Alien parakeets as a potential threat to the common noctule Nyctalus noctula

The ring-necked parakeet Psittacula krameri (Aves: Psittaciformes) is a widely distributed species of Asian and African origin, which occurs with over 40 alien populations in the rest of the world. Most established populations of this species are showing a clear trend of territorial expansion and numerical growth. Recent reviews highlighted that one of the main impacts by alien ring-necked parakeets is the competition with threatened bat spe cies using trunk cavities as roosts. In Italy, the only known reproductive population of Nyctalus bats (Mammalia: Chiroptera) occurs in an urban area in the central part of the country, surrounded by increasing and expanding populations of ring-necked parakeets. In this work, we updated the population status of both ring-necked and Alexandrine parakeets and breeding noctule bats in the region. Then, we ran a species distribution model using Maxent software to analyze the environmental suitability of the region for the ring-necked parakeet and a connectivity model using Circuitscape software to predict the possibility of its expansion in the area occupied by breeding noctule bats. We recorded a high number of individual parakeets and breeding colonies, together with a remarkable noctule popula tion decline, from about 400 to about 120 individuals, in the last 20 years, possibly due to urban green management practices. Although some ring-necked parakeets have already been observed in the study area, there is no evidence of reproduction in the surroundings of the noctule colony. However, our model showed a high environmental suitability for the ring-necked parakeet in the area occupied by breeding noctules. As well, the connectivity model showed the potential for a direct fow of individuals from the main urban centers to the area used by noctule bats. The arrival of alien parakeets to the area occupied by the bat breeding colony should be tightly monitored by surveying the suitable areas for this bird, as well as the identifed ecological corridors. Early detection of new invasions, together with a sustainable urban green management practice, may prevent the extinction of the southernmost breeding colony of the common noctule

Machine learning use for prognostic purposes in multiple sclerosis

The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge

Robotic "Double Loop" Roux-en-Y gastric bypass reduces the risk of postoperative internal hernias: a prospective observational study

BACKGROUND: Internal herniation (IH) is a potentially serious complication after laparoscopic Roux-en-Y gastric bypass (RYGB). The aim of the study is to evaluate the incidence of IH after robot-assisted RYGB (RA-RYGB) performed with the “Double Loop” technique at our Institution. METHODS: Prospective cohort study of patients submitted to RA-RYGB with the “Double Loop” technique, with a minimum follow-up of 2 years. Patients with complaints of abdominal pain at clinical visits or entering the emergency department were evaluated. Primary outcome was the incidence of IH, defined as the presence of herniated bowel through a mesenteric defect, diagnosed at imaging or at surgical exploration. RESULTS: A total of 129 patients were included: 65 (50.4%) were primary procedures, while 64 (49.6%) were revisional operations after primary restrictive bariatric surgery. Mean age was 47.9 ± 10.2 years, mean weight, and body mass index were, respectively, 105.3 ± 22.6 kg and 39.7 ± 9.6 kg/m(2). Postoperative morbidity rate was 7.0%. Mean follow-up was 53.2 ± 22.6 (range 24–94) months. During the follow-up period, a total of 14 (10.8%) patients entered the emergency department: 1 patient had melena, 4 renal colic, 1 acute cholecystitis, 2 gynecologic pathologies, 2 anastomotic ulcers, 1 perforated gastric ulcer, 1 diverticulitis and 2 gastroenteritis. There were no diagnoses of IH. During the follow-up period, no patient experienced recurrence of symptoms. CONCLUSIONS: In the present study, the robotic approach confirms the low complication rate and absence of IH after “Double Loop” RA-RYGB in a large case-series at a medium-term follow-up

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only “real world” data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used. Predictions at 180, 360 or 720 days from the last visit were obtained considering either the data of the last available visit (Visit-Oriented setting), comparing four classical ML methods (Random Forest, Support Vector Machine, K-Nearest Neighbours and AdaBoost) or the whole clinical history of each patient (History-Oriented setting), using a Recurrent Neural Network model, specifically designed for historical data. Missing values were handled by removing either all clinical records presenting at least one missing parameter (Feature-saving approach) or the 3 clinical parameters which contained missing values (Record-saving approach). The performances of the classifiers were rated using common indicators, such as Recall (or Sensitivity) and Precision (or Positive predictive value). In the visit-oriented setting, the Record-saving approach yielded Recall values from 70% to 100%, but low Precision (5% to 10%), which however increased to 50% when considering only predictions for which the model returned a probability above a given “confidence threshold”. For the History-oriented setting, both indicators increased as prediction time lengthened, reaching values of 67% (Recall) and 42% (Precision) at 720 days. We show how “real world” data can be effectively used to forecast the evolution of MS, leading to high Recall values and propose innovative approaches to improve Precision towards clinically useful values

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease

Background: Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated. Objectives: To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD. Methods: From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis < 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD. Results: Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11 years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23. Conclusions: Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations. Clinical trial registration: NCT02737982

User profile based Quality of Experience

The Quality of Experience (QoE) is a subjective measure of the quality experienced by an user with respect to a service or a class of services. This measure takes into account a pervasive and holistic evaluation of the service as a whole. It typically differs from objective and structured measure of quality parameters subject to a service provider’s control. In this paper we consider the problem of identifying general behavioural profiles with respect to a generic service starting from raw data describing the user’s feedback in different circumstances. The aim is that of analyzing these profiles in order to recognize what are the most influencing components for QoE

A reinforcement learning approach for QoS/QoE model identification

In the last decade, researchers has focused their studies on the mathematical relation between the Quality of Service (QoS) and the user Quality of Experience (QoE). This paper investigates the problem of modelling the user QoE feedback in the next generation networks. The problem has been formulated and solved using a reinforcement learning technique. The proposed approach is innovative since it does not require an explicit knowledge of the mathematical model describing the network dynamics or the QoS/QoE relationship since it is learnt on-line. Simulation results shows that the proposed solution can adapt dynamically to the user behavior

Profiled QoE based network controller

Internet evolution follows the customer needs, each algorithm, protocol, architecture, equipment, functionality succeeded when the users perceived a real benefit in using it. Taking into account the impact of customer experience when designing promising and future proof technologies is essential. In this paper we investigate how it is possible to control network resources on the base of the Quality of Experience (QoE), defined as the quality of service perceived by a user when using a specific service. QoE is a subjective measure and typically differs from objective and structured measures of quality of service that are under the service provider's control. We consider the problem of identifying a set of QoE profiles that describes the user behavior when enjoying specific class of services, by analyzing the data related to the users' feedback in different contextual scenarios. We formulated the mathematical model and performed a validation on the base of preliminary field trials

Improvement of intrinsic myocardial contractility and cardiac fibrosis degree in acromegalic patients treated with somatostatin analogues: a prospective study

Background Acromegalic patients have increased left ventricular (LV) mass (M) and impaired diastolic function.Aim Using ultrasonic cardiac tissue characterization, we evaluated the early changes in cardiac fibrosis (IBS) and intrinsic myocardial contractility (CVI) as well as their reversibility after treatment with somatostatin analogues (SMSA) in patients with acromegaly.Patients and Methods Twenty-two acromegalic patients with active untreated disease (Acro(UNTR)) underwent conventional Doppler echocardiography and integrated backscattering; 25 healthy subjects (controls) and eight patients with acromegaly in remission after pituitary adenomectomy (Acro(REM)) served as controls.Results As expected, Acro(UNTR) at baseline had higher LVM than controls or Acro(REM) (P < 0.001); LVM reduced in acromegalic patients after SMSA (P < 0.005 vs. baseline) while LV ejection fraction did not change. LV diastolic function was reduced in all acromegalic patients, either at baseline or after SMSA therapy (E/A ratio, 0.96 +/- 0.3 and 1.1 +/- 0.3, respectively, P < 0.002 vs. controls, 1.6 +/- 0.3). CVI was reduced in Acro(UNTR) (14.3 +/- 5.8%, P < 0.003 vs. controls, 28.7 +/- 7.5%) and greatly improved after SMSA (22.5 +/- 4.5%, P < 0.003 vs. baseline). Cardiac fibrosis was increased in Acro(UNTR) (IBSMSI, 53.7 +/- 5.3%P < 0.002 vs. controls) and reduced after SMSA (43.7 +/- 4.2%P < 0.002 vs. baseline) albeit not reaching values observed in controls. More importantly, five of 22 (23%) Acro(UNTR) patients had normal LVM, but increased cardiac fibrosis as revealed by back scattering. IBS values and CVI% were related with serum GH and IGF-1 (P < 0.0001) levels, and the estimated duration of disease (P < 0.005).Conclusions The present study demonstrated that active acromegalic patients had early impairment of systolic function and increased cardiac fibrosis; increased fibrosis may precede LV hypertrophy; these changes are related to the activity of disease and might improve during treatment with SMSA