Miracle-dz/UniFAR-FTUs huggingface dataset:
Monitoring of Biodistribution and Persistence of Conditionally Replicative Adenovirus in a Murine Model of Ovarian Cancer Using Capsid-Incorporated mCherry and Expression of Human Somatostatin Receptor Subtype 2 Gene

A significant limiting factor to the human clinical application of conditionally replicative adenovirus (CRAd)-based virotherapy is the inability to noninvasively monitor these agents and their potential persistence. To address this issue, we proposed a novel imaging approach that combines transient expression of the human somatostatin receptor (SSTR) subtype 2 reporter gene with genetic labeling of the viral capsid with mCherry fluorescent protein. To test this dual modality system, we constructed the Ad5/3Δ24pIXcherry/SSTR CRAd and validated its capacity to generate fluorescent and nuclear signals in vitro and following intratumoral injection. Analysis of 64Cu-CB-TE2A-Y3-TATE biodistribution in mice revealed reduced uptake in tumors injected with the imaging CRAd relative to the replication-incompetent, Ad-expressing SSTR2 but significantly greater uptake compared to the negative CRAd control. Optical imaging demonstrated relative correlation of fluorescent signal with virus replication as determined by viral genome quantification in tumors. Positron emission tomography/computed tomography studies demonstrated that we can visualize radioactive uptake in tumors injected with imaging CRAd and the trend for greater uptake by standardized uptake value analysis compared to control CRAd. In the aggregate, the plasticity of our dual imaging approach should provide the technical basis for monitoring CRAd biodistribution and persistence in preclinical studies while offering potential utility for a range of clinical applications.
A Spectral Bundle Method for Semidefinite Programming

A central drawback of primal-dual interior point methods for semidefinite programs is their lack of ability to exploit problem structure in cost and coefficient matrices. This restricts applicability to problems of small dimension. Typically, semidefinite relaxations arising in combinatorial applications have sparse and well-structured cost and coefficient matrices of huge order. We present a method that allows us to compute acceptable approximations to the optimal solution of large problems within reasonable time. Semidefinite programming problems with constant trace on the primal feasible set are equivalent to eigenvalue optimization problems. These are convex nonsmooth programming problems and can be solved by bundle methods. We propose replacing the traditional polyhedral cutting plane model constructed from subgradient information by a semidefinite model that is tailored to eigenvalue problems. Convergence follows from the traditional approach but a proof is included for completeness. We present numerical examples demonstrating the efficiency of the approach on combinatorial examples.
Adaptive Extended Kalman Filter for asynchronous shuttering error of stereo vision localization

Stereo Vision (SV) Localization is an advanced sensory tool for motion estimation and tracking applications especially in robotics. Low cost and ease of use are the core benefits of this sensory system. One of the major problems in SV localization is the error in the estimation of object location because of the delay which is produced by asynchronous shuttering of each camera. A usual technique to overcome this problem is to make cameras operate synchronously. In this paper we propose Adaptive Extended Kalman Filter (AEKF) to compensate this asynchronization error by modeling the object's movement in image plane. In this approach after getting each cameras' frame we perform a localization process which results in a higher frame rate compared with the synchronous approach. It has also advantage of having more adaptable performance especially in the presence of uncertainty in arbitrary movements over conventional Kalman Filter and other linear techniques.
Ku‐deficient yeast strains exhibit alternative states of silencing competence

In Saccharomyces cerevisiae, efficient silencer function requires telomere proximity, i.e. compartments of the nucleoplasm enriched in silencing factors. Accordingly, silencers located far from telomeres function inefficiently. We show here that cells lacking yKu balance between two mitotically stable states of silencing competence. In one, a partial delocalization of telomeres and silencing factors throughout the nucleoplasm correlates with enhanced silencing at a non‐telomeric locus, while in the other, telomeres retain their focal pattern of distribution and there is no repression at the non‐telomeric locus, as observed in wild‐type cells. The two states also differ in their level of residual telomeric silencing. These findings indicate the existence of a yKu‐independent pathway of telomere clustering and Sir localization. Interestingly, this pathway appears to be under epigenetic control.
Using Histograms for Skyline Size Estimation

Let T be a table of n points described by a set of d attributes/dimensions. Let p, q ∈ T. p dominates q iff it is better than q in every dimension and there exists at least one attribute for which p is strictly better than p. p is a skyline point of T iff it is not dominated by any point of T. A skyline query returns the set of all skyline points. In order to integrate Skyline queries into database management systems, deriving an estimation of the skyline cardinality is important for query optimization purposes. We propose techniques for estimating skyline cardinality when data distribution is known. We first provide an unbiased estimator which requires one traversal of the whole data which is much faster than computing the exact skyline. Then, we show that this estimator can be used on a sample of the underlying data while preserving the estimation quality, i.e., it is still unbiased. Next, we provide a convergent estimator which does not require any data access but the data distribution. It estimates skyline cardinality expectation for those data sets respecting data distribution. The advantages of these solutions are their ease of implementation and, by contrast to other proposals, no costly subskyline queries are required. Our solutions are implemented and some experiments are reported showing both the accuracy of the estimations and the execution time efficiency by which they are obtained.
7.10 EVALUATING NEW CLOUD-RADIATION AND HYDROLOGIC CYCLE PARAMETERIZATIONS

Most modern GCMs now include prognostic cloud parameterizations that treat cloud fraction and cloud water/ice amount as interactive variables. These cloud water/ice amounts are used to prognostically calculate the cloud optical properties that can have a large influence on the model's radiation budget. Thus, the accurate parameterization of the auto-conversion process is important for both modeling of precipitation rates and specification of cloud optical properties (Liu and Daum, 2004; Rotstayn 2000).
Organisational Sustainability Modelling for Return on Investment (ROI): Case Studies Presented by a National Health Service (NHS) Trust UK

Organisational Sustainability Modelling is a new way to measure Cloud business performance quantitatively and accurately. It combines statistical computation and 3D Visualisation to present the Return on Investment arising from the adoption of Cloud Computing by organisations. The Cloud Return On Investment methodology described in this paper makes use of a highly structured and organised process to review and evaluate Cloud business performance. We illustrate its use with two case studies. The first case study concerns a National Health Service (NHS) Trust UK Infrastructure and confirms that using Cloud infrastructures can improve efficiency. It also results in raising benchmark, the minimum acceptance level to complete concurrent tasks. The second case study shows 3D Visualisation being used to confirm incremental improvements to an NHS Bioinformatics project. The low risk-free rate may imply code development allows reduced time to complete, and objective is clearly met and project delivery is straightforward. We introduce a structured Quality Assurance process, and demonstrate how to ensure the quality of our data analysis, which other researchers miss out.
A Critical Role for the Cannabinoid CB1 Receptors in Alcohol Dependence and Stress-Stimulated Ethanol Drinking

Although many people drink alcohol regularly, only some become addicted. Several studies have shown that genetic and environmental factors contribute to individual differences in the vulnerability to the effects of alcohol (Nestler, 2000; Kreek, 2001; Crabbe, 2002). Among the environmental factors, stress is perhaps the most important trigger for relapse after a period of abstinence (Koob and Nestler, 1997; Piazza and Le Moal, 1998; Koob and Le Moal, 2001; Weiss et al., 2001). Here we show that ethanol withdrawal symptoms were completely absent in cannabinoid CB1 receptor-deficient mice, although acute effects of ethanol and ethanol tolerance and preference were basically normal. Furthermore, foot-shock stress had no affect on alcohol preference in Cnr1−/− mice, although it induced a dramatic increase in Cnr1+/+ animals. These results reveal a critical role for the CB1 receptor in clinically important aspects of alcohol dependence and provide a rationale for the use of CB1 receptor antagonists in the treatment of alcohol addiction.
Machine Learning based Efficient Image colorization

Image colorization is a process that adds color to grayscale images. Manual image colorization is tedious and prone to human error. Existing approaches to colorize images automatically use either pixel-based scanning (expensive), are scribble-based (manual), assume strong correlation with target grayscale image, or require large number of training examples. In this project, we aim to look at approaches that perform automatic image colorization with a small number of similar reference images using Machine Learning. Initially, (1) we plan to design features which would capture different properties of a grayscale image for training ML models. Then, (2) grouping pixels into superpixels enables capturing of local information with reduced complexity. If we have a corpus of training images, (3) one or more of Machine Learning algorithms like support vector regression or k-means could be used to train an algorithm to colorize parts of images with reasonable accuracy. Based on the output of the model, (4) we perform some post-processing (like smoothing) to re-assign colors of segments predicted with low confidence. Via these four steps, we color images with reasonable accuracy using only a small subset of training images than needed in other approaches.
Multiple-valued caches for power-efficient embedded systems

In this paper, we propose three novel cache models using multiple-valued logic (MVL) paradigm to reduce the cache data storage area and cache energy consumption for embedded systems. Multiple-valued caches have significant potential for compact and power-efficient cache array design. The cache models differ from each other depending on whether they store tag and data in binary, radix-r or a mix of both. Our analytical study of cache silicon area shows that an embedded system-on-a-chip (SoC) equipped with a multiple-valued cache model can reduce the cache data storage area up to 6% regardless of cache parameters. Also, our experiments on several embedded benchmarks demonstrate that dynamic cache energy consumption can be reduced up to 62% in a multiple-valued instruction cache in an embedded SoC.
SMT-based Bounded Model Checking for Weighted Interpreted Systems and for Weighted Epistemic ECTL

We define the SMT-based bounded model checking (BMC) method for Weighted Interpreted Systems and for the existential fragment of the Weighted Epistemic Computation Tree Logic. We implemented the new BMC algorithm and compared it with the SATbased BMC method for the same systems and the same property language on several benchmarks for multi-agent systems.
A 3D segmentation method of lung parenchyma based on CT image sequences

Three dimensional (3D) pulmonary parenchyma segmentation is an indispensable step in CAD (Computer-Aided Detection) that used for pulmonary nodule detection based on CT images. In this paper, we proposed a simple and effective 3D lung parenchyma segmentation method, which combined the adaptive threshold, connected regional labeling and morphological operations. The method has four main steps. Firstly, the CT sequences images were binarized. Secondly, the lung parenchyma is extracted from the CT images by 3D connected component labeling. And then, the trachea is removed by 3D region growing. Finally, a sequence of morphological operations is used to smooth the boundaries and fill the holes caused by small vessels, nodules and trachea/bronchus. Using our method to segment 20 group lung CT clinical data, the average segmentation accuracy is 91.55%, and the average time-consuming to deal with a single group data is 167.4563s(about 0.6 second for processing a single slice). The experimental results showed that this method can automatically and quickly segments the lung parenchyma, and which formed the basis for the follow-up pulmonary nodules computer-aided detection technology study.
A Two Stage Similarity-aware Indexing for Real-time Large Scale Entity Resolution

Entity resolution, or data integration, is the process that identifies and matches data records that refer to the same real world entity. In many cases, entity resolution needs to be performed in sub-seconds. The challenge is especially enormous when entity resolution is processed on large scale datasets. Some techniques have been proposed to solve the problem. Indexing is a technique that enables real-time entity resolution by only comparing record pairs that have the same encoding value and thus largely reduce the number of comparisons. Similarity-aware Indexing (SAI) improves the performance of traditional indexing by pre-calculating similarities of attribute values. Another technique known as Locality Sensitive Hashing (LSH) provides a similarity based filtering. Minhash is an implementation of LSH. In this report, a two-stage approach which combines LSH with SAI is proposed. At the first stage, LSH is adopted to approximate filter data records and preserve the potential matches. At the second stage, SAI is used to compare potential matches to obtain a precise and accurate result. This approach is evaluated experimentally on two large scale datasets taken from real-world databases. Experimental results show that the two-stage approach can processes queries 10 times faster than the original similarity-aware indexing. This approach can also perform well at scenarios where precise query results are needed.
Noninvasive Monitoring of Murine Tumor Blood Flow During and After Photodynamic Therapy Provides Early Assessment of Therapeutic Efficacy

Purpose: To monitor tumor blood flow noninvasively during photodynamic therapy (PDT) and to correlate flow responses with therapeutic efficacy. Experimental Design: Diffuse correlation spectroscopy (DCS) was used to measure blood flow continuously in radiation-induced fibrosarcoma murine tumors during Photofrin (5 mg/kg)/PDT (75 mW/cm2, 135 J/cm2). Relative blood flow (rBF; i.e., normalized to preillumination values) was compared with tumor perfusion as determined by power Doppler ultrasound and was correlated with treatment durability, defined as the time of tumor growth to a volume of 400 mm3. Broadband diffuse reflectance spectroscopy concurrently quantified tumor hemoglobin oxygen saturation (SO2). Results: DCS and power Doppler ultrasound measured similar flow decreases in animals treated with identical protocols. DCS measurement of rBF during PDT revealed a series of PDT-induced peaks and declines dominated by an initial steep increase (average ± SE: 168.1 ± 39.5%) and subsequent decrease (59.2 ± 29.1%). The duration (interval time; range, 2.2-15.6 minutes) and slope (flow reduction rate; range, 4.4 -45.8% minute−1) of the decrease correlated significantly (P = 0.0001 and 0.0002, r2 = 0.79 and 0.67, respectively) with treatment durability. A positive, significant (P = 0.016, r2 = 0.50) association between interval time and time-to-400 mm3 was also detected in animals with depressed pre-PDT blood flow due to hydralazine administration. At 3 hours after PDT, rBF and SO2 were predictive (P ≤ 0.015) of treatment durability. Conclusion: These data suggest a role for DCS in real-time monitoring of PDT vascular response as an indicator of treatment efficacy.
Age cutoff for Epstein-Barr virus-positive diffuse large B-cell lymphoma–is it necessary?

Epstein-Barr virus-positive diffuse large B-cell lymphoma of the elderly (EBV+ DLBCL-e) is a molecularly distinct variant of DLBCL, characterized by a monoclonal B-cell proliferation that occurs in patients >50 years of age without a history or clinicopathologic evidence of immunodeficiency. However, patients with EBV+ DLBCL younger than 50-years-old also exist in Western countries. We evaluated the clinicopathologic, immunophenotypic and genetic features in Cacausian patients with EBV+ DLBCL who are ≤50 years of age and compared this patient group to patients who are >50 years. In patients who are ≤50 years, less frequent expression of BCL6 and a trend of more frequent expression of CD30 and pSTAT3 were found in patients with EBV+ DLBCL. In patients who are >50 years, common expression of CD30, p50, pSTAT3 and less frequent expression of BCL6 were observed. Older patients also more commonly had a poor performance status (ECOG≥2). Comparing EBV+ DLBCL patients in ≤50 years versus >50 years, both groups had similar clinicopathologic, immunophenotypic and genetic features. Gene expression profiling, microRNA profiling and treatment outcome of the younger patients with EBV+ DLBCL was not distinctive from tumors in older patients. Based on our data, we suggest that the arbitrary age cutoff for EBV+ DLBCL is unnecessary and should be eliminated in the WHO lymphoma classification scheme.
Incidence of Mindfulness and Qi Gong on the State of Health, Psychological Well-Being, Life Satisfaction and Work Stress

This study was conducted through a pre-experimental methodological approach with pre-test and post-test measurements. 52 workers from the Universidad de la Costa participated were invited to attend the eight-hour mindfulness and qi gong workshop for improving health, psychological well-being, life satisfaction and stress reduction. The instruments used were the Health Survey sf-11, Scale of Psychological Well-Being (Ryff & Keyes, 1995), Life Satisfaction Scale (Cortés, 2013) and Occupational Stress Numerical Scale. The combination of mindfulness and qi gong produced an improvement in the health of the workers —general health, energy, efficiency, domestic activities and diminution of pain, important changes in the psychological well-being — adaptation, confidence, clarity, openness and self-esteem, and improvement in satisfaction— health and productivity. No significant differences in work stress levels were found.
A reverse write assist circuit for SRAM dynamic write VMIN tracking using canary SRAMs

SRAMs occupy a large amount of area in modern system on chip circuits. With the growing trend of device scaling in deep sub-micron technologies, the 6T SRAM write operation is more vulnerable than the read operation from a failure standpoint. In order to make the SRAMs operate correctly, we must design them with some guard band above the minimum operating voltage (VMIN) by designing for the worst case. In this paper, we investigate a reverse write assist circuit scheme that enables the tracking of SRAM write VMIN by using canary SRAM bitcells to track dynamic voltage, temperature fluctuations and aging effects. This circuit ultimately allows us to lower the write VMIN below the worst case corner (SF_85C) VMIN, which saves a minimum of 30.7% energy per cycle at the SS_85C, and a maximum of 51.5% energy per cycle at the FS_85C corner.
GDCLU: A New Grid-Density Based ClustrIng Algorithm

This paper addresses the density based clustering problem in data mining where clusters are established based on density of regions. The most well-known algorithm proposed in this area is DBSCAN [1] which employs two parameters influencing the shape of resulted clusters. Therefore, one of the major weaknesses of this algorithm is lack of ability to handle clusters in multi-density environments. In this paper, a new density based grid clustering algorithm, GDCLU, is proposed which uses a new definition for dense regions. It determines dense grids based on densities of their neighbors. This new definition enables GDCLU to handle different shaped clusters in multi-density environments. Also this algorithm benefits from scale independency feature. The time complexity of the algorithm is O(n) in which n is number of points in dataset. Several examples are presented showing promising improvement in performance over other basic algorithms like optics in multi-density environments.
Children view own-age faces qualitatively differently to other-age faces

ABSTRACT Like most own-group biases in face recognition, the own-age bias (OAB) is thought to be based either on perceptual expertise or socio-cognitive motivational mechanisms [Wolff, N. , Kemter, K. , Schweinberger, S. R. , & Wiese, H. (2013). What drives social in-group biases in face recognition memory? ERP evidence from the own-gender bias. Social Cognitive and Affective Neuroscience. doi:10.1093/scan/nst024]. The present study employed a recognition paradigm with eye-tracking in order to assess whether participants actively viewed faces of their own-age differently to that of other-age faces. The results indicated a significant OAB (superior recognition for own-age relative to other-age faces), provided that they were upright, indicative of expertise being employed for the recognition of own-age faces. However, the eye-tracking results indicate that viewing other-age faces was qualitatively different to the viewing of own-age faces, with more nose fixations for other-age faces. These results are interpreted as supporting the socio-cognitive model of the OAB.
O ct 2 00 6 Complementarity in Quantum Systems

Reduction of a state of a quantum system to a subsystem gives partial quantum information about the true state of the total system. Two subalgebras A1 and A2 of B(H) are called complementary if the traceless subspaces of A1 and A2 are orthogonal (with respect to the Hilbert-Schmidt inner product). When both subalgebras are maximal Abelian, then the concept reduces to complementary observables or mutually unbiased bases. In the paper several characterizations of complementary subalgebras are given in the general case and several examples are presented. For a 4-level quantum system, the structure of complementary subalgebras can be described very well, the Cartan decomposition of unitaries plays a role. It turns out that a measurement corresponding to the Bell basis is complementary to any local measurement of the two-qubit-system.