Diagnosis of Schizophrenia Using EEG Sensor Data: A Novel Approach with Automated Log Energy-Based Empirical Wavelet Reconstruction and Cepstral Features.

Schizophrenia (SZ) is a severe mental disorder characterised by disruptions in cognition, behaviour, and perception, significantly impacting an individual's life. Traditional SZ diagnosis methods are labour-intensive and prone to errors. This study presents an innovative automated approach for detecting SZ acquired through electroencephalogram (EEG) sensor signals, aiming to improve diagnostic efficiency and accuracy. We utilised Fast Independent Component Analysis to remove artefacts from raw EEG sensor data. A novel Automated Log Energy-based Empirical Wavelet Reconstruction (ALEEWR) technique was introduced to reconstruct decomposed modes based on their variability, ensuring effective extraction of meaningful EEG signatures. Cepstral-based features-cepstral activity, cepstral mobility, and cepstral complexity-were used to capture the power, rate of change, and irregularity of the cepstrum of preprocessed EEG signals. ANOVA-based feature selection was applied to refine these features before classification using the K-Nearest Neighbour (KNN) algorithm. Our approach achieved an exceptional accuracy of 99.4%, significantly surpassing previous methods. The proposed ALEEWR and cepstral analysis demonstrated high precision, sensitivity, and specificity in the automated diagnosis of schizophrenia. This study introduces a highly accurate and efficient method for SZ detection using EEG technology. The proposed techniques offer significant improvements in diagnostic accuracy, with potential implications for enhancing SZ diagnosis and patient care through automated systems.

Multilevel Pain Assessment with Functional Near-Infrared Spectroscopy: Evaluating ΔHBO2 and ΔHHB Measures for Comprehensive Analysis.

Assessing pain in non-verbal patients is challenging, often depending on clinical judgment which can be unreliable due to fluctuations in vital signs caused by underlying medical conditions. To date, there is a notable absence of objective diagnostic tests to aid healthcare practitioners in pain assessment, especially affecting critically-ill or advanced dementia patients. Neurophysiological information, i.e., functional near-infrared spectroscopy (fNIRS) or electroencephalogram (EEG), unveils the brain's active regions and patterns, revealing the neural mechanisms behind the experience and processing of pain. This study focuses on assessing pain via the analysis of fNIRS signals combined with machine learning, utilising multiple fNIRS measures including oxygenated haemoglobin (ΔHBO2) and deoxygenated haemoglobin (ΔHHB). Initially, a channel selection process filters out highly contaminated channels with high-frequency and high-amplitude artifacts from the 24-channel fNIRS data. The remaining channels are then preprocessed by applying a low-pass filter and common average referencing to remove cardio-respiratory artifacts and common gain noise, respectively. Subsequently, the preprocessed channels are averaged to create a single time series vector for both ΔHBO2 and ΔHHB measures. From each measure, ten statistical features are extracted and fusion occurs at the feature level, resulting in a fused feature vector. The most relevant features, selected using the Minimum Redundancy Maximum Relevance method, are passed to a Support Vector Machines classifier. Using leave-one-subject-out cross validation, the system achieved an accuracy of 68.51%±9.02% in a multi-class task (No Pain, Low Pain, and High Pain) using a fusion of ΔHBO2 and ΔHHB. These two measures collectively demonstrated superior performance compared to when they were used independently. This study contributes to the pursuit of an objective pain assessment and proposes a potential biomarker for human pain using fNIRS.

Experimental Exploration of Multilevel Human Pain Assessment Using Blood Volume Pulse (BVP) Signals.

Critically ill patients often lack cognitive or communicative functions, making it challenging to assess their pain levels using self-reporting mechanisms. There is an urgent need for an accurate system that can assess pain levels without relying on patient-reported information. Blood volume pulse (BVP) is a relatively unexplored physiological measure with the potential to assess pain levels. This study aims to develop an accurate pain intensity classification system based on BVP signals through comprehensive experimental analysis. Twenty-two healthy subjects participated in the study, in which we analyzed the classification performance of BVP signals for various pain intensities using time, frequency, and morphological features through fourteen different machine learning classifiers. Three experiments were conducted using leave-one-subject-out cross-validation to better examine the hidden signatures of BVP signals for pain level classification. The results of the experiments showed that BVP signals combined with machine learning can provide an objective and quantitative evaluation of pain levels in clinical settings. Specifically, no pain and high pain BVP signals were classified with 96.6% accuracy, 100% sensitivity, and 91.6% specificity using a combination of time, frequency, and morphological features with artificial neural networks (ANNs). The classification of no pain and low pain BVP signals yielded 83.3% accuracy using a combination of time and morphological features with the AdaBoost classifier. Finally, the multi-class experiment, which classified no pain, low pain, and high pain, achieved 69% overall accuracy using a combination of time and morphological features with ANN. In conclusion, the experimental results suggest that BVP signals combined with machine learning can offer an objective and reliable assessment of pain levels in clinical settings.

Intensive Stuttering Therapy with Telepractice Follow-Up: Longitudinal Outcomes.

PURPOSE: This study estimated the treatment outcomes of a behavioral stuttering therapy program that blended a combination of intensive face-to-face therapy with telepractice-based follow-up therapy. METHOD: A total of 17 participants (mean age = 22 years) who stutter participated in the program, preceded by an extended baseline period. The participants completed a series of assessments conducted over multiple time points, spanning a total of 42 weeks. Growth curve modeling was used to analyze the changes participants experienced in the frequency of stuttering, stuttering severity, communication attitudes, and quality of life. RESULTS: The participants demonstrated stability throughout the extended baseline period, and experienced positive outcomes from the intensive program and the gains in communication attitudes and quality of life were largely maintained with weekly follow-up telepractice sessions. However, stuttering frequency and severity increased when the telepractice follow-up session frequency transitioned to a biweekly basis. Neither gender nor age group predicted the treatment outcomes for frequency or severity of stuttering. Gender-based differences were found for the treatment outcomes of specific self-report measures, with male participants having demonstrated a greater proportional decline on their standard scores, relative to female participants. Outcomes were similar for both adolescents and adults. CONCLUSIONS: Participants attending the intensive stuttering therapy program experienced positive and significant changes in their speech, attitudes toward communication, and overall quality of life, which were maintained over time with structured, weekly telepractice follow-up sessions.

Self-Regulation Development Among Young Spanish-English Dual Language Learners.

Despite strong evidence self-regulation skills are critical for school readiness, there remains a dearth of longitudinal studies that describe developmental trajectories of self-regulation, particularly among low-resource and underrepresented populations such as Spanish-English dual-language learners (DLLs). The present study examined individual differences in trajectories of self-regulation among 459 Spanish-English DLLs who were Hispanic from four different samples and three geographic locations in the U.S. Self-regulation was assessed in all samples using repeated administration of the Head-Toes-Knees-Shoulders (HTKS) task from early childhood through early elementary school. Results of latent growth curve analyses revealed that growth was best represented by quadratic trajectories. Latent class growth analyses captured significant individual differences in self-regulation trajectories. One group of children (41%) started with higher HTKS scores and displayed rapid early growth in performance. A similar percentage of children (41%) displayed intermediate growth in self-regulation, starting with lower HTKS scores but displaying rapid growth commencing arrange 4.5 years. Finally, about 18% of the sample did not display growth in HTKS performance until after entry to elementary school, around age 6 years. Girls were half as likely as boys to be in this later developing group. Likewise, children from families at the upper end of the socioeconomic distribution in this low-income sample were significantly less likely to be in the later developing group relative to children from families with lower SES. Study findings indicate the importance of monitoring growth rates in self-regulation as a means of identifying children at risk for entering school without the requisite self-regulation skills.

Osteosarcoma of the Trapezium.

Osteogenic sarcoma is a malignant tumor that rarely affects the hand. When it does, it most often involves the phalanges or metacarpal heads. We present the case of a 51-year-old woman with a low-grade osteosarcoma affecting the trapezium bone of her left hand. A total trapeziectomy with partial removal of the first metatarsal, scaphoid, trapezoid, and capitate bones was performed, and no adjuvant therapy was administered. Six years after the intervention, the patient is disease-free, with excellent functionality and yearly imaging tests showing no signs of recurrence.

The Quality of Mother-Toddler Communication Predicts Language and Early Literacy in Mexican-American Children from Low-Income Households.

This longitudinal study documents the key role of early joint engagement in the language and early literacy development of Mexican-American children from low-income households. This rapidly growing population often faces challenges as sequential Spanish-English language learners. Videos of 121 mothers and their 2.5-year-old children interacting in Spanish for 15 min were recorded in 2009-2011 in the Dallas-Fort Worth metropolitan area. Researchers reliably rated general dyadic features of joint engagement-symbol-infused joint engagement, shared routines and rituals, and fluency and connectedness-that have been found to facilitate language development in young English-speaking children. The construct respeto, a valued aspect of traditional Latino parenting, was also rated using two culturally specific items-the parent's calm authority and the child's affiliative obedience. In addition, three individual contributions-maternal sensitivity, quality of maternal language input, and quality of child language production-were assessed. General features of joint engagement at 2.5 years predicted expressive and receptive language at 3.6 years and receptive language and early literacy at 7.3 years, accounting for unique variance over and above individual contributions at 2.5 years, with some effects being stronger in girls than boys. The level of culturally specific joint engagement did not alter predictions made by general features of joint engagement. These findings highlight the importance of the quality of early communication for language and literacy success of Mexican-American children from low-income households and demonstrate that culturally specific aspects of early interactions can align well with general features of joint engagement.

Axial superresolution via multiangle TIRF microscopy with sequential imaging and photobleaching.

We report superresolution optical sectioning using a multiangle total internal reflection fluorescence (TIRF) microscope. TIRF images were constructed from several layers within a normal TIRF excitation zone by sequentially imaging and photobleaching the fluorescent molecules. The depth of the evanescent wave at different layers was altered by tuning the excitation light incident angle. The angle was tuned from the highest (the smallest TIRF depth) toward the critical angle (the largest TIRF depth) to preferentially photobleach fluorescence from the lower layers and allow straightforward observation of deeper structures without masking by the brighter signals closer to the coverglass. Reconstruction of the TIRF images enabled 3D imaging of biological samples with 20-nm axial resolution. Two-color imaging of epidermal growth factor (EGF) ligand and clathrin revealed the dynamics of EGF-activated clathrin-mediated endocytosis during internalization. Furthermore, Bayesian analysis of images collected during the photobleaching step of each plane enabled lateral superresolution (<100 nm) within each of the sections.

Cortical Network Response to Acupuncture and the Effect of the Hegu Point: An fNIRS Study.

Acupuncture is a practice of treatment based on influencing specific points on the body by inserting needles. According to traditional Chinese medicine, the aim of acupuncture treatment for pain management is to use specific acupoints to relieve excess, activate qi (or vital energy), and improve blood circulation. In this context, the Hegu point is one of the most widely-used acupoints for this purpose, and it has been linked to having an analgesic effect. However, there exists considerable debate as to its scientific validity. In this pilot study, we aim to identify the functional connectivity related to the three main types of acupuncture manipulations and also identify an analgesic effect based on the hemodynamic response as measured by functional near-infrared spectroscopy (fNIRS). The cortical response of eleven healthy subjects was obtained using fNIRS during an acupuncture procedure. A multiscale analysis based on wavelet transform coherence was employed to assess the functional connectivity of corresponding channel pairs within the left and right somatosensory region. The wavelet analysis was focused on the very-low frequency oscillations (VLFO, 0.01⁻0.08 Hz) and the low frequency oscillations (LFO, 0.08⁻0.15 Hz). A mixed model analysis of variance was used to appraise statistical differences in the wavelet domain for the different acupuncture stimuli. The hemodynamic response after the acupuncture manipulations exhibited strong activations and distinctive cortical networks in each stimulus. The results of the statistical analysis showed significant differences ( p < 0.05 ) between the tasks in both frequency bands. These results suggest the existence of different stimuli-specific cortical networks in both frequency bands and the anaesthetic effect of the Hegu point as measured by fNIRS.

Moving Forward by Looking Back: Understanding Why Some Spanish-Speaking English Learners Fall Behind.

This study investigated early indicators of Spanish-speaking English learners (ELs) at risk for reading difficulties at the end of Grade 2 by examining their early bilingual oral language development, taking into account language of academic instruction. Standardized measures of reading and narrative samples were collected in English and Spanish from kindergarten to Grade 2 from 1,243 ELs primarily instructed in English or Spanish. Conditional growth curve models yielded four primary findings of reading and oral language development. First, ELs with low reading achievement at the end of Grade 2 demonstrated early reading difficulties during kindergarten. Second, although ELs demonstrated overall higher reading achievement in their instructed language, this difference decreased over time. Third, ELs with low reading achievement at the end of Grade 2 demonstrated lower oral language skills in each language over time. Fourth, ELs demonstrated overall higher oral language skills in their instructed language, yet these differences varied over time. The study provided a detailed description of the longitudinal relations among the bilingual reading and oral language skills of Spanish-speaking ELs during the early school years. These findings help to inform the processes of early identification and intervention for Spanish-speaking ELs who are likely to demonstrate reading achievement difficulties.

IQ-Achievement Discrepancy for Identification of Disabilities in Spanish-speaking English Learners.

This article examines the validity of IQ-achievement discrepancy and low achievement as criteria for the identification of disabilities in Spanish-speaking English-language learners (ELs) and the factors that moderate the validity of these approaches as bases for identification. While there has been a long history of examining the validity of different approaches to disability identification in monolinguals, there are no systematic approaches taken for ELs. Data from Grades 1 and 2 of a large longitudinal data set consisting of young Spanish-speaking students attending schools in the United States were used to empirically examine criteria for disability identification among language minority children-one of the first large-scale attempts. Findings indicated significant overidentification when the language of assessment was not matched to the language of the instruction, although the effects varied predictably over time and by language of instruction. Validation of classifications using measures external to the classification found that low achieving and discrepant children differ from typically developing children, and from one another in predictable ways based on differences in IQ. The study highlights the importance of taking into account the language of instruction and the severity of the cut-off to reduce misidentification of typically developing children.

Speaking and Reading in Two Languages: On the Identification of Reading and Language Disabilities in Spanish-Speaking English Learners.

Articles in this issue examine (1) the primary sources of variability in reading and language achievement among Spanish-speaking English learners (ELs) in the United States, (2) the extent to which poor performance at the end of grade 2 is identifiable in developmental trajectories beginning in kindergarten, (3) the relations among core reading constructs of phonological awareness and decoding in both English and Spanish and the factors that affect their relationship, (4) the performance of different approaches to identification and the factors that influence how well they work, as well as (5) the growing literature focused on intervention for reading problems in this population. This article examines the literature on language minority students and disability identification and analyzes a large-scale longitudinal dataset (>4,000 ELs; >15,000 observations) to systematically characterize and describe the oral language and reading development of Spanish-speaking children designated as ELs from kindergarten to second grade, considering a range of factors that may potentially contribute to that characterization and its relation to academic performance. This systematic characterization should facilitate the development of an empirical basis for a theoretically grounded framework of typical development in ELs in order to more precisely identify those children with language and learning disabilities.

Clinical impact of wordless picture storybooks on bilingual narrative language production: A comparison of the 'Frog' stories.

BACKGROUND: Language sampling, recognized as a gold standard for expressive language assessment, is often elicited using wordless picture storybooks. A series of wordless storybooks, commonly referred to as 'Frog' stories, have been frequently used in language-based research with children from around the globe. AIMS: To examine the impact that differences in stories have on narrative output by comparing narrative productions across a series of five storybooks produced by 831 bilingual (Spanish-English) children in kindergarten through third grade. METHODS & PROCEDURES: Each participant produced oral narratives using one of the five Frog storybooks in both English and Spanish. The narratives were recorded, transcribed and coded for a variety of measures of language production. OUTCOMES & RESULTS: Negligible differences were observed in language measures when comparing groups of children who told different stories, with the exception of lexical diversity. CONCLUSIONS & IMPLICATIONS: The implications of using different storybooks to elicit narrative language samples from children are discussed from the perspectives of research and clinical practice.

Child Vocabulary, Maternal Behavior, and Inhibitory Control Development Among Spanish-Speaking Children.

RESEARCH FINDINGS: The roles of child lexical diversity and maternal sensitivity in the development of young children's inhibitory control were examined in 100 low-income Hispanic Spanish-speaking children. Child communication utterances at age 2½ years were transcribed from 10-min mother-child interactions to quantify lexical diversity. Maternal behavior was rated independently from the interactions. Inhibitory control was measured with a battery of tasks at ages 2½ and 3½. Greater maternal sensitivity was correlated with higher vocabulary at 2½. Greater vocabulary predicted positive growth in child inhibitory control skills from ages 2½ to 3½ in multivariable regression models that controlled for maternal education, family income, the home environment, and mothering quality. PRACTICE OR POLICY: These findings suggest that supporting vocabulary development in low-income Spanish-speaking children is important for the development of inhibitory control skills, an important foundation for school readiness and academic success.

A computational and experimental study of O-glycosylation. Catalysis by human UDP-GalNAc polypeptide:GalNAc transferase-T2.

It is estimated that >50% of proteins are glycosylated with sugar tags that can modulate protein activity through what has been called the sugar code. Here we present the first QM/MM calculations of human GalNAc-T2, a retaining glycosyltransferase, which initiates the biosynthesis of mucin-type O-glycans. Importantly, we have characterized a hydrogen bond between the ß-phosphate of UDP and the backbone amide group from the Thr7 of the sugar acceptor (EA2 peptide) that promotes catalysis and that we propose could be a general catalytic strategy used in peptide O-glycosylation by retaining glycosyltransferases. Additional important substrate-substrate interactions have been identified, for example, between the ß-phosphate of UDP with the attacking hydroxyl group from the acceptor substrate and with the substituent at the C2' position of the transferred sugar. Our results support a front-side attack mechanism for this enzyme, with a barrier height of ~20 kcal mol(-1) at the QM(M05-2X/TZVP//BP86/SVP)/CHARMM22 level, in reasonable agreement with the experimental kinetic data. Experimental and in silico mutations show that transferase activity is very sensitive to changes in residues Glu334, Asn335 and Arg362. Additionally, our calculations for different donor substrates suggest that human GalNAc-T2 would be inactive if 2'-deoxy-Gal or 2'-oxymethyl-Gal were used, while UDP-Gal is confirmed as a valid sugar donor. Finally, the analysis herein presented highlights that both the substrate-substrate and the enzyme-substrate interactions are mainly concentrated on stabilizing the negative charge developing at the UDP leaving group as the transition state is approached, identifying this as a key aspect of retaining glycosyltransferases catalysis.

La Rana or El Rana: Dual Language Learners' Grammatical Variability in Narrative Retells.

PURPOSE: This longitudinal study investigated the trajectory of Spanish article accuracy in Spanish-English dual language learners (DLLs) from preschool to first grade, addressing the need for longitudinal data on the variability of Spanish grammatical skills in DLLs in English immersion classrooms. METHOD: Language sample analysis was conducted on 336 Spanish and English narrative retells elicited from 31 Spanish-English DLLs (range: 45-85 months). Growth curve models captured within- and between-individual change in article accuracy from the beginning of preschool to the end of first grade. RESULTS: As a group, DLLs did not exhibit significant positive or negative growth in Spanish article accuracy over time. On average, article accuracy remained stable at 76% from preschool throughout first grade. Participants exhibited significant variability in article accuracy that was partly explained by changes in Spanish proficiency. Spanish article accuracy was lower for DLLs with lower Spanish proficiency indexed by measures from the Spanish language samples, while English proficiency indexed by the English language samples did not affect Spanish article accuracy. CONCLUSIONS: These findings suggest that expectations for Spanish grammatical performance in DLLs need to be adjusted to account for the possible impact of not receiving Spanish support in English immersion school settings. DLLs in these instructional programs do not exhibit article accuracy at a level expected for monolingual Spanish speakers. Significant individual differences in both individual status and growth rates of Spanish article accuracy highlight the broad variability in Spanish language skills of DLLs in the United States.

Empirical comparison of deep learning models for fNIRS pain decoding.

Introduction: Pain assessment is extremely important in patients unable to communicate and it is often done by clinical judgement. However, assessing pain using observable indicators can be challenging for clinicians due to the subjective perceptions, individual differences in pain expression, and potential confounding factors. Therefore, the need for an objective pain assessment method that can assist medical practitioners. Functional near-infrared spectroscopy (fNIRS) has shown promising results to assess the neural function in response of nociception and pain. Previous studies have explored the use of machine learning with hand-crafted features in the assessment of pain. Methods: In this study, we aim to expand previous studies by exploring the use of deep learning models Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and (CNN-LSTM) to automatically extract features from fNIRS data and by comparing these with classical machine learning models using hand-crafted features. Results: The results showed that the deep learning models exhibited favourable results in the identification of different types of pain in our experiment using only fNIRS input data. The combination of CNN and LSTM in a hybrid model (CNN-LSTM) exhibited the highest performance (accuracy = 91.2%) in our problem setting. Statistical analysis using one-way ANOVA with Tukey's (post-hoc) test performed on accuracies showed that the deep learning models significantly improved accuracy performance as compared to the baseline models. Discussion: Overall, deep learning models showed their potential to learn features automatically without relying on manually-extracted features and the CNN-LSTM model could be used as a possible method of assessment of pain in non-verbal patients. Future research is needed to evaluate the generalisation of this method of pain assessment on independent populations and in real-life scenarios.

Functional architecture of the retromer cargo-recognition complex.

The retromer complex is required for the sorting of acid hydrolases to lysosomes, transcytosis of the polymeric immunoglobulin receptor, Wnt gradient formation, iron transporter recycling and processing of the amyloid precursor protein. Human retromer consists of two smaller complexes: the cargo recognition VPS26-VPS29-VPS35 heterotrimer and a membrane-targeting heterodimer or homodimer of SNX1 and/or SNX2 (ref. 13). Here we report the crystal structure of a VPS29-VPS35 subcomplex showing how the metallophosphoesterase-fold subunit VPS29 (refs 14, 15) acts as a scaffold for the carboxy-terminal half of VPS35. VPS35 forms a horseshoe-shaped, right-handed, alpha-helical solenoid, the concave face of which completely covers the metal-binding site of VPS29, whereas the convex face exposes a series of hydrophobic interhelical grooves. Electron microscopy shows that the intact VPS26-VPS29-VPS35 complex is a stick-shaped, flexible structure, approximately 21 nm long. A hybrid structural model derived from crystal structures, electron microscopy, interaction studies and bioinformatics shows that the alpha-solenoid fold extends the full length of VPS35, and that VPS26 is bound at the opposite end from VPS29. This extended structure presents multiple binding sites for the SNX complex and receptor cargo, and appears capable of flexing to conform to curved vesicular membranes.

The language growth of spanish-speaking English language learners.

Although the research literature regarding language growth trajectories is burgeoning, the shape and direction of English Language Learners' (ELLs) language growth trajectories are largely not known. This study used growth curve modeling to determine the shape of ELLs' language growth trajectories across 12,248 oral narrative language samples (6,516 Spanish; 5,732 English) produced by 1,723 ELLs during the first 3 years of formal schooling (M age at first observation = 5 years 7 months). Results indicated distinct trajectories of language growth over time for each language differentially impacted by summer vacation and gender, significant intra- and interindividual differences in initial status and growth rates across both languages, and language-specific relations between language growth and initial status. Implications of ELLs' language growth are discussed.

A systematic review of neurophysiological sensing for the assessment of acute pain.

Pain is a complex and personal experience that presents diverse measurement challenges. Different sensing technologies can be used as a surrogate measure of pain to overcome these challenges. The objective of this review is to summarise and synthesise the published literature to: (a) identify relevant non-invasive physiological sensing technologies that can be used for the assessment of human pain, (b) describe the analytical tools used in artificial intelligence (AI) to decode pain data collected from sensing technologies, and (c) describe the main implications in the application of these technologies. A literature search was conducted in July 2022 to query PubMed, Web of Sciences, and Scopus. Papers published between January 2013 and July 2022 are considered. Forty-eight studies are included in this literature review. Two main sensing technologies (neurological and physiological) are identified in the literature. The sensing technologies and their modality (unimodal or multimodal) are presented. The literature provided numerous examples of how different analytical tools in AI have been applied to decode pain. This review identifies different non-invasive sensing technologies, their analytical tools, and the implications for their use. There are significant opportunities to leverage multimodal sensing and deep learning to improve accuracy of pain monitoring systems. This review also identifies the need for analyses and datasets that explore the inclusion of neural and physiological information together. Finally, challenges and opportunities for designing better systems for pain assessment are also presented.