Generic Method Research Articles

Efficient Scenario Generation for Heavy-Tailed Chance Constrained Optimization

We consider a generic class of chance-constrained optimization problems with heavy-tailed (i.e., power-law type) risk factors. As the most popular generic method for solving chance constrained optimization, the scenario approach generates sampled optimization problem as a precise approximation with provable reliability, but the computational complexity becomes intractable when the risk tolerance parameter is small. To reduce the complexity, we sample the risk factors from a conditional distribution given that the risk factors are in an analytically tractable event that encompasses all the plausible events of constraints violation. Our approximation is proven to have optimal value within a constant factor to the optimal value of the original chance constraint problem with high probability, uniformly in the risk tolerance parameter. To the best of our knowledge, our result is the first uniform performance guarantee of this type. We additionally demonstrate the efficiency of our algorithm in the context of solvency in portfolio optimization and insurance networks. Funding: The research of B. Zwart is supported by the NWO (Dutch Research Council) [Grant 639.033.413]. The research of J. Blanchet is supported by the Air Force Office of Scientific Research [Award FA9550-20-1-0397], the National Science Foundation [Grants 1820942, 1838576, 1915967, and 2118199], Defense Advanced Research Projects Agency [Award N660011824028], and China Merchants Bank.

Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features.

Decoding human visual neural representations is a challenging task with great scientific significance in revealing vision-processing mechanisms and developing brain-like intelligent machines. Most existing methods are difficult to generalize to novel categories that have no corresponding neural data for training. The two main reasons are 1) the under-exploitation of the multimodal semantic knowledge underlying the neural data and 2) the small number of paired (stimuli-responses) training data. To overcome these limitations, this paper presents a generic neural decoding method called BraVL that uses multimodal learning of brain-visual-linguistic features. We focus on modeling the relationships between brain, visual and linguistic features via multimodal deep generative models. Specifically, we leverage the mixture-of-product-of-experts formulation to infer a latent code that enables a coherent joint generation of all three modalities. To learn a more consistent joint representation and improve the data efficiency in the case of limited brain activity data, we exploit both intra- and inter-modality mutual information maximization regularization terms. In particular, our BraVL model can be trained under various semi-supervised scenarios to incorporate the visual and textual features obtained from the extra categories. Finally, we construct three trimodal matching datasets, and the extensive experiments lead to some interesting conclusions and cognitive insights: 1) decoding novel visual categories from human brain activity is practically possible with good accuracy; 2) decoding models using the combination of visual and linguistic features perform much better than those using either of them alone; 3) visual perception may be accompanied by linguistic influences to represent the semantics of visual stimuli.

Minimally Invasive Versus Transcatheter Aortic Valve Replacement: An Updated Meta-Analysis and Systematic Review.

Transcatheter aortic valve replacement (TAVR) has arisen as a viable alternative to surgery. Similarly, minimally invasive surgical aortic valve replacement (mini-SAVR), such as ministernotomy and minithoracotomy, have also gained interest. We conducted a pairwise meta-analysis to further investigate the efficacy of TAVR versus mini-SAVR. Medical databases were comprehensively searched for studies comparing TAVR with a mini-SAVR modality, defined as minimally invasive aortic surgery, ministernotomy, minithoracotomy, or rapid-deployment or sutureless SAVR. Random-effects meta-analysis was conducted using the generic inverse variance method. Primary outcomes included 30-day mortality, midterm mortality, 30-day stroke, acute kidney injury (AKI), paravalvular leak (PVL), new permanent pacemaker (PPM), new-onset atrial fibrillation, and postintervention mean and peak valve pressure gradients and were pooled as risk ratio (RR), mean difference (MD), or hazard ratio (HR) with 95% confidence interval (CI). A total of 5,071 patients (2,505 mini-SAVR vs 2,566 TAVR) from 12 studies were pooled. Compared with TAVR, mini-SAVR showed significantly lower rates of both 30-day (RR = 0.63, 95% CI: 0.42 to 0.96, P = 0.03) and midterm mortality at 4 years of follow-up (HR = 0.76, 95% CI: 0.67 to 0.87, P < 0.001). Furthermore, mini-SAVR was protective against 30-day PVL (RR = 0.07, 95% CI: 0.04 to 0.13, P < 0.001) and new PPM (RR = 0.25, 95% CI: 0.11 to 0.57, P < 0.001). Conversely, TAVR was protective against 30-day AKI (RR = 1.67, 95% CI: 1.20 to 2.32, P = 0.002) and postinterventional mean gradients (MD = 1.65, 95% CI: 0.25 to 3.05, P = 0.02). No difference was observed for 30-day stroke (RR = 0.84, 95% CI: 0.56 to 1.24, P = 0.38), new-onset atrial fibrillation (RR = 1.79, 95% CI: 0.93 to 3.44, P = 0.08), or postinterventional peak gradients (MD = 3.24, 95% CI: -1.10 to 7.59, P = 0.14). Compared with TAVR, mini-SAVR was protective against 30-day and midterm mortality, 30-day PVL, and new permanent pacemaker, while TAVR patients had lower 30-day AKI. Future randomized trials comparing the efficacy of mini-SAVR approaches with TAVR are needed.

Quality improvement interventions to prevent the use of hospital services among nursing home residents: protocol for a systematic review and meta-analysis

IntroductionQuality improvement interventions are a promising strategy for reducing hospital services use among nursing home residents. However, evidence for their effectiveness is limited. It is unclear which characteristics of the...

Combining chains of Bayesian models with Markov melding.

A challenge for practitioners of Bayesian inference is specifying a model that incorporates multiple relevant, heterogeneous data sets. It may be easier to instead specify distinct submodels for each source of data, then join the submodels together. We consider chains of submodels, where submodels directly relate to their neighbours via common quantities which may be parameters or deterministic functions thereof. We propose chained Markov melding, an extension of Markov melding, a generic method to combine chains of submodels into a joint model. One challenge we address is appropriately capturing the prior dependence between common quantities within a submodel, whilst also reconciling differences in priors for the same common quantity between two adjacent submodels. Estimating the posterior of the resulting overall joint model is also challenging, so we describe a sampler that uses the chain structure to incorporate information contained in the submodels in multiple stages, possibly in parallel. We demonstrate our methodology using two examples. The first example considers an ecological integrated population model, where multiple data sets are required to accurately estimate population immigration and reproduction rates. We also consider a joint longitudinal and time-to-event model with uncertain, submodel-derived event times. Chained Markov melding is a conceptually appealing approach to integrating submodels in these settings.

Generic method of local thickening in shell element FEM models for orthotropic bridge decks

AbstractThis paper presents a method of estimating the hot‐spot stress in orthotropic bridge decks (OBD) using a shell Finite Element (FE) model, in which local thickening is applied around the welded. The dimensions of the area of local thickening are a function of the weld throat thickness and the plate thickness. A comparison of this method with 3D solid element FE models is presented for various fatigue sensitive details. The comparison is carried out for OBD with trapezoidal stiffeners, both with continuous stiffeners through crossbeams and with stiffeners welded in between crossbeams. It is concluded that the accuracy of estimating the hot‐spot stress with a shell element model using local thickening is significantly higher as compared to a conventional shell element model without thickening. The method of local thickening is adopted in the new prTS 1993‐1‐901. This paper functions as the background to this technical specification.

Early Life Nutrition Factors and Risk of Acute Leukemia in Children: Systematic Review and Meta-Analysis.

Acute leukemia commonly occurs in young children with peak incidence at the age of 2-5 years. However, the etiology is still unclear and many preventable risk factors still deserve to be reviewed. The focus of this systematic review and meta-analysis is to summarize the evidence concerning early life nourishment (breastfeeding, early life diet), neonatal vitamin K administration and the risk of acute leukemia. All epidemiological studies published up to June 2023 and assessing diet-related risk factors for childhood acute leukemia were identified in two electronic databases (PubMed and Web of Science), with no limits on publication year or language. A total of 38 studies (37 case-control studies and 1 study with pooled analysis) were included. The published risk estimates were combined into a meta-analysis using the Generic Inverse Variance method. The current evidence shows that breastfeeding (yes vs. no) has a protective effect against acute lymphoblastic leukemia (odds ratio = 0.85; 95% CI, 0.76-0.94). Evidence related to the role of other studied factors (foods and supplements) is inconclusive. Further research into the potential role of diet in early life and the risk of acute leukemia is needed to develop prevention strategies at population level. Review Registration: PROSPERO registration no. CRD42019128937.

A novel interactive intuitionistic fuzzy approach to find satisfactory Pareto-optimal solutions for general bilevel multi-followers programming

This paper focuses on the approach for solving the satisfactory Pareto-optimal solutions of the general form of bilevel multi-followers programming(BMFP), including the BMFP with partial shared variables among followers, the so-called referential-uncooperative BMFP, the uncooperative BMFP, and the cooperative BMFP. Without losing generality, the BMFP with partial shared variables among followers will be taken as a representative to explain the universal applicability of the proposed generic interactive intuitionistic fuzzy method. Based on the theory of intuitionistic fuzzy set, the new definition of satisfactory Pareto-optimal solutions is introduced by means of membership function, non-membership function, and score function. In order to obtain satisfactory Pareto-optimal solutions of BMFP with partial shared variables among followers, a prior minimization optimization problem and a series of corresponding auxiliary maximization optimization problems are formulated, and we strictly prove that the optimal solution of the prior minimization optimization problem is not necessarily the satisfactory Pareto-optimal solutions, but at least one satisfactory Pareto optimal solution can be derived by further solving these auxiliary maximization optimization problems. Then, a detailed interactive intuitionistic fuzzy algorithm framework that consider both the balance of satisfactory degree between the leader and the followers and the balance of satisfactory degree among all followers is proposed. In the part of numerical experiments, four examples(including a linear BMFP problem with partial shared variables among followers, a BMFP model of a road network problem, an uncooperative BMFP problem, and a nonlinear BMFP problem with partial shared variables among followers) are illustrated to demonstrate the applicability, effectiveness and superior performance of the proposed technique.

Development of a generic approach for monitoring leachable compounds in hospital pharmacy-prepared prefilled plastic packaging by ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry with postcolumn infusion

Prefilled plastic packaging is time- and cost-effective in hospital pharmacy because it prevents waste, preparation errors, dosage errors, microbial contamination and accidents. This packaging mostly includes prefilled syringes (PFS), intravenous (IV) bags and vials intended for long-term storage that can be used for immediate treatment. There is a rising availability in the market for prefilled drug products due to their practical approach. Leachable compounds could be evaluated in hospital pharmacy-prepared prefilled drug solutions. The Pharmacy Department at the Lausanne University Hospital has developed an innovative, highly sensitive, and generic method by postcolumn infusion based on ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) for the analysis of plastic additives in hospital pharmacies. The postcolumn infusion solution was developed with 2% ammonium hydroxide in methanol on a representative set of 30 candidate compounds with different physical-chemical properties, such as log P and molecular structure, to represent the most important categories of additives. The LODs obtained for all compounds ranged from 0.03 to 7.91 ng/mL with linearity up to 250 ng/mL. Through this screening method, plastic additives can be rapidly identified due to the combined use of retention time, exact mass (including isotopic pattern) and MS/MS spectra. In addition, the users can screen for vast categories of plastic additives, including plasticizers, epoxy monomers, antioxidants, UV stabilizers, and others. The screening is facilitated by assessments of a complex in-house-built database for extractable and leachable trace assessment (DELTA), containing 205 compounds for unambiguous identification. Relative response factors were established for all analytes to obtain a semiquantitation of compounds. Moreover, the database also contains valuable estimative toxicology information, which was obtained through calculating their permissible dose exposure threshold; thus, estimative toxicology assessment can be performed for identified compounds in prefilled drug products. This method and the database were applied to a hospital pharmacy-prepared prefilled vancomycin syringe for paediatric use. Ultrasound-assisted dispersive liquid-liquid microextraction (UA-DLLME) was used to prepare the samples for leachable analysis. As a result, 17 plastic additives were formally identified, and their concentrations were estimated. A toxicology assessment was performed by comparing their concentrations with their theoretical PDE thresholds. In conclusion, the prefilled drug solution released a negligible amount of known leachables that appeared to be safe for use in neonates and children.

Simultaneous determination of low molecular weight nitrosamines in pharmaceutical products by fast gas chromatography mass spectrometry

Control of N-nitrosamines has been in the focus of health authorities in recent years because many of these compounds are probable human carcinogens. In July 2018 the U.S. Food and Drug Administration (FDA) announced a recall for valsartan-containing medicines due to contamination with the carcinogenic low molecular weight nitrosamine, N-nitrosodimethylamine (NDMA). It has become clear that the problem can not only exist in the case of sartans, but in any active pharmaceutical ingredient (API)/drug product in which secondary or tertiary amines are present (as API or as impurities) and a nitrosating agent is available. The decision was made by regulators, according to which manufacturers of pharmaceutical products are obliged to perform a risk assessment for the potential presence of nitrosamines in active pharmaceutical ingredients and drug products. This resulted in a high demand for validated analytical methods that are able to quantify N-nitrosamines at low ppb levels in pharmaceutical products.In this work we have developed and validated a generic fast GC-MS method suitable for the quantitative determination of a wide range of low molecular weight nitrosamines, which include N-nitrosodiethylamine (NDEA), N-nitrosodimethylamine (NDMA), N-nitroso-diphenylamine (NDPh), N-nitrosodipropylamine (NDPA), N-nitrosomethylethylamine (NMEA), N-nitrosomorpholine (NMOR), N-nitrosopiperidine (NPIP), N-nitroso-ethylisopropylamine (EIPNA), N-nitroso-diisopropylamine (DIPNA), N-nitroso-N-methylaniline (NMPA), 1-Methyl-4-nitrosopiperazine (MeNP) and N-nitroso-pyrrolidine (NPYR). The advantage of the method is that it is possible to screen low molecular weight nitrosamines in low concentrations with a short analysis time in a wide range of APIs and drug products.

Significantly Enhanced Poling Efficiency of Piezocomposites by Tuning Resistivity of a Polymer Matrix.

Although the ability to convert biomechanical vibrations into electric energy has been demonstrated in organic-inorganic piezocomposites, it is challenging to improve their piezoelectric properties owing to insufficient electric field poling. Here, we propose a facile and effective approach to enhance the poling efficiency of a barium calcium zirconate titanate/polydimethylsiloxane (BCZT/PDMS) composite by introducing copper nanowires (Cu NWs) to tune the resistivity of the PDMS matrix. The Cu NW-modified PDMS weakens the resistivity mismatch between the BCZT filler and the PDMS matrix, allowing a higher poling electric field to be applied to the BCZT filler during poling. As a result, the BCZT/Cu-PDMS piezocomposite exhibited a high piezoelectric quality factor (d33 × g33) of 2.58 pm2/N, which was about 7 times higher than that of BCZT/PDMS (d33 × g33 = 0.38 pm2/N). Moreover, BCZT/Cu-PDMS showed a much higher power density (3.18 μW/cm2) and a faster charging capability. This composite approach of introducing metal nanowires can be considered as a generic poling-improvement method that can be extended to other organic-inorganic piezocomposite systems.

PrimitivePose: Generic Model and Representation for 3D Bounding Box Prediction of Unseen Objects

A considerable amount of research is concerned with the challenging task of estimating three-dimensional (3D) pose and size for multi-object indoor scene configurations. Many existing models rely on a priori known object models, such as 3D CAD models and are therefore limited to a predefined set of object categories. This closed-set constraint limits the range of applications for robots interacting in dynamic environments where previously unseen objects may appear. This paper addresses this problem with a highly generic 3D bounding box detection method that relies entirely on geometric cues obtained from depth data percepts. While the generation of synthetic data, e.g. synthetic depth maps, is commonly used for this task, the well-known synth-to-real gap often emerges, which prohibits transition of models trained solely on synthetic data to the real world. To ameliorate this problem, we use stereo depth computation on synthetic data to obtain pseudo-realistic disparity maps. We then propose an intermediate representation, namely disparity-scaled surface normal (SN) images, which encodes geometry and at the same time preserves depth/scale information unlike the commonly used standard SNs. In a series of experiments, we demonstrate the usefulness of our approach, detecting everyday objects on a captured data set of tabletop scenes, and compare it to the popular PoseCNN model. We quantitatively show that standard SNs are less adequate for challenging 3D detection tasks by comparing predictions from the model trained on disparity alone, SNs and disparity-scaled SNs. Additionally, in an ablation study we investigate the minimal number of training samples required for such a learning task. Lastly, we make the tool used for 3D object annotation publicly available at: https://preview.tinyurl.com/3ycn8v5k . A video showcasing our results can be found at: https://preview.tinyurl.com/dzdzabek .

Analysing ecological dynamics with relational event models: The case of biological invasions

AbstractAimsSpatio‐temporal processes play a key role in ecology, from genes to large‐scale macroecological and biogeographical processes. Existing methods studying such spatio‐temporally structured data either simplify the dynamic structure or the complex interactions of ecological drivers. The aim of this paper is to present a generic method for ecological research that allows analysing spatio‐temporal patterns of biological processes at large spatial scales by including the time‐varying variables that drive these dynamics.LocationGlobal analysis at the level of 272 regions.MethodsWe introduce a method called relational event modelling (REM). REM relies on temporal interaction dynamics that encode sequences of relational events connecting a sender node to a recipient node at a specific point in time. We apply REM to the spread of alien species around the globe between 1880 and 2005, following accidental or deliberate introductions into geographical regions outside of their native range. In this context, a relational event represents the new occurrence of an alien species given its former distribution.ResultsThe application of relational event models to the first reported invasions of 4835 established alien species outside of their native ranges from four major taxonomic groups enables us to unravel the main drivers of the dynamics of the spread of invasive alien species. Combining the alien species first records data with other spatio‐temporal information enables us to discover which factors have been responsible for the spread of species across the globe. Besides the usual drivers of species invasions, such as trade, land use and climatic conditions, we also find evidence for species‐interconnectedness in alien species spread.ConclusionsRelational event models offer the capacity to account for the temporal sequences of ecological events such as biological invasions and to investigate how relationships between these events and potential drivers change over time.

Facile growth of perovskite nanoparticles confined on dimension-controlled Si nanorod arrays for various promising photophysical applications

In this study, a facile growth of hybrid perovskite nanoparticles (NPs) has been demonstrated on mesoporous, high-aspect ratio, morphology-controlled Si nanorod (NR) arrays. A generic method of directly confining perovskite nanocrystallites (average size <7 nm) on a mesoporous substrate without the use of colloidal stabilization was introduced. The perovskite NPs coated on dimension-and position-controlled Si NR arrays were systematically investigated by their various photophysical properties such as optical reflectance, cathodoluminescence (CL), and photoluminescence (PL) behaviors at both room temperature and low temperature. The dimension and position of the Si NR arrays were controlled by e-beam lithography followed by selective metal-assisted chemical etching (MACE). Organic-inorganic perovskite NPs were synthesized on the surface of Si NR array by spin coating of perovskite precursor solution and followed by annealing. The porous and rough sites on the surface of the NR arrays acted as nucleation centers for the formation of perovskite NPs. Due to the excitonic recombination of CH3NH3PbBr3, the NPs confined on the various NR templates exhibited strong PL emission in the 505–510 nm region. Furthermore, due to greater radiative recombination and lesser carrier trapping in the NPs, the PL and CL emission intensities of the perovskite NPs localized on the surface of the NR array were dramatically increased (PL enhancement factor ∼ 7.7, when NR aspect ratio ∼ 17.2) as compared to the bulk perovskite microcrystals. The low-temperature PL study revealed higher exciton binding energy of the NPs as compared to the bulk microcrystalline film. A systematic investigation revealed that the optical absorption, as well as the emission color of the perovskite NPs, can be tuned by the aspect ratio of the Si NR arrays. The results of our studies indicate the application of bandgap engineering of perovskite nanocrystals through confinement in a mesoporous, well-organized, regularly-ordered template as a powerful tool for tuning the emission wavelength in next-generation photonic sources. Furthermore, the important findings on the periodic array of photoactive nanoscale materials introduced in this manuscript may open up huge opportunities in numerous cutting-edge applications such as light emitting diode arrays, photodetector arrays, individually addressable devices, display devices with controlled pixel size and pixel density, etc.

A spatio-temporal fusion method based on Landsat and MODIS normalized vegetation index data

Abstract. Normalized difference vegetation index (NDVI) can simply and effectively reflect the growth status of plants, and has a linear relationship with vegetation coverage, thus it is an important indicator to identify vegetation growth status and coverage. However, the commonly used MODIS NDVI or Landsat NDVI cannot simultaneously achieve the high temporal and spatial resolution. To this end, we proposed a generic and automatic method to fuse MODIS and Landsat satellite vegetation index (NDVI) into daily high-resolution products which takes time series NDVI data from MODIS/Landsat as input, filters noise pixels, and generates high-resolution and high-frequency products through a fusion process of temporal-linearly interpolation to increase the temporal resolution and spatial filtering to alleviate the blocky artifacts from MODIS pixel border. The fused NDVI maps are visually compared to evaluate the performance of this method, and more quantitative assessment will be continued in the next step. This study can provide important support for large-scale long-term surface vegetation monitoring.

Short-lived product returns forecasting when customers are unwilling to return the product: A grey-graphical evaluation and review technique

Consumer electronics, such as mobile phones, laptops, and tablets, encounter the challenge of short product life cycles due to the rapid advancement of technology, marketing models, and rapidly changing consumer tastes. This study attempts to provide a forecasting model for a short-lived product for closed-loop supply chain planning. Importantly, we model the setting where the customers prefer to store the product instead of returning it for various reasons such as security and secondary use. We proposed a generic method that can be implemented for any technology-driven product's returns and remanufacturing process. We used the Graphical Evaluation and Review Technique to calculate the probability and quantity of product returns, the quantity of modules, parts, materials, and disposal. Then, we employed the Grey-Graphical Evaluation and Review Technique to calculate the expected time interval for core returns and the expected interval for the availability of module/parts/material for reconditioning/ remanufacturing of the product. An illustrative case study demonstrates the results of the forecasting model. Further, we analyzed the effect of customers' unwillingness to return the product on the reverse network. Our analysis asserts that the customer storing an end-of-use product has a greater impact on the probability of overall product return. Our analysis found that when customers tend to return the product instead of storing it for secondary use, the equivalence probability of returns can increase up to 91 %. In addition, we also address the effect of product return on the “ecological rucksack” of mobile phones. This can help policymakers design hassle-free return experiences and attractive customer incentives.

Increased Prevalence of Gastroesophageal Reflux Disease Among Patients With Rheumatoid Arthritis: A Systematic Review and Meta-analysis.

This study was conducted to determine the association between gastroesophageal reflux disease (GERD) and rheumatoid arthritis (RA) by pooling the evidence from all available studies. Potentially eligible studies were identified from MEDLINE and EMBASE database from inception to April 2021 employing a search strategy that consisted of terms for "Rheumatoid Arthritis" and "Gastroesophageal Reflux Disease". Eligible studies for the meta-analysis were recruited with conditions of being cohort studies that included rheumatoid arthritis and without rheumatoid arthritis individuals. Together with this, prevalence of GERD in both groups and the odds ratio (OR) comparing the prevalence of GERD between the two cohorts have been reported. The retrieved point estimates with standard errors from each study were pooled into the final result by the random-effect model and generic inverse variance method as described by DerSimonian and Laird. A total of 3,646 articles were identified. After two rounds of independent review by two investigators, five cohort studies were included in the meta-analysis as they met the eligibility criteria. The pooled analysis demonstrated a significant association between RA and GERD with the pooled odds ratio of 1.98 (95% CI, 1.49 - 2.65). High statistical heterogeneity with I2 of 83% was observed. The funnel plot was symmetric and publication bias was not observed. This systematic review and meta-analysis found a significant association between GERD and RA.

A Generic and Effective System Dispersion Compensation Method: Development and Validation in Visible-Light OCT

Compared with optical coherence tomography (OCT) in the near-infrared domain, the visible-light OCT (vis-OCT) system affords a higher axial resolution for discerning subtle pathological changes associated with early diseases. However, the significant material dispersion at the visible-light range leads to a severe problem for dispersion management in vis-OCT systems, which results in a compromised axial resolution. While dispersion compensators (such as prism pairs) are commonly used, a digital method is still highly desirable and has been widely used to compensate for the residual dispersion imbalance between the reference and sample arms in an OCT system. In this paper, we develop a generic approach to effectively compensate for the system dispersion, especially the higher-order dispersion in the vis-OCT system, by using a single arbitrary measurement of the mirror-reflection (SAMMR) method and its resulting phase information. Compared with the previous methods, including the method based on the Taylor series iterative fitting and differential method, the proposed method does not need to extract the dispersion coefficients or use the metric functions and affords a better performance for axial resolution and the signal-to-noise ratio in vis-OCT systems. Its effectiveness is further validated in an OCT system operating in the near-infrared domain.

Association between cannabis use and risk of diabetes mellitus type 2: A systematic review and meta-analysis.

Cannabis consumption exerts multiple effects on metabolism via various pathways, including glucose regulation and insulin secretion. Studies concerning the association between cannabis use and diabetes mellitus type 2 are discrepant. This study was conducted to evaluate the association between cannabis use and type 2 diabetes mellitus (T2DM). We searched PubMed, Scopus, Embase, Proquest, Web of Science, and Cochrane Library with no time, language or study types restriction until July 1, 2022, using various forms of "cannabis" and "diabetes mellitus" search terms. Randomized control trials, cohort, and case-control studies investigating the relationship between cannabis consumption and diabetes mellitus type 2 were included. The Newcastle-Ottawa scale was used to assess the quality of studies. We pooled odds ratio (OR) with 95% confidence interval (CI) using the random-effects model, generic inverse variance method, DerSimonian and Laird approach. A meta-analysis of seven studies, containing 11 surveys and 4 cohorts, revealed that the odds of developing T2DM in individuals exposed to cannabis was 0.48 times (95% CI: 0.39 to 0.59) lower than in those without cannabis exposure. A protective effect of cannabis consumption on the odds of diabetes mellitus type 2 development has been suggested. Yet given the considerable interstudy heterogeneity, the upward trend of cannabis consumption and cannabis legalization is recommended to conduct studies with higher levels of evidence.

Transforming the untransformable with knockout minicircles: High-efficiency transformation and vector-free allelic exchange knockout in the fish pathogen Photobacterium damselae.

Gene inactivation studies are critical in pathogenic bacteria, where insights into species biology can guide the development of vaccines and treatments. Allelic exchange via homologous recombination is a generic method of targeted gene editing in bacteria. However, generally applicable protocols are lacking, and suboptimal approaches are often used for nonstandard but epidemiologically important species. Photobacterium damselae subsp. piscicida (Pdp) is a primary pathogen of fish in aquaculture and has been considered hard to transform since the mid-1990s. Consequently, conjugative transfer of RK2/RP4 suicide vectors from Escherichiacoli S17-1/SM10 donor strains, a system prone to off-target mutagenesis, was used to deliver the allelic exchange DNA in previous studies. Here we have achieved efficient electrotransformation in Pdp using a salt-free highly concentrated sucrose solution, which performs as a hypertonic wash buffer, cryoprotectant, and electroporation buffer. High-efficiency transformation has enabled vector-free mutagenesis for which we have employed circular minimalistic constructs (knockout minicircles) containing only allelic exchange essentials that were generated by Gibson assembly. Preparation of competent cells using sucrose and electroporation/integration of minicircles had virtually no detectable off-target promutagenic effect. In contrast, a downstream sacB selection apparently induced several large deletions via mobilization of transposable elements. Electroporation of minicircles into sucrose-treated cells is a versatile broadly applicable approach that may facilitate allelic exchange in a wide range of microbial species. The method permitted inactivation of a primary virulence factor unique to Pdp, apoptogenic toxin AIP56, demonstrating the efficacy of minicircles for difficult KO targets located on the high copy number of small plasmids.