Profiles of functioning of a group of children with cerebral palsy in Argentina: Preliminary data from the first national study. / Perfiles de funcionamiento en un grupo de niños con parálisis cerebral en Argentina: datos preliminares del primer estudio nacional.

Cerebral palsy (CP) affects body posture and movement coordination and is the most common cause of severe disability in the pediatric population. The diagnosis of CP is not a description of a person's functioning or interaction with their environment. Therefore, the diagnosis should be complemented with a description of functioning, using tools based on the biopsychosocial model proposed by the World Health Organization's International Classification of Functioning, Disability and Health (ICF). This report describes the preliminary data from a multicenter study conducted in Argentina with the aim of standardizing the description of the profiles of functioning of children and adolescents with CP. These data showed that the participants had some skills in sleep functions, mental functions of language, seeing functions, and in some environmental factors. They showed significant difficulties in categories such as maintaining body position, walking, and toileting.

La parálisis cerebral (PC) afecta la postura y la coordinación del movimiento, y es la causa más común de discapacidad grave en la población pediátrica. El diagnóstico de PC no describe el funcionamiento ni la interacción de la persona con el contexto en el cual se desarrolla. Por lo tanto, el diagnóstico se debe complementar con una descripción del funcionamiento, utilizando herramientas basadas en el modelo biopsicosocial de la Clasificación del Funcionamiento (CIF) de la Organización Mundial de la Salud (OMS). En esta comunicación, describimos los datos preliminares de un estudio multicéntrico en Argentina destinado a estandarizar la descripción del perfil de funcionamiento en niños y adolescentes con PC. Estos datos mostraron que los participantes tienen algunas habilidades en las funciones del sueño, las funciones mentales del lenguaje y las visuales, así como en algunas relacionadas con el contexto. Presentan dificultades importantes en categorías tales como mantener la posición del cuerpo, el andar y la higiene.

Immune infiltration at the primary tumor is associated with clinical outcome of patients with extranodal extension of lymph node metastasis in oral cancer.

BACKGROUND: Extranodal extension (ENE) of lymph node metastasis is one of the most reliable prognostic indicators for patients with locally advanced oral cancer. Although multiple reports have found a close relationship between immune infiltration of tumors and patient clinical outcomes, its association with ENE is unknown. METHODS: We identified 234 human papillomavirus-negative (HPV-) oral cavity squamous cell carcinoma (OSCC) patients in The Cancer Genome Atlas and investigated the immune infiltration profiles of primary tumors and their association with survival. RESULTS: Hierarchical clustering analysis clearly classified the overall immune infiltration status in OSCC into high immune or low immune groups. The combination of ENE positivity and low immune infiltration was strongly associated with poor overall survival (OS) compared to the combination of ENE positivity and high immune infiltration [hazard ratio 2.04 (95 %CI, 1.08-3.83); p = 0.024]. The immune infiltration status was not associated with OS rates in patients with ENE-negative or node negative tumors. CONCLUSION: Overall Immune infiltration at the primary site was significantly associated with clinical outcome of OSCC patients with ENE.

Quantum-Enhanced Hybrid Feature Engineering in Thoracic CT Image Analysis for State-of-the-Art Nodule Classification: An Advanced Lung Cancer Assessment.

The intricate nature of lung cancer treatment poses considerable challenges upon diagnosis. Early detection plays a pivotal role in mitigating its escalating global mortality rates. Consequently, there are pressing demands for robust and dependable early detection and diagnostic systems. However, the technological limitations and complexity of the disease make it challenging to implement an efficient lung cancer screening system. AI-based CT image analysis techniques are showing significant contributions to the development of computer-assisted detection (CAD) systems for lung cancer screening. Various existing research groups are working on implementing CT image analysis systems for assessing and classifying lung cancer. However, the complexity of different structures inside the CT image is high and comprehension of significant information inherited by them is more complex even after applying advanced feature extraction and feature selection techniques. Traditional and classical feature selection techniques may struggle to capture complex interdependencies between features. They may get stuck in local optima and sometimes require additional exploration strategies. When applied to a prominent feature space, traditional techniques may also struggle with combinatorial optimization problems. This paper proposed a methodology to overcome the existing challenges by applying feature extraction using Vision Transformer (FexViT) and Feature selection using the Quadratic unconstrained binary optimization (FSelQUBO) technique. This algorithm shows better performance when compared with other existing techniques. The proposed methodology showed better performance as compared to other existing techniques when evaluated by applying necessary output measures, such as accuracy, Area under roc (receiver operating characteristics) curve, precision, sensitivity, and specificity, obtained as 94.28%, 99.10%, 96.17%, 90.16% and 97.46%. The further advancement of CAD systems is essential to meet the demand for more reliable detection and diagnosis of cancer, which can be addressed by leading the proposed quantum computation and growing AI-based technology ahead. .

A novel classification of juvenile and adolescent idiopathic scoliosis for conservative treatment.

BACKGROUND: The operative classification of scoliosis is well-developed but inadequate for guiding conservative treatment. The current conservative classification for juvenile and adolescent idiopathic scoliosis (JAIS) exhibits noticeable deficiencies. This study aimed to establish the Peking Union Medical College Hospital (PUMCH) classification and assess its clinical value in the conservative treatment of JAIS. METHODS: This study consisted of two parts. First, it involved a retrospective analysis of patients treated for JAIS in the Department of Rehabilitation Medicine, the *** Union Medical College Hospital, between January 2013 and June 2020. Second, it involved an ambispective cohort study that enrolled patients with JAIS in the above hospital between July and December 2020. RESULTS: A total of 989 patients with JAIS were enrolled, with 899 patients for establishing the PUMCH classification and 90 patients with JAIS for validating the PUMCH classification. The classification demonstrated an average reliability of 88.22% with a kappa coefficient of 0.862. After 1 week, the remeasured results presented a mean reproducibility of 92.78% and a kappa coefficient of 0.908. After 1-year follow-up, the Cobb angle decreased significantly from 16.61°±2.88° to 12.16°±9.97° (P=0.002) in 51 patients with PUMCH-SSE treatment, while the Cobb angle increased significantly from 15.74°±2.75° to 17.64°±5.60° (P=0.014) in 39 patients without PUMCH-SSE treatment. CONCLUSIONS: The PUMCH-SSE classification demonstrates good inter-observer reliability and intra-observer reproducibility. In addition, the classification may be used to guide the conservative treatment of JAIS in clinical settings.

Are the ICHD-3 criteria for headache attributed to idiopathic intracranial hypertension valid? Headache phenotyping and field-testing in newly diagnosed idiopathic intracranial hypertension.

BACKGROUND: Headache burden is substantial in idiopathic intracranial hypertension. The classification of idiopathic intracranial hypertension headache by the International Classification of Headache Disorders (ICHD) is an important tool for research and clinical purposes. METHODS: We phenotyped headaches and tested sensitivity and specificity of the ICHD-3 criteria for idiopathic intracranial hypertension headache in a prospective cohort of patients suspected of idiopathic intracranial hypertension at two tertiary headache centers. RESULTS: Sensitivity was 93% and specificity was 100% of ICHD-3 criteria for idiopathic intracranial hypertension-related headache validated in idiopathic intracranial hypertension (n = 140) and patients in whom idiopathic intracranial hypertension was suspected but disproven (n = 103). The phenotype of new/worsened headaches related to idiopathic intracranial hypertension suspicion was equally migraine-like (p = 0.76) and tension-type-like (p = 0.08). Lumbar puncture opening pressure was higher (p < 0.0001) and pulsatile tinnitus more frequent (p < 0.0001) in idiopathic intracranial hypertension patients, but neither improved the applicability of the headache criteria, nor did papilledema. CONCLUSION: Headache phenotype is not distinct in idiopathic intracranial hypertension. ICHD-3 criteria for idiopathic intracranial hypertension headache are sensitive and specific, but simplicity can be improved without compromising accuracy. We propose that a new or worsened headache temporally related to active idiopathic intracranial hypertension is a sufficient criterion for idiopathic intracranial hypertension headache regardless of headache phenotype or accompanying symptoms, and that elements of idiopathic intracranial hypertension diagnostics (papilledema and opening pressure) be segregated from headache criteria.Trial Registration: ClinicalTrials.gov Identifier: NCT04032379.

Identifying prehospital trauma patients from ambulance patient care records; comparing two methods using linked data in New South Wales, Australia.

BACKGROUND: Linked datasets for trauma system monitoring should ideally follow patients from the prehospital scene to hospital admission and post-discharge. Having a well-defined cohort when using administrative datasets is essential because they must capture the representative population. Unlike hospital electronic health records (EHR), ambulance patient-care records lack access to sources beyond immediate clinical notes. Relying on a limited set of variables to define a study population might result in missed patient inclusion. We aimed to compare two methods of identifying prehospital trauma patients: one using only those documented under a trauma protocol and another incorporating additional data elements from ambulance patient care records. METHODS: We analyzed data from six routinely collected administrative datasets from 2015 to 2018, including ambulance patient-care records, aeromedical data, emergency department visits, hospitalizations, rehabilitation outcomes, and death records. Three prehospital trauma cohorts were created: an Extended-T-protocol cohort (patients transported under a trauma protocol and/or patients with prespecified criteria from structured data fields), T-protocol cohort (only patients documented as transported under a trauma protocol) and non-T-protocol (extended-T-protocol population not in the T-protocol cohort). Patient-encounter characteristics, mortality, clinical and post-hospital discharge outcomes were compared. A conservative p-value of 0.01 was considered significant RESULTS: Of 1 038 263 patient-encounters included in the extended-T-population 814 729 (78.5 %) were transported, with 438 893 (53.9 %) documented as a T-protocol patient. Half (49.6 %) of the non-T-protocol sub-cohort had an International Classification of Disease 10th edition injury or external cause code, indicating 79644 missed patients when a T-protocol-only definition was used. The non-T-protocol sub-cohort also identified additional patients with intubation, prehospital blood transfusion and positive eFAST. A higher proportion of non-T protocol patients than T-protocol patients were admitted to the ICU (4.6% vs 3.6 %), ventilated (1.8% vs 1.3 %), received in-hospital transfusion (7.9 vs 6.8 %) or died (1.8% vs 1.3 %). Urgent trauma surgery was similar between groups (1.3% vs 1.4 %). CONCLUSION: The extended-T-population definition identified 50 % more admitted patients with an ICD-10-AM code consistent with an injury, including patients with severe trauma. Developing an EHR phenotype incorporating multiple data fields of ambulance-transported trauma patients for use with linked data may avoid missing these patients.

A novel classification of posterior pelvic exenteration to assess prognosis in female patients with locally advanced primary rectal cancer: a retrospective cohort study from China PelvEx collaborative.

PURPOSE: Surgical techniques and the prognosis of posterior pelvic exenteration for locally advanced primary rectal cancer in female patients pose challenges that need to be addressed. Therefore, we investigated the short-term and survival outcomes of posterior pelvic exenteration in female patients using a novel Peking classification. METHODS: We retrospectively analysed a prospective database from China PelvEx Collaborative across three tertiary referral centres. A total of 172 patients who underwent combined resection for locally advanced primary rectal cancer were classified based on four subtypes (PPE-I [64/172], PPE-II [68/172], PPE-III [21/172], and PPE-IV [19/172]) according to the Peking classification; perioperative characteristics and short-term and oncological outcomes were analysed. RESULTS: Differences were significant among the four groups regarding colorectal reconstruction (p < 0.001), perineal reconstruction (p < 0.001), in-hospital complications (p < 0.05), and urinary retention (p < 0.05). The R0 resection rates for PPE-I, PPE-II, PPE-III, and PPE-IV were 90.6%, 89.7%, 90.5%, and 89.5%, respectively. The 5-year overall survival rates of the PPE-I, PPE-II, PPE-III, and PPE-IV groups were 73.4%, 68.8%, 54.7%, and 37.3%, respectively. Correspondingly, their 5-year disease-free survival rates were 76.0%, 62.5%, 57.7%, and 43.1%, respectively. Notably, the PPE-IV group demonstrated the lowest 5-year overall survival rate (p < 0.001) and 5-year disease-free survival rate (p < 0.001). CONCLUSION: The Peking classification can aid in determining suitable surgical techniques and conducting prognostic assessments in female patients with locally advanced primary rectal cancer.

Predictive modeling of initiation and delayed mental health contact for depression.

BACKGROUND: Depression is prevalent among Operation Enduring Freedom and Operation Iraqi Freedom (OEF/OIF) Veterans, yet rates of Veteran mental health care utilization remain modest. The current study examined: factors in electronic health records (EHR) associated with lack of treatment initiation and treatment delay; the accuracy of regression and machine learning models to predict initiation of treatment. METHODS: We obtained data from the VA Corporate Data Warehouse (CDW). EHR data were extracted for 127,423 Veterans who deployed to Iraq/Afghanistan after 9/11 with a positive depression screen and a first depression diagnosis between 2001 and 2021. We also obtained 12-month pre-diagnosis and post-diagnosis patient data. Retrospective cohort analysis was employed to test if predictors can reliably differentiate patients who initiated, delayed, or received no mental health treatment associated with their depression diagnosis. RESULTS: 108,457 Veterans with depression, initiated depression-related care (55,492 Veterans delayed treatment beyond one month). Those who were male, without VA disability benefits, with a mild depression diagnosis, and had a history of psychotherapy were less likely to initiate treatment. Among those who initiated care, those with single and mild depression episodes at baseline, with either PTSD or who lacked comorbidities were more likely to delay treatment for depression. A history of mental health treatment, of an anxiety disorder, and a positive depression screen were each related to faster treatment initiation. Classification of patients was modest (ROC AUC = 0.59 95%CI = 0.586-0.602; machine learning F-measure = 0.46). CONCLUSIONS: Having VA disability benefits was the strongest predictor of treatment initiation after a depression diagnosis and a history of mental health treatment was the strongest predictor of delayed initiation of treatment. The complexity of the relationship between VA benefits and history of mental health care with treatment initiation after a depression diagnosis is further discussed. Modest classification accuracy with currently known predictors suggests the need to identify additional predictors of successful depression management.

The effect of movement system impairment-based classification treatment compared to routine physiotherapy on pain, disability, alignment, and movement impairments in individuals with tibiofemoral rotation syndrome: a randomized controlled trial.

BACKGROUND: Knee pain is a common musculoskeletal problem. Lower extremity movement impairments could alter stresses in different planes and contribute to knee pain. Classifying these impairments may be helpful in the diagnosis and treatment of knee problems. Movement system impairment (MSI)-based classification is a system to evaluate movement impairments. Trials that involve this classification are limited. Therefore, it will be of interest to examine the effect of movement system impairment-based classification treatment compared to routine physiotherapy in individuals with tibiofemoral rotation syndrome. METHODS: Twenty-two individuals with knee pain aged 18-40 years (2 males, 20 females) diagnosed with tibiofemoral rotation (TFR) syndrome were included. After initial evaluation, individuals were randomly assigned into two treatment groups (MSI-based treatment and routine physiotherapy). Both treatment groups contained 8 treatment sessions over 4 weeks. Alignment and movement impairments data form, a numeric rating scale (NRS), and the Kujala Disability Questionnaire were assessed at baseline and after a four-week intervention. Independent samples t-test and Mann-Whitney U test were used for quantitative variables, and Fisher's exact test was employed for qualitative variables to compare the groups. One-way Analysis of variance (ANOVA) and paired samples t-test were utilized to within-group changes of quantitative variables, and qualitative variables were analyzed with the McNemar test. RESULTS: The results showed that pain intensity and disability significantly decreased within and between groups after intervention (P > 0.05). There were also statistically significant differences between treatment groups for 3 out of 6 alignment and movement impairments (PS-FAdd/IR, Step down-Add/Valgus, and STS-Add/Valgus) (P > 0.05). Within-group differences for alignment and movement impairments were significant only for the MSI-based treatment group (P > 0.05). CONCLUSIONS: The findings suggest that a specific MSI-based treatment, considering a homogenous group of individuals with knee pain, may contribute to pain, disability, and alignment and movement impairments improvement. Therefore, it is important to notice the classification-based treatment for individuals with knee pain. TRIAL REGISTRATION NUMBER (TRN) AND DATE OF REGISTRATION: The trial was registered at the ( https://www.irct.ir ), (IRCT20210505051181N3) on 17/7/2021.

Margin assessment during breast conserving surgery using diffuse reflectance spectroscopy.

Significance: During breast-conserving surgeries, it is essential to evaluate the resection margins (edges of breast specimen) to determine whether the tumor has been removed completely. In current surgical practice, there are no methods available to aid in accurate real-time margin evaluation. Aim: In this study, we investigated the diagnostic accuracy of diffuse reflectance spectroscopy (DRS) combined with tissue classification models in discriminating tumorous tissue from healthy tissue up to 2 mm in depth on the actual resection margin of in vivo breast tissue. Approach: We collected an extensive dataset of DRS measurements on ex vivo breast tissue and in vivo breast tissue, which we used to develop different classification models for tissue classification. Next, these models were used in vivo to evaluate the performance of DRS for tissue discrimination during breast conserving surgery. We investigated which training strategy yielded optimum results for the classification model with the highest performance. Results: We achieved a Matthews correlation coefficient of 0.76, a sensitivity of 96.7% (95% CI 95.6% to 98.2%), a specificity of 90.6% (95% CI 86.3% to 97.9%) and an area under the curve of 0.98 by training the optimum model on a combination of ex vivo and in vivo DRS data. Conclusions: DRS allows real-time margin assessment with a high sensitivity and specificity during breast-conserving surgeries.

Understanding Female and Male Insights in Psychology: Who Thinks What?

Evolutionary psychology is the study of human psychological behavior. During childhood, men and women behave similarly; however, as a child approaches puberty, new physical and behavioral changes emerge. Behavioral psychology focuses on understanding the functioning and thought processes of the human mind. The general population lacks knowledge of basic behavioral differences between men and women, leaving them unaware of their role, limitations, societal responsibilities, resulting in an underestimation of their own natural talents and biology. Thus, people tend to follow societal norms rather than exploring and utilizing their natural talents. The current review was designed and conducted to enforce compression on behavioral psychology in both genders as well as to identify variations in hormonal activity and sexual preferences.

BT-CNN: a balanced binary tree architecture for classification of brain tumour using MRI imaging.

Deep learning is a very important technique in clinical diagnosis and therapy in the present world. Convolutional Neural Network (CNN) is a recent development in deep learning that is used in computer vision. Our medical investigation focuses on the identification of brain tumour. To improve the brain tumour classification performance a Balanced binary Tree CNN (BT-CNN) which is framed in a binary tree-like structure is proposed. It has a two distinct modules-the convolution and the depthwise separable convolution group. The usage of convolution group achieves lower time and higher memory, while the opposite is true for the depthwise separable convolution group. This balanced binarty tree inspired CNN balances both the groups to achieve maximum performance in terms of time and space. The proposed model along with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. are experimented on public datasets. Before we feed the data into model the images are pre-processed using CLAHE, denoising, cropping, and scaling. The pre-processed dataset is partitioned into training and testing datasets as per 5 fold cross validation. The proposed model is trained and compared its perforarmance with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. The proposed model reported average training accuracy of 99.61% compared to other models. The proposed model achieved 96.06% test accuracy where as other models achieved 68.86%, 85.8%, 86.88%, and 90.41% respectively. Further, the proposed model obtained lowest standard deviation on training and test accuracies across all folds, making it invariable to dataset.

Validity of the Functional Classification of the Upper Extremities for Duchenne Muscular Dystrophy.

Objectives: Some upper-limb function assessments can evaluate treatments in the non-ambulatory stage of Duchenne muscular dystrophy (DMD). The Functional Classification of the Upper Extremities (FCUE) was developed for DMD in Japan. The FCUE is easier to use than the Performance of Upper Limb (PUL) and is more detailed than the Brooke Upper Extremity Scale. This study aimed to determine the concurrent validity of FCUE with other methods of assessment for DMD. Methods: This retrospective study reviewed the medical records of 39 boys with DMD from the National Center of Neurology and Psychiatry to evaluate the concurrent validity of the FCUE and PUL using non-parametric Spearman rank correlation (ρ). We also determined the concurrent validity of the Brooke Upper Extremity Scale and PUL for comparison. Results: The ρ value between the FCUE and PUL was -0.914 (P<0.001). The FCUE showed robust concurrent validity with the PUL. That correlation between the FCUE and Brooke Upper Extremity Scale gave a ρ value of -0.854 (P<0.001). Conclusions: The FCUE had a higher concurrent validity with the PUL than with the Brooke Upper Extremity Scale. The FCUE is considered a valid assessment tool of upper-limb function in boys with DMD. Selecting the best assessment method depends on the severity of the patient's condition and a balance between assessment accuracy and evaluation time.

Mind your prevalence!

Multiple metrics are used when assessing and validating the performance of quantitative structure-activity relationship (QSAR) models. In the case of binary classification, balanced accuracy is a metric to assess the global performance of such models. In contrast to accuracy, balanced accuracy does not depend on the respective prevalence of the two categories in the test set that is used to validate a QSAR classifier. As such, balanced accuracy is used to overcome the effect of imbalanced test sets on the model's perceived accuracy. Matthews' correlation coefficient (MCC), an alternative global performance metric, is also known to mitigate the imbalance of the test set. However, in contrast to the balanced accuracy, MCC remains dependent on the respective prevalence of the predicted categories. For simplicity, the rest of this work is based on the positive prevalence. The MCC value may be underestimated at high or extremely low positive prevalence. It contributes to more challenging comparisons between experiments using test sets with different positive prevalences and may lead to incorrect interpretations. The concept of balanced metrics beyond balanced accuracy is, to the best of our knowledge, not yet described in the cheminformatic literature. Therefore, after describing the relevant literature, this manuscript will first formally define a confusion matrix, sensitivity and specificity and then present, with synthetic data, the danger of comparing performance metrics under nonconstant prevalence. Second, it will demonstrate that balanced accuracy is the performance metric accuracy calibrated to a test set with a positive prevalence of 50% (i.e., balanced test set). This concept of balanced accuracy will then be extended to the MCC after showing its dependency on the positive prevalence. Applying the same concept to any other performance metric and widening it to the concept of calibrated metrics will then be briefly discussed. We will show that, like balanced accuracy, any balanced performance metric may be expressed as a function of the well-known values of sensitivity and specificity. Finally, a tale of two MCCs will exemplify the use of this concept of balanced MCC versus MCC with four use cases using synthetic data. SCIENTIFIC CONTRIBUTION: This work provides a formal, unified framework for understanding prevalence dependence in model validation metrics, deriving balanced metric expressions beyond balanced accuracy, and demonstrating their practical utility for common use cases. In contrast to prior literature, it introduces the derived confusion matrix to express metrics as functions of sensitivity, specificity and prevalence without needing additional coefficients. The manuscript extends the concept of balanced metrics to Matthews' correlation coefficient and other widely used performance indicators, enabling robust comparisons under prevalence shifts.

The Evolving Clinical Trajectory From a Sliding Hiatus Hernia to Mixed Sliding and Para-Oesophageal Subtype: A Case Report.

We present a compelling case of a patient initially diagnosed with a simple sliding hiatus hernia (HH), which was managed conservatively through optimised medical therapy. Over the span of a few years, she developed new symptoms which included epigastric discomfort and pain, prompting further clinical review and imaging investigation. These revealed the progression of her HH from a simple form to a more complex rolling or para-oesophageal type. This outcome highlights the importance of recognising a potential for progression during the clinical assessment of patients with a history of reflux symptoms and the onset of new epigastric discomfort or pain. Understanding this continuum of HHs is essential for physicians as management plans may need to switch from a conservative to a more invasive approach.

A pixel-wise labelled dataset of Moroccan aircraft emergency landing sites for semantic segmentation applications.

Detecting emergency aircraft landing sites is crucial for ensuring passenger and crew safety during unexpected forced landings caused by factors like engine malfunctions, adverse weather, or other aviation emergencies. In this article, we present a dataset consisting of Google Maps images with their corresponding masks, specifically crafted with manual annotations of emergency aircraft landing sites, distinguishing between safe areas with suitable conditions for emergency landings and unsafe areas presenting hazardous conditions. Drawing on detailed guidelines from the Federal Aviation Administration, the annotations focus on key features such as slope, surface type, and obstacle presence, with the goal of pinpointing appropriate landing areas. The proposed dataset has 4180 images, with 2090 raw images accompanied by their corresponding annotation instances. This dataset employs a semantic segmentation approach, categorizing the image pixels into two "Safe" and "Unsafe" classes based on authenticated terrain-specific attributes, thereby offering a nuanced understanding of the viability of various landing sites in emergency scenarios.

Noisecut: a python package for noise-tolerant classification of binary data using prior knowledge integration and max-cut solutions.

BACKGROUND: Classification of binary data arises naturally in many clinical applications, such as patient risk stratification through ICD codes. One of the key practical challenges in data classification using machine learning is to avoid overfitting. Overfitting in supervised learning primarily occurs when a model learns random variations from noisy labels in training data rather than the underlying patterns. While traditional methods such as regularization and early stopping have demonstrated effectiveness in interpolation tasks, addressing overfitting in the classification of binary data, in which predictions always amount to extrapolation, demands extrapolation-enhanced strategies. One such approach is hybrid mechanistic/data-driven modeling, which integrates prior knowledge on input features into the learning process, enhancing the model's ability to extrapolate. RESULTS: We present NoiseCut, a Python package for noise-tolerant classification of binary data by employing a hybrid modeling approach that leverages solutions of defined max-cut problems. In a comparative analysis conducted on synthetically generated binary datasets, NoiseCut exhibits better overfitting prevention compared to the early stopping technique employed by different supervised machine learning algorithms. The noise tolerance of NoiseCut stems from a dropout strategy that leverages prior knowledge of input features and is further enhanced by the integration of max-cut problems into the learning process. CONCLUSIONS: NoiseCut is a Python package for the implementation of hybrid modeling for the classification of binary data. It facilitates the integration of mechanistic knowledge on the input features into learning from data in a structured manner and proves to be a valuable classification tool when the available training data is noisy and/or limited in size. This advantage is especially prominent in medical and biomedical applications where data scarcity and noise are common challenges. The codebase, illustrations, and documentation for NoiseCut are accessible for download at https://pypi.org/project/noisecut/ . The implementation detailed in this paper corresponds to the version 0.2.1 release of the software.

Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal.

PREMISE: Spectroscopy is a powerful remote sensing tool for monitoring plant biodiversity over broad geographic areas. Increasing evidence suggests that foliar spectral reflectance can be used to identify trees at the species level. However, most studies have focused on only a limited number of species at a time, and few studies have explored the underlying phylogenetic structure of leaf spectra. Accurate species identifications are important for reliable estimations of biodiversity from spectral data. METHODS: Using over 3500 leaf-level spectral measurements, we evaluated whether foliar reflectance spectra (400-2400 nm) can accurately differentiate most tree species from a regional species pool in eastern North America. We explored relationships between spectral, phylogenetic, and leaf functional trait variation as well as their influence on species classification using a hurdle regression model. RESULTS: Spectral reflectance accurately differentiated tree species (κ = 0.736, ±0.005). Foliar spectra showed strong phylogenetic signal, and classification errors from foliar spectra, although present at higher taxonomic levels, were found predominantly between closely related species, often of the same genus. In addition, we find functional and phylogenetic distance broadly control the occurrence and frequency of spectral classification mistakes among species. CONCLUSIONS: Our results further support the link between leaf spectral diversity, taxonomic hierarchy, and phylogenetic and functional diversity, and highlight the potential of spectroscopy to remotely sense plant biodiversity and vegetation response to global change.

EM-COGLOAD: An investigation into age and cognitive load detection using eye tracking and deep learning.

Alzheimer's Disease is the most prevalent neurodegenerative disease, and is a leading cause of disability among the elderly. Eye movement behaviour demonstrates potential as a non-invasive biomarker for Alzheimer's Disease, with changes detectable at an early stage after initial onset. This paper introduces a new publicly available dataset: EM-COGLOAD (available at https://osf.io/zjtdq/, DOI: 10.17605/OSF.IO/ZJTDQ). A dual-task paradigm was used to create effects of declined cognitive performance in 75 healthy adults as they carried out visual tracking tasks. Their eye movement was recorded, and time series classification of the extracted eye movement traces was explored using a range of deep learning techniques. The results of this showed that convolutional neural networks were able to achieve an accuracy of 87.5% when distinguishing between eye movement under low and high cognitive load, and 76% when distinguishing between the oldest and youngest age groups.

Multidirectional instability of the shoulder: a systematic review with a novel classification.

Purpose: A variety of instabilities are grouped under multidirectional instability (MDI) of the shoulder. This makes understanding its diagnostic process, presentation and treatment difficult due to lack of evidence-based consensus. This review aims to propose a novel classification for subtypes of MDI. Methods: A systematic search was performed on PubMed Medline and Embase. A combination of the following 'MeSH' and 'non-MesH' search terms were used: (1) Glenohumeral joint[tiab] OR Glenohumeral[tiab] OR Shoulder[tiab] OR Shoulder joint[tiab] OR Shoulder[MeSH] OR Shoulder joint[MeSH], (2) Multidirectional[tiab], (3) Instability[tiab] OR Joint instability[MeSH]. Sixty-eight publications which met our criteria were included. Results: There was a high degree of heterogeneity in the definition of MDI. Thirty-one studies (46%) included a trauma etiology in the definition, while 23 studies (34%) did not. Twenty-five studies (37%) excluded patients with labral or bony injuries. Only 15 (22%) studies defined MDI as a global instability (instability in all directions), while 28 (41%) studies considered MDI to be instability in two directions, of which one had to include the inferior direction. Six (9%) studies included the presence of global ligamentous laxity as part of the definition. To improve scientific accuracy, the authors propose a novel AB classification which considers traumatic etiology and the presence of hyperlaxity when subdividing MDI. Conclusion: MDI is defined as symptomatic instability of the shoulder joint in two or more directions. A comprehensive classification system that considers predisposing trauma and the presence of hyperlaxity can provide a more precise assessment of the various existing subtypes of MDI. Level of Evidence: III.