Zinc transporter 8 and MAP3865c homologous epitopes are recognized at T1D onset in Sardinian children

Our group has recently demonstrated that Mycobacterium avium subspecies paratuberculosis (MAP) infection significantly associates with T1D in Sardinian adult patients. Due to the potential role played by MAP in T1D pathogenesis, it is relevant to better characterize the prevalence of anti-MAP antibodies (Abs) in the Sardinian population, studying newly diagnosed T1D children. Therefore, we investigated the seroreactivity against epitopes derived from the ZnT8 autoantigen involved in children at T1D onset and their homologous sequences of the MAP3865c protein. Moreover, sera from all individuals were tested for the presence of Abs against: the corresponding ZnT8 C-terminal region, the MAP specific protein MptD, the T1D autoantigen GAD65 and the T1D unrelated Acetylcholine Receptor. The novel MAP3865c281–287 epitope emerges here as the major C-terminal epitope recognized. Intriguingly ZnT8186–194 immunodominant peptide was cross-reactive with the homologous sequences MAP3865c133–141, strengthening the hypothesis that MAP could be an environmental trigger of T1D through a molecular mimicry mechanism. All eight epitopes were recognized by circulating Abs in T1D children in comparison to healthy controls, suggesting that these Abs could be biomarkers of T1D. It would be relevant to investigate larger cohorts of children, followed over time, to elucidate whether Ab titers against these MAP/Znt8 epitopes wane after diagnosis

Immersions into Sasakian space forms

We study immersions of Sasakian manifolds into finite and infinite dimensional Sasakian space forms. After proving Calabi’s rigidity results in the Sasakian setting, we characterise all homogeneous Sasakian manifolds which admit a (local) Sasakian immersion into a nonelliptic Sasakian space form. Moreover, we give a characterisation of homogeneous Sasakian manifolds which can be embedded into the standard sphere both in the compact and noncompact case

A deep architecture based on attention mechanisms for effective end-to-end detection of early and mature malaria parasites

Malaria is a severe infectious disease caused by the Plasmodium parasite. The early and accurate detection of this disease is crucial to reducing the number of deaths it causes. However, the current method of detecting malaria parasites involves manual examination of blood smears, which is a time-consuming and labor-intensive process, mainly performed by skilled hematologists, especially in underdeveloped countries. To address this problem, we have developed two deep learning-based systems, YOLO-SPAM and YOLO-SPAM++, which can detect the parasites responsible for malaria at an early stage. Our evaluation of these systems using two public datasets of malaria parasite images, MP-IDB and IML, shows that they outperform the current state-of-the-art, with more than 11M fewer parameters than the baseline YOLOv5m6. YOLO-SPAM++ demonstrated a substantial 10% improvement over YOLO-SPAM and up to 20% against the best-performing baseline in preliminary experiments conducted on the Plasmodium Falciparum species of MP-IDB. On the other hand, YOLO-SPAM showed slightly better results than YOLO-SPAM++ in subsets without tiny parasites, while YOLO-SPAM++ performed better in subsets with tiny parasites, with precision values up to 94%. Further cross-species generalization validations, conducted by merging training sets of various species within MP-IDB, showed that YOLO-SPAM++ consistently outperformed YOLOv5 and YOLO-SPAM across all species, emphasizing its superior performance and precision in detecting tiny parasites. These architectures can be integrated into computer-aided diagnosis systems to create more reliable and robust systems for the early detection of malaria

A Shallow Learning Investigation for COVID-19 Classification

COVID-19, an infectious coronavirus disease, triggered a pandemic that resulted in countless deaths. Since its inception, clinical institutions have used computed tomography as a supplemental screening method to reverse transcription-polymerase chain reaction. Deep learning approaches have shown promising results in addressing the problem; however, less computationally expensive techniques, such as those based on handcrafted descriptors and shallow classifiers, may be equally capable of detecting COVID-19 based on medical images of patients. This work proposes an initial investigation of several handcrafted descriptors well known in the computer vision literature already been exploited for similar tasks. The goal is to discriminate tomographic images belonging to three classes, COVID-19, pneumonia, and normal conditions, and present in a large public dataset. The results show that kNN and ensembles trained with texture descriptors achieve outstanding accuracy in this task, reaching accuracy and F-measure of 93.05% and 89.63%, respectively. Although it did not exceed state of the art, it achieved satisfactory performance with only 36 features, enabling the potential to achieve remarkable improvements from a computational complexity perspective

SAMMI: Segment Anything Model for Malaria Identification

Malaria, a life-threatening disease caused by the Plasmodium parasite, is a pressing global health challenge. Timely detection is critical for effective treatment. This paper introduces a novel computer-aided diagnosis system for detecting Plasmodium parasites in blood smear images, aiming to enhance automation and accessibility in comprehensive screening scenarios. Our approach integrates the Segment Anything Model for precise unsupervised parasite detection. It then employs a deep learning framework, combining Convolutional Neural Networks and Vision Transformer to accurately classify malaria-infected cells. We rigorously evaluate our system using the IML public dataset and compare its performance against various off-the-shelf object detectors. The results underscore the efficacy of our method, demonstrating superior accuracy in detecting and classifying malaria-infected cells. This innovative Computer-aided diagnosis system presents a reliable and near real-time solution for malaria diagnosis, offering significant potential for widespread implementation in healthcare settings. By automating the diagnosis process and ensuring high accuracy, our system can contribute to timely interventions, thereby advancing the fight against malaria globally

Understanding cheese ripeness: An artificial intelligence-based approach for hierarchical classification

Within the contemporary dairy industry, the effective monitoring of cheese ripeness constitutes a critical yet challenging task. This paper proposes the first public dataset encompassing images of cheese wheels that depict various products at distinct stages of ripening and introduces an innovative hybrid approach, integrating machine learning and computer vision techniques to automate the detection of cheese ripeness. By leveraging deep learning and shallow learning techniques, the proposed method endeavors to overcome the limitations associated with conventional assessment methodologies. It aims to provide automation, precision, and consistency in the evaluation of cheese ripeness, delving into a hierarchical classification for the simultaneous classification of distinct cheese types and ripeness levels and presenting a comprehensive solution to enhance the efficiency of the cheese production process. By employing a lightweight hierarchical feature aggregation methodology, this investigation navigates the intricate landscape of preprocessing steps, feature selection, and diverse classifiers. We report a noteworthy achievement, attaining a best F-measure score of 0.991 through the merging of features extracted from EfficientNet and DarkNet-53, opening the field to concretely address the complexity inherent in cheese quality assessment

Global symplectic coordinates on gradient Kaehler-Ricci solitons

A classical result of D. McDuff asserts that a simply-connected complete Kaehler manifold $(M,g,\omega)$ with non positive sectional curvature admits global symplectic coordinates through a symplectomorphism $\Psi: M\rightarrow R^{2n}$ (where $n$ is the complex dimension of $M$), satisfying the following property (proved by E. Ciriza): the image $\Psi (T)$ of any complex totally geodesic submanifold $T\subset M$ through the point $p$ such that $\Psi(p)=0$, is a complex linear subspace of $C^n \simeq R^{2n}$. The aim of this paper is to exhibit, for all positive integers $n$, examples of $n$-dimensional complete Kaehler manifolds with non-negative sectional curvature globally symplectomorphic to $R^{2n}$ through a symplectomorphism satisfying Ciriza's property.Comment: 8 page

Aromatase immunoreactivity in fetal ovine neuronal cell cultures exposed to oxidative injury

A lot of evidence testifies that aromatase is expressed in the central nervous system where it has been detected not only in hypothalamic and limbic regions but also in the cerebral cortex and spinal cord. In physiological conditions, aromatase is expressed exclusively by neurons, where it has been mainly found in cell bodies, processes and synaptic terminals. Moreover, primary cultured cortical astrocytes from female rats are more resistant to oxidant cell death than those from males, suggesting a protective role of estradiol. The aim of this study was to evaluate changes in aromatase expression in response to 3-nitro-L-tyrosine, a marker of oxidative stress, in primary neuronal cell cultures from brains of 60-day old sheep fetuses. Cells were identified as neurons by using class III β-tubulin, a marker of neuronal cells. Two morphological types were consistently recognizable: i) bipolar cells with an oval cell body; ii) multipolar cells whose processes formed a wide net with those of adjacent cells. In situ hybridization technique performed on 60-day old fetal neurons revealed that in baseline conditions aromatase gene expression occurs. Importantly, cells exposed to 360 µM 3-nitro-L-tyrosine were fewer and showed more globular shape and shorter cytoplasmic processes in comparison to control cells. The immunocytochemical study with anti-aromatase antibody revealed that cells exposed to 360 µM 3-nitro-L-tyrosine were significantly more immunoreactive than control cells. Thus, it can be postulated that the oxidant effects of the amino acid analogue 3-nitro-L-tyrosine could be counterbalanced by an increase in aromatase expression that in turn can lead to the formation of neuroprotective estradiol via aromatization of testosterone

Dynamic surface electromyography using stretchable screen-printed textile electrodes

Objective. Wearable devices have created new opportunities in healthcare and sport sciences by unobtrusively monitoring physiological signals. Textile polymer-based electrodes proved to be effective in detecting electrophysiological potentials but suffer mechanical fragility and low stretch resistance. The goal of this research is to develop and validate in dynamic conditions cost-effective and easily manufacturable electrodes characterized by adequate robustness and signal quality. Methods. We here propose an optimized screen printing technique for the fabrication of PEDOT:PSS-based textile electrodes directly into finished stretchable garments for surface electromyography (sEMG) applications. A sensorised stretchable leg sleeve was developed, targeting five muscles of interest in rehabilitation and sport science. An experimental validation was performed to assess the accuracy of signal detection during dynamic exercises, including sit-to-stand, leg extension, calf raise, walking, and cycling. Results. The electrodes can resist up to 500 stretch cycles. Tests on five subjects revealed excellent contact impedance, and cross-correlation between sEMG envelopes simultaneously detected from the leg muscles by the textile and Ag/AgCl electrodes was generally greater than 0.9, which proves that it is possible to obtain good quality signals with performance comparable with disposable electrodes. Conclusions. An effective technique to embed polymer-based electrodes in stretchable smart garments was presented, revealing good performance for dynamic sEMG detections. Significance. The achieved results pave the way to the integration of unobtrusive electrodes, obtained by screen printing of conductive polymers, into technical fabrics for rehabilitation and sport monitoring, and in general where the detection of sEMG in dynamic conditions is necessary

Balanced metrics on Cartan and Cartan-Hartogs domains

This paper consists of two results dealing with balanced metrics (in S. Donaldson terminology) on nonconpact complex manifolds. In the first one we describe all balanced metrics on Cartan domains. In the second one we show that the only Cartan-Hartogs domain which admits a balanced metric is the complex hyperbolic space. By combining these results with those obtained in [13] (Kaehler-Einstein submanifolds of the infinite dimensional projective space, to appear in Mathematische Annalen) we also provide the first example of complete, Kaehler-Einstein and projectively induced metric g such that $\alpha g$ is not balanced for all $\alpha >0$.Comment: 11 page