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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.

El objetivo de este trabajo es discutir el papel de la regulación bancaria en la creación de los primeros bancos emisores iberoamericanos hasta 1870. Nos centramos en el estudio de la regulación de los bancos emisores fundados en España, Portugal, Argentina, Brasil y Chile, exmetrópolis y excolonias ibéricas. Nuestro marco de análisis toma en consideración la importancia de la ley en la construcción de las finanzas, aunque hace énfasis en la importancia del contexto y las instituciones más que en el marco legal para entender la evolución de los sistemas bancarios iberoamericanos. Analizamos las primeras constituciones, códigos de comercio, leyes bancarias y estatutos bancarios, así como la creación de bancos emisores. La principal aportación consiste en poner en evidencia los estatutos bancarios como origen de la banca emisora así como la comparación de los estatutos de los bancos analizados.

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.

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.

In the field of physiological signals monitoring and its applications, non-contact technology is often proposed as a possible alternative to traditional contact devices. The ability to extract information about a patient’s health status in an unobtrusive way, without stressing the subject and without the need of qualified personnel, fuels research in this growing field. Among the various methodologies, RADAR-based non-contact technology is gaining great interest. This scoping review aims to summarize the main research lines concerning RADAR-based physiological sensing and machine learning applications reporting recent trends, issues and gaps with the scientific literature, best methodological practices, employed standards to be followed, challenges, and future directions. After a systematic search and screening, two hundred and seven papers were collected following the guidelines of PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). The included records covered two macro-areas being regression of physiological signals or physiological features (n=77 papers) and the other a cluster of papers regarding the processing of RADAR-based physiological signals and features; the latter cluster concerns four fields of interest, being RADAR-based diagnosis (n=77), RADAR-based human behaviour monitoring (n=25), RADAR-based biometric authentication (n=19) and RADAR-based affective computing (n=9). Papers collected under the diagnosis category were further divided, on the basis of their aims: in breath pattern classification (n=41), infection detection (n=10), sleep stage classification (n=9), heart disease detection (n=9) and quality detection (n=8). Papers collected under the human behaviour monitoring were further divided based on their aims: fatigue detection (n=9), human detection (n=7), human localisation (n=4), human orientation (n=2), and activities classification (n=3).

Clostridium species are known for their impact on animal and human health, but also for the spoilage of foodstuffs. Their spores contaminate milk and result in germination and gas production, the latter being particularly evident in the cheeses that suffer severe depreciation. To address this issue, the Primary Production Department of the IZSLER institute in Brescia, Italy conducts the Most Probable Number (MPN) method on bovine milk samples collected from Northern Italian dairies between 2004 and 2023. This approach leverages two semi-quantitative protocols, S2 and S3, to detect Clostridium species spore forms upon customer request. Here, we would like to present an a-posteriori analysis on the results of the S2 and S3 protocols. The goal of this study is to highlight the differences between these two methods and provide evidence of the actual decrease in Clostridium species in raw cow milk over a 20-year period. Our analysis shows that client demand for S2 has progressively decreased, while S3’s has remained constant, and both protocols reveal a significant reduction in positives; furthermore, S3’s greater sensitivity made it more responsive to environmental changes. This highlights the necessity of choosing the appropriate testing protocol that accounts for both regulatory standards and environmental factors. Overall, our findings underscore the importance of continued monitoring to manage Clostridium species contamination and ensure milk quality.

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.

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.

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

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.

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%.

Functional magnetic resonance imaging (fMRI) based on the Blood Oxygen Level Dependent (BOLD) contrast has been extensively used to map brain activity and connectivity in health and disease. Standard fMRI preprocessing includes different steps to remove confounds unrelated to neuronal activity. First, this narrative review explores how signal fluctuations due to cardiac and respiratory activity, usually considered as “physiological noise” and regressed out from fMRI time series. However, these signal components bear useful information about some mechanisms of brain functioning (e.g., glymphatic clearance) or cerebrovascular compliance in response to arterial pressure waves. Aging and chronic diseases can cause stiffening of the aorta and other main arteries, with a reduced dampening effect resulting in greater transmission of pressure impulses to the brain. Importantly, the continuous hammering of cardiac pulsations can produce local alterations of the mechanical properties of the small cerebral vessels, with a progressive deterioration that ultimately affects neuronal functionality. Second, the review emphasizes how fMRI can study the brain patterns most affected by cardiac pulsations in health and disease with high spatiotemporal resolution, offering the opportunity to identify much more specific risk markers than systemic factors based on measurements of the vascular compliance of large arteries or other global risk factors. In this regard, modern fast fMRI acquisition techniques allow a better characterization of these pulsatile signal components due to reduced aliasing effects, turning what has been traditionally considered as noise in a signal of interest that can be used to develop novel non-invasive biomarkers in different clinical contexts.

With the recent introduction of NarrowBand Internet of Things (NB-IoT) technology in the 4th and 5th generations of mobile radio networks, the mobile communications context opens up significantly to the world of sensors. By means of NB-IoT, the mobile systems within 3GPP standardization introduce the peculiar functions of sensor networks, thus making it possible to satisfy very specific requirements with respect to those which characterize traditional mobile telecommunications. Among the functions of interest for sensor networks, the possibility of locating the positions of the sensors without an increase in costs and energy consumption of the sensor nodes is of utmost interest. The present work describes a procedure for locating the NB-IoT nodes based on the quality of radio signals received by the mobile terminals, which therefore does not require further hardware implementations on board the nodes. This procedure, based on the RF fingerprinting technique and on machine learning processing, has been tested experimentally and has achieved interesting performances.

Goal: To evaluate suitability of respiratory signals derived from clinical 12-lead electrocardiograms (ECGs) and wearable 1-lead ECG to identify different respiration types. Methods: ECGs were simultaneously acquired through the M12R ECG Holter by Global Instrumentation and the chest strap BioHarness 3.0 by Zephyr from 42 healthy subjects alternating normal breathing, breath holding, and deep breathing. Respiration signals were derived from the ECGs through the Segmented-Beat Modulation Method (SBMM)-based algorithm and the algorithms by Van Gent, Charlton, Soni and Sarkar, and characterized in terms of breathing rate and amplitude. Respiration classification was performed through a linear support vector machine and evaluated by F1 score. Results: Best F1 scores were 86.59%(lead V2) and 80.57%, when considering 12-lead and 1-lead ECGs, respectively, and using SBMM-based algorithm. Conclusion: ECG-derived respiratory signals allow reliable identification of different respiration types even when acquired through wearable sensors, if associated to appropriate processing algorithms, such as the SBMM-based algorithm.

Nematodes represent the most abundant benthic metazoan of all seas and oceans, and their relative importance increases with increasing water depth. Understanding the biodiversity patterns of this dominant phylum could be a critical step towards our comprehension of the evolutionary patterns across the largest biome of the biosphere. For instance, it has been assumed for a long time that nematodes are ubiquitous across depths, latitudes and biogeographic regions, but there is still little scientific evidence for this lack of endemism. The present study is based on a meta-analysis of nematode biodiversity data collected from 246 deep-sea sites of the Atlantic Ocean and Mediterranean Sea. We explored the cosmopolitanism, rareness and potential endemism of nematode genera in deep-sea sediments. The results of this analysis indicate that only one-third of nematode families are widely distributed and could potentially be cosmopolitan, whereas 94% of the nematode genera are linked to specific habitats or bathymetric ranges. Singleton nematode genera (i.e. genera presenting as a single individual only in one specific habitat) increased in importance with increasing water depth. We conclude that rareness and endemism may be a far more common feature than previously thought in deep-sea nematode assemblages and hypothesise that the deep ocean interior could be a huge reservoir of endemic nematode species.

Microorganisms interact with all biological components in a variety of ways. They contribute to increase the efficiency of marine food webs and facilitate the adaptation of multicellular organisms to climate change and other human-induced impacts. Increasing evidence suggests that microbiomes are essential for the health of marine species, for maintaining productive marine ecosystems, and thus for the sustainable functioning of the global biosphere. Marine microbiomes are typically species- or habitat-specific and are susceptible to environmental and human-driven changes. The microbiota of seagrasses, macroalgae, mangroves or tropical corals benefits their hosts by increasing their fitness, contributing to the removal of toxic compounds, conferring protection against pathogens, and/or supporting nutrient requirements. Alterations of the microbiomes might have negative consequences on species’ health, survival, and overall ecosystem functioning. Despite the key ecological role of microbiomes in all ecosystems, their potential for the restoration of degraded habitats is still largely unexplored. Here we present a literature survey of the existing information on the microbiota associated with habitat-forming species and suggest that the resilience/recovery of damaged marine habitats can depend largely on the changes in the microbiota. Nature-based solutions relying on microbiome analyses (also through omics approaches) enable health monitoring of transplanted organisms/metacommunities and potential identification/production of probiotics/bio-promoters to stabilize unhealthy conditions of transplants. In the context of international strategies concerning ecological restoration, the use of the scientific knowledge acquired on the marine microbiome deserves to be exploited to assist both traditional and innovative restoration approaches. The success of habitat restoration may depend on our ability to maintain, along with the restored species and habitats, a functional microbiota.

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.

Timely and reliable fetal monitoring is crucial to prevent adverse events during pregnancy and delivery. Fetal phonocardiography, i.e., the recording of fetal heart sounds, is emerging as a novel possibility to monitor fetal health status. Indeed, due to its passive nature and its noninvasiveness, the technique is suitable for long-term monitoring and for telemonitoring applications. Despite the high share of literature focusing on signal processing, no previous work has reviewed the technological hardware solutions devoted to the recording of fetal heart sounds. Thus, the aim of this scoping review is to collect information regarding the acquisition devices for fetal phonocardiography (FPCG), focusing on technical specifications and clinical use. Overall, PRISMA-guidelines-based analysis selected 57 studies that described 26 research prototypes and eight commercial devices for FPCG acquisition. Results of our review study reveal that no commercial devices were designed for fetal-specific purposes, that the latest advances involve the use of multiple microphones and sensors, and that no quantitative validation was usually performed. By highlighting the past and future trends and the most relevant innovations from both a technical and clinical perspective, this review will represent a useful reference for the evaluation of different acquisition devices and for the development of new FPCG-based systems for fetal monitoring.

Introduction Chronic constipation (CC) is a highly prevalent disease in Western society. Chronic constipation can have a different etiology in patients who underwent a cesarean section and result from postoperative stress and metabolic response to trauma, analgesic agents, immobilization, and dietary restrictions. Chronic constipation may also occur due to puerperium-related psychological changes and to the stretching and weakening of the perineal and abdominal muscles after childbirth. Objectives The present study analyzes intestinal transit restoration after a cesarean section and the influence of osmotic laxative agents. Methods The present prospective, nonrandomized sample study used the ROME III questionnaire and the Bristol stool scale in adult women who underwent a cesarean section. We divided the subjects into 2 groups, each with 30 patients, to compare the effect of the prophylactic administration of an osmotic laxative. Results We evaluated 60 randomly-chosen pregnant women from the Obstetrics ward of Hospital Santa Marcelina, São Paulo, SP, Brazil, from October 2019 to March 2020. Their mean age was 26.8 years old, and the mean gestation time was 37.95 weeks. Ten patients (16.7%) presented with constipation before the cesarean section, and 38 (63.3%) had a bowel movement after the procedure. However, in 84.2% of these patients, the usual stool consistency worsened. After the cesarean section, 46.7% of the women who did not receive laxative agents had a bowel movement, compared with 80% of those who did (p = 0.0074). Conclusion Some factors, including those related to the procedure, may hamper intestinal transit restoration after a cesarean section. Osmotic laxative agents can facilitate transit restoration with no negative effects in this group of patients.

The Mediterranean Sea is one of the most exploited regions of the world’s oceans. Here industrial activities have determined either acute or long-term impacts on coastal marine ecosystems. In this study, we investigated macrofauna distribution and diversity, and food-web functioning in a coastal area of the Mediterranean Sea facing an industrial chemical plant abandoned in the ‘90s to assess benthic ecosystem health. This area has been identified as a Site of National Interest (SNI) since 2002 and has been closed to any human activity awaiting to be remediated according to national laws. Our results indicate that, two decades after the SNI declaration (a decade after the plant decommissioning), there is no longer any sign of the impact of historical contaminations on macrofauna and benthic food web functioning. Overall, all the thirty-six sites showed high/good ecological quality according to the score assigned by AMBI and M-AMBI indexes, reflecting the absence of chronic impacts. Our findings reveal, for the first time, the positive effects of passive restoration (i.e., unassisted, or spontaneous recovery following cessation of anthropogenic impacts) on historically impacted coastal ecosystems since their health conditions, in terms of both abiotic (environmental variables and contaminant concentration) and biotic (macrofauna diversity and community composition, and benthic food-web structure) factors, were indistinguishable from surrounding non-impacted areas. These findings also suggest that other effective area-based conservation measures (OECMs) could be useful not only for biodiversity conservation of vulnerable and priority habitats in larger ocean sectors but also to promote the passive recovery of historically contaminated ecosystems.

I tentativi attuati all'interno della tradizione tomista per individuare la dottrina ufficiale dell’analogia elaborata da Tommaso d’Aquino si sono dovuti confrontare con la varietà di distinzioni tracciate dall’Aquinate nei suoi scritti. In particolare, l’importanza inedita assegnata alla convenienza di proporzionalità nelle Quaestiones disputatae de veritate ha suscitato un dibattito secolare, che oppone quanti riconoscono in questa soluzione la dottrina autentica a coloro che la valutano meramente come uno stadio provvisorio all’interno di un’evoluzione riscontrabile nelle opere di Tommaso. Nel presente contributo si intende segnalare alcuni dati testuali che aiutano a chiarire il passaggio dalla concezione dell’analogia presentata nello Scriptum super sententiis a quella del De veritate, un passaggio strettamente legato alla funzione euristica svolta dall’analogia. The attempts made within the Thomist tradition to identify the official doctrine of analogy elaborated by Thomas Aquinas had to deal with the various distinctions drawn by Aquinas in his writings. In particular, the great importance given to the agreement of proportionality in the Quaestiones disputatae de veritate has elicited a long debate, in which those who consider such a solution as the authentic doctrine are opposed to those who see it merely as a temporary stage of an evolution which could be observed in Thomas’s works. The aim of this essay is to present some textual data that contribute to clarify the transition from the analogy as conceived in the Scriptum super Sententiis to that of the De veritate, a transition closely related to the heuristic function of analogy.

Aflatoxin M1 (AFM1) is a well-known carcinogenic compound that may contaminate milk and dairy products. Thus, with the regulation 1881/2006, the European Union established a concentration limit for AFM1 in milk and insisted on the importance of defining enrichment factors (EFs) for cheese. In 2019, the Italian Ministry of Health proposed four different EFs based on cheese’s moisture content on a fat-free basis (MMFB) for bovine dairy products. This study aimed to define the EFs of cheese with different MFFB. The milk used for cheesemaking was naturally contaminated with different AFM1 concentrations. Results showed that all the EF average values from this study were lower than those of the Italian Ministry of Health. Hence, the current EFs might need to be reconsidered for a better categorization of AFM1 risk in cheese.