Near-infrared spectroscopy to quantify overall thermal process intensity during high-moisture extrusion of soy protein concentrate.

High-moisture extrusion (HME) is widely used to produce meat analogues. During HME the plant-based materials experience thermal and mechanical stresses. It is complicated to separate their effects on the final products because these effects are interrelated. In this study we hypothesize that the intensity of the thermal treatment can explain a large part of the physicochemical changes that occur during extrusion. For this reason, near-infrared (NIR) spectroscopy was used as a novel method to quantify the thermal process intensity during HME. High-temperature shear cell (HTSC) processing was used to create a partial least squares (PLS) regression curve for processing temperature under controlled processing conditions (root mean standard error of cross-validation (RMSECV) = 4.00 °C, coefficient of determination of cross-validation (R2CV) = 0.97). This PLS regression model was then applied to HME extrudates produced at different screw speeds (200-1200 rpm) and barrel temperatures (100-160 °C) with two different screw profiles to calculate the equivalent shear cell temperature as a measure for thermal process intensity. This equivalent shear cell temperature reflects the effects of changes in local temperature conditions, residence time and thermal stresses. Furthermore, it can be related to the degree of texturization of the extrudates. This information can be used to gain new insights into the effect of various process parameters during HME on the thermal process intensity and extrudate quality.

The design and baseline characteristics for the HOPE Consortium Trial to reduce pain and opioid use in hemodialysis.

The HOPE Consortium Trial to Reduce Pain and Opioid Use in Hemodialysis (HOPE Trial) is a multicenter randomized trial addressing chronic pain among patients receiving maintenance hemodialysis for end-stage kidney disease. The trial uses a sequential, multiple assignment design with a randomized component for all participants (Phase 1) and a non-randomized component for a subset of participants (Phase 2). During Phase 1, participants are randomized to Pain Coping Skills Training (PCST), an intervention designed to increase self-efficacy for managing pain, or Usual Care. PCST consists of weekly, live, coach-led cognitive behavioral therapy sessions delivered by video- or tele-conferencing for 12 weeks followed by daily interactive voice response sessions delivered by telephone for an additional 12 weeks. At 24 weeks (Phase 2), participants in both the PCST and Usual Care groups taking prescription opioid medications at an average dose of ≥20 morphine milligram equivalents per day are offered buprenorphine, a partial opioid agonist with a more favorable safety profile than full-agonist opioids. All participants are followed for 36 weeks. The primary outcome is pain interference ascertained, for the primary analysis, at 12 weeks. Secondary outcomes include additional patient-reported measures and clinical outcomes including falls, hospitalizations, and death. Exploratory outcomes include acceptability, tolerability, and efficacy of buprenorphine. The enrollment target of 640 participants was met 27 months after trial initiation. The findings of the trial will inform the management of chronic pain, a common and challenging issue for patients treated with maintenance hemodialysis. NCT04571619.

Oxidative stress mediates associations between preoperative psychosocial phenotype and pain-related outcomes at 6 months following total knee arthroplasty: a longitudinal cohort study.

OBJECTIVE: Greater preoperative depression, anxiety, and pain catastrophizing are associated with more severe long-term pain following total knee arthroplasty (TKA). In a secondary analysis of previously reported data, we tested the hypothesis that these associations are mediated by oxidative stress (OS). DESIGN: A mixed between/within-subjects longitudinal cohort design. SETTING: A single academic medical center. SUBJECTS: Osteoarthritis patients (n = 91; 62.6% female) undergoing unilateral TKA. METHODS: We assessed depression, anxiety, and catastrophizing, as well as markers of central sensitization (widespread pain, temporal summation of pain) preoperatively. Blood samples were then obtained immediately prior to intraoperative tourniquet placement for quantification of in vivo biomarkers of systemic OS, F2-isoprostanes and isofurans. Post-TKA pain intensity (numeric rating scale worst pain [NRS], McGill Pain Questionnaire-2 [MPQ-2]) and function (PROMIS Pain Interference) were assessed at 6 months following TKA. RESULTS: Greater preoperative depression, catastrophizing, and widespread pain were associated with higher intraoperative combined OS (F2-isoprostanes+isofurans/2), which was in turn associated with higher post-TKA pain intensity and worse function (P < .05). All preoperative phenotype predictors except anxiety were correlated positively with post-TKA pain and/or function (P < .05). Bootstrapped mediation analyses revealed significant (P < .05) indirect (mediated) effects of depression (NRS Worst Pain, MPQ-2, PROMIS Pain Interference), anxiety (MPQ-2, PROMIS Pain Interference), and catastrophizing (PROMIS Pain Interference) on adverse long-term post-TKA outcomes via elevated OS. Central sensitization-related predictors demonstrated only direct effects (P < .05) on post-TKA outcomes that were independent of OS mechanisms. CONCLUSIONS: Results suggest that the adverse impact of depression, anxiety, and pain catastrophizing on post-TKA pain and functional outcomes are mediated in part by elevated OS.

Liquid-Liquid Interface-Assisted Self-Assembly of Ag-Doped ZnO Nanosheets for Atomic Switch Application.

Developing facile and inexpensive methods for obtaining large-area two-dimensional semiconducting nanosheets is highly desirable for mass-scale device application. Here, we report a method for producing uniform and large-area films of a Ag-doped ZnO (AZO) nanosheet network via self-assembly at the hexane-water interface by controlling the solute/solvent ratio. The self-assembled film comprises of uniformly tiled nanosheets with size ∼1 µm and thicknesses∼60-100 nm. Using these films in a Pt/AZO/Ag structure, an atomic switch operation is realized. The switching mechanism is found to be governed by electrochemical metallization with nucleation as the rate-limiting step. Our results establish the protocol for large-scale device applications of AZO nanosheets for exploring advanced atomic switch-based neuromorphic systems.

Preoperative Predictors of Prolonged Opioid Use in the 6 Months After Total Knee Arthroplasty.

OBJECTIVES: Prolonged postoperative opioid use increases the risk for new postsurgical opioid use disorder. We evaluated preoperative phenotypic factors predicting prolonged postoperative opioid use. METHODS: We performed a secondary analysis of a prospective observational cohort (n=108) undergoing total knee arthroplasty (TKA) for osteoarthritis with 6-week and 6-month follow-up. Current opioid use and psychosocial, pain, and opioid-related characteristics were assessed at preoperative baseline. Primary outcomes were days/week of opioid use at follow-up. RESULTS: At 6 weeks, preoperative opioid use and greater cumulative opioid exposure, depression, catastrophizing, anxiety, pain interference, sleep disturbance, and central sensitization were significantly associated with more days/week of opioid use after controlling for contemporaneous pain intensity. Prior euphoric response to opioids were also significant predictors at 6 months. All 6-week predictors except anxiety remained significant after controlling for preoperative opioid use; at 6 months, cumulative opioid exposure, catastrophizing, pain interference, and sleep disturbance remained significant after this adjustment ( P <0.05). In multivariable models, a psychosocial factor reflecting negative affect, sleep, and pain accurately predicted 6-week opioid use (area under the curve=0.84). A combined model incorporating psychosocial factor scores, opioid-related factor scores, and preoperative opioid use showed near-perfect predictive accuracy at 6 months (area under the curve=0.97). DISCUSSION: Overall, preoperative psychosocial, pain-related, and opioid-related phenotypic characteristics predicted prolonged opioid use after total knee arthroplasty.

An improved nonlinear deloading approach based on the fuzzy controller for wind turbine generators in an islanded microgrid.

The wind turbine generators (WTG's) incapability of primary frequency support during system contingencies due to its decoupled nature from the system frequency causes profound integration and stability issues. The present study focuses on resolving such issues by enabling the WTGs to participate in long-term frequency support under the derated operation of WTGs. The deloading operation of WTGs can provide a specific reserve power margin by reducing its rotor speed, which can be utilized during system contingencies. In literature, linear and quadratic deloading techniques have been proposed but these fail to replicate the nonlinear characteristics of the WTG accurately, thereby making deloading ineffective. To effectively implement the deloading, this work uses a more-accurate higher-order Newton's interpolation polynomial (HNIP), to cope with the highly nonlinear characteristics of WTG. The proposed deloading approach is also augmented with a fuzzy-based intelligent supplementary control structure to handle the inherent and incorporated nonlinearities in WTG. The microgrid system, consisting of a conventional energy source with WTG, has been considered as system under investigation. The integral time absolute error for step wind profile and variable speed wind profile was found to be improved by 97.65% and 97.29%, respectively, with the proposed novel deloading technique with fuzzy-PID compared to PID. Further, to ensure the implementation viability of the proposed control scheme, real-time validation of the same is carried out on OPAL-RT 4510, having a Xilinx Kintex-7 FPGA board. It was found that for all the scenarios considered for real-time digital simulation purposes, the results unerringly matched with MATLAB/Simulink.

Sequentially orthogonalized canonical partial least squares for improved multiple responses modeling in multiblock data sets.

Multiblock data sets and modeling techniques are widely encountered in the chemometric community. Although the currently available techniques, such as sequential orthogonalized partial least squares (SO-PLS) regression are mainly focused on the prediction of a single response and deal with the multiple response(s) case using PLS2 type approach. Recently, a new approach called canonical PLS (CPLS) was proposed for extracting the subspaces efficiently for multiple response(s) cases, supporting both regression and classification. 'Efficiently' here means more information in fewer latent variables. This work suggests a combination of SO-PLS and CPLS, sequential orthogonalized canonical partial least squares (SO-CPLS), to model multiple response(s) for multiblock data sets. The cases of SO-CPLS for modeling multiple response(s) regression and classification were demonstrated on several data sets. Also, the capability of SO-CPLS to incorporate meta-information related to samples for efficient subspace extraction is demonstrated. Furthermore, a comparison with the commonly used sequential modeling technique, called sequential orthogonalized partial least squares (SO-PLS), is also presented. The SO-CPLS approach can benefit both the multiple response(s) regression and classification modeling and can be of high importance when meta-information such as experimental design or sample classes is available.

Deciphering pressure-induced nanoarchitectonics in a monolayer of heterocoronene-based discotics at air-water and air-solid interfaces.

Understanding and control of molecular alignment at the nanoscale in self-assembled supramolecular structures is a prerequisite for the subsequent exploitation of molecules in functional devices. Here, we have clarified the surface-pressure induced molecular nanoarchitectures in a monolayer of a heterocoronene-based discotic liquid crystal (DLC) at air-water and air-solid interfaces using surface manometry, real-time Brewster angle microscopy, and real-space atomic force microscopy (AFM). Chloroform-spread DLCs at a concentration of ∼108 µM exhibit floating domains at the air-water interface comprising small aggregates of edge-on stacked molecules interacting via peripheral alkyl chains. Detailed analysis of surface manometry and relaxation measurements reveal that, upon compression, these domains coalesce to form a coherent monolayer which then undergoes irreversible structural transformations via mechanisms such as monolayer loss due to desorption and localized nucleation of defects. AFM images of the films transferred on a hydrophilic substrate reveal that with increasing surface-pressure, the nanoscale structure of the monolayer transforms from randomly oriented nanowires to tightly-packed nanowire domains, and finally to fragmented wire segments which diffuse locally above the film. These results provide a facile method for the preparation of compact, two-dimensional films of ambipolar DLC molecules with a tunable nanoarchitecture which will be crucial for their applications in nanoscale electronic devices.

Perioperative oxidative stress predicts subsequent pain-related outcomes in the 6 months after total knee arthroplasty.

ABSTRACT: Total knee arthroplasty (TKA) is effective for pain reduction in most patients, but 15% or more report unsatisfactory long-term pain outcomes. We tested whether oxidative stress (OS) related to extended tourniquet application during TKA and subsequent ischemic reperfusion (IR) contributed to adverse post-TKA pain outcomes. Blood samples were obtained in 91 patients with osteoarthritis (63% female) undergoing TKA before tourniquet placement (T1), 45 minutes after tourniquet inflation (T2), and 15 minutes after tourniquet removal (T3). Plasma levels of F 2 -isoprostanes and isofurans, the most specific measures of in vivo OS, were quantified. Pain intensity and function were assessed at baseline and again at 6 weeks and 6 months after TKA. Results indicated that higher Combined OS (F 2 -isoprostanes + isofurans/2) at T1 baseline and larger increases in Combined OS from T1 to T2 were associated with higher baseline-corrected past 24-hour worst and average pain intensity (numeric rating scale) and higher past week McGill Pain Questionnaire-2 total scores at 6-month follow-up ( P 's < 0.05). Increases in Combined OS from T1 to T3, which should most directly capture OS and IR injury related to tourniquet use, were not associated with short-term or long-term post-TKA pain outcomes. Longer ischemia duration was unexpectedly associated with lower baseline-corrected pain intensity at 6-month follow-up. Combined OS was not linked to functional outcomes at either follow-up. Elevated perioperative OS seems to exert small but significant adverse effects on long-term post-TKA pain outcomes, although this OS seems unrelated to IR injury associated with extended tourniquet use.

META-PLS modelling: An integrated approach to automatic model optimization for near-infrared spectra.

Predictive latent space near-infrared (NIR) spectral modelling with PLS (Partial Least Squares) has two main tasks that require user input to achieve optimal models. The first is the selection of the optimal pre-processing of NIR spectra and the second is the selection of the optimal number of PLS model components assuming the data is outlier free. Often the two tasks are performed in an exhaustive search to find the best pre-processing as well as the optimal number of model components. We propose a novel approach called meta partial least square (META-PLS) which drops the need for both the pre-processing optimisation and exhaustive search for optimal model components. We utilise the stepwise nature of the PLS algorithm to learn complementary information from different pre-processed forms of the same data set as performed in multiblock pre-processing ensemble models to avoid pre-processing selection but receive help from the pre-processing ensembles, and deploy a weighted randomisation test to decide the optimal number of model components automatically. The performance of the approach for performing automatic NIR spectral modelling is demonstrated with several real data sets.

Preoperative Predictors of Complex Regional Pain Syndrome Outcomes in the 6 Months Following Total Knee Arthroplasty.

This prospective observational study evaluated preoperative predictors of complex regional pain syndrome (CRPS) outcomes in the 6 months following total knee arthroplasty (TKA). Participants were n = 110 osteoarthritis patients (64.5% female) undergoing unilateral TKA with no prior CRPS history. Domains of negative affect (depression, anxiety, catastrophizing), pain (intensity, widespread pain, temporal summation of pain [TSP]), pain interference, sleep disturbance, and pro-inflammatory status (tumor necrosis factor-alpha [TNF-a]) were assessed preoperatively. CRPS outcomes at 6-week and 6-month follow-up included the continuous CRPS Severity Score (CSS) and dichotomous CRPS diagnoses (2012 IASP criteria). At 6 months, 12.7% of participants met CRPS criteria, exhibiting a "warm CRPS" phenotype. Six-week CSS scores were predicted by greater preoperative depression, anxiety, catastrophizing, TSP, pain intensity, sleep disturbance, and TNF-a (P's < .05). Provisional CRPS diagnosis at 6 weeks was predicted by higher preoperative TSP, sleep disturbance, and TNF-a (P's < .05). CSS scores at 6 months were predicted by more widespread and intense preoperative pain, and higher preoperative TSP, pain interference, and TNF-a (P's < .01). CRPS diagnosis at 6 months was predicted only by more widespread and intense pain preoperatively (P's < .05). Risk for CRPS following TKA appears to involve preoperative central sensitization and inflammatory mechanisms. Preoperative negative affect is unlikely to directly influence long-term CRPS risk. PERSPECTIVE: This article identifies preoperative predictors of CRPS features at 6 months following total knee arthroplasty, including more widespread pain and higher pain intensity, temporal summation of pain, pain interference, and tumor necrosis factor-alpha levels. Findings suggest the importance of central sensitization and inflammatory mechanisms in CRPS risk following tissue trauma.

Swiss knife partial least squares (SKPLS): One tool for modelling single block, multiblock, multiway, multiway multiblock including multi-responses and meta information under the ROSA framework.

In the domain of chemometrics and multivariate data analysis, partial least squares (PLS) modelling is a widely used technique. PLS gains its beauty by handling the high collinearity found in multivariate data by replacing highly covarying variables with common subspaces spanned by orthogonal latent variables. Furthermore, all can be achieved with simple steps of linear algebra requiring minimal computation power and time usage compared to current high-end computing and substantial hyperparameter tuning required by methods such as deep learning. PLS can be used for a wide variety of tasks, for example, single block modelling, multiblock modelling, multiway data modelling and for task such as regression and classification. Furthermore, new PLS based approaches can also incorporate meta information to improve the PLS subspace extraction. However, in the current scenario, there is a wide range of separate tools and codes available to perform different PLS tasks. Often when the user needs to perform a new PLS task, they need to start with a separate mathematical implementation of the PLS techniques. This study aims to provide a single solution, i.e., the Swiss knife PLS (SKPLS) modelling approach to enable a single mathematical implementation to perform analyses of single block, multiblock, multiway, multiblock multiway, multi-response, and incorporation of meta information in PLS modelling. It contains all that is needed for any PLS practitioner to perform both classification and regression tasks. The SKPLS backbone is the stepwise PLS strategy called response oriented sequential alternation (ROSA) which we generalize to enable all the mentioned analysis possibilities. The basic structure of the algorithm is highlighted, and some example cases of performing single block, multiblock, multiway, multiblock multiway, multi-response PLS modelling and the incorporation of meta information in PLS modelling are included.

Combining deep learning with chemometrics when it is really needed: A case of real time object detection and spectral model application for spectral image processing.

Deep learning (DL) being popularly used in computer vision applications is still in its early stage in chemometric domain for spectral image processing. Often the challenge is that there are too few samples from analytical laboratory experiments to preform DL. In this study, we present a novel combination of DL and chemometrics to process spectral images even with as few as < 100 spectral images. We divided the image processing part such as object detection and recognition as the DL task and prediction of chemical property as the chemometric task based on latent space modelling. For image processing tasks of object detection and recognition, transfer learning was performed on the pretrained YOLOv4 object detection network weights to adapt the model to work well on spectral images captured in laboratory settings. Once the object is identified with DL, a background query is performed for the pre-built chemometric models to select the model for predicting the properties for specific object. The obtained results showed good potential of using DL and chemometric approaches in conjunction to reap the best of both scientific domains. This approach is of high interest to whoever involved in spectral imaging and dealing with object detection and physicochemical properties prediction of the samples with chemometric approaches.

High-throughput plant phenotyping: a role for metabolomics?

High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.

Deep generative neural networks for spectral image processing.

An artificial intelligence approach based on deep generative neural networks for spectral imaging processing was proposed. The key idea was to treat different spectral image processing operations such as segmentation, regression, and classification as image-to-image translation tasks. For the image-to-image translation, the conditional generative adversarial networks were used. As a baseline comparison, the traditional chemometric approach based on pixels wise modelling was demonstrated. The analysis was presented with two real data sets related to fruit property prediction and kernel and shell classification of walnuts. The presented artificial intelligence approach for spectral image processing can provide benefits for any field of science where spectral imaging and processing is widely performed.

Oxidative stress is associated with characteristic features of the dysfunctional chronic pain phenotype.

ABSTRACT: The dysfunctional chronic pain (Dysfunctional CP) phenotype is an empirically identifiable CP subtype with unclear pathophysiological mechanisms that cuts across specific medical CP diagnoses. This study tested whether the multidimensional pain and psychosocial features that characterize the dysfunctional CP phenotype are associated broadly with elevated oxidative stress (OS). Measures of pain intensity, bodily extent of pain, catastrophizing cognitions, depression, anxiety, sleep disturbance, pain interference, and function were completed by 84 patients with chronic osteoarthritis before undergoing total knee arthroplasty. Blood samples were obtained at the initiation of surgery before incision or tourniquet placement. Plasma levels of F2-isoprostanes and isofurans, the most highly specific measures of in vivo OS, were quantified using gas chromatography/negative ion chemical ionization mass spectrometry. The results indicated that controlling for differences in age, sex, and body mass index, higher overall OS (mean of isoprostanes and isofurans) was associated with significantly (P < 0.05) greater pain intensity, more widespread pain, greater depressive symptoms and pain catastrophizing, higher pain interference, and lower function. OS measures were not significantly associated with sleep disturbance or anxiety levels (P >0.10). The results build on prior case-control findings suggesting that presence of a CP diagnosis is associated with elevated OS, highlighting that it may specifically be individuals displaying characteristics of the dysfunctional CP phenotype who are characterized by elevated OS. Clinical implications of these findings remain to be determined.

Enhancing the performance of a deregulated nonlinear integrated power system utilizing a redox flow battery with a self-tuning fractional-order fuzzy controller.

Load frequency regulation is one of the most vital and complex ancillary services in a deregulated power system. Increasing penetration from renewable energy sources in an integrated power system (IPS) further escalates the related control complexity due to a considerable decrement in IPS's effective inertia. This may incur additional costs and can even lead to the destabilization of IPS. To overcome these problems in frequency regulation, this work proposes and investigates the use of an intelligent, direct adaptive control scheme, i.e., self-tuning fractional order fuzzy PID (STFOFPID) controller with and without the presence of a recently devised energy storage unit, i.e., the redox flow battery. The IPS' efficacy with the STFOFPID controller is validated for various contracts in a deregulated operation mode for considered three area IPS. Extensive simulation studies are carried out, and detailed comparative studies have been drawn with conventional PID and fractional order PID controllers for load frequency regulation in Poolco, bilateral, and contract-violation mode of operation. Robustness analysis in terms of parametric variations in different nonlinearities present in a reheated thermal power plant is also carried out, and the efficacy of the STFOFPID controller is established using a thorough quantitative comparative analysis. The real-time digital simulation validation of the investigated control structure has been carried out on OPAL-RT 4150 based on Xilinx Kintex-7 FPGA board with INTEL multi-core processor.

All-in-one: A spectral imaging laboratory system for standardised automated image acquisition and real-time spectral model deployment.

Spectral imaging (SI) in analytical chemistry is widely used for the assessment of spatially distributed physicochemical properties of samples. Although massive development in instrument and chemometrics modelling has taken place in the recent years, the main challenge with SI is that available sensors require extensive system integration and calibration modelling before their use for routine analysis. Further, the models developed during one experiment are rarely useful once the system is reintegrated for a new experiment. To avoid system reintegration and reuse calibrated models, this study presents an intelligent All-In-One SI (ASI) laboratory system allowing standardised automated data acquisition and real-time spectral model deployment. The ASI system supplies a controlled standardised illumination environment, an in-built computing system, embedded software for automated image acquisition, and model deployment to predict the spatial distribution of sample properties in real-time. To show the capability of the ASI framework, exemplary cases of fruit property prediction in different fruits are presented. Furthermore, ASI is also benchmarked in performance against the current commercially available portable as well as high-end laboratory spectrometers.

Chemometric approaches for calibrating high-throughput spectral imaging setups to support digital plant phenotyping by calibrating and transferring spectral models from a point spectrometer.

Visible and near-infrared (Vis-NIR) spectral imaging is appearing as a potential tool to support high-throughput digital agricultural plant phenotyping. One of the uses of spectral imaging is to predict non-destructively the chemical constituents in the plants such as nitrogen content which can be related to the functional status of plants. However, before using high-throughput spectral imaging, it requires extensive calibration, just as needed for any other spectral sensor. Calibrating the high-throughput spectral imaging setup can be a challenging task as the resources needed to run experiments in high-throughput setups are far more than performing measurements with point spectrometers. Hence, to supply a resource-efficient approach to calibrate spectral cameras integrated with high-throughput plant phenotyping setups, this study proposes the use of chemometric calibration transfer (CT) and model update. The main idea was to use a point spectrometer to develop the primary model and transfer it to the spectral cameras integrated into the high-throughput setups. The potential of the approach was showed using a real Vis-NIR dataset related to nitrogen prediction in wheat plants measured with point spectrometer, tabletop spectral cameras and spectral cameras integrated with a high-throughput plant phenotyping setup. For CT and model update, direct standardization and parameter-free calibration enhancement approaches were explored. A key aim of this study was to only use and compare techniques that does not require any further optimization as they can be easily implemented by the plant biologist in future applications. The proposed approach based on the transfer of point spectroscopy models to spectral cameras in a high-throughput setup can allow spectral calibrations to be sharable and widely applicable, thus helping the global digital plant phenotyping community.