Sensitive Balance Research Articles

Sensitivity analysis of the energy balance of dynamic soaring

Dynamic soaring is a periodic flying technique observed in large seabirds that enables the extraction of energy from wind and thereby facilitates covering large distances with minimum effort. It requires wind shears as seen close to the ocean surface or behind mountain ridges. At sufficiently strong winds it becomes feasible to extract at least the amount of energy required for overcoming the aircraft’s dissipation. This situation is often referred to as energy-neutral dynamic soaring and would theoretically enable an abiding effortless flight. An energy-neutral dynamic soaring cycle has already been formulated and solved as a parametric optimal control problem. Due to the inherent variability of the wind, the sensitivity of the energy balance of an open type dynamic soaring cycle to wind conditions will be investigated within this work. Of particular interest is the wind direction and the wind speed. To expose the parameters with the strongest influence for some given conditions, state sensitivities will be computed after the optimization by applying the sensitivity differential equation approach.

A Comparative Evaluation of the Apically Extruded Debris from Root Canals Prepared by R-Motion NiTi File System.

Apically extruded debris (AED) is an inherent concern during root canal treatment for both endodontists and general practitioners. The present study investigates the AED of the novel R-Motion single-file reciprocating system compared to standard single reciprocating and multifile rotary systems. Fifty-six moderately curved palatal roots of upper maxillary first molars were selected for the present study. The samples were then divided randomly into four groups (n = 14)- Group I: R-motion (RM), Group II: WaveOne Gold (WOG), Group III: ProTaper Next (PTN), and Group IV: HyFlex EDM (HFEDM). The researcher has modified Myers and Montgomery's method to simulate human body temperature. Vials were used to collect debris and weighted using a 0.00001 sensitive balance before and after instrumentation. The instrumentation of all experimented groups was done at 37°C, terminated at master apical file #25. An auto syringe with a side vented needle was used to deliver 8 ml of deionized water for irrigation of each sample during preparation. Vials were stored in a dry sealed desiccator which contained CaSO4 crystals, for 24 hr before weighing. The weight of the collected debris was obtained by subtracting the preinstrumentation weight from the postinstrumentation weight for each vial. The Kruskal-Wallis and Mann-Whitney U tests were performed to analyse the statistical difference in the amount of debris between the tested groups at a 0.05 significance level. The RM system produced less debris extrusion than all tested groups, with a significant difference between the former and the WOG and the PTN systems. However, WOG, PTN, and HFEDM showed no statistically significant difference in the amount of AED. All tested groups produced apical debris in different amounts. The RM produced substantially less AED than WOG and PTN. Meanwhile, WOG, PTN, and HFEDM caused a comparable amount of AED.

ARMO: automated and reliable multi-objective model for lymph node metastasis prediction in head and neck cancer

Objective. Accurate diagnosis of lymph node metastasis (LNM) is critical in treatment management for patients with head and neck cancer. Positron emission tomography and computed tomography are routinely used for identifying LNM status. However, for small or less fluorodeoxyglucose (FDG) avid nodes, there are always uncertainties in LNM diagnosis. We are aiming to develop a reliable prediction model is for identifying LNM. Approach. In this study, a new automated and reliable multi-objective learning model (ARMO) is proposed. In ARMO, a multi-objective model is introduced to obtain balanced sensitivity and specificity. Meanwhile, confidence is calibrated by introducing individual reliability, whilst the model uncertainty is estimated by a newly defined overall reliability in ARMO. In the training stage, a Pareto-optimal model set is generated. Then all the Pareto-optimal models are used, and a reliable fusion strategy that introduces individual reliability is developed for calibrating the confidence of each output. The overall reliability is calculated to estimate the model uncertainty for each test sample. Main results. The experimental results demonstrated that ARMO obtained more promising results, which the area under the curve, accuracy, sensitivity and specificity can achieve 0.97, 0.93, 0.88 and 0.94, respectively. Meanwhile, based on calibrated confidence and overall reliability, clinicians could pay particular attention to highly uncertain predictions. Significance. In this study, we developed a unified model that can achieve balanced prediction, confidence calibration and uncertainty estimation simultaneously. The experimental results demonstrated that ARMO can obtain accurate and reliable prediction performance.

The Contribution of the Nrf2/ARE System to Mechanotransduction in Musculoskeletal and Periodontal Tissues.

Mechanosensing plays an essential role in maintaining tissue functions. Across the human body, several tissues (i.e., striated muscles, bones, tendons, ligaments, as well as cartilage) require mechanical loading to exert their physiological functions. Contrary, mechanical unloading triggers pathological remodeling of these tissues and, consequently, human body dysfunctions. At the cellular level, both mechanical loading and unloading regulate a wide spectrum of cellular pathways. Among those, pathways regulated by oxidants such as reactive oxygen species (ROS) represent an essential node critically controlling tissue organization and function. Hence, a sensitive balance between the generation and elimination of oxidants keeps them within a physiological range. Here, the Nuclear Factor-E2-related factor 2/Antioxidant response element (Nrf2/ARE) system plays an essential role as it constitutes the major cellular regulation against exogenous and endogenous oxidative stresses. Dysregulations of this system advance, i.a., liver, neurodegenerative, and cancer diseases. Herein, we extend our comprehension of the Nrf2 system to the aforementioned mechanically sensitive tissues to explore its role in their physiology and pathology. We demonstrate the relevance of it for the tissues' functionality and highlight the imperative to further explore the Nrf2 system to understand the physiology and pathology of mechanically sensitive tissues in the context of redox biology.

68Ga-Prostate-Specific Membrane Antigen PET Radiomics For the Prediction of PostSurgical International Society of Urological Pathology Grade in Patients with Primary Prostate Cancer

INTRODUCTION Radiomics has been proven effective for the characterisation of primary prostate cancer (PCa).1,2 However, the limited interpretability of the proposed models represents one of the major limitations in this field.3,4 This study investigated 68Ga-prostate-specific membrane antigen (PSMA) PET radiomics for the prediction of post-surgical International Society of Urological Pathology (ISUP) grade in patients with primary PCa, ensuring model interpretability. MATERIALS AND METHODS Forty-seven patients with PCa were examined with 68Ga-PSMA PET at the authors’ institution. Those patients were enrolled in this study prior to radical prostatectomy. Images were acquired using either PET/MRI or PET/CT. ISUP grade was available at both biopsy and radical prostatectomy for all patients. A radiologist manually segmented the whole prostate on PET images using the co-registered CT or MRI for anatomical localisation on 3D Slicer software (Brigham and Women’s Hospital, Boston, Massachusetts, USA).5 The whole prostate was used as volume of interest (VOI) to avoid the limitations of radiomics for small volumes.6 VOIs were normalised, resampled, and discretised. A total of 103 image biomarker standardisation initiative-compliant, radiomic features (RF) were extracted using PyRadiomics (Python Software Foundation, Beaverton, Oregon, USA).7 RFs were harmonised with the ComBat method8 to control for the scanner effect, and selected using the minimum redundancy maximum relevance algorithm. Combinations of the four most relevant RFs were used to train 12 radiomics machine learning models for the prediction of post-surgical ISUP ≥4 versus ISUP <4 that were validated by five-fold repeated stratified cross-validation. To ensure that results were not driven by spurious associations, two ad hoc control models were generated. The first one Creative Commons Attribution-Non Commercial 4.0 ● April 2023 ● Urology 37 EAU 2023 • Abstract had SUVmax and VOI volume as input (radiomics baseline), while the other was made by setting to zero all voxel values prior features extraction (PET zeros). Balanced accuracy, sensitivity, specificity, and positive and negative predictive values were collected. The performance of the best developed model was compared with that of ISUP grade biopsy. RESULTS ISUP grade at biopsy was upgraded in 9 out of 47 patients after prostatectomy, resulting in a balanced accuracy of 85.9%; sensitivity of 71.9%; specificity of 100.0%; positive predicted value of 100.0%; and negative predictive value of 62.5%. The best performing radiomic model yielded a balanced accuracy of 87.6%; sensitivity of 88.6%; specificity of 86.7%; positive predicted value of 94.0%; and negative predicted value of 82.5%. All radiomic models trained with at least two RFs (grey level size zone matrix; zone entropy and shape; least axis length) outperformed the control models. Conversely, no significant differences were found for radiomic models trained with two or more RFs (Mann–Whitney U test; p>0.05). See Table 1 for a detailed report of all the generated models’ performance. CONCLUSION These findings support the role of 68Ga-PSMA PET radiomics for the accurate and non-invasive prediction of post-surgical ISUP grade. Future multicentre studies will be needed to establish with certainty the accuracy and reproducibility of the radiomic signature proposed here.

DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features

Despite the increasing use of genomic sequencing in clinical practice, the interpretation of rare genetic variants remains challenging even in well-studied disease genes, resulting in many patients with Variants of Uncertain Significance (VUSs). Computational Variant Effect Predictors (VEPs) provide valuable evidence in variant assessment, but they are prone to misclassifying benign variants, contributing to false positives. Here, we develop Deciphering Mutations in Actionable Genes (DeMAG), a supervised classifier for missense variants trained using extensive diagnostic data available in 59 actionable disease genes (American College of Medical Genetics and Genomics Secondary Findings v2.0, ACMG SF v2.0). DeMAG improves performance over existing VEPs by reaching balanced specificity (82%) and sensitivity (94%) on clinical data, and includes a novel epistatic feature, the ‘partners score’, which leverages evolutionary and structural partnerships of residues. The ‘partners score’ provides a general framework for modeling epistatic interactions, integrating both clinical and functional information. We provide our tool and predictions for all missense variants in 316 clinically actionable disease genes (demag.org) to facilitate the interpretation of variants and improve clinical decision-making.

Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study.

Postoperative systemic inflammatory response syndrome (SIRS) is common in surgical patients especially in older patients, and the geriatric population with SIRS is more susceptible to sepsis, MODS, and even death. We aimed to develop and validate a model for predicting postoperative SIRS in older patients. Patients aged ≥65 years who underwent general anesthesia in two centers of Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2020 were included. The cohort was divided into training and validation cohorts. A simple nomogram was developed to predict the postoperative SIRS in the training cohort using two logistic regression models and the brute force algorithm. The discriminative performance of this model was determined by area under the receiver operating characteristics curve (AUC). The external validity of the nomogram was assessed in the validation cohort. A total of 5,904 patients spanning from January 2015 to December 2019 were enrolled in the training cohort and 1,105 patients from January 2020 to September 2020 comprised the temporal validation cohort, in which incidence rates of postoperative SIRS were 24.6 and 20.2%, respectively. Six feature variables were identified as valuable predictors to construct the nomogram, with high AUCs (0.800 [0.787, 0.813] and 0.822 [0.790, 0.854]) and relatively balanced sensitivity (0.718 and 0.739) as well as specificity (0.718 and 0.729) in both training and validation cohorts. An online risk calculator was established for clinical application. We developed a patient-specific model that may assist in predicting postoperative SIRS among the aged patients.

Digital and Absolute Assays for Low Abundance Molecular Biomarkers.

There has been a recent surge of advances in biomolecular assays based on the measurement of discrete molecular targets as opposed to signals averaged across molecular ensembles. Many of these "digital" assay designs derive from now-mature technologies involving single-molecule imaging and microfluidics and provide an assortment of new modalities to quantify nucleic acids and proteins in biospecimens such as blood and tissue homogenates. A primary new benefit is the robust detection of trace analytes at attomolar to femtomolar concentrations for which many ensemble assays cannot distinguish signals above noise levels. In addition, multiple biomolecules can be differentiated within a mixture using optical barcodes, with much faster and simpler readouts compared with sequencing methods. In ideal digital assays, signals should, in theory, further represent absolute molecular counts, rather than relative levels, eliminating the need for calibration standards that are the mainstay of typical assays. Several digital assay platforms have now been commercialized but challenges hinder the adoption and diversification of these new formats, as there are broad needs to balance sensitivity and dynamic range of detection, increase analyte multiplexing, improve sample throughput, and reduce cost. Our lab and others have developed technologies to address these challenges by redesigning molecular probes and labels, improving molecular transport within detection focal volumes, and applying solution-based readout methods in flow.This Account describes the principles, formats, and design constraints of digital biomolecular assays that apply optical labels toward the goal of simple and routine target counting that may ultimately approach absolute readout standards. The primary challenges can be understood from fundamental concepts in thermodynamics and kinetics of association reactions, mass transport, and discrete statistics. Major advances include (1) new inorganic nanocrystal probes for more robust counting compared with dyes, (2) diverse molecular amplification tools that endow attachment of numerous labels to single targets, (3) specialized surfaces with patterned features for electromagnetic coupling to labels for signal amplification, (4) surface capture enhancement methods to concentrate targets through disruption of diffusion depletion zones, and (5) flow counting in which analytes are rapidly counted in solution without pull-down to a surface. Further progress and integration of these tools for biomolecular counting could improve the precision of laboratory measurements in life sciences research and benefit clinical diagnostic assays for low abundance biomarkers in limiting biospecimen volumes that are out of reach of traditional ensemble-level bioassays.

The accuracy of international and national fetal growth charts in detecting small-for-gestational-age infants using the Lambda-Mu-Sigma method.

To construct a national fetal growth chart using retrospective data and compared its diagnostic accuracy in predicting SGA at birth with existing international growth charts. This is a retrospective study where datasets from May 2011 to Apr 2020 were extracted to construct the fetal growth chart using the Lambda-Mu-Sigma method. SGA is defined as birth weight <10th centile. The local growth chart's diagnostic accuracy in detecting SGA at birth was evaluated using datasets from May 2020 to Apr 2021 and was compared with the WHO, Hadlock, and INTERGROWTH-21st charts. Balanced accuracy, sensitivity, and specificity were reported. A total of 68,897 scans were collected and five biometric growth charts were constructed. Our national growth chart achieved an accuracy of 69% and a sensitivity of 42% in identifying SGA at birth. The WHO chart showed similar diagnostic performance as our national growth chart, followed by the Hadlock (67% accuracy and 38% sensitivity) and INTERGROWTH-21st (57% accuracy and 19% sensitivity). The specificities for all charts were 95-96%. All growth charts showed higher accuracy in the third trimester, with an improvement of 8-16%, as compared to that in the second trimester. Using the Hadlock and INTERGROWTH-21st chart in the Malaysian population may results in misdiagnose of SGA. Our population local chart has slightly higher accuracy in predicting preterm SGA in the second trimester which can enable earlier intervention for babies who are detected as SGA. All growth charts' diagnostic accuracies were poor in the second trimester, suggesting the need of improvising alternative techniques for early detection of SGA to improve fetus outcomes.

Deep Learning-Based Algorithm for Automatic Detection of Pulmonary Embolism in Chest CT Angiograms

Since the prompt recognition of acute pulmonary embolism (PE) and the immediate initiation of treatment can significantly reduce the risk of death, we developed a deep learning (DL)-based application aimed to automatically detect PEs on chest computed tomography angiograms (CTAs) and alert radiologists for an urgent interpretation. Convolutional neural networks (CNNs) were used to design the application. The associated algorithm used a hybrid 3D/2D UNet topology. The training phase was performed on datasets adequately distributed in terms of vendors, patient age, slice thickness, and kVp. The objective of this study was to validate the performance of the algorithm in detecting suspected PEs on CTAs. The validation dataset included 387 anonymized real-world chest CTAs from multiple clinical sites (228 U.S. cities). The data were acquired on 41 different scanner models from five different scanner makers. The ground truth (presence or absence of PE on CTA images) was established by three independent U.S. board-certified radiologists. The algorithm correctly identified 170 of 186 exams positive for PE (sensitivity 91.4% [95% CI: 86.4-95.0%]) and 184 of 201 exams negative for PE (specificity 91.5% [95% CI: 86.8-95.0%]), leading to an accuracy of 91.5%. False negative cases were either chronic PEs or PEs at the limit of subsegmental arteries and close to partial volume effect artifacts. Most of the false positive findings were due to contrast agent-related fluid artifacts, pulmonary veins, and lymph nodes. The DL-based algorithm has a high degree of diagnostic accuracy with balanced sensitivity and specificity for the detection of PE on CTAs.

MP30-10 OPERATING CHARACTERISTICS OF SPERM CONCENTRATION AND FSH FOR PREDICTING CHROMOSOMAL ABNORMALITIES

You have accessJournal of UrologyCME1 Apr 2023MP30-10 OPERATING CHARACTERISTICS OF SPERM CONCENTRATION AND FSH FOR PREDICTING CHROMOSOMAL ABNORMALITIES Colin McLain, Matthew Christman, Eric Biewenga, and Donald Crain Colin McLainColin McLain More articles by this author , Matthew ChristmanMatthew Christman More articles by this author , Eric BiewengaEric Biewenga More articles by this author , and Donald CrainDonald Crain More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003258.10AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Current AUA/EUA guidelines recommend obtaining Yq microdeletion analysis, karyotype, and genetic counseling in men with non-obstructive azoospermia or severe oligospermia (<5 mil/mL). A recent retrospective cohort study suggests that Yq microdeletions are primarily found at a sperm concentration < 0.5 mil/mL and that by lowering the concentration threshold for genetic testing, specificity could be increased and relative financial cost decreased without adversely affecting the sensitivity. Studies have shown that FSH is the best predictor of non-obstructive azoospermia (NOA) in combination with testicular volume as well as a significant predictor of microdeletion presence. We hypothesized that the combination of sperm concentration and FSH would better predict the presence of chromosome abnormalities in infertile men. METHODS: A retrospective cohort study was conducted with subjects who were referred to a military tertiary Urology clinic between 2010 to 2020 and who underwent an infertility evaluation to include genetic testing, hormone profile, and at least one semen analysis. Sperm density and FSH were analyzed as independent predictors of chromosomal abnormalities using logistic regression. Receiver operating characteristics were generated for each independent variable that achieved significance in regression. RESULTS: 356 patients met inclusion criteria. Over the ten-year period, the prevalence of Y-chromosome microdeletions and karyotype abnormalities was 3.3% (n=12) and 8.4% (n=30), respectively. The ability to predict chromosomal abnormality based on logistic regression was statistically significant for each independent variable. The area under the curve for sperm concentration and FSH was found to be statistically better in combination than concentration alone (0.71 vs 0.66, p=0.033). The best balance of sensitivity and specificity (i.e. for a diagnostic test) for concentration and FSH were found at cut-points of <0.215 mil/mL and >8.345 mIU/mL, respectively. To optimize performance as a screening test and maintain sensitivity >90%, the cut points for concentration and FSH were <1 mil/mL and >2.74 mIU/mL, respectively. There was no difference in sensitivity for concentration between 2-7 mil/mL and no abnormalities were found if concentration exceeded 7 mil/mL. CONCLUSIONS: Our military single-institution ten-year cohort, exemplifies the potential improvement in patient selection and results that can be achieved if the sperm concentration is lowered from the current cutoff as well as possible addition of FSH to algorithm. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e394 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Colin McLain More articles by this author Matthew Christman More articles by this author Eric Biewenga More articles by this author Donald Crain More articles by this author Expand All Advertisement PDF downloadLoading ...

Adaptation and validation of the Italian Singing Voice Handicap Index-10 (SVHI-10-IT).

The aim of this study was to adapt and validate the Italian version of the singing Voice Handicap Index-10 (SVHI-10-IT). 99 Italian singers were enrolled in the study. All subjects underwent videolaryngostroboscopic examination and were asked to fill out the self-reported 10-item SVHI-10-IT. Laryngostroboscopic examination was pathological in 56 subjects (study group) (56.6%), while it was normal in the remaining 43 singers (control group) (43.4%). Dimensionality, test retest and internal validity for SVHI-10-IT were performed. Videolaryngostroboscopy was used as gold-standard for external validity. The items of SVHI-10-IT were uni-dimensional and Cronbach's α was 0.853 (95% CI = 0.805-0.892). High and comparable area under curve (AUC:0.93 95% CI = 0.88-0.98) values indicate a good ability of the scale to distinguish between the study and control groups. Based on balanced sensitivity (Se = 83.9%) and specificity (Sp = 86.0%), the optimal cut-off score for a singer's perceived voice handicap was 12. The SVHI-10-IT is a reliable and valid instrument to evaluate the self-reported singing voice handicap among singers. It can also be used as a quick screening tool since a score higher than 12 is indicative of a problematic voice as perceived by singers.

Benchmarking the Drude Polarizable Force Field Using the r(GACC) Tetranucleotide.

Polarizable force fields, in particular, the Drude polarizable force field (Drude FF), may hold the key to more accurately modeling biomolecules with molecular dynamics simulations by explicitly accounting for atomic polarizability. Previous work has shown promising results in simulating duplex nucleic acids and protein structures with excellent agreement with experimental values. However, benchmarking the Drude polarizable force field with highly flexible, single-stranded structures has yet to be achieved. In this work, the r(GACC) tetranucleotide is simulated over a multimicrosecond time scale, starting with various different initial conformations. Despite the starting conformation, including starting from the expected dominant A-form major conformation, the experimental structural distribution is not matched. In fact, the major NMR conformation is never resampled. Instead, the r(GACC) tetranucleotide becomes stabilized in anomalous structures that are inconsistent with the NMR data and that favor base-pairing and electrostatic interactions over base stacking. These structures are maintained for lengthy time scales (>1 μs) themselves, suggesting a misbalance of forces in the Drude polarizable force field itself. This model system is suggestive of the fact that currently the Drude polarizable force field does not appear to produce the sensitive balance of forces required to accurately model other single-stranded or noncanonical RNA structures.

Modulatory mechanisms of TARP γ8-selective AMPA receptor therapeutics

AMPA glutamate receptors (AMPARs) mediate excitatory neurotransmission throughout the brain. Their signalling is uniquely diversified by brain region-specific auxiliary subunits, providing an opportunity for the development of selective therapeutics. AMPARs associated with TARP γ8 are enriched in the hippocampus, and are targets of emerging anti-epileptic drugs. To understand their therapeutic activity, we determined cryo-EM structures of the GluA1/2-γ8 receptor associated with three potent, chemically diverse ligands. We find that despite sharing a lipid-exposed and water-accessible binding pocket, drug action is differentially affected by binding-site mutants. Together with patch-clamp recordings and MD simulations we also demonstrate that ligand-triggered reorganisation of the AMPAR-TARP interface contributes to modulation. Unexpectedly, one ligand (JNJ-61432059) acts bifunctionally, negatively affecting GluA1 but exerting positive modulatory action on GluA2-containing AMPARs, in a TARP stoichiometry-dependent manner. These results further illuminate the action of TARPs, demonstrate the sensitive balance between positive and negative modulatory action, and provide a mechanistic platform for development of both positive and negative selective AMPAR modulators.

Increased muscle responses to balance perturbations in children with cerebral palsy can be explained by increased sensitivity to center of mass movement

BackgroundBalance impairments are common in children with cerebral palsy (CP). Muscle activity during perturbed standing is higher in children with CP than in typically developing (TD) children, but we know surprisingly little about how sensorimotor processes for balance control are altered in CP. Sensorimotor processing refers to how the nervous system translates incoming sensory information about body motion into motor commands to activate muscles. In healthy adults, muscle activity in response to backward support-surface translations during standing can be reconstructed by center of mass (CoM) feedback, i.e., by a linear combination of delayed (due to neural transmission times) CoM displacement, velocity, and acceleration. The level of muscle activity in relation to changes in CoM kinematics, i.e., the feedback gains, provides a metric of the sensitivity of the muscle response to CoM perturbations. Research questionCan CoM feedback explain reactive muscle activity in children with CP, yet with higher feedback gains than in TD children? MethodsWe perturbed standing balance by backward support-surface translations of different magnitudes in 20 children with CP and 20 age-matched TD children and investigated CoM feedback pathways underlying reactive muscle activity in the triceps surae and tibialis anterior. ResultsReactive muscle activity could be reconstructed by delayed feedback of CoM kinematics and hence similar sensorimotor pathways might underlie balance control in children with CP and TD children. However, sensitivities of both agonistic and antagonistic muscle activity to CoM displacement and velocity were higher in children with CP than in TD children. The increased sensitivity of balance correcting responses to CoM movement might explain the stiffer kinematic response, i.e., smaller CoM movement, observed in children with CP. SignificanceThe sensorimotor model used here provided unique insights into how CP affects neural processing underlying balance control. Sensorimotor sensitivities might be a useful metric to diagnose balance impairments.

Development of prediction models for one-year brain tumour survival using machine learning: a comparison of accuracy and interpretability

Background and ObjectivePrediction of survival in patients diagnosed with a brain tumour is challenging because of heterogeneous tumour behaviours and treatment response. Advances in machine learning have led to the development of clinical prognostic models, but due to the lack of model interpretability, integration into clinical practice is almost non-existent. In this retrospective study, we compare five classification models with varying degrees of interpretability for the prediction of brain tumour survival greater than one year following diagnosis. Methods1028 patients aged ≥16 years with a brain tumour diagnosis between April 2012 and April 2020 were included in our study. Three intrinsically interpretable ‘glass box’ classifiers (Bayesian Rule Lists [BRL], Explainable Boosting Machine [EBM], and Logistic Regression [LR]), and two ‘black box’ classifiers (Random Forest [RF] and Support Vector Machine [SVM]) were trained on electronic patients records for the prediction of one-year survival. All models were evaluated using balanced accuracy (BAC), F1-score, sensitivity, specificity, and receiver operating characteristics. Black box model interpretability and misclassified predictions were quantified using SHapley Additive exPlanations (SHAP) values and model feature importance was evaluated by clinical experts. ResultsThe RF model achieved the highest BAC of 78.9%, closely followed by SVM (77.7%), LR (77.5%) and EBM (77.1%). Across all models, age, diagnosis (tumour type), functional features, and first treatment were top contributors to the prediction of one year survival. We used EBM and SHAP to explain model misclassifications and investigated the role of feature interactions in prognosis. ConclusionInterpretable models are a natural choice for the domain of predictive medicine. Intrinsically interpretable models, such as EBMs, may provide an advantage over traditional clinical assessment of brain tumour prognosis by weighting potential risk factors and their interactions that may be unknown to clinicians. An agreement between model predictions and clinical knowledge is essential for establishing trust in the models decision making process, as well as trust that the model will make accurate predictions when applied to new data.

Machine Learning Systems Detecting Illicit Drugs Based on Their ATR-FTIR Spectra

We present a comparative study aiming to determine the most efficient multivariate model screening for the main drugs of abuse based on their ATR-FTIR spectra. A preliminary statistical analysis of selected spectra data extracted from the public SWGDRUG IR Library was first performed. The results corroborated those of an exploratory analysis that was based on several dimensionality reduction methods, i.e., Principal Component Analysis (PCA), Independent Component Analysis (ICA), and autoencoders. Then, several machine learning methods, i.e., Support Vector Machines (SVM), eXtreme Gradient Boosting (XGB), Random Forest, Gradient Boosting, and K-Nearest Neighbors (KNN), were used to assign the drug class membership. In order to account for the stochastic nature of these machine learning methods, both models were evaluated 10 times on a randomly distributed subset of the whole SWGDRUG IR Library, and the results were compared in detail. Finally, their performance in assigning the class identity of three classes of drugs of abuse, i.e., hallucinogenic (2C-x, DOx, and NBOMe) amphetamines, cannabinoids, and opioids, were compared based on confusion matrices and various classification parameters, such as balanced accuracy, sensitivity, and specificity. The advantages of each of the illicit drug-detecting systems and their potential as forensic screening tools used in field scenarios are also discussed.

Mapping hyperspectral NIR images using supervised self-organizing maps: Discrimination of weedy rice seeds

Weedy rice is one of the most problematic weeds in rice-growing regions, particularly in Southeast Asia. Unlike other types of weeds, it is extremely difficult to distinguish weedy rice from cultivated rice directly from paddy seeds as they exhibit common morphological features. As such weed management can be a difficult, time-consuming, and inaccurate process that is often carried out manually. This study offers a novel classification approach based on an artificial neural network (ANN), utilizing self-organizing maps (SOMs), to directly discriminate weedy rice using the near-infrared (NIR) hyperspectral imaging (HSI) technique. The physical attributes, thermal behavior, and chemical profiles of the weedy and cultivated rice were thoroughly investigated by a range of analytical techniques, including optical microscopy, scanning electron microscopy, thermogravimetric analysis, and direct analysis in real-time mass spectrometry (DART-MS). For direct sample analysis by HSI, a global self-organizing map was generated with optimized parameters (scaling value and map size). The color indices (Red, Green, Blue values) of the sample image were defined to obtain a color ratio that can be used to classify unknown samples. The optimal threshold for classification was carefully determined using a receiver operating characteristic (ROC) curve. Performance metrics (sensitivity, specificity, precision, accuracy, and misclassification error) were used to evaluate the performance of the model. Classification accuracies of 98% (Weedy vs PL2) and 88% (Weedy vs RD49) were obtained with balanced sensitivity and specificity. The classification was assessed from the whole sample image, which was completely independent of the selected region of interest. As far as we know, this is the first instance where SOMs have been utilized to appraise seed quality by means of authentic HSI images.

Predicting Suicide Attempts among Major Depressive Disorder Patients with Structural Neuroimaging: A Machine Learning Approach

IntroductionEvery year at least one million people die by suicide, with major depressive disorder (MDD) being one of the major causes of suicide deaths. Current suicide risk assessments rely on subjective information, are time consuming, low predictive, and poorly reliable. Thus, finding objective biomarkers of suicidality is crucial to move clinical practice towards a precision psychiatry framework, enhancing suicide risk detection and prevention for MDD.ObjectivesThe present study aimed at applying machine learning (ML) algorithms on both grey matter and white-matter voxel-wise data to discriminate MDD suicide attempters (SA) from non-attempters (nSA).Methods91 currently depressed MDD patients (24 SA, 67 nSA) underwent a structural MRI session. T1-weighted images and diffusion tensor imaging scans were respectively pre-processed using Computational Atlas Toolbox 12 (CAT12) and spatial tract-based statistics (TBSS) on FSL, to obtain both voxel-based morphometry (VBM) and fractional anisotropy (FA) measures. Three classification models were built, entering whole-brain VBM and FA maps alone into a Support Vector Machine (SVM) and combining both modalities into a Multiple Kernel Learning (MKL) algorithm. All models were trained through a 5-fold nested cross-validation with subsampling to calculate reliable estimates of balanced accuracy, specificity, sensitivity, and area under the receiver operator curve (AUC).ResultsModels’ performances are summarized in Table 1.Table 1.Models’ performances.Input featuresAlgorithmSpecificitySensitivityBalanced accuracyAUCVBMSVM55.00%50.00%52.50%0.55FASVM72.00%54.00%63.00%0.62VBM and FAMKL68.00%54.00%61.00%0.58Abbreviations: AUC, area under the receiver operator curve; FA, fractional anisotropy; VBM, voxel-based morphometry.ConclusionsOverall, although overcoming the random classification accuracy (i.e., 50%), performances of all models classifying SA and nSA MDD patients were moderate, possibly due to the imbalanced numerosity of classes, with SVM on FA reaching the highest accuracy. Thus, future studies may enlarge the sample and add different features (e.g., functional neuroimaging data) to develop an objective and reliable predictive model to assess and hence prevent suicide risk among MDD patients.Disclosure of InterestNone Declared

Bisnitramide-Bridged N-Substituted Tetrazoles with Balanced Sensitivity and High Performance.

In this study, a simple synthetic strategy for bridged bis(nitramide)-based N-substituted tetrazoles is described. All new compounds were isolated and fully characterized by sophisticated analytical techniques. The structures of the intermediate derivative and two final compounds were determined by single-crystal X-ray data. The structures of the intermediate derivative and two final compounds were determined by single crystal X-ray data. Thermostabilities and energetic properties of new bridged bisnitramide-based N-substituted tetrazoles were discussed and compared with known materials.