The KM3NeT data acquisition system - Status and evolution

In this contribution, we present the data acquisition system system of the KM3NeT neutrino telescopes, ARCA and ORCA, already operating while under construction at the bottom of the Mediterranean Sea. The DAQ system implements a modular and scalable hardware-triggerless streaming readout of the optical modules in the telescopes. Handling a raw incoming throughput up to hundreds of Gbps, it is integrated with the online alert system of the KM3NeT Multimessenger program. The system will evolve according to a new design, currently under validation, which will grant the extension of the telescope to the aimed cubic kilometer scale

Interpopulation variation in pollinators and floral scent of the lady's-slipper orchid Cypripedium calceolus L.

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In vitro generation of pancreatic endocrine cells from human adult fibroblast-like limbal stem cells

Stem cells might provide unlimited supply of transplantable cells for β-cell replacement therapy in diabetes. The human limbus is a highly specialized region hosting a well-recognized population of epithelial stem cells, which sustain the continuous renewal of the cornea, and the recently identified stromal fibroblast-like stem cells (f-LSCs), with apparent broader plasticity. However, the lack of specific molecular markers for the identification of the multipotent limbal subpopulation has so far limited the investigation of their differentiation potential. In this study we show that the human limbus contains uncommitted cells that could be potentially harnessed for the treatment of diabetes. Fourteen limbal biopsies were obtained from patients undergoing surgery for ocular diseases not involving the conjunctiva or corneal surface. We identified a subpopulation of f-LSCs characterized by robust proliferative capacity, expressing several pluripotent stem cell markers and exhibiting self-renewal ability. We then demonstrated the potential of f-LSCs to differentiate in vitro into functional insulin-secreting cells by developing a four-step differentiation protocol that efficiently directed f-LSCs towards the pancreatic endocrine cell fate. The expression of specific endodermal, pancreatic, islet, and β-cell markers, as well as functional properties of f-LSC-derived insulin-producing cells, were evaluated during differentiation. With our stage-specific approach, up to 77% of f-LSCs eventually differentiated into cells expressing insulin (also assessed as C-peptide) and exhibited phenotypic features of mature β-cells, such as expression of critical transcription factors and presence of secretory granules. Although insulin content was about 160-fold lower than what observed in adult islets, differentiated cells processed ∼98% of their proinsulin content, similar to mature β-cells. Moreover, they responded in vitro in a regulated manner to multiple secretory stimuli, including glucose. In conclusion, f-LSCs represent a possible relevant source of autologous, transplantable, insulin-producing cells that could be tested for the reversal of diabetes

Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer

The current guidelines recommend the sentinel lymph node biopsy to evaluate the lymph node involvement for breast cancer patients with clinically negative lymph nodes on clinical or radiological examination. Machine learning (ML) models have significantly improved the prediction of lymph nodes status based on clinical features, thus avoiding expensive, time-consuming and invasive procedures. However, the classification of sentinel lymph node status represents a typical example of an unbalanced classification problem. In this work, we developed a ML framework to explore the effects of unbalanced populations on the performance and stability of feature ranking for sentinel lymph node status classification in breast cancer. Our results indicate state-of-the-art AUC (Area under the Receiver Operating Characteristic curve) values on a hold-out set (67%) while providing particularly stable features related to tumor size, histological subtype and estrogen receptor expression, which should therefore be considered as potential biomarkers

Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features

The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest performance, but sometimes surgery is achieved without a clear diagnosis and also with possible serious complications. In this work, we developed a machine learning model to predict the sentinel lymph nodes positivity in clinically negative patients. Breast cancer clinical and immunohistochemical features of 907 patients characterized by a clinically negative lymph node status were collected. We trained different machine learning algorithms on the retrospective collected data and selected an optimal subset of features through a sequential forward procedure. We found comparable performances for different classification algorithms: on a hold-out training set, the logistics regression classifier with seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status reached an AUC value of 71.5% and showed a better trade-off between sensitivity and specificity (69.4 and 66.9%, respectively) compared to other two classifiers. On the hold-out test set, the performance dropped by five percentage points in terms of accuracy. Overall, the histological characteristics alone did not allow us to develop a support tool suitable for actual clinical application, but it showed the maximum informative power contained in the same for the resolution of the clinical problem. The proposed study represents a starting point for future development of predictive models to obtain the probability for lymph node metastases by using histopathological features combined with other features of a different nature

A HGF/cMET Autocrine Loop Is Operative in Multiple Myeloma Bone Marrow Endothelial Cells and May Represent a Novel Therapeutic Target

Purpose: The aim of this study was to investigate the angiogenic role of the hepatocyte growth factor (HGF)/cMET pathway and its inhibition in bone marrow endothelial cells (EC) from patients with multiple myeloma versus from patients with monoclonal gammopathy of undetermined significance (MGUS) or benign anemia (control group). Experimental Design: The HGF/cMET pathway was evaluated in ECs from patients with multiple myeloma (multiple myeloma ECs) at diagnosis, at relapse after bortezomib- or lenalidomide-based therapies, or on refractory phase to these drugs; in ECs from patients with MGUS (MGECs); and in those patients from the control group. The effects of a selective cMET tyrosine kinase inhibitor (SU11274) on multiple myeloma ECs' angiogenic activities were studied in vitro and in vivo. Results: Multiple myeloma ECs express more HGF, cMET, and activated cMET (phospho (p)-cMET) at both RNAand protein levels versus MGECs and control ECs. Multiple myeloma ECs are able to maintain the HGF/cMET pathway activation in absence of external stimulation, whereas treatment with anti-HGF and anti-cMET neutralizing antibodies (Ab) is able to inhibit cMET activation. The cMET pathway regulates several multiple myeloma EC activities, including chemotaxis, motility, adhesion, spreading, and whole angiogenesis. Its inhibition by SU11274 impairs these activities in a statistically significant fashion when combined with bortezomib or lenalidomide, both in vitro and in vivo. Conclusions: An autocrine HGF/cMET loop sustains multiple myeloma angiogenesis and represents an appealing new target to potentiate the antiangiogenic management of patients with multiple myeloma

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research

Introduction: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). Methods and objectives: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. Conclusion: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project

Tracker Operation and Performance at the Magnet Test and Cosmic Challenge

During summer 2006 a fraction of the CMS silicon strip tracker was operated in a comprehensive slice test called the Magnet Test and Cosmic Challenge (MTCC). At the MTCC, cosmic rays detected in the muon chambers were used to trigger the readout of all CMS sub-detectors in the general data acquisition system and in the presence of the 4 T magnetic field produced by the CMS superconducting solenoid. This document describes the operation of the Tracker hardware and software prior, during and after data taking. The performance of the detector as resulting from the MTCC data analysis is also presented

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches