Dirac Gauginos and the 125 GeV Higgs

We investigate the mass, production and branching ratios of a 125 GeV Higgs in models with Dirac gaugino masses. We give a discussion of naturalness, and describe how deviations from the Standard Model in the key Higgs search channels can be simply obtained. We then perform parameter scans using a SARAH package upgrade, which produces SPheno code that calculates all relevant quantities, including electroweak precision and flavour constraint data, to a level of accuracy previously impossible for this class of models. We study three different variations on the minimal Dirac gaugino extension of the (N)MSSM.Comment: 32 pages, 9 figure

Exploring the Landscape of Natural Language Processing Research

As an efficient approach to understand, generate, and process natural language texts, research in natural language processing (NLP) has exhibited a rapid spread and wide adoption in recent years. Given the increasing amount of research work in this area, several NLP-related approaches have been surveyed in the research community. However, a comprehensive study that categorizes established topics, identifies trends, and outlines areas for future research remains absent to this day. Contributing to closing this gap, we have systematically classified and analyzed research papers included in the ACL Anthology. As a result, we present a structured overview of the research landscape, provide a taxonomy of fields-of-study in NLP, analyze recent developments in NLP, summarize our findings, and highlight directions for future work.Comment: Accepted to the 14th International Conference on Recent Advances in Natural Language Processing (RANLP 2023

Statistical learning on randomized data to verify quantum state k-designs

Random ensembles of pure states have proven to be extremely important in various aspects of quantum physics such as benchmarking the performance of quantum circuits, testing for quantum advantage, providing novel insights for many-body thermalization and studying the black hole information paradox. Although generating a fully random ensemble is experimentally challenging, approximations of it are just as useful and are known to emerge naturally in a variety of physical models, including Rydberg setups. These are referred to as approximate quantum state designs, and verifying their degree of randomness can be an expensive task, similar to performing full quantum state tomography on many-body systems. In this theoretical work, we efficiently validate the character of approximate quantum designs with respect to data size acquisition when compared to the conventional frequentist approach. This is achieved by translating the information residing in the complex many-body state into a succinct representation of classical data using a random projective measurement basis, which is then processed using methods of statistical inference such as maximum likelihood estimation and neural networks and benchmarked against the predictions of shadow tomography. Our scheme of combining machine learning methods for postprocessing the data obtained from randomized measurements for efficient characterisation of (approximate) quantum state k designs is applicable to any noisy quantum platform that can generate quantum designs.Comment: 11+5 pages, 6+3 figure

Writer-Defined AI Personas for On-Demand Feedback Generation

Compelling writing is tailored to its audience. This is challenging, as writers may struggle to empathize with readers, get feedback in time, or gain access to the target group. We propose a concept that generates on-demand feedback, based on writer-defined AI personas of any target audience. We explore this concept with a prototype (using GPT-3.5) in two user studies (N=5 and N=11): Writers appreciated the concept and strategically used personas for getting different perspectives. The feedback was seen as helpful and inspired revisions of text and personas, although it was often verbose and unspecific. We discuss the impact of on-demand feedback, the limited representativity of contemporary AI systems, and further ideas for defining AI personas. This work contributes to the vision of supporting writers with AI by expanding the socio-technical perspective in AI tool design: To empower creators, we also need to keep in mind their relationship to an audience.Comment: 25 pages, 7 figures, 2 table

Streamflow Modeling in a Highly Managed Mountainous Glacier Watershed Using SWAT: The Upper Rhone River Watershed Case in Switzerland

Streamflow simulation is often challenging in mountainous watersheds because of irregular topography and complex hydrological processes. Rates of change in precipitation and temperature with respect to elevation often limit the ability to reproduce stream runoff by hydrological models. Anthropogenic influence, such as water transfers in high altitude hydropower reservoirs increases the difficulty in modeling since the natural flow regime is altered by long term storage of water in the reservoirs. The Soil and Water Assessment Tool (SWAT) was used for simulating streamflow in the upper Rhone watershed located in the south western part of Switzerland. The catchment area covers 5220km2, where most of the land cover is dominated by forest and 14% is glacier. Streamflow calibration was done at daily time steps for the period of 2001-2005, and validated for 2006-2010. Two different approaches were used for simulating snow and glacier melt process, namely the temperature index approach with and without elevation bands. The hydropower network was implemented based on the intake points that form part of the inter-reservoir network. Subbasins were grouped into two major categories with glaciers and without glaciers for simulating snow and glacier melt processes. Model performance was evaluated both visually and statistically where a good relation between observed and simulated discharge was found. Our study suggests that a proper configuration of the network leads to better model performance despite the complexity that arises for water transaction. Implementing elevation bands generates better results than without elevation bands. Results show that considering all the complexity arising from natural variability and anthropogenic influences, SWAT performs well in simulating runoff in the upper Rhone watershed. Findings from this study can be applicable for high elevation snow and glacier dominated catchments with similar hydro-physiographic constraint

Structure–function studies of the RNA polymerase II elongation complex

X-ray crystallographic and complementary functional studies have contributed significantly to the current understanding of gene transcription. Here, recent structure–function studies on various aspects of the elongation phase of transcription are summarized

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

BioPrev-C - development and validation of a contemporary prostate cancer risk calculator

OBJECTIVES To develop a novel biopsy prostate cancer (PCa) prevention calculator (BioPrev-C) using data from a prospective cohort all undergoing mpMRI targeted and transperineal template saturation biopsy. MATERIALS AND METHODS Data of all men who underwent prostate biopsy in our academic tertiary care center between 11/2016 and 10/2019 was prospectively collected. We developed a clinical prediction model for the detection of high-grade PCa (Gleason score ≥7) based on a multivariable logistic regression model incorporating age, PSA, prostate volume, digital rectal examination, family history, previous negative biopsy, 5-alpha-reductase inhibitor use and MRI PI-RADS score. BioPrev-C performance was externally validated in another prospective Swiss cohort and compared with two other PCa risk-calculators (SWOP-RC and PBCG-RC). RESULTS Of 391 men in the development cohort, 157 (40.2%) were diagnosed with high-grade PCa. Validation of the BioPrev C revealed good discrimination with an area under the curve for high-grade PCa of 0.88 (95% Confidence Interval 0.82-0.93), which was higher compared to the other two risk calculators (0.71 for PBCG and 0.84 for SWOP). The BioPrev-C revealed good calibration in the low-risk range (0 - 0.25) and moderate overestimation in the intermediate risk range (0.25 - 0.75). The PBCG-RC showed good calibration and the SWOP-RC constant underestimation of high-grade PCa over the whole prediction range. Decision curve analyses revealed a clinical net benefit for the BioPrev-C at a clinical meaningful threshold probability range (≥4%), whereas PBCG and SWOP calculators only showed clinical net benefit above a 30% threshold probability. CONCLUSION BiopPrev-C is a novel contemporary risk calculator for the prediction of high-grade PCa. External validation of the BioPrev-C revealed relevant clinical benefit, which was superior compared to other well-known risk calculators. The BioPrev-C has the potential to significantly and safely reduce the number of men who should undergo a prostate biopsy

Cytoplasmic PAR-3 protein expression is associated with adverse prognostic factors in clear cell renal cell carcinoma and independently impacts survival.

International audienceClear cell renal cell carcinomas (ccRCCs) represent 70% of renal cancers, and several clinical and histolopathological factors are implicated in their prognosis. We recently demonstrated that the overexpression of PAR-3 protein encoded by the PARD3 gene could be implicated in renal oncogenesis. The object of this work was to study the association of intratumoral PAR-3 expression with known prognostic parameters and clinical outcome. In this aim, PAR-3 expression was assessed by immunohistochemistry in ccRCC tumors of 101 patients from 2003 to 2005. The immunostaining of PAR-3 was scored either as membranous (mPAR-3) or as both membranous and cytoplasmic (cPAR-3). Cytoplasmic PAR-3 was significantly associated with worse histopathological and clinical prognostic factors: Fuhrman grades 3 and 4, tumor necrosis, sarcomatoid component, adrenal invasion, renal and hilar fat invasion, eosinophilic component, a noninactivated VHL gene, higher tumor grade, lymph node involvement, metastasis, and worse clinical Eastern Cooperative Oncology Group and S classification scores. After multivariate analysis, 2 parameters were independently associated with cPAR-3: necrosis and eosinophilic components. In addition, cPAR-3 patients had shorter overall and progression-free survivals independently from strong prognostic validated factors like metastases. A cytoplasmic expression of PAR-3 is therefore implicated in worse clinical and pathological cancer features in ccRCC and could be useful to identify patients with high-risk tumors

Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts and tumors. In this study, we seek to investigate the ability with which 24 oral and maxillofacial (OMF) surgeons assess the presence of periapical lucencies on panoramic radiographs, and we compare these findings to the performance of a predictive deep learning algorithm that we have developed using a curated data set of 2902 de-identified panoramic radiographs. The mean diagnostic positive predictive value (PPV) of OMF surgeons based on their assessment of panoramic radiographic images was 0.69(± 0.13), indicating that dentists on average falsely diagnose 31% of cases as radiolucencies. However, the mean diagnostic true positive rate (TPR) was 0.51(± 0.14), indicating that on average 49% of all radiolucencies were missed. We demonstrate that the deep learning algorithm achieves a better performance than 14 of 24 OMF surgeons within the cohort, exhibiting an average precision of 0.60(± 0.04), and an F1 score of 0.58(± 0.04) corresponding to a PPV of 0.67(± 0.05) and TPR of 0.51(± 0.05). The algorithm, trained on limited data and evaluated on clinically validated ground truth, has potential to assist OMF surgeons in detecting periapical lucencies on panoramic radiographs