scSID: A lightweight algorithm for identifying rare cell types by capturing differential expression from single-cell sequencing data

Single-cell RNA sequencing (scRNA-seq) is currently an important technology for identifying cell types and studying diseases at the genetic level. Identifying rare cell types is biologically important as one of the downstream data analyses of single-cell RNA sequencing. Although rare cell identification methods have been developed, most of these suffer from insufficient mining of intercellular similarities, low scalability, and being time-consuming. In this paper, we propose a single-cell similarity division algorithm (scSID) for identifying rare cells. It takes cell-to-cell similarity into consideration by analyzing both inter-cluster and intra-cluster similarities, and discovers rare cell types based on the similarity differences. We show that scSID outperforms other existing methods by benchmarking it on different experimental datasets. Application of scSID to multiple datasets, including 68K PBMC and intestine, highlights its exceptional scalability and remarkable ability to identify rare cell populations.

Surface engineering of multifunctional nanostructured adsorbents for enhanced wastewater treatment: A review

Rapid urbanization and industrialization have significantly contributed to the contamination of the environment through the discharge of wastewater containing various pollutants. The development of high-performance surface functional nanostructured adsorbents is of wide interest for researchers. Therefore, we explore the significant advancements in this field, focusing on the efficiency of nanostructured materials, as well as their nanocomposites, for wastewater treatment applications. The crucial role of surface modification in enhancing the affinity of these nanostructured adsorbents towards targeted pollutants, addressing a key bottleneck in the utilization of nanomaterials for wastewater treatment, was specifically emphasized. In addition to highlighting the advantages of surface engineering in enhancing the efficiency of nanostructured adsorbents, this review also provides a comprehensive overview of the limitations and challenges associated with surface-modified nanostructured adsorbents, including high cost, low stability, poor scalability, and potential nanotoxicity. Addressing these limitations is essential for realizing the commercial viability of these state-of-the-art materials for large-scale wastewater treatment applications. This review also thoroughly discusses the potential scalability and environmental safety aspects of surface-modified nanostructured adsorbents, offering insights into their future prospects for wastewater treatment. It is believed that this review will contribute significantly to the existing body of knowledge in the field and provide valuable information for researchers and practitioners working in the area of environmental remediation and nanomaterials.

GeSe ovonic threshold switch: the impact of functional layer thickness and device size

Three-dimensional phase change memory (3D PCM), possessing fast-speed, high-density and nonvolatility, has been successfully commercialized as storage class memory. A complete PCM device is composed of a memory cell and an associated ovonic threshold switch (OTS) device, which effectively resolves the leakage current issue in the crossbar array. The OTS materials are chalcogenide glasses consisting of chalcogens such as Te, Se and S as central elements, represented by GeTe6, GeSe and GeS. Among them, GeSe-based OTS materials are widely utilized in commercial 3D PCM, their scalability, however, has not been thoroughly investigated. Here, we explore the miniaturization of GeSe OTS selector, including functional layer thickness scalability and device size scalability. The threshold switching voltage of the GeSe OTS device almost lineally decreases with the thinning of the thickness, whereas it hardly changes with the device size. This indicates that the threshold switching behavior is triggered by the electric field, and the threshold switching field of the GeSe OTS selector is approximately 105 V/μm, regardless of the change in film thickness or device size. Systematically analyzing the threshold switching field of Ge-S and Ge-Te OTSs, we find that the threshold switching field of the OTS device is larger than 75 V/μm, significantly higher than PCM devices (8.1-56 V/μm), such as traditional Ge2Sb2Te5, Ag-In-Sb-Te, etc. Moreover, the required electric field is highly correlated with the optical bandgap. Our findings not only serve to optimize GeSe-based OTS device, but also may pave the approach for exploring OTS materials in chalcogenide alloys.

PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies

Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from background make this task very challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module to counteract potential perturbations such as microscope movements and frame blurring. We also propose an explainable cell segmentation module to improve the interpretability of deep learning methods compared to black-box algorithms. This includes two interpretable deep learning capabilities: visual explanation and model simplification. We demonstrate that interpretability in deep learning is not the opposite of high performance, by additionally providing essential deep learning algorithm optimization insights and solutions. Besides, incorporating interpretable modules results in an efficient architecture design and optimized execution time. We apply this pipeline to quantify and analyze microglial cell phagocytosis in frontotemporal dementia (FTD) and obtain statistically reliable results showing that FTD mutant cells are larger and more aggressive than control cells. The method has been tested and validated on several public benchmarks by generating state-of-the art performances. To stimulate translational approaches and future studies, we release an open-source end-to-end pipeline and a unique microglial cells phagocytosis dataset for immune system characterization in neurodegenerative diseases research. This pipeline and the associated dataset will consistently crystallize future advances in this field, promoting the development of efficient and effective interpretable algorithms dedicated to the critical domain of neurodegenerative diseases' characterization. https://github.com/ounissimehdi/PhagoStat .

Molecular self-assembled cellulose enabling durable, scalable, high-power osmotic energy harvesting

In recent years, renewable cellulose-based ion exchange membranes have emerged as promising candidates for capturing green, abundant osmotic energy. However, the low power density and structural/performance instability are challenging for such cellulose membranes. Herein, cellulose-molecule self-assembly engineering (CMA) is developed to construct environmentally friendly, durable, scalable cellulose membranes (CMA membranes). Such a strategy enables CMA membranes with ideal nanochannels (∼7 nm) and tailored channel lengths, which support excellent ion selectivity and ion fluxes toward high-performance osmotic energy harvesting. Finite element simulations also verified the function of tailored nanochannel length on osmotic energy conversion. Correspondingly, our CMA membrane shows a high-power density of 2.27 W/m2 at a 50-fold KCl gradient and super high voltage of 1.32 V with 30-pair CMA membranes (testing area of 22.2 cm2). In addition, the CMA membrane demonstrates long-term structural and dimensional integrity in saline solution, due to their high wet strength (4.2 MPa for N-CMA membrane and 0.5 MPa for P-CMA membrane), and correspondingly generates ultrastable yet high power density more than 100 days. The self-assembly engineering of cellulose molecules constructs high-performance ion-selective membranes with environmentally friendly, scalable, high wet strength and stability advantages, which guide sustainable nanofluidic applications beyond the blue energy.

Assessing the scalability of healthy eating interventions within the early childhood education and care setting: secondary analysis of a Cochrane systematic review

OBJECTIVE

Early childhood education and care (ECEC) is a recommended setting for the delivery of health eating interventions 'at scale' (i.e. to large numbers of childcare services) to improve child public health nutrition. Appraisal of the 'scalability' (suitability for delivery at scale) of interventions is recommended to guide public health decision-making. This study describes the extent to which factors required to assess scalability are reported among ECEC-based healthy eating interventions.

DESIGN

Studies from a recent Cochrane systematic review assessing the effectiveness of healthy eating interventions delivered in ECEC for improving child dietary intake were included. The reporting of factors of scalability was assessed against domains outlined within the Intervention Scalability Assessment Tool (ISAT). The tool recommends decision makers consider the problem, the intervention, strategic and political context, effectiveness, costs, fidelity and adaptation, reach and acceptability, delivery setting and workforce, implementation infrastructure and sustainability. Data were extracted by one reviewer and checked by a second reviewer.

SETTING

ECEC.

PARTICIPANTS

Children 6 months to 6 years.

RESULTS

Of thirty-eight included studies, none reported all factors within the ISAT. All studies reported the problem, the intervention, effectiveness and the delivery workforce and setting. The lowest reported domains were intervention costs (13 % of studies) and sustainability (16 % of studies).

CONCLUSIONS

Findings indicate there is a lack of reporting of some key factors of scalability for ECEC-based healthy eating interventions. Future studies should measure and report such factors to support policy and practice decision makers when selecting interventions to be scaled-up.

Human Schwann Cells in vitro II. Passaging, Purification, Banking, and Labeling of Established Cultures

This manuscript describes step-by-step procedures to establish and manage fresh and cryopreserved cultures of nerve-derived human Schwann cells (hSCs) at the desired scale. Adaptable protocols are provided to propagate hSC cultures through serial passaging and perform routine manipulations such as enzymatic dissociation, purification, cryogenic preservation, live-cell labeling, and gene delivery. Expanded hSCs cultures are metabolically active, proliferative, and phenotypically stable for at least three consecutive passages. Cell yields are expected to be variable as determined by the rate of growth of individual batches and the rounds of subculture. The purity, however, can be maintained high at >95% hSC regardless of passage. The cells obtained in this manner are suitable for various applications, including small drug screens, in vitro modeling of neurodevelopmental processes, and cell transplantation. One caveat of this protocol is that continued expansion of same-batch hSC populations is eventually restricted due to senescence-linked growth arrest.

A scalable solution recipe for a Ag-based neuromorphic device

Integration and scalability have posed significant problems in the advancement of brain-inspired intelligent systems. Here, we report a self-formed Ag device fabricated through a chemical dewetting process using an Ag organic precursor, which offers easy processing, scalability, and flexibility to address the above issues to a certain extent. The conditions of spin coating, precursor dilution, and use of solvents were varied to obtain different dewetted structures (broadly classified as bimodal and nearly unimodal). A microscopic study is performed to obtain insight into the dewetting mechanism. The electrical behavior of selected bimodal and nearly unimodal devices is related to the statistical analysis of their microscopic structures. A capacitance model is proposed to relate the threshold voltage (Vth) obtained electrically to the various microscopic parameters. Synaptic functionalities such as short-term potentiation (STP) and long-term potentiation (LTP) were emulated in a representative nearly unimodal and bimodal device, with the bimodal device showing a better performance. One of the cognitive behaviors, associative learning, was emulated in a bimodal device. Scalability is demonstrated by fabricating more than 1000 devices, with 96% exhibiting switching behavior. A flexible device is also fabricated, demonstrating synaptic functionalities (STP and LTP).

A survey on blockchain sharding

As a promising technology, blockchain has found widespread application in numerous decentralized systems. However, the scalability problem of blockchain has drawn considerable criticism. Sharding, an effective technology, offers a solution to enhance blockchain scalability by enabling parallel validation and confirmation of transactions or new block generation. Although extensive research has been conducted on sharding, the existing literature still lacks a thorough review on its current state of arts with comprehensive analysis and evaluation. In this paper, we propose a series of evaluation criteria regarding scalability, applicability, and reliability. Additionally, we classify the cutting-edge sharding schemes based on blockchain type and sharding techniques. We then provide a comprehensive overview of these existing schemes by analyzing their respective advantages and disadvantages according to the proposed criteria. At the end of the survey, we highlight open issues and suggest future research directions based on the results of our meticulous analysis.

Failure to scale in digital agronomy: An analysis of site-specific nutrient management decision-support tools in developing countries

While many have extolled the potential impacts of digital advisory services for smallholder agriculture, the evidence for sustained uptake of such tools remains limited. This paper utilizes a survey of tool developers and researchers, as well as a systematic meta-analysis of prior studies, to assess the extent and challenges of scaling decision support tools for site-specific soil nutrient management (SSNM-DST) across smallholder farming systems, where "scaling" is defined as a significant increase in tool usage beyond pilot levels. Our evaluation draws on relevant literature, expert opinion and apps available in different repositories. Despite their acclaimed yield benefits, we find that SSNM-DST have struggled to reach scale over the last few decades and, with strong heterogeneity in adoption among intended stakeholders and tools. For example, the log odds of a SSNM-DST reaching 5-10 % of the target farmers compared with reaching none, decreases by ∼200% when a technical problem is stated as a reason for the tools' failure to be used at scale. We find a similar decrease in odds ratios when technical, socioeconomic, policy, and R&D constraints were identified as barriers to scaling by national extension and private systems. Meta-regression analysis indicates that the response ratio of using SSNM-DST over Farmer Fertilizer Practice (FFP) varies by non-tool related covariates, such as initial crop yield potential under FFP, current and past crop types, acidity class of the soil, temperature and rainfall regimes, and the amount of input under FFP. In general, the SSNM-DST have moved one step forward compared with the traditional 'blanket' fertilizer recommendation by accounting for in-field heterogeneities in soil and crop characteristics, while remaining undifferentiated in terms of demographic and socioeconomic heterogeneities among users, which potentially constrains adoption at scale. The SSNM-DSTs possess reasonable applicability and can be labeled 'ready' from purely scientific viewpoints, although their readiness for system-level uptake at scale remains limited, especially where socio-technical and institutional constraints are prevalent.

Assessing the sustainability and scalability of a diabetes eHealth innovation: a mixed-methods study

BACKGROUND

To date, little is known about the sustainability and scalability of MyDiabetesPlan, an eHealth innovation designed to facilitate shared decision-making within diabetes care. To avoid the possibility of its short-lived implementation and promote wider adoption so as to promote patient-centred diabetes care, it is critical to understand MyDiabetesPlan's sustainability and scalability in order to ensure its long-term impact at a greater scale. We sought to identify the sustainability and scalability potential of MyDiabetesPlan and its limiting factors.

METHODS

Using a concurrent triangulation mixed-methods approach, data were collected from 20 individuals involved in the development and implementation of MyDiabetesPlan. The National Health Services Sustainability Model (NHSSM) and the Innovation Scalability Self-administered Questionnaire (ISSaQ) were administered using a 'think-aloud' approach and subsequently, short semi-structured interviews were conducted. Mean aggregate scores and stakeholder-specific scores were generated for the NHSSM and ISSaQ, to quantitatively determine facilitating and limiting factors to sustainability and scalability. Content analysis occurred iteratively with qualitative data, to examine commonalities and differences with the quantitative findings.

RESULTS

The top facilitating factor to sustaining MyDiabetesPlan was "Staff involvement and training to sustain the process.", whereas the top limiting factors were: "Adaptability of Improved Process", "Senior Leadership Engagement" and "Infrastructure for Sustainability". The top three facilitating factors for scale-up were "Acceptability", "Development with Theory" and "Consistency with Policy Directives." Conversely, the top three limiting factors were "Financial and Human Resources", "Achievable Adoption" and "Broad Reach". Qualitative findings corroborated the limiting/facilitating factors identified.

CONCLUSIONS

Addressing staff involvement throughout the dynamic care contexts, and resource constraints impacting scale-up can enhance the sustainability and scalability of MyDiabetesPlan. As such, future plans will focus on garnering organizational leadership buy-in and support, which may address the resource constraints associated with sustainability and scalability and improve the capacity for adequate staff involvement. eHealth researchers will be able to prioritize these limiting factors from the outset of their tool development to purposefully optimize its sustainability and scalability performance.

Health Research Requires Efficient Platforms for Data Collection from Personal Devices

Data from consumer-based devices for collecting personal health-related data could be useful in diagnostics and treatment. This requires a flexible and scalable software and system architecture to handle the data. This study examines the existing mSpider platform, addresses shortcomings in security and development, and suggests a full risk analysis, a more loosely coupled component- based system for long term stability, better scalability, and maintainability. The goal is to create a human digital twin platform for an operational production environment.

Secure data sharing with blockchain for remote health monitoring applications: a review

Remote Health Monitoring (RHM) is going to reinvent the future healthcare industry and bring about abundant value to hospitals, doctors, and patients by overcoming the many challenges currently being faced in monitoring patient's well-being, promoting preventive care, and managing the quality of drugs and equipment. Despite the many benefits of RHM, it is yet to be widely deployed due to the healthcare data security and privacy challenges. Healthcare data are highly sensitive and require fail-safe measures against unauthorized data access, leakages, and manipulations, and as such, there are stringent regulations governing how healthcare data can be secured, communicated, and stored, such as General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). The challenges and regulatory demands in RHM applications can be addressed using blockchain technology due to its distinguishing features of decentralization, immutability, and transparency to address the challenges of data security and privacy. This article will provide a systematic review on the use of blockchain in RHM, focusing primarily on data security and privacy.

Intensification of Implementation Strategies: Developing a Model of Foundational and Enhanced Implementation Approaches to Support National Adoption and Scale-up

Implementation strategies are activities to support integration of evidence-based programs (EBPs) into routine care. Comprised of 170+ facilities, the Veterans Affairs Healthcare System is conducive to evaluating feasibility and scalability of implementation strategies on a national level. In previous work evaluating implementation of three EBPs for older Veterans (hospital-based walking, caregiver skills training, group physical therapy), we found facilities varied in their need for implementation support, with some needing minimal guidance and others requiring intensive support. Committed to national scalability, our team developed an implementation intensification model consisting of foundational (low-touch) and enhanced (high-touch) implementation support. This Forum article describes our multilevel and multistep process to develop and evaluate implementation intensification. Steps included (a) review completed trial data; (b) conduct listening sessions; (c) review literature; (d) draft foundational and enhanced implementation support packages; (e) iteratively refine packages; and (7) devise an evaluation plan. Our model of implementation intensification may be relevant to other health care systems seeking strategies that can adapt to diverse delivery settings, optimize resources, help build capacity, and ultimately enhance implementation outcomes. As more health care systems focus on spread of EBPs into routine care, identifying scalable and effective implementation strategies will be critical.

Evaluating change in body image concerns following a single session digital intervention

Many young individuals at risk for eating disorders spend time on social media and frequently search for information related to their body image concerns. In a large randomized study, we demonstrated that a guided chat-based intervention could reduce weight and shape concerns and eating disorder pathology. The goal of the current study was to determine if a modified single session mini-course, derived from the aforementioned chat-based intervention, could reduce body image concerns among individuals using eating disorder related search terms on a social media platform. Over a two-month period of prompting individuals, 525 people followed the link to the web-based application where the intervention was hosted and subsequently completed the mini-course. This resulted in a significant improvement on the one-time body image satisfaction question pre-to post intervention (p < .001) with a moderate effect size (Cohen's d = 0.54). Additionally, individuals completing the program showed significant improvement on motivation to change their body image (p < .001) with a small effect size (Cohen's d = 0.28). Additionally, users reported that the program was enjoyable and easy to use. These results suggest that a single session micro-intervention, offered to individuals on social media, can help improve body image.

A Simple, Transition Metal Catalyst-Free Method for the Design of Complex Organic Building Blocks Used to Construct Porous Metal-Organic Frameworks

The design of metal-organic frameworks (MOFs) having large pore sizes and volumes often requires the use of complex organic ligands, currently synthesized using costly and time-consuming palladium-catalyzed coupling chemistry. Thus, in the present work, a new strategy for ligand design is reported, where piperazine and dihydrophenazine units are used as substitutes for benzene rings, which are the basic building block of most MOF ligands. This chemistry, which is based on simple, nucleophilic aromatic substitution (S_N Ar) reactions, is used for the transition metal catalyst-free construction of 21 new, carboxylate-based ligands with varying sizes, shapes, and denticity and 15 linear di- and tetra-nitriles. Moreover, to demonstrate the utility of the ligands as building blocks, 16 new structurally diverse MOFs having surface areas up to 3100 m²  g^-1 were also synthesized.

Application of a membrane-less air cathode microbial fuel cell to treat municipal waste composting leachate

The adverse effects of high strength wastewaters on the microbial activities have created a challenge to biological treatments. Microbial fuel cell has been considered as a promising process because the electrical potential generation can stimulate microorganisms and overcome the inhibitory effect. However, several issues (e.g., scalability, high costs and maintenance) have prevented the process from the industrial applications. Elimination of the proton exchange membrane has been suggested as a remedy to the mentioned problems. In this work, a membrane-less microbial fuel cell was modified by putting the cathode within a thin sand layer (instead of the proton exchange membrane) to treat a high strength wastewater sample. The influences of the feed organic load and time of treatment in the modified system were studied in batch and continuous operations. It was revealed that the batch operation efficiency was higher for the lower feed loadings as a 5-day batch treatment removed 66 ± 4% of the 15,000 ± 500 mg/L initial chemical oxygen demand while the continuous process efficiency with 9-day hydraulic residence time was slightly more than 50%. However, the efficiency of the continuous operation for treatment of higher initial loading values was better than the batch mode with the removal efficiency of 41 ± 2% versus 12 ± 2% for a more concentrated leachate feed (45,000 ± 1000 mg/L). Finally, it was disclosed that the modified membrane-less MFC employed in this work can be effective in treatment of high strength wastewaters in larger scales with lower costs.

Remote mental health care interventions during the COVID-19 pandemic: An umbrella review

Mitigating the COVID-19 related disruptions in mental health care services is crucial in a time of increased mental health disorders. Numerous reviews have been conducted on the process of implementing technology-based mental health care during the pandemic. The research question of this umbrella review was to examine what the impact of COVID-19 was on access and delivery of mental health services and how mental health services have changed during the pandemic. A systematic search for systematic reviews and meta-analyses was conducted up to August 12, 2022, and 38 systematic reviews were identified. Main disruptions during COVID-19 were reduced access to outpatient mental health care and reduced admissions and earlier discharge from inpatient care. In response, synchronous telemental health tools such as videoconferencing were used to provide remote care similar to pre-COVID care, and to a lesser extent asynchronous virtual mental health tools such as apps. Implementation of synchronous tools were facilitated by time-efficiency and flexibility during the pandemic but there was a lack of accessibility for specific vulnerable populations. Main barriers among practitioners and patients to use digital mental health tools were poor technological literacy, particularly when preexisting inequalities existed, and beliefs about reduced therapeutic alliance particularly in case of severe mental disorders. Absence of organizational support for technological implementation of digital mental health interventions due to inadequate IT infrastructure, lack of funding, as well as lack of privacy and safety, challenged implementation during COVID-19. Reviews were of low to moderate quality, covered heterogeneously designed primary studies and lacked findings of implementation in low- and middle-income countries. These gaps in the evidence were particularly prevalent in studies conducted early in the pandemic. This umbrella review shows that during the COVID-19 pandemic, practitioners and mental health care institutions mainly used synchronous telemental health tools, and to a lesser degree asynchronous tools to enable continued access to mental health care for patients. Numerous barriers to these tools were identified, and call for further improvements. In addition, more high quality research into comparative effectiveness and working mechanisms may improve scalability of mental health care in general and in future infectious disease outbreaks.

A portable clustering algorithm based on compact neighbors for face tagging

We focus on the following problem: Given a collection of unlabeled facial images, group them into the individual identities where the number of subjects is not known. To this end, a Portable clustering algorithm based on Compact Neighbors called PCN is proposed. (1) Benefiting from the compact neighbor, the local density of each sample can be determined automatically and only one user-specified parameter, the number of nearest neighbors k, is involved in our model. (2) More importantly, the performance of PCN is not sensitive to the number of nearest neighbors. Therefore this parameter is relatively easy to determine in practical applications. (3) The computational overhead of PCN is O(nk(k²+log(nk))) that is nearly linear with respect to the number of samples, which means it is easily scalable to large-scale problems. In order to verify the effectiveness of PCN on the face clustering problem, extensive experiments based on a two-stage framework (extracting features using a deep model and performing clustering in the feature space) have been conducted on 16 middle- and 5 large-scale benchmark datasets. The experimental results have shown the efficiency and effectiveness of the proposed algorithm, compared with state-of-the-art methods. [code].

Fast increased fidelity samplers for approximate Bayesian Gaussian process regression

Gaussian processes (GPs) are common components in Bayesian non-parametric models having a rich methodological literature and strong theoretical grounding. The use of exact GPs in Bayesian models is limited to problems containing several thousand observations due to their prohibitive computational demands. We develop a posterior sampling algorithm using H -matrix approximations that scales at O ( n log 2 n ) . We show that this approximation's Kullback-Leibler divergence to the true posterior can be made arbitrarily small. Though multidimensional GPs could be used with our algorithm, d-dimensional surfaces are modeled as tensor products of univariate GPs to minimize the cost of matrix construction and maximize computational efficiency. We illustrate the performance of this fast increased fidelity approximate GP, FIFA-GP, using both simulated and non-synthetic data sets.

Design of scalable metalens array for optical addressing

Large-scale trapped-ion quantum computers hold great promise to outperform classical computers and are crucially desirable for finance, pharmaceutical industry, fundamental chemistry and other fields. Currently, a big challenge for trapped-ion quantum computers is the poor scalability mainly brought by the optical elements that are used for optical addressing. Metasurfaces provide a promising solution due to their excellent flexibility and integration ability. Here, we propose and numerically demonstrate a scalable off-axis metalens array for optical addressing working at the wavelength of 350 nm. Metalens arrays designed for x linearly polarized and left circularly polarized light respectively can focus the collimated addressing beam array into a compact focused spot array with spot spacing of 5 μm, featuring crosstalk below 0.82%.

Assessing the scalability of a health management-strengthening intervention at the district level: a qualitative study in Ghana, Malawi and Uganda

Background

The scale-up of successfully tested public health interventions is critical to achieving universal health coverage. To ensure optimal use of resources, assessment of the scalability of an intervention is recognized as a crucial step in the scale-up process. This study assessed the scalability of a tested health management-strengthening intervention (MSI) at the district level in Ghana, Malawi and Uganda.

Methods

Qualitative interviews were conducted with intervention users (district health management teams, DHMTs) and implementers of the scale-up of the intervention (national-level actors) in Ghana, Malawi and Uganda, before and 1 year after the scale-up had started. To assess the scalability of the intervention, the CORRECT criteria from WHO/ExpandNet were used during analysis.

Results

The MSI was seen as credible, as regional- and national-level Ministry of Health officials were championing the intervention. While documented evidence on intervention effectiveness was limited, district- and national-level stakeholders seemed to be convinced of the value of the intervention. This was based on its observed positive results regarding management competencies, teamwork and specific aspects of health workforce performance and service delivery. The perceived need for strengthening of management capacity and service delivery showed the relevance of the intervention, and relative advantages of the intervention were its participatory and sustainable nature. Turnover within the DHMTs and limited (initial) management capacity were factors complicating implementation. The intervention was not contested and was seen as compatible with (policy) priorities at the national level.

Conclusion

We conclude that the MSI is scalable. However, to enhance its scalability, certain aspects should be adapted to better fit the context in which the intervention is being scaled up. Greater involvement of regional and national actors alongside improved documentation of results of the intervention can facilitate scale-up. Continuous assessment of the scalability of the intervention with all stakeholders involved is necessary, as context, stakeholders and priorities may change. Therefore, adaptations of the intervention might be required. The assessment of scalability, preferably as part of the monitoring of a scale-up strategy, enables critical reflections on next steps to make the intervention more scalable and the scale-up more successful.

Scalability of an ACT-Based strategy for improving well-being in health care providers: A mix-method and preliminary evaluation of efficacy

The burden of the COVID-19 pandemic has been mainly carried by health care providers. Technology-Mediated Interventions (TMI) seem to be a feasible alternative to increase access to behavioral health resources in this population. However, scaling-up treatments into TMI requires developing user-friendly, accepted, and accessible formats. A two-stage study was conducted to assess scalability of an Acceptance and Commitment Therapy (ACT) based strategy (named FACE COVID) delivered using technology. First, a mix-method design connected qualitative and quantitative data from health providers and ACT experts by which changes were performed to enhance scalability. Second, a pretest-posttest study was conducted to preliminary evaluate the efficacy of FACE COVID intervention on well-being, psychological distress, and psychological flexibility. Results showed a positive impact on well-being, but not distress and psychological flexibility. While this intervention has promising results, changes in dose intensity, social support, and mental health literacy could improve retention as well as increase opportunities to target distress and psychological flexibility in future studies.

Circuit-level convergence of electronics and photonics: basic concepts and recent advances

Integrated photonics is widely regarded as an important post-Moore's law research direction. However, it suffers from intrinsic limitations, such as lack of control and satisfactory photonic memory, that cannot be solved in the optical domain and must be combined with electronics for practical use. Inevitably, electronics and photonics will converge. The photonic fabrication and integration technology is gradually maturing and electronics-photonics convergence (EPC) is experiencing a transition from device integration to circuit design. We derive a conceptual framework consisting of regulator, oscillator, and memory for scalable integrated circuits based on the fundamental concepts of purposeful behavior in cybernetics, entropy in information theory, and symmetry breaking in physics. Leveraging this framework and emulating the successes experienced by electronic integrated circuits, we identify the key building blocks for the integrated circuits for EPC and review the recent advances.

Tools for assessing the scalability of innovations in health: a systematic review

BACKGROUND

The last decade has seen growing interest in scaling up of innovations to strengthen healthcare systems. However, the lack of appropriate methods for determining their potential for scale-up is an unfortunate global handicap. Thus, we aimed to review tools proposed for assessing the scalability of innovations in health.

METHODS

We conducted a systematic review following the COSMIN methodology. We included any empirical research which aimed to investigate the creation, validation or interpretability of a scalability assessment tool in health. We searched Embase, MEDLINE, CINAHL, Web of Science, PsycINFO, Cochrane Library and ERIC from their inception to 20 March 2019. We also searched relevant websites, screened the reference lists of relevant reports and consulted experts in the field. Two reviewers independently selected and extracted eligible reports and assessed the methodological quality of tools. We summarized data using a narrative approach involving thematic syntheses and descriptive statistics.

RESULTS

We identified 31 reports describing 21 tools. Types of tools included criteria (47.6%), scales (33.3%) and checklists (19.0%). Most tools were published from 2010 onwards (90.5%), in open-access sources (85.7%) and funded by governmental or nongovernmental organizations (76.2%). All tools were in English; four were translated into French or Spanish (19.0%). Tool creation involved single (23.8%) or multiple (19.0%) types of stakeholders, or stakeholder involvement was not reported (57.1%). No studies reported involving patients or the public, or reported the sex of tool creators. Tools were created for use in high-income countries (28.6%), low- or middle-income countries (19.0%), or both (9.5%), or for transferring innovations from low- or middle-income countries to high-income countries (4.8%). Healthcare levels included public or population health (47.6%), primary healthcare (33.3%) and home care (4.8%). Most tools provided limited information on content validity (85.7%), and none reported on other measurement properties. The methodological quality of tools was deemed inadequate (61.9%) or doubtful (38.1%).

CONCLUSIONS

We inventoried tools for assessing the scalability of innovations in health. Existing tools are as yet of limited utility for assessing scalability in health. More work needs to be done to establish key psychometric properties of these tools. Trial registration We registered this review with PROSPERO (identifier: CRD42019107095).

Microbial fuel cell scale-up options: Performance evaluation of membrane (c-MFC) and membrane-less (s-MFC) systems under different feeding regimes

In recent years, bioelectrochemical systems have advanced towards upscaling applications and tested during field trials, primarily for wastewater treatment. Amongst reported trials, two designs of urine-fed microbial fuel cells (MFCs) were tested successfully on a pilot scale as autonomous sanitation systems for decentralised area. These designs, known as ceramic MFCs ( c -MFCs) and self-stratifying MFCs ( s -MFC), have never been calibrated under similar conditions. Here, the most advanced versions of both designs were assembled and tested under similar feeding conditions. The performance and efficiency were evaluated under different hydraulic retention times (HRT), through chemical oxygen demand measures and polarisation experiments. Results show that c -MFCs displayed constant performance independently from the HRT (32.2 ± 3.9 W m-3) whilst displaying high energy conversion efficiency at longer HRT (NER COD  = 2.092 ± 0.119 KWh.Kg COD -1, at 24h HRT). The s -MFC showed a correlation between performance and HRT. The highest performance was reached under short HRT (69.7 ± 0.4 W m-3 at 3h HRT), but the energy conversion efficiency was constant independently from the HRT (0.338 ± 0.029 KWh.Kg COD -1). The c -MFCs and s -MFCs similarly showed the highest volumetric efficiency under long HRT (65h) with NER V of 0.747 ± 0.010 KWh.m-3 and 0.825 ± 0.086 KWh.m-3, respectively. Overall, c -MFCs seems more appropriate for longer HRT and s -MFCs for shorter HRT.

Clinical translation of nanomedicines: Challenges, opportunities, and keys

Despite the massive interest and recent developments in the field of nanomedicine, only a limited number of formulations have found their way to the clinics. This shortcoming reveals the challenges facing the clinical translation of this technology. In the current article, we summarize and evaluate the status, market situation, and clinical profiles of the reported nanomedicines, the shortcomings limiting their clinical translation, as well as some approaches designed to break through this barrier. Moreover, some emerging technologies that have the potential to compete with nanomedicines are highlighted. Lastly, we identify the key factors that should be considered in nanomedicine-related research to be clinically-translatable. These can be classified into five areas: rational design during the research and development stage, the recruitment of representative preclinical models, careful design of clinical trials, development of specific and uniform regulatory protocols, and calls for non-classic sponsorship. This new field of endeavor was firmly established during the last two decades and more in-depth progress is expected in the coming years.

Simple Scalable Protein Expression and Extraction Using Two-stage Autoinducible Cell Autolysis and DNA/RNA Autohydrolysis in Escherichia coli

Recombinant protein expression is extensively used in biological research. Despite this, current protein expression and extraction methods are not readily scalable or amenable for high-throughput applications. Optimization of protein expression conditions using traditional methods, reliant on growth-associated induction, is non-trivial. Similarly, protein extraction methods are predominantly restricted to chemical methods, and mechanical methods reliant on expensive specialized equipment more tuned for large-scale applications. In this article, we outline detailed protocols for the use of an engineered autolysis/autohydrolysis E. coli strain, in two-stage fermentations in shake-flasks. This two-stage fermentation protocol does not require optimization of expression conditions and results in high protein titers. Cell lysis in an engineered strain is tightly controlled and only triggered post-culture by addition of a 0.1% detergent solution. Upon cell lysis, a nuclease digests contaminating host oligonucleotides, which facilitates sample handling. This method has been validated for use at different scales, from microtiter plates to instrumented bioreactors. Graphic abstract: Two-stage protein expression, cell autolysis and DNA/RNA autohydrolysis. Reprinted with permission from Menacho-Melgar et al. (2020a). Copyright 2020 John Wiley and Sons.

Assessing the scalability of an integrated falls prevention service for community-dwelling older people: a mixed methods study

Background

There is growing acknowledgement of the need for a phased approach to scaling up health interventions, beginning with an assessment of ‘scalability’, that is, the capacity of an individual intervention to be scaled up. This study aims to assess the scalability of a multi-component integrated falls prevention service for community-dwelling older people and to examine the applicability of the Intervention Scalability Assessment Tool (ISAT). The ISAT consists of 10 domains for consideration when determining the scalability of an intervention, and each domain comprises a series of questions aimed at examining readiness for scale-up.

Methods

Multiple methods were used sequentially as recommended by the ISAT: a review of policy documents, results from a service evaluation and falls-related literature; one-to-one interviews (n = 11) with key stakeholders involved in management and oversight of the service; and a follow-up online questionnaire (n = 10) with stakeholders to rate scalability and provide further feedback on reasons for their scores.

Results

Three of the ISAT domains were rated highly by the participants. Analysis of the qualitative feedback and documents indicated that the issue of falls prevention among older people was of sufficient priority to warrant scale-up of the service and that the service aligned with national health policy priorities. Some participants also noted that benefits of the service could potentially outweigh costs through reduced hospital admissions and serious injuries such as hip fracture. The remaining domains received a moderate score from participants, however, indicating considerable barriers to scale-up. In the qualitative feedback, barriers identified included the perceived need for more healthcare staff to deliver components of the service, for additional infrastructure such as adequate room space, and for an integrated electronic patient management system linking primary and secondary care and to prevent duplication of services.

Conclusions

Plans to scale up the service are currently under review given the practical barriers that need to be addressed. The ISAT provides a systematic and structured framework for examining the scalability of this multi-component falls prevention intervention, although the iterative nature of the process and detailed and technical nature of its questions require considerable time and knowledge of the service to complete.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02717-6.

Juggling with theory, evidence, practice, and real-world circumstances: Development of a complex community intervention to increase physical activity in inactive adults aged 50 years and older - The Move for Life Study

Despite well-known benefits of physical activity, in Ireland only 38 % of older adults are sufficiently active. Behavioural interventions are rarely developed systematically and, when reported, inadequate description often becomes a barrier for subsequent replication and scalability. In this article, we describe the development and characteristics of Move for Life, an intervention to reach and help inactive adults aged 50 years and older increase their physical activity. It was designed to fit within existing group-based structured physical activity programmes run by Local Sports Partnerships, thus maximising the likelihood of translation into policy and practice. Constructs from social cognitive theory, self-determination theory, and the conceptual model of group cohesion in exercise informed the conceptual model and the development of behavioural skills, social support, and group cohesion intervention strategies. Physical activity instructors supported by peer mentors, who also contributed to sustaining the intervention, implemented these strategies. Moving away from accounts of intervention development as a relatively simple linear process, we illustrate the complex interplay of theory, evidence, practice, and real-world contextual circumstances that shaped the development of Move for Life. Against this backdrop, we discuss issues relevant to the planning and reporting of behavioural and physical activity interventions in public health.

Market viability: a neglected concept in implementation science

This debate paper asserts that implementation science needs to incorporate a key concept from entrepreneurship—market demand—and demonstrates how assessing an innovation’s potential market viability might advance the pace and success of innovation adoption and sustainment. We describe key concepts, language distinctions, and questions that entrepreneurs pose to implementation scientists—many of which implementation scientists appear ill-equipped to answer. The paper concludes with recommendations about how concepts from entrepreneurship, notably market viability assessment, can enhance the translation of research discoveries into real-world adoption, sustained use, and population health benefits. The paper further proposes activities that can advance implementation science’s capacity to draw from the field of entrepreneurship, along with the data foundations required to assess and cultivate market demand.

Microchromatography integrated with impedance sensor for bioprocess optimization: Experimental and numerical study of column efficiency for evaluation of scalability

In the last decade, there has been a growing interest in developing microfluidic systems as new scale-down models for accelerated and cost-effective biopharmaceutical process development. Nonetheless, the research in this field is still in its infancy and requires further investigation to simplify and accelerate the microfabrication process. In addition, integration of different label-free sensors into the microcolumn systems has utmost importance to minimize result discrepancies during the scale-up process. In this study, we developed a simple, low-cost integrated microcolumn (26 µl). Micromilling technology was employed to define the geometry and shape of microfluidic structures using poly(methylmethacrylate) (PMMA). The design of PMMA microstructure was transferred to polydimethylsiloxane (PDMS), and interdigitated planar microelectrodes (IDE) were integrated into the system. To evaluate the scalability of the developed microcolumn column, column performance was assessed and compared with a conventional 1-ml prepacked column. Computational Fluid Dynamics (CFD) studies were performed for both columns to understand the differences between theoretical and experimental results regarding retention time and peak broadening. Despite obtaining an acceptable asymmetric factor for the microcolumn (1.03 ± 0.02), the reduced plate height value was still higher than the recommended range with the value of 4.14 ± 0.18. Nevertheless, the consistency and significant improvement of microcolumn efficiency compared to previous studies provide the possibility of developing robust simulation tools for transferring acquired experimental data for larger-scale units.

coli

We report that two-stage dynamic control improves bioprocess robustness as a result of the dynamic deregulation of central metabolism. Dynamic control is implemented during stationary phase using combinations of CRISPR interference and controlled proteolysis to reduce levels of central metabolic enzymes. Reducing the levels of key enzymes alters metabolite pools resulting in deregulation of the metabolic network. Deregulated networks are less sensitive to environmental conditions improving process robustness. Process robustness in turn leads to predictable scalability, minimizing the need for traditional process optimization. We validate process robustness and scalability of strains and bioprocesses synthesizing the important industrial chemicals alanine, citramalate and xylitol. Predictive high throughput approaches that translate to larger scales are critical for metabolic engineering programs to truly take advantage of the rapidly increasing throughput and decreasing costs of synthetic biology.

Evidence on scaling in health and social care: protocol for a living umbrella review

BACKGROUND

There is a growing interest in scaling effective health innovations to promote equitable access to high-quality health services worldwide. However, multiple challenges persist in scaling innovations. In this study, we aim to summarize the scaling evidence in the health and social care literature and identify current knowledge gaps.

METHODS

We will conduct a living umbrella review according to the Joanna Briggs Institute Reviewers' Manual. We will consider all knowledge syntheses addressing scaling in health or social care (e.g., any setting, any clinical area) and conducted in a systematic way. We will search the following electronic databases: MEDLINE (Ovid), Embase, PsychINFO (Ovid), CINAHL (EBSCO), Web of Science, The Cochrane Library, Sociological Abstract (Proquest), Academic Search Premier (EBSCO), and Proquest Dissertations & Theses Global, from inception. Furthermore, we will conduct searches of the grey literature. No restriction regarding date or language will be applied. Each phase of the review will be processed by two independent reviewers. We will develop a data extraction form on Covidence. We will assess the methodological quality of the included reviews using AMSTAR2 and the risk of bias using ROBIS. Results will be presented in tabular form and accompanied by a narrative synthesis covering the traditional themes of scaling science that emerge from the analysis, such as coverage, range, and sustainability, as well as themes less covered in the literature, including reporting guidance, models, tools, barriers, and/or facilitators to scaling innovations, evidence regarding application in high-income or low-income countries, and end-user engagement. We will disseminate the findings via publications and through relevant networks.

DISCUSSION

The findings of the umbrella review will facilitate access to scaling evidence in the literature and help strengthen the science of scaling for researchers, policy makers, and program managers. Finally, this work will highlight important knowledge gaps and help prioritize future research questions.

SYSTEMATIC REVIEW REGISTRATION

This protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on November 11, 2020 (registration number: CRD42020183774 ).

Biocompatible Multifunctional E-Skins with Excellent Self-Healing Ability Enabled by Clean and Scalable Fabrication

Electronic skins (e-skins) with an excellent sensing performance have been widely developed over the last few decades. However, wearability, biocompatibility, environmental friendliness and scalability have become new limitations. Self-healing ability can improve the long-term robustness and reliability of e-skins. However, self-healing ability and integration are hardly balanced in classical structures of self-healable devices. Here, cellulose nanofiber/poly(vinyl alcohol) (CNF/PVA), a biocompatible moisture-inspired self-healable composite, was applied both as the binder in functional layers and the substrate. Various functional layers comprising particular carbon materials and CNF/PVA were patterned on the substrate. A planar structure was beneficial for integration, and the active self-healing ability of the functional layers endowed self-healed e-skins with a higher toughness. Water served as both the only solvent throughout the fabrication process and the trigger of the self-healing process, which avoids the pollution and bioincompatibility caused by the application of noxious additives. Our e-skins could achieve real-time monitoring of whole-body physiological signals and environmental temperature and humidity. Cross-interference between different external stimuli was suppressed through reasonable material selection and structural design. Combined with conventional electronics, data could be transmitted to a nearby smartphone for post-processing. This work provides a previously unexplored strategy for multifunctional e-skins with an excellent practicality.

Youth Mental Health First Aid: Examining the Influence of Pre-Existing Attitudes and Knowledge on Training Effectiveness

Youth Mental Health First Aid (YMHFA) is a gatekeeper training designed to assist professionals in their identification and response to youth mental illness. Despite widespread implementation, few studies have examined how individual-level trainee characteristics may impact the effectiveness of this training. In this study, we examined how pre-existing knowledge and attitudes about youth mental illness (measured prior to participation in the program) influenced training outcomes. We used a sample of 987 child-serving professionals who completed the YMHFA training program to identify demographic and work-related factors that predicted pre-existing attitudes and knowledge. The sample was then divided into two groups based on pre-existing attitudes and knowledge. We examined training effectiveness across four constructs: knowledge, confidence, intentions to intervene, and stigma. Findings indicate that gender and work-related characteristics were associated with pre-existing knowledge and attitudes about youth mental illness. The magnitude of training effectiveness varied across pre-existing attitudes and knowledge. Those participants with above average attitudes and knowledge benefited little from the program, suggesting that universal implementation of YMHFA may not be an efficient use of resources. Suggestions for a tiered implementation plan that prioritizes professionals most in need of the training are discussed.

Virus filter scalability: Demonstration of consistent viral clearance across laboratory and manufacturing scales

Virus removal filtration processes in biopharmaceutical manufacturing are developed, optimized and validated for viral clearance using laboratory scale filters. Thus, the scalability of these filters is critical for accurately extrapolating filtration performance and reliably extending viral clearance to manufacturing scale. Virus removal filter manufacturers generally validate scalability of filtration performance based on various filtration parameters, and virus removal capability is extended to manufacturing scale filters using inert, size-appropriate particles such as gold nanoparticles to avoid the risks associated with using mammalian viruses in large feed volumes. In this study, we use bacteriophage PP7 as a parvovirus model to directly demonstrate viral clearance on Planova™ BioEX virus removal filters across all scales, including manufacturing scale. Filters with hollow fibers from three spinning series with filter sizes ranging from 0.0003 to 4.0 m² were tested for virus removal, flux, and protein recovery performance using BSA spiked with PP7. Complete viral clearance was observed across all filter sizes with PP7 LRV of ≥4.7 or higher. Flux and protein recovery were also consistent. These results demonstrate the scalability of filtration performance and consistent virus removal at all sizes, supporting the use of laboratory scale filters to validate viral clearance at manufacturing scales.

Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets

BACKGROUND

Recent advances in 3D imaging technologies provide novel insights to researchers and reveal finer and more detail of examined specimen, especially in the biomedical domain, but also impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing dataset sizes. In particular, existing research towards automated vessel network analysis does not always consider memory requirements of proposed algorithms and often generates a large number of spurious branches for structures consisting of many voxels. Additionally, very often these algorithms have further restrictions such as the limitation to tree topologies or relying on the properties of specific image modalities.

RESULTS

We propose a scalable iterative pipeline (in terms of computational cost, required main memory and robustness) that extracts an annotated abstract graph representation from the foreground segmentation of vessel networks of arbitrary topology and vessel shape. The novel iterative refinement process is controlled by a single, dimensionless, a-priori determinable parameter.

CONCLUSIONS

We are able to, for the first time, analyze the topology of volumes of roughly 1 TB on commodity hardware, using the proposed pipeline. We demonstrate improved robustness in terms of surface noise, vessel shape deviation and anisotropic resolution compared to the state of the art. An implementation of the presented pipeline is publicly available in version 5.1 of the volume rendering and processing engine Voreen.

ProkEvo: an automated, reproducible, and scalable framework for high-throughput bacterial population genomics analyses

Whole Genome Sequence (WGS) data from bacterial species is used for a variety of applications ranging from basic microbiological research, diagnostics, and epidemiological surveillance. The availability of WGS data from hundreds of thousands of individual isolates of individual microbial species poses a tremendous opportunity for discovery and hypothesis-generating research into ecology and evolution of these microorganisms. Flexibility, scalability, and user-friendliness of existing pipelines for population-scale inquiry, however, limit applications of systematic, population-scale approaches. Here, we present ProkEvo, an automated, scalable, reproducible, and open-source framework for bacterial population genomics analyses using WGS data. ProkEvo was specifically developed to achieve the following goals: (1) Automation and scaling of complex combinations of computational analyses for many thousands of bacterial genomes from inputs of raw Illumina paired-end sequence reads; (2) Use of workflow management systems (WMS) such as Pegasus WMS to ensure reproducibility, scalability, modularity, fault-tolerance, and robust file management throughout the process; (3) Use of high-performance and high-throughput computational platforms; (4) Generation of hierarchical-based population structure analysis based on combinations of multi-locus and Bayesian statistical approaches for classification for ecological and epidemiological inquiries; (5) Association of antimicrobial resistance (AMR) genes, putative virulence factors, and plasmids from curated databases with the hierarchically-related genotypic classifications; and (6) Production of pan-genome annotations and data compilation that can be utilized for downstream analysis such as identification of population-specific genomic signatures. The scalability of ProkEvo was measured with two datasets comprising significantly different numbers of input genomes (one with ~2,400 genomes, and the second with ~23,000 genomes). Depending on the dataset and the computational platform used, the running time of ProkEvo varied from ~3-26 days. ProkEvo can be used with virtually any bacterial species, and the Pegasus WMS uniquely facilitates addition or removal of programs from the workflow or modification of options within them. To demonstrate versatility of the ProkEvo platform, we performed a hierarchical-based population structure analyses from available genomes of three distinct pathogenic bacterial species as individual case studies. The specific case studies illustrate how hierarchical analyses of population structures, genotype frequencies, and distribution of specific gene functions can be integrated into an analysis. Collectively, our study shows that ProkEvo presents a practical viable option for scalable, automated analyses of bacterial populations with direct applications for basic microbiology research, clinical microbiological diagnostics, and epidemiological surveillance.

FastGAE: Scalable graph autoencoders with stochastic subgraph decoding

Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues. In this paper, we introduce FastGAE, a general framework to scale graph AE and VAE to large graphs with millions of nodes and edges. Our strategy, based on an effective stochastic subgraph decoding scheme, significantly speeds up the training of graph AE and VAE while preserving or even improving performances. We demonstrate the effectiveness of FastGAE on various real-world graphs, outperforming the few existing approaches to scale graph AE and VAE by a wide margin.

Simplitigs as an efficient and scalable representation of de Bruijn graphs

de Bruijn graphs play an essential role in bioinformatics, yet they lack a universal scalable representation. Here, we introduce simplitigs as a compact, efficient, and scalable representation, and ProphAsm, a fast algorithm for their computation. For the example of assemblies of model organisms and two bacterial pan-genomes, we compare simplitigs to unitigs, the best existing representation, and demonstrate that simplitigs provide a substantial improvement in the cumulative sequence length and their number. When combined with the commonly used Burrows-Wheeler Transform index, simplitigs reduce memory, and index loading and query times, as demonstrated with large-scale examples of GenBank bacterial pan-genomes.

Translating cell and gene therapies from concept to commercialisation. A commentary on a historical, ongoing journey

This commentary will summarise opinions on the progression of the next pillar of patient treatment; cell and gene therapy. The development of exemplar products will be discussed along with their respective journeys. This will be in comparison to present-day developments and the direction in which the field is heading with respect to future products. Important topics for consideration will be the use of various technologies, automation, scalability and the key considerations for those already involved, or those striving to enter the field. Discussions will consider the current Chemistry, manufacturing and control (CMC) procedures and where these will require consideration for improvement as we proceed into the future.

"Top-down bottom-up" estimation of per capita cost of new-born care interventions in four regions of Ghana: beyond implementation to scalability and sustainability

BACKGROUND

Limited financial, human and material health resources coupled with increasing demand for new-born care services require efficiency in health systems to maximize the available sources for improved health outcomes. Making Every Baby Count Initiative (MEBCI) implemented by local and international partners in 2013 in Ghana aimed at attaining neonatal mortality of 21 per 1000 livebirths by 2018 in four administrative regions in Ghana. MEBCI interventions benefited 4027 health providers, out of which 3453 (86%) were clinical healthcare staff.

OBJECTIVE

Determine the per capita cost of the MEBCI interventions towards enhancing new-born care best practices through capacity trainings for frontline clinical and non-clinical staff.

METHODS

Parameters for determining per capita cost of the new-born care interventions were estimated using expenditure on trainings, supervisions, monitoring and evaluation, advocacy, administrative/services and medical logistics. Data collection started in October 2017 and ended in September 2018. Data sources for the per capita cost estimations were invoices, expense reports and ledger books at the national, regional and district levels of the health system.

RESULTS

Total of 4027 healthcare providers benefited from the MEBCI training activities comprising of 3453 clinical staff and 574 non-clinical personnel. Cumulative cost of implementing the MEBCI interventions did not necessarily match the cost per capita in staff capacity building; average cost per capita for all staff (clinical and non-clinical staff) was approximately US$ 982 compared to a per capita cost of US$ 799 for training only core clinical staff. Average cost per capita for all regions was approximately US$ 965 for all staff compared to US$ 777 per capita cost for only clinical staff. Per capita cost of training was relatively lower in regions with more staff than regions with lower numbers, perhaps due to economies of scale.

CONCLUSION

The MEBCI intervention had a wide coverage in terms of training for frontline healthcare providers albeit the associated cost may be potentially unsustainable for Ghana's health system. Emerging digital training platforms could be leveraged to reduce per capita cost of training. Large-scale on-site batch-training approach could also be replaced with facility-based workshops using training of trainers (TOTs) approach to promote efficiency.

Impact of concurrency on the performance of a whole exome sequencing pipeline

Background

Current high-throughput technologies—i.e. whole genome sequencing, RNA-Seq, ChIP-Seq, etc.—generate huge amounts of data and their usage gets more widespread with each passing year. Complex analysis pipelines involving several computationally-intensive steps have to be applied on an increasing number of samples. Workflow management systems allow parallelization and a more efficient usage of computational power. Nevertheless, this mostly happens by assigning the available cores to a single or few samples’ pipeline at a time. We refer to this approach as naive parallel strategy (NPS). Here, we discuss an alternative approach, which we refer to as concurrent execution strategy (CES), which equally distributes the available processors across every sample’s pipeline.

Results

Theoretically, we show that the CES results, under loose conditions, in a substantial speedup, with an ideal gain range spanning from 1 to the number of samples. Also, we observe that the CES yields even faster executions since parallelly computable tasks scale sub-linearly. Practically, we tested both strategies on a whole exome sequencing pipeline applied to three publicly available matched tumour-normal sample pairs of gastrointestinal stromal tumour. The CES achieved speedups in latency up to 2–2.4 compared to the NPS.

Conclusions

Our results hint that if resources distribution is further tailored to fit specific situations, an even greater gain in performance of multiple samples pipelines execution could be achieved. For this to be feasible, a benchmarking of the tools included in the pipeline would be necessary. It is our opinion these benchmarks should be consistently performed by the tools’ developers. Finally, these results suggest that concurrent strategies might also lead to energy and cost savings by making feasible the usage of low power machine clusters.

MaskLayer: Enabling scalable deep learning solutions by training embedded feature sets

Deep learning-based methods have shown to achieve excellent results in a variety of domains, however, some important assets are absent. Quality scalability is one of them. In this work, we introduce a novel and generic neural network layer, named MaskLayer. It can be integrated in any feedforward network, allowing quality scalability by design by creating embedded feature sets. These are obtained by imposing a specific structure of the feature vector during training. To further improve the performance, a masked optimizer and a balancing gradient rescaling approach are proposed. Our experiments show that the cost of introducing scalability using MaskLayer remains limited. In order to prove its generality and applicability, we integrated the proposed techniques in existing, non-scalable networks for point cloud compression and semantic hashing with excellent results. To the best of our knowledge, this is the first work presenting a generic solution able to achieve quality scalable results within the deep learning framework.

Designing research to inform sustainability and scalability of digital technology innovations

Considerably more research needs to be done to understand how successful technological innovations and change processes are sustained and scaled to new learning contexts. Without a better understanding of successful technological innovation, the wider field and education community are unable to benefit and build capacity. A model is presented that explores how research can better inform sustainability and scalability in technological innovations. It is represented as four loops: Organization, Innovation, Research and New Contexts. Three international case studies of technological innovation are analyzed to demonstrate use of the framework and model. Results show that research can be designed to support sustainability and scalability, but that this needs to be balanced with other factors to support a successful technological innovation. Implications for supporting technological innovations are explored.

Increasing SARS-CoV-2 RT-qPCR testing capacity by sample pooling

OBJECTIVES

Limited testing capacity has characterized the ongoing coronavirus disease 2019 (COVID-19) pandemic in Spain, hampering timely control of outbreaks and opportunities to reduce the escalation of community transmission. This study investigated the potential to use sample pooling, followed by one-step retrotranscription and real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) to increase testing capacity for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).

METHODS

Various pool sizes (five, 10 and 15 samples) were evaluated prior to RNA extraction followed by standard RT-qPCR for the diagnosis of COVID-19. The pool size achieving reproducible results with individual sample testing was subsequently used to assess nasopharyngeal samples in a tertiary hospital in August 2020.

RESULTS

A pool size of five samples had higher sensitivity compared with pool sizes of 10 and 15 samples, showing a mean cycle threshold (Ct) shift of 3.5 [standard deviation (SD) 2.2] between the pooled test and positive samples in the pool. Next, a pool size of five was used to test a total of 895 pools (4475 prospective samples) using two different RT-qPCR kits. The Real Accurate Quadruplex corona-plus PCR Kit (PathoFinder) reported the lowest mean Ct shift [2.2 (SD 2.4)] between the pool and individual samples. This strategy enables detection of individual positive samples in positive pools with Ct of 16.7-39.4.

CONCLUSIONS

Grouping samples into pools of five for RT-qPCR resulted in an increase in SARS-CoV-2 testing capacity with minimal loss of sensitivity compared with testing each sample individually.

CIDACS-RL: a novel indexing search and scoring-based record linkage system for huge datasets with high accuracy and scalability

Background

Record linkage is the process of identifying and combining records about the same individual from two or more different datasets. While there are many open source and commercial data linkage tools, the volume and complexity of currently available datasets for linkage pose a huge challenge; hence, designing an efficient linkage tool with reasonable accuracy and scalability is required.

Methods

We developed CIDACS-RL (Centre for Data and Knowledge Integration for Health – Record Linkage), a novel iterative deterministic record linkage algorithm based on a combination of indexing search and scoring algorithms (provided by Apache Lucene). We described how the algorithm works and compared its performance with four open source linkage tools (AtyImo, Febrl, FRIL and RecLink) in terms of sensitivity and positive predictive value using gold standard dataset. We also evaluated its accuracy and scalability using a case-study and its scalability and execution time using a simulated cohort in serial (single core) and multi-core (eight core) computation settings.

Results

Overall, CIDACS-RL algorithm had a superior performance: positive predictive value (99.93% versus AtyImo 99.30%, RecLink 99.5%, Febrl 98.86%, and FRIL 96.17%) and sensitivity (99.87% versus AtyImo 98.91%, RecLink 73.75%, Febrl 90.58%, and FRIL 74.66%). In the case study, using a ROC curve to choose the most appropriate cut-off value (0.896), the obtained metrics were: sensitivity = 92.5% (95% CI 92.07–92.99), specificity = 93.5% (95% CI 93.08–93.8) and area under the curve (AUC) = 97% (95% CI 96.97–97.35). The multi-core computation was about four times faster (150 seconds) than the serial setting (550 seconds) when using a dataset of 20 million records.

Conclusion

CIDACS-RL algorithm is an innovative linkage tool for huge datasets, with higher accuracy, improved scalability, and substantially shorter execution time compared to other existing linkage tools. In addition, CIDACS-RL can be deployed on standard computers without the need for high-speed processors and distributed infrastructures.

Investigation of acoustic and geometric effects on the sonoreactor performance

In this work, three design configurations of a sonoreactor are considered under various operating conditions, and the acoustic characteristics during water sonication are investigated while using an immersed-type ultrasonic flat transducer probe in a sonoreactor model. Numerical models are also developed to simulate the sonication process, and they are successfully validated and compared with available data in the literature. Several sets of numerical investigations are conducted using the finite-element method and solved by the computational acoustics module in the COMSOL Multiphysics. The effects of the acoustical and geometrical parameters are investigated, analyzed, and reported, including the ultrasonic frequency, acoustic intensity, and scaling-up the reactor. The present study includes a parametric investigation examining the change of the ultrasonic frequency, intensity, and probe immersion depth on the performance. The results of the parametric study show that the highest cavitation energy corresponds to the maximum magnitude of negative pressure that takes place in the range of 60-80 kHz. The cavitation energy analyses are conducted under the conditions of 20 kHz of frequency and at 36 W input power. It is found that the cavitation energy of 15.87 W could produce 2.98 × 10^-10 mol/J of sonochemical efficiency. In addition, the effect of altering the transducer probe depth changes the acoustic pressure field insignificantly. Furthermore, a recommendation is made to improve the sonochemical efficiency by introducing more considerable ultrasound input power while operating the sonoreactor at an ultrasonic frequency lower than 60 kHz. The results presented in this paper provide a comprehensive assessment of different sonoreactors and the feasibility of scaling-up their production rate.