Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fast Hydrogen Sulfide–Releasing Donor Increases the Tumor Response to Radiotherapy

Hydrogen sulfide (H2S) is the last gaseous transmitter identified in mammals, and previous studies have reported disparate conclusions regarding the implication of H2S in cancer progression. In the present study, we hypothesized that sodium hydrosulfide (NaHS), a fast H2S-releasing donor, might interfere with the mitochondrial respiratory chain of tumor cells, increase tumor oxygenation, and potentiate the response to irradiation. Using electron paramagnetic resonance (EPR) oximetry, we found a rapid increase in tumor pO2 after NaHS administration (0.1 mmol/kg) in two human tumor models (breast MDA-MB-231 and cervix SiHa), an effect that was due to a decreased oxygen consumption and an increased tumor perfusion. Tumors irradiated 15 minutes after a single NaHS administration were more sensitive to irradiation compared with those that received irradiation alone (increase in growth delay by 50%). This radiosensitization was due to the oxygen effect, as the increased growth delay was abolished when temporarily clamped tumors were irradiated. In contrast, daily NaHS injection (0.1 mmol/kg/day for 14 days) did not provide any effect on tumor growth in vivo. To understand these paradoxical data, we analyzed the impact of external factors on the cellular response to NaHS. We found that extracellular pH had a dramatic effect on the cell response to NaHS, as the proliferation rate (measured in vitro by BrdU incorporation) was increased at pH = 7.4, but decreased at pH = 6.5. Overall, our study highlights the complex role of environmental components in the response of cancer cells to H2S and suggests a new approach for the use of H2S donors in combination with radiotherapy. Mol Cancer Ther; 15(1); 154–61. ©2015 AACR.
Replicability of SF-36 summary scores by the SF-12 in stroke patients.

BACKGROUND AND PURPOSE The replicability of the physical and mental component summary scores of the Short Form (SF)-36 has been established using the SF-12 in selected patient populations but has yet to be assessed in stroke patients. If the summary scores of the SF-12 are highly correlated with those of the SF-36, the benefits of using a shorter health-status measure may be realized without substantial loss of information or precision. Both self-reported and proxy assessments were evaluated for replicability. METHODS Intraclass correlation coefficients (ICCs) and linear regression were used to assess the ability of the SF-12 physical component summary (PCS-12) scores to predict PCS-36 scores and the SF-12 mental component summary (MCS-12) scores to predict MCS-36 scores. Multivariate regression was used to explore the relationship between SF-12 and SF-36 scores. RESULTS The MCS-12 and PCS-12 scores were strongly correlated with the corresponding SF-36 summary scores for surveys completed by proxy or self-report (ICCs ranged from 0.954 to 0.973). Regression analysis of the proxy assessments indicated that patient age was an important effect modifier in the relationship between MCS-12 and MCS-36 scores. CONCLUSIONS The SF-12 reproduced SF-36 summary scores without substantial loss of information in stroke patients. Accordingly, the SF-12 can be used at the summary score level as a substitute for the SF-36 in stroke survivors capable of self-report. However, the mental health summary scores of proxy assessments are influenced by patient age, thereby limiting the replicability of the SF-36 by the SF-12 under these conditions.
Oncogenic transformation confers a selective susceptibility to the combined suppression of the proteasome and autophagy

The proteasome and the autophagy systems are two evolutionarily conserved mechanisms for degrading intracellular materials. They are functionally coupled and suppression of the proteasome promotes autophagy. Although suppression of the proteasome leads to cell death, suppression of autophagy can be either prodeath or prosurvival. To understand the underlining mechanism of this dichotomy and its potential clinical implications, we treated various transformed and nontransformed human cells with proteasome inhibitors. We found that whether autophagy served a prosurvival role in this scenario was contingent on the cellular oncogenic status. Thus, autophagy suppression enhanced apoptosis induced by proteasome inhibitors in transformed cells, but not in nontransformed cells. Oncogenic transformation enhanced the ability of cells to initiate autophagy in response to stress, reflecting a stronger dependence of transformed cells on autophagy for survival. Indeed, a combined use of bortezomib, the only Food and Drug Administration–approved proteasome inhibitor for clinical use, and chloroquine, which inhibits autophagy by disturbing lysosomal functions, suppressed tumor growth more significantly than either agent alone in a xenograft model. These findings indicate that suppression of both intracellular degradation systems could constitute a novel strategy for enhanced cancer control in a tumor-specific way. [Mol Cancer Ther 2009;8(7):2036–45]
Segmentation of Dual-Axis Swallowing Accelerometry Signals in Healthy Subjects With Analysis of Anthropometric Effects on Duration of Swallowing Activities

Dysphagia (swallowing difficulty) is a serious and debilitating condition that often accompanies stroke, acquired brain injury, and neurodegenerative illnesses. Individuals with dysphagia are prone to aspiration (the entry of foreign material into the airway), which directly increases the risk of serious respiratory consequences such as pneumonia. Swallowing accelerometry is a promising noninvasive tool for the detection of aspiration and the evaluation of swallowing. In this paper, dual-axis accelerometry was implemented since the motion of the hyolaryngeal complex occurs in both anterior-posterior and superior-inferior directions during swallowing. Dual-axis cervical accelerometry signals were acquired from 408 healthy subjects during dry, wet, and wet chin tuck swallowing tasks. The proposed segmentation algorithm is based on the idea of sequential fuzzy partitioning of the signal and is well suited for long signals with nonstationary variance. The algorithm was validated with simulated signals with known swallowing locations and a subset of 295 real swallows manually segmented by an experienced speech language pathologist. In both cases, the algorithm extracted individual swallows with over 90% accuracy. The time duration analysis was carried out with respect to gender, body mass index (BMI), and age. Demographic and anthropometric variables influenced the duration of these segmented signals. Male participants exhibited longer swallows than female participants (p=0.05). Older participants and participants with higher BMIs exhibited swallows with significantly longer (p=0.05) duration than younger participants and those with lower BMIs, respectively.
Sofya: A Flexible Framework for Development of Dynamic Program Analyses for Java Software

Dynamic analysis techniques are well established in the software engineering community as methods for validating, understanding, maintaining, and improving programs. Generally, this class of techniques requires developers to instrument programs to generate events that capture, or observe, relevant features of program execution. Streams of these events are then processed to achieve the goals of the dynamic analysis. The lack of high-level tools for defining program observations, automating their mapping to efficient low- level implementations, and supporting the flexible combination of different event-stream-based processing com ponents hampers the development and evaluation of new dynamic analysis techniques. For example, mapping non-trivial program observations to existing low-level instrumentation facilities is a time-consuming and error-prone process tha t can easily result in poorly performing analyses. In this paper, we present Sofya - a framework that we have developed for building dynamic analysis tools. We describe the architecture of Sofya, and explain how it meets the challenges faced by developers of a wide-range of dynamic analyses. We survey existing dynamic analysis tools to show how they relate to the capabilities of the Sofya framework, and we show howSofya improves on their shortcomings. Finally, to illustrate the flexibility and effectiveness of the framework, we describe our experiences developing several state-of-the-art dynamic analyses using Sofya.
Parallel Time O(log n) Acceptance of . . .

Previously Fortune and Wyllie showed that any language accepted by a determinis-tic Turing machine with space bound s(n)>=log n is accepted in time O(s(n)) by a PRAM. Also, Ruzzo showed in [10] that any language accepted by a nondeterministic auxiliary pushdown machine with space s(n)>= log n and time 2 sCn)) can be accepted in time O(s(n)') by an apparently weaker parallel machine model, the uniform circuit family. In particular, he gave a parallel algorithm for recognition of general context-free languages that requires O(log 2 n) time and /,/6 processors on a uniform circuit family. Although Ruzzo's algorithm requires only O(log n) time on a more powerful parallel model in which concurrent writes are permitted, there seems no way to take advantage of the more limited power of the exclusive-write PRAM to achieve O(log n) time for recognition of an arbitrary context-free language. Our contribution is to show that, if a context-free language is in fact deterministic, then O(log n) time recognition is indeed possible on a PRAM. We give a very simple algorithm to solve this problemmso simple, it might be practical to implement. Its proof, however, is rather complicated. This is perhaps understandable in view of the fact that our result implies Cook's result [2] that a deterministic context-free language can be recognized in O(log 2 n) space and polynomial time on a Turing machine. We also show as a corollary to our main result that a PRAM can simulate a deterministic s(n) space-bounded, 2 n)) time-bounded auxiliary pushdown automaton in parallel time O(s(n)) with 2 (s(")) processors. One further difference between Ruzzo's algorithm and ours may be of greater practical significance: the processor bounds. Ruzzo's algorithm for context-free language recognition seems to require rt 6 processors to achieve O(log 2 n) parallel time on a uniform circuit family. However, if the language is deterministic context-free, our algorithm can recognize it in O(log n) time on only n processors of a PRAM. Moreover, our algorithm can be modified [6] to run on only n log n processors (but in O(log n) time). The form of the paper is as follows" in 2, we describe a well-known parallel algorithm for simulating a deterministic space-bounded Turing machine; the algorithm and its proof serve to prepare the ground for our algorithm for simulating a deterministic push-down automaton (DPDA). In 3, we describe some assumptions we make of the simulated DPDA in …
Extracting Semantic Concept Relations from Wikipedia

Background knowledge as provided by repositories such as WordNet is of critical importance for linking or mapping ontologies and related tasks. Since current repositories are quite limited in their scope and currentness, we investigate how to automatically build up improved repositories by extracting semantic relations (e.g., is-a and part-of relations) from Wikipedia articles. Our approach uses a comprehensive set of semantic patterns, finite state machines and NLP-techniques to process Wikipedia definitions and to identify semantic relations between concepts. Our approach is able to extract multiple relations from a single Wikipedia article. An evaluation for different domains shows the high quality and effectiveness of the proposed approach.
Region of interest analysis using an SPM toolbox

Most functional imaging studies use analyses that look for effects anywhere in the brain. The standard approach is to calculate a statistic relating the experimental effect of interest to the data for each brain voxel. This method has the advantage that it can detect strong effects without apriori constraint on the area that activation will occur. Problems arise when we wish to ask the question whether a particular brain area has been activated: if we know the shape and location of the expected activity, then voxel by voxel approaches have low power, because of the multiple comparisons across voxels. Whole brain analyses usually use image smoothing in order to increase signal to noise; however, the best smoothing filter will depend on the shape of the activation, which may well not be matched by a standard kernel such as a Gaussian. The most direct answer to the question "has this area been activated" is to use a region of interest analysis. Here we define a region, and perform the statistical test on the mean time course of the voxels within the region. The contribution of the voxels may be weighted using the expected shape of the activation. This approach has two advantages. First, we increase power by avoiding the multiple comparison problem. Second, if we are correct about the shape and location of the region, the process of taking the mean is equivalent to using the best smoothing kernel to recover the signal. This method has proved very powerful in analysing the activation in well defined regions. For example, Kanwisher et al have used screening tasks and voxel by voxel statistics to define regions of interest, and used these regions to investigate the nature of the original activation in further experiments of visual analysis of faces, scenes, objects and body parts. We have implemented a toolbox called "MarsBar" for region of interest analysis within the SPM99 software package (available for free download from http://www.mrc-cbu.cam.ac.uk/Imaging/marsbar.html). The user can define regions using activations from previous SPM analyses, binary or weighted images, or simple shapes (boxes or spheres). Regions can be combined using a full range of algebra to give new regions. Functions include overlap between regions (logical and) or combination (logical or). The software can then extract raw or filtered time courses from the region for futher analysis outside SPM. It can also use new or previous SPM analysis files to analyze the regional time course. t or F statistics for multiple regions can be computed, and the results plotted using the SPM graphical interface. We are currently working on estimation of percentage signal change for the region data. Region of interest analyses are likely to become more important as prior hypotheses about the location of activation become more specific. This may have many advantages in terms of statistical power and the ease of interpretation of neuroimaging data. We hope that this toolbox will make such analyses simpler to implement.
A retrospective review of the outcome of plasma exchange and aggressive medical therapy in antibody mediated rejection of renal allografts: A single center experience

Antibody‐mediated rejection (AMR) has been recognized as a major cause of renal allograft loss. Protocols using plasma exchange (PE) to reverse rejection have mixed results. Methods: A retrospective chart review was performed to determine the clinical response to PE inpatients with AMR of renal allograft. A good response to treatment was defined as a decline in serum creatinine (SCr) to within 25% above the prerejection value or discontinuation of dialysis with a SCr <2 mg/dl within 3 months of discharge from the hospital and disappearance of donor‐specific alloantibodies (DSA). Results: Twenty‐two patients, treated with PE for biopsy proven AMR with or without acute‐cellular rejection (ACR), were included in the study. Sixty‐four percent of patients had concurrent AMR and ACR. Fifty‐two percent of all patients had a good response to antirejection therapy, whereas 63% of patients with only AMR and 46% of patients with both AMR and ACR had a good response. Good response to PE did not correlate with the number of plasma volumes exchanged (P = 0.09), but correlated with a shorter period from transplantation to the rejection episode (P = 0.002). Conclusion: Only a shorter interval between transplantation and the acute rejection episode correlated with a good response to PE. J. Clin. Apheresis, 2008. © 2008 Wiley‐Liss, Inc.
Generalized optimal sub-pattern assignment metric

This paper presents the generalized optimal sub-pattern assignment (GOSPA) metric on the space of finite sets of targets. Compared to the well-established optimal sub-pattern assignment (OSPA) metric, GOSPA is not normalised by the cardinality of the largest set and it penalizes cardinality errors differently, which enables us to express it as an optimisation over assignments instead of permutations. An important consequence of this is that GOSPA allows us to penalize localization errors for detected targets and the errors due to missed and false targets, as indicated by traditional multiple target tracking (MTT) performance measures, in a sound manner. In addition, we extend the GOSPA metric to the space of random finite sets, which is important to evaluate MTT algorithms via simulations in a rigorous way.
Plasmodesmal-Associated Protein Kinase in Tobacco and Arabidopsis Recognizes a Subset of Non-Cell-Autonomous Proteinsw⃞

Cell-to-cell communication in plants involves the trafficking of macromolecules through specialized intercellular organelles, termed plasmodesmata. This exchange of proteins and RNA is likely regulated, and a role for protein phosphorylation has been implicated, but specific components remain to be identified. Here, we describe the molecular characterization of a plasmodesmal-associated protein kinase (PAPK). A 34-kD protein, isolated from a plasmodesmal preparation, exhibits calcium-independent kinase activity and displays substrate specificity in that it recognizes a subset of viral and endogenous non-cell-autonomous proteins. This PAPK specifically phosphorylates the C-terminal residues of tobacco mosaic virus movement protein (TMV MP); this posttranslational modification has been shown to affect MP function. Molecular analysis of purified protein established that tobacco (Nicotiana tabacum) PAPK is a member of the casein kinase I family. Subcellular localization studies identified a possible Arabidopsis thaliana PAPK homolog, PAPK1. TMV MP and PAPK1 are colocalized within cross-walls in a pattern consistent with targeting to plasmodesmata. Moreover, Arabidopsis PAPK1 also phosphorylates TMV MP in vitro at its C terminus. These results strongly suggest that Arabidopsis PAPK1 is a close homolog of tobacco PAPK. Thus, PAPK1 represents a novel plant protein kinase that is targeted to plasmodesmata and may play a regulatory role in macromolecular trafficking between plant cells.
A performance analysis framework for identifying potential benefits in GPGPU applications

Tuning code for GPGPU and other emerging many-core platforms is a challenge because few models or tools can precisely pinpoint the root cause of performance bottlenecks. In this paper, we present a performance analysis framework that can help shed light on such bottlenecks for GPGPU applications. Although a handful of GPGPU profiling tools exist, most of the traditional tools, unfortunately, simply provide programmers with a variety of measurements and metrics obtained by running applications, and it is often difficult to map these metrics to understand the root causes of slowdowns, much less decide what next optimization step to take to alleviate the bottleneck. In our approach, we first develop an analytical performance model that can precisely predict performance and aims to provide programmer-interpretable metrics. Then, we apply static and dynamic profiling to instantiate our performance model for a particular input code and show how the model can predict the potential performance benefits. We demonstrate our framework on a suite of micro-benchmarks as well as a variety of computations extracted from real codes.
Genetic Targets of Hydrogen Sulfide in Ventilator-Induced Lung Injury – A Microarray Study

Recently, we have shown that inhalation of hydrogen sulfide (H2S) protects against ventilator-induced lung injury (VILI). In the present study, we aimed to determine the underlying molecular mechanisms of H2S-dependent lung protection by analyzing gene expression profiles in mice. C57BL/6 mice were subjected to spontaneous breathing or mechanical ventilation in the absence or presence of H2S (80 parts per million). Gene expression profiles were determined by microarray, sqRT-PCR and Western Blot analyses. The association of Atf3 in protection against VILI was confirmed with a Vivo-Morpholino knockout model. Mechanical ventilation caused a significant lung inflammation and damage that was prevented in the presence of H2S. Mechanical ventilation favoured the expression of genes involved in inflammation, leukocyte activation and chemotaxis. In contrast, ventilation with H2S activated genes involved in extracellular matrix remodelling, angiogenesis, inhibition of apoptosis, and inflammation. Amongst others, H2S administration induced Atf3, an anti-inflammatory and anti-apoptotic regulator. Morpholino mediated reduction of Atf3 resulted in elevated lung injury despite the presence of H2S. In conclusion, lung protection by H2S during mechanical ventilation is associated with down-regulation of genes related to oxidative stress and inflammation and up-regulation of anti-apoptotic and anti-inflammatory genes. Here we show that Atf3 is clearly involved in H2S mediated protection.
Markov decision process-based analysis of rechargeable nodes in wireless sensor networks

Wireless Sensor Networks (WSNs) are popular nowadays for applications ranging from environmental monitoring to object tracking and surveillance. But the sensors which are deployed are energy constrained. The nodes in WSNs deplete of their very limited energy over time. In this work, we consider a network of rechargeable sensors deployed in a random sensing environment. Using Markov Decision Processes (MDP), we analyze the sensor nodes having different levels of energies in the batteries, so as to maximize a generalized system performance in terms of recharge delay and the number of sensor nodes recharged. We have formulated a relationship between the residual energy level of a sensor node and the recharge delay. The simulation results establish the effectiveness of our approach in decreasing the number of passive nodes and the recharge delay in WSNs.
Frontal Conversion and Uniformity in 3D Printing by Photopolymerisation

We investigate the impact of the non-uniform spatio-temporal conversion, intrinsic to photopolymerisation, in the context of light-driven 3D printing of polymers. The polymerisation kinetics of a series of model acrylate and thiol-ene systems, both neat and doped with a light-absorbing dye, is investigated experimentally and analysed according to a descriptive coarse-grained model for photopolymerisation. In particular, we focus on the relative kinetics of polymerisation with those of 3D printing, by comparing the evolution of the position of the conversion profile (zf) to the sequential displacement of the object stage (∆z). After quantifying the characteristic sigmoidal monomer-to-polymer conversion of the various systems, with a combination of patterning experiments, FT-IR mapping, and modelling, we compute representative regimes for which zf is smaller, commensurate with, or larger than ∆z. While non-monotonic conversion can be detrimental to 3D printing, for instance in causing differential shrinkage of inhomogeneity in material properties, we identify opportunities for facile fabrication of modulated materials in the z-direction (i.e., along the illuminated axis). Our simple framework and model, based on directly measured parameters, can thus be employed in photopolymerisation-based 3D printing, both in process optimisation and in the precise design of complex, internally stratified materials by coupling the z-stage displacement and frontal polymerisation kinetics.