Semi-stable and splitting models for unitary Shimura varieties over ramified places. I

We consider Shimura varieties associated to a unitary group of signature $(n-s,s)$ where $n$ is even. For these varieties, we construct smooth $p$-adic integral models for $s=1$ and regular $p$-adic integral models for $s=2$ and $s=3$ over odd primes $p$ which ramify in the imaginary quadratic field with level subgroup at $p$ given by the stabilizer of a $\pi$-modular lattice in the hermitian space. Our construction, which has an explicit moduli-theoretic description, is given by an explicit resolution of a corresponding local model.Comment: 35 pp. In this version we added section 7 where we give an explicit moduli theoretic description of our construction

Weight status and depressive symptoms in 18 year-old Greek adolescents

Depressive symptoms in adolescence have been a subject of considerable controversy in terms of their nature, severity and identification. The aim of the study was to investigate the possible association between weight status and depressive symptoms among 18 year-old Greek adolescents. A cross-sectional study design was used. The study population consisted of 200 students of the University of Athens who fulfilled the following criteria: age 18 years, absence of clinical depression, no history of hospitalization in a mental institution, no history of alcohol abuse. Weight status was assessed by Body Mass Index (BMI) (kg/m2) and calculated from weight and height measurements. Severity of depressive symptoms was assessed by Centre for Epidemiologic Studies-Depression Scale (CES-D). In univariate analysis, CES-D score was significantly associated with adolescents' gender and BMI. The multivariate analysis showed that CES-D score was negatively related to BMI even after controlling the confounding effect of gender (P=0.018, B=-0.378). Depressive symptoms are related to weight status of adolescents

Leiomyosarcoma of the Prostate: Case Report and Review of 54 Previously Published Cases

Prostate leiomyosarcoma is an extremely rare and highly aggressive neoplasm that accounts for less than 0.1% of primary prostate malignancies. We present a patient with primary leiomyosarcoma of the prostate and review 54 cases reported in the literature to discuss the clinical, diagnostic and therapeutic aspects of this uncommon tumor. Median survival was estimated at 17 months (95% C.I. 20.7–43.7 months) and the 1-, 3-, and 5-year actuarial survival rates were 68%, 34%, and 26%, respectively. The only factors predictive of long-term survival were negative surgical margins and absence of metastatic disease at presentation. A multidisciplinary approach is necessary for appropriate management of this dire entity

Table inference for combinatorial origin-destination choices in agent-based population synthesis

A key challenge in agent‐based mobility simulations is the synthesis of individual agent socioeconomic profiles. Such profiles include locations of agent activities, which dictate the quality of the simulated travel patterns. These locations are typically represented in origin‐destination matrices that are sampled using coarse travel surveys. This is because fine‐grained trip profiles are scarce and fragmented due to privacy and cost reasons. The discrepancy between data and sampling resolutions renders agent traits nonidentifiable due to the combinatorial space of data‐consistent individual attributes. This problem is pertinent to any agent‐based inference setting where the latent state is discrete. Existing approaches have used continuous relaxations of the underlying location assignments and subsequent ad hoc discretisation thereof. We propose a framework to efficiently navigate this space offering improved reconstruction and coverage as well as linear‐time sampling of the ground truth origin‐destination table. This allows us to avoid factorially growing rejection rates and poor summary statistic consistency inherent in discrete choice modelling. We achieve this by introducing joint sampling schemes for the continuous intensity and discrete table of agent trips, as well as Markov bases that can efficiently traverse this combinatorial space subject to summary statistic constraints. Our framework's benefits are demonstrated in multiple controlled experiments and a large‐scale application to agent work trip reconstruction in Cambridge, UK

Predictive valve of telomerase reverse transcriptase expression in patients with high risk superficial bladder cancer treated with adjuvvant BCG immunotherapy

Purpose We conducted a prospective study to determine whether expression of telomerase reverse transcriptase (hTERT) is associated with recurrence-free-survival (RFS) or development of invasive disease in patients with high risk superficial bladder cancer (SBC) that received adjuvant BCG immunotherapy. Methods Thirty patients with high-grade T1 tumors were evaluated. Pre-BCG TURBT and post-BCG specimens were analyzed for hTERT nucleolar expression by immunohistochemistry. Results Post-BCG hTERT expression was statistically significantly lower than pre-BCG hTERT expression. Pre-BCG hTERT nucleolar staining in more than 75% of cells was associated with worse RFS (9 months vs. not yet reached, P = 0.05), while post-BCG hTERT nucleolar staining in more than 50% of the cells was associated with worse RFS (6 months vs. not yet reached, P = 0.001) and development of invasive disease. In multivariate analysis, post-BCG hTERT expression was independently associated with RFS and development of invasive disease. Conclusions Immunohistochemical evaluation of hTERT may help define a subset of high risk SBC patients that will eventually fail BCG and may therefore benefit from early salvage cystectomyΣκοπός της μελέτης Πραγματοποιήσαμε μια προοπτική μελέτη, με σκοπό να ερευνήσουμε εάν η έκφραση της ανάστροφης μεταγραφάσης της τελομεράσης (hTERT), σχετίζεται με την ελεύθερης υποτροπή επιβίωση (RFS) ή την ανάπτυξη διηθητικής νόσου, σε ασθενείς με υψηλού κινδύνου, σταδίου Τα-Τ1 καρκίνο της κύστης, οι οποίοι έλαβαν ανοσοθεραπεία με BCG. Ασθενείς και Μέθοδος Εκτιμήθηκαν 30 ασθενείς με Τα-Τ1 υψηλής κακοήθειας όγκους. Αναλύσαμε την ανοσοιστοχημική έκφραση της hTERT σε παρασκευάσματα των ίδιων ασθενών μετά από διουρηθρική εκτομή προ της ανοσοθεραπείας με BCG καθώς και σε παρασκευάσματα από ψυχρές βιοψίες μετά την ανοσοθεραπεία BCG . Αποτελέσματα Η μετά BCG έκφραση της hTERT ήταν στατιστικώς σημαντικά χαμηλότερη σε σχέση με τα προ BCG δείγματα. Η προ BCG ανοσοθετικότητα της hTERT όταν χρησιμοποιήσαμε cut-off θετικότητας της hTERT το 75% των νεοπλασματικών κυττάρων, σχετίζονταν με χειρότερη επιβίωση ελεύθερης υποτροπής ενώ η μετά BCG ανοσοθετικότητα της hTERT σε περισσότερο από το 50% των κυττάρων σχετίζονταν με οι ασθενείς με χειρότερη επιβίωση ελεύθερης υποτροπής και με την ανάπτυξη διηθητικής νόσου. Σε πολυπαραγοντική ανάλυση συμπεριλαμβανομένου την ηλικία, το φύλο και την προ και μετά-BCG έκφραση της hTERT, έδειξε ότι, μόνο η μετά-BCG hTERT ανοσοθετικότητα συσχετίζονταν ανεξάρτητα με χειρότερη επιβίωση ελεύθερης υποτροπής και ανάπτυξη διηθητικής νόσου. Συμπεράσματα Η ανοιστοχημική εκτίμηση της hTERT μπορεί να βοηθήσει στον καθορισμό μιάς ομάδας ασθενών υψηλού κινδύνου, οι οποίοι πιθανότατα θα αποτύχουν στην ανοσοθεραπεία με BCG και ενδεχομένως θα ωφεληθούν από μία πρόωρη κυστεκτομή

Table inference for combinatorial origin-destination choices in agent-based population synthesis

A key challenge in agent-based mobility simulations is the synthesis of individual agent socioeconomic profiles. Such profiles include locations of agent activities, which dictate the quality of the simulated travel patterns. These locations are typically represented in origin-destination matrices that are sampled using coarse travel surveys. This is because fine-grained trip profiles are scarce and fragmented due to privacy and cost reasons. The discrepancy between data and sampling resolutions renders agent traits non-identifiable due to the combinatorial space of data-consistent individual attributes. This problem is pertinent to any agent-based inference setting where the latent state is discrete. Existing approaches have used continuous relaxations of the underlying location assignments and subsequent ad-hoc discretisation thereof. We propose a framework to efficiently navigate this space offering improved reconstruction and coverage as well as linear-time sampling of the ground truth origin-destination table. This allows us to avoid factorially growing rejection rates and poor summary statistic consistency inherent in discrete choice modelling. We achieve this by introducing joint sampling schemes for the continuous intensity and discrete table of agent trips, as well as Markov bases that can efficiently traverse this combinatorial space subject to summary statistic constraints. Our framework's benefits are demonstrated in multiple controlled experiments and a large-scale application to agent work trip reconstruction in Cambridge, UK.Comment: 17 pages, 8 figures, 2 table

Table inference for combinatorial origin-destination choices in agent-based population synthesis

A key challenge in agent-based mobility simulations is the synthesis of individual agent socioeconomic profiles. Such profiles include locations of agent activities, which dictate the quality of the simulated travel patterns. These locations are typically represented in origin-destination matrices that are sampled using coarse travel surveys. This is because fine-grained trip profiles are scarce and fragmented due to privacy and cost reasons. The discrepancy between data and sampling resolutions renders agent traits non-identifiable due to the combinatorial space of data-consistent individual attributes. This problem is pertinent to any agent-based inference setting where the latent state is discrete. Existing approaches have used continuous relaxations of the underlying location assignments and subsequent ad-hoc discretisation thereof. We propose a framework to efficiently navigate this space offering improved reconstruction and coverage as well as linear-time sampling of the ground truth origin-destination table. This allows us to avoid factorially growing rejection rates and poor summary statistic consistency inherent in discrete choice modelling. We achieve this by introducing joint sampling schemes for the continuous intensity and discrete table of agent trips, as well as Markov bases that can efficiently traverse this combinatorial space subject to summary statistic constraints. Our framework's benefits are demonstrated in multiple controlled experiments and a large-scale application to agent work trip reconstruction in Cambridge, UK