From Pointwise to Powerhouse: Initialising Neural Networks with Generative Models

Traditional initialisation methods, e.g. He and Xavier, have been effective in avoiding the problem of vanishing or exploding gradients in neural networks. However, they only use simple pointwise distributions, which model one-dimensional variables. Moreover, they ignore most information about the architecture and disregard past training experiences. These limitations can be overcome by employing generative models for initialisation. In this paper, we introduce two groups of new initialisation methods. First, we locally initialise weight groups by employing variational autoencoders. Secondly, we globally initialise full weight sets by employing graph hypernetworks. We thoroughly evaluate the impact of the employed generative models on state-of-the-art neural networks in terms of accuracy, convergence speed and ensembling. Our results show that global initialisations result in higher accuracy and faster initial convergence speed. However, the implementation through graph hypernetworks leads to diminished ensemble performance on out of distribution data. To counteract, we propose a modification called noise graph hypernetwork, which encourages diversity in the produced ensemble members. Furthermore, our approach might be able to transfer learned knowledge to different image distributions. Our work provides insights into the potential, the trade-offs and possible modifications of these new initialisation methods

Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning

Federated and Continual Learning have emerged as potential paradigms for the robust and privacy-aware use of Deep Learning in dynamic environments. However, Client Drift and Catastrophic Forgetting are fundamental obstacles to guaranteeing consistent performance. Existing work only addresses these problems separately, which neglects the fact that the root cause behind both forms of performance deterioration is connected. We propose a unified analysis framework for building a controlled test environment for Client Drift -- by perturbing a defined ratio of clients -- and Catastrophic Forgetting -- by shifting all clients with a particular strength. Our framework further leverages this new combined analysis by generating a 3D landscape of the combined performance impact from both. We demonstrate that the performance drop through Client Drift, caused by a certain share of shifted clients, is correlated to the drop from Catastrophic Forgetting resulting from a corresponding shift strength. Correlation tests between both problems for Computer Vision (CelebA) and Medical Imaging (PESO) support this new perspective, with an average Pearson rank correlation coefficient of over 0.94. Our framework's novel ability of combined spatio-temporal shift analysis allows us to investigate how both forms of distribution shift behave in mixed scenarios, opening a new pathway for better generalization. We show that a combination of moderate Client Drift and Catastrophic Forgetting can even improve the performance of the resulting model (causing a "Generalization Bump") compared to when only one of the shifts occurs individually. We apply a simple and commonly used method from Continual Learning in the federated setting and observe this phenomenon to be reoccurring, leveraging the ability of our framework to analyze existing and novel methods for Federated and Continual Learning

Vesico-Acetabular Fistula and Urolithiasis in the Hip Joint Cavity due to Persistent Bladder Entrapment after Acetabular Fracture

We report a rare case of vesico-acetabular fistula due to an improperly treated pelvic fracture with urinary stone formation in the joint cavity. This complication was related to an unrecognized mechanism of bladder wall entrapment in the acetabular floor defect during weight bearing. This situation led to several mistreatment decisions in our case and should be always considered by urologists dealing with patients after major pelvic trauma. In this case report, we analyze the publications related to this problem, discuss the main mechanisms of bladder wall damage after acetabular fracture, and propose tips for diagnosis and treatment

Integrative clinical transcriptome analysis reveals TMPRSS2-ERG dependency of prognostic biomarkers in prostate adenocarcinoma

In prostate adenocarcinoma (PCa), distinction between indolent and aggressive disease is challenging. Around 50% of PCa are characterized by TMPRSS2-ERG (T2E)-fusion oncoproteins defining two molecular subtypes (T2E-positive/negative). However, current prognostic tests do not differ between both molecular subtypes, which might affect outcome prediction. To investigate gene-signatures associated with metastasis in T2E-positive and T2E-negative PCa independently, we integrated tumor transcriptomes and clinicopathological data of two cohorts (total n = 783), and analyzed metastasis-associated gene- signatures regarding the T2E-status. Here, we show that the prognostic value of biomarkers in PCa critically depends on the T2E-status. Using gene-set enrichment analyses, we uncovered that metastatic T2E-positive and T2E-negative PCa arecharacterized by distinct gene-signatures. In addition, by testing genes shared by several functional gene-signatures for theirassociation with event-free survival in a validation cohort (n=272), we identifiedfive genes (ASPN,BGN,COL1A1,RRM2andTYMS)—three of which are included in commercially available prognostic tests—whose high expression was significantlyassociated with worse outcome exclusively in T2E-negative PCa. Among these genes,RRM2andTYMSwere validated byimmunohistochemistry in another validation cohort (n=135), and several of them proved to add prognostic information tocurrent clinicopathological predictors, such as Gleason score, exclusively for T2E-negative patients. No prognostic biomarkerswere identified exclusively for T2E-positive tumors. Collectively, our study discovers that the T2E-status, which ispersenot astrong prognostic biomarker, crucially determines the prognostic value of other biomarkers. Our data suggest that themolecular subtype needs to be considered when applying prognostic biomarkers for outcome prediction in PCa. What’s new? Genetic rearrangements involving androgen-regulated transmembrane protease serine 2 and genes from the ETS transcription factor family (T2E), most commonly ERG and ETV1, occur in half of prostate cancers but are currently not considered in risk predictions. The authors integrate clinical and transcriptomic data from multiple studies and show that the prognostic value of biomarkers critically depends on the T2E-status. They identify five biomarkers that predict negative outcome exclusively in T2E-negative prostate cancers, which has implications for outcome prediction based on the molecular subtype.Deutsche Forschungsgemeinschaft 391665916Deutsche Krebshilfe 70112257Dr Leopold and Carmen Ellinger FoundationDr Rolf M. Schwiete FoundationFriedrich-Baur FoundationGert and Susanna Mayer FoundationKind-Philipp FoundationMatthias-Lackas FoundationMehr LEBEN fur Krebskranke Kinder-Bettina-Brau-StiftungWilhelm Sander-Stiftung 2016.167.

Comparison to PSA, Prostein, Androgen Receptor, ERG, NKX3.1, PSAP, and PSMA

Aims: Determining the origin of metastases is an important task of pathologists to allow for the initiation of a tumor-specific therapy. Recently, homeobox protein Hox-B13 (HOXB13) has been suggested as a new marker for the detection of prostatic origin. The aim of this study was to evaluate the diagnostic sensitivity of HOXB13 in comparison to commonly used immunohistochemical markers for prostate cancer. Materials and methods: Histologically confirmed prostate cancer lymph node metastases from 64 cases were used to test the diagnostic value of immunohistochemical markers: prostate specific antigen (PSA), Prostatic acid phosphatase (PSAP), prostate specific membrane antigen (PSMA), homeobox gene NKX3.1, prostein, androgen receptor (AR), HOXB13, and ETS-related gene (ERG). All markers were evaluated semi-quantitatively using Remmele's immune reactive score. Results: The detection rate of prostate origin of metastasis for single markers was 100% for NKX3.1, 98.1% for AR, 84.3% for PSMA, 80.8% for PSA, 66% for PSAP, 60.4% for HOXB13, 59.6% for prostein, and 50.0% for ERG. Conclusions: Our data suggest that HOXB13 on its own lacks sensitivity for the detection of prostatic origin. Therefore, this marker should be only used in conjunction with other markers, preferably the highly specific PSA. The combination of PSA with NKX3.1 shows a higher sensitivity and thus appears preferable in this setting. View Full-Tex

Acceptance, Prevalence and Indications for Robot-Assisted Laparoscopy - Results of a Survey Among Urologists in Germany, Austria and Switzerland

Background: Robotic-assisted laparoscopy (RAL) is being widely accepted in the field of urology as a replacement for conventional laparoscopy (CL). Nevertheless, the process of its integration in clinical routines has been rather spontaneous. Objective: To determine the prevalence of robotic systems (RS) in urological clinics in Germany, Austria and Switzerland, the acceptance of RAL among urologists as a replacement for CL and its current use for 25 different urological indications. Materials and Methods: To elucidate the practice patterns of RAL, a survey at hospitals in Germany, Austria and Switzerland was conducted. All surgically active urology departments in Germany (303), Austria (37) and Switzerland (84) received a questionnaire with questions related to the one-year period prior to the survey. Results: The response rate was 63%. Among the participants, 43% were universities, 45% were tertiary care centres, and 8% were secondary care hospitals. A total of 60 RS (Germany 35, Austria 8, Switzerland 17) were available, and the majority (68%) were operated under public ownership. The perception of RAL and the anticipated superiority of RAL significantly differed between robotic and non-robotic surgeons. For only two urologic indications were more than 50% of the procedures performed using RAL: pyeloplasty (58%) and transperitoneal radical prostatectomy (75%). On average, 35% of robotic surgeons and only 14% of non-robotic surgeons anticipated RAL superiority in some of the 25 indications. Conclusions: This survey provides a detailed insight into RAL implementation in Germany, Austria and Switzerland. RAL is currently limited to a few urological indications with a small number of high-volume robotic centres. These results might suggest that a saturation of clinics using RS has been achieved but that the existing robotic capacities are being utilized ineffectively. The possible reasons for this finding are discussed, and certain strategies to solve these problems are offered

Mitophagy-associated genes PINK1 and PARK2 are independent prognostic markers of survival in papillary renal cell carcinoma and associated with aggressive tumor behavior

Abstract The aim of this study was to investigate the mitophagy-related genes PINK1 and PARK2 in papillary renal cell carcinoma and their association with prognosis. In silico data of PINK1 and PARK2 were analyzed in TCGA cohorts of papillary renal cell carcinoma comprising 290 tumors and 33 corresponding non-neoplastic renal tissues. Protein expression data from a cohort of 95 papillary renal cell carcinoma patients were analyzed and associated with clinical-pathological parameters including survival. PINK1 and PARK2 were significantly downregulated in papillary renal cell carcinoma at transcript and protein levels. Reduced transcript levels of PINK1 and PARK2 were negatively associated with overall survival (p < 0.05). At the protein level, PARK2 and PINK1 expression were positively correlated (correlation coefficient 0.286, p = 0.04) and reduced PINK1 protein expression was prognostic for shorter survival. Lower PINK1 protein levels were found in tumors with metastases at presentation and in tumors of higher pT-stages. The multivariate analysis revealed mRNA expression of PINK1 and PARK2 as well as PINK1 protein expression as independent prognostic factors for shorter overall survival. The downregulation of PINK1 is a strong predictor of poor survival in papillary renal cell carcinoma. Immunohistochemical PINK1 expression in resected pRCC should be considered as an additional prognostic marker for routine practice

Integrative clinical transcriptome analysis reveals TMPRSS2‐ERG dependency of prognostic biomarkers in prostate adenocarcinoma

In prostate adenocarcinoma (PCa), distinction between indolent and aggressive disease is challenging. Around 50% of PCa are characterized by TMPRSS2‐ERG (T2E)‐fusion oncoproteins defining two molecular subtypes (T2E‐positive/negative). However, current prognostic tests do not differ between both molecular subtypes, which might affect outcome prediction. To investigate gene‐signatures associated with metastasis in T2E‐positive and T2E‐negative PCa independently, we integrated tumor transcriptomes and clinicopathological data of two cohorts (total n = 783), and analyzed metastasis‐associated gene‐signatures regarding the T2E‐status. Here, we show that the prognostic value of biomarkers in PCa critically depends on the T2E‐status. Using gene‐set enrichment analyses, we uncovered that metastatic T2E‐positive and T2E‐negative PCa are characterized by distinct gene‐signatures. In addition, by testing genes shared by several functional gene‐signatures for their association with event‐free survival in a validation cohort (n = 272), we identified five genes (ASPN, BGN, COL1A1, RRM2 and TYMS)—three of which are included in commercially available prognostic tests—whose high expression was significantly associated with worse outcome exclusively in T2E‐negative PCa. Among these genes, RRM2 and TYMS were validated by immunohistochemistry in another validation cohort (n = 135), and several of them proved to add prognostic information to current clinicopathological predictors, such as Gleason score, exclusively for T2E‐negative patients. No prognostic biomarkers were identified exclusively for T2E‐positive tumors. Collectively, our study discovers that the T2E‐status, which is per se not a strong prognostic biomarker, crucially determines the prognostic value of other biomarkers. Our data suggest that the molecular subtype needs to be considered when applying prognostic biomarkers for outcome prediction in PCa