Exploiting CNN’s visual explanations to drive anomaly detection

Nowadays, deep learning is a key technology for many applications in the industrial area such as anomaly detection. The role of Machine Learning (ML) in this field relies on the ability of training a network to learn to inspect images to determine the presence or not of anomalies. Frequently, in Industry 4.0 w.r.t. the anomaly detection task, the images to be analyzed are not optimal, since they contain edges or areas, that are not of interest which could lead the network astray. Thus, this study aims at identifying a systematic way to train a neural network to make it able to focus only on the area of interest. The study is based on the definition of a loss to be applied in the training phase of the network that, using masks, gives higher weight to the anomalies identified within the area of interest. The idea is to add an Overlap Coefficient to the standard cross-entropy. In this way, the more the identified anomaly is outside the Area of Interest (AOI) the greater is the loss. We call the resulting loss Cross-Entropy Overlap Distance (CEOD). The advantage of adding the masks in the training phase is that the network is forced to learn and recognize defects only in the area circumscribed by the mask. The added benefit is that, during inference, these masks will no longer be needed. Therefore, there is no difference, in terms of execution times, between a standard Convolutional Neural Network (CNN) and a network trained with this loss. In some applications, the masks themselves are determined at run-time through a trained segmentation network, as we have done for instance in the "Machine learning for visual inspection and quality control" project, funded by the MISE Competence Center Bi-REX

Neural-Symbolic Ensemble Learning for early-stage prediction of critical state of Covid-19 patients

Recently, Artificial Intelligence (AI) and Machine Learning (ML) have been successfully applied to many domains of interest including medical diagnosis. Due to the availability of a large quantity of data, it is possible to build reliable AI systems that assist humans in making decisions. The recent Covid-19 pandemic quickly spread over the world causing serious health problems and severe economic and social damage. Computer scientists are actively working together with doctors on different ML models to diagnose Covid-19 patients using Computed Tomography (CT) scans and clinical data. In this work, we propose a neural-symbolic system that predicts if a Covid-19 patient arriving at the hospital will end in a critical condition. The proposed system relies on Deep 3D Convolutional Neural Networks (3D-CNNs) for analyzing lung CT scans of Covid-19 patients, Decision Trees (DTs) for predicting if a Covid-19 patient will eventually pass away by analyzing its clinical data, and a neural system that integrates the previous ones using Hierarchical Probabilistic Logic Programs (HPLPs). Predicting if a Covid-19 patient will end in a critical condition is useful for managing the limited number of intensive care at the hospital. Moreover, knowing early that a Covid-19 patient could end in serious conditions allows doctors to gain early knowledge on patients and provide special treatment to those predicted to finish in critical conditions. The proposed system, entitled Neural HPLP, obtains good performance in terms of area under the receiver operating characteristic and precision curves with values of about 0.96 for both metrics. Therefore, with Neural HPLP, it is possible not only to efficiently predict if Covid-19 patients will end in severe conditions but also possible to provide an explanation of the prediction. This makes Neural HPLP explainable, interpretable, and reliable. Graphical abstract Representation of Neural HPLP. From top to bottom, the two different types of data collected from the same patient and used in this project are represented. This data feeds the two different machine learning systems and the integration of the two systems using Hierarchical Probabilistic Logic Program

Machine learning techniques for extracting relevant features from clinical data for COVID-19 mortality prediction

The role of Machine Learning (ML) in healthcare is based on the ability of a machine to analyse the huge amounts of data available for each patient, like age, medical history, overall health status, test results, etc. With ML algorithms it is possible to learn models from data for the early identification of pathologies and their severity. Early identification is crucial to proceed as soon as possible with the necessary therapeutic actions. This work applies modern ML techniques to clinical data of either COVID-19 positive and COVID-19 negative patients with pulmonary complications, to learn mortality prediction models for both groups of patients, and compare results. We have focused on symbolic methods for building classifiers able to extract patterns from clinical data. This approach leads to predictive Artificial Intelligence (AI) systems working on medical data, and able to explain the reasons that lead the systems themselves to reach a certain conclusion

The Key Role of IP6K: A Novel Target for Anticancer Treatments?

Inositol and its phosphate metabolites play a pivotal role in several biochemical pathways and gene expression regulation: inositol pyrophosphates (PP-IPs) have been increasingly appreciated as key signaling modulators. Fluctuations in their intracellular levels hugely impact the transfer of phosphates and the phosphorylation status of several target proteins. Pharmacological modulation of the proteins associated with PP-IP activities has proved to be beneficial in various pathological settings. IP 7 has been extensively studied and found to play a key role in pathways associated with PP-IP activities. Three inositol hexakisphosphate kinase (IP 6 K) isoforms regulate IP 7 synthesis in mammals. Genomic deletion or enzymic inhibition of IP 6 K1 has been shown to reduce cell invasiveness and migration capacity, protecting against chemical-induced carcinogenesis. IP 6 K1 could therefore be a useful target in anticancer treatment. Here, we summarize the current understanding that established IP 6 K1 and the other IP 6 K isoforms as possible targets for cancer therapy. However, it will be necessary to determine whether pharmacological inhibition of IP 6 K is safe enough to begin clinical study. The development of safe and selective inhibitors of IP 6 K isoforms is required to minimize undesirable effects

Efficient Resource-Aware Neural Architecture Search with a Neuro-Symbolic Approach

Hardware-aware Neural Architectural Search (NAS) is gaining momentum to enable the deployment of deep learning on edge devices with limited computing capabilities. Incorporating device-related objectives such as affordable floating point operations, latency, power, memory usage, etc. into the optimization process makes searching for the most efficient neural architecture more complicated, since both model accuracy and hardware cost should guide the search. The main concern with most state-of-the-art hardware-aware NAS strategies is that they propose for evaluation also trivially infeasible network models for the capabilities of the hardware platform at hand. Moreover, previously generated models are frequently not exploited to intelligently generate new ones, leading to prohibitive computational costs for practical relevance. This paper aims to boost the computational efficiency of hardware-aware NAS by means of a neuro-symbolic framework revolving around a Probabilistic Inductive Logic Programming module to define and exploit a set of symbolic rules. This component learns and refines the probabilities associated with the rules, allowing the framework to adapt and improve over time, thus quickly narrowing down the search space toward the most promising neural architectures

The activation of miR-125a-5p/IP6K1 axis in breast cancer cells upon treatment with myo-Inositol.

Several studies have been performed with the aim of identifying drugs able in inhibiting Epithelial-Mesenchymal Transition (EMT), chiefly by blocking PI3K/Akt pathway. We have already demonstrated that treatment with myo-Inositol at the pharmacological dose can block EMT in breast cancer cells by downregulating PI3K/Akt and inducing changes in cytoskeletal architecture. Herewith, we investigated the mechanism of action of myo-inositol in both highly (MDA-MB-231) and low (MCF-7) invasive human breast cancer cells. After 30’ and 24h from treatment, gene expression analysis revealed a significant downregulation of Pi3k and Psen1 after 30’ in both cell lines. Psen1 downregulation was maintained in MDA-MB-231 at 24h. Likewise, we explored the modulation of Ip6k1, Dnmt3b, Isyna1 and p53. In MDA-MB-231, a strong downregulation of Ip6k1 expression was recorded at 30’ and 24h, whilst Dnmt3b was reduced only at 30’. On the contrary, in MCF-7, Ip6k1 downregulation was unexpectedly associated to the upregulation of Dnmt3b at 30’. IP6K1 is a key enzyme of inositol metabolism, inhibits ISYNA1, probably inducing de novo DNA methylation (i.e., DNMT3B). Furthermore, IP6K1 inhibition correlates with a decrease of cancer cells motility. The upregulation of Isyna1 was observed in both cell lines at 30’, together with p53. ISYNA1 activates myo-Inositol intracellular biosynthesis starting from glucose-6-phosphate. In this activation, p53 plays a key role in binding Isyna1 promoter and eventually enabling its expression. Western-blot of MDA-MB-231 confirmed that changes in gene expression were also mirrored by concurrently modifications in IP6K1 and p53 protein levels, altogether with a decrease of both MDM2 and YAP/TAZ. It is worth noting that in MCF-7, no changes were observed in protein levels. In-silico analysis was performed using TCGA miRNA-Seq data to identify differentially expressed miRNAs between normal and tumoral tissue in breast cancer patients. To further gain mechanistic insights on myo-Inositol effects, we compared these data with main differentially expressed cancer-related miRNAs in MDA-MB-231 cells after 30’ from treatment. This analysis allowed to identify two mRNAs, downregulated in tumor tissues, that were significantly increased with myo-Inositol: miR-92a-3p and miR-125a-5p. Using DIANA tools, miR-92a-3p was predicted to interact with Notch-1 and PI3K, linking it to cytoskeletal rearrangement. Moreover, a strong interaction was predicted between miR-125a-5p and IP6K1 in 3’-UTR site. Indeed, the upregulation of miR-125a-5p is usually correlated with metastasis inhibition in breast cancer. In MDA-MB-231, miR-125a-5p upregulation was maintained at 24h, while in MCF-7 was slightly upregulated at 30’ and downregulated at 24h. Our results suggest that myo-Inositol causes early changes in gene expression, probably led by miRNAs and methylation remodeling. Elucidation of the role of miR-125a-5p/IP6K1 axis will reveal strategies for molecular targeted therapies in breast cancer

Myo-Inositol treatment inhibits motility in triple negative breast cancer via miR-125a-5p/IP6K1 axis

Background: Several researches have been performed with the aim of identifying drugs able in blocking PI3K/Akt pathway. We have already demonstrated that myo-Inositol (myo-Ins) treatment can block EMT in breast cancer cells by downregulating PI3K/Akt and inducing changes in cytoskeletal architecture. Aim: Herein, we set our experiments to investigate migration/invasiveness inhibition though in vitro and in vivo models upon myo-Ins administration. Methods: In vitro experiments were performed using both mesenchymal-like (MDA-MB-231) and epithelial-like (MCF-7) invasive human breast cancer cells. We used transwell assays for in vitro and Zebrafish embryos as in vivo models to evaluate migration and invasiveness. The expression of key genes involved in the mechanism was evaluated by qPCR, while gain- and loss-of-function approaches allowed identifying the specific dynamical relationships. Results: Myo-Ins inhibits motility and invasiveness only in MDA-MB-231 cells both in vitro and in vivo. In MDA cells, miR-125a-5p upregulation was linked to IP6K1 downregulation triggered by myo-Ins treatment. Silencing and overexpression experiments confirmed the key role of miR-125a-5p/IP6K1 axis in blocking cell motility. This effect was demonstrated to be myo-Ins-dependent MDM2 inhibition. Given that MDM2 in MCF-7 cells was unaffected by treatment, in these cells myo-Ins was unable in antagonizing motility. Conversely, both miR-125a-5p and IP6K1 were not modulated. However, MDM2 silencing restore sensitivity to myo-Ins, thus leading to a significant inhibition of the MCF-7 cells motility capability. Conclusions: Our results suggest that myo-Ins can inhibit motility in triple negative breast cancer. Such an effect is likely mediated by MDM2 inhibition, which, in turn, triggers a complex tumor reversion promoted by the miR-125a-5p/IP6K1 axis modulation. Elucidation of the role of miR-125a- 5p/IP6K1 axis will reveal strategies for molecular targeted therapies in breast cancer

Complete response in a patient with gynecological hidradenocarcinoma treated with exclusive external beam radiotherapy and brachytherapy: a case report

Hidradenocarcinoma (HC) is a very rare disease. This case report illustrates a successful treatment of a 60-year-old woman with vulvo-vaginal localization of hidradenocarcinoma treated with external beam radiotherapy delivered by helical tomotherapy with a simultaneous integrated boost (SIB), followed by brachytherapy. External beam radiotherapy dose prescription was 50.4 Gy in 28 fractions, five fractions per week to whole pelvis (planning target volume 1 \ue2\u80\u93 PTV1), 60.2 Gy in 28 fractions to SIB1 (fundus of uterus and right inguinal node), and 58.8 Gy in 28 fractions to SIB2 (lower/middle third of vagina, paraurethral region and right inguinal lymph nodes). Brachytherapy dose prescription was 28 Gy in 4 fractions for cervix, fundus of uterus and upper third of vagina (HR-CTV1), and 22 Gy in 4 fractions to middle third of vagina and paraurethral region (HR-CTV2). D90for whole treatment was 91.9 Gy and 86.0 Gy for HR-CTV1 and HR-CTV2, respectively. Patient remained 12-months disease-free without treatment related side effects