Sabancı University

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    The effects of boron nitride/hydroxyapatite compounds on bone defects in osteoporotic rats

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    This study investigated the effects of poly-(lactide-co-glycolide) (PLGA) scaffolds containing different concentrations of boron nitride (BN) and/or hydroxyapatite (HA) on bone defects in osteoporotic rats. The control group consisted of healthy rats. A standard non-critical size defect was induced in the osteoporotic rat femurs 12 weeks post-ovariectomy. PLGA scaffolds containing different concentrations of BN+HA were then applied to the defect area. In one group, defect was induced but no PLGA was applied. Computed tomography images were obtained and tissue samples were collected in the first, second, and fourth weeks postoperatively. PLGA scaffolds were classified as no BN + HA, only 10% HA, 2.5% BN + 10% HA, 5% BN + 10% HA, 10% BN + 10% HA or only 2.5%, 5% or 10% BN. No healing was determined in the first and second weeks postoperatively. However, in the fourth week healing was observed in the groups treated with PLGA scaffolds containing 10% HA, 2.5% BN + 10% HA, 2.5% BN and 5% BN, and especially in the 2.5% BN + 10% HA group. The findings of this study suggest that BN may represent a novel target for treating osteoporotic bone defects for physicians and engineers

    Biomedical applications of engineered heparin-based materials

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    Heparin is a negatively charged polysaccharide with various chain lengths and a hydrophilic backbone. Due to its fascinating chemical and physical properties, nontoxicity, biocompatibility, and biodegradability, heparin has been extensively used in different fields of medicine, such as cardiovascular and hematology. This review highlights recent and future advancements in designing materials based on heparin for various biomedical applications. The physicochemical and mechanical properties, biocompatibility, toxicity, and biodegradability of heparin are discussed. In addition, the applications of heparin-based materials in various biomedical fields, such as drug/gene delivery, tissue engineering, cancer therapy, and biosensors, are reviewed. Finally, challenges, opportunities, and future perspectives in preparing heparin-based materials are summarized

    Effect of mixed wettability surfaces on flow boiling heat transfer at subatmospheric pressures

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    Subatmospheric flow boiling heat transfer is a promising method for electronics cooling due to lower saturation temperatures. However, pressure is a crucial parameter that affects surface tension and vapor density. In this study, the effect of surface mixed wettability configuration on bubble dynamics and flow boiling was investigated under atmospheric and subatmospheric pressure conditions. Superhydrophilic, superhydrophobic, and mixed-wettability surfaces were prepared and tested at various heat fluxes and three system pressures of 48 kPa, 68 kPa, and 101 kPa. The channel dimensions were 50 mm × 15 mm, and the channel had a depth of 1 mm. The results showed that biphilic surfaces enhanced the performance up to 28% compared to superhydrophilic surfaces at high heat fluxes for subatmospheric boiling. Flow visualization efforts reveal that mixed-wettability surfaces improve heat transfer by extending the efficient slug regime to higher heat fluxes by preventing dried spot formation. These surfaces benefit from high density nucleation sites at low and medium heat fluxes, resulting in a noticeable performance improvement compared to the superhydrophilic surface. The obtained experimental data in this study will be helpful for the development of thermal-fluid systems operating under subatmospheric conditions

    Deeper look into deprovincialization hypothesis: the mediating role of ingroup identification in contact-prejudice association

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    Deprovincialization hypothesis of intergroup contact has great potential to demonstrate favorable effects of contact in improving intergroup attitudes, yet previous studies did not find a consistent relationship between contact and deprovincialization. With two studies, I aimed to investigate if the multicomponent model of ingroup identification and deprovincialization mediates contact - prejudice link. Study 1 involved two serial mediation models including first the components of ingroup identification in parallel and deprovincialization in sequel; second, satisfaction and collective narcissism in parallel and deprovincialization in sequel, in the association between contact and attitudes (N = 315, Mage = 33.96, SDage = 13.15). Results indicated that only the centrality component mediated contact and attitudes through deprovincialization. Also, collective narcissism but not satisfaction mediated this association via deprovincialization. Study 2 (N = 144, Mage = 21.69, SDage = 2.56) aimed to replicate Study 1 with imagined contact manipulation, yet the models did not show the previous effects with the use of imagined contact with Syrians. Findings emphasized the deprovincializing role of centrality and collective narcissism in association with more positive outgroup attitudes

    Data driven intrusion detection for 6LoWPAN based IoT systems

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    Wide adoption of Internet of Things (IoT) devices and their limitations in terms of hardware cause them to be easy targets for attackers. This, in turn, requires monitoring such systems using intrusion detection systems and take mitigative actions against insider and outsider attackers. Recent studies have explored that machine learning based intrusion detection systems are quite successful in detecting different types of cyber threats targeting IoT systems. However, the proposed systems in these studies incurred limitations in terms of the characteristics of their datasets and detection models. Specifically, a big proportion of the proposed models were developed using simulation-based data generated through specific simulators. Some of these studies also used previously published testbed data that contain the samples of outdated IoT attacks and vulnerabilities. Furthermore, they focused on a lower attack variety and proposed binary classifiers which do not scale in multi-attack scenarios. In this study, we propose a machine learning based multi-class classifier that can classify 6 attack types together with the benign traffic. Our node based feature extraction and detection methodology allows locating the network addresses of the attackers, rather than a rough network level attack existence information, by modeling their traffic characteristics over a sliding time window. For training and testing our models, we also propose an intrusion detection dataset generated using the traffic data collected from real IoT devices running with 6LoWPAN and RPL protocols. Besides having RPL routing attacks in the dataset, we leverage Mirai botnet, employed frequently to target IoT devices. The results show that the proposed intrusion detection system can detect 6 attack types with high recall scores ranging from 79% to 100%. We also illustrate the practicality of the developed model via deployment in a proof of concept implementation over a testbed

    Predicting structural susceptibility of proteins to proteolytic processing

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    The importance of 3D protein structure in proteolytic processing is well known. However, despite the plethora of existing methods for predicting proteolytic sites, only a few of them utilize the structural features of potential substrates as predictors. Moreover, to our knowledge, there is currently no method available for predicting the structural susceptibility of protein regions to proteolysis. We developed such a method using data from CutDB, a database that contains experimentally verified proteolytic events. For prediction, we utilized structural features that have been shown to influence proteolysis in earlier studies, such as solvent accessibility, secondary structure, and temperature factor. Additionally, we introduced new structural features, including length of protruded loops and flexibility of protein termini. To maximize the prediction quality of the method, we carefully curated the training set, selected an appropriate machine learning method, and sampled negative examples to determine the optimal positive-to-negative class size ratio. We demonstrated that combining our method with models of protease primary specificity can outperform existing bioinformatics methods for the prediction of proteolytic sites. We also discussed the possibility of utilizing this method for bioinformatics prediction of other post-translational modifications

    iBiR: bug-report-driven fault injection

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    Much research on software engineering relies on experimental studies based on fault injection. Fault injection, however, is not often relevant to emulate real-world software faults since it "blindly"injects large numbers of faults. It remains indeed challenging to inject few but realistic faults that target a particular functionality in a program. In this work, we introduce iBiR , a fault injection tool that addresses this challenge by exploring change patterns associated to user-reported faults. To inject realistic faults, we create mutants by re-targeting a bug-report-driven automated program repair system, i.e., reversing its code transformation templates. iBiR is further appealing in practice since it requires deep knowledge of neither code nor tests, just of the program's relevant bug reports. Thus, our approach focuses the fault injection on the feature targeted by the bug report. We assess iBiR by considering the Defects4J dataset. Experimental results show that our approach outperforms the fault injection performed by traditional mutation testing in terms of semantic similarity with the original bug, when applied at either system or class levels of granularity, and provides better, statistically significant estimations of test effectiveness (fault detection). Additionally, when injecting 100 faults, iBiR injects faults that couple with the real ones in around 36% of the cases, while mutation testing achieves less than 4%

    Non-coding RNA transcripts in cancer therapy: pre-clinical and clinical implications

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    "Non-coding RNA Transcripts in Cancer Therapy: Pre-clinical and Clinical Implications" provides a new insight towards role of non-coding RNAs molecules including microRNAs, long non-coding RNAs and circular RNAs in cancer. The reason of focusing on cancer is that this malignant disease is responsible for high death around the world and after cardiovascular diseases, it is a leading cause of death. In the current book, the basic information and knowledge about ncRNAs, their biogenesis and their biological functions are covered. It is shown that ncRNAs can regulate expression level of genes at transcriptional and post-transcriptional levels and therefore, ncRNAs are master and key players in molecular pathways. Then, the impact of regulation of molecular pathways by ncRNAs on important molecular mechanisms such as proliferation, metastasis, epithelial-to-mesenchymal transition, apoptosis and autophagy are described. All of these aspects are integrated in cancer and for treatment of this disease. In case of miRNAs, their biogenesis and biological functions in physiological conditions are described. Then, impact of miRNAs on proliferation, metastasis, therapy response (drug resistance and radio-resistance) is described. For lncRNAs and circRNAs, a same way is followed and their biogenesis and functional roles in cells are discussed. At the next step, function of lncRNAs and circRNAs in regulating proliferation, metastasis and therapy response (chemoresistance and radio-resistance) is introduced. These subjects are discussed based on pre-clinical studies and in order to make the book more comprehensive and broaden its scope, the function of exosomal ncRNAs in diagnosis and prognosis of cancer patients is described

    Balance and posture control of legged robots: a survey

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    Legged robots excel in navigating challenging natural environments, such as steep obstructions or wide gaps in the ground. In addition to rough terrain, they may confront unexpected impact forces during their leaping gaits. While facing external disturbances, legged robots should maintain and (if necessary) restore their stability while completing their gaits. To this end, external disturbances and body orientation errors should be identified, and appropriate actions have to be taken to restore the balance of the robot and to provide advantageous landing circumstances. This paper briefly surveys the developments for balance and posture control of legged robots. The primary focus of these studies is on balancing legged robots under external disturbances or performing dynamic gaits. This paper also includes a brief focus on the literature that present research on balance and posture control strategies using the angular momentum approach

    Upcycled graphene integrated fiber-based photothermal hybrid nanocomposites for solar-driven interfacial water evaporation

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    Solar-driven interfacial evaporation is an efficient and viable solution for providing freshwater, especially in remote areas that utilize sunlight for water purification and desalination systems. This study proposes a practical preparation method for a photothermal nanocomposite, compromising Polyacrylonitrile (PAN) nanofibrous membrane, crosslinked PVA, and upcycled Graphene Nanoplatelets (GNP). The synergistic effect between the PAN nanofibers and PVA/GNP nanocomposite and the contributing factors to the overall performance is examined. It was found that the initial thickness of the PAN nanofibrous layer has an inverse effect on the evaporation rate. The obtained results indicated that while the GNP content enhances the photothermal activity, it deteriorates the water absorbency of the nanocomposite; thus, an optimized concentration should be obtained. By investigating different parameters for the evaporator, we obtained an evaporation rate of 1.40 kg/m2h under 1 sun of illumination

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