VR, followed by an interview. Thematic analyses revealed two themes that contributed to the simulation’s realism: the immersive nature of the virtual environment and the accuracy of the avatar’s visual, verbal, and behavioral characteristics. Moreover, the factors influencing the successful implementation of VR simulations in long-term care home settings include sufficient technological infrastructure and workplace personnel who can assist with Be EPIC-VR implementation. The findings highlight the potential to scale Be EPIC using VR and provides insights into the iterative process involved in translating an in-person training into a VR training program.

Einstein material balance and modeling of the flow of compressible fluid near the boundary

We consider sewing machinery between nite difference and analytical solutions de ned at different scales: far away and near the source of the perturbation of the flow. One of the essences of the approach is that the coarse problem and the boundary-value problem in the proxy of the source model two different flows. In his remarkable paper, Peaceman proposes a framework for dealing with solutions de ned on different scales for linear time independent problems by introducing the famous Peaceman well block radius. In this article, we consider a novel problem: how to solve this issue for transient flow generated by the compressibility of the fluid. We are proposing a method to glue solution via total fluxes, which are prede ned on coarse grid, and changes in pressure, due to compressibility, in the block containing production (injection) well. It is important to mention that the coarse solution “does not see” the boundary. From an industrial point of view, our report provides a mathematical tool for the analytical interpretation of simulated data for compressible fluid flow around a well in a porous medium. It can be considered a mathematical “shirt” on the Peaceman well-block radius formula for linear (Darcy) transient flow but can be applied in much more general scenarios. In the article, we use the Einstein approach to derive the material balance equation, a key instrument to de ne R0. We will expand the Einstein approach for three regimes of Darcy and non-Darcy flows for compressible fluids (time-dependent): 1. stationary; 2. pseudostationary; 3. boundary dominated. Note that in all known authors literature, the rate of production on the well is time-independent.

External labor migration in modern Russia: socio-legal and criminological contexts

In modern Russia, criminological and sociological problems of external labor migration are relevant and topical, and primarily associated with large-scale uncontrolled and illegal migration, as well as with the ongoing criminalization of the sphere of migration relations. The problems accumulated in the migration area pose a significant threat to the national security of our country. The author has made an attempt to identify social hotbeds and determinants of the criminalization of migration processes and to formulate special measures and means aimed at decriminalizing the sphere of migration relations and reducing the level of illegal migration in Russia. The aim was to consider the current processes in the field of external labor migration in the Russian Federation and specific tasks to identify the main problems in this area in the socio-legal and criminological context and to propose effective ways to solve them, which are characterized by novelty and originality both in practical and theoretical terms. The research was carried out on the basis of empirical methods of cognition (conversations, in-depth interviews, sociological survey), as well as general scientific and interdisciplinary methods of cognition (comparative legal, formal legal and structural-functional). The key idea of this publication is that the scale and level of criminological and sociological problems in the field of external labor migration require development and adoption of a codified regulatory legal act — Migration Code of the Russian Federation, which would prescribe in detail and enshrine legal status, order of entry and stay, duties and rights of external migrants in Russia. Due to the special criminological and social significance of the processes of social adaptation, acculturation and integration of external labor migrants, the author of the publication proposes to develop and adopt a Federal Law «On social and cultural adaptation and integration of external labor migrants in the Russian Federation» with detailed regulation of appropriate measures, procedures and mechanisms.

Confirmatory factor analysis and gender invariance of the Persian version of psychological control scale: association with internalizing and externalizing behavior problems.

The current cross-sectional study aimed to examine the reliability, construct validity, gender invariance and concurrent validity of the psychological control scale-youth self-report (PCS-YSR) among Iranian adolescents. A total of 1,453 high school students (49.2% boys; Mage = 15.48, SD = 0.97), who aged between 14 and 18 years old completed the PCS-YSR and the youth self-report (YSR) scale of behavior problems. Reliability was established using Cronbach's alpha and ordinal alpha for maternal and paternal psychological control. The confirmatory factor analysis (CFA) results supported the original unidimensional model of the PCS-YSR scale for both mother and father forms. Results also revealed that mother and father forms of PCS-YSR were invariant across adolescents' gender. When comparing the mean differences, mothers were more psychologically controlling toward their sons, compared to their daughters. The mother and father forms of PCS-YSR were found to have acceptable concurrent validity through their relationship to internalizing and externalizing behavioral problems. Overall, our findings supported the psychometric properties of the Persian version of the Psychological Control Scale-Youth Self-Report among Iranian adolescents. This scale can be used as an efficient tool for parental psychological control among adolescents in Iran. The negative effect of the intrusive parenting behavior on child' negative outcomes in Iran, irrespective of culture, was shown.

Closed-Loop Control and Plant Co-Design of a Hybrid Electric Unmanned Air Vehicle

Abstract Novel conceptual aircraft designs have been enabled by more electrified aircraft components providing enhanced capability and versatility. Through the advancement of multidisciplinary design optimization, control co-design methods have become a popular approach for system design conceptualization wherein the plant and control actions are designed simultaneously to account for the coupling between vehicle subsystems and power management systems. Many prior efforts have focused on open-loop control co-design that can later be adapted for a more realistic operating case. This work focuses on the development and scalability of closed-loop control co-design that would result in a physically realizable plant and closed-loop control law. The theoretical approach is demonstrated practically through the design of a hybrid electric unmanned air vehicle and two feedback controllers that operate the hybrid power split and propulsion system. The system is designed to complete a dynamic seven phase mission consisting of multiple cruise, dash, engage, dive, and climb segments as quickly as possible. Given the scale of the dynamic design problem, a convergence study is introduced that facilitates accurate and computationally tractable design optimization studies. The study is conducted for independent, sequential, and simultaneous design approaches. The results indicate high-speed motors, high voltage batteries, and responsive control gains result in a fast vehicle with high thrust-to-weight ratio. The simultaneous design solution had the best closed-loop performance, outclassing a baseline system design by over 30%.

Design and optimization of molten salt printed circuit steam generators

Among the array of heat exchangers available, printed circuit steam generators emerge as a particularly promising choice for high-temperature applications. This is primarily attributed to their augmented heat transfer surface area, which enables the efficient transfer of thermal energy within the Rankine power conversion cycle, especially in the context of molten salt-based thermal power systems, such as those employed in molten salt reactors and concentrated solar power plants. It is imperative to analytically model the heat transfer characteristics of mini-channel flow boilings for a printed circuit steam generator. To resolve new technical design issues that the single hot fluid flow channel is cooled by multiple cold fluid flow channels, an improved heat exchanger model is proposed to assist the numerical modeling of printed circuit steam generators for molten salt based on the equilibrium thermodynamics quality of water/steam flow. The existing flow boiling heat transfer coefficient correlations of mini-channels are leveraged to optimally design the straight-passage based printed circuit layouts and narrow down their optimal operational parameters. With various selections of subcooled water, saturated water, and superheated steam heat transfer correlations, The best combination of heat transfer correlations is found to assess the axial distributions of cold fluid temperatures and pressure drops. Two multi-objective optimizations are formulated to respectively solve thermal sizing and scaling problems for the optimal designs and operations of printed circuit steam generators. The computational results indicate that the overall heat transfer coefficient for an optimized 50 MW(th) printed circuit steam generator assessed at the transition point is about 3,828 W/(m2 K), which remarkably surpasses the 1,544 W/(m2 K) value of the conventional shell-tube steam generators by a factor of three. This implies that printed circuit steam generators would be a promising replacement of the traditional shell-tube steam generator.

Carsharing equitable relocation problem: A two-stage stochastic programming approach with learning-embedded endogenous uncertainty in demand

Uncertain demand may exacerbate the imbalance of the supply–demand for a one-way carsharing system and complicate vehicle relocation decisions. To consider the effect of the uncertainty, this study proposes a two-stage stochastic nonlinear programming model, integrating long-term and short-term decisions and maximizing the profit of the carsharing companies. Specifically, in the first stage, tactical decisions of fleet sizing and initial vehicle distribution are determined before the realization of the uncertain demand. Operational decisions of both operator-based and user-based relocation are optimized in the second stage. Moreover, this paper first studies the user-based relocation incentives, which affect the distribution of uncertain demand with an endogenous relationship. A learning-embedded optimization method is introduced to learn such a distribution, enabling the decision-making optimization model to achieve higher performance under the guidance of the demand uncertainty. Second, we envision an equitable relocation issue that considers an uneven distribution of the unsatisfied demand with two different equity criteria measured from the aspects of stations and OD pairs, respectively. Third, the large problem scale, the nonlinear objective function and constraints, and the endogenous demand uncertainty constitute the nontrivial challenges to the feasible solution. For solving the problem efficiently, we linearize the nonlinear terms and develop a dedicated two-phase solution algorithm with a learning-embedded trust-region method in phase I to solve the continuous relaxation problem and a mixed-integer linear programming guided iterative rounding in phase II to obtain the integer solutions of carsharing operations. The solution algorithm adaptively bridges the learning and optimization process via the trust-region method with flexible sample generation. Finally, we conduct numerical experiments based on a real-world one-way carsharing system in Beijing to demonstrate the effectiveness and applicability of the proposed method and reveal some insights for the carsharing service.

A discontinuous smooth particle hydrodynamics method for modeling deformation and failure processes of fractured rocks

A discontinuous smoothed particle hydrodynamics (DSPH) method considering block contacts is originally developed to model the cracking, frictional slip and large deformation in rock masses, and is verified by theoretical, numerical and/or experimental results. In the DSPH method, cracking is realized by breaking the virtual bonds via a pseudo-spring method based on Mohr–Coulomb failure criteria. The damaged particles are instantaneously replaced by discontinuous particles and the contact bond between the original and discontinuous particles is formed to simulate the frictional slip and separation/contraction between fracture surfaces based on the block contact algorithm. The motion of rock blocks and the contact force of discontinuous particles are determined following Newton's second law. The results indicate that the DSPH method precisely captures the cracking, contact formation and complete failure across six numerical benchmark tests. This single smoothed particle hydrodynamics (SPH) framework could significantly improve computational efficiency and is potentially applicable to broad multi-physical rock engineering problems of different scales.

3D Cellular Solar Crystallizer for Stable and Ultra-Efficient High-Salinity Wastewater Treatment.

Recent developed interfacial solar brine crystallizers, which employ solar-driven water evaporation for salts crystallization from the near-saturation brine to achieve zero liquid discharge (ZLD) brine treatment, are promising due to their excellent energy efficiency and sustainability. However, most existing interfacial solar crystallizers are only tested using NaCl solution and failed to maintain high evaporation capability when treating real seawater due to the scaling problem caused by the crystallization of high-valent cations. Herein, an artificial tree solar crystallizer (ATSC) with a multi-branched and interconnected open-cell cellular structure that significantly increased evaporation surface is rationally designed, achieving an ultra-high evaporation rate (2.30kgm-2 h-1 during 2h exposure) and high energy efficiency (128%) in concentrated real seawater. The unit cell design of ATSC promoted salt crystallization on the outer frame rather than the inner voids, ensuring that salt crystallization does not affect the continuous transport of brine through the pores inside the unit cell, thus ATSC can maintain a stable evaporation rate of 1.94kgm-2 h-1 on average in concentrated seawater for 80h continuous exposure. The design concept of ATSC represents a major step forward toward ZLD treatment of high-salinity brine in many industrial processes is believed.

Scaling the Inference of Digital Pathology Deep Learning Models Using CPU-Based High-Performance Computing

Digital pathology whole-slide images (WSIs) are large-size gigapixel images and image analysis based on deep learning artificial intelligence (AI) technology often involves pixel-wise testing of a trained deep learning neural network (DLNN) on hundreds of WSI images, which is time consuming. We take advantage of High-Performance Computing (HPC) facilities to parallelize this procedure into multiple independent (and hence delightfully parallel) tasks. However, traditional software parallelization techniques and regular file formats can have significant scaling problems on HPC clusters. In this work, a useful computational strategy is designed to localize and extract relevant patches in WSI files and group them in HDF5 files well suited for parallel I/O. HPC's array job facilities are adapted for hierarchical scaling and parallelization of WSI pre-processing and testing of trained algorithms. Applying these techniques to testing a trained DLNN on the CAMELYON datasets with 399 WSIs reduced the theoretical processing time of 18 years on a single CPU or 30 days on a single GPU to less than 45 hours on an HPC cluster of 4,000 CPU cores. The efficiency-accuracy trade-off we demonstrated on this dataset further reinforced the importance of efficient computation techniques, without which the accuracy may be sacrificed. The framework developed here for testing DLNNs does not rely on any specific neural network architecture and HPC cluster setup and can be utilized for any large-scale image processing and big-data analysis.

Cost-Effective Traffic Scheduling and Resource Allocation for Edge Service Provisioning

The multi-access edge computing (MEC) paradigm has emerged as a critical solution to address the exponential growth in mobile web services and devices. By implementing an edge-based service provisioning system (EPS) with servers located at the network’s edge, both transmission and computation efficiency can be significantly enhanced. Nevertheless, it is also essential to carefully consider the resource allocation for services, the traffic management of requests, and the path arrangement for data delivery to ensure the cost-effective operation of the EPS. Therefore, we investigate and quantify the relationship between the performance and cost of the EPS in this paper, and model the cost-effective service provisioning problem as a multi-phase convex optimization problem. An online algorithm whose name is RDC based on the Lyapunov framework is proposed to decompose this problem into several sub-problems.Additionally, a heuristic approach that partitions edge servers into several clusters, called RDC-NeP and based on RDC, has also been proposed to reduce computational complexity. A series of experiments were conducted to evaluate the proposed approach. The results demonstrate that RDC can effectively balance expense and performance, while RDC-NeP significantly simplifies the processing of RDC when the problem scale increases.

A bi-objective model for aircraft and crew recovery considering individual flight legs and maintenance requirements

This paper proposes a new bi-objective aircraft and crew recovery model. Unlike previous researches, the proposed model considers all individual flight legs in the recovery process. The objective functions minimize the recovery cost regarding crew swapping, flight cancellation, flight delay, and crew deadhead costs. Besides, the crew sit time is reduced considering the maximum legal working hours and the minimum essential rest time. Moreover, maintenance operations can be done during the ground time, not necessarily at the end of duty day. The model is never infeasible because flight cancelation is an alternative solution for recovering a schedule. A modified Tabu Search (TS) approach has been developed to solve the model. The performance of the model and solution approach is examined by several test problems in small, medium, and large scales. The results indicate that TS reaches optimal and close-optimal in a short computational time, especially in large scale problems.

Collaborative Inference Acceleration Integrating DNN Partitioning and Task Offloading in Mobile Edge Computing

In mobile edge computing environment, intelligent inference services driven by DNN are highly sensitive to latency. Recently, collaborative inference between User Devices and Edge Servers (ESs) based on Deep Neural Networks (DNN) partition has achieved success in service acceleration. However, most of the existing collaborative acceleration schemes are partitioned for a single DNN inference task, which cannot quickly make partition decisions for a set of concurrent inference tasks, and often sacrifice inference accuracy. In addition, due to the limited resources of ESs, there is resource competition among concurrent requests, which makes the partitioned tasks cannot be offloaded to ESs in time for processing. Therefore, designing an efficient offloading scheme becomes essential. The task offloading schemes based on deep reinforcement learning can solve complex decision-making problems in high-dimensional state space, but they have problems such as insufficient sample diversity and easily falling into local optimum. In this paper, a Collaborative Inference Acceleration Scheme integrating DNN Partitioning and Task Offloading (CIAS-PnO) is proposed. First, while ensuring inference accuracy, the Collaborative DNN Layer Partitioning (CDLP) algorithm is designed with the goal of optimal latency. CDLP can reduce the problem scale of concurrent inference tasks partition by pruning operation and determine the partition decisions in time. Then, the Distributed Soft Actor-Critic (SAC)-based Partition Task Offloading algorithm (DSACO) is designed. DSACO supports SAC Agents to explore samples in parallel and share learning experiences, and uses the automatic entropy adjustment mechanism to improve the exploration efficiency of Agents, so as to avoid falling into local optimum and achieve efficient offloading of partition tasks. Experimental results on DNN benchmarks show that compared with the baseline acceleration schemes, CIAS-PnO achieves more than 19.8% acceleration performance improvement, and has higher convergence performance and task success rate.

Associations between the Use of Pain Management Strategies and Self-Reported Emotional and Behavioral Functioning of Adolescent and Young Adults with Sickle Cell Disease

Background: Sickle Cell Disease (SCD) often causes significant pain that may require the use of various pain management strategies. These strategies are typically categorized into two broad groups, pharmacological and nonpharmacological, but a need exists to understand how specific strategies may impact an individual's functioning. This understanding is especially critical when considering adolescents and young adults (AYA) with SCD, as this age range may experience increased pain and other SCD complications that coincide with the transition from pediatric to adult care. Methods: Adolescent and young adult patients with SCD completed the Behavioral Assessment System for Children, Third Edition (BASC-3), a measure of emotional and behavioral functioning, and a questionnaire about their pain management strategy use. These strategies were categorized as: Over the Counter Medication (acetaminophen, ibuprofen, naproxen), Prescription Medication (codeine, short-acting morphine, long-acting morphine, oxycodone, hydrocodone, hydromorphone, gabapentin, amitriptyline), Psychological Strategies (deep breathing, imagery, distraction, talking with friends/family, prayer/meditation), and Behavioral Strategies (hydrate, apply heat, apply cold, massage, smoking, rest/sleep). Analyses included Spearman rank correlations to determine the relationship between categories of pain management strategies and self-reported BASC scores. Based on correlation results, independent sample t-tests were conducted to further examine the relationship between the use of specific pain management strategies and self-reported BASC scores. Results: Participants included 94 AYA patients with SCD (Mean age = 18.1 ± 1.5 years; 51.1% female; 52.1% HbSS). Of the pain management categories, Prescription Medication and Behavioral Strategies had significant correlations with self-reported BASC scores. Within the Prescription Medication category, participants that reported use of hydrocodone (40.9%) self-reported higher scores on the Social Stress (p = 0.01) and Somatization (p = 0.001) scales of the BASC-3 as well as lower scores on the Interpersonal Relations (p = 0.048) scale compared to participants that did not use hydrocodone. Those using oxycodone (25%) self-reported higher scores on the Somatization (p = 0.002) scale and lower scores on the Interpersonal Relations (p = 0.036) scale compared to those that did not use oxycodone. Participants that reported use of gabapentin (6.8%) self-reported higher scores on the Social Stress (p = 0.006) and Somatization (p = 0.009) scales compared to those that did not use of gabapentin. Compared to participants that did not use amitriptyline, those that reported use (3.4%) self-reported higher scores on the Somatization (p = 0.017) scale. Within the Behavioral Strategies category, adolescents and young adults that reported the use of smoking to manage pain (7.3%) self-reported higher scores on the Somatization (p < 0.001) and Attention Problems (p < 0.001) scales than those that did not smoke. Additionally, participants that reported the application of cold temperature to manage pain (15.9%) self-reported higher scores on the Attitude to School (p = 0.03) and Attention Problems (p = 0.006) scales compared to those that did not use this strategy. Conclusions: Results indicate that specific pain management strategies may be associated with different social, emotional, and behavioral functioning. Further exploration to understand the mechanisms between specific pain management strategies and patient functioning during the AYA period is warranted. Given the unique challenges of adolescents and young adults with SCD, future work should also investigate how specific pain management strategies may improve the experience of those transitioning from pediatric to adult care.

A viable supply chain by considering vendor-managed-inventory with a consignment stock policy and learning approach

Inventory management challenges are optimizing inventory levels, minimizing stockouts, and streamlining the supply chain process for the vendor and the customer. The study tries to improve these challenges by proposing a new Viable Supply Chain (VSC) incorporating Vendor-Managed-Inventory (VMI) with a Consignment Stock (SC) policy for managing inventory between SC components. These strategies access the inventory level of the customer in the vendor, and if the items are sold, the customer pays the vendor for the inventory that was sold. The Robust Stochastic Optimization (RSO) with Conditional Value at Risk (CVaR) as risk criteria is utilized to solve the model. The objective function of this model combines the expected value, maximum, and CVaR of cost. To promote viability against disruption, sustainability requirements such as CO2 emissions and energy consumption are considered. An agile approach that includes a learning factor is suggested. In addition, a resiliency strategy by addressing order disruption requirements is proposed. The case study of this research is a small healthcare industry that prepares drug products. It is one of the largest industries with high sales and added value. The results show that the inclusion of VMI and CS in the main problem-solving approach resulted in a 14.8 % cost reduction compared to the scenario where VMI and CS were not considered. Finally, sensitivity analysis is performed on CVaR confidence, conservatism coefficient, learning rate, agility rate and scale of the main problem.

Quick taxi route assignment via real-time intersection state prediction with a spatial-temporal graph neural network

The optimization of airport taxi routes is crucial for increasing airport capacity and resource utilization. However, existing methods for preplanning schedules are often time-consuming. To address this, we propose a new approach that reduces the problem scale by predicting the intersection state in the airport taxiway network using a spatial–temporal graph neural network (STGNN) that incorporates a graph convolutional network and recursive neural network with an attention mechanism. A mixed-integer linear problem (MILP) model for taxi route assignment is established based on a subgraph obtained from the prediction results. Our approach treats time as a continuous variable without discretization, allowing aircraft to stop and wait at any node to avoid conflict. Finally, we demonstrate the effectiveness of our approach by testing it on actual operation data from Beijing Capital International Airport (PEK). Compared with several baseline algorithms, the method developed in this study has a significant impact on improving airport transportation efficiency. The results indicate that our method not only produces high-quality solutions but also operates in real time.

On scaling robust feedback control and state estimation problems in power networks

Many mainstream robust control/estimation algorithms for power networks are designed using the Lyapunov theory as it provides performance guarantees for linear/nonlinear models of uncertain power networks but comes at the expense of scalability and sensitivity. In particular, Lyapunov-based approaches rely on forming semi-definite programs (SDPs) that are (i) not scalable and (ii) extremely sensitive to the choice of the bounding scalar that ensures the strict feasibility of the linear matrix inequalities (LMIs). This paper addresses these two issues by employing a celebrated non-Lyapunov approach (NLA) from the control theory literature. In lieu of linearized models of power grids, we focus on (the more representative) nonlinear differential algebraic equation (DAE) models and showcase the simplicity, scalability, and parameter-resiliency of NLA. For some power systems, the approach is nearly fifty times faster than solving SDPs via standard solvers with almost no impact on the performance. The case studies also demonstrate that NLA can be applied to more realistic scenarios in which (i) only partial state data is available and (ii) sparsity structures are imposed on the feedback gain. The paper also showcases that virtually no degradation in state estimation quality is experienced when applying NLA.

Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications.

Antibodies are critical reagents to detect and characterize proteins. It is commonly understood that many commercial antibodies do not recognize their intended targets, but information on the scope of the problem remains largely anecdotal, and as such, feasibility of the goal of at least one potent and specific antibody targeting each protein in a proteome cannot be assessed. Focusing on antibodies for human proteins, we have scaled a standardized characterization approach using parental and knockout cell lines (Laflamme et al., 2019) to assess the performance of 614 commercial antibodies for 65 neuroscience-related proteins. Side-by-side comparisons of all antibodies against each target, obtained from multiple commercial partners, have demonstrated that: (i) more than 50% of all antibodies failed in one or more applications, (ii) yet, ~50-75% of the protein set was covered by at least one high-performing antibody, depending on application, suggesting that coverage of human proteins by commercial antibodies is significant; and (iii) recombinant antibodies performed better than monoclonal or polyclonal antibodies. The hundreds of underperforming antibodies identified in this study were found to have been used in a large number of published articles, which should raise alarm. Encouragingly, more than half of the underperforming commercial antibodies were reassessed by the manufacturers, and many had alterations to their recommended usage or were removed from the market. This first study helps demonstrate the scale of the antibody specificity problem but also suggests an efficient strategy toward achieving coverage of the human proteome; mine the existing commercial antibody repertoire, and use the data to focus new renewable antibody generation efforts.

Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications

Antibodies are critical reagents to detect and characterize proteins. It is commonly understood that many commercial antibodies do not recognize their intended targets, but information on the scope of the problem remains largely anecdotal, and as such, feasibility of the goal of at least one potent and specific antibody targeting each protein in a proteome cannot be assessed. Focusing on antibodies for human proteins, we have scaled a standardized characterization approach using parental and knockout cell lines (Laflamme et al., 2019) to assess the performance of 614 commercial antibodies for 65 neuroscience-related proteins. Side-by-side comparisons of all antibodies against each target, obtained from multiple commercial partners, have demonstrated that: (i) more than 50% of all antibodies failed in one or more applications, (ii) yet, ~50–75% of the protein set was covered by at least one high-performing antibody, depending on application, suggesting that coverage of human proteins by commercial antibodies is significant; and (iii) recombinant antibodies performed better than monoclonal or polyclonal antibodies. The hundreds of underperforming antibodies identified in this study were found to have been used in a large number of published articles, which should raise alarm. Encouragingly, more than half of the underperforming commercial antibodies were reassessed by the manufacturers, and many had alterations to their recommended usage or were removed from the market. This first study helps demonstrate the scale of the antibody specificity problem but also suggests an efficient strategy toward achieving coverage of the human proteome; mine the existing commercial antibody repertoire, and use the data to focus new renewable antibody generation efforts.