Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables

Background Advanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics. Methods Thirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor (“trained” data) were then applied to data for a “new” patient to predict ICU LOS for that individual. Results Factors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p <0.0001. Cross validation in prediction of a “new” patient yielded r2 = 0.200, p <0.0001. The same 8 factors analyzed with ANN yielded a training prediction r2 of 0.535 (p <0.0001) and a cross validation prediction r2 of 0.410, p <0.0001. Two additional predictive algorithms were studied, but they had lower prediction accuracies. Our validated neural network model identified the upper quartile of ICU LOS with an odds ratio of 9.8(p <0.0001). Conclusions ANN demonstrated a 2-fold greater accuracy than ALM in prediction of observed ICU LOS. This greater accuracy would be presumed to result from the capacity of ANN to capture nonlinear effects and higher order interactions. Predictive modeling may be of value in early anticipation of risks of post-operative morbidity and utilization of ICU facilities.
An Adaptive Mechanism for High Efficiency and Fairness in IEEE 802.11 Wireless Lans

Wireless local area networks own their popularity mainly to the robust characteristics of distributed medium access schemes. However, as new user applications diverge from the traditional data-centric model, the introduction of distributed medium access methods that achieve high efficiency in terms of both throughput and fairness becomes of utmost importance. IEEE 802.11 is the most widely used access scheme for wireless networks. In this paper, we address a shortcoming of IEEE 802.11 as indicated by previous studies, namely the inability of the protocol to adapt to different working conditions. We study and evaluate a low complexity mechanism that allows a network employing the IEEE 802.11 protocol to adapt its operating parameters according to the offered load. Simulation studies further document and confirm the high throughput and fairness performance of the proposed scheme.
GAUSSIAN PROCESS FOR ACTIVITY MODELING AND ANOMALY DETECTION

Complex activity modeling and identification of anomaly is one of the most interesting and desired capabilities for automated video behavior analysis. A number of different approaches have been proposed in the past to tackle this problem. There are two main challenges for activity modeling and anomaly detection: 1) most existing approaches require sufficient data and supervision for learning; 2) the most interesting abnormal activities arise rarely and are ambiguous among typical activities, i.e. hard to be precisely defined. In this paper, we propose a novel approach to model complex activities and detect anomalies by using non-parametric Gaussian Process (GP) models in a crowded and complicated traffic scene. In comparison with parametric models such as HMM, GP models are nonparametric and have their advantages. Our GP models exploit implicit spatial-temporal dependence among local activity patterns. The learned GP regression models give a probabilistic prediction of regional activities at next time interval based on observations at present. An anomaly will be detected by comparing the actual observations with the prediction at real time. We verify the effectiveness and robustness of the proposed model on the QMUL Junction Dataset. Furthermore, we provide a publicly available manually labeled ground truth of this data set.
A novel droplet routing algorithm for digital microfluidic biochips

One of the recent areas of research interest is the use of microfluidics for building up biochips, the digital microfluidic biochips (DMFB). This paper deals with a challenging problem related to the design of DMFB. Specifically the design problem considered is related to high performance droplet routing, where each droplet has single source location and single target location. The objectives are (i) minimizing the number of electrodes used in the DMFB, and (ii) minimizing the total routing time of all the droplets or arrival time of a droplet that is the last to arrive at its target(latest arrival time). We propose a simple algorithm for concurrent path allocation to multiple droplets, based on the Soukup's routing algorithm [22], together with the use of stalling, and possible detouring of droplets in cases of contentions. Selection of the droplets is based on their respective source to target Manhattan paths. The empirical results are quite encouraging.
Bayesian prognostic model for genomic discovery in bipolar disorder

Integrative approaches that incorporate multiple experiments have shown a potential application in the discovery of disease-related attributes. This study presents a unique, data-driven, integrative, Bayesian approach to merge gene expression data from various experiments into prognostic models and evaluate them for the discovery of bipolar-related attributes. Two prognostic models were constructed: a singly-structured Bayesian and a Bayesian multi-net model, which differentiated bipolar disease state at a higher level of abstraction. These prognostic models were evaluated to find the most common attributes responsible for the disease and their AUROC, using external cross-validation. The multi-net model achieved an AUROC of 0.907 significantly outperforming the single- structured model with an AUROC of 0.631. The study found six new genes and five chromosomal regions associated with the bipolar state. We anticipate this method and results will be used in the future to integrate information from multiple experiments for the same or related phenotypes of various diseases and also to predict the disease state earlier.
Some notes on analytic torsion of the Rumin complex on contact manifolds

We propose a definition for analytic torsion of the Rumin complex on contact manifolds. This is given by the derivative at zero of a well-chosen combination of zeta functions of a fourth-order modified Rumin Laplacian. The regular value at zero (before differentiation) of this well-chosen combination of zeta functions is shown to be a contact invariant. The variation of our analytic torsion is given as the integral of local terms, together with a global term coming from the null-space of the Laplacian.
The benefits of social capital: close social bonds among female baboons enhance offspring survival

Sociality has evolved in many animal taxa, but primates are unusual because they establish highly differentiated bonds with other group members. Such bonds are particularly pronounced among females in species like baboons, with female philopatry and male dispersal. These relationships seem to confer a number of short-term benefits on females, and sociality enhances infant survival in some populations. However, the long-term consequences of social bonds among adult females have not been well established. Here we provide the first direct evidence that social relationships among female baboons convey fitness benefits. In a group of free-ranging baboons, Papio cynocephalus ursinus, the offspring of females who formed strong social bonds with other females lived significantly longer than the offspring of females who formed weaker social bonds. These survival benefits were independent of maternal dominance rank and number of kin and extended into offspring adulthood. In particular, females who formed stronger bonds with their mothers and adult daughters experienced higher offspring survival rates than females who formed weaker bonds. For females lacking mothers or adult daughters, offspring survival was closely linked to bonds between maternal sisters. These results parallel those from human studies, which show that greater social integration is generally associated with reduced mortality and better physical and mental health, particularly for women.
Incorporation of Tumor Vasculature Targeting Motifs into Moloney Murine Leukemia Virus Env Escort Proteins Enhances Retrovirus Binding and Transduction of Human Endothelial Cells

ABSTRACT Adhesion receptors expressed on the surfaces of tumor-activated endothelial cells provide an advantageous locus for targeting gene therapy vectors to angiogenic tissues and/or tumor vasculature. In this study, we engineered a series of Asn-Gly-Arg (NGR)-containing congeners of the presumptive cell binding motif contained within the ninth type III repeat of fibronectin and displayed these tumor vasculature targeting motifs (TVTMs) within the context of Moloney murine leukemia envelope “escort” proteins. Comparative studies of envelope incorporation into viral particles and evaluation of the cell binding properties of the targeted vectors revealed critical structural features, thus identifying a subset of optimal TVTMs. Utilizing a modified ELISA to evaluate viral binding to target cells, we observed a significant down-regulation of TVTM-virion binding to human endothelial cells following sustained (48-h) exposure to VEGF. Normalized for equivalent titers (106 CFU/ml), as assayed on NIH 3T3 cells, vectors displaying TVTM escort proteins significantly enhanced the transduction efficiency from 12.2 to 37.4% in human KSY-1 endothelial cell cultures (P < 0.001) and from 0.4 to 4.1% in human umbilical vein endothelial cell (HUVEC) cultures (P < 0.001). In summary, these studies utilized an engineering approach to identify a subset of TVTMs that are stably incorporated as envelope “escort” proteins into retroviral vectors and that, by functioning to improve the binding efficiency and transduction of both HUVEC and KSY1 endothelial cells, may have therapeutic potential for targeting gene delivery to the tumor-associated vasculature.
Degree of local cooperation and its implication on global utility

In a cooperative multi-agent system that is situated in an evolving environment, agents need to dynamically adjust their negotiation attitudes towards different agents in order to achieve optimal system performance. In this paper, we construct a statistical model for a small cooperative multi-linked negotiation system. It presents the relationship between the environment, the level of local cooperation and the global system performance in a formal and clear way that allows us to explain system behavior and predict system performance. The analysis results in a set of design equations that can be used to develop distributed mechanisms that optimize the performance of the system dynamically. It helps us more concretely understand the important issue of distraction and provides us with the local attitude parameter to handle distraction effectively. This research demonstrates that sophisticated probabilistic modelling can be used to understand the behaviors of a system with complex agent interactions, and provide guidelines for the development of effective distributed control mechanisms.
Real-time Musical Applications on an Experimental Operating System for Multi-Core Processors

A natural approach to increasing the performance of musical applications is to exploit their inherent parallel structure on general-purpose multi-core architectures. In this paper, we discuss opportunities for exploiting parallelism in audio DSP graphs as well as within select audio processing components. We describeTessellation OS, an experimental operating system structured around resource distribution, performance isolation, and QoS guarantees, andLithe, a user-level runtime framework that enables construction of composable, application-specific schedulers. We present the design and implementation of a realtime parallelmusical application on topof Tessellation OS and conclude with some preliminary experimental results.
Learning transition models of biological regulatory and signaling networks from noisy data

In this paper, we present an extended 2-step probabilistic LGTS (PLGTS) transition system which aims to identify the network structure and stochastic nature of biological processes using time series data. This work is a step towards system identification in a noisy environment using transition systems. Here, the noise implies noise in transitions between states in the observed data. Interestingly, noise in the data helps in assisting system identification. Experimental results on synthetic data show that noise actually helps in understanding the system dynamics as well as constraining the solution space; thus helping to identify the most probable network structure for a given data set.
Inverse Procedural Modeling by Automatic Generation of L‐systems

We present an important step towards the solution of the problem of inverse procedural modeling by generating parametric context‐free L‐systems that represent an input 2D model. The L‐system rules efficiently code the regular structures and the parameters represent the properties of the structure transformations. The algorithm takes as input a 2D vector image that is composed of atomic elements, such as curves and poly‐lines. Similar elements are recognized and assigned terminal symbols of an L‐system alphabet. The terminal symbols' position and orientation are pair‐wise compared and the transformations are stored as points in multiple 4D transformation spaces. By careful analysis of the clusters in the transformation spaces, we detect sequences of elements and code them as L‐system rules. The coded elements are then removed from the clusters, the clusters are updated, and then the analysis attempts to code groups of elements in (hierarchies) the same way. The analysis ends with a single group of elements that is coded as an L‐system axiom. We recognize and code branching sequences of linearly translated, scaled, and rotated elements and their hierarchies. The L‐system not only represents the input image, but it can also be used for various editing operations. By changing the L‐system parameters, the image can be randomized, symmetrized, and groups of elements and regular structures can be edited. By changing the terminal and non‐terminal symbols, elements or groups of elements can be replaced.
A novel approach to proactive human-robot cooperation

This paper introduces the concept of proactive execution of robot tasks in the context of human-robot cooperation with uncertain knowledge of the human's intentions. We present a system architecture that defines the necessary modules of the robot and their interactions with each other. The two key modules are the intention recognition that determines the human user's intentions and the planner that executes the appropriate tasks based on those intentions. We show how planning conflicts due to the uncertainty of the intention information are resolved by proactive execution of the corresponding task that optimally reduces the system's uncertainly. Finally, we present an algorithm for selecting this task and suggest a benchmark scenario.
Colorectal Cancer Chemoprevention: Is This the Future of Colorectal Cancer Prevention?

Colorectal cancer (CRC) is presently one of the most common causes of cancer-related death in our setting and affects a great number of people each year. Screening strategies are commonly used but they do not seem enough to avoid CRC development or prevent completely its mortality. Because of this fact other prevention strategies have gained interest in recent years. Chemoprevention seems to be an attractive option in this setting and several drugs have been studied in this field. This review is focused on salicylates, nonsteroidal anti-inflammatory drugs (NSAIDs) and cycloxygenase-2 inhibitors (COXIBs), whose mechanism of action could be directly related to colon cancer chemoprevention.
Predicate Oriented Pattern Analysis for Biomedical Knowledge Discovery

In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF, OWL). With the increasing number of semi-structured data coming into the biomedical community, data integration and knowledge discovery from heterogeneous domains become important research problem. In the application level, detection of related concepts among medical ontologies is an important goal of life science research. It is more crucial to figure out how different concepts are related within a single ontology or across multiple ontologies by analysing predicates in different knowledge bases. However, the world today is one of information explosion, and it is extremely difficult for biomedical researchers to find existing or potential predicates to perform linking among cross domain concepts without any support from schema pattern analysis. Therefore, there is a need for a mechanism to do predicate oriented pattern analysis to partition heterogeneous ontologies into closer small topics and do query generation to discover cross domain knowledge from each topic. In this paper, we present such a model that predicates oriented pattern analysis based on their close relationship and generates a similarity matrix. Based on this similarity matrix, we apply an innovated unsupervised learning algorithm to partition large data sets into smaller and closer topics and generate meaningful queries to fully discover knowledge over a set of interlinked data sources. We have implemented a prototype system named BmQGen and evaluate the proposed model with colorectal surgical cohort from the Mayo Clinic.
Otherworld: giving applications a chance to survive OS kernel crashes

The default behavior of all commodity operating systems today is to restart the system when a critical error is encountered in the kernel. This terminates all running applications with an attendant loss of "work in progress" that is nonpersistent. Otherworld is a mechanism that microreboots the operating system kernel when a critical error is encountered in the kernel, and it does so without clobbering the state of the running applications. After the kernel microreboot, Otherworld attempts to resurrect the applications that were running at the time of failure. It does so by restoring the application memory spaces, open files and other resources. In the default case it then continues executing the processes from the point at which they were interrupted by the failure. Optionally, applications can have user-level recovery procedures registered with the kernel, in which case Otherworld passes control to these procedures after having restored their process state. Recovery procedures might check the integrity of application data and restore resources Otherworld was not able to restore. We implemented Otherworld in Linux, but we believe that the technique can be applied to all commodity operating systems. In an extensive set of experiments on real-world applications (MySQL, Apache/PHP, Joe, vi), we show that Otherworld is capable of successfully microrebooting the kernel and restoring the applications in over 97% of the cases. In the default case, Otherworld adds zero overhead to normal execution. In an enhanced mode, Otherworld can provide extra application memory protection with overhead of between 4% and 12%.
Distinct pharmacological properties of second generation HDAC inhibitors with the benzamide or hydroxamate head group

Advanced second generation inhibitors of histone deacetylases (HDAC) are currently used in clinical development. This study aimed at comparing the pharmacological properties of selected second generation HDAC inhibitors with the hydroxamate and benzamide head group, namely SAHA, LAQ824/LBH589, CI994, MS275 and MGCD0103. In biochemical assays using recombinant HDAC1, 3, 6 and 8 isoenzymes, SAHA and LAQ824/LBH589 behave as quite unselective HDAC inhibitors. In contrast, the benzamides CI994, MS275 and MGCD0103 are more selective, potent inhibitors of at least HDAC1 and HDAC3. All HDAC inhibitors induce histone H3 hyperacetylation, correlating with inhibition of proliferation, induction of cell differentiation and apoptosis. A broad cytotoxicity is seen across cell lines from different tumor entities with LAQ824/LBH589 being the most potent agents. The apoptosis inducing activity is evident in arrested and proliferating RKO colon cancer cells with inducible, heterologous p21waf1 expression, indicative for a cell‐cycle independent mode‐of‐action. Differentiation of MDA‐MB468 breast cancer cells is induced by benzamide and hydroxamate analogs. The reversibility of drug action was evaluated by pulse treatment of A549 lung cancer cells. Whereas paclitaxel induced irreversible cell cycle alterations already after 6 hr treatment, HDAC inhibitor action was retarded and irreversible after >16 hr treatment. Interestingly, pulse treatment was equally effective as continous treatment. Finally, the efficacy of LAQ824, SAHA and MS275 in A549 nude mice xenografts was comparable to that of paclitaxel at well tolerated doses. We conclude that despite a different HDAC isoenzyme inhibition profile, hydroxamate and benzamide analogs as studied display similar cellular profiles. © 2007 Wiley‐Liss, Inc.
Inhibition of DNA synthesis in SV3T3 cultures by isolated 3T3 plasma membranes

3T3 plasma membranes were added to subconfluent cultures of SV3T3 cells in the presence of fusogens. If this protocol results in the introduction into the SV3T3 cell membrane of 3T3 plasma membrane components responsible for density-dependent inhibition of growth, then the SV3T3 cell cultures would be expected to show decreased rates of DNA synthesis as they approach confluence. Results of these experiments indicate that rates of DNA synthesis in SV3T3 cultures so treated were as much as 63% less than in untreated controls. This effect could not be attributed to the fusogens or to the 3T3 plasma membranes alone. This growth-inhibitory effect is specific for 3T3 membranes and is not observed when SV3T3 plasma membranes are fused with SV3T3 cell cultures. These data support the hypothesis that one aspect of the loss of density-dependent inhibition of growth in SV3T3 cells is a deletion or alteration in plasma membrane components and, further, that density- dependent inhibition of growth can be in part restored to SV3T3 cell cultures by fusing the cells with 3T3 plasma membranes.