Dynamic Decision Models for Managing the Major Complications of Diabetes

Diabetes is the sixth-leading cause of death and a major cause of cardiovascular and renal diseases in the U.S. In this dissertation, we consider the major complications of diabetes and develop dynamic decision models for two important timing problems: Transplantation in prearranged paired kidney exchanges (PKEs) and statin initiation. Transplantation is the most viable renal replacement therapy for end-stage renal disease (ESRD) patients, but there is a severe disparity between the demand and supply of kidneys for transplantation. PKE, a cross-exchange of kidneys between incompatible patient-donor pairs, overcomes many difficulties in matching patients with incompatible donors. In a typical PKE, transplantation surgeries take place simultaneously so that no donor may renege after her intended recipient receives the kidney. We consider two autonomous patients with probabilistically evolving health statuses in a PKE, and model their transplant timing decisions as a discrete-time non-zero-sum stochastic game. We explore necessary and sufficient conditions for patients' decisions to form a stationary-perfect equilibrium, and formulate a mixed-integer linear programming (MIP) representation of equilibrium constraints to characterize a socially optimal stationary-perfect equilibrium. We calibrate our model using large scale clinical data. We quantify the social welfare loss due to patient autonomy and demonstrate that the objective of maximizing the number of transplants may be undesirable. Patients with Type 2 diabetes have higher risk of heart attack and stroke, and if not treated these risks are confounded by lipid abnormalities. Statins can be used to treat such abnormalities, but their use may lead to adverse outcomes. We consider the question of when to initiate statin therapy for patients with Type 2 diabetes. We formulate a Markov decision process (MDP) to maximize the patient's quality-adjusted life years (QALYs) prior to the first heart attack or stroke. We derive sufficient conditions for the optimality of control-limit policies with respect to patient's lipid-ratio (LR) levels and age and parameterize our model using clinical data. We compute the optimal treatment policies and illustrate the importance of individualized treatment factors by comparing their performance to those of the guidelines in use in the U.S

MelNet: A Real-Time Deep Learning Algorithm for Object Detection

In this study, a novel deep learning algorithm for object detection, named MelNet, was introduced. MelNet underwent training utilizing the KITTI dataset for object detection. Following 300 training epochs, MelNet attained an mAP (mean average precision) score of 0.732. Additionally, three alternative models -YOLOv5, EfficientDet, and Faster-RCNN-MobileNetv3- were trained on the KITTI dataset and juxtaposed with MelNet for object detection. The outcomes underscore the efficacy of employing transfer learning in certain instances. Notably, preexisting models trained on prominent datasets (e.g., ImageNet, COCO, and Pascal VOC) yield superior results. Another finding underscores the viability of creating a new model tailored to a specific scenario and training it on a specific dataset. This investigation demonstrates that training MelNet exclusively on the KITTI dataset also surpasses EfficientDet after 150 epochs. Consequently, post-training, MelNet's performance closely aligns with that of other pre-trained models.Comment: 11 pages, 9 figures, 5 table

GenPluSSS: A Genetic Algorithm Based Plugin for Measured Subsurface Scattering Representation

This paper presents a plugin that adds a representation of homogeneous and heterogeneous, optically thick, translucent materials on the Blender 3D modeling tool. The working principle of this plugin is based on a combination of Genetic Algorithm (GA) and Singular Value Decomposition (SVD)-based subsurface scattering method (GenSSS). The proposed plugin has been implemented using Mitsuba renderer, which is an open source rendering software. The proposed plugin has been validated on measured subsurface scattering data. It's shown that the proposed plugin visualizes homogeneous and heterogeneous subsurface scattering effects, accurately, compactly and computationally efficiently

ExTTNet: A Deep Learning Algorithm for Extracting Table Texts from Invoice Images

In this work, product tables in invoices are obtained autonomously via a deep learning model, which is named as ExTTNet. Firstly, text is obtained from invoice images using Optical Character Recognition (OCR) techniques. Tesseract OCR engine [37] is used for this process. Afterwards, the number of existing features is increased by using feature extraction methods to increase the accuracy. Labeling process is done according to whether each text obtained as a result of OCR is a table element or not. In this study, a multilayer artificial neural network model is used. The training has been carried out with an Nvidia RTX 3090 graphics card and taken $162$ minutes. As a result of the training, the F1 score is $0.92$.Comment: 6 pages, 4 figures, 3 table

ZNAČAJ EFEKTIVNOG BROJA KLONOVA U KLONSKIM SJEMENSKIM PLANTAŽAMA: KOMPARATIVNO ISTRAŽIVANJE ZA SEDAM CRNOGORIČNIH VRSTA U TURSKOJ

The Mediterranean Basin is one of the major plant diversity centers in the northern hemisphere. The Eastern Mediterranean Basin is also a hotspot region of gene diversity for conifer species. In this study, Turkey’s conifer seed orchards were investigated for their effective number of clones. The mean census number of clones (N) was estimated 33.12. The mean effective number of clones (Nc) was calculated as 27.59. The mean relative effective number of clones (Nr = Nc/N) was 0.827. The estimated proportional gene diversity was found 0.973, with a range from 0.922 to 0.983. Thus, considerable attention should be given to use nearly equal ramet numbers during seed orchard establishment and management operations. Threats such as climatic change, fire, disease and insects should be considered during seed orchards establishment. High number of populations from wide range of species should be sampled and seed orchards should be established locally depending on ecological requirements of species. This is also essential for sustainable management of forest genetic resources. Information both from phenotypic selection and molecular genetic analysis should be used to establish future seed orchards.Mediteranski bazen jedan je od glavnih središta biljne raznolikosti u Sjevernoj hemisferi. Istočni Mediteran također je područje iznimne genetske raznolikosti četinjača. Istra­živanjem su obuhvaćene klonske sjemenske plantaže Turske s ciljem utvrđivanja efektivnog broja klonova. Srednji broj klonova (N) procijenjen je na 33,12. Srednji efektivni broj klonova (Nc) iznosi 27,59, a srednji relativni efektivni broj klonova (Nr = Nc / N) je 0,827. Procijenjeni proporcionalni genetski diverzitet iznosio je 0,973, s rasponom od 0,922 – 0,983. Stoga je potrebno voditi računa da se koristi gotovo podjednaki broj rameta tijekom osnivanja i održavanja klonskih sjemenskih plantaža. Prijetnje poput klimatskih promjena, požara, bolesti i kukaca moraju se uzeti u obzir kod osnivanja plantaža. Potrebno je uzorkovati velik broj populacija sa šireg područja, a klonske sjemenske plantaže najbolje je osnivati lokalno uvažavajući ekološka obilježja četinjača. Ovime se također osigurava održivo gospodarenje šumskim genetskim bogatstvom. Kod osnivanja budućih klonskih sjemenskih plantaža nužno je pribaviti podatke o fenotipskoj selekciji kao i rezultate molekularne genetičke analize potencijalnih klonova

Endocan Overexpression in Pterygium

WOS: 000403262500013PubMed ID: 28410547Purpose: The aim of this study was to evaluate the possible role of endocan in the pathogenesis of pterygium. Methods: The study was conducted on 33 patients with primary pterygium and 20 control subjects with normal bulbar conjunctiva. Patients with pterygium were graded into 3 groups as atrophic, fleshy, and intermediate, according to the Tan classification. Primary nasal pterygia and normal bulbar conjunctivas were surgically removed. Endocan expression was immunohistochemically investigated. Results: Endocan expression in epithelial and endothelial cells was statistically significantly higher in pterygium tissues than control tissues (P = 0.001). No significant correlation was observed between pterygium classification groups and endocan expression in both epithelial and endothelial cells (P > 0.05). Conclusions: The results suggest that endocan may have a role in the pathogenesis of pterygium