Miracle-dz/UniFAR-FTUs huggingface dataset:
ATP Augments von Willebrand Factor-dependent Shear-induced Platelet Aggregation through Ca2+-Calmodulin and Myosin Light Chain Kinase Activation*

Shear stress triggers von Willebrand factor (VWF) binding to platelet glycoprotein Ibα and subsequent integrin αIIbβ3-dependent platelet aggregation. Concomitantly, nucleotides are released from plateletdense granules, and ADP is known to contribute to shear-induced platelet aggregation (SIPA). We found that the impaired SIPA of platelets from a Hermansky-Pudlak patient lacking dense granules was restored by exogenous l-β,γ-methylene ATP, a stable P2X1 agonist, as well as by ADP, confirming that in addition to ADP (via P2Y1 and P2Y12), ATP (via P2X1) also contributes to SIPA. Likewise, SIPA of apyrase-treated platelets was restored upon P2X1 activation with l-β,γ-methylene ATP, which promoted granule centralization within platelets and stimulated P-selectin expression, which is a marker of α-granule release. In addition, during SIPA, platelet degranulation required both extracellular Ca2+ and VWF-glycoprotein Ibα interactions without involving αIIbβ3. Neither platelet release nor SIPA was affected by protein kinase C inactivation, even though protein kinase C blockade inhibits platelet responses to collagen and thrombin in stirring conditions. In contrast, inhibiting myosin light chain (MLC) kinase with ML-7 reduced platelet release and SIPA by 30%. Accordingly, the potentiating effect of P2X1 stimulation on the aggregation of apyrase-treated platelets coincided with intensified phosphorylation of MLC and was abrogated by ML-7. SIPA-induced MLC phosphorylation occurred exclusively through released nucleotides and selective antagonism of P2X1 with MRS2159-reduced SIPA, ATP release, and potently inhibited MLC phosphorylation. We conclude that the P2X1 ion channel induces MLC-mediated cytoskeletal rearrangements, thus contributing to SIPA and degranulation during VWF-triggered platelet activation.
Learning Graph Representations for Defect Prediction

We propose to study the impact of the representation of the data in defect prediction models. For this study, we focus on the use of developer activity data, from which we structure dependency graphs. Then, instead of manually generating features, such as network metrics, we propose a model inspired in recent advances in Representation Learning which are able to automatically learn representations from graph data. These new representations are compared against manually crafted features for defect prediction in real world software projects.
Adaptive-Rate Transmission With Opportunistic Scheduling in Wireless Networks

In this paper, we propose an adaptive-rate transmission scheme with opportunistic scheduling in a two-hop relay network over a Rayleigh fading environment. We assume that only channel state information (CSI) at receivers is available in a decentralized network, where <formula formulatype="inline"><tex Notation="TeX">$n$</tex></formula> source-to-destination pairs are completely independent and <formula formulatype="inline"><tex Notation="TeX">$m$</tex></formula> half-duplex relays cooperate by exchanging their selected source IDs at the beginning of the two-hop transmission. In the limit of large <formula formulatype="inline"><tex Notation="TeX">$n$</tex></formula> and fixed <formula formulatype="inline"> <tex Notation="TeX">$m$</tex></formula>, the system throughput of the scheme scales as <formula formulatype="inline"><tex Notation="TeX">$(m/ \hbox{2})\log\, \log\, n$</tex></formula>. It is shown that this is the same achievable scaling even with perfect CSI assumptions at transmitters and full cooperation among nodes. Furthermore, it is shown that the optimal scaling of <formula formulatype="inline"><tex Notation="TeX">$m$</tex></formula> is <formula formulatype="inline"><tex Notation="TeX">$\Theta(\log\, n)$ </tex></formula>, under which a linear increase in throughput with <formula formulatype="inline"><tex Notation="TeX">$m$</tex> </formula> is obtained. A closed-form delay expression of the proposed scheme is also presented.
Using Home Visits to Understand Medication Errors in Children

Current research methods are not well designed to detect medication errors that occur at home. We developed home visit methods to investigate home medication errors in children with chronic conditions. These methods include observation of parent administration of medication to the child by a trained nurse observer who takes detailed ethnographic notes; review of all prescription and over-the-counter medications for dispensing errors, pill counts, and medication reconciliation; and parent interviews to identify barriers to effective home use of medications, prior home medication errors that parents are aware of, and suggestions for systemic improvements. Details about each possible error detected are recorded using a structured data collection form (allergies, medication list, dispensing errors, administration errors). We conducted several pilot home visits and found that this approach has the potential to help understand home medication errors in order to develop interventions to improve the safety of medication self-management. Introduction Despite over 3,000 publications about medication safety over the last 5 years, there continue to be “enormous gaps in the knowledge required to implement a safe medication-use system,” according to the July 2006 Institute of Medicine (IOM) report, Preventing Medication Errors. This report called for research on the rate of errors in ambulatory care, particularly home care and pediatric care, and support for medication self-management. Among children, the rate of potentially dangerous medication errors is three times that of adults and outpatient wrong dose ordering errors are common, due to the complexity of weight-based dosing. 3, 4, 5, 6 Although the majority of pediatric medications are taken in the home, data on pediatric medication errors in the home setting are limited, and risks for children with chronic conditions, who use man medicines, may be great. y 7, 8 Research methods are needed to describe errors in the home use of pediatric medications. Medical record review is not well suited for detecting medication administration errors. The most efficient and accurate method to detect medication administration errors in the hospital setting is through direct observation of nurses by a trained researcher. 11, 12 It is reasonable to expect that direct observation would also be a good method of detecting medication errors in the home setting, and so we sought to develop comparable methods. To that end, we reviewed the literature, developed home visit methods, and conducted a pilot study. Each of these steps is described in separate sections in this article.
Evolutionary computer vision

Evolutionary Computer Vision (ECV) is a recent research area devoted to the study of artificial vision through evolutionary and genetic computing approaches. 1. Computer vision as a scientific discipline is concerned with the theory and technology for building artificial systems that obtain information from images or multi-dimensional data. 2. Evolutionary computation is a research field of computational intelligence devoted to the study and application of artificial evolution to develop problem solving systems. slide #2
Validating and Highlighting the Advantages of the Optimal Estimation Method For Rayleigh Lidar Middle Atmospheric Temperature Retrievals

An improved understanding of temperature variations in Earth’s middle atmosphere is important for the improvement of our understanding of climate and weather on the surface. The optimal estimation method (OEM) is an inversion modeling approach, which uses regularized nonlinear regression to retrieve, in this case, the temperature of Earth’s middle atmosphere using Rayleigh-scatter lidar measurements. The OEM regularization term is the a priori knowledge of the atmospheric temperature profile. In this thesis I use lidar temperatures in the altitude range 30–110 km to construct a temperature climatology using over 500 nights of measurements obtained by the Purple Crow Lidar in London, Ontario. The OEM produces several diagnostic tools, such as averaging kernels and an uncertainty budget which includes both systematic and statistical uncertainties important for atmospheric applications. Using OEM allows for the quantitative calculation of the maximum valid altitude of the retrieval by determining at which altitude the a priori temperature profile influences the retrieval by more than 10%. This new knowledge extends the temperature retrievals 5 to 10 km higher in altitude than traditional methods. The OEM retrievals are validated by comparison of the PCL temperature climatology with other measurements. Excellent agreement is found between the PCL and sodium lidar climatologies in the upper mesosphere and lower thermosphere, where the temperature variability is highest. Thus validated, the OEM can now be applied to other similar lidar systems. Lidar retrievals of atmospheric temperature profiles using the OEM typically use a retrieval grid whose number of points is larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from the a priori values, which can affect the temperatures at higher altitudes. I present a method for removing the a priori information from the retrieved profiles. The OEM provides averaging kernels, or weighting functions, at each level. I applied the OEM to measurements obtained from two lidars during a coincident measurement campaign between the Deutscher Wetterdienst and
Overexpression of oligouridylate binding protein 1b results in ABA hypersensitivity

ABSTRACT Oligouridylate binding protein 1b (UBP1b), a marker protein of plant stress granules (SGs), plays a role in heat stress tolerance in plants. A previous microarray analysis revealed that the expression of several ABA signaling-related genes is higher in UBP1b-overexpressing Arabidopsis plants (UBP1b-ox) subjected to both non-stressed and heat stress conditions. Root elongation and seed germination assays demonstrated that UBP1b-ox exhibited hypersensitivity to ABA. RT-qPCR analysis confirmed that mitogen-activated protein kinase (MAPK) cascade genes, such as MPK3, MKK4, and MKK9 were upregulated in UBP1b-ox plants. ABA receptor genes, including PYL5 and PYL6, were also upregulated in UBP1b-ox plants. mRNA of WRKY33 – a downstream gene of MPK3 and an upstream gene of ethylene biosynthesis, exhibited high levels of accumulation, although the level of endogenous ABA was not significantly different between UBP1b-ox and control plants. In addition, RNA decay analysis revealed that WRKY33 was more stable in UBP1b-ox plants, indicating that the mRNA of WRKY33 was protected within UBP1b SGs. Collectively, these data demonstrate that UBP1b plays an important role in plant response to ABA.
Evolution of CO 2 and soil carbon dynamics in biologically managed , row-crop agroecosystems

Field CO2 production was related to soil carbon pools and ̄uxes determined by laboratory incubation of soils from agroecosystems designed to test the possibility of substituting biological for chemical inputs. Treatments included: conventional and organic-based row crops, woody and herbaceous perennial crops and historically tilled and never tilled successional ®elds. The CO2 ef ̄ux in corn and soybeans was affected by crop residues from previous years and growing season temperatures but not soil moisture. Overwinter cover crops and perennials such as alfalfa and poplar, resulted in fairly uniform ̄uxes of approximately 20 kg CO2±C ha ÿ1 dayÿ1 throughout the non-frozen period. Highest ̄uxes occurred in alfalfa, historically tilled successional and never tilled, grassland successional treatments, although, highest aboveground productivity occurred in the corn and poplar. Summed, ®eld CO2 ̄uxes were similar to residue-C inputs. Measurement of CO2 mineralized in extended incubations in the laboratory made it possible to use soil enzyme activity to determine the size and dynamics of soil C pools. The residue of acid hydrolysis de®ned the size of the resistant pool Cr. Carbon dating determined its mean residence time (MRT). Curve analyses of CO2 evolution plotted on a per unit time basis gave the active (Ca) and slow (Cs) pool sizes and decomposition rate constants ka and ks. Temperature correction factors provided ®eld MRTs. The active pool of this coarse textured soil represents 2% of the soil C with a MRT of 30±66 days. The slow pool represents 40±45% of the SOC with ®eld MRTs of 9±13 years. The poplar soil has the greatest MRT for both the active and slow pools. The system approach to land use sustainability (SALUS) model, which predicts CO2 evolution from decomposition in the ®eld as part of a plant growth ± soil process model, was tested using the decomposition parameters determined by incubation and C dating. The model satisfactorily predicted the intra and inter year differences in ®eld CO2 but over predicted ̄uxes from residues in the fall. It does not yet adequately consider a lag period during which the residues lose their hydrophobicity, are comminuted and colonized. # 1999 Elsevier Science B.V.
Systematic expression shearing in symbolic CMOS circuits analysis based on topology reduction

This paper proposes a novel approach to the exact symbolic analysis of analog electronic circuits. The new method is entirely topology oriented. The procedure introduces a new graph representation of exact transfer function called Topology Decision Diagram (TDD). Its construction is based on topological interpretation of network function factorization process. The method considers every factorized part of the function as a subcircuit. Each subsequent subcircuit is obtained by replacing a regular circuit element with a singular element, when a circuit parameter is taken to a limit of infinity or zero. The new algorithm significantly improves compactness of the generated circuit equations by introduction of static element ordering and dynamic reordering during the TDD construction. Experimental results confirm the efficiency of the method.
Setaria digitata in advancing our knowledge of human lymphatic filariasis

Abstract Setaria digitata is a filarial parasite that causes fatal cerebrospinal nematodiasis in goats, sheep and horses, resulting in substantial economic losses in animal husbandry in the tropics. Due to its close resemblance to Wuchereria bancrofti, this nematode is also frequently used as a model organism to study human lymphatic filariasis. This review highlights numerous insights into the morphological, histological, biochemical, immunological and genetic aspects of S. digitata that have broadened our understanding towards the control and eradication of filarial diseases.
An Experience Based Learning Controller

The autonomous mobile robots must be flexible to learn the new complex control behaviours in order to adapt effectively to a dynamic and varying environment. The proposed approach of this paper is to create a controller that learns the complex behaviours incorporating the learning from demonstration to reduce the search space and to improve the demonstrated task geometry by trial and corrections. The task faced by the robot has uncertainty that must be learned. Simulation results indicate that after the handful of trials, robot has learned the right policies and avoided the obstacles and reached the goal.
Requirements for Pseudosubstrate Arginine Residues during Autoinhibition and Phosphatidylinositol 3,4,5-(PO4)3-dependent Activation of Atypical PKC*

Background: The mechanisms whereby atypical PKCs (aPKCs) are silenced and activated by phosphatidylinositol 3,4,5-(PO4)3 are uncertain. Results: Mutagenesis of arginine residues in the pseudosubstrate of aPKCs caused constitutive activation that was inhibited by exogenous pseudosubstrate. Pseudosubstrate-dependent inhibition was reversed by phosphatidylinositol 3,4,5-(PO4)3. Conclusion: Pseudosubstrate arginine residues are required for autoinhibition and are targeted by phosphatidylinositol 3,4,5-(PO4)3 during aPKC activation. Significance: Pseudosubstrate arginine residues are key regulators of aPKC. Atypical PKC (aPKC) isoforms are activated by the phosphatidylinositol 3-kinase product phosphatidylinositol 3,4,5-(PO4)3 (PIP3). How PIP3 activates aPKC is unknown. Although Akt activation involves PIP3 binding to basic residues in the Akt pleckstrin homology domain, aPKCs lack this domain. Here we examined the role of basic arginine residues common to aPKC pseudosubstrate sequences. Replacement of all five (or certain) arginine residues in the pseudosubstrate sequence of PKC-ι by site-directed mutagenesis led to constitutive activation and unresponsiveness to PIP3 in vitro or insulin in vivo. However, with the addition of the exogenous arginine-containing pseudosubstrate tridecapeptide to inhibit this constitutively active PKC-ι, PIP3-activating effects were restored. A similar restoration of responsiveness to PIP3 was seen when exogenous pseudosubstrate was used to inhibit mouse liver PKC-λ/ζ maximally activated by insulin or ceramide and a truncated, constitutively active PKC-ζ mutant lacking all regulatory domain elements and containing “activating” glutamate residues at loop and autophosphorylation sites (Δ1–247/T410E/T560E-PKC-ζ). NMR studies suggest that PIP3 binds directly to the pseudosubstrate. The ability of PIP3 to counteract the inhibitory effects of the exogenous pseudosubstrate suggests that basic residues in the pseudosubstrate sequence are required for maintaining aPKCs in an inactive state and are targeted by PIP3 for displacement from the substrate-binding site during kinase activation.
DARCAAT: DARPA Competence Assessment and Alarms for Teams

Assessing teams in complex military environments requires effective tracking of individual and team performance. Indeed, performance measures must be both accurate and timely in order to provide effective real-time alarms. However, current methods of monitoring team and group performance often rely on delayed outcomes or global metrics that are insufficiently detailed to detect failures until recovery is impossible, and are often unable to reveal the causes of failures. An untapped source of timely and diagnostic information lies in the communications among team members. The DARCAAT program developed and tested a toolset for automating team assessment and near real-time alarms. The toolset uses Automated Speech Recognition and Statistical Natural Language-based techniques for embedding automatic, continuous, and cumulative analysis of team communication in training and operational environments. The techniques include measures of the content, patterns, and style of team members’ communications. These measures were combined using machine learning techniques to develop performance models based on Subject Matter Expert (SME) ratings of teams. Focusing on the domain of convoy training, we collected team performance and communication data from the Fort Lewis DARWARS Ambush! convoy training virtual environment and from the National Training Center’s live convoy STX lane training. Tests of the performance models and critical incident detection capabilities showed that the technology agreed significantly with SMEs’ ratings of teams, and could identify a majority of the team critical incidents. In this paper we discuss the implications for modeling team performance based on communication, describe the development of the technology, and demonstrate how it can process communication to detect critical incidents and to generate team performance metrics. Finally we describe how this technology can be integrated into training systems for automatic team assessment. These systems can provide automated feedback and can alert teams and commanders of potential problems before they occur.
Slow memory: weakening consistency to enhance concurrency in distributed shared memories

The use of weakly consistent memories in distributed shared memory systems to combat unacceptable network delay and to allow such systems to scale is proposed. Proposed memory correctness conditions are surveyed, and how they are related by a weakness hierarchy is demonstrated. Multiversion and messaging interpretations of memory are introduced as means of systematically exploring the space of possible memories. Slow memory is presented as a memory that allows the effects of writes to propagate slowly through the system, eliminating the need for costly consistency maintenance protocols that limit concurrency. Slow memory processes a valuable locality property and supports a reduction from traditional atomic memory. Thus slow memory is as expressive as atomic memory. This expressiveness is demonstrated by two exclusion algorithms and a solution to M.J. Fischer and A. Michael's (1982) dictionary problem on slow memory. <<ETX>>
Axions: Past, present, and future

I give a pedagogical and historical introduction to axion physics, and briefly review the present status of axions in our understanding of particle physics and cosmology. This is a contribution to Continuous Advances in QCD 2002/Arkadyfest, held in honor of Arkady Vainshtein's 60th birthday.
On Predictive Modeling for Optimizing Transaction Execution in Parallel OLTP Systems

A new emerging class of parallel database management systems (DBMS) is designed to take advantage of the partitionable workloads of on-line transaction processing (OLTP) applications [23, 20]. Transactions in these systems are optimized to execute to completion on a single node in a shared-nothing cluster without needing to coordinate with other nodes or use expensive concurrency control measures [18]. But some OLTP applications cannot be partitioned such that all of their transactions execute within a single-partition in this manner. These distributed transactions access data not stored within their local partitions and subsequently require more heavy-weight concurrency control protocols. Further difficulties arise when the transaction's execution properties, such as the number of partitions it may need to access or whether it will abort, are not known beforehand. The DBMS could mitigate these performance issues if it is provided with additional information about transactions. Thus, in this paper we present a Markov model-based approach for automatically selecting which optimizations a DBMS could use, namely (1) more efficient concurrency control schemes, (2) intelligent scheduling, (3) reduced undo logging, and (4) speculative execution. To evaluate our techniques, we implemented our models and integrated them into a parallel, main-memory OLTP DBMS to show that we can improve the performance of applications with diverse workloads.
What do we mean by validating a prognostic model?

Prognostic models are used in medicine for investigating patient outcome in relation to patient and disease characteristics. Such models do not always work well in practice, so it is widely recommended that they need to be validated. The idea of validating a prognostic model is generally taken to mean establishing that it works satisfactorily for patients other than those from whose data it was derived. In this paper we examine what is meant by validation and review why it is necessary. We consider how to validate a model and suggest that it is desirable to consider two rather different aspects - statistical and clinical validity - and examine some general approaches to validation. We illustrate the issues using several case studies.