Miracle-dz/UniFAR-FTUs huggingface dataset:
BRCA1-BARD1 Complexes Are Required for p53Ser-15 Phosphorylation and a G1/S Arrest following Ionizing Radiation-induced DNA Damage*

BRCA1 is a major player in the DNA damage response. This is evident from its loss, which causes cells to become sensitive to a wide variety of DNA damaging agents. The major BRCA1 binding partner, BARD1, is also implicated in the DNA damage response, and recent reports indicate that BRCA1 and BARD1 co-operate in this pathway. In this report, we utilized small interfering RNA to deplete BRCA1 and BARD1 to demonstrate that the BRCA1-BARD1 complex is required for ATM/ATR (ataxia-telangiectasia-mutated/ATM and Rad3-related)-mediated phosphorylation of p53Ser-15 following IR- and UV radiation-induced DNA damage. In contrast, phosphorylation of a number of other ATM/ATR targets including H2AX, Chk2, Chk1, and c-jun does not depend on the presence of BRCA1-BARD1 complexes. Moreover, prior ATM/ATR-dependent phosphorylation of BRCA1 at Ser-1423 or Ser-1524 regulates the ability of ATM/ATR to phosphorylate p53Ser-15 efficiently. Phosphorylation of p53Ser-15 is necessary for an IR-induced G1/S arrest via transcriptional induction of the cyclin-dependent kinase inhibitor p21. Consistent with these data, repressing p53Ser-15 phosphorylation by BRCA1-BARD1 depletion compromises p21 induction and the G1/S checkpoint arrest in response to IR but not UV radia-tion. These findings suggest that BRCA1-BARD1 complexes act as an adaptor to mediate ATM/ATR-directed phosphorylation of p53, influencing G1/S cell cycle progression after DNA damage.
National Programs to Assess IEQ Effects of Building Materials and Products

Background: Building materials, interior furnishings, surface treatments, paints, coatings and consumer products have been shown to contribute to indoor contaminants. Currently, information on potential hazards of these chemicals prior to specification, purchase or use of these products is limited. Except for a few cases, regulations to limit or prevent exposures to product-related emissions do not exist in the US. There have been several recent circumstances where introduction of new materials/products have resulted in wide scale exposures followed by research examining potential harmful effects.
Integrating Translation Memory into Phrase-Based Machine Translation during Decoding

Since statistical machine translation (SMT) and translation memory (TM) complement each other in matched and unmatched regions, integrated models are proposed in this paper to incorporate TM information into phrase-based SMT. Unlike previous multi-stage pipeline approaches, which directly merge TM result into the final output, the proposed models refer to the corresponding TM information associated with each phrase at SMT decoding. On a Chinese–English TM database, our experiments show that the proposed integrated Model-III is significantly better than either the SMT or the TM systems when the fuzzy match score is above 0.4. Furthermore, integrated Model-III achieves overall 3.48 BLEU points improvement and 2.62 TER points reduction in comparison with the pure SMT system. Besides, the proposed models also outperform previous approaches significantly.
Preserving knowledge in design projects: what designers need to know

In order to inform the design of technology support and new procedural methods for software design, we analyzed the content of real design meetings in three organizations, focusing in particular on the questions the designers ask of each other. We found that most questions concerned the project requirements, particularly what the software was supposed to do and, somewhat less frequently, scenarios of use. Questions about functions to be performed by software components and how these functions were to be realized were also fairly frequent. Rationales for design decisions were seldom asked about. The implications of this research for design tools and methods are discussed.
Fast User Classifying to Establish Forensic Analysis Priorities

In computer and common crimes, important evidence or clues are increasingly stored in the computers hard disks. The huge and increasing penetration of computers in the daily life together with a considerable increase of storage capacity in mass-market computers, pose, currently, new challenges to forensic operators. Usually a digital forensic investigator has to spend a lot of time in order to find documents, clues or evidence related to the investigation among the huge amount of data extracted from one or more sized hard drive. In particular, the seized material could be very huge, and, very often, only few devices are considered relevant for the investigation. In this paper we propose a methodology and a tool to support a fast computer user profiling via a classification into investigator-defined categories in order to quickly classify the seized computer user. The main purpose of the methodology discussed is to define the class of the user in order to establish an effective schedule with priorities based on the computer user content.
Gravel: Fine-Grain GPU-Initiated Network Messages

Distributed systems incorporate GPUs because they provide massive parallelism in an energy-efficient manner. Unfortunately, existing programming models make it difficult to route a GPU-initiated network message. The traditional coprocessor model forces programmers to manually route messages through the host CPU. Other models allow GPU-initiated communication, but are inefficient for small messages. To enable fine-grain PGAS-style communication between threads executing on different GPUs, we introduce Gravel. GPU-initiated messages are offloaded through a GPU-efficient concurrent queue to an aggregator (implemented with CPU threads), which combines messages targeting to the same destination. Gravel leverages diverged work-group-level semantics to amortize synchronization across the GPU’s data-parallel lanes. Using Gravel, we can distribute six applications, each with frequent small messages, across a cluster of eight GPU-accelerated nodes. Compared to one node, these applications run 5.3x faster, on average. Furthermore, we show Gravel is more programmable and usually performs better than prior GPU networking models. CCS CONCEPTS Computer methodologies→Massively parallel algorithms;
Distribution-Based Categorization of Classifier Transfer Learning

Transfer Learning (TL) aims to transfer knowledge acquired in one problem, the source problem, onto another problem, the target problem, dispensing with the bottom-up construction of the target model. Due to its relevance, TL has gained significant interest in the Machine Learning community since it paves the way to devise intelligent learning models that can easily be tailored to many different applications. As it is natural in a fast evolving area, a wide variety of TL methods, settings and nomenclature have been proposed so far. However, a wide range of works have been reporting different names for the same concepts. This concept and terminology mixture contribute however to obscure the TL field, hindering its proper consideration. In this paper we present a review of the literature on the majority of classification TL methods, and also a distribution-based categorization of TL with a common nomenclature suitable to classification problems. Under this perspective three main TL categories are presented, discussed and illustrated with examples.
Evaluasi Kondisi Lingkungan Perairan Kawasan Pulau Abang, Galang Baru, Batam Berdasarkan Analisa Indeks Storet dan Similaritas Canbera

Evaluation of the water quality status at Abang Island Region was conducted by using the STORET and Canbera Similarity Indeks during Mei, June and October 2006. This analysis result shows that the water quality within this region was within the range of good to moderate. This condition indicats that the water quality status is still suitable to support the marine biotic life. During May, the water quality was good, but in June and October was decreased due to the increasing of the organic material transported by land surface run off driven by precipitation as response to the seasonal change.
Single Stage Knee Arthroplasty Revision Surgery: A Systematic Review of the Literature

Total Knee Arthroplasty is an increasingly common procedure and revision surgery, particularly for infection, is associated with significant morbidity and healthcare costs. The current gold standard is a two stage revision procedure but single stage revision is increasingly being used in some departments to improve patient outcomes. We conducted a systematic review of the literature to determine the up-to-date evidence underlying the use of a single stage knee approach in revision surgery. A total of 12 studies were included in this review amounting to 433 revision surgeries. This is the largest review of single stage knee revision surgery. The procedures described were heterogenous and included the ‘two-in-one’ technique as well as other single stage revision procedures. There were also differences in implants and antibiotic regimens. The mean re-infection rates described in 10 studies was 9.4% (range 0-19.2%) after a mean follow-up of 40.3 months (range 7-180 months). The re-infection rates in the studies published over the last 30 years are falling, and this is not accounted for by any significant change in duration of follow-up during this period. The outcome scores varied, but patients generally showed an improvement. The Knee Society Score and the Oxford Knee Score were the most commonly used in five and three studies respectively. We conclude that the current evidence for single stage revision is variable and there is a lack of good quality evidence to address whether single stage revisions is thorough enough to eradicate deep infection and is able to restore adequate function. There is a need for larger prospective studies with standardised procedures and protocol, and with adequate follow-up. Till then, patients considered for a single stage approach should be thoroughly assessed and the surgery should be performed by a senior surgeon with experience in single stage knee revisions.
On remote sensing of convective clouds over Indian continent and quantification of their variability in a warming environment

ABSTRACT Convective clouds are associated with extreme precipitation events triggering floods. They are an important part of atmospheric circulation and hydrological cycle. Changes in convective clouds in changing climate remain one of the most challenging aspects of forecasting future climate change. The present research focuses on identification of convective clouds using multispectral measurements at split window channels (near 10.5 µm and 12.5 µm) and water vapour absorption channels (near 6.7 µm) from Meteosat 7 observations. Variability of convective clouds has been examined in warming climate using observations from Meteosat First Generation (MFG). It has been reported that convective clouds show high density over Western, Central, North Eastern Indian region, and the Western Ghats during the monsoon period. This observation is consistent with measurement from Precipitation Radar (PR) (reflectivity-based threshold) on-board Tropical Rainfall Measuring Mission (TRMM) and rain gauge-based product. The present technique fails to detect shallow convective clouds over the Western Ghats. An increase of about 32.68% ± 5.81% per degree increase in temperature has been reported in convective clouds over India.
MetaKernels and fault containment wrappers

This paper addresses the problem of using COTS microkernels in dependable systems. Because they are not developed with this aim, their behavior in the presence of faults is a main concern to system designers. We propose a novel approach to contain the effect of both external and internal faults that may affect their behavior. As microkernels can be decomposed into simple components, modeling of their expected behavior in the absence of faults is most often possible, which allows for the easy definition of dynamic predicates. For an efficient implementation of fault containment wrappers checking for these predicates, we introduce the notion of MetaKernel to reify the information required for implementing the predicates and to reflect appropriate actions. This approach is exemplified on a case study using an open version of the Chorus microkernel. MAFALDA, a software-implemented fault injection tool, is used to illustrate the benefits procured by the proposed wrappers.
High-dimensional mutual information estimation for image registration

We present a new algorithm for mutual information estimation for image registration based on the nearest neighbor entropy estimator of Kozachenko and Leonenko. We modify the algorithm to be numerically robust and computationally efficient, with optimal asymptotic complexity O(N/sub pixels/d/sub dim/). We propose two MI-based criteria exploiting the high-dimensionality of the feature space and show their effectiveness in determining the correct alignment even in difficult cases when classical criteria fail.
Multi-task Learning for Continuous Control

Reliable and effective multi-task learning is a prerequisite for the development of robotic agents that can quickly learn to accomplish related, everyday tasks. However, in the reinforcement learning domain, multi-task learning has not exhibited the same level of success as in other domains, such as computer vision. In addition, most reinforcement learning research on multi-task learning has been focused on discrete action spaces, which are not used for robotic control in the real-world. In this work, we apply multi-task learning methods to continuous action spaces and benchmark their performance on a series of simulated continuous control tasks. Most notably, we show that multi-task learning outperforms our baselines and alternative knowledge sharing methods.
A STUDY COMPARING CHEMICAL PEELING USING MODIFIED JESSNER'S SOLUTION AND 15%TRICHLOROACETIC ACID VERSUS 15% TRICHLOROACETIC ACID IN THE TREATMENT OF MELASMA

Background: Melasma is a symmetric progressive hyperpigmentation of the facial skin that occurs in all races but has a predilection for darker skin phenotypes. Depigmenting agents, laser and chemical peeling as classic Jessner's solution, modified Jessner's solution and trichloroacetic acid have been used alone and in combination in the treatment of melasma. Objectives: The aim of the study was to compare the therapeutic effect of combined 15% Trichloroacetic acid (TCA) and modified Jessner's solution with 15% TCA on melasma. Materials and Methods: Twenty married females with melasma (epidermal type), with a mean age of 38.25 years, were included in this study. All were of skin type III or IV. Fifteen percent TCA was applied to the whole face, with the exception of the left malar area to which combined TCA 15% and modified Jessner's solution was applied. Results: Our results revealed statistically highly significant difference between MASI Score (Melasma Area and Severity Index) between the right malar area and the left malar area. Conclusion: Modified Jessner's solution proved to be useful as an adjuvant treatment with TCA in the treatment of melasma, improving the results and minimizing postinflammatory hyperpigmentation.
Mismatch Patterns and Adaptation Aspects: A Foundation for Rapid Development of Web Service Adapters

Standardization in Web services simplifies integration. However, it does not remove the need for adapters due to possible heterogeneity among service interfaces and protocols. In this paper, we characterize the problem of Web services adaptation focusing on business interfaces and protocols adapters. Our study shows that many of the differences between business interfaces and protocols are recurring. We introduce mismatch patterns to capture these recurring differences and to provide solutions to resolve them. We leverage mismatch patterns for service adaptation with two approaches: by developing stand-alone adapters and via service modification. We then dig into the notion of adaptation aspects that, following aspect-oriented programming paradigm and service modification approach, allow for rapid development of adapters. We present a study showing that it is a preferable approach in many cases. The proposed approach is implemented in a proof-of-concept prototype tool, and evaluated using both qualitative and quantitative methods.
Semantic Association Identification and Knowledge Discovery for National Security Applications

Public and private organizations have access to a vast amount of internal, deep Web and open Web information. Transforming this heterogeneous and distributed information into actionable and insightful information is the key to the emerging new classes of business intelligence and national security applications. Although the role of semantics in search and integration has been often talked about, in this paper we discuss semantic approaches to support analytics on vast amounts of heterogeneous data. In particular, we bring together novel academic research and commercialized Semantic Web technology. The academic research related to semantic association identification is built upon commercial Semantic Web technology for semantic metadata extraction. A prototypical demonstration of this research and technology is presented in the context of an aviation security application of significance to national security.
THE PERFORMANCE OF MICROPHYSICS SCHEME IN WRF MODEL FOR SIMULATING EXTREME RAINFALL EVENTS

In this research, microphysics scheme is one main important scheme to simulate high-resolution rainfall and extreme rainfall event. It has impacted increase natural or relating to aerosols in the atmosphere. It can modify climate in the atmosphere because it can change precipitation efficiency. This research, two microphysics schemes (Lin scheme and WSM6 scheme) in the Weather Research and Forecasting (WRF) model were used to simulate three extreme rainfall events over Thailand with three nested domains. The innermost domain was 4 km × 4 km and covered Thailand area. The results of extreme rainfall events given by the simulations from two microphysics schemes can be compared with the trend of rainfall spatial distribution from the TRMM data. The spatial pattern results of extreme rainfall events given by the two microphysics schemes. It can capture the trend of extreme spatial rainfall distribution with TRMM data. The statistical comparison was supported by the spatial pattern. It was shown the value less than 0 almost three cases that mean the results from the simulation were underestimate than TRMM data. However, the statistical comparison was shown a good trend from two microphysics scheme. In general, the trend of rainfall from two microphysics schemes of the WRF model good approximately extreme rainfall three cases with TRMM data over Thailand.
Event graph model and its properties: A method of mining trends in text streams

Massive text stream is a very important source of information. There are rich graph structures contained in massive texts, which can be used as an effective method to mine trends embodied in the contents in text streams. This paper formally defines the event graph model based on systems science theory, and discusses its properties. This model aims to extract the potential events and relationships between them from text streams.
Methanotrophy under Versatile Conditions in the Water Column of the Ferruginous Meromictic Lake La Cruz (Spain)

Lakes represent a considerable natural source of methane to the atmosphere compared to their small global surface area. Methanotrophs in sediments and in the water column largely control methane fluxes from these systems, yet the diversity, electron accepting capacity, and nutrient requirements of these microorganisms have only been partially identified. Here, we investigated the role of electron acceptors alternative to oxygen and sulfate in microbial methane oxidation at the oxycline and in anoxic waters of the ferruginous meromictic Lake La Cruz, Spain. Active methane turnover in a zone extending well below the oxycline was evidenced by stable carbon isotope-based rate measurements. We observed a strong methane oxidation potential throughout the anoxic water column, which did not vary substantially from that at the oxic/anoxic interface. Both in the redox-transition and anoxic zones, only aerobic methane-oxidizing bacteria (MOB) were detected by fluorescence in situ hybridization and sequencing techniques, suggesting a close coupling of cryptic photosynthetic oxygen production and aerobic methane turnover. Additions of nitrate, nitrite and to a lesser degree iron and manganese oxides also stimulated bacterial methane consumption. We could not confirm a direct link between the reduction of these compounds and methane oxidation and we cannot exclude the contribution of unknown anaerobic methanotrophs. Nevertheless, our findings from Lake La Cruz support recent laboratory evidence that aerobic methanotrophs may be able to utilize alternative terminal electron acceptors under oxygen limitation.
Social influences on Cigarette Smoking Among Mainland Chinese and Chinese Americans: A Comparative Study.

The purpose of this cross-national study was to determine the leading social influences on smoking among Chinese Americans and mainland Chinese adults with the aim of improving prevention and intervention strategies to reduce smoking rates. A comparative cross-sectional design was used and a stratified-cluster sampling technique was employed in identifying the study sample. An 80-item questionnaire was administered in Chinese to 1222 participants, 812 in China and 410 in the U.S. Data were analyzed using descriptive and inferential statistical tests. Results contain comparative data on social influences of smoking among Chinese Americans and mainland Chinese, as well as factors influencing smoking behavior which include cultural beliefs, gender roles, and family relationship dynamics. Grandparents were influential and significantly correlated with current smokers in China. Findings indicate differential effects of social influences on smoking behavior. Similarities and differences provide a basis for improving and/or developing smoking intervention programs of mutual benefit to China and the U.S.
Colloidal Photonic Crystals Containing Silver Nanoparticles with Tunable Structural Colors

Polystyrene (PS) colloidal photonic crystals (CPhCs) containing silver nanoparticles (AgNPs) present tunable structural colors. PS CPhC color films containing a high concentration of AgNPs were prepared using self-assembly process through gravitational sedimentation method. High-concentration AgNPs were deposited on the bottom of the substrate and acted as black materials to absorb background and scattering light. Brilliant structural colors were enhanced because of the absorption of incoherent scattering light, and color saturation was increased by the distribution AgNPs on the PS CPhC surfaces. The vivid iridescent structural colors of AgNPs/PS hybrid CPhC films were based on Bragg diffraction and backward scattering absorption using AgNPs. The photonic stop band of PS CPhCs and AgNPs/PS hybrid CPhCs were measured by UV–visible reflection spectrometry and calculated based on the Bragg–Snell law. In addition, the tunable structural colors of AgNPs/PS hybrid CPhC films were evaluated using color measurements according to the Commission International d’Eclairage standard colorimetric system. This paper presents a simple and inexpensive method to produce tunable structural colors for numerous applications, such as textile fabrics, bionic colors, catalysis, and paints.