α,β-D-Constrained Nucleic Acids Are Strong Terminators of Thermostable DNA Polymerases in Polymerase Chain Reaction

(SC5′, RP) α,β-D- Constrained Nucleic Acids (CNA) are dinucleotide building blocks that can feature either B-type torsional angle values or non-canonical values, depending on their 5′C and P absolute stereochemistry. These CNA are modified neither on the nucleobase nor on the sugar structure and therefore represent a new class of nucleotide with specific chemical and structural characteristics. They promote marked bending in a single stranded DNA so as to preorganize it into a loop-like structure, and they have been shown to induce rigidity within oligonucleotides. Following their synthesis, studies performed on CNA have only focused on the constraints that this family of nucleotides introduced into DNA. On the assumption that bending in a DNA template may produce a terminator structure, we investigated whether CNA could be used as a new strong terminator of polymerization in PCR. We therefore assessed the efficiency of CNA as a terminator in PCR, using triethylene glycol phosphate units as a control. Analyses were performed by denaturing gel electrophoresis and several PCR products were further analysed by sequencing. The results showed that the incorporation of only one CNA was always skipped by the polymerases tested. On the other hand, two CNA units always stopped proofreading polymerases, such as Pfu DNA polymerase, as expected for a strong replication terminator. Non-proofreading enzymes, e.g. Taq DNA polymerase, did not recognize this modification as a strong terminator although it was predominantly stopped by this structure. In conclusion, this first functional use of CNA units shows that these modified nucleotides can be used as novel polymerization terminators of proofreading polymerases. Furthermore, our results lead us to propose that CNA and their derivatives could be useful tools for investigating the behaviour of different classes of polymerases

Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions

Although satellite-based variables have for long been expected to be key components to a unified and global biodiversity monitoring strategy, a definitive and agreed list of these variables still remains elusive. The growth of interest in biodiversity variables observable from space has been partly underpinned by the development of the essential biodiversity variable (EBV) framework by the Group on Earth Observations – Biodiversity Observation Network, which itself was guided by the process of identifying essential climate variables. This contribution aims to advance the development of a global biodiversity monitoring strategy by updating the previously published definition of EBV, providing a definition of satellite remote sensing (SRS) EBVs and introducing a set of principles that are believed to be necessary if ecologists and space agencies are to agree on a list of EBVs that can be routinely monitored from space. Progress toward the identification of SRS-EBVs will require a clear understanding of what makes a biodiversity variable essential, as well as agreement on who the users of the SRS-EBVs are. Technological and algorithmic developments are rapidly expanding the set of opportunities for SRS in monitoring biodiversity, and so the list of SRS-EBVs is likely to evolve over time. This means that a clear and common platform for data providers, ecologists, environmental managers, policy makers and remote sensing experts to interact and share ideas needs to be identified to support long-term coordinated actions

Lack of Correlation of Sinonasal and Otologic Reported Symptoms With Objective Measurements Among Patients With Primary Ciliary Dyskinesia: An International Study.

peer reviewedSinonasal and otologic symptoms are common among patients with primary ciliary dyskinesia (PCD) of all ages. We used baseline data from the ENT Prospective International Cohort of PCD patients (EPIC-PCD), the first PCD cohort focused on ENT disease manifestations. We assessed agreement between patient- or parent-reported symptoms and relevant examination findings, and calculated unweighted Cohen’s kappa to adjust for agreement by chance. We included 404 participants, from 12 centres. We found no correlation between patient-reported sinonasal symptoms and relevant clinical examination findings. Otologic symptoms correlated poorly or weakly with otoscopy and audiometry findings, with age and centre identified as determinants of agreement

Effect of Early Surgery vs Endoscopy-First Approach on Pain in Patients With Chronic Pancreatitis The ESCAPE Randomized Clinical Trial:The ESCAPE Randomized Clinical Trial

IMPORTANCE For patients with painful chronic pancreatitis, surgical treatment is postponed until medical and endoscopic treatment have failed. Observational studies have suggested that earlier surgery could mitigate disease progression, providing better pain control and preserving pancreatic function. OBJECTIVE To determine whether early surgery is more effective than the endoscopy-first approach in terms of clinical outcomes. DESIGN, SETTING, AND PARTICIPANTS The ESCAPE trial was an unblinded, multicenter, randomized clinical superiority trial involving 30 Dutch hospitals participating in the Dutch Pancreatitis Study Group. From April 2011 until September 2016, a total of 88 patients with chronic pancreatitis, a dilated main pancreatic duct, and who only recently started using prescribed opioids for severe pain (strong opioids for INTERVENTIONS There were 44 patients randomized to the early surgery group who underwent pancreatic drainage surgery within 6 weeks after randomization and 44 patients randomized to the endoscopy-first approach group who underwent medical treatment, endoscopy including lithotripsy if needed, and surgery if needed. MAIN OUTCOMES AND MEASURES The primary outcome was pain, measured on the Izbicki pain score and integrated over 18 months (range, 0-100 [increasing score indicates more pain severity]). Secondary outcomes were pain relief at the end of follow-up; number of interventions, complications, hospital admissions; pancreatic function; quality of life (measured on the 36-Item Short Form Health Survey [SF-36]); and mortality. RESULTS Among 88 patients who were randomized (mean age, 52 years; 21 (24%) women), 85 (97%) completed the trial. During 18 months of follow-up, patients in the early surgery group had a lower Izbicki pain score than patients in the group randomized to receive the endoscopy-first approach group (37 vs 49; between-group difference, -12 points [95% CI, -22 to -2]; P = .02). Complete or partial pain relief at end of follow-up was achieved in 23 of 40 patients (58%) in the early surgery vs 16 of 41 (39%)in the endoscopy-first approach group (P = .10). The total number of interventions was lower in the early surgery group (median, 1 vs 3; P <.001). Treatment complications (27% vs 25%), mortality (0% vs 0%), hospital admissions, pancreatic function, and quality of life were not significantly different between early surgery and the endoscopy-first approach. CONCLUSIONS AND RELEVANCE Among patients with chronic pancreatitis, early surgery compared with an endoscopy-first approach resulted in lower pain scores when integrated over 18 months. However, further research is needed to assess persistence of differences over time and to replicate the study findings

A review of applying second-generation wavelets for noise removal from remote sensing data.

The processing of remotely sensed data includes compression, noise reduction, classification, feature extraction, change detection and any improvement associated with the problems at hand. In the literature, wavelet methods have been widely used for analysing remote sensing images and signals. The second-generation of wavelets, which is designed based on a method called the lifting scheme, is almost a new version of wavelets, and its application in the remote sensing field is fresh. Although first-generation wavelets have been proven to offer effective techniques for processing remotely sensed data, second-generation wavelets are more efficient in some respects, as will be discussed later. The aim of this review paper is to examine all existing studies in the literature related to applying second-generation wavelets for denoising remote sensing data. However, to make a better understanding of the application of wavelet-based denoising methods for remote sensing data, some studies that apply first-generation wavelets are also presented. In the part of hyperspectral data, there is a focus on noise removal from vegetation spectrum

30-day morbidity and mortality of sleeve gastrectomy, Roux-en-Y gastric bypass and one anastomosis gastric bypass: a propensity score-matched analysis of the GENEVA data

Background: There is a paucity of data comparing 30-day morbidity and mortality of sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and one anastomosis gastric bypass (OAGB). This study aimed to compare the 30-day safety of SG, RYGB, and OAGB in propensity score-matched cohorts. Materials and methods: This analysis utilised data collected from the GENEVA study which was a multicentre observational cohort study of bariatric and metabolic surgery (BMS) in 185 centres across 42 countries between 01/05/2022 and 31/10/2020 during the Coronavirus Disease-2019 (COVID-19) pandemic. 30-day complications were categorised according to the Clavien–Dindo classification. Patients receiving SG, RYGB, or OAGB were propensity-matched according to baseline characteristics and 30-day complications were compared between groups. Results: In total, 6770 patients (SG 3983; OAGB 702; RYGB 2085) were included in this analysis. Prior to matching, RYGB was associated with highest 30-day complication rate (SG 5.8%; OAGB 7.5%; RYGB 8.0% (p = 0.006)). On multivariate regression modelling, Insulin-dependent type 2 diabetes mellitus and hypercholesterolaemia were associated with increased 30-day complications. Being a non-smoker was associated with reduced complication rates. When compared to SG as a reference category, RYGB, but not OAGB, was associated with an increased rate of 30-day complications. A total of 702 pairs of SG and OAGB were propensity score-matched. The complication rate in the SG group was 7.3% (n = 51) as compared to 7.5% (n = 53) in the OAGB group (p = 0.68). Similarly, 2085 pairs of SG and RYGB were propensity score-matched. The complication rate in the SG group was 6.1% (n = 127) as compared to 7.9% (n = 166) in the RYGB group (p = 0.09). And, 702 pairs of OAGB and RYGB were matched. The complication rate in both groups was the same at 7.5 % (n = 53; p = 0.07). Conclusions: This global study found no significant difference in the 30-day morbidity and mortality of SG, RYGB, and OAGB in propensity score-matched cohorts

30-Day morbidity and mortality of bariatric metabolic surgery in adolescence during the COVID-19 pandemic – The GENEVA study

Background: Metabolic and bariatric surgery (MBS) is an effective treatment for adolescents with severe obesity. Objectives: This study examined the safety of MBS in adolescents during the coronavirus disease 2019 (COVID-19) pandemic. Methods: This was a global, multicentre and observational cohort study of MBS performed between May 01, 2020, and October 10,2020, in 68 centres from 24 countries. Data collection included in-hospital and 30-day COVID-19 and surgery-specific morbidity/mortality. Results: One hundred and seventy adolescent patients (mean age: 17.75 ± 1.30 years), mostly females (n = 122, 71.8%), underwent MBS during the study period. The mean pre-operative weight and body mass index were 122.16 ± 15.92 kg and 43.7 ± 7.11 kg/m2, respectively. Although majority of patients had pre-operative testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (n = 146; 85.9%), only 42.4% (n = 72) of the patients were asked to self-isolate pre-operatively. Two patients developed symptomatic SARS-CoV-2 infection post-operatively (1.2%). The overall complication rate was 5.3% (n = 9). There was no mortality in this cohort. Conclusions: MBS in adolescents with obesity is safe during the COVID-19 pandemic when performed within the context of local precautionary procedures (such as pre-operative testing). The 30-day morbidity rates were similar to those reported pre-pandemic. These data will help facilitate the safe re-introduction of MBS services for this group of patients

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Spectral adaptation of hyperspectral flight lines using VHR contextual information

Due to technological constraints, hyperspectral earth observation imagery are often a mosaic of overlapping flight lines collected in different passes over the area of interest. This causes variations in aqcuisition conditions such that the reflected spectrum can vary significantly between these flight lines. Partly, this problem is solved by atmospherical correction, but residual spectral differences often remain. A probabilistic domain adaptation framework based on graph matching using Hidden Markov Random Fields was recently proposed for transforming hyperspectral data from one image to better correspond to the other. This paper investigates the use of scale and angle invariant textural features for improving the performance of the used Hidden Markov Random Field matching framework in the case of hyperspectral flight lines. These textural features are derived from the filtering of VHR optical imagery with a bank of Gabor filters with varying orientation, scale and frequency and subsequently rendering them invariant to scale and frequency by applying the 2D DFT on the filter responses in the scale and frequency space.</p

Modelling biochemical processes in orchards at leaf- and canopy-level using hyperspectral data

Presented at the Airborne Imaging Spectroscopy Workshop, BruHyp 2006, 10 October 2006, Bruges, Belgium.This research was conducted to evaluate the potential and limitations of hyperspectral remote sensing to detect iron deficiency in capital-intensive multi-annual crop systems, e.g. peach orchards. The noted deficiency can be regarded as a proxy for deviations from optimal plant functioning, while detection of such deviations is in turn of significant importance to monitoring and modelling efforts of orchards as production systems. Hyperspectral leaf, canopy, and airborne reflectance measurements were acquired in a peach (Prunus persica L.) orchard in Zaragoza, Spain. Leaf- and canopy-level data were collected with a handheld spectroradiometer (ASD, Inc.), while the AHS-160 hyperspectral sensor provided airborne data. Blocks of trees were treated with different amount of iron chelates (Sequestrene) which created a dynamic range of chlorophyll concentration as measured in leaves. Hyperspectral measurements at leaf-level were carried out to characterize the physiological aspects of nutrient stress, as opposed to the evaluation of plant nutrient status at the complete plant-level. Stressinduced physiological changes make stress detection at the leaf-level possible at an early stage of suboptimal photosynthetic functioning. Airborne imagery, however, is difficult to interpret due to altering illumination angles, BRDF effects, and intervening atmospheric light interactions resulting in an alteration of the vegetative reflectance spectrum. Although many studies have implemented hyperspectral analysis of nutrient status at large scales, this research field is still in its infancy phase, since the link between airborneand leaf-level measurements is lacking. This inevitably makes the physiological interpretation of existing hyperspectral research more complex. The multi-level (leaf, canopy, and airborne) approach presented here enabled the assessment of vegetation indices and their relationship with pigment concentration at each monitoring level. Pertinent classical chlorophyll-related vegetation indices were tested and new indices were extracted from the spectral profiles by means of band reduction techniques and narrow-waveband rationing, which involved all possible 2-band combinations. Robustness was evaluated by studying the index performance for datasets of increasing complexity, from leaf- to canopy- and airborne-level. Physiological interpretations extracted from leaf-level experiments were extrapolated to canopy- and airborne level. The measured spectra and estimated biochemical parameters were related via inversion of a linked directional homogeneous canopy reflectance model (ACRM) and the PROSPECT leaf model. Numerical model inversion was conducted by minimizing the difference between the measured reflectance samples and modelled reflectance values. An improved optimization method is presented. Results are compared with a simple linear regression analysis, linking chlorophyll to the reflectance measured at the leaf level and at the Top of Canopy (TOC), while optimal band regions and bandwidths also were analyzed.We would like to thank the Belgian Science Policy Office for financing this work.Peer reviewe