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The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvements over conventional statistical techniques. However, deep learning models can underperform if trained with bad processed data. Preprocessing is crucial for EEG data analysis, yet there is no consensus on the optimal strategies in deep learning scenarios, leading to uncertainty about the extent of preprocessing required for optimal results. This study is the first to thoroughly investigate the effects of EEG preprocessing in deep learning applications, drafting guidelines for future research. It evaluates the effects of varying preprocessing levels, from raw and minimally filtered data to complex pipelines with automated artifact removal algorithms. Six classification tasks (eye blinking, motor imagery, Parkinson's, Alzheimer's disease, sleep deprivation, and first episode psychosis) and four established EEG architectures were considered for the evaluation. The analysis of 4800 trained models revealed statistical differences between preprocessing pipelines at the intra-task level for each model and at the inter-task level for the largest model. Models trained on raw data consistently performed poorly, always ranking last in average scores. In addition, models seem to benefit more from minimal pipelines without artifact handling methods. These findings suggest that EEG artifacts may affect the performance and generalizability of deep neural networks.

Stroke remains a leading cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. To address these challenges, we established the NeuralCup consortium to benchmark predictive models of stroke outcome through a collaborative, data-driven approach. This study presents findings from 15 international teams who used a comprehensive dataset including clinical and imaging data, to identify and compare predictors of motor, cognitive, and emotional outcomes one year post-stroke. Our analyses integrated traditional statistical approaches and novel machine learning algorithms to uncover 'optimal recipes' for predicting each domain. The differences in these 'optimal recipes' reflect distinct brain mechanisms in response to different tasks. Key predictors across all domains included infarct characteristics, T1-weighted MRI sequences, and demographic factors. Additionally, integrating FLAIR imaging and white matter tract analysis significantly improved the prediction of cognitive and motor outcomes, respectively. These findings support a multifaceted approach to stroke outcome prediction, underscoring the potential of collaborative data science to develop personalized care strategies that enhance recovery and quality of life for stroke survivors. To encourage further model development and validation, we provide access to the training dataset at http://neuralcup.bcblab.com.

We report herein on the magneto-chiral dichroism (MChD), investigated through near infrared light absorption, of a chiral nanomagnet showing room temperature magneto-electric coupling. The MChD signal associated with the Yb III center is driven by the magnetic dipole allowed character of the 2 F 7/2 ← 2 F 5/2 electronic transition (|Δ J | = 1). Magnetic field and temperature dependence studies reveal an MChD signal that follows the material magnetization and persists at room temperature. These results represent the first evidence of the coexistence of magneto-chiral dichroism and magneto-electric coupling at room temperature in a molecular nanomagnet and pave the way for further studies where magneto-chiral anisotropy and magneto-electric coupling interact.

Here we report on the Magneto-Chiral Dichroism (MChD) detected through visible and near-infrared light absorption of two enantiomeric pairs of Er III and Tm III chiral complexes featuring a propeller-like molecular structure. The magnetic properties show typical features of isolated paramagnetic ions associated with 4 I 15/2 and 3 H 6 ground state terms. MChD spectroscopy shows high g MChD dissymmetry factors of ca. 0.12 T -1 and 0.05 T -1 (T=4.0 K and B=1.0 T) for Er III and Tm III , respectively, associated with the magnetic-dipole allowed 4 I 13/2 ← 4 I 15/2 and 3 H 5 ← 3 H 6 transitions. MChD signals of the two complexes were detected up to room temperature and under magnetic fields up to 5.0 T. For the first time, the MChD results are discussed in the context of the Richardson theory of lanthanide optical activity and provide clear indications on the strongest MChD-active electronic transitions of lanthanide complexes., (© 2024 Wiley-VCH GmbH.)

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Advancements in neural network approaches have enhanced the effectiveness of surface Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity. However, current deep learning architectures struggle to achieve good generalization and robustness, often demanding significant computational resources. The goal of this paper was to develop a robust model that can quickly adapt to new users using Transfer Learning. We propose a Multi-Scale Convolutional Neural Network (MSCNN), pre-trained with various strategies to improve inter-subject generalization. These strategies include domain adaptation with a gradient-reversal layer and self-supervision using triplet margin loss. We evaluated these approaches on several benchmark datasets, specifically the NinaPro databases. This study also compared two different Transfer Learning frameworks designed for user-dependent fine-tuning. The second Transfer Learning framework achieved a 97% F1 Score across 14 classes with an average of 1.40 epochs, suggesting potential for on-site model retraining in cases of performance degradation over time. The findings highlight the effectiveness of Transfer Learning in creating adaptive, user-specific models for sEMG-based prosthetic hands. Moreover, the study examined the impacts of rectification and window length, with a focus on real-time accessible normalizing techniques, suggesting significant improvements in usability and performance.

The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially in domains where collecting large volumes of annotated data is not trivial. Biomedical data include several modalities containing complementary information, such as medical images and reports: images are often large and encode low-level information, while reports include a summarized high-level description of the findings identified within data and often only concerning a small part of the image. However, only a few methods allow to effectively link the visual content of images with the textual content of reports, preventing medical specialists from properly benefitting from the recent opportunities offered by deep learning models. This paper introduces a multimodal architecture creating a robust biomedical data representation encoding fine-grained text representations within image embeddings. The architecture aims to tackle data scarcity (combining supervised and self-supervised learning) and to create multimodal biomedical ontologies. The architecture is trained on over 6,000 colon whole slide Images (WSI), paired with the corresponding report, collected from two digital pathology workflows. The evaluation of the multimodal architecture involves three tasks: WSI classification (on data from pathology workflow and from public repositories), multimodal data retrieval, and linking between textual and visual concepts. Noticeably, the latter two tasks are available by architectural design without further training, showing that the multimodal architecture that can be adopted as a backbone to solve peculiar tasks. The multimodal data representation outperforms the unimodal one on the classification of colon WSIs and allows to halve the data needed to reach accurate performance, reducing the computational power required and thus the carbon footprint. The combination of images and reports exploiting self-supervised algorithms allows to mine databases without needing new annotations provided by experts, extracting new information. In particular, the multimodal visual ontology, linking semantic concepts to images, may pave the way to advancements in medicine and biomedical analysis domains, not limited to histopathology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Philippe Weitz reports a relationship with Stratipath AB that includes: employment. Mattias Rantalainen reports a relationship with Stratipath AB that includes: equity or stocks. Johan Hartman reports a relationship with Stratipath AB that includes: equity or stocks. Kimmo Kartasalo reports a relationship with Clinsight AB that includes: equity or stocks. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

In ophthalmology, Optical Coherence Tomography (OCT) has become a daily used tool in the diagnostics and therapeutic planning of various diseases. Publicly available datasets play a crucial role in advancing research by providing access to diverse imaging data for algorithm development. The accessibility, data format, annotations, and metadata are not consistent across OCT datasets, making it challenging to efficiently use the available resources. This article provides a comprehensive analysis of different OCT datasets, with particular attention to dataset properties, disease representation, accessibility, and aims to create a catalog of all publicly available OCT datasets. The goal is to improve accessibility to OCT data, increase openness about the availability, and give important new perspectives on the state of OCT imaging resources. Our findings reveal the need for improved data-sharing practices and standardized documentation.

Magnetic materials are widely used for many technologies in energy, health, transportation, computation, and data storage. For the latter, the readout of the magnetic state of a medium is crucial. Optical readout based on the magneto-optical Faraday effect was commercialized but soon abandoned because of the need for a complex circular polarization-sensitive readout. Combining chirality with magnetism can remove this obstacle, as chiral magnetic materials exhibit magneto-chiral dichroism, a differential absorption of unpolarized light dependent on their magnetic state. Molecular chemistry allows the rational introduction of chirality into single-molecule magnets (SMMs), ultimate nanoobjects capable of retaining magnetization. Here, we report the first experimental demonstration of optical detection of the magnetic state of an SMM using unpolarized light on a novel air-stable Dy-based chiral SMM featuring a strong single-ion magnetic anisotropy. These findings might represent a paradigm shift in the field of optical data readout technologies.

Objective. This study aims to address the challenges associated with data-driven electroencephalography (EEG) data analysis by introducing a standardised library called BIDSAlign . This library efficiently processes and merges heterogeneous EEG datasets from different sources into a common standard template. The goal of this work is to create an environment that allows to preprocess public datasets in order to provide data for the effective training of deep learning (DL) architectures. Approach. The library can handle both Brain Imaging Data Structure (BIDS) and non-BIDS datasets, allowing the user to easily preprocess multiple public datasets. It unifies the EEG recordings acquired with different settings by defining a common pipeline and a specified channel template. An array of visualisation functions is provided inside the library, together with a user-friendly graphical user interface to assist non-expert users throughout the workflow. Main results. BIDSAlign enables the effective use of public EEG datasets, providing valuable medical insights, even for non-experts in the field. Results from applying the library to datasets from OpenNeuro demonstrate its ability to extract significant medical knowledge through an end-to-end workflow, facilitating group analysis, visual comparison and statistical testing. Significance. BIDSAlign solves the lack of large EEG datasets by aligning multiple datasets to a standard template. This unlocks the potential of public EEG data for training DL models. It paves the way to promising contributions based on DL to clinical and non-clinical EEG research, offering insights that can inform neurological disease diagnosis and treatment strategies., (Creative Commons Attribution license.)

The problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows and research-oriented settings, necessitates human intervention and re-scanning. Overcoming this challenge requires developing quality control algorithms, that are hindered by the limited availability of relevant annotated data in histopathology. The manual annotation of ground-truth for artifact detection methods is expensive and time-consuming. This work addresses the issue by proposing a method dedicated to augmenting whole slide images with artifacts. The tool seamlessly generates and blends artifacts from an external library to a given histopathology dataset. The augmented datasets are then utilized to train artifact classification methods. The evaluation shows their usefulness in classification of the artifacts, where they show an improvement from 0.10 to 0.01 AUROC depending on the artifact type. The framework, model, weights, and ground-truth annotations are freely released to facilitate open science and reproducible research., (© 2024. The Author(s).)

Artificial intelligence has transformed medical diagnostic capabilities, particularly through medical image analysis. AI algorithms perform well in detecting abnormalities with a strong performance, enabling computer-aided diagnosis by analyzing the extensive amounts of patient data. The data serve as a foundation upon which algorithms learn and make predictions. Thus, the importance of data cannot be underestimated, and clinically corresponding datasets are required. Many researchers face a lack of medical data due to limited access, privacy concerns, or the absence of available annotations. One of the most widely used diagnostic tools in ophthalmology is Optical Coherence Tomography (OCT). Addressing the data availability issue is crucial for enhancing AI applications in the field of OCT diagnostics. This review aims to provide a comprehensive analysis of all publicly accessible retinal OCT datasets. Our main objective is to compile a list of OCT datasets and their properties, which can serve as an accessible reference, facilitating data curation for medical image analysis tasks. For this review, we searched through the Zenodo repository, Mendeley Data repository, MEDLINE database, and Google Dataset search engine. We systematically evaluated all the identified datasets and found 23 open-access datasets containing OCT images, which significantly vary in terms of size, scope, and ground-truth labels. Our findings indicate the need for improvement in data-sharing practices and standardized documentation. Enhancing the availability and quality of OCT datasets will support the development of AI algorithms and ultimately improve diagnostic capabilities in ophthalmology. By providing a comprehensive list of accessible OCT datasets, this review aims to facilitate better utilization and development of AI in medical image analysis.

Prostate cancer is the second most frequent cancer in men worldwide after lung cancer. Its diagnosis is based on the identification of the Gleason score that evaluates the abnormality of cells in glands through the analysis of the different Gleason patterns within tissue samples. The recent advancements in computational pathology, a domain aiming at developing algorithms to automatically analyze digitized histopathology images, lead to a large variety and availability of datasets and algorithms for Gleason grading and scoring. However, there is no clear consensus on which methods are best suited for each problem in relation to the characteristics of data and labels. This paper provides a systematic comparison on nine datasets with state-of-the-art training approaches for deep neural networks (including fully-supervised learning, weakly-supervised learning, semi-supervised learning, Additive-MIL, Attention-Based MIL, Dual-Stream MIL, TransMIL and CLAM) applied to Gleason grading and scoring tasks. The nine datasets are collected from pathology institutes and openly accessible repositories. The results show that the best methods for Gleason grading and Gleason scoring tasks are fully supervised learning and CLAM, respectively, guiding researchers to the best practice to adopt depending on the task to solve and the labels that are available., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Here we report on the strong magneto-chiral dichroism (MChD) detected through visible and near-infrared light absorption up to 5.0 T on {Er 5 Ni 6 } metal clusters obtained by reaction of enantiopure chiral ligands and Ni II and Er III precursors. Single-crystal diffraction analysis reveals that these compounds are 3 d- 4 f heterometallic clusters, showing helical chirality. MChD spectroscopy reveals a high g MChD dissymmetry factor of ca. 0.24 T -1 ( T = 4.0 K, B = 1.0 T) for the 4 I 13/2 ← 4 I 15/2 magnetic-dipole allowed electronic transition of the Er III centers. This record value is 1 or 2 orders of magnitude higher than that of the d-d electronic transitions of the Ni II ions and the others f-f electric-dipole induced transitions of the Er III centers. These findings clearly show the key role that magnetic-dipole allowed transitions have in the rational design of chiral lanthanide systems showing strong MChD.

Background and Objective: The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from different visible structures. It is also useful to quickly transfer annotations between consecutive or restained slides, thus significantly reducing the annotation time and associated costs. Nevertheless, the slide preparation is different for each stain and the tissue undergoes complex and large deformations. Therefore, a robust, efficient, and accurate registration method is highly desired by the scientific community and hospitals specializing in digital pathology., Methods: We propose a two-step hybrid method consisting of (i) deep learning- and feature-based initial alignment algorithm, and (ii) intensity-based nonrigid registration using the instance optimization. The proposed method does not require any fine-tuning to a particular dataset and can be used directly for any desired tissue type and stain. The registration time is low, allowing one to perform efficient registration even for large datasets. The method was proposed for the ACROBAT 2023 challenge organized during the MICCAI 2023 conference and scored 1st place. The method is released as open-source software., Results: The proposed method is evaluated using three open datasets: (i) Automatic Nonrigid Histological Image Registration Dataset (ANHIR), (ii) Automatic Registration of Breast Cancer Tissue Dataset (ACROBAT), and (iii) Hybrid Restained and Consecutive Histological Serial Sections Dataset (HyReCo). The target registration error (TRE) is used as the evaluation metric. We compare the proposed algorithm to other state-of-the-art solutions, showing considerable improvement. Additionally, we perform several ablation studies concerning the resolution used for registration and the initial alignment robustness and stability. The method achieves the most accurate results for the ACROBAT dataset, the cell-level registration accuracy for the restained slides from the HyReCo dataset, and is among the best methods evaluated on the ANHIR dataset., Conclusions: The article presents an automatic and robust registration method that outperforms other state-of-the-art solutions. The method does not require any fine-tuning to a particular dataset and can be used out-of-the-box for numerous types of microscopic images. The method is incorporated into the DeeperHistReg framework, allowing others to directly use it to register, transform, and save the WSIs at any desired pyramid level (resolution up to 220k x 220k). We provide free access to the software. The results are fully and easily reproducible. The proposed method is a significant contribution to improving the WSI registration quality, thus advancing the field of digital pathology., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: Oral-facial-digital (OFD) syndromes are genetically heterogeneous developmental disorders, caused by pathogenic variants in genes involved in primary cilia formation and function. We identified a previously undescribed type of OFD with brain anomalies, ranging from alobar holoprosencephaly to pituitary anomalies, in 6 unrelated families., Methods: Exome sequencing of affected probands was supplemented with alternative splicing analysis in patient and control lymphoblastoid and fibroblast cell lines, and primary cilia structure analysis in patient fibroblasts., Results: In 1 family with 2 affected males, we identified a germline variant in the last exon of ZRSR2, NM_005089.4:c.1211_1212del NP_005080.1:p.(Gly404GlufsTer23), whereas 7 affected males from 5 unrelated families were hemizygous for the ZRSR2 variant NM_005089.4:c.1207_1208del NP_005080.1:p.(Arg403GlyfsTer24), either occurring de novo or inherited in an X-linked recessive pattern. ZRSR2, located on chromosome Xp22.2, encodes a splicing factor of the minor spliceosome complex, which recognizes minor introns, representing 0.35% of human introns. Patient samples showed significant enrichment of minor intron retention. Among differentially spliced targets are ciliopathy-related genes, such as TMEM107 and CIBAR1. Primary fibroblasts containing the NM_005089.4:c.1207_1208del ZRSR2 variant had abnormally elongated cilia, confirming an association between defective U12-type intron splicing, OFD and abnormal primary cilia formation., Conclusion: We introduce a novel type of OFD associated with elongated cilia and differential splicing of minor intron-containing genes due to germline variation in ZRSR2., Competing Interests: Conflict of Interest The authors declare no conflicts of interest., (Copyright © 2024 American College of Medical Genetics and Genomics. Published by Elsevier Inc. All rights reserved.)

Down syndrome (DS) is characterized by skeletal and brain structural malformations, cognitive impairment, altered hippocampal metabolite concentration and gene expression imbalance. These alterations were usually investigated separately, and the potential rescuing effects of green tea extracts enriched in epigallocatechin-3-gallate (GTE-EGCG) provided disparate results due to different experimental conditions. We overcame these limitations by conducting the first longitudinal controlled experiment evaluating genotype and GTE-EGCG prenatal chronic treatment effects before and after treatment discontinuation. Our findings revealed that the Ts65Dn mouse model reflected the pleiotropic nature of DS, exhibiting brachycephalic skull, ventriculomegaly, neurodevelopmental delay, hyperactivity, and impaired memory robustness with altered hippocampal metabolite concentration and gene expression. GTE-EGCG treatment modulated most systems simultaneously but did not rescue DS phenotypes. On the contrary, the treatment exacerbated trisomic phenotypes including body weight, tibia microarchitecture, neurodevelopment, adult cognition, and metabolite concentration, not supporting the therapeutic use of GTE-EGCG as a prenatal chronic treatment. Our results highlight the importance of longitudinal experiments assessing the co-modulation of multiple systems throughout development when characterizing preclinical models in complex disorders and evaluating the pleiotropic effects and general safety of pharmacological treatments., Competing Interests: SL, BT, ES, MA, KW, VV, MP, LV, NG, LG, HP, LV, UH, AC, CA, ZC, WG, NM, GV No competing interests declared, (© 2023, Llambrich et al.)

The analysis of veterinary radiographic imaging data is an essential step in the diagnosis of many thoracic lesions. Given the limited time that physicians can devote to a single patient, it would be valuable to implement an automated system to help clinicians make faster but still accurate diagnoses. Currently, most of such systems are based on supervised deep learning approaches. However, the problem with these solutions is that they need a large database of labeled data. Access to such data is often limited, as it requires a great investment of both time and money. Therefore, in this work we present a solution that allows higher classification scores to be obtained using knowledge transfer from inter-species and inter-pathology self-supervised learning methods. Before training the network for classification, pretraining of the model was performed using self-supervised learning approaches on publicly available unlabeled radiographic data of human and dog images, which allowed substantially increasing the number of images for this phase. The self-supervised learning approaches included the Beta Variational Autoencoder, the Soft-Introspective Variational Autoencoder, and a Simple Framework for Contrastive Learning of Visual Representations. After the initial pretraining, fine-tuning was performed for the collected veterinary dataset using 20% of the available data. Next, a latent space exploration was performed for each model after which the encoding part of the model was fine-tuned again, this time in a supervised manner for classification. Simple Framework for Contrastive Learning of Visual Representations proved to be the most beneficial pretraining method. Therefore, it was for this method that experiments with various fine-tuning methods were carried out. We achieved a mean ROC AUC score of 0.77 and 0.66, respectively, for the laterolateral and dorsoventral projection datasets. The results show significant improvement compared to using the model without any pretraining approach., (© 2023. The Author(s).)

Here we report magneto-chiral dichroism (MChD) detected through visible and near-infrared light absorption of a chiral dysprosium(III) coordination polymer. The two enantiomers of [Dy III (H6(py) 2 )(hfac) 3 ] n [H6(py) 2 = 2,15-bis(4-pyridyl)ethynylcarbo[6]helicene; hfac - = 1,1,1,5,5,5-hexafluoroacetylacetonate], where the chirality is provided by a functionalized helicene ligand, were structurally, spectroscopically, and magnetically investigated. Magnetic measurements reveal a slow relaxation of the magnetization, with differences between enantiopure and racemic systems rationalized on the basis of theoretical calculations. When the enantiopure complexes are irradiated with unpolarized light in a magnetic field, they exhibit multiple MChD signals associated with the f-f electronic transitions of Dy III , thus providing the coexistence of MChD-active absorptions and single-molecule-magnet (SMM) behavior. These findings clearly show the potential that rationally designed chiral SMMs have in enabling the optical readout of magnetic memory through MChD.

Computational pathology can significantly benefit from ontologies to standardize the employed nomenclature and help with knowledge extraction processes for high-quality annotated image datasets. The end goal is to reach a shared model for digital pathology to overcome data variability and integration problems. Indeed, data annotation in such a specific domain is still an unsolved challenge and datasets cannot be steadily reused in diverse contexts due to heterogeneity issues of the adopted labels, multilingualism, and different clinical practices., Material and Methods: This paper presents the ExaMode ontology, modeling the histopathology process by considering 3 key cancer diseases (colon, cervical, and lung tumors) and celiac disease. The ExaMode ontology has been designed bottom-up in an iterative fashion with continuous feedback and validation from pathologists and clinicians. The ontology is organized into 5 semantic areas that defines an ontological template to model any disease of interest in histopathology., Results: The ExaMode ontology is currently being used as a common semantic layer in: (i) an entity linking tool for the automatic annotation of medical records; (ii) a web-based collaborative annotation tool for histopathology text reports; and (iii) a software platform for building holistic solutions integrating multimodal histopathology data., Discussion: The ontology ExaMode is a key means to store data in a graph database according to the RDF data model. The creation of an RDF dataset can help develop more accurate algorithms for image analysis, especially in the field of digital pathology. This approach allows for seamless data integration and a unified query access point, from which we can extract relevant clinical insights about the considered diseases using SPARQL queries., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Gianmaria Silvello reports financial support was provided by 10.13039/501100000780European Commission. Filippo Fragetta is an author of the paper and a member of the editorial board of JPI., (© 2023 The Authors.)

Because the combination of chiral and magnetic properties is becoming more and more attractive for magneto-chiral phenomena, we here aim at exploring the induction of chirality to achiral magnetic molecules as a strategy for the preparation of magneto-chiral objects. To this end, we have associated free base- and metallo-porphyrins with silica nano helices, using a variety of elaboration methods, and have studied them mainly by electronic natural circular dichroism (NCD) and magnetic circular dichroism (MCD) spectroscopies. While electrostatic or covalent surface grafting uniformly yielded very low induced CD (ICD) for the four assayed porphyrins, a moderate response was observed when the porphyrins were incorporated into the interior of the double-walled helices, likely due to the association of the molecules with the chirally-organized gemini surfactant. A generally stronger, but more variable, ICD was observed when the molecules were drop casted onto the helices immobilised on a quartz plate, likely due to the different capacities of the porphyrins to aggregate into chiral assemblies. Electronic spectroscopy, electron microscopy and IR spectroscopy were used to interpret the patterns of aggregation and their influence on ICD and MCD. No enhancement of MCD was observed as a result of association with the nanohelices except in the case of the free base, 5,10,15,20-tetra-(4-sulfonatophenyl)porphyrin (TPPS). This nanocomposite demonstrated a large ICD in the Soret region and a large MCD in the Q-region due to J-aggregation. However, no induced MChD was observed, possibly due to the spectral mismatch between the ICD and MCD peaks.

The acquisition of whole slide images is prone to artifacts that can require human control and re-scanning, both in clinical workflows and in research-oriented settings. Quality control algorithms are a first step to overcome this challenge, as they limit the use of low quality images. Developing quality control systems in histopathology is not straightforward, also due to the limited availability of data related to this topic. We address the problem by proposing a tool to augment data with artifacts. The proposed method seamlessly generates and blends artifacts from an external library to a given histopathology dataset. The datasets augmented by the blended artifacts are then used to train an artifact detection network in a supervised way. We use the YOLOv5 model for the artifact detection with a slightly modified training pipeline. The proposed tool can be extended into a complete framework for the quality assessment of whole slide images.Clinical relevance- The proposed method may be useful for the initial quality screening of whole slide images. Each year, millions of whole slide images are acquired and digitized worldwide. Numerous of them contain artifacts affecting the following AI-oriented analysis. Therefore, a tool operating at the acquisition phase and improving the initial quality assessment is crucial to increase the performance of digital pathology algorithms, e.g., early cancer diagnosis.

Muscle synergy analysis investigates the neurophysiological mechanisms that the central nervous system employs to coordinate muscles. Several models have been developed to decompose electromyographic (EMG) signals into spatial and temporal synergies. However, using multiple approaches can complicate the interpretation of results. Spatial synergies represent invariant muscle weights modulated with variant temporal coefficients; temporal synergies are invariant temporal profiles that coordinate variant muscle weights. While non-negative matrix factorization allows to extract both spatial and temporal synergies, the comparison between the two approaches was rarely investigated targeting a large set of multi-joint upper-limb movements. Spatial and temporal synergies were extracted from two datasets with proximal (16 subjects, 10M, 6F) and distal upper-limb movements (30 subjects, 21M, 9F), focusing on their differences in reconstruction accuracy and inter-individual variability. We showed the existence of both spatial and temporal structure in the EMG data, comparing synergies with those from a surrogate dataset in which the phases were shuffled preserving the frequency content of the original data. The two models provide a compact characterization of motor coordination at the spatial or temporal level, respectively. However, a lower number of temporal synergies are needed to achieve the same reconstruction R 2 : spatial and temporal synergies may capture different hierarchical levels of motor control and are dual approaches to the characterization of low-dimensional coordination of the upper-limb. Last, a detailed characterization of the structure of the temporal synergies suggested that they can be related to intermittent control of the movement, allowing high flexibility and dexterity. These results improve neurophysiology understanding in several fields such as motor control, rehabilitation, and prosthetics., (Copyright © 2023 IBRO. Published by Elsevier Ltd. All rights reserved.)

The combination of physical properties sensitive to molecular chirality in a single system allows the observation of fascinating phenomena such as magneto-chiral dichroism (MChD) and circularly polarized luminescence (CPL) having potential applications for optical data readout and display technology. Homochiral monodimensional coordination polymers of Yb III were designed from a 2,15-bis-ethynyl-hexahelicenic scaffold decorated with two terminal 4-pyridyl units. Thanks to the coordination of the chiral organic chromophore to Yb(hfac) 3 units (hfac - =1,1,1,5,5,5-hexafluoroacetylaconate), efficient NIR-CPL activity is observed. Moreover, the specific crystal field around the Yb III induces a strong magnetic anisotropy which leads to a single-molecule magnet (SMM) behaviour and a remarkable room temperature MChD. The MChD-structural correlation is supported by computational investigations., (© 2022 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

Several challenges prevent extracting knowledge from biomedical resources, including data heterogeneity and the difficulty to obtain and collaborate on data and annotations by medical doctors. Therefore, flexibility in their representation and interconnection is required; it is also essential to be able to interact easily with such data. In recent years, semantic tools have been developed: semantic wikis are collections of wiki pages that can be annotated with properties and so combine flexibility and expressiveness, two desirable aspects when modeling databases, especially in the dynamic biomedical domain. However, semantics and collaborative analysis of biomedical data is still an unsolved challenge. The aim of this work is to create a tool for easing the design and the setup of semantic databases and to give the possibility to enrich them with biostatistical applications. As a side effect, this will also make them reproducible, fostering their application by other research groups. A command-line software has been developed for creating all structures required by Semantic MediaWiki. Besides, a way to expose statistical analyses as R Shiny applications in the interface is provided, along with a facility to export Prolog predicates for reasoning with external tools. The developed software allowed to create a set of biomedical databases for the Neuroscience Department of the University of Padova in a more automated way. They can be extended with additional qualitative and statistical analyses of data, including for instance regressions, geographical distribution of diseases, and clustering. The software is released as open source-code and published under the GPL-3 license at https://github.com/mfalda/tsv2swm ., (© 2023. The Author(s).)

Computational pathology targets the automatic analysis of Whole Slide Images (WSI). WSIs are high-resolution digitized histopathology images, stained with chemical reagents to highlight specific tissue structures and scanned via whole slide scanners. The application of different parameters during WSI acquisition may lead to stain color heterogeneity, especially considering samples collected from several medical centers. Dealing with stain color heterogeneity often limits the robustness of methods developed to analyze WSIs, in particular Convolutional Neural Networks (CNN), the state-of-the-art algorithm for most computational pathology tasks. Stain color heterogeneity is still an unsolved problem, although several methods have been developed to alleviate it, such as Hue-Saturation-Contrast (HSC) color augmentation and stain augmentation methods. The goal of this paper is to present Data-Driven Color Augmentation (DDCA), a method to improve the efficiency of color augmentation methods by increasing the reliability of the samples used for training computational pathology models. During CNN training, a database including over 2 million H&E color variations collected from private and public datasets is used as a reference to discard augmented data with color distributions that do not correspond to realistic data. DDCA is applied to HSC color augmentation, stain augmentation and H&E-adversarial networks in colon and prostate cancer classification tasks. DDCA is then compared with 11 state-of-the-art baseline methods to handle color heterogeneity, showing that it can substantially improve classification performance on unseen data including heterogeneous color variations., Competing Interests: The authors declare that there are no competing interests., (© 2022 The Authors.)

Exa-scale volumes of medical data have been produced for decades. In most cases, the diagnosis is reported in free text, encoding medical knowledge that is still largely unexploited. In order to allow decoding medical knowledge included in reports, we propose an unsupervised knowledge extraction system combining a rule-based expert system with pre-trained Machine Learning (ML) models, namely the Semantic Knowledge Extractor Tool (SKET). Combining rule-based techniques and pre-trained ML models provides high accuracy results for knowledge extraction. This work demonstrates the viability of unsupervised Natural Language Processing (NLP) techniques to extract critical information from cancer reports, opening opportunities such as data mining for knowledge extraction purposes, precision medicine applications, structured report creation, and multimodal learning. SKET is a practical and unsupervised approach to extracting knowledge from pathology reports, which opens up unprecedented opportunities to exploit textual and multimodal medical information in clinical practice. We also propose SKET eXplained (SKET X), a web-based system providing visual explanations about the algorithmic decisions taken by SKET. SKET X is designed/developed to support pathologists and domain experts in understanding SKET predictions, possibly driving further improvements to the system., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Filippo Fraggetta is an author of this work and a member of the editorial board., (© 2022 The Authors.)

The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer diagnosis. However, the availability of medical specialists to annotate digitized images and free-text diagnostic reports does not scale with the need for large datasets required to train robust computer-aided diagnosis methods that can target the high variability of clinical cases and data produced. This work proposes and evaluates an approach to eliminate the need for manual annotations to train computer-aided diagnosis tools in digital pathology. The approach includes two components, to automatically extract semantically meaningful concepts from diagnostic reports and use them as weak labels to train convolutional neural networks (CNNs) for histopathology diagnosis. The approach is trained (through 10-fold cross-validation) on 3'769 clinical images and reports, provided by two hospitals and tested on over 11'000 images from private and publicly available datasets. The CNN, trained with automatically generated labels, is compared with the same architecture trained with manual labels. Results show that combining text analysis and end-to-end deep neural networks allows building computer-aided diagnosis tools that reach solid performance (micro-accuracy = 0.908 at image-level) based only on existing clinical data without the need for manual annotations., (© 2022. The Author(s).)

Muscle synergies have been largely used in many application fields, including motor control studies, prosthesis control, movement classification, rehabilitation, and clinical studies. Due to the complexity of the motor control system, the full repertoire of the underlying synergies has been identified only for some classes of movements and scenarios. Several extraction methods have been used to extract muscle synergies. However, some of these methods may not effectively capture the nonlinear relationship between muscles and impose constraints on input signals or extracted synergies. Moreover, other approaches such as autoencoders (AEs), an unsupervised neural network, were recently introduced to study bioinspired control and movement classification. In this study, we evaluated the performance of five methods for the extraction of spatial muscle synergy, namely, principal component analysis (PCA), independent component analysis (ICA), factor analysis (FA), nonnegative matrix factorization (NMF), and AEs using simulated data and a publicly available database. To analyze the performance of the considered extraction methods with respect to several factors, we generated a comprehensive set of simulated data (ground truth), including spatial synergies and temporal coefficients. The signal-to-noise ratio (SNR) and the number of channels (NoC) varied when generating simulated data to evaluate their effects on ground truth reconstruction. This study also tested the efficacy of each synergy extraction method when coupled with standard classification methods, including K-nearest neighbors (KNN), linear discriminant analysis (LDA), support vector machines (SVM), and Random Forest (RF). The results showed that both SNR and NoC affected the outputs of the muscle synergy analysis. Although AEs showed better performance than FA in variance accounted for and PCA in synergy vector similarity and activation coefficient similarity, NMF and ICA outperformed the other three methods. Classification tasks showed that classification algorithms were sensitive to synergy extraction methods, while KNN and RF outperformed the other two methods for all extraction methods; in general, the classification accuracy of NMF and PCA was higher. Overall, the results suggest selecting suitable methods when performing muscle synergy-related analysis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhao, Wen, Zhang, Atzori, Müller, Xie and Scano.)

Here, we report the molecular self-assembly of hydroxido-bridged {Ln 5 Ni 6 } ((Ln 3+ = Dy 3+ , Y 3+ ) metal clusters by the reaction of enantiopure chiral ligands, namely, ( R / S )-(2-hydroxy-3-methoxybenzyl)-serine), with Ni II and Ln III precursors. Single-crystal diffraction analysis reveals that these compounds are isostructural sandwich-like 3d-4f heterometallic clusters showing helical chirality. Direct current magnetic measurements on {Dy 5 Ni 6 } indicates ferromagnetic coupling between Dy III and Ni II centers, whereas those on {Y 5 Ni 6 } denote that the Ni II centers are antiferromagnetically coupled and/or magnetically anisotropic. Magneto-chiral dichroism (MChD) measurements on {Dy 5 Ni 6 } and its comparison to that of {Y 5 Ni 6 } provide the first experimental observation of intense multimetal site MChD signals in the visible-near-infrared region. Moreover, the comparison of MChD with natural and magnetic circular dichroism spectra unambiguously demonstrate for the first time that the MChD signals associated with the Ni II d-d transitions are mostly driven by natural optical activity and those associated with the Dy III f-f transitions are driven by magnetic optical activity.