Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Multihop Partition-Based Clustering Routing Protocol for WSNs

Wireless sensor networks (WSNs) have great potential in the application of partition parameter observation; however, the high correlation with applications makes WSNs working in specific environments require specific work patterns. In this article, a hybrid multihop partition-based clustering (HMPBC) routing protocol is proposed, which can fit a specific environment, meet interests and needs, balance network load, and prolong the network lifetime. In HMPBC, both the single-chain structure within cluster and the cluster heads selection depending on residual energy are operated by self-organization, and the region minimum spanning tree structure among clusters is established by the base station. The proposed protocol is simulated in MATLAB with the network model of 200m $\times$ 200m 400 nodes. Simulation results show that HMPBC has 33.31 and 39.70 longer lifetime, smaller fluctuations of average energy consumption of each node per round, and more precise measurement of zone parameters than LEACH-MLOR and the energy efficient hybrid multihop clustering scheme.
Impact of non-synchronous generation on transmission oscillations paths

The large scale penetration of non-synchronous generation has been causing several impacts on the power systems dynamics. The low-frequency oscillations affect the power exchanged along the transmission lines/corridors. This paper uses the Multi-Prony Analysis mode estimation technique to monitor and suggest the dominant oscillation modes which can be useful for wide-area control purposes. Moreover, the oscillation modes are also monitored under gradual cases of non-synchronous generation integration in the system. The methodology is applied to two different test transmission systems: i) the two area system and, ii) the Nordic 32 system. The results illustrate the similarity and differences in the scenarios proposed.
Evaluation of Potential Mechanisms Controlling the Catalase Expression in Breast Cancer Cells

Development of cancer cell resistance against prooxidant drugs limits its potential clinical use. MCF-7 breast cancer cells chronically exposed to ascorbate/menadione became resistant (Resox cells) by increasing mainly catalase activity. Since catalase appears as an anticancer target, the elucidation of mechanisms regulating its expression is an important issue. In MCF-7 and Resox cells, karyotype analysis showed that chromosome 11 is not altered compared to healthy mammary epithelial cells. The genomic gain of catalase locus observed in MCF-7 and Resox cells cannot explain the differential catalase expression. Since ROS cause DNA lesions, the activation of DNA damage signaling pathways may influence catalase expression. However, none of the related proteins (i.e., p53, ChK) was activated in Resox cells compared to MCF-7. The c-abl kinase may lead to catalase protein degradation via posttranslational modifications, but neither ubiquitination nor phosphorylation of catalase was detected after catalase immunoprecipitation. Catalase mRNA levels did not decrease after actinomycin D treatment in both cell lines. DNMT inhibitor (5-aza-2′-deoxycytidine) increased catalase protein level in MCF-7 and its resistance to prooxidant drugs. In line with our previous report, chromatin remodeling appears as the main regulator of catalase expression in breast cancer after chronic exposure to an oxidative stress.
The Interplay between Microscopic and Mesoscopic Structures in Complex Networks

Understanding a complex network's structure holds the key to understanding its function. The physics community has contributed a multitude of methods and analyses to this cross-disciplinary endeavor. Structural features exist on both the microscopic level, resulting from differences between single node properties, and the mesoscopic level resulting from properties shared by groups of nodes. Disentangling the determinants of network structure on these different scales has remained a major, and so far unsolved, challenge. Here we show how multiscale generative probabilistic exponential random graph models combined with efficient, distributive message-passing inference techniques can be used to achieve this separation of scales, leading to improved detection accuracy of latent classes as demonstrated on benchmark problems. It sheds new light on the statistical significance of motif-distributions in neural networks and improves the link-prediction accuracy as exemplified for gene-disease associations in the highly consequential Online Mendelian Inheritance in Man database.
Smart Markers for Watershed-Based Cell Segmentation

Automated cell imaging systems facilitate fast and reliable analysis of biological events at the cellular level. In these systems, the first step is usually cell segmentation that greatly affects the success of the subsequent system steps. On the other hand, similar to other image segmentation problems, cell segmentation is an ill-posed problem that typically necessitates the use of domain-specific knowledge to obtain successful segmentations even by human subjects. The approaches that can incorporate this knowledge into their segmentation algorithms have potential to greatly improve segmentation results. In this work, we propose a new approach for the effective segmentation of live cells from phase contrast microscopy. This approach introduces a new set of “smart markers” for a marker-controlled watershed algorithm, for which the identification of its markers is critical. The proposed approach relies on using domain-specific knowledge, in the form of visual characteristics of the cells, to define the markers. We evaluate our approach on a total of 1,954 cells. The experimental results demonstrate that this approach, which uses the proposed definition of smart markers, is quite effective in identifying better markers compared to its counterparts. This will, in turn, be effective in improving the segmentation performance of a marker-controlled watershed algorithm.
Obliv-C: A Language for Extensible Data-Oblivious Computation

Many techniques for secure or private execution depend on executing programs in a data-oblivious way, where the same instructions execute independent of the private inputs which are kept in encrypted form throughout the computation. Designers of such computations today must either put substantial effort into constructing a circuit representation of their algorithm, or use a high-level language and lose the opportunity to make important optimizations or experiment with protocol variations. We show how extensibility can be improved by judiciously exposing the nature of dataoblivious computation. We introduce a new language that allows application developers to program secure computations without being experts in cryptography, while enabling programmers to create abstractions such as oblivious RAM and width-limited integers, or even new protocols without needing to modify the compiler. This paper explains the key language features that safely enable such extensibility and describes the simple implementation approach we use to ensure security properties are preserved.
Testing of a 32-bit High Performance Embedded Microprocessor

This paper describes the testable design and fault coverage analysis for a 32-bit high performance embedded microprocessor which is compatible with PowerPC750 ISA. In the test structure, the TAP controller do on-chip standard boundary scan test, full scan test for core logic and MBIST test for embedded memories. In full scan test, all scan registers are organized to 32 scan chains, the area overhead is less than 3%, the critical path is less than 4ns and meets timing requirement. The fault coverage analysis and chip test result demonstrate that the test strategy and test structure are effective for our embedded processor.
A Stochastic Model of Optimal Debt Management and Bankruptcy

A problem of optimal debt management is modeled as a noncooperative interaction between a borrower and a pool of lenders, in an infinite time horizon with exponential discount. The yearly income of the borrower is governed by a stochastic process. When the debt-to-income ratio $x(t)$ reaches a given size $x^*$, bankruptcy instantly occurs. The interest rate charged by the risk-neutral lenders is precisely determined in order to compensate for this possible loss of their investment. For a given bankruptcy threshold $x^*$, existence and properties of optimal feedback strategies for the borrower are studied, in a stochastic framework as well as in a limit deterministic setting. The paper also analyzes how the expected total cost to the borrower changes, depending on different values of $x^*$.
Salient object extraction for user-targeted video content association

The increasing amount of videos on the Internet and digital libraries highlights the necessity and importance of interactive video services such as automatically associating additional materials (e.g., advertising logos and relevant selling information) with the video content so as to enrich the viewing experience. Toward this end, this paper presents a novel approach for user-targeted video content association (VCA). In this approach, the salient objects are extracted automatically from the video stream using complementary saliency maps. According to these salient objects, the VCA system can push the related logo images to the users. Since the salient objects often correspond to important video content, the associated images can be considered as content-related. Our VCA system also allows users to associate images to the preferred video content through simple interactions by the mouse and an infrared pen. Moreover, by learning the preference of each user through collecting feedbacks on the pulled or pushed images, the VCA system can provide user-targeted services. Experimental results show that our approach can effectively and efficiently extract the salient objects. Moreover, subjective evaluations show that our system can provide content-related and user-targeted VCA services in a less intrusive way.
Recognition of unvoiced human utterances using brain-computer interface

This paper presents a Brain Computer Interface (BCI) for recognizing unvoiced human utterances through EEG brainwaves as thoughts and predicts the intended speech. The proposed system provides a new and efficient mode of communication for people with speech impairment disability. The dataset used for the experimentation has been collected using RMS-EEG machine. The feature extraction is done by the using Fast Fourier Transform (FFT) based on welch method power spectrum density estimation (PSD). These features are trained for various facial muscles artifacts, epiglottis stress and stomach contraction using Probabilistic K-Nearest Neighbor (KNN) classifier. The performance of Probabilistic KNN is found to be superior compared to existing KNN with reduced classification error.
Decreasing Mortality in Severe Sepsis and Septic Shock Patients by Implementing a Sepsis Bundle in a Hospital Setting

Background The Surviving Sepsis Campaign (SSC) guidelines for the management of severe sepsis (SS) and septic shock (SSh) have been recommended to reduce morbidity and mortality. Materials and Methods A quasi-experimental study was conducted in a medical-surgical ICU. Multiple interventions to optimize SS and SSh shock patients' clinical outcomes were performed by applying sepsis bundles (6- and 24-hour) in May 2006. We compared bundle compliance and patient outcomes before (July 2005-April 2006) and after (May 2006-December 2009) implementation of the interventions. Results A total of 564 SS and SSh patients were identified. Prior to the intervention, compliance with the 6 hour-sepsis resuscitation bundle was only 6%. After the intervention, compliance was as follows: 8.2% from May to December 2006, 9.3% in 2007, 21.1% in 2008 and 13.7% in 2009. For the 24 hour-management bundle, baseline compliance was 15.0%. After the intervention, compliance was 15.1% from May to December 2006, 21.4% in 2007, 27.8% in 2008 and 44.4% in 2009. The in-hospital mortality was 54.0% from July 2005 to April 2006, 41.1% from May to December 2006, 39.3% in 2007, 41.4% in 2008 and 16.2% in 2009. Conclusion These results suggest reducing SS and SSh patient mortality is a complex process that involves multiple performance measures and interventions.
High-accuracy crack detection for concrete bridge based on sub-pixel

Crack is one of the main reasons for the damage of Concrete Bridges. Crack detection is a critical responsibility and essential task. The accuracy of crack detection is very important. A high-accuracy edge detection algorithm at sub-pixel level is proposed in this work. A blurred edge model is adopted here, and a least-squared-error based solution is derived. Compared with other algorithms, the algorithm proposed in this paper creatively uses the Sigmoid model, this makes the fitting error smaller and the solving process simpler. Its applications to both synthetic and real images are presented for evaluation. Finally, the algorithm is applied to crack detection of concrete Bridges based on climbing robot, and a high-resolution camera is used to collect crack images. It is very difficult to distinguish real fractures from false ones without damaging the bridge. We use the red laser line to illuminate the crack, then we detect the breakpoints of the laser line, by this way, the false crack and noise interference can be.
Selective Association Between Tetris Game Play and Visuospatial Working Memory: A Preliminary Investigation

Summary Recent experimental and clinical research has suggested that Tetris game play can disrupt maladaptive forms of mental imagery because Tetris competes for limited cognitive resources within visuospatial working memory (WM) that contribute to imagery. Whether or not Tetris performance is selectively associated with visuospatial WM remains to be tested. In this study, young adults (N = 46) completed six standardized measures indexing verbal and non‐verbal reasoning, verbal and visuospatial short‐term memory, and verbal and visuospatial WM. They also played Tetris. Consistent with the hypothesis that visuospatial WM resources support Tetris game play, there was a significant moderate positive relationship between Tetris scores and visuospatial WM performance but no association with other cognitive ability measures. Findings suggest that Tetris game play involves both storage and processing resources within visuospatial WM. These preliminary results can inform interventions involving computer games to disrupt the development of maladaptive visual imagery, for example, intrusive memories of trauma. © 2017 The Authors. Applied Cognitive Psychology Published by John Wiley & Sons Ltd.
Mutations at multiple CDK phosphorylation consensus sites on Cdt2 increase the affinity of CRL4Cdt2 for PCNA and its ubiquitination activity in S phase

CRL4Cdt2 ubiquitin ligase plays an important role maintaining genome integrity during the cell cycle. A recent report suggested that Cdk1 negatively regulates CRL4Cdt2 activity through phosphorylation of its receptor, Cdt2, but the involvement of phosphorylation remains unclear. To address this, we mutated all CDK consensus phosphorylation sites located in the C‐terminal half region of Cdt2 (Cdt2‐18A) and examined the effect on substrate degradation. We show that both cyclinA/Cdk2 and cyclinB/Cdk1 phosphorylated Cdt2 in vitro and that phosphorylation was reduced by the 18A mutation both in vitro and in vivo. The 18A mutation increased the affinity of Cdt2 to PCNA, and a high amount of Cdt2‐18A was colocalized with PCNA foci during S phase in comparison with Cdt2‐WT. Poly‐ubiquitination activity to Cdt1 was concomitantly enhanced in cells expressing Cdt2‐18A. Other CRL4Cdt2 substrates, Set8 and thymine DNA glycosylase, begin to accumulate around late S phase to G2 phase, but the accumulation was prevented in Cdt2‐18A cells. Furthermore, mitotic degradation of Cdt1 after UV irradiation was induced in these cells. Our results suggest that CDK‐mediated phosphorylation of Cdt2 inactivates its ubiquitin ligase activity by reducing its affinity to PCNA, an important strategy for regulating the levels of key proteins in the cell cycle.
Large Amplitude Vibration of Polymer Composite Stiffened Laminates by the Finite Element Method

Large amplitude free flexural vibration analyses of composite stiffened plates have been carried out using a C nine noded Lagrangian element. The element, capable of incorporating curved boundaries, is based on the first order shear deformation theory. The large deformation effect of the stiffened plated structures has been taken care of by the dynamic version of von Karman's field equations. The resulting nonlinear equations have been solved by the direct iteration technique using linear mode shapes as the starting vectors. The stiffeners have been modeled with the same shape functions as that of plate element enforcing compatibility without introduction of additional unknowns. A large number of problems of isotropic and composite bare and stiffened plates have been analyzed to validate the formulation and some new results have been put forward for future reference.
Antiretroviral Simplification with Darunavir/Ritonavir Monotherapy in Routine Clinical Practice: Safety, Effectiveness, and Impact on Lipid Profile

Background Simplification of antiretroviral treatment (ART) with darunavir/ritonavir (DRV/r) monotherapy has achieved sustained suppression of plasma viral load (pVL) in clinical trials; however, its effectiveness and safety profile has not been evaluated in routine clinical practice. Methodology/Principal Findings We performed a retrospective cohort analysis of HIV-1-infected patients who initiated DRV/r monotherapy once daily with a pVL <50 copies/mL under ART and at least 1 subsequent follow-up visit in our clinic. The primary study endpoints were the percentage of patients with virological failure (VF, defined as 2 consecutive pVL>50 copies/mL) at week 48, and time to VF. Other causes of treatment discontinuation and changes in lipid profile were evaluated up to week 48. Ninety-two patients were followed for a median (IQR) of 73 (57–92) weeks. The median baseline and nadir CD4+ T-cell counts were 604 (433–837) and 238 (150–376) cells/mm3, respectively. Patients had previously received a median of 5 (3–9) ART lines and maintained a pVL<50 copies/mL for a median of 76 (32–176) weeks before initiating DRV/r monotherapy. Nine (9.8%) patients developed VF at week 48; time to VF was 47.1 (IQR: 36.1–47.8) weeks among patients with VF. Other reasons for changing ART were gastrointestinal disturbances (n = 3), rash (n = 1), and impaired CD4 recovery (n = 2). Median low-density lipoprotein cholesterol levels increased from 116.1 mg/dL at baseline to 137.3 mg/dL at 48 weeks (p = 0.001). Conclusions/Significance Treatment simplification with DRV/r monotherapy seems safe and effective in routine clinical practice. Further research is needed to elucidate the effect of DRV/r monotherapy on cholesterol levels.
Semen amyloids participate in spermatozoa selection and clearance

Unlike other human biological fluids, semen contains multiple types of amyloid fibrils in the absence of disease. These fibrils enhance HIV infection by promoting viral fusion to cellular targets, but their natural function remained unknown. The similarities shared between HIV fusion to host cell and sperm fusion to oocyte led us to examine whether these fibrils promote fertilization. Surprisingly, the fibrils inhibited fertilization by immobilizing sperm. Interestingly, however, this immobilization facilitated uptake and clearance of sperm by macrophages, which are known to infiltrate the female reproductive tract (FRT) following semen exposure. In the presence of semen fibrils, damaged and apoptotic sperm were more rapidly phagocytosed than healthy ones, suggesting that deposition of semen fibrils in the lower FRT facilitates clearance of poor-quality sperm. Our findings suggest that amyloid fibrils in semen may play a role in reproduction by participating in sperm selection and facilitating the rapid removal of sperm antigens. DOI: http://dx.doi. org/10.7554/eLife.24888. 001
Light Transport Simulation via Generalized Multiple Importance Sampling

Multiple importance sampling (MIS) is employed to reduce variance of estimators, but when sampling and weighting are not suitable to the integrand, the estimators would have extra variance. Therefore, robust light transport simulation algorithms based on Monte Carlo sampling for different types of scenes are still uncompleted. In this paper, we address this problem by present a general method, named generalized multiple importance sampling (GMIS), to enhance the robustness of light transport simulation based on MIS. GMIS combines different sampling techniques and weighting functions, extending MIS to a more generalized framework. Meanwhile, we implement the GMIS in common renderers and illustrate how it increase the robustness of light transport simulation. Experiments show that, by applying GMIS, we obtain better convergence performance and lower variance, and increase the rendering of ambient light and specular shadow effects apparently.
A Poorly Known High-Latitude Parasitoid Wasp Community: Unexpected Diversity and Dramatic Changes through Time

Climate change will have profound and unanticipated effects on species distributions. The pace and nature of this change is largely unstudied, especially for the most diverse elements of terrestrial communities – the arthropods – here we have only limited knowledge concerning the taxonomy and the ecology of these groups. Because Arctic ecosystems have already experienced significant increases in temperature over the past half century, shifts in community structure may already be in progress. Here we utilise collections of a particularly hyperdiverse insect group – parasitoid wasps (Hymenoptera; Braconidae; Microgastrinae) – at Churchill, Manitoba, Canada in the early and mid-twentieth century to compare the composition of the contemporary community to that present 50–70 years ago. Morphological and DNA barcoding results revealed the presence of 79 species of microgastrine wasps in collections from Churchill, but we estimate that 20% of the local fauna awaits detection. Species composition and diversity between the two time periods differ significantly; species that were most common in historic collections were not found in contemporary collections and vice versa. Using barcodes we compared these collections to others from across North America; contemporary Churchill species are most affiliated with more south-western collections, while historic collections were more affiliated with eastern collections. The past five decades has clearly seen a dramatic change of species composition within the area studied coincident with rising temperature.