Miracle-dz/UniFAR-FTUs huggingface dataset:
CEBPD Reverses RB/E2F1-Mediated Gene Repression and Participates in HMDB-Induced Apoptosis of Cancer Cells

Purpose: Recent evidence indicates that a tumor suppressor gene CEBPD (CCAAT/enhancer-binding protein delta) is downregulated in many cancers including cervical cancer, which provides a therapeutic potential associated with its reactivation. However, little is known for CEBPD activators and the effect of reactivation of CEBPD transcription upon anticancer drug treatment. In this study, we identified a novel CEBPD activator, 1-(2-hydroxy-5-methylphenyl)-3-phenyl-1,3-propanedione (HMDB). The purpose of this study is to characterize the mechanism of HMDB-induced CEBPD activation and its potential effect in cancer therapy. Experimental Design: Methylation-specific PCR assay, reporter assay, and chromatin immunoprecipitation (ChIP) assay were performed to dissect the signaling pathway of HMDB-induced CEBPD transcription. Furthermore, a consequence of HMDB-induced CEBPD expression was linked with E2F1 and retinoblastoma (RB), which discloses the scenario of CEBPD, E2F1, and RB bindings and transcriptional regulation on the promoters of proapoptotic genes, PPARG2 and GADD153. Finally, the anticancer effect of HMDB was examined in xenograft mice. Results: We demonstrate that CEBPD plays an essential role in HMDB-mediated apoptosis of cancer cells. HMDB up-regulates CEBPD transcription through the p38/CREB pathway, thus leading to transcriptional activation of PPARG2 and GADD153. Furthermore, increased level of CEBPD attenuates E2F1-induced cancer cell proliferation and partially rescues RB/E2F1-mediated repression of PPARG2 and GADD153 transcription. Moreover, HMDB treatment attenuates the growth of A431 xenografts in severe combined immunodeficient mice mice. Conclusions: These results clearly demonstrate that HMDB kills cancer cells through activation of CEBPD pathways and suggest that HMDB can serve as a superior chemotherapeutic agent with limited potential for adverse side effects. Clin Cancer Res; 16(23); 5770–80. ©2010 AACR.
Covolumes of uniform lattices acting on polyhedral complexes

Let X be a polyhedral complex with finitely many isometry classes of links. We establish a restriction on the covolumes of uniform lattices acting on X. When X is two‐dimensional and has all links isometric to either a complete bipartite graph or the building for a Chevalley group of rank 2 over a field of prime order, we obtain further restrictions on covolumes.
A 2D Texture Image Retrieval Technique based on Texture Energy Filters

In this paper, a database of texture images is analyzed by the Laws’ texture energy measure technique. The Laws’ technique has been used in a number of fields, such as com puter vision and pattern recognition. Although most applications use Laws’ convolution filters with sizes of 3× 3 and 5× 5 for extracting image features, our experimental system uses extended resolutio ns of filters with sizes of 7×7 and 9×9. The use of multiple resolutions of filters makes it possible to extra c various image features from 2D texture images of a database. In our study, the extracted image features wer e selected based on statistical analysis, and the analysis results were used for determining which resolutio ns f features were dominant to classify texture images. A texture energy computation technique was impleme nted for an experimental texture image retrieval system. Our preliminary experiments showed that the system can classify certain texture images based on texture features, and also it can retrieve texture images re flecting texture pattern similarities.
Expression of Abelson Interactor 1 (Abi1) Correlates with Inflammation, KRAS Mutation and Adenomatous Change during Colonic Carcinogenesis

Background Abelson interactor 1 (Abi1) is an important regulator of actin dynamics during cytoskeletal reorganization. In this study, our aim was to investigate the expression of Abi1 in colonic mucosa with and without inflammation, colonic polyps, colorectal carcinomas (CRC) and metastases as well as in CRC cell lines with respect to BRAF/KRAS mutation status and to find out whether introduction of KRAS mutation or stimulation with TNFalpha enhances Abi1 protein expression in CRC cells. Methodology/Principal Findings We immunohistochemically analyzed Abi1 protein expression in 126 tissue specimens from 95 patients and in 5 colorectal carcinoma cell lines with different mutation status by western immunoblotting. We found that Abi1 expression correlated positively with KRAS, but not BRAF mutation status in the examined tissue samples. Furthermore, Abi1 is overexpressed in inflammatory mucosa, sessile serrated polyps and adenomas, tubular adenomas, invasive CRC and CRC metastasis when compared to healthy mucosa and BRAF-mutated as well as KRAS wild-type hyperplastic polyps. Abi1 expression in carcinoma was independent of microsatellite stability of the tumor. Abi1 protein expression correlated with KRAS mutation in the analyzed CRC cell lines, and upregulation of Abi1 could be induced by TNFalpha treatment as well as transfection of wild-type CRC cells with mutant KRAS. The overexpression of Abi1 could be abolished by treatment with the PI3K-inhibitor Wortmannin after KRAS transfection. Conclusions/Significance Our results support a role for Abi1 as a downstream target of inflammatory response and adenomatous change as well as oncogenic KRAS mutation via PI3K, but not BRAF activation. Furthermore, they highlight a possible role for Abi1 as a marker for early KRAS mutation in hyperplastic polyps. Since the protein is a key player in actin dynamics, our data encourages further studies concerning the exact role of Abi1 in actin reorganization upon enhanced KRAS/PI3K signalling during colonic tumorigenesis.
Involvement of the Arcuate Nucleus of the Hypothalamus in Interleukin-1-Induced Anorexia

Cytokine-mediated anorexia is a component of “sickness behavior” and presents a significant obstacle in the treatment of chronic illnesses. We hypothesized an involvement of the hypothalamic arcuate nucleus (ARH) in mediating the anorexic effects of a systemic interleukin-1 (IL-1) challenge based on its content of peptidergic neurons involved in feeding, its expression of IL-1 receptors and its sensitivity to systemic IL-1. IL-1 (6 μg/kg, i.v.) was found to induce Fos expression in both pro-opiomelanocortin- and neuropeptide Y-expressing neurons in and around the ARH. Contrary to expectations, rats that had sustained lesions of the arcuate nucleus, produced by neonatal monosodium glutamate treatment, displayed a more pronounced suppression (by 25%) of food intake than nonlesioned controls when treated with IL-1 after a 20 hr fast. To confirm and further characterize this unexpected result, a second ablation method was used in a similar paradigm. Animals bearing knife cuts designed to sever major ARH projections displayed an even more accentuated loss of appetite (by 60%, relative to controls) in response to systemic IL-1. This effect exhibited at least some degree of specificity, because the knife cuts did not alter either IL-1 effects on another centrally mediated acute phase response (fever) or the anorexia produced by an alternate agent, fenfluramine. These results fail to support the hypothesized ARH mediation of IL-1-induced anorexia and may suggest rather that the net output of this cell group may serve normally to restrain cytokine-induced reductions in food intake.
Answering and Questioning for Machine Reading

Machine reading can be defined as the automatic understanding of text. One way in which human understanding of text has been gauged is to measure the ability to answer questions pertaining to the text. In this paper, we present a brief study designed to explore how a natural language processing component for the recognition of textual entailment bears on the problem of answering questions in a basic, elementary school reader. An alternative way of testing human understanding is to assess one's ability to ask sensible questions for a given text. We survey current computational systems that are capable of generating questions automatically, and suggest that understanding must comprise not only a grasp of semantic equivalence, but also an assessment of the importance of information conveyed by the test. We suggest that this observation should contribute in the design of an overall evaluation methodology for machine reading.
Improving Second-Order DPA Attacks with New Modeled Power Leakages

Higher Order Differential Power Analysis (HO-DPA) has been introduced for more than ten years. Various combining functions are applied to attack the masked AES implementation, but finding an appropriate combining function is still a big issue. We study the weakness of absolute difference method when using multi-bit variables to mount an attack. In this paper, an optimized method is proposed to improve its efficiency. Simulation results based on different combining functions show that the success rate of our method is twice as high as that of normal absolute difference and sum combining function. Our idea is also confirmed by physical traces measured on a FPGA platform.
Improving Educational Assessment: Multivariate Statistical Methods

This review presents two case studies that illustrate how multivariate statistical modeling applies to the specific goal of improving educational assessment. The first case study involves the development of a new large-scale English language proficiency assessment system (called the English Language Proficiency Assessment for the 21st Century; ELPA21). The second application concerns efforts to quantify student progress in learning using conditional growth models, a topic of current debate about assessment policy. A popular measure, Student Growth Percentile (SGP), is explored through the lens of multivariate statistical analysis. It is concluded that collaboration between researchers and practice stakeholders can improve assessments that benefit student learning.
Research on Intracranial Atherosclerosis from the East and West: Why Are the Results Different?

Intracranial atherosclerosis (ICAS) is a major cause of stroke worldwide and is more common in Asians than Caucasians. The study results from the East and West are generally similar, but notable differences exist. For example, studies from the East have reported that ICAS is associated with young age, whereas ICAS seems to be associated with old age in the West. Studies from the East have strongly suggested that mild ICAS associated with branch occlusion is one of the main causes of single subcortical infarction, whereas this aspect has not been considered in stroke classification systems developed in the West. While clopidogrel is commonly used in patients with large artery disease in the West, cilostazol has been more extensively studied and commonly used in ICAS patients in the East. A randomized controlled study from the West reported negative results regarding the efficacy of stenting in ICAS patients due largely to a relatively high rate of periprocedural adverse events, whereas research papers from the East have reported a relatively lower rate of complications. Studies to narrow these East-West gaps should be performed, including risk factor studies using homogenous ethnic populations, studies investigating appropriate classification systems, drug trials in different ethnic populations, and rigorous high standard randomized controlled studies on the efficacy of stenting in Eastern populations.
Revealing Context-Specific Conditioned Fear Memories with Full Immersion Virtual Reality

The extinction of conditioned fear is known to be context-specific and is often considered more contextually bound than the fear memory itself (Bouton, 2004). Yet, recent findings in rodents have challenged the notion that contextual fear retention is initially generalized. The context-specificity of a cued fear memory to the learning context has not been addressed in the human literature largely due to limitations in methodology. Here we adapt a novel technology to test the context-specificity of cued fear conditioning using full immersion 3-D virtual reality (VR). During acquisition training, healthy participants navigated through virtual environments containing dynamic snake and spider conditioned stimuli (CSs), one of which was paired with electrical wrist stimulation. During a 24-h delayed retention test, one group returned to the same context as acquisition training whereas another group experienced the CSs in a novel context. Unconditioned stimulus expectancy ratings were assayed on-line during fear acquisition as an index of contingency awareness. Skin conductance responses time-locked to CS onset were the dependent measure of cued fear, and skin conductance levels during the interstimulus interval were an index of context fear. Findings indicate that early in acquisition training, participants express contingency awareness as well as differential contextual fear, whereas differential cued fear emerged later in acquisition. During the retention test, differential cued fear retention was enhanced in the group who returned to the same context as acquisition training relative to the context shift group. The results extend recent rodent work to illustrate differences in cued and context fear acquisition and the contextual specificity of recent fear memories. Findings support the use of full immersion VR as a novel tool in cognitive neuroscience to bridge rodent models of contextual phenomena underlying human clinical disorders.
Parameterizing the winner determination problem for combinatorial auctions

Combinatorial auctions (CAs) have been studied by the multiagent systems community for some time, since these auctions are an effective mechanism for resource allocation when agents are self-interested. One challenge, however, is that the winner-determination problem (WDP) for combinatorial auctions is NP-hard in the general case. However, there are ways to leverage meaningful structure in the auction so as to achieve a polynomial-time algorithm for the WDP. In this paper, using the formal scope of parameterized complexity theory, we systematically investigate alternative parameterizations of the bids made by the agents (i.e. the input to the WDP for combinatorial auctions) and are able to determine when a parameterization reduces the complexity of the WDP (fixed-parameter tractable), and when a particular parameterization results in the WDP remaining hard (fixed-parameter intractable). Our results are relevant to auction designers since they provide information as to what types of bidding-restrictions are effective for simplifying the winner determination problem, and which would simply limit the expressiveness of the agents while not providing any additional computational gains.
Energy-Aware Massively Distributed Cloud Facilities: The DISCOVERY Initiative

Instead of the current trend consisting of building larger and larger data centers (DCs) in few strategic locations, the DISCOVERY initiative proposes to leverage any network point of presences (PoP, i.e., a small or medium-sized network center) available through the Internet. The key idea is to demonstrate a widely distributed Cloud platform that can better match the geographical dispersal of users and of renewable energy sources. This involves radical changes in the way resources are managed, but leveraging computing resources around the end-users will enable to deliver a new generation of highly efficient and sustainable Utility Computing (UC) platforms, thus providing a strong alternative to the actual Cloud model based on mega DCs (i.e., DCs composed of tens of thousands resources). This poster will present the DISCOVERY initiative efforts towards achieving energy-aware massively distributed cloud facilities.
Image barcodes

A Visually significant two-dimensional barcode (VSB) developed by Shaked et. al. is a method used to design an information carrying two-dimensional barcode, which has the appearance of a given graphical entity such as a company logo. The encoding and decoding of information using the VSB, uses a base image with very few graylevels (typically only two). This typically requires the image histogram to be bi-modal. For continuous-tone images such as digital photographs of individuals, the representation of tone or "shades of gray" is not only important to obtain a pleasing rendition of the face, but in most cases, the VSB renders these images unrecognizable due to its inability to represent true gray-tone variations. This paper extends the concept of a VSB to an image bar code (IBC). We enable the encoding and subsequent decoding of information embedded in the hardcopy version of continuous-tone base-images such as those acquired with a digital camera. The encoding-decoding process is modeled by robust data transmission through a noisy print-scan channel that is explicitly modeled. The IBC supports a high information capacity that differentiates it from common hardcopy watermarks. The reason for the improved image quality over the VSB is a joint encoding/halftoning strategy based on a modified version of block error diffusion. Encoder stability, image quality vs. information capacity tradeoffs and decoding issues with and without explicit knowledge of the base-image are discussed.
Augmented Reality for Depth Cues in Monocular Minimally Invasive Surgery

One of the major challenges in Minimally Invasive Surgery (MIS) such as laparoscopy is the lack of depth perception. In recent years, laparoscopic scene tracking and surface reconstruction has been a focus of investigation to provide rich additional information to aid the surgical process and compensate for the depth perception issue. However, robust 3D surface reconstruction and augmented reality with depth perception on the reconstructed scene are yet to be reported. This paper presents our work in this area. First, we adopt a state-of-the-art visual simultaneous localization and mapping (SLAM) framework - ORB-SLAM - and extend the algorithm for use in MIS scenes for reliable endoscopic camera tracking and salient point mapping. We then develop a robust global 3D surface reconstruction frame- work based on the sparse point clouds extracted from the SLAM framework. Our approach is to combine an outlier removal filter within a Moving Least Squares smoothing algorithm and then employ Poisson surface reconstruction to obtain smooth surfaces from the unstructured sparse point cloud. Our proposed method has been quantitatively evaluated compared with ground-truth camera trajectories and the organ model surface we used to render the synthetic simulation videos. In vivo laparoscopic videos used in the tests have demonstrated the robustness and accuracy of our proposed framework on both camera tracking and surface reconstruction, illustrating the potential of our algorithm for depth augmentation and depth-corrected augmented reality in MIS with monocular endoscopes.
Classification of Tumor Samples from Expression Data Using Decision Trunks

We present a novel machine learning approach for the classification of cancer samples using expression data. We refer to the method as “decision trunks,” since it is loosely based on decision trees, but contains several modifications designed to achieve an algorithm that: (1) produces smaller and more easily interpretable classifiers than decision trees; (2) is more robust in varying application scenarios; and (3) achieves higher classification accuracy. The decision trunk algorithm has been implemented and tested on 26 classification tasks, covering a wide range of cancer forms, experimental methods, and classification scenarios. This comprehensive evaluation indicates that the proposed algorithm performs at least as well as the current state of the art algorithms in terms of accuracy, while producing classifiers that include on average only 2–3 markers. We suggest that the resulting decision trunks have clear advantages over other classifiers due to their transparency, interpretability, and their correspondence with human decision-making and clinical testing practices.
CUPULIFORMIS establishes lateral organ boundaries in Antirrhinum

cupuliformis mutants are defective in shoot apical meristem formation, but cup plants overcome this early barrier to development to reach maturity. CUP encodes a NAC-domain transcription factor, homologous to the Petunia NAM and Arabidopsis CUC proteins. The phenotype of cup mutants differs from those of nam and cuc1 cuc2 in that dramatic organ fusion is observed throughout development. In addition to cotyledon and floral organ fusions, severe lateral organ fusion is found in leaves and inflorescences, and the apical meristem becomes highly fasciated. These features reveal a role for CUP in the establishment of all above ground organ boundaries. Consistent with this function, CUP is expressed at the boundaries of all lateral organs and meristems. It is not currently known how NAC-domain genes act to establish organ boundaries. Here, we show that CUP directly interacts with a TCP-domain transcription factor. Members of the TCP-domain family have previously been shown to regulate organ outgrowth. Our results suggest a model for the establishment of organ boundaries based on the localised expression of NAC-domain and TCP-domain factors.
Energy efficiency in cloud-based MapReduce applications through better performance estimation

An important issue for efficient execution of MapReduce jobs on a cloud platform is selecting the best fitting virtual machine (VM) configuration(s) among the miscellany of choices that cloud providers offer. Wise selection of VM configurations can lead to better performance, cost and energy consumption. Therefore, it is crucial to explore the available configurations and choose the best one for each given MapReduce application. Executing the given application on all the configurations for comparison is a costly, time and energy consuming process. An alternative is to run the application on a subset of configurations (sample configurations) and estimate its performance on other configurations based on the obtained values on those sample configurations. We show that the choice of these sample configurations highly affects accuracy of later estimations. Our Smart Configuration Selection (SCS) scheme chooses better representatives from among all configurations by once-off analysis of given performance figures of the benchmarks so as to increase the accuracy of estimations of missing values, and consequently, to more accurately choose the configuration providing the highest performance. The results show that the SCS choice of sample configurations is very close to the best choice, and can reduce estimation error to 7.11% from the original 16.02% of random configuration selection. Furthermore, this more accurate performance estimation saves 24.3% energy on average.
Determination of Fetal State from Cardiotocogram Using LS-SVM with Particle Swarm Optimization and Binary Decision Tree

We use least squares support vector machine (LS-SVM) utilizing a binary decision tree for classification of cardiotocogram to determine the fetal state. The parameters of LS-SVM are optimized by particle swarm optimization. The robustness of the method is examined by running 10-fold cross-validation. The performance of the method is evaluated in terms of overall classification accuracy. Additionally, receiver operation characteristic analysis and cobweb representation are presented in order to analyze and visualize the performance of the method. Experimental results demonstrate that the proposed method achieves a remarkable classification accuracy rate of 91.62%.