Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events

One of the most crucial applications of radar-based precipitation nowcasting systems is the short-term forecast of extreme rainfall events such as flash floods and severe thunderstorms. While deep learning nowcasting models have recently shown to provide better overall skill than traditional echo extrapolation models, they suffer from conditional bias, sometimes reporting lower skill on extreme rain rates compared to Lagrangian persistence, due to excessive prediction smoothing. This work presents a novel method to improve deep learning prediction skills in particular for extreme rainfall regimes. The solution is based on model stacking, where a convolutional neural network is trained to combine an ensemble of deep learning models with orographic features, doubling the prediction skills with respect to the ensemble members and their average on extreme rain rates, and outperforming them on all rain regimes. The proposed architecture was applied on the recently released TAASRAD19 radar dataset: the initial ensemble was built by training four models with the same TrajGRU architecture over different rainfall thresholds on the first six years of the dataset, while the following three years of data were used for the stacked model. The stacked model can reach the same skill of Lagrangian persistence on extreme rain rates while retaining superior performance on lower rain regimes

TAASRAD19, a high-resolution weather radar reflectivity dataset for precipitation nowcasting

none6We introduce TAASRAD19, a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps. The dataset includes 894,916 timesteps of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section at 5 min sampling rate, covering an area of 240 km of diameter at 500 m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validate TAASRAD19 as a benchmark for nowcasting methods by introducing a TrajGRU deep learning model to forecast reflectivity, and a procedure based on the UMAP dimensionality reduction algorithm for interactive exploration. Software methods for data pre-processing, model training and inference, and a pre-trained model are publicly available on GitHub (https://github.com/MPBA/TAASRAD19) for study replication and reproducibility.noneFranch, Gabriele; Maggio, Valerio; Coviello, Luca; Pendesini, Marta; Jurman, Giuseppe; Furlanello, CesareFranch, Gabriele; Maggio, Valerio; Coviello, Luca; Pendesini, Marta; Jurman, Giuseppe; Furlanello, Cesar

Recurrence of immunoglobulin A nephropathy after kidney transplantation: a narrative review on incidence, risk factors, pathophysiology and management of immunosuppressive therapy

Abstract Glomerulonephritis (GN) is the underlying cause of end-stage renal failure in 30–50% of kidney transplant recipients. It represents the primary cause of end-stage renal disease for 25% of the dialysis population and 45% of the transplant population. For patients with GN requiring renal replacement therapy, kidney transplantation is associated with superior outcomes compared with dialysis. Recurrent GN was previously considered to be a minor contributor to graft loss, but with the prolongation of graft survival, the effect of recurrent disease on graft outcome assumes increasing importance. Thus the extent of recurrence of original kidney disease after kidney transplantation has been underestimated for several reasons. This review aims to provide updated knowledge on one particular recurrent renal disease after kidney transplantation, immunoglobulin A nephropathy (IgAN). IgAN is one of the most common GNs worldwide. The pathogenesis of IgAN is complex and remains incompletely understood. Evidence to date is most supportive of a several hit hypothesis. Biopsy is mandatory not only to diagnose the disease in the native kidney, but also to identify and characterize graft recurrence of IgAN in the kidney graft. The optimal therapy for IgAN recurrence in the renal graft is unknown. Supportive therapy aiming to reduce proteinuria and control hypertension is the mainstream, with corticosteroids and immunosuppressive treatment tailored for certain subgroups of patients experiencing a rapidly progressive course of the disease with active lesions on renal biopsy and considering safety issues related to infectious complications

Analytical Methods for Extraction and Identification of Primary and Secondary Metabolites of Apple (Malus domestica) Fruits: A Review

Apples represent a greater proportion of the worldwide fruit supply, due to their availability on the market and to the high number of existing cultivar varieties and apple-based products (fresh fruit, fruit juice, cider, and crushed apples). Several studies on apple fruit metabolites are available, with most of them focusing on their healthy properties’ evaluation. In general, the metabolic profile of apple fruits strongly correlates with most of their peculiar characteristics, such as taste, flavor and color. At the same time, many bioactive molecules could be identified as markers of a specific apple variety. Therefore, a complete description of the analytical protocols commonly used for apple metabolites’ characterization and quantification could be useful for researchers involved in the identification of new phytochemical compounds from different apple varieties. This review describes the analytical methods published in the last ten years, in order to analyze the most important primary and secondary metabolites of Malus Domestica fruits. In detail, this review gives an account of the spectrophotometric, chromatographic, and mass spectrometric methods. A discussion on the quantitative and qualitative analytical shortcomings for the identification of sugars, fatty acids, polyphenols, organic acids, carotenoids, and terpenes found in apple fruits is reported

A Modulator-less Beam Steering Transmitter based on a revised DDS-PLL Phase Shifter Architecture

This paper details the design and implementation of a modulator-less beam steering transmitter based on a revised DDS-PLL phase shifter architecture. The proposed topology targets low data rate communications for Internet-of-Things systems, and has been demonstrated using an FPGA evaluation board and a custom PCB with four PLLs centered at 2.453-GHz. Measured system performance for an experimental 32-kbps data rate achieved through a 16-PSK modulation scheme are discussed. The proposed architecture is frequency independent, can be used in multi-band devices and has the potential for being integrated as an RF System-on-Chip

Why is the video analytics accuracy fluctuating, and what can we do about it?

It is a common practice to think of a video as a sequence of images (frames), and re-use deep neural network models that are trained only on images for similar analytics tasks on videos. In this paper, we show that this leap of faith that deep learning models that work well on images will also work well on videos is actually flawed. We show that even when a video camera is viewing a scene that is not changing in any human-perceptible way, and we control for external factors like video compression and environment (lighting), the accuracy of video analytics application fluctuates noticeably. These fluctuations occur because successive frames produced by the video camera may look similar visually, but these frames are perceived quite differently by the video analytics applications. We observed that the root cause for these fluctuations is the dynamic camera parameter changes that a video camera automatically makes in order to capture and produce a visually pleasing video. The camera inadvertently acts as an unintentional adversary because these slight changes in the image pixel values in consecutive frames, as we show, have a noticeably adverse impact on the accuracy of insights from video analytics tasks that re-use image-trained deep learning models. To address this inadvertent adversarial effect from the camera, we explore the use of transfer learning techniques to improve learning in video analytics tasks through the transfer of knowledge from learning on image analytics tasks. In particular, we show that our newly trained Yolov5 model reduces fluctuation in object detection across frames, which leads to better tracking of objects(40% fewer mistakes in tracking). Our paper also provides new directions and techniques to mitigate the camera's adversarial effect on deep learning models used for video analytics applications

A Modulator-less Beam Steering Transmitter based on a revised DDS-PLL Phase Shifter Architecture

This paper details the design and implementation of a modulator-less beam steering transmitter based on a revised DDS-PLL phase shifter architecture. The proposed topology targets low data rate communications for Internet-of-Things systems, and has been demonstrated using an FPGA evaluation board and a custom PCB with four PLLs centered at 2.453-GHz. Measured system performance for an experimental 32-kbps data rate achieved through a 16-PSK modulation scheme are discussed. The proposed architecture is frequency independent, can be used in multi-band devices and has the potential for being integrated as an RF System-on-Chip

Laparoscopic gynecological surgery under minimally invasive anesthesia: a prospective cohort study

The purpose of this study is to assess the feasibility and the perioperative outcomes of laparoscopic gynecological surgery in regional anesthesia (RA) from the point of view of the surgeon, anesthesiologist and patient. This is a prospective cohort study comprising sixty-six women planned to undergo gynecologic laparoscopy surgery for benign pathology at tertiary care gynecolgical center of the University Federico II of Naples. Women were assigned, according to their preference, to either RA (Group A) or general anesthesia (GA) (Group B). Surgical, anesthesiologic and postoperative recovery data were recorded. Postoperative pain was considered as the primary outcome. Secondary outcomes included mobilization, length of hospital stay, global surgeons and patient satisfaction, intraoperative pain assessment in Group A. Immediate postoperative pain was significantly lower in Group A 0 vs 2 (p < 0.001), with no significant differences at 24 h. The secondary outcome demonstrated early patient's mobilization (p < 0.001) as well as early discharge (p < 0.001) and greater patient's satisfaction for the Group A. In these patients, a maximum pain score of 3 points out of 5 was recorded through the entire surgery. RA showed to decrease the impact of surgical stress and to guarantee a quicker recovery without compromising surgical results. Although several surgical approaches can be employed to treat different conditions, RA technique could be a viable option for well-selected patients affected by gynecological diseases

Role of BRCA2 mutation status on overall survival among breast cancer patients from Sardinia

Background: Germline mutations in BRCA1 or BRCA2 genes have been demonstrated to increase the risk of developing breast cancer. Conversely, the impact of BRCA mutations on prognosis and survival of breast cancer patients is still debated. In this study, we investigated the role of such mutations on breast cancer-specific survival among patients from North Sardinia. Methods: Among incident cases during the period 1997–2002, a total of 512 breast cancer patients gave their consent to undergo BRCA mutation screening by DHPLC analysis and automated DNA sequencing. The Hakulinen, Kaplan-Meier, and Cox regression methods were used for both relative survival assessment and statistical analysis. Results: In our series, patients carrying a germline mutation in coding regions and splice boundaries of BRCA1 and BRCA2 genes were 48/512 (9%). Effect on overall survival was evaluated taking into consideration BRCA2 carriers, who represented the vast majority (44/48; 92%) of mutation-positive patients. A lower breast cancer-specific overall survival rate was observed in BRCA2 mutation carriers after the first two years from diagnosis. However, survival rates were similar in both groups after five years from diagnosis. No significant difference was found for age of onset, disease stage, and primary tumour histopathology between the two subsets. Conclusion: In Sardinian breast cancer population, BRCA2 was the most affected gene and the effects of BRCA2 germline mutations on patients' survival were demonstrated to vary within the first two years from diagnosis. After a longer follow-up observation, breast cancer-specific rates of death were instead similar for BRCA2 mutation carriers and non-carriers

Direct Anterior versus Lateral Approach for Femoral Neck Fracture: Role in COVID-19 Disease

Background: During the COVID-19 emergency, the incidence of fragility fractures in elderly patients remained unchanged. The management of these patients requires a multidisciplinary approach. The study aimed to assess the best surgical approach to treat COVID-19 patients with femoral neck fracture undergoing hemiarthroplasty (HA), comparing direct lateral (DL) versus direct anterior approach (DAA). Methods: A single-center, observational retrospective study including 50 patients affected by COVID-19 infection (30 males, 20 females) who underwent HA between April 2020 to April 2021 was performed. The patients were allocated into two groups according to the surgical approach used: lateral approach and anterior approach. For each patient, the data were recorded: age, sex, BMI, comorbidity, oxygen saturation (SpO2), fraction of the inspired oxygen (FiO2), type of ventilation invasive or non-invasive, HHb, P/F ratio (PaO2/FiO2), hemoglobin level the day of surgery and 1 day post operative, surgical time, Nottingham Hip Fractures Score (NHFS) and American Society of Anesthesiologists Score (ASA). The patients were observed from one hour before surgery until 48 h post-surgery of follow-up. The patients were stratified into five groups according to Alhazzani scores. A non-COVID-19 group of patients, as the control, was finally introduced. Results: A lateral position led to a better level of oxygenation (p < 0.01), compared to the supine anterior approach. We observed a better post-operative P/F ratio and a reduced need for invasive ventilation in patients lying in the lateral position. A statistically significant reduction in the surgical time emerged in patients treated with DAA (p < 0.01). Patients within the DAA group had a significantly lower blood loss compared to direct lateral approach. Conclusions: DL approach with lateral decubitus seems to preserved respiratory function in HA surgery. Thus, the lateral position may be associated with beneficial effects on gas exchange