Machine-Learning-Based Proteomic Predictive Modeling with Thermally-Challenged Caribbean Reef Corals

Coral health is currently diagnosed retroactively; colonies are deemed “stressed” upon succumbing to bleaching or disease. Ideally, health inferences would instead be made on a pre-death timescale that would enable, for instance, environmental mitigation that could promote coral resilience. To this end, diverse Caribbean coral (Orbicella faveolata) genotypes of varying resilience to high temperatures along the Florida Reef Tract were exposed herein to elevated temperatures in the laboratory, and a proteomic analysis was taken with a subset of 20 samples via iTRAQ labeling followed by nano-liquid chromatography + mass spectrometry; 46 host coral and 40 Symbiodiniaceae dinoflagellate proteins passed all stringent quality control criteria, and the partial proteomes of biopsies of (1) healthy controls, (2) sub-lethally stressed samples, and (3) actively bleaching corals differed significantly from one another. The proteomic data were then used to train predictive models of coral colony bleaching susceptibility, and both generalized regression and machine-learning-based neural networks were capable of accurately forecasting the bleaching susceptibility of coral samples based on their protein signatures. Successful future testing of the predictive power of these models in situ could establish the capacity to proactively monitor coral health.

Chronic pain in older and senile patients. Clinical guidelines

The present document developed by the Russian Association of Gerontologists and Geriatricians represents the Clinical guidelines on chronic pain in older and senile patients. The Clinical guidelines were endorsed by Scientific Council of the Ministry of Health of the Russian Federation in December 2020.Clinical guidelines are focused on geriatricians providing medical care in outpatient and hospital settings, general practitioners, and physiotherapists.The document sets out the pain syndrom screening and diagnosis principles in older patients, management and treatment approaches in patients with chronic pain, as well as the prevention, rehabilitation, medical care organization issues and quality control criteria for its provision in this category of persons.

External Quality of Mandarins: Influence of Fruit Appearance Characteristics on Consumer Choice

In a pre-purchase situation, consumer perception of mandarin quality is almost exclusively based on fruit appearance. Determination of consumer requirements in this regard is needed to preserve the current success of this crop in markets worldwide. In this study, the effect on consumer choice of different appearance characteristics that can occur mainly in early-season mandarins was quantified. Two independent Choice-Based Conjoint Analyses were performed to evaluate the effect of different external mandarin factors: (1) two factors linked mainly with harvesting practices: ‘Leaf’ (no leaves but sound calyx/fresh leaf/dehydrated leaf) and ‘Rind Colour’ (orange colour/rind with slightly greenish areas); (2) three factors related to postharvest handling: ‘Calyx Condition’ (sound/blackening/detached),’Waxing’ (absence/presence of wax), and ‘Rind Condition’ (dehydrated/turgid). A total of 280 consumers participated in each study. The evaluation of the factors linked with harvesting revealed four different choice patterns. Leaf presence was appreciated only by a small group of consumers (20%), and the presence of greenish areas on the rind was perceived mostly as a negative characteristic. Among the evaluated postharvest-related factors, ‘Waxing’ and ‘Calyx condition’ had the strongest effect on consumer choice. Consumers showed marked preferences for mandarins that had been waxed and presented shine and gloss. Calyx blackening and detachment had a negative effect mainly on waxed fruit, while rind dehydration more negatively affected the fruit that had not been waxed. Consumer perception of mandarin quality is importantly affected by external mandarin characteristics. The information herein reported can be very useful for the citrus industry for adapting quality control criteria that respond to consumer demands.

Assessment of SARS-CoV-2 genome sequencing: quality criteria and low frequency variants

Although many laboratories worldwide have developed their sequencing capacities in response to the need for SARS-CoV-2 genome-based surveillance of variants, only few reported some quality criteria to ensure sequence quality before lineage assignment and submission to public databases. Hence, we aimed here to provide simple quality control criteria for SARS-CoV-2 sequencing to prevent erroneous interpretation of low quality or contaminated data. We retrospectively investigated 647 SARS-CoV-2 genomes obtained over ten tiled amplicons sequencing runs. We extracted 26 potentially relevant metrics covering the entire workflow from sample selection to bioinformatics analysis. Based on data distribution, critical values were established for eleven selected metrics to prompt further quality investigations for problematic samples, in particular those with a low viral RNA quantity. Low frequency variants (<70% of supporting reads) can result from PCR amplification errors, sample cross contaminations or presence of distinct SARS-CoV2 genomes in the sample sequenced. The number and the prevalence of low frequency variants can be used as a robust quality criterion to identify possible sequencing errors or contaminations. Overall, we propose eleven metrics with fixed cutoff values as a simple tool to evaluate the quality of SARS-CoV-2 genomes, among which cycle thresholds, mean depth, proportion of genome covered at least 10x and the number of low frequency variants combined with mutation prevalence data.

Response to Response

We challenge the view that “one is better than none” on grounds that single-item assessments perpetuate a simplistic view of well-being, which is out of touch with how the field has progressed over recent decades. We also question blanket advocacy for measures in the absence of substantive scientific questions that require thoughtful engagement with the prior literature to make sound measurement choices. Substantive illustrations, invoking research on well-being and health in different cultural and socioeconomic contexts, are provided. Quality control is also essential in making sound measurement choices. Numerous contenders fail at this juncture because they have no conceptual foundation and also lack rigorous psychometric analyses documenting their empirical credibility. Another critical element in adjudicating measurement quality is extent of prior usage: evidence that the measures have taken hold in the scientific community, indicated by citation counts and number of published studies. We conclude that all such quality control criteria were inadequately addressed or missing in the measurement recommendations put forth in Chapter 17.

Deep learning algorithm helps to standardise ATS/ERS spirometric acceptability and usability criteria

RationaleWhile ATS/ERS quality control criteria for spirometry include several quantitative limits, it also requires manual visual inspection. The current approach is time consuming and leads to high inter-technician variability. We propose a deep learning approach called convolutional neural network (CNN), to standardise spirometric manoeuvre acceptability and usability.Methods and methodsIn 36 873 curves from the national health and nutritional examination survey (NHANES) USA 2011–12, technicians labelled 54% of curves as meeting ATS/ERS 2005 acceptability criteria with satisfactory start and end of test but identified 93% of curves with a usable FEV1. We processed raw data into images of maximal expiratory flow-volume curve (MEFVC), calculated ATS/ERS quantifiable criteria, and developed CNNs to determine manoeuvre acceptability and usability on 90% of the curves. The models were tested on the remaining 10% of curves. We calculated Shapley values to interpret the models.ResultsIn the test set (N=3738), CNN showed an accuracy of 87% for acceptability and 92% for usability, with the latter demonstrating a high sensitivity (92%) and specificity (96%). They were significantly superior (p<0.0001) to ATS/ERS quantifiable rule-based models. Shapley interpretation revealed MEFVC<1 s (MEFVC pattern within first second of exhalation) and plateau in volume-time were most important in determining acceptability, while MEFVC<1 s entirely determined usability.ConclusionThe CNNs identified relevant attributes in spirometric curves to standardise ATS/ERS manoeuvre acceptability and usability recommendations, and further provides individual manoeuvre feedback. Our algorithm combines the visual experience of skilled technicians and ATS/ERS quantitative rules in automating the critical phase of spirometry quality control.

Multi-residue Analysis of Pesticides in Turmeric (Powder and Rhizome) Using Gas Chromatography Tandem Mass Spectrometry

Background and objectives Turmeric is widely used as an ingredient of food and medicinal products. There exists no validated method for multi-residue analysis of pesticides in turmeric. Objective This study was undertaken to develop a simple and robust method for the quantitative determination of multi-class pesticides in turmeric powder and rhizome by GC-MS/MS. Method Initially, the samples were soaked in water for 30 min and homogenized to a fine paste. A portion of this paste (2 g) was extracted with acetonitrile (2 mL) and partitioned with hexane (2 mL) after adding 5 mL of 20% NaCl. The cleanup step involved dispersive solid phase extraction with graphitized carbon black (GCB, 5 mg/mL). Its performance was evaluated against primary secondary amine (PSA) and C18 sorbents. The cleaned extract was evaporated to dryness and reconstituted in ethyl acetate before GC-MS/MS analysis. The method was validated for a mixture of 208 multi-class pesticides at 10 ng/g and higher levels (i.e., 20 and 50 ng/g). Results The findings, which demonstrated a satisfactory recovery and precision (RSDs &lt;20%) for all compounds at 10 ng/g and higher spiking levels, are aligned with the analytical quality control criteria of SANTE/12682/2019 guidelines. The cleanup effect of GCB was much superior to that of PSA, C18, and their combinations. The solvent exchange step with hexane was effective in removing co-extractives and minimizing matrix effects. Conclusions This method complies with the regulatory requirements and is fit-for-purpose for pesticide residue monitoring in turmeric. Highlights The study reports a validated GC-MS/MS method for multi-residue analysis of pesticides in turmeric for the first time. The method provided a high throughput analysis of multi-class pesticides in turmeric rhizome and powder matrices with satisfactory selectivity, sensitivity, accuracy, and precision.