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Word segmentation is a crucial step in children's vocabulary learning. While computational models of word segmentation can capture infants' performance in small-scale artificial tasks, the examination of early word segmentation in naturalistic settings has been limited by the lack of measures that can relate models' performance to developmental data. Here, we extended CLASSIC (Chunking Lexical and Sublexical Sequences in Children; Jones et al., 2021), a corpus-trained chunking model that can simulate several memory and phonological and vocabulary learning phenomena to allow it to perform word segmentation using utterance boundary information, and we have named this extended version CLASSIC utterance boundary (CLASSIC-UB). Further, we compared our model to the performance of children on a wide range of new measures, capitalizing on the link between word segmentation and vocabulary learning abilities. We showed that the combination of chunking and utterance-boundary information used by CLASSIC utterance boundary allowed a better prediction of English-learning children's output vocabulary than did other models.

Resumo: Embora se reconheça clara mudança em seu posicionamento político, de liberal a conservador, pode-se dizer que, no campo das ideias econômicas, Francisco de Salles Torres Homem (visconde de Inhomirim) manteve a lógica do que defendeu desde o início de suas atividades intelectuais. Considerando seus escritos de 1836, sua atuação nos debates parlamentares da década de 1850 e seu discurso no Senado em favor da Lei do Ventre Livre em 1871, nota-se uma linha coerente de argumentação especialmente em três assuntos: escravidão, moeda e finanças públicas. A fim de sustentar a hipótese de manutenção da coerência de suas ideias econômicas no decorrer de sua atuação política, a primeira seção deste trabalho analisa os artigos publicados nos periódicos Nitheroy e Minerva Fluminense entre 1836 e 1844, bem como os discursos parlamentares proferidos entre 1857 e 1871. Por ter sido ministro da Fazenda (1858-1859) e presidente do Banco do Brasil (1866-1869), a segunda seção reforça a hipótese em tela ao cotejar sua argumentação teórica com as medidas adotadas enquanto gestor de política econômica. Para tanto, valeu-se do relatório do Ministério da Fazenda (1858) e das atas de reunião da diretoria do Banco do Brasil.

Aging is the main risk factor of cognitive neurodegenerative diseases such as Alzheimer's disease, with epigenome alterations as a contributing factor. Here, we compared transcriptomic/epigenomic changes in the hippocampus, modified by aging and by tauopathy, an AD-related feature. We show that the cholesterol biosynthesis pathway is severely impaired in hippocampal neurons of tauopathic but not of aged mice pointing to vulnerability of these neurons in the disease. At the epigenomic level, histone hyperacetylation was observed at neuronal enhancers associated with glutamatergic regulations only in the tauopathy. Lastly, a treatment of tau mice with the CSP-TTK21 epi-drug that restored expression of key cholesterol biosynthesis genes counteracted hyperacetylation at neuronal enhancers and restored object memory. As acetyl-CoA is the primary substrate of both pathways, these data suggest that the rate of the cholesterol biosynthesis in hippocampal neurons may trigger epigenetic-driven changes, that may compromise the functions of hippocampal neurons in pathological conditions.

Abstract Background Pallister-Killian syndrome (PKS) is a rare genetic disorder caused by mosaic tetrasomy of 12p with wide neurological involvement. Intellectual disability, developmental delay, behavioral problems, epilepsy, sleep disturbances, and brain malformations have been described in most individuals, with a broad phenotypic spectrum. This observational study, conducted through brain MRI scan analysis on a cohort of patients with genetically confirmed PKS, aims to systematically investigate the neuroradiological features of this syndrome and identify the possible existence of a typical pattern. Moreover, a literature review differentiating the different types of neuroimaging data was conducted for comparison with our population. Results Thirty-one individuals were enrolled (17 females/14 males; age range 0.1–17.5 years old at first MRI). An experienced pediatric neuroradiologist reviewed brain MRIs, blindly to clinical data. Brain abnormalities were observed in all but one individual (compared to the 34% frequency found in the literature review). Corpus callosum abnormalities were found in 20/30 (67%) patients: 6 had callosal hypoplasia; 8 had global hypoplasia with hypoplastic splenium; 4 had only hypoplastic splenium; and 2 had a thin corpus callosum. Cerebral hypoplasia/atrophy was found in 23/31 (74%) and ventriculomegaly in 20/31 (65%). Other frequent features were the enlargement of the cisterna magna in 15/30 (50%) and polymicrogyria in 14/29 (48%). Conversely, the frequency of the latter was found to be 4% from the literature review. Notably, in our population, polymicrogyria was in the perisylvian area in all 14 cases, and it was bilateral in 10/14. Conclusions Brain abnormalities are very common in PKS and occur much more frequently than previously reported. Bilateral perisylvian polymicrogyria was a main aspect of our population. Our findings provide an additional tool for early diagnosis.Further studies to investigate the possible correlations with both genotype and phenotype may help to define the etiopathogenesis of the neurologic phenotype of this syndrome.

IntroductionCoronavirus disease 2019 (COVID-19) is a highly contagious viral illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has had a dramatic effect on the world, resulting in millions of deaths worldwide and causing drastic changes in daily life. A study reported that septic complications were associated with high mortality in COVID-19 patients. This study aimed to evaluate how the COVID-19 pandemic changed the pre-pandemic and post-pandemic prevalence of sepsis in ICUs and to evaluate the different risk factors associated with mortality and the different diffusion of microorganisms and their resistance.Materials and methodsWe conducted a single-center retrospective observational clinical study, observing all patients in the ICU of the SS Annunziata Hospital in Chieti (Italy) who were diagnosed with sepsis and had a bacterial isolate from their blood culture. Sepsis was diagnosed by SEPSIIS III criteria. We enrolled all in-patients in the ICU from January 2018 to December 2021. We divided the patients into three groups: (1) non-pandemic period (Np) hospitalized in 2018–2019, (2) pandemic period (Pp)-COVID hospitalized in 2020–2021 with a diagnosis of COVID-19, and (3) Pp-non-COVID patients hospitalized in 2020–2021 without a diagnosis of COVID-19.ResultsFrom January 2018 to December 2021, 1,559 patients were admitted to the ICU, of which 211 patients [36 (17.1%) in 2018, 52 (24.6%) in 2019, 73 (34.6%) in 2020, and 50 (23.7%) in 2021, respectively] met the selection criteria: 88 patients in period Np, 67 patients in Pp without COVID-19, and 56 patients Pp with COVID-19. The overall mortality of these patients was high (65.9% at 30 days in Np), but decreased during the Pp (60.9%): Pp-non-COVID was 56.7% vs. Pp-COVID 66.1%, with a statistically significant association with APACHE III score (OR 1.08, 95%CI 1.04–1.12, p

Abstract Nematodes represent >3/5 of the abundance of the world’s metazoans and usually account for nearly 90% of the total benthic fauna, playing a key ecological role in the benthic ecosystem functioning on a global scale. These small metazoans include a relevant number of microscopic predators and, in turn, are the most abundant preys of macro-megafauna and fish juveniles thus playing a key role in marine food webs. Here, using two independent approaches, we test the bioaccumulation in marine nematodes of several heavy metals present in contaminated sediments. We report here that nematodes, despite their short life cycle and small size, bioaccumulate significantly heavy metals. Bioaccumulation increases from deposit feeders and microalgal grazers to predators of microbes and other tiny metazoans. These results suggest that nematodes also contribute to their biomagnification along the food webs and can contribute to increase the transfer of contaminants from the sediments to larger organisms.

Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset of 40 subjects was used to evaluate PHASOR against state-of-the-art feature sets in a five-phase gait recognition problem. Additionally, fully data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. The separability index (SI) and mean semi-principal axis (MSA) analyses showed mean SI and MSA metrics of 7.7 and 0.5, respectively, indicating the proposed approach’s ability to effectively decode gait phases through EMG activity. The SVM classifier demonstrated the highest accuracy of 82% using a five-fold leave-one-trial-out testing approach, outperforming Rocket and Mini-Rocket. This study confirms that in gait phase recognition based on EMG signals, novel and efficient muscle synergy information feature extraction schemes, such as PHASOR, can compete with deep learning approaches that require greater processing time for feature extraction and classification.

Introduction Appendicitis is the surgical disease with the highest prevalence in emergency rooms. Its clinical and/or surgical complications are associated with the time course of symptoms, age, comorbidities, and stages of the disease.

Recent evidence indicates that children represent and learn multiple meanings of ambiguous words from early in development (e.g., mail letter, alphabetic letter). This raises the question of which naturalistic factors might allow young children to resolve lexical ambiguities. Previous research has shown that children’s processing of ambiguous words is facilitated by verb-related information. However, it is still unclear whether such facilitation comes from bottom-up (lexical associations) or top-down information sources (verb-event structures). In this study, we leveraged a large sense-annotated child-directed speech corpus to disentangle the effect of bottom-up lexical and top-down event structure cues. Preliminary results show that 4-year-olds might rely on verb-event structures when these are put in competition with lexical association. We discuss implications for theories of sentence parsing and word learning.

The development of contactless methods to assess the degree of personal hygiene in elderly people is crucial for detecting frailty and providing early intervention to prevent complete loss of autonomy, cognitive impairment, and hospitalisation. The unobtrusive nature of the technology is essential in the context of maintaining good quality of life. The use of cameras and edge computing with sensors provides a way of monitoring subjects without interrupting their normal routines, and has the advantages of local data processing and improved privacy. This work describes the development an intelligent system that takes the RGB frames of a video as input to classify the occurrence of brushing teeth, washing hands, and fixing hair. No action activity is considered. The RGB frames are first processed by two Mediapipe algorithms to extract body keypoints related to the pose and hands, which represent the features to be classified. The optimal feature extractor results from the most complex Mediapipe pose estimator combined with the most complex hand keypoint regressor, which achieves the best performance even when operating at one frame per second. The final classifier is a Light Gradient Boosting Machine classifier that achieves more than 94% weighted F1-score under conditions of one frame per second and observation times of seven seconds or more. When the observation window is enlarged to ten seconds, the F1-scores for each class oscillate between 94.66% and 96.35%.

Recent studies have highlighted the possibility of using surface electromyographic (EMG) signals to develop human–computer interfaces that are also able to recognize complex motor tasks involving the hand as the handwriting of digits. However, the automatic recognition of words from EMG information has not yet been studied. The aim of this study is to investigate the feasibility of using combined forearm and wrist EMG probes for solving the handwriting recognition problem of 30 words with consolidated machine-learning techniques and aggregating state-of-the-art features extracted in the time and frequency domains. Six healthy subjects, three females and three males aged between 25 and 40 years, were recruited for the study. Two tests in pattern recognition were conducted to assess the possibility of classifying fine hand movements through EMG signals. The first test was designed to assess the feasibility of using consolidated myoelectric control technology with shallow machine-learning methods in the field of handwriting detection. The second test was implemented to assess if specific feature extraction schemes can guarantee high performances with limited complexity of the processing pipeline. Among support vector machine, linear discriminant analysis, and K-nearest neighbours (KNN), the last one showed the best classification performances in the 30-word classification problem, with a mean accuracy of 95% and 85% when using all the features and a specific feature set known as TDAR, respectively. The obtained results confirmed the validity of using combined wrist and forearm EMG data for intelligent handwriting recognition through pattern recognition approaches in real scenarios.