The Littlewood problem and non-harmonic Fourier series

In this paper, we give a direct quantitative estimate of $L^1$norms of non-harmonic trigonometric polynomials over large enough intervals. This extends the result by Konyagin and Mc Gehee, Pigno, Smith to the settingof trigonometric polynomials with non-integer frequencies.The result is a quantitative extension of a result by Nazarov and also covers a resultby Hudson and Leckband when the length of the interval goes to infinity.Comment: Tis version has been revised according to the referees remarks.The appendixes are not present in the final version, only in the arxiv/hal versio

On the analysis of big data indexing execution strategies

Efficient response to search queries is very crucial for data analysts to obtain timely results from big data spanned over heterogeneous machines. Currently, a number of big-data processing frameworks are available in which search operations are performed in distributed and parallel manner. However, implementation of indexing mechanism results in noticeable reduction of overall query processing time. There is an urge to assess the feasibility and impact of indexing towards query execution performance. This paper investigates the performance of state-of-the-art clustered indexing approaches over Hadoop framework which is de facto standard for big data processing. Moreover, this study leverages a comparative analysis of non-clustered indexing overhead in terms of time and space taken by indexing process for varying volume data sets with increasing Index Hit Ratio. Furthermore, the experiments evaluate performance of search operations in terms of data access and retrieval time for queries that use indexes. We then validated the obtained results using Petri net mathematical modeling. We used multiple data sets in our experiments to manifest the impact of growing volume of data on indexing and data search and retrieval performance. The results and highlighted challenges favorably lead researchers towards improved implication of indexing mechanism in perspective of data retrieval from big data. Additionally, this study advocates selection of a non-clustered indexing solution so that optimized search performance over big data is obtained

Therapy decisions after diagnosis of prostate cancer in men with negative prostate MRI

Background: To investigate the clinical implications of magnetic resonance imaging (MRI) negative prostate cancer (PCa) in a cohort of men undergoing transperineal prostate biopsy. Methods: We included all men without prior diagnosis of PCa undergoing transperineal template saturation ± fusion-guided targeted biopsy of the prostate between November 2014 and March 2018. Before biopsy, all patients underwent MRI and biopsies were performed irrespective of imaging results. Baseline characteristics, imaging, biopsy results, and follow-up information were retrieved from the patient charts. Patients were classified as either MRI negative (Prostate Imaging Reporting and Data System [PIRADS] ≤ 2) or positive (PIRADS ≥ 3). ISUP grade group 1 was defined as clinically nonsignificant (nsPCa) and ≥2 as clinically significant PCa (csPCa). Primary outcome was the individual therapeutic decision after diagnosis of PCa stratified according to MRI visibility. Secondary outcomes were the sensitivity and specificity of MRI, and the urooncological outcomes after radical prostatectomy (RP). Results: From 515 patients undergoing prostate biopsy, 171 (33.2%) patients had a negative and 344 (66.8%) a positive MRI. Pathology review stratified for MRI negative and positive cases revealed nsPCa in 27 (15.8%) and 32 (9.3%) and csPCa in 26 (15.2%) and 194 (56.4%) of the patients, respectively. The rate of active treatment in the MRI negative was lower compared with the MRI positive cohort (12.3% vs. 53.2%; odd ratio [OR] = 0.12; p < 0.001). While men with negative MRI were more likely to undergo active surveillance (AS) than MRI positive patients (18.1% vs. 10.8%; OR = 1.84; p = 0.027), they rarely underwent RP (6.4% vs. 40.7%, OR = 0.10; p < 0.001). Logistic regression revealed that a negative MRI was independently protective for active treatment (OR = 0.32, p = 0.014). The specificity, sensitivity, negative, and positive predictive value of MRI for detection of csPCa were 49.2%, 88.2%, 56.4%, and 84.8%, respectively. The rate of adverse clinicopathological outcome features (pT3/4, ISUP ≥4, or prostate-specific antigen [PSA]-persistence) following RP was 4.7% for men with MRI negative compared to 17.4% for men with MRI positive PCa (OR = 3.1, p = 0.19). Conclusion: Only few men with MRI negative PCa need active cancer treatment at the time of diagnosis, while the majority opts for AS. Omitting prostate biopsies and performing a follow-up MRI may be a safe alternative to reduce the number of unnecessary interventions. Keywords: PIRADS; biopsy-naïve; imaging; invisible prostate cancer; transperineal biopsy; treatmen

Aircraft to operations communication analysis and architecture for the future aviation environment

Fifth Generation (5G) systems are envisaged to support a wide range of applications scenarios with varying requirements. 5G architecture includes network slicing abilities which facilitate the partitioning of a single network infrastructure on to multiple logical networks, each tailored to a given use case, providing appropriate isolation and Quality of Service (QoS) characteristics. Radio Access Network (RAN) slicing is key to ensuring appropriate QoS over multiple domains; achieved via the configuration of multiple RAN behaviours over a common pool of radio resources. This Paper proposes a novel solution for efficient resource allocation and assignment among a variety of heterogeneous services, to utilize the resources while ensuring maximum QoS for network services. First, this paper evaluates the effectiveness of different wireless data bearers. Secondly, the paper proposes a novel dynamic resource allocation algorithm for RAN slicing within 5G New Radio (NR) networks utilising cooperative game theory combined with priority-based bargaining. The impact of this work to industry is to provide a new technique for resource allocation that utilizes cooperative bargaining to ensure all network services achieve minimum QoS requirements – while using application priority to reduce data transfer time for key services to facilitate decreased turnaround time at the gate

External Validation and Comparison of Prostate Cancer Risk Calculators Incorporating Multiparametric Magnetic Resonance Imaging for Prediction of Clinically Significant Prostate Cancer

PURPOSE: To externally validate recently published prostate cancer risk calculators (PCa-RCs) incorporating multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer (csPCa) and compare their performance to mpMRI-naïve PCa-RCs. MATERIAL AND METHODS: Men without previous PCa diagnosis undergoing transperineal template saturation prostate biopsy with fusion-guided targeted biopsy between 11/2014 and 03/2018 in our academic tertiary referral center were identified. Any Gleason pattern ≥4 was defined to be csPCa. Predictors (age, PSA, DRE, prostate volume, family history, previous prostate biopsy and highest region of interest according to PIRADS) were retrospectively collected. Four mpMRI-PCa-RCs and two mpMRI-naïve PCa-RCs were evaluated for their discrimination, calibration and clinical net benefit using a ROC analysis, calibration plots and a decision curve analysis, respectively. RESULTS: Out of 468 men, 193 (41%) were diagnosed with csPCa. Three mpMRI-PCa-RCs showed similar discrimination with area-underneath-the-receiver-operating-characteristic-curves (AUC) from 0.83 to 0.85, which was significantly higher than the other PCa-RCs (AUCs: 0.69-0.74). Calibration-in-the-large showed minimal deviation from the true amount of csPCa by 2% for two mpMRI-PCa-RCs, while the other PCa-RCs showed worse calibration (11-27%). A clinical net benefit could only be observed for three mpMRI-PCa-RCs at biopsy thresholds ≥15%, while none of the six investigated PCa-RCs demonstrated clinical utility against a biopsy all strategy at thresholds <15%. CONCLUSIONS: Performance of the mpMRI-PCa-RCs varies, but they generally outperform mpMRI-naïve PCa-RCs in regard to discrimination, calibration and clinical usefulness. External validation in other biopsy settings is highly encouraged

Prostate cancer detection rate in men undergoing transperineal template-guided saturation and targeted prostate biopsy

OBJECTIVES To compare prostate cancer (PCa) detection rate of transperineal template-guided saturation prostate biopsy (SBx) and multiparametric magnetic resonance imaging (mpMRI)/transrectal ultrasound fusion guided targeted biopsy (TBx). MATERIALS AND METHODS: We prospectively enrolled 392 men who underwent SBx and TBx in case of suspicious lesions from November 2016 to October 2019. Triggers for a biopsy were an elevated prostate-specific antigen (PSA) and/or positive digital rectal examination and only treatment naïve patients without a previous diagnosis of PCa were included. Study inclusion occurred before biopsy and a prebiopsy mpMRI was available in all men. SBx were taken from 20 different locations according to the modified Barzell zones. The primary endpoint was the detection rate of clinically significant PCa (csPCa) and insignificant PCa (ciPCa) by SBx and/or TBx by comparing the two methods alone and in combination. Additional TBx were taken for any prostate imaging-reporting and data system (PI-RADS) lesion ≥3 seen on the mpMRI. csPCa was defined as any Gleason score ≥7 and ciPCa as Gleason score 6. RESULTS A total of 392 men with a median age of 64 years (interquartile range [IQR]: 58-69), a median PSA of 7.0 ng/ml (IQR: 4.8-10.1) were enrolled. Overall, PCa was found in 200 (51%) of all biopsied men, with 158 (79%) being csPCa and 42 (21%) ciPCa. A total of 268 (68%) men with a suspicious mpMRI and underwent a combined TBx and SBx, of whom csPCa was found in 139 (52%). In this subgroup, 116/139 (83%) csPCa would have been detected by TBx alone, and an additional 23 (17%) were found by SBx. Men with a negative mpMRI (PI-RADS < 3, n = 124, 32%) were found to have csPCa in 19 (15%) cases. In patients with a negative mpMRI in combination with a PSA density <0.1 ng/ml$^{2}$ , only 8% (3/36) had csPCa. If only TBx would have been performed and all men with a negative mpMRI would not have been biopsed, 42/158 (27%) of csPCa would have been missed, and 38/42 (90%) ciPCa would have not been detected. On multivariable analysis, significant predictors of csPCa were increasing PSA (odds ratio, OR: 1.07 [95% confidence interval, CI: 1.03-1.11]), increasing age (OR: 1.07 [95% CI: 1.03-1.11]), PI-RADS score ≥ 3 (OR: 6.49 [95% CI: 3.55-11.89]), and smaller prostate volume (OR: 0.96 [95% CI: 0.95 -0.97] (p < 0.05 for all parameters). CONCLUSION In comparison to SBx, TBx alone detects csPCa in only ¾ of all men with a positive mpMRI lesion. Thus, systematic biopsies in addition to TBx have to be considered at least in some who undergo a prostate biopsy. In men with a negative mpMRI, SBx still detects 15% csPCa, but similarly overdetecting ciPCa. According to our results, low PSA density and negative mpMRI findings could be used to decide which men can safely avoid biopsy

Identification of Urine Biomarkers to Improve Eligibility for Prostate Biopsy and Detect High-Grade Prostate Cancer

PCa screening is based on the measurements of the serum prostate specific antigen (PSA) to select men with higher risks for tumors and, thus, eligible for prostate biopsy. However, PSA testing has a low specificity, leading to unnecessary biopsies in 50–75% of cases. Therefore, more specific screening opportunities are needed to reduce the number of biopsies performed on healthy men and patients with indolent tumors. Urine samples from 45 patients with elevated PSA were collected prior to prostate biopsy, a mass spectrometry (MS) screening was performed to identify novel biomarkers and the best candidates were validated by ELISA. The urine quantification of PEDF, HPX, CD99, CANX, FCER2, HRNR, and KRT13 showed superior performance compared to PSA. Additionally, the combination of two biomarkers and patient age resulted in an AUC of 0.8196 (PSA = 0.6020) and 0.7801 (PSA = 0.5690) in detecting healthy men and high-grade PCa, respectively. In this study, we identified and validated novel urine biomarkers for the screening of PCa, showing that an upfront urine test, based on quantitative biomarkers and patient age, is a feasible method to reduce the number of unnecessary prostate biopsies and detect both healthy men and clinically significant PCa

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

Objective: We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. Study Design and Setting: Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group greater or equal 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. Results: Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history