Detection of Anomalies in Household Appliances from Disaggregated Load Consumption

The detection of anomalous power consumption in household appliances plays a key role for the optimization of grid operations and for reducing unwanted electrical absorptions in residential buildings. Smart Plugs, Smart Appliances and other appliance-level monitoring devices allow to continuously monitor the power consumption of individual appliances present in the house. This work is aimed at detecting electrical anomalies in household appliances by analyzing the disaggregated load consumption derived from appliance-level monitoring devices. For this purpose, we implemented an anomaly detection framework which monitors the hourly energy consumption of three common sources of power absorption: the baseline, the fridge and the electrical devices. Here, we focused our analysis on two kinds of anomalies: single-point deviations and anomalous trends. The analysis of single-point deviations allowed us to identify short-term power peaks due either to unexpected electrical faults or sudden variations in end-users routines. The analysis of anomalous trends revealed several cases in which the end-users gradually increased their ordinary power consumption profile towards more energy-intensive practices. In summary, the results of our work showed that the power consumption derived from appliance-level load monitoring can be used to detect several anomalous power consumption in household appliances

Event-Driven User-Centric Middleware for Energy Efficient Buildings and Public Spaces

In this work, the design of an event-driven user-centric middleware for monitoring and managing energy consumption in public buildings and spaces is presented. The main purpose is to increase the energy efficiency, reducing consumption, in buildings and public spaces. To achieve this, the proposed service-oriented middleware has been designed to be event based, also exploiting the user behaviours patterns of the people who live and work into the building. Furthermore, it allows an easy integration of heterogeneous technologies in order to enable a hardware independent interoperability between them. Moreover, a Heating Ventilation and Air Conditioning (HVAC) control strategy has been developed and the whole infrastructure has been deployed in a real-world case study consisting of a historical building. Finally the results will be presented and discusse

ChatGPT as a New Tool to Select a Biological for Chronic Rhino Sinusitis with Polyps, “Caution Advised” or “Distant Reality”?

ChatGPT is an advanced language model developed by OpenAI, designed for naturallanguage understanding and generation. It employs deep learning technology to comprehend andgenerate human-like text, making it versatile for various applications. The aim of this study is toassess the alignment between the Rhinology Board’s indications and ChatGPT’s recommendationsfor treating patients with chronic rhinosinusitis with nasal polyps (CRSwNP) using biologic ther-apy. An observational cohort study involving 72 patients was conducted to evaluate various param-eters of type 2 inflammation and assess the concordance in therapy choices between ChatGPT andthe Rhinology Board. The observed results highlight the potential of Chat-GPT in guiding optimalbiological therapy selection, with a concordance percentage = 68% and a Kappa coefficient = 0.69(CI95% [0.50; 0.75]). In particular, the concordance was, respectively, 79.6% for dupilumab, 20% formepolizumab, and 0% for omalizumab. This research represents a significant advancement in man-aging CRSwNP, addressing a condition lacking robust biomarkers. It provides valuable insightsinto the potential of AI, specifically ChatGPT, to assist otolaryngologists in determining the optimalbiological therapy for personalized patient care. Our results demonstrate the need to implement theuse of this tool to effectively aid clinicians

Effects of 5-Week of FIFA 11+ Warm-Up Program on Explosive Strength, Speed, and Perception of Physical Exertion in Elite Female Futsal Athletes

Futsal is a sport that originates from soccer and is increasingly practiced all over the world. Since training and warm-up protocols should be sport-specific in order to reduce injuries and maximize performance, this study aimed to evaluate the effects of 5 weeks of the FIFA 11+ warm-up program on explosive strength, speed, and perception of physical exertion in elite female futsal athletes. Twenty-nine elite female futsal athletes participating in the Italian national championships were divided into two groups: the experimental group (EG) underwent 5 weeks of the FIFA 11+ warmup program, and the control group (CG) underwent 5 weeks of a dynamic warm-up. We evaluated any effect on explosive strength (by Squat Jump test), speed (by Agility T-test), and perception of physical exertion (by Borg CR-10 scale). All measurements were carried out by a technician of the Italian Football Federation before (T0), at the middle (T1), and at the end (T2) of the protocol. The EG showed significant improvements on performances between T0 vs. T1 and T0 vs. T2 both in the Squat Jump test (p = 0.0057 and p = 0.0030, respectively) and in the Agility T-test (p = 0.0075 and p = 0.0122). No significant differences were found in the Squat Jump test performances in the CG, while significant improvements were detected in the Agility T-test performances (p = 0.0004 and p = 0.0053, T0 vs. T1 and T0 vs. T2, respectively). As for the Borg CR-10 scale, we found a significant difference between T0 and T2 in the EG (p = 0.017) and no differences in the CG. This study showed that 5 weeks of the FIFA 11+ warm-up program improves the jumping performance of female futsal athletes without adversely affecting speed. These findings can be useful for coaches and athletic trainers in order to consider FIFA 11+ warm-up program also in female futsal athletes

MicroRNAs as Regulators of Neo-Angiogenesis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a highly vascularized neoplasm. In the tumor niche, abundant angiogenesis is fundamental in providing nutrients for tumor growth and represents the first escape route for metastatic cells. Active angiogenesis, together with metastasis, are responsible for the reduction of recurrence-free survival of HCC. MicroRNAs (miRNAs) are small non-coding RNAs that have recently drawn attention in molecular targeted therapy or as diagnostic and prognostic biomarkers. MiRNA expression in HCC has been widely studied in the last decade. Some miRNAs have been found to be up- or down-regulated, besides association with apoptosis, metastasis progression and drug resistance have been found. This review article aims to summarize the angiogenetic process in tumor diseases and to update on what has been found in the vast world of HCC-related-miRNAs and, eventually, to report the latest finding on several miRNAs involved in HCC angiogenesis. We searched the state of the arts for the 12 miRNAs found to be involved with angiogenesis in HCC (miR-29b, miR-126-3p, miR-144-3p, miR-146a, miR-195, miR-199a-3p, miR-210-3p, miR-338- 3p, mir-491, mir-497, mir-638, mir-1301) and reported their main molecular targets and their overall effect in the sprouting of new vessels

Moving Toward a Strategy for Addressing Climate Displacement of Marine Resources: A Proof-of-Concept

Realistic predictions of climate change effects on natural resources are central to adaptation policies that try to reduce these impacts. However, most current forecasting approaches do not incorporate species-specific, process-based biological information, which limits their ability to inform actionable strategies. Mechanistic approaches, incorporating quantitative information on functional traits, can potentially predict species- and population-specific responses that result from the cumulative impacts of small-scale processes acting at the organismal level, and can be used to infer population-level dynamics and inform natural resources management. Here we present a proof-of-concept study using the European anchovy as a model species that shows how a trait-based, mechanistic species distribution model can be used to explore the vulnerability of marine species to environmental changes, producing quantitative outputs useful for informing fisheries management. We crossed scenarios of temperature and food to generate quantitative maps of selected mechanistic model outcomes (e.g., Maximum Length and Total Reproductive Output). These results highlight changing patterns of source and sink spawning areas as well as the incidence of reproductive failure. This study demonstrates that model predictions based on functional traits can reduce the degree of uncertainty when forecasting future trends of fish stocks. However, to be effective they must be based on high spatial- and temporal resolution environmental data. Such a sensitive and spatially explicit predictive approach may be used to inform more effective adaptive management strategies of resources in novel climatic conditions

Real-world Outcomes of Relapsed/Refractory Diffuse Large B-cell Lymphoma Treated With Polatuzumab Vedotin-based Therapy

: After FDA and EMA approval of the regimen containing polatuzumab vedotin plus rituximab and bendamustine (PolaBR), eligible relapsed/refractory diffuse large B-cell lymphoma (DLBCL) patients in Italy were granted early access through a Named Patient Program. A multicentric observational retrospective study was conducted focusing on the effectiveness and safety of PolaBR in everyday clinical practice. Fifty-five patients were enrolled. There were 26 females (47.3%), 32 patients were primary refractory and 45 (81.8%) resulted refractory to their last therapy. The decision to add or not bendamustine was at physician's discretion. Thirty-six patients underwent PolaBR, and 19 PolaR. The 2 groups did not differ in most of baseline characteristics. The final overall response rate was 32.7% (18.2% complete response rate), with a best response rate of 49.1%. Median disease-free survival was reached at 12 months, median progression-free survival at 4.9 months and median overall survival at 9 months, respectively. Overall, 88 adverse events (AEs) were registered during treatment in 31 patients, 22 of grade ≥3. Eight cases of neuropathy occurred, all of grades 1-2 and all related to polatuzumab. The two groups of treatment did not differ for effectiveness endpoints but presented statistically significant difference in AEs occurrence, especially in hematological AEs, in AEs of grade equal or greater than 3 and in incidence of neuropathy. Our data add useful information on the effectiveness of Pola(B)R in the setting of heavily pretreated DLBCL and may also suggest a better tolerability in absence of bendamustine without compromise of efficacy

A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial

BACKGROUND Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313). METHODS We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0-9.6; High→Int, HR: 2.3, 95% CI: 1.5-4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential

A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial

SIMPLE SUMMARY: The interest in using Machine-Learning (ML) techniques in clinical research is growing. We applied ML to build up a novel prognostic model from patients affected with Mantle Cell Lymphoma (MCL) enrolled in a phase III open-labeled, randomized clinical trial from the Fondazione Italiana Linfomi (FIL)—MCL0208. This is the first application of ML in a prospective clinical trial on MCL lymphoma. We applied a novel ML pipeline to a large cohort of patients for which several clinical variables have been collected at baseline, and assessed their prognostic value based on overall survival. We validated it on two independent data series provided by European MCL Network. Due to its flexibility, we believe that ML would be of tremendous help in the development of a novel MCL prognostic score aimed at re-defining risk stratification. ABSTRACT: Background: Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313). Methods: We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0–9.6; High→Int, HR: 2.3, 95% CI: 1.5–4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential

Lopinavir/Ritonavir and Darunavir/Cobicistat in Hospitalized COVID-19 Patients: Findings From the Multicenter Italian CORIST Study

Background: Protease inhibitors have been considered as possible therapeutic agents for COVID-19 patients. Objectives: To describe the association between lopinavir/ritonavir (LPV/r) or darunavir/cobicistat (DRV/c) use and in-hospital mortality in COVID-19 patients. Study Design: Multicenter observational study of COVID-19 patients admitted in 33 Italian hospitals. Medications, preexisting conditions, clinical measures, and outcomes were extracted from medical records. Patients were retrospectively divided in three groups, according to use of LPV/r, DRV/c or none of them. Primary outcome in a time-to event analysis was death. We used Cox proportional-hazards models with inverse probability of treatment weighting by multinomial propensity scores. Results: Out of 3,451 patients, 33.3% LPV/r and 13.9% received DRV/c. Patients receiving LPV/r or DRV/c were more likely younger, men, had higher C-reactive protein levels while less likely had hypertension, cardiovascular, pulmonary or kidney disease. After adjustment for propensity scores, LPV/r use was not associated with mortality (HR = 0.94, 95% CI 0.78 to 1.13), whereas treatment with DRV/c was associated with a higher death risk (HR = 1.89, 1.53 to 2.34, E-value = 2.43). This increased risk was more marked in women, in elderly, in patients with higher severity of COVID-19 and in patients receiving other COVID-19 drugs. Conclusions: In a large cohort of Italian patients hospitalized for COVID-19 in a real-life setting, the use of LPV/r treatment did not change death rate, while DRV/c was associated with increased mortality. Within the limits of an observational study, these data do not support the use of LPV/r or DRV/c in COVID-19 patients