Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular and Cellular Pathobiology PCTAIRE 1 Phosphorylates p 27 and Regulates Mitosis in Cancer Cells

PCTAIRE1 is distant relative of the cyclin-dependent kinase family that has been implicated in spermatogenesis and neuronal development, but it has not been studied in cancer. Here, we report that PCTAIRE1 is expressed in prostate, breast, and cervical cancer cells, where its RNAi-mediated silencing causes growth inhibition with aberrant mitosis due to defects in centrosome dynamics. PCTAIRE1 was not similarly involved in proliferation of nontransformed cells, including diploid human IMR-90 fibroblasts. Through yeast two-hybrid screening, we identified tumor suppressor p27 as a PCTAIRE1 interactor. In vitro kinase assays showed PCTAIRE1 phosphorylates p27 at Ser10. PCTAIRE1 silencing modulated Ser10 phosphorylation on p27 and led to its accumulation in cancer cells but not in nontransformed cells. In a mouse xenograft model of PPC1 prostate cancer, conditional silencing of PCTAIRE1 restored p27 protein expression and suppressed tumor growth. Mechanistic studies in HeLa cells showed that PCTAIRE1 phosphorylates p27 during the S and M phases of the cell cycle. Notably, p27 silencing was sufficient to rescue cells from mitotic arrest caused by PCTAIRE1 silencing. Clinically, PCTAIRE1 was highly expressed in primary breast and prostate tumors compared with adjacent normal epithelial tissues. Together our findings reveal an unexpected role for PCTAIRE1 in regulating p27 stability, mitosis, and tumor growth, suggesting PCTAIRE1 as a candidate cancer therapeutic target. Cancer Res; 74(20); 1–13. 2014 AACR.
MEC-IDC: joint load balancing and power control for distributed Internet Data Centers

Internet Data Center (IDC) supports the reliable operations of many important Internet on-line services. As the demand on Internet services and cloud computing keep increasing in recent years, the power usage associated with IDC operations has been uprising significantly. The cyber and physical aspects of IDCs interact with each other, and brings unprecedented challenges in power management. While most existing research focuses on reducing power consumptions of IDCs, this paper studies the problem of minimizing the total electricity cost geared to quality of service constraint as well as the location diversity and time diversity of electricity price under multiple electricity markets. We jointly consider both the cyber and physical management capabilities of IDCs, and exploit both the center-level load balancing, and the server-level power control in a unified scheme. We model the problem as a constrained mixed integer programming based on Generalized Benders Decomposition (GBD) technique. Extensive evaluations based on real-life electricity price data for multiple IDC locations demonstrates the effectiveness of our scheme.
Image analysis via fuzzy reasoning approach: prototype applications at NASA

A set of imaging techniques based on the fuzzy reasoning (FR) approach was built for NASA at Kennedy Space Center (KSC) to perform complex real-time visual-related safety prototype tasks, such as detection and tracking of moving foreign objects debris (FOD) during the NASA Space Shuttle liftoff and visual anomaly detection on slidewires used in the emergency egress system for Space Shuttle at the launch pad. The system capability was used as well during the imaging analysis of the Space Shuttle Columbia accident. These FR-based imaging techniques include novel adaptive image segmentation, edge extraction, and image enhancement. Commercially available probabilistic neural network (PNN) scheme was also used along with this set of FR-based imaging techniques to add powerful learning and image classification capabilities. NASA filed for patents on these FR-based imaging techniques and is currently offering them for their commercialization.
TRUS Image Classification for Prostate cancer using Computational Intelligence

In medical image analysis image classification is key task for diagnosing the disease of the patient. The classification results assist the doctors to treat the patient according to the severances of the diseases. TRUS imaging is one of the most important medical imaging technologies for scanning the prostate to detect the dissimilarity in the images. It is hard to extort the region of interest from the original TRUS prostate image, since the problems with images are low of intensity contrast and inherent with speckle noise. The M3-Filter is applied to remove the speckle noise. Then, the region of interest is extorted using DBSCAN clustering with morphological operators. The twenty two features are extracted from Gray Level Co-occurrence Matrix (GLCM) which is constructed using extracted ROI. Further, QR-ACO feature selection algorithm is adapted to select the optimum features from the constructed features set. This paper proposes Complex-valued Support Vector Machine (C-SVM) for the classification of TRUS prostate cancer images. And also investigates proposed approaches with SVM according to prostate cancer based on the underlying texture contained within the region of interest. Receiver Operating Characteristic (ROC) analysis is used to evaluate the performance of the proposed classifiers. Experimental results demonstrate that the proposed approach gives the best performance compare to SVM.
INVITED COMMENTARY: Rating the Quality of Trials in Systematic Reviews of Physical Therapy Interventions

&NA; Physical therapists seeking to use evidence to guide their practice may have limited time to read research reports. One way to reduce the time required to identify and read about the research that is relevant to a particular clinical question is to read a systematic review that summarizes multiple studies. This paper explains the process that is used to conduct systematic reviews, which includes the establishment of a protocol, comprehensive searching, appraisal of the quality of the included studies, data extraction and metaanalysis, and consideration of the clinical and research implications of the findings. We also consider how the reader of a systematic review can determine whether the review is likely to provide an unbiased (believable) estimate of the treatment effect. A systematic review of randomized trials of a cardiopulmonary physical therapy intervention is used as an example. The issue of appraisal of quality is then discussed further, with a demonstration of how one validated tool for quality appraisal‐‐the PEDro scale‐‐can be used to evaluate a randomized trial in cardiopulmonary physical therapy.
ProMode-Oligomer: Database of Normal Mode Analysis in Dihedral Angle Space for a Full-Atom System of Oligomeric Proteins

The database ProMode-Oligomer (http://promode.socs.waseda.ac.jp/promode_oligomer) was constructed by collecting normal-mode-analysis (NMA) results for oligomeric proteins including protein-protein complexes. As in the ProMode database developed earlier for monomers and individual subunits of oligomers (Bioinformatics vol. 20, pp. 2035-2043, 2004), NMA was performed for a full-atom system using dihedral angles as independent variables, and we re- leased the results (fluctuations of atoms, fluctuations of dihedral angles, correlations between atomic fluctuations, etc.). The vibrating oligomer is visualized by animation in an interactive molecular viewer for each of the 20 lowest-frequency normal modes. In addition, displacement vectors of constituent atoms for each normal mode were decomposed into two characteristic motions in individual subunits, i.e., internal and external (deformation and rigid-body movements of the in- dividual subunits, respectively), and then the mutual movements of the subunits and the movement of atoms around the interface regions were investigated. These results released in ProMode-Oligomer are useful for characterizing oligomeric proteins from a dynamic point of view. The analyses are illustrated with immunoglobulin light- and heavy-chain variable domains bound to lysozyme and to a 12-residue peptide.
Continuous systolic and diastolic blood pressure estimation utilizing long short-term memory network

A novel blood pressure estimation method based on long short-term memory neural network, one of the recurrent neural networks being commonly used nowadays, is proposed in this paper for better chronic diseases monitoring. Along with the neural network, a newly proposed ambulatory blood pressure (ABP) processing technique called Two-stage Zero-order Holding (TZH) algorithm has also been presented in the paper. The proposed methodology has the advantages over traditional blood pressure estimation algorithms which are based on Pulse Transit time (PTT). The paper addresses the effectiveness of the algorithm by computing the Root-Mean-Squared Errors (RMSE) between the BP estimated and the ground truth. Our algorithm shows precise systolic blood pressure and diastolic blood pressure estimation with the average RMSE values in 2.751 mmHg and 1.604 mmHg respectively across the sample used. Experimental results suggest that BP estimation based on LSTM has great potential to be embedded into monitoring system for better accuracy and generalization.
Coronary atherosclerosis distribution and the effect of blood glucose level on operative mortality/morbidity in diabetic patients undergoing coronary artery bypass grafting surgery: a single center experience.

OBJECTIVE The study aim was to investigate the effect of blood glucose level on atherosclerotic lesion distribution and the contribution to the operative mortality/morbidity in diabetic patients who underwent coronary artery bypass grafting (CABG). METHODS Between 1986-2003, a total of 2095 patients with diabetes mellitus underwent CABG. The analysis was carried out retrospectively from the clinical records. The patients were divided into four groups according to the blood glucose levels that were obtained when they first applied to hospital; Group 1 (492 patients with blood glucose < 120 mg/dl), group 2 (1112 patients with blood glucose - 120-200 mg/dl, group 3 (261 patients with blood glucose 201-250 mg/dl) and group 4 (230 patients with blood glucose > 250 mg/dl). One way ANOVA test was used for the statistical analysis of continuous variables and Chi-square test was used for analyzing the categorical variables. RESULTS Emergent operation rate due to acute ischemia gradually increased from Group 1 to Group 4 and reached 6.6% in Group 4 (p=0.005). Operation time and the duration of cardiopulmonary bypass and cross clamp were significantly longer in patients with high blood glucose levels (p<0.05). Necessity for inotropic drug administration postoperatively (p<0.05) and mechanical support (p<0.05) were significantly higher also. The hospital mortality in group 3 was 9.6% and in group 4 was 11.3% (p=0.09). No statistically significant difference was found in terms of morbidity between the groups (p>0.05). The multi-vessel coronary artery disease was more common in groups with high blood glucose level (p<0.05). As the blood glucose level raised, patients were more frequently (p<0.05) confronted with distal left anterior descending artery, middle circumflex artery and right coronary artery lesions. CONCLUSION Uncontrolled blood glucose level not only increased the perioperative complications but also the incidence of middle and distal coronary artery lesions. It is necessary to diagnose and aggressively treat the high blood glucose level especially before the CABG.
Understandable Learning of Privacy Preferences Through Default Personas and Suggestions

As mobile and social networking applications continue to proliferate, they also increasingly rely on the collection of an ever wider range of contextual attributes, location being a prime example. Prior work has shown that people’s privacy preferences when it comes to sharing this information are often complex and that expecting users to spend the time necessary to tediously specify these preferences is unrealistic. Yet research has also shown that users are concerned about their privacy and that adequately capturing their privacy will likely be necessary for some of these mobile and social networking applications to be more broadly adopted. The present article reports on research aimed at reducing user burden through the development of two types of user-oriented machine learning techniques: (1) techniques to automatically generate small numbers of user-understandable privacy profiles (or “personas”) that users can chose from when configuring their privacy settings, (2) techniques to turn user feedback into suggestions for incrementally modifying a user’s existing privacy settings. We study to what extent these techniques, by themselves and in combination, can help users rapidly converge towards their preferred privacy settings in the context of location sharing scenarios where the settings control when and where a user’s location is shared with different groups of recipients (close friends and family, Facebook friends, members of the university community, and advertisers). Our results, which are based on data collected from 27 subjects over a period of 3 weeks, suggest that both types of techniques can significantly help users converge towards their desired privacy settings, with the biggest improvements typically resulting from the use of privacy personas.
HMM-Based Alignment of Inaccurate Transcriptions for Historical Documents

For historical documents, available transcriptions typically are inaccurate when compared with the scanned document images. Not only the position of the words and sentences are unknown, but also the correct image transcription may not be matched exactly. An error-tolerant alignment is needed to make the document images amenable to browsing and searching in digital libraries. In this paper, we propose a novel multi-pass alignment method based on Hidden Markov Models (HMM) that combines text line recognition, string alignment, and keyword spotting to cope with word substitutions, deletions, and insertions in the transcription. In a segmentation-free approach, transcriptions of complete pages are aligned with sequences of text line images. On the Parzival data set, results are reported for several degrees of artificial distortions. Both the accuracy and the efficiency of the proposed system are promising for real-world applications.
Calibration-Free Localizations and Applications on U-Care Cloud

The key technique of the location-based service (LBS) is localization which is a kind of techniques for determining the location of mobile users (MU). One of the most common techniques for the location estimation of mobile users (MU) is the radio frequency (RF) site survey. The main idea of the technique is to build a signal strength model, called a radio map, in the off-line phase and to estimate the location of an MU by finding the best match from the radio map in the on-line phase. However, when signal strength values vary frequently due to the characteristics of environmental dynamics, the radio map will quickly become outdated, and recalibration requires considerable manual effort. A good positioning technique should be able to adapt to a dynamically changing environment. In this paper, the authors describe the design and implementation of a positioning system which can provide low-cost, but highly adaptable and precise positioning in the context of changeable radio environments. Instead of constructing a radio map, the authors use reference points (RP) installed in the localization area to continuously monitor changes in the environment. The authors then employ the minimum mean square error (MMSE) method to estimate the location of the MU. Experimental results show that the average error distance is about 3 meters. The positioning system has been implemented as a subsystem of the U-care project in the Chang Gung Health and Culture Village. From the results of questionnaire made by residents, the authors find that the most satisfaction system in the U-care project is the positioning and rescuing system because it can locate the exact position of a resident in need of emergency. Calibration-Free Localizations and Applications on U-Care Cloud
Influence of Culture Conditions on Expression of the 40-Kilodalton Porin Protein of Vibrio anguillarum Serotype O2

ABSTRACT Vibrio anguillarum serotype O2 strains express a 40-kDa outer membrane porin protein. Immunoblot analysis revealed that antigenic determinants of the V. anguillarum O2 40-kDa porin were conserved within bacterial species of the genusVibrio. The relative amounts of the V. anguillarum O2 40-kDa porin were enhanced by growth of V. anguillarum O2 in CM9 medium containing 5 to 10% sucrose or 0.1 to 0.5 M NaCl. In contrast, the levels of the porin were significantly reduced when cells were grown at 37°C, and a novel 60-kDa protein was also observed. However, the osmolarity or ionic concentration of the growth medium did not influence expression of the 60-kDa protein. Growth in medium containing greater than 0.6 mM EDTA reduced production of the V. anguillarum O2 40-kDa porin and enhanced levels of a novel 19-kDa protein. Thus, expression of the V. anguillarum O2 40-kDa porin was osmoregulated and possibly coregulated by temperature. The N-terminal amino acid sequence of theV. anguillarum O2 40-kDa protein and the effect of environmental factors on the cellular levels of the porin suggested that the V. anguillarum O2 40-kDa porin was functionally similar to the OmpC porin of Escherichia coli. However, pore conductance assays revealed that the V. anguillarum O2 40-kDa porin was a general diffusion porin with a pore size in the range of that of the OmpF porin of E. coli.
Bloom Filter Based Inter-Domain Name Resolution: A Feasibility Study

The enormous size of the information space raises significant concerns regarding the scalability of name resolution in Information-Centric Networks, especially at a global scale. Recently, the use of Bloom filters has been proposed as a means to achieve a more compact representation of name resolution state. However, little attention has been paid to the expected performance or even the feasibility of such an approach. In this paper, we aim to fill this gap, presenting a feasibility study and performance analysis of Bloom filter based route-by-name inter-domain name resolution schemes. We propose a methodology for assessing the memory and processing resource requirements of the considered schemes and apply it on top of the inter-domain topology. Our investigation reveals that the skewed distribution of state across the inter-network results in a hard to balance trade-off between memory and processing resource requirements. We show that hardly any Bloom filter configuration i.e., size and bits-per-element, is able to reduce both types of resource requirements for all Autonomous Systems (ASes) in the network, while lowering resource requirements at one area of the inter-domain topology inflates resource requirements at another. Detailed simulations further show that the direct connection of multiple stub networks to tier-1 ASes, results in a dramatic increase of false positives, questioning the reliability of a BF-based inter-domain name resolution scheme.
Faster Heuristic Search Algorithms for Planning with Uncertainty and Full Feedback

Recent algorithms like RTDP and LAO* combine the strength of Heuristic Search (HS) and Dynamic Programming (DP) methods by exploiting knowledge of the initial state and an admissible heuristic function for producing optimal policies without evaluating the entire space. In this paper, we introduce and analyze three new HS/DP algorithms. A first general algorithm schema that is a simple loop in which 'inconsistent' reachable states (i.e., with residuals greater than a given c) are found and updated until no such states are found, and serves to make explicit the basic idea underlying HS/DP algorithms, leaving other commitments aside. A second algorithm, that builds on the first and adds a labeling mechanism for detecting solved states based on Tarjan's strongly-connected components procedure, which is very competitive with existing approaches. And a third algorithm, that approximates the latter by enforcing the consistency of the value function over the likely' reachable states only, and leads to great time and memory savings, with no much apparent loss in quality, when transitions have probabilities that differ greatly in value.
Facility Location with Matroid or Knapsack Constraints

In the classical k-median problem, we are given a metric space and want to open k centers so as to minimize the sum (over all the vertices) of the distance of each vertex to its nearest open center. In this paper we present the first constant-factor approximation algorithms for two natural generalizations of this problem that handle matroid or knapsack constraints.In the matroid median problem, there is an underlying matroid on the vertices and the set of open centers is constrained to be independent in this matroid. When the matroid is uniform, we recover the k -median problem. Another previously studied special case is the red-blue median problem where we have a partition matroid with two parts. Our algorithm for matroid median is based on rounding a natural linear programming relaxation in two stages, and it relies on a connection to matroid intersection.In the knapsack median problem, centers have weights and the total weight of open centers is constrained to be at most a given capacity. When all weights are uniform, this reduces to the k -median problem. The algorithm for knapsack median is based on a novel LP relaxation that constrains the set of centers that each vertex can get connected to. The rounding procedure uses a two-stage approach similar to that for matroid median.
Adaptable Markov models in industrial planning

A significant number of scientific and economic problems is characterised by a large number of interrelated variables. But with larger variable number, the domain under consideration may grow fast, so that analyses and reasoning become increasingly difficult. Graphical models allow to represent the combined distributions compactly and are suitable for dealing with uncertain and incomplete information. We describe their application to a problem of industrial planning. We also demonstrate how the iterative planning process can be supported by allowing the users to adapt the model using revision and updating operators. Moreover we discuss the problem of inconsistent inputs.
Component‐wise Controllers for Structure‐Preserving Shape Manipulation

Recent shape editing techniques, especially for man‐made models, have gradually shifted focus from maintaining local, low‐level geometric features to preserving structural, high‐level characteristics like symmetry and parallelism. Such new editing goals typically require a pre‐processing shape analysis step to enable subsequent shape editing. Observing that most editing of shapes involves manipulating their constituent components, we introduce component‐wise controllers that are adapted to the component characteristics inferred from shape analysis. The controllers capture the natural degrees of freedom of individual components and thus provide an intuitive user interface for editing. A typical model usually results in a moderate number of controllers, allowing easy establishment of semantic relations among them by automatic shape analysis supplemented with user interaction. We propose a component‐wise propagation algorithm to automatically preserve the established inter‐relations while maintaining the defining characteristics of individual controllers and respecting the user‐specified modeling constraints. We extend these ideas to a hierarchical setup, allowing the user to adjust the tool complexity with respect to the desired modeling complexity. We demonstrate the effectiveness of our technique on a wide range of man‐made models with structural features, often containing multiple connected pieces.