Miracle-dz/UniFAR-FTUs huggingface dataset:
Bone Augmentation Using Novel Unidirectional Porous Hydroxyapatite with Bone Morphogenetic Protein-2 on Rat Skull

Unidirectional porous hydroxyapatite (UDPHAp) is a material whose microstructure consists of cross- sectional oval pores with a pore diameter in the range of 100-300 m that penetrate through the material. UDPHAp has proven suitable for osteogenesis and angiogenesis. The present study was designed to evaluate the osteogenic effect of a newly developed UDPHAp as a carrier of recombinant human bone morphogenetic protein-2 (rhBMP-2) in a rat onlay graft model. UDPHAp was implanted beneath the calvarial periosteum of rats to simulate alveolar bone augmentation in a clinical condition. At 2 weeks after implantation, UDPHAp with rhBMP-2 resulted in active bone formation, and the augmented bone was connected directly with the original bone, whereas commercialized porous hydroxyapatite (PHAp) with rhBMP-2 showed little bone formation. These results suggest that UDPHAp in this model is suitable for onlay graft and is an effective biomaterial for the rhBMP-2 delivery system.
Pacemaker Programming, Physiologic Pacing Settings, and Clinical Outcomes in Real-world Practice: Results from the OPTI-MIND Clinical Study

The aim was to observe the relationship of pacemaker programming and patients’ outcome in an unselected population of recipients in real-world practice. Pacemaker programming at hospital discharge and at scheduled follow-up visits was recorded and classified as physiologic based on simple principles: avoidance of unnecessary ventricular stimulation in patients without atrioventricular block, avoidance of unnecessary atrial stimulation in those without sinus node dysfunction, rate increase in those with chronotropic incompetence, and avoidance of hypotension-related symptoms. All-cause 2-year mortality was the primary endpoint; atrial fibrillation (AF) episodes leading to unplanned visits/hospitalizations was the secondary endpoint. A total of 1740 patients were followed for 24±3 months, 89% had DDD(R) pacemakers; physiologic programming was not associated with better survival, but it was associated with a lower incidence of AF episodes in the 810 patients with AV block as primary pacemaker indication (6.5% versus 11%, po0.01). Physiologic pacemaker programming occurred in only 41.4% of patients at hospital discharge; reprogramming towards a physiologic setting occurred in 8.7% at follow-up, mainly (76%) within the first 6 months. Physiologic programming is associated with a lower incidence of AF episodes in AV block patients beyond maintenance of AV synchrony. Proactive pacemaker behavior to increase physiologic programming should be considered, in view of physicians’ reluctance to change programming from shipment settings.
Solving the Multi-Instance Problem with Neural Networks

Multi-instance learning was coined by Dietterich et al. recently in their investigation on drug activity prediction. In such a learning framework, the training examples are bags composed of instances, and the task is to predict the labels of unseen bags through analyzing the training bags with known labels. A bag is positive if it contains at least one positive instance, while it is negative if it contains no positive instance. However, the labels of the instances constituting the training bags are unknown. In this paper, the open problem of designing multiinstance modification for neural networks is addressed. In detail, a neural network algorithm named Backpropagation-MIP is presented, which is derived from the popular Backpropagation algorithm through employing a new error function capturing the nature of multi-instance learning, i.e. the labels of the training bags instead of that of the training instances are known. Experiments on both the real-world and artificial benchmark multi-instance data show that the performance of Backpropagation-MIP is comparable to that of some well-established multi-instance learning methods.
Quantifying quantumness of correlations using Gaussian Rényi-2 entropy in optomechanical interfaces

Abstract Using the Gaussian Rényi-2 entropy, we analyse the behaviour of two different aspects of quantum correlations (entanglement and quantum discord) in two optomechanical subsystems (optical and mechanical). We work in the resolved sideband and weak coupling regimes. In experimentally accessible parameters, we show that it is possible to create entanglement and quantum discord in the considered subsystems by quantum fluctuations transfer from either light to light or light to matter. We find that both mechanical and optical entanglement are strongly sensitive to thermal noises. In particular, we find that the mechanical one is more affected by thermal effects than that optical. Finally, we reveal that under thermal noises, the discord associated with the entangled state decays aggressively, whereas the discord of the separable state (quantumness of correlations) exhibits a freezing behaviour, seeming to be captured over a wide range of temperature.
Cockle Shell-Based Biocomposite Scaffold for Bone Tissue Engineering

Bone transplantation is a rapidly growing and expanding field, and has a significant impact on improving the quality of life of patients suffering from bone tissue damage and disease. Bone is the second most frequently transplanted tissue in humans after the blood. Secondary bone tumour, trauma or deformity often presents a significant problem for bone surgery (Boyne et al., 2002). Grafting is regularly used in medical procedures to replace damaged tissue. Bone grafting involves replacing damaged bone with harvested bone from donors or from another location within the body. Traditionally, bone graft treatments, such as autografting, allografting, and xenografting were used to replace or repair damaged bone tissue. These processes can be long, painful, and have the possibility of being rejected by the body. Autografts are osteoinductive, osteoconductive and have osteogenic properties (Cypher and Grossman, 1996). Although autografts are considered the gold standard of bone transplantation, they also have certain limitations due to possible donor site morbidity, low tissue availability and may introduce additional medical complications (Younger and Chapman, 1989; Moore et al., 2001). Allografts are grafts made of tissue from a human donor, usually during post-mortem. This method rules out the limitations of auotgrafts technique, but have their own limitations including donor shortages and risks of infections. Xenografts are bone grafts from different species such as bovine, porcine or coralline bone that can be implanted into human graft site. Xenografts offer the advantage of availability in a variety of shapes and sizes, but they are also subjected to problems of immunogenicity and have the tendency to denature or decompose in room temperature (Vaccaro et al., 2002). Distraction osteogenesis is another technique for bone treatment used to promote bone growth using the body’s innate bone-healing mechanisms. The process is lengthy and painful, in which the two sides of a bone fracture are separated by a short distance every few weeks until the desired length of bone has been regrown (Chang et al., 2004). Other treatment options, the man-made devices, such as bone cement fillers and prosthetics made of metals, ceramics and polymers are also used for bone defect repair or replacing damaged bone tissue. All the conventional methods for bone repair and replacement can be long, painful and have the possibility of being rejected by the body (Ducheyne et al., 1992). Alternative approaches have been heavily researched and investigated based on a tissue engineering strategy, in an effort to overcome the inherent limitations of the currently available solutions to bone defects. In this approach, a tissue engineered bone is produced by seeding cells that can become osteoblasts on highly porous biomaterials (Hardin-Young et al., 2000).
Hypersonic Vehicle Model and Control Law Development Using Hc_ and # Synthesis

Irene M. GregoryLangley Research Center ,, Hampton, VirginiaRajiv S. ChozodhryLockheed Engineering & Sciences Company • Hampton, VirginiaJohn D. McMinn and John D. ShaughnessyLangley Research Center • Hampton, VirginiaNational Aeronautics and Space AdministrationLangley Research Center • Hampton, Virginia 23681-0001
BIOMETRIC SYSTEMS BASED ON FACIAL FEATURES AND THEIR RELIABILITY

Today, user identification based on biometrics is a very topical issue. The most widespread methods of identification include: fingerprints, identification based on face recognition and bloodstream scans. For testing, systems were selected that identify a person based on facial features. Testing showed errors in these readers under both standard and difficult conditions. Difficult conditions that we frequently encounter in everyday life were chosen. In the test of biometric identification systems the probability of erroneous user acceptance and probability of false rejection of the user was calculated and shown graphically. Wrong user acceptance was based on unfavorable conditions. Unfavorable conditions were: pollution with dark oil, pollution with soil, pollution with makeup, pollution with soot, pollution with black coal, pollution with paints. Another problem is the surrounding environment of the scanner device, in particular light conditions. The testing demonstrated that it is necessary to conduct identification of people under laboratory conditions, which is not acceptable from the user perspective. The results of the measurements showed that the measured reliability values do not correspond to those of the manufacturers. It is necessary to adapt and perfect these biometric identification systems for use in industrial areas, as they are often used in these areas as access or attendance systems.
Enhanced spin-orbit optical mirages from dual nanospheres

This research was supported by the Spanish Ministerio de Economia y Competitividad (MICINN) and European Regional Development Fund (ERDF) Projects No. FIS2014-55987-P, No. FIS2015-69295-C3-3-P, and No. FIS2017-82804-P, by the Basque Dep. de Educacion Project No. PI-2016-1-0041 and by the Basque Government ELKARTEK program (KK-2016/00030, KK-2017/00089). A.G. -E. received funding from the Fellows Gipuzkoa fellowship of the Gipuzkoako Foru Aldundia through FEDER “Una Manera de hacer Europa. ”
Wave propagation, stress relaxation, and grain-to-grain shearing in saturated, unconsolidated marine sediments

A linear theory of wave propagation in saturated, unconsolidated granular materials, including marine sediments, is developed in this article. Since the grains are unbonded, it is assumed that the shear rigidity modulus of the medium is zero, implying the absence of a skeletal elastic frame. The analysis is based on two types of shearing, translational and radial, which occur at grain contacts during the passage of a wave. These shearing processes act as stress-relaxation mechanisms, which tend to return the material to equilibrium after the application of a dynamic strain. The stress arising from shearing is represented as a random stick-slip process, consisting of a random succession of deterministic stress pulses. Each pulse is produced when micro-asperities on opposite surfaces of a contact slide against each other. The quantity relevant to wave propagation is the average stress from all the micro-sliding events, which is shown to be a temporal convolution between the deterministic stress, h(t), from .
Combined Optical and MR Bioimaging Using Rare Earth Ion Doped NaYF4 Nanocrystals

Here, novel nanoprobes for combined optical and magnetic resonance (MR) bioimaging are reported. Fluoride (NaYF4) nanocrystals (20–30 nm size) co‐doped with the rare earth ions Gd3+ and Er3+/Yb3+/Eu3+ are synthesized and dispersed in water. An efficient up‐ and downconverted photoluminescence from the rare‐earth ions (Er3+ and Yb3+ or Eu3+) doped into fluoride nanomatrix allows optical imaging modality for the nanoprobes. Upconversion nanophosphors (UCNPs) show nearly quadratic dependence of the photoluminescence intensity on the excitation light power, confirming a two‐photon induced process and allowing two‐photon imaging with UCNPs with low power continuous wave laser diodes due to the sequential nature of the two‐photon process. Furthermore, both UCNPs and downconversion nanophosphors (DCNPs) are modified with biorecognition biomolecules such as anti‐claudin‐4 and anti‐mesothelin, and show in vitro targeted delivery to cancer cells using confocal microscopy. The possibility of using nanoprobes for optical imaging in vivo is also demonstrated. It is also shown that Gd3+ co‐doped within the nanophosphors imparts strong T1 (Spin‐lattice relaxation time) and T2 (spin‐spin relaxation time) for high contrast MR imaging. Thus, nanoprobes based on fluoride nanophosphors doped with rare earth ions are shown to provide the dual modality of optical and magnetic resonance imaging.
Leaf Biochemistry Estimation on EU High-Value Crops with ROSIS and DAIS Hyperspectral Data and Radiative Transfer Simulation

This manuscript provides a description of progress made on the investigation of estimation of leaf biochemistry on high-value crops using hyperspectral remote sensing imagery. Hyperspectral optical indices related to leaf chlorophyll content were used to test different assumptions under open and row-crop canopies, specifically Olea europaea L. (olive trees) and Vitis vinifera L. (vineyards). Scaling-up methods were tested as a function of the different spatial resolutions of ROSIS and DAIS datasets acquired over two olive groves and ten vineyard fields in southern and northern Spain, respectively, during the HySens 2002 campaign. Leaf-level biochemical estimation from 1-m ROSIS and 5-m DAIS data required different modeling assumptions, enabling in some cases the use of PROSPECT-SAILH radiative transfer simulation when targeting olive-tree crowns. At lower spatial resolutions the soil and shadow scene components were considered through the linked PROSPECT-SAILH-FLIM models. These considerations along with the calculation of predictive equations using MCARI/OSAVI to minimize soil background variations in these canopies are also discussed.
DESIGN PRINCIPLES AND TOOLS FOR MULTIMODAL DIALOG SYSTEMS

We review e orts in de ning design principles and creating tools for building multimodal dialog systems with emphasis on the speech modality. General design principles and challenges are outlined. The focus is on system architecture, application and speech interface design, data collection and evaluation tools. We conclude that modularity, exibility, customizability, domain-independence and automatic dialog generation are some important features of successful dialog systems and de-
T he E ffect of Sim ulation on Recurrence after Breast- C onserving Surgery and Radiotherapy : Prelim inary Results

Purpose: To evaluate the effect of the simulation method on recurrence among the patients who received radiotherapy after breast-conserving surgery (BCS) for early breast carcinoma. Methods and Materials: Between 1995 and 2000, 70 patients with stage I-II breast carcinoma underwent breast-conserving surgery and adjuvant radiotherapy. Twenty nine patients (41.4% ) were simulated with the 2D contour-based method (September 1995 to August 1997) and 41 patients (58.6% ) were simulated with the 3D CT-based method (September 1997 to February 2000). To analyze the effect of the simulation method, the patient and treatment characteristics were compared. Results: The characteristics were similar for the patients between the 2D contour-based simulation group and the 3D CT-based simulation group. During a median follow-up period of 75 months, 4 (13.8% ) of 29 patients who were treated with 2D simulation and 1 (2.4% ) of 41 patients who were treated with 3D simulation group developed treatment failure. The five-year survival rates were 89.2% and 95.1% between the 2D and 3D simulation groups (p=0.196). The five-year disease free survival (DFS ) rates were 86.2% and 97.5% between the 2D and 3D simulation groups (p=0.0636). On univariate analysis, age > 40 (p= 0.0226) and the number of dissected axillary lymph node ≥ 10 (p=0.0435) were independent predictors of improved 5-year DFS. Conclusions: Although our data showed marginal significance for the DFS between the two groups, it is insufficient, due to the small number of patients in our study, to prove whether 3D CT-based simulation might improve the DFS and reduce the risk of recurrence when compared with 2D contour-based simulation. Further study is needed with a larger group of patients. (Cancer Res Treat. 2006;38:40-47) 󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏 Key W ords: Breast neoplasms, Radiotherapy, Simulation, Recurrence
INTRUSION DETECTION SYSTEM ON A COMPUTER NETWORK USING AN ENSEMBLE OF RANDOMIZABLE FILTERED CLASSIFIER , K-NEAREST NEIGHBOR ALGORITHM

Intrusion detection is the process of monitor the event occurring in a computer network and analyzing them for signs of intrusions. In recent years, the needs of internet are felt in lives of many people. Accordingly, many studies have been done on security in virtual environments. The earliest techniques such as authentication, firewalls and encryption could not be utilized toprovide the complete internet security. Similarly, the motivations to create a new solution approach and a defense system in cyber environment led to introduction of numerous intrusion detection systems (IDS); i.e. different algorithms. However, the results have shown that using a machine learning and knowledge discovery techniques are very effective and increase the detection accuracy of anomalies on a real time computer networks. Therefore, this study presents an ensemble of randomizable filtered and KNearest Neighbor classifier for selecting features in order to enhance network intrusion detection and increase the accuracy of anomaly detection in a real time computer network. Furthermore, data preprocessing and analysis are undertaken using KDDcup99 dataset and a filter, such that best features are selected and irrelevant, redundant, noisy data are removed. The selected features are passed as input to the based classifier for classification and optimization. The based classifier KNN is employed to increase the amount of learning, the efficiency of classification and thereby increasing the authenticity of intrusion detection. Experimental results obtained reveals that the proposed algorithm is very promising accurately detecting anomalies on a computer network.
NİTRİK OKSİTİN DİABET PATOGENEZİ VE KOMPLİKASYONLARINDAKİ ROLÜ

ÖZET Vasküler dinlenim tonüsünün önemli aracısı olan nitrik oksit (NO) endotelyal hücrelere ve makrofajlara ek olarak vücudun pek çok hücresinden köken alarak farklı aracı roller üstlenir. Diabetes Mellitus (DM) sendromunda NO makrofajların rol aldığı sitotoksik aktivite ve sitokin etkileri ile oluşan pankreas adacık hücre harabiyetine aracılık eder. Diğer yandan da kronik hiperglisemi sırasında oluşumu hızlanan ileri glikozilasyon ürünleri tarafından NO'in hem vazodilatasyon oluşturucu hemde antiproliferatif etkilerinin önlenmesi diabetin komplikasyonlarının gelişiminde önemli bir rol alır. Anahtar kelimeler: NO, DM, glikozilasyon. SUMMARY The Role of Nitric Oxide in The Pathogenesis and Complications of Diabetes Mellitus Niitric Oxide (NO) is an important mediator in the regulation of vascular tone. İn addition to endothelial cells and macrophages, a variety of other celi types produce NO as a multifunctional mediator. İn Diabetes Mellitus (DM) syndrome, NO mediates the cytotoxic activity of macrophages and also the cytokine effects on the pancreas islet celi destruction. On the other hand, during chronic hyperglycemia, accelerated formation of advancedglycosylation endproducts (ACEs) inactivates the vasodilatatory and antiproliferative effects of NO, hence contribute to the pathogenesis of diabetic complications. Key vvords: NO, DM, glycosylation.
AuTO: scaling deep reinforcement learning for datacenter-scale automatic traffic optimization

Traffic optimizations (TO, e.g. flow scheduling, load balancing) in datacenters are difficult online decision-making problems. Previously, they are done with heuristics relying on operators' understanding of the workload and environment. Designing and implementing proper TO algorithms thus take at least weeks. Encouraged by recent successes in applying deep reinforcement learning (DRL) techniques to solve complex online control problems, we study if DRL can be used for automatic TO without human-intervention. However, our experiments show that the latency of current DRL systems cannot handle flow-level TO at the scale of current datacenters, because short flows (which constitute the majority of traffic) are usually gone before decisions can be made. Leveraging the long-tail distribution of datacenter traffic, we develop a two-level DRL system, AuTO, mimicking the Peripheral & Central Nervous Systems in animals, to solve the scalability problem. Peripheral Systems (PS) reside on end-hosts, collect flow information, and make TO decisions locally with minimal delay for short flows. PS's decisions are informed by a Central System (CS), where global traffic information is aggregated and processed. CS further makes individual TO decisions for long flows. With CS&PS, AuTO is an end-to-end automatic TO system that can collect network information, learn from past decisions, and perform actions to achieve operator-defined goals. We implement AuTO with popular machine learning frameworks and commodity servers, and deploy it on a 32-server testbed. Compared to existing approaches, AuTO reduces the TO turn-around time from weeks to ~100 milliseconds while achieving superior performance. For example, it demonstrates up to 48.14% reduction in average flow completion time (FCT) over existing solutions.
Dictionary Learning for Classification

The techniques involving representation of image in sparser domain have been used profoundly in many image processing applications. These include denoising, compressive sensing, feature extraction, classification and many more. The idea that image is sparse in some domain is modelled by design matrix called Dictionary which comprises set of prototype signals known as dictionary atoms. This dictionary can be prespecified or learned adaptively to fit the data. In this presented work , dictionary learning algorithm K-SVD is studied together with sparse coding algorithms like OMP (Orthogonal Matching Pursuit) and ISTA(Iterated Soft Thresholding Algorithm). Further, the learned Dictionary that best fits data can be used for task like classification, however it is expected that Dictionary will perform better if it has discrimination ability. This aspect is addressed by FDDL (Fisher Discimination based Dictionary Learning) model, reported to have better classification rate with learned dictionaries in the literature.
Performance measurement in the Australian water supply industry: A preliminary analysis

Various government-owned businesses provide water supply services to Australian residents. With the advent of recent competition and regulatory reforms in infrastructure industries in Australia, more and more of these businesses are now facing new types of incentive-based regulatory regimes. This has led to a desire for more information on the performance of these businesses, both relative to each other and over time. In this study we use panel data on the 18 largest Australian water services businesses, observed over an eight-year period from 1995/6 to 2002/3, to measure the relative efficiency and productivity growth of these businesses. Data envelopment analysis (DEA) methods are used to obtain estimates of the multi-input, multi-output production technology. The potential use of these performance measures in price-cap regulation is discussed, where the effects of variable selection and data quality upon empirical results is emphasised.