Generic Method Research Articles

Genicular Nerve Block for Knee Osteoarthritis: A Systematic Review and Meta-analysis of Randomized Clinical Trials.

Genicular nerve block (GNB) has emerged as a novel nonsurgical therapy for symptomatic knee osteoarthritis (KOA). The objective was to evaluate GNB versus placebo and other intra-articular (IA) therapies. The Medline, Embase, and Scopus databases were searched from their inception to January 2021. Only randomized controlled trials (RCTs) were included. A random-effects model and the generic inverse variance method were used for quantitative data synthesis. Heterogeneity was tested with the I2 index. Regarding pain statistically significant scores were demonstrated at 1 and 3 months overall total effect of 1.43 (95% CI, 0.86, 1.99; P= 0.00001; I2 =85%). Similarly, for knee function a total effect of 0.71 (95% CI, 0.35, 1.06; P= 0.00001; I2 =69%) at 1 and 3 months, statistically significant. The minimal clinically important difference regarding pain was achieved at 1 and 3 months. Although, GNB achieved the MCID for KOA pain and statistically significant results for knee functions. We cannot recommend its routine use besides being a safe procedure so far, evidence is fair on GNB for primary KOA due to study heterogeneity and limited follow-up.

The effect of deprescribing interventions on mortality and health outcomes in older people: An updated systematic review and meta-analysis.

Previous systematic reviews suggest that deprescribing may improve survival, particularly in frail older people. Evidence is rapidly accumulating, suggesting a need for an updated review of the literature. We updated a 2016 systematic review and meta-analysis to include studies published from inception to 26 April 2024 from specified databases. Studies in which older people had at least one medication deprescribed were included and grouped by study designs and targeted medications. The risk of bias was assessed using the Cochrane tool and the Newcastle-Ottawa tool. Odds ratios (OR) or mean differences were calculated as the effect measures using either the Mantel-Haenszel or generic inverse-variance method with fixed- or random-effects meta-analyses. The primary outcome was mortality. Secondary outcomes were adverse drug withdrawal events, physical health, cognitive function, quality of life and effect on medication regimen. Subgroup analyses were performed based on age and intervention types. A total of 259 studies (reported in 286 papers) were included in this updated review. Deprescribing polypharmacy did not result in a significant reduction in mortality in both randomized (OR 0.96, 95% confidence interval [CI] 0.84-1.09) and non-randomized studies (OR 0.70, 95% CI 0.36-1.38). Further subgroup analyses of randomized studies on deprescribing polypharmacy demonstrated a significant reduction in mortality in the young old (aged 65-79) (OR 0.71, 95% CI 0.51-0.99) and when patient-specific interventions were applied (OR 0.79, 95% CI 0.63-0.99). Deprescribing can be achieved with potentially important benefits in terms of improved survival, particularly when patient-specific interventions are applied and initiated early in the young old.

CE-MS and CE-MS/MS for the multiattribute analysis of monoclonal antibody variants at the subunit level

The analysis of product-related substances and impurities is a critical step in the biopharmaceutical quality control of multiattribute monoclonal antibodies (mAbs), as posttranslational modifications or other variants can influence the product's biological activity. Many approaches are available for variant analysis; however, they are either variant-specific, mAb-specific, time-consuming, or require expensive equipment. Here, we present a generic capillary electrophoretic method based on a neutral-coated capillary which was coupled to mass spectrometry (MS) via the nanoCEasy interface for mAb variant analysis at the subunit level (enzymatically digested and reduced mAb). The method enabled the separation of several (i) size variants (e.g. glycosylation variants) and (ii) charge variants (e.g. c-terminal lysin clipping) as well as (iii) multiple other proteoforms (e.g. additional glycation) and (iv) incompletely reduced subunits. Separated variants were confirmed by MS/MS fragmentation even for small mass deviations like deamidation or open disulfide bridges. The system, initially developed for one mAb, was tested with nine other IgG1s to show the general applicability of the system. The presented multiattribute method enables fast and detailed characterization of mAb variants with little sample preparation and relatively simple separation equipment enabling the separation of a large set of mAb variants.

Improved computation of polynomial roots over number fields when using complex embeddings

We explore a fairly generic method to compute roots of polynomials over number fields through complex embeddings. Our main contribution is to show how to use a structure of a relative extension to decode in a subfield. Additionally we describe several heuristic options to improve practical efficiency. We provide experimental data from our implementation and compare our methods to the state of the art algorithm implemented in Pari/Gp.

A Financing Constraints Paradox? Internal Finance and Capital Structure Link for Tourism Firms: Asian Emerging Market evidence

The aim of the study is to investigate the relationship between the internal financial and capital structure of an organization in the travel and tourism sector. The study shows how financial constraints on tourism companies affect their ability to finance themselves. The study also shows a relationship between tax shielding and financial leverage. This study analyzes panel data collected over a 21 year period, from 1998 to 2019, from over 100 publicly traded tourism companies in three Asian countries. A panel data methodology and the generalized method of moments estimation (GMM) were used in this empirical study. To support our hypothesis, using a generic method to evaluate parameter estimation will be our most effective method to improve the literature. Overall, the results show that financially stressed tourism firms use their option of debt-free tax avoidance to increase leverage. There is also a positive correlation between tax haven and leverage for firms with limited resources. The correlations between financial leverage and corporate debt supported by the results theoretically support the pecking order. By providing useful insights into the tax shielding of non-debt, which is significantly correlated with leverage for firms operating in tourism, this study closes the gap in firms’ capital structure decisions regarding access to finance. JEL Classification: G0, G01, L25

Expert opinion aggregation-based decision support for human-robot collaboration digital twin maturity assessment

Human-centered smart manufacturing is an essential direction for the future development of manufacturing. Safe and reliable smart human-robot collaboration is the foundation for realizing human-centered smart manufacturing. Digital twin-based human-robot collaboration has been proposed as a new manufacturing paradigm to devise collaborative strategies, simulate collaborative processes, and ensure worker safety. Establishing a maturity model is essential to accurately assess the capabilities of the constructed human-robot collaboration digital twin. This paper aims to contribute to the formalization and standardization of the human-robot collaboration digital twin. It constructs a novel assessment framework for the overall maturity measurement of existing digital twin-based human-robot collaboration projects. The developed human-robot collaboration digital twin maturity model includes 5 evaluation dimensions and 24 evaluation factors. Additionally, 5 maturity levels and their definitions are defined for each evaluation factor for maturity scoring. The expert opinion aggregation approach is proposed to quantify the evaluation factor metrics and ultimately to obtain a maturity level for the human-robot collaboration digital twin. The effectiveness and feasibility of the proposed method are verified through a collaborative assembly case study. This paper provides a generic method for assessing the competency level of human-robot collaboration digital twins, which can provide insights into the maturity of digital twins for practitioners in the human-robot collaboration field to develop targeted strategies for optimizing and upgrading human-robot collaboration digital twins.

Stability Conditions and Stiffness Variability of General Tensegrity Systems with Kinematic Joints

Abstract Tensegrity systems represent promising candidate mechanisms with in-situ stiffness variability through changing the cables' prestress levels. However, prestress-based stiffness behaviors of tensegrity systems with arbitrary kinematic joints have not been analyzed systematically. This paper adopts the natural absolute coordinates for static modeling of tensegrity systems consisting of rigid members and tension elements. Then, a generic stiffness analysis method is developed to formulate the reduced-basis tangent stiffness matrix, which is found to include three parts: positive semi-definite material and geometric stiffness matrices, and an indefinite constraint stiffness matrix. Based on these findings, a systematic stability-checking procedure is derived to determine prestress and super stability, which are qualitative indicators of the softening and stiffening effects in different tensegrity systems. Then, we proceed to quantify the range of prestress-based stiffness variability by formulating semidefinite programming problems that numerically pinpoint the maximum and zero stiffness points. Furthermore, this paper reveals the composable nature of multiple self-stress states, enabling the composability of stiffness properties in mechanism designs. Several numerical examples verify the efficacy and versatility of the proposed method and demonstrate interesting stiffness behaviors of tensegrity systems with kinematic joints.

A guidance for the enrichment of micropollutants from wastewater by solid-phase extraction before bioanalytical assessment

BackgroundWastewater can contain a complex mixture of organic micropollutants, with both chemical analysis and effect-based methods needed to identify relevant micropollutants and detect mixture effects. Solid-phase extraction (SPE) is commonly used to enrich micropollutants prior to analysis. While the recovery and stability of individual micropollutants by SPE has been well studied, few studies have optimized SPE for effect-based methods. The aim of the current study was to develop and evaluate two standard operating procedures (SOPs) for the enrichment of micropollutants in preparation for chemical analysis and bioanalysis, one covering a broad range of chemicals and the other selective for estrogenic chemicals.ResultsPristine surface water spiked with > 600 micropollutants was used to develop a generic extraction method for micropollutants with a wide range of physiochemical properties, while water spiked with estrogenic chemicals was used to identify a selective extraction method. Three different SPE sorbents were tested, with recoveries of individual chemicals and effect in assays indicative of mutagenicity, estrogenic activity, and fish embryo toxicity assessed. The sorbent HRX at pH 7 was selected for the generic extraction method as it showed the best recovery of both individual chemicals and effect in the bioassays. The sorbent HLB at pH 3 showed optimal recovery of estrogenic chemicals and estrogenic activity. The two optimal SPE methods were applied to spiked and unspiked wastewater effluents, with the concentrations of detected chemicals and observed effects similar to those of previous studies. The long-term storage of both extracts and SPE cartridges for estrogens and estrogenic activity after extraction with the HRX and HLB methods were evaluated, with estrogenic effectiveness close to 100% after 112 days when HLB was used.ConclusionsHRX is recommended for generic extraction, while HLB is optimal for the selective extraction of estrogenic micropollutants. However, if a laboratory only wants to use a single SPE sorbent, HLB can be used for both generic and selective extraction as it yielded similar chemical and effect recovery as HRX for a wide range of micropollutants. This paper is supplemented by the final SOP that includes a variant for generic extraction and one for the extraction of estrogenic chemicals.

“I married because …”: Motivations to marry early among female spouses in child marriages in northern Ghana

Child marriage is practised in Ghana with a prevalence rate of 27.2 %. This high prevalence is due to how marriage is conceptualized and constructed in communities that practice child marriage. Though reasons for the practice of child marriage are well documented, empirical studies of the married adolescent girls’ reasons for marrying early in the Ghanaian context are scant. This study, therefore, sought to present experiential factors that influenced the early marriage of adolescent girls. Twenty-one married girls aged between 13 and 17 years were recruited from selected communities in the Northern region of Ghana using purposeful, convenient and snowball sampling techniques. Individual face-to-face in-depth interviews with a semi-structured interview guide were adopted for the collection of data. A generic inductive qualitative analysis method was used to analyse the accounts of their experiences. Three core themes emerged as reasons influencing participants’ decision to marry. Personal motivating factors focused on adolescent girls’ personal need for children, idleness, peer group comparison and love. Economically motivated reasons include poverty, the quest for a better life and enticement by men. Sociocultural reasons include teenage pregnancy, family pressure and societal views on marriage among others. Information on the motivations for marriage among adolescent girls may be relevant to policymakers and interventionists in developing measures and interventions that are tailored to the needs of individuals affected by the practice of child marriage.

A Novel Topology Adaptation Strategy for Dynamic Sparse Training in Deep Reinforcement Learning.

Deep reinforcement learning (DRL) has been widely adopted in various applications, yet it faces practical limitations due to high storage and computational demands. Dynamic sparse training (DST) has recently emerged as a prominent approach to reduce these demands during training and inference phases, but existing DST methods achieve high sparsity levels by sacrificing policy performance as they rely on the absolute magnitude of connections for pruning and randomly generating connections. Addressing this, our study presents a generic method that can be seamlessly integrated into existing DST methods in DRL to enhance their policy performance while preserving their sparsity levels. Specifically, we develop a novel method for calculating the importance of connections within the model. Subsequently, we dynamically adjust the sparse network topology by dropping existing connections and introducing new connections based on their respective importance values. Through validation on eight widely used simulation tasks, our method improves two state-of-the-art (SOTA) DST approaches by up to 70% in episode return and average return across all episodes under various sparsity levels.

Association between obesity paradox in the all-cause mortality among patients with cardiac resynchronization therapy device.

Recent studies have demonstrated an obesity paradox, where obese patients with cardiovascular disease have a better outcome compared to those with normal weight. However, the effect of obesity and body mass index (BMI) on the outcome of patients with cardiac resynchronization therapy (CRT) devices remains unclear. The current study aims to investigate this relationship using all available published data. We systematically reviewed studies from Medline and EMBASE databases from inception to January 2024. Eligible studies must investigate the association between BMI status and all-cause mortality in individuals with CRT devices. Relative risk (RR) or hazard ratio (HR) and 95% CIs were retrieved from each study and combined using the generic inverse variance method. A total of 12 cohort studies were included in the meta-analysis. Pooled analysis showed that overweight and obesity patients had lower all-cause mortality compared to those with normal body weight with the pooled risk ratios (RR) for overweight of 0.77 (95% CI 0.69-0.87, I2 47%) and for obesity of 0.81 (95% CI 0.67-0.97, I2 59%). Conversely, the underweight exhibited higher all-cause mortality than the group with normal weight, with a pooled RR of 1.37 (95% CI 1.14-1.64, I2 0%). Additionally, higher BMI as continuous data was associated with decreased all-cause mortality, with a pooled HR of 0.94 (95% CI 0.89-0.98, I2 72%). The pooled analyses observed an obesity paradox in patients with CRT, where overweight and obesity were associated with reduced all-cause mortality, while underweight individuals exhibited higher all-cause mortality. Further research is necessary to investigate the underlying mechanisms and their implications for clinical practice.

Brand Building in a VUCA Environment: A Study based on Star Hotels in Sri Lanka

The business world is becoming more unstable than ever before where organizations all over the world are influenced by the VUCA environment. Accordingly, the business organizations are required to devise and implement strategies to deal with the disruptions generated by the VUCA environment. Particularly, the tourism and hospitality industry are more sensitive towards the instable environment and the organizations in this industry need to be more responsive for sustaining their growth and survival. Hence, the purpose of this study is to explore the influence of VUCA environment on brand building and responses taken by the high-end star class hotels in Sri Lanka. Researchers have used a qualitative research method and particularly the multiple case study approach in conducting this study. Accordingly, the data was collected using in-depth semi-structured interviews, images, and company documents and the data was analyzed using the generic qualitative data analysis method consists of coding, sorting, synthesizing and theorizing. The findings of the study highlighted the challenges encountered by the hotels in sustaining their brand during a VUCA environment, including threat for customer relations, challenges for internal operational activities and insufficiency in employee talent. The study also identified the strategies used by these hotels to sustain their brand, including innovations for sustaining the brand, novelty in marketing for enhancing customer experience and enhancement of stakeholder wellbeing.The findings imply that hotels are required to focus on innovations while addressing the needs of stakeholders by being more resilient towards the influences from VUCA environment.

Energy Hub Model for the Massive Adoption of Hydrogen in Power Systems

A promising energy carrier and storage solution for integrating renewable energies into the power grid currently being investigated is hydrogen produced via electrolysis. It already serves various purposes, but it might also enable the development of hydrogen-based electricity storage systems made up of electrolyzers, hydrogen storage systems, and generators (fuel cells or engines). The adoption of hydrogen-based technologies is strictly linked to the electrification of end uses and to multicarrier energy grids. This study introduces a generic method to integrate and optimize the sizing and operation phases of hydrogen-based power systems using an energy hub optimization model, which can manage and coordinate multiple energy carriers and equipment. Furthermore, the uncertainty related to renewables and final demands was carefully assessed. A case study on an urban microgrid with high hydrogen demand for mobility demonstrates the method’s applicability, showing how the multi-objective optimization of hydrogen-based power systems can reduce total costs, primary energy demand, and carbon equivalent emissions for both power grids and mobility down to −145%. Furthermore, the adoption of the uncertainty assessment can give additional benefits, allowing a downsizing of the equipment.

Prognostic impact of myosteatosis on survival with immune checkpoint inhibitors: A systematic review and meta-analysis

Myosteatosis has emerged as a promising prognostic biomarker for survival outcomes in patients with advanced cancer. However, recent research has yielded conflicting results on the association between myosteatosis and survival in patients treated with immune checkpoint inhibitors (ICIs). Therefore, we performed this systematic review and meta-analysis to evaluate the association between myosteatosis and survival outcomes in patients treated with ICIs. We conducted a systematic review using Pubmed, Web of Science, and Scopus databases for studies published until June 10, 2024. This protocol was registered in the PROSPERO database (Registration Number: CRD42023466337). We performed the meta-analyses with the generic inverse-variance method with a random effects model. Eleven studies involving 1362 patients were included. The pooled analysis showed that patients with myosteatosis had a significantly higher risk of death compared to patients without myosteatosis (HR: 1.61, 95% CI: 1.23-2.12, p<0.001). Subgroup analysis revealed this association was stronger in melanoma patients (HR: 2.07, 95% CI: 1.09-3.94, p=0.030). Furthermore, patients with myosteatosis had an increased risk of progression or death than those without myosteatosis (HR: 1.31, 95% CI: 1.05-1.64, p=0.020). Myosteatosis is associated with a higher risk of death in ICI-treated patients. Further research in larger cohorts is needed to standardize the definition of myosteatosis as well as the true mechanistic association between myosteatosis and survival in patients treated with ICIs.

GameBus on FHIR.

The interoperability of healthcare data across various systems remains a big challenge, largely attributable to the disparate data schemas and APIs in use. This study showcases the integration of a FHIR layer into GameBus, a gamified health platform, aiming to enhance its interoperability. Traditionally, GameBus has relied on proprietary data schemas and REST APIs, which restricted data exchange with other platforms. The incorporation of the FHIR standard significantly mitigates these constraints. The FHIR layer, constructed with open-source technologies - including the Google HCLS Data Harmonization tool for data transformation and the HAPI FHIR framework for RESTful services - allows GameBus to engage in data sharing using standardized FHIR formats and APIs. Implemented as a standalone microservice, this layer requires no alterations to the pre-existing architecture of GameBus. Furthermore, the design and implementation of the FHIR layer illustrate a generic method for achieving interoperability across diverse healthcare platforms.

Sensory analysis of the flavor profile of full immersion hot, room temperature, and cold brewed coffee over time

With the growing popularity of cold brewed coffee comes a need for brewing efficiency while preserving the desirable flavor profile. Despite the wide use of full immersion brewing techniques, the effect of brew time on the dynamic sensory profiles of full immersion brewed coffee remains underexplored. Here, we investigated the relationship between coffee sensory quality and extraction dynamics, measured as Total Dissolved Solids (TDS) and Extraction (E) of full immersion brewed coffee at various roast levels, and brewing temperatures (4 °C, 22 °C and 92 °C), over brew time using a generic descriptive analysis method. Specifically, different brew time points were selected for different temperatures based on five targeted coffee extraction stages. Furthermore, the unique experimental design also explored a sensory-driven engineering research process. Roast level had the greatest impact on the sensory profile of the coffees, followed by brewing temperature, but brew time, especially the longer brew times as TDS plateaued, had subtler impacts than expected. Twenty-five of 28 sensory attributes were significantly different among the 30 coffee samples, indicating a single source green coffee blend can produce a wide range of complex sensory profiles using different combinations of roast level, temperature, and brew time. Specifically, the intensity of sweetness was negatively correlated with TDS, and 19 other attribute intensities were positively correlated with TDS. Interestingly, we found that certain long time cold brews had similar sensory profiles to those of some short time hot brews, suggesting the sensory profiles of certain hot brews and cold brews could possibly be matched through controlled preparation. Overall, our study demonstrated an approach of integrating food engineering and sensory analysis for product development, and our findings provide valuable insights into the extraction dynamics and sensory quality of full immersion brewed coffee and opens new brewing avenues for the coffee industry.

Effect of collagen supplementation on knee osteoarthritis: an updated systematic review and meta-analysis of randomised controlled trials.

To perform a systematic review and meta-analysis to assess the clinical efficacy of collagen-based supplements on knee osteoarthritis (OA) symptoms. Until October 2023, we conducted searches on the MEDLINE, EMBASE, Web of Science, and Scopus databases to identify randomised controlled trials (RCTs) that reported the effects of oral collagen-based supplements on knee OA. Quantitative data from outcomes were pooled using a random- or fixed-effects model (depending on inter-study variability) and the generic inverse variance method. The Cochrane Risk of Bias 2.0 tool was employed to assess the risk of bias. This systematic review incorporated information of 870 participants included from 11 RCTs, with 451 allocated to the collagen supplementation group and 419 to the placebo group. The meta-analysis revealed an overall significant improvement of both function [MD, -6.46 (95% CI -9.52, -3.40); I2=75%; p=0.00001] and pain scores [MD, -13.63 (95% CI -20.67, -6.58); I2=88%; p=0.00001], favouring collagen supplementation. The results of this meta-analysis suggest that oral collagen administration relieves OA symptoms. Our findings revealed noteworthy improvements, statistically and clinically, in both functional and pain scores.

An interlaboratory proficiency test using metagenomic sequencing as a diagnostic tool for the detection of RNA viruses in swine fecal material.

Metagenomic shotgun sequencing (mNGS) is a generic molecular diagnostic method, involving laboratory preparation of samples, sequencing, bioinformatic analysis of millions of short sequences, and interpretation of the results. In this paper, we investigated the performance of mNGS on the detection of porcine astroviruses, a model for RNA viruses in a pig fecal material, among six European veterinary and public health laboratories. We showed that different methods for data generation affect mNGS performance among participants and that the selection of reference genomes is crucial for read classification. Follow-up investigation remains a critical factor for the diagnostic interpretation of mNGS results. The paper contributes to potential improvements of mNGS as a diagnostic tool in clinical settings.

Simple generic self-assembly fabrication of transition metal borates on polyaminophenylboric acid modified foam nickel for highly-efficient overall water splitting

Water electrocatalysis for hydrogen production as a prospect strategy to store renewable energy and reduce carbon emissions has attracted great attention, and exploring water electrolysis catalysts has become a research hotspot for widespread industrial water electrolysis application. Very recently, transition metal borates have been demonstrated great potential for electrocatalytic hydrogen or oxygen evolution in alkaline media. However, a generic method for fabricating transition metal borates as bifunctional oxygen catalysts are still challenging. Herein, through a two-step process including poly-aminophenylboronic acid electrodeposition and subsequent electrostatic self-assembly of transitional metal ions, for the first time, to our best knowledge, a simple and generic approach to fabricate polyaminophenylborate transition metal salt modified nickel foam electrodes (PAB-M/NF, M represents transition metal element) was developed for highly efficient water splitting. As a proof of concept, the obtained PAB-Ru/NF exhibited remarkable bifunctional HER and OER catalytic activities and stability in alkaline medium. To drive the HER and OER with a current density of 10 mA cm−2 in 1 M KOH electrolyte, PAB-Ru/NF only required an overpotential of 14 mV and 301 mV, respectively. Moreover, even at a great current density of 250 mA cm−2, PAB-Ru/NF can exhibit very favorable long-term stability for over 35 h. When assembled into an electrolyzer with PAB-Ru/NF as both the anode and cathode, a current density of 10 mA cm−2 can be achieved at a very low cell voltage of 1.50 V. In addition, the other PAB-M/NF catalysts (M including but not limited to Fe, Co and Ni) prepared by the same way also show excellent bifunctional HER and OER catalytic activities and durability. This study provides a prospective and economically feasible generic approach for developing practical bifunctional catalysts for industrial water electrolysis.

FLAT: Fusing layer representations for more efficient transfer learning in NLP

Parameter efficient transfer learning (PETL) methods provide an efficient alternative for fine-tuning. However, typical PETL methods inject the same structures to all Pre-trained Language Model (PLM) layers and only use the final hidden states for downstream tasks, regardless of the knowledge diversity across PLM layers. Additionally, the backpropagation path of existing PETL methods still passes through the frozen PLM during training, which is computational and memory inefficient. In this paper, we propose FLAT, a generic PETL method that explicitly and individually combines knowledge across all PLM layers based on the tokens to perform a better transferring. FLAT considers the backbone PLM as a feature extractor and combines the features in a side-network, hence the backpropagation does not involve the PLM, which results in much less memory requirement than previous methods. The results on the GLUE benchmark show that FLAT outperforms other tuning techniques in the low-resource scenarios and achieves on-par performance in the high-resource scenarios with only 0.53% trainable parameters per task and 3.2× less GPU memory usagewith BERTbase. Besides, further ablation study is conducted to reveal that the proposed fusion layer effectively combines knowledge from PLM and helps the classifier to exploit the PLM knowledge to downstream tasks. We will release our code for better reproducibility.