Miracle-dz/UniFAR-FTUs huggingface dataset:
Left atrial appendage occluder thrombosis after successful implantation.

We report the case of a 69-year-old woman admitted for recurrent left atrial appendage (LAA) thrombosis occurring 8 months after implantation of an Amplatzer Cardiac Plug device. The indication for LAA closure was paroxysmal atrial fibrillation with high thromboembolic risk (CHA2DS2-Vasc score, 4) and a history of intracranial bleeding 6 months previously under vitamin K antagonist with an international normalized ratio ranging from 2 to 3. The Amplatzer Cardiac Plug …
Unsupervised texture segmentation using Gabor filters

A texture segmentation algorithm inspired by the multichannel filtering theory for visual information processing in the early stages of the human visual system is presented. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain. A systematic filter selection scheme based on reconstruction of the input image from the filtered images is proposed. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of energy in a window around each pixel. An unsupervised square-error clustering algorithm is then used to integrate the feature images and produce a segmentation. A simple procedure to incorporate spatial adjacency information in the clustering process is proposed. Experiments on images with natural textures as well as artificial textures with identical second and third-order statistics are reported. The algorithm appears to perform as predicted by preattentive texture discrimination by a human. <<ETX>>
cAMP and Serum and Glucocorticoid-inducible Kinase (SGK) Regulate the Epithelial Na+ Channel through Convergent Phosphorylation of Nedd4-2*

The epithelial Na+ channel (ENaC) functions as a pathway for epithelial Na+ transport, contributing to Na+ homeostasis and blood pressure control. Vasopressin increases ENaC expression at the cell surface through a pathway that includes cAMP and cAMP-dependent protein kinase (PKA), but the mechanisms that link PKA to ENaC are unknown. Here we found that cAMP regulates Na+ transport in part by inhibiting the function of Nedd4-2, an E3 ubiquitin-protein ligase that targets ENaC for degradation. Consistent with this model, we found that cAMP inhibited Nedd4-2 by decreasing its binding to ENaC. Moreover, decreased Nedd4-2 expression (RNA interference) or overexpression of a dominant negative Nedd4-2 construct disrupted ENaC regulation by cAMP. Nedd4-2 was a substrate for phosphorylation by PKA in vitro and in cells; three Nedd4-2 residues were phosphorylated by PKA and were required for cAMP to inhibit Nedd4-2 (relative functional importance Ser-327 > Ser-221 > Thr-246). Previous work found that these residues are also phosphorylated by serum and glucocorticoid-inducible kinase (SGK), a downstream mediator by which aldosterone regulates epithelial Na+ transport. Consistent with a functional interaction between these pathways, overexpression of SGK blunted ENaC stimulation by cAMP, whereas inhibition of SGK increased stimulation. Conversely, cAMP agonists decreased ENaC stimulation by SGK. The data suggest that cAMP regulates ENaC in part by phosphorylation and inhibition of Nedd4-2. Moreover, Nedd4-2 is a central convergence point for kinase regulation of Na+ transport.
≫MB-Activity Measure Based Slice Grouping and Unequal Error Protection for H.264/AVC Video Transmission over Wireless Networks

A novel scheme for transmitting H.264/AVC video streams under error-prone environments is presented in this paper. The proposed scheme exploits a slice grouping method which bases on a macroblock (MB)- activity measure, and employs an effective unequal error protection (UEP) scheme using Turbo-codes as forward error correction (FEC) tools to the output H.264/AVC streams. Experiments show that our proposed UEP approach outperforms the conventional H.264/AVC error-resilient method.
Tropical West Pacific moisture dynamics and climate controls on rainfall isotopic ratios in southern Papua, Indonesia

Understanding the controls on stable isotopologues of tropical rainfall is critical for paleoclimatic reconstruction from tropical ice core records. The southern Papua region, Indonesia, has a unique climate regime that allows for the evaluation of the influence of precipitation and convective activity on seasonal rainfall δ18O. The influence of the El Niño–Southern Oscillation (ENSO) on interannual rainfall δ18O variation is also important for paleoclimate reconstruction. Here we present stable isotope analyses of 1332 rain samples collected daily during the period from January 2013 to February 2014 (ENSO‐normal) and December 2014 to September 2015 (El Niño) at various elevation stations (9 to 3945 m above sea level) on the southern slope of the central mountain ranges in Papua. The results suggest an altitude effect with an isotopic lapse rate for δ18O (δD) of −2.4‰/km (−18.2‰/km). The temporal δ18O variability (daily to interannual) is controlled mostly by regional convective activity rather than local/regional precipitation amount. The intraseasonal δ18O variation resembles the Madden‐Julian Oscillation cycle with major δ18O depletion events associated with active (wet) phases. Moisture origins, transport pathways, moisture convergence, and raindrop evaporation appear to have no significant seasonal effects on δ18O, leading to the conclusion that condensation temperature controls δ18O depletion associated with convective activity. Seasonal δ18O variation is likely associated with atmospheric temperature at the mean condensation level as indicated by the altitude of latent heat release in the troposphere. Rainfall δ18O (δD) is generally enriched by 1.6‰–2‰ (11‰–15‰) during El Niño than during ENSO‐normal periods.
Agile offshore techniques - a case study

The case study gives a brief insight into the different techniques used by an agile project team which was located at two different geographical locations. It demonstrates how the teams used effective tools and techniques for collaborating. The case study also talks in detail about team dynamics and how the team gelled together as a unit in spite of being physically distant. It demonstrates how a flat organization structure can be more effective than a hierarchical structure. The case study also shows how the team switched from the development to maintenance mode with a different set of techniques which mapped to those used in the development mode.
Radiographic skills learning: procedure simulation using adaptive hypermedia.

The design and development of a simulation tool supporting learning of radiographic skills is reported. This tool has by textual, graphical and iconic resources, organized according to a building-block, adaptive hypermedia approach, which is described and supported by an image base of radiographs. It offers interactive user-controlled simulation of radiographic imaging procedures. The development is based on a commercially available environment (Toolbook 3.0, Asymetrix Corporation). The core of the system is an attributed precedence (priority) graph, which represents a task outline (concept and resources structure), which is dynamically adjusted to selected procedures. The user interface imitates a conventional radiography system, i.e. operating console, tube, table, patient and cassette. System parameters, such as patient positioning, focus-to-patient distance, magnification, field dimensions, tube voltage and mAs are under user control. Their effects on image quality are presented, by means of an image base acquired under controlled exposure conditions. Innovative use of hypermedia, computer based learning and simulation principles and technology in the development of this tool resulted in an enhanced interactive environment providing radiographic parameter control and visualization of parameter effects on image quality.
A participatory video and audio platform for community interaction using DVDs and IVR systems

Community screenings in rural India through DVDs and projectors have had an impact in improving education [1], agricultural information dissemination [3] and community empowerment [4]. These methods have been successful primarily because local people and references are featured in the videos making the information contextual, and interactive sessions are held with the audience making the screenings engaging. Nonetheless, there are some challenges. First, sustaining the interest of the audience during a screening depends heavily on training the field staff to remember various discussion topics they should initiate to make the screenings interactive. Second, even though a significant number of people may show up at the screening, it is hard for the field staff to keep them engaged beyond the screenings to offer them continued information support and on-going learning.
Comparison of multisensory and strength training for postural control in the elderly

Objective The objective of this study was to analyze the efficacy of multisensory versus muscle strengthening to improve postural control in healthy community-dwelling elderly. Participants We performed a single-blinded study with 46 community-dwelling elderly allocated to strength (GS, n = 23; 70.18 ± 4.8 years 22 women and 1 man) and multisensory exercises groups (GM, n = 23; 68.8 ± 5.9 years; 22 women and 1 man) for 12 weeks. Methods We performed isokinetic evaluations of muscle groups in the ankle and foot including dorsiflexors, plantar flexors, inversion, and eversion. The oscillation of the center of pressure was assessed with a force platform. Results The GM group presented a reduction in the oscillation (66.8 ± 273.4 cm2 to 11.1 ± 11.6 cm2; P = 0.02), which was not observed in the GS group. The GM group showed better results for the peak torque and work than the GS group, but without statistical significance. Conclusion Although the GM group presented better results, it is not possible to state that one exercise regimen proved more efficacious than the other in improving balance control.
Defective transcription of the right end of bacteriophage T7 DNA during an abortive infection of F plasmid-containing Escherichia coli

Transcription of T7 and mutant T3 DNA during infections of F plasmid-containing cells has been analyzed by using Southern hybridization. A transcriptional defect is apparent in these abortively infected cells that is most severe in the class III region of the phage genome. In particular, RNAs that are initiated from the gene 13 promoter are not elongated to give full-length molecules. It is suggested that the transcription defect results from positive supercoiling of the template DNA and that torsional constraints may even prevent the complete entry of the phage genome into an abortively infected cell.
Face/Non-face Classification Method Based on Partial Face Classifier Using LDA and MLP

In this paper, we proposed a face / non-face classification method based on a partial face classifier using LDA and MLP for face detection system. General classifier for face and non-face classification requires many face images and background images for training. In contrast, the classification method based on the partial face classifier uses a constraint of facial structure that a pattern on the center of face must be surrounded with patterns on the boundary of face. So the partial face classifier in this method can reduce the affect of non-face image because it’s trained by only face images without non-face images. For improving the classification performance and computation efficiency of partial face classifier, the input pattern has to be represented as a low dimensionality by removing redundant and irrelevant features in the high dimensional image. To achieve this purpose, we transform input image into low dimensional pattern by LDA. In the experimental results, the time and space complexity are reduced by 48.9% and 43.6% when the 8 LDA features are used instead of image features. Also in the experiment of partial face classification, the proposed method which uses 8 LDA feature obtains the classification rate 1.7% higher than using image features.
An Expressive Language and Efficient Execution System for Software Agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information -- typically, a slow, I/O-bound process -- it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as nonstreaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine.
Using matching distance in size theory: A survey

In this survey we illustrate how the matching distance between reduced size functions can be applied for shape comparison. We assume that each shape can be thought of as a compact connected manifold with a real continuous function defined on it, that is a pair (ℳ︁,φ : ℳ︁ → ℝ), called size pair. In some sense, the function φ focuses on the properties and the invariance of the problem at hand. In this context, matching two size pairs (ℳ︁, φ) and (𝒩, ψ) means looking for a homeomorphism between ℳ︁ and 𝒩 that minimizes the difference of values taken by φ and ψ on the two manifolds. Measuring the dissimilarity between two shapes amounts to the difficult task of computing the value δ = inff maxP∈ℳ︁ |φ(P) − ψ(f(P))|, where f varies among all the homeomorphisms from ℳ︁ to 𝒩. From another point of view, shapes can be described by reduced size functions associated with size pairs. The matching distance between reduced size functions allows for a robust to perturbations comparison of shapes. The link between reduced size functions and the dissimilarity measure δ is established by a theorem, stating that the matching distance provides an easily computable lower bound for δ. Throughout this paper we illustrate this approach to shape comparison by means of examples and experiments. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 154–161, 2006
Language modeling with stochastic automata

It is well known that language models are effective for increasing the accuracy of speech and handwriting recognizers, but large language models are often required to achieve low model perplexity (or entropy) and yet still have adequate language coverage. We study three efficient methods for stochastic language modeling in the context of the stochastic pattern recognition problem (variable-length Markov models, variable n-gram stochastic automata and refined probabilistic finite automata), and we give the results of a comparative performance analysis. In addition, we show that a method which combines two of these language modeling techniques yields an even better performance than the best of the single techniques tested.
Bestrophin-Encoded Ca2+-Activated Cl− Channels Underlie a Current with Properties Similar to the Native Current in the Moth Spodoptera littoralis Olfactory Receptor Neurons

Responses of insect olfactory receptor neurons (ORNs) involve an entry of Ca2+ through olfactory heterodimeric receptor complexes. In moths, the termination of ORN responses was found to strongly depend on the external Ca2+ concentration through the activation of unknown Ca2+-dependent Cl− channels. We thus investigated the molecular identity of these Cl− channels. There is compelling evidence that bestrophins form Cl− channels when expressed in heterologous systems. Here we provide evidence that antennae of the moth Spodoptera littoralis express three transcripts encoding proteins with hallmarks of bestrophins. One of these transcripts, SlitBest1b, is expressed in ORNs. The heterologous expression of SlitBest1b protein in CHO-K1 cells yielded a Ca2+-activated Cl− current that shares electrophysiological properties with the native Ca2+-activated Cl− current of ORNs. Both currents are anionic, present similar dependence on the intracellular Ca2+ concentration, partly inactivate over time, have the same anion permeability sequence, the same sequence of inhibitory efficiency of blockers, the same almost linear I–V relationships and finally both currents do not depend on the cell volume. Therefore, our data suggest that SlitBest1b is a good candidate for being a molecular component of the olfactory Ca2+-activated Cl− channel and is likely to constitute part of the insect olfactory transduction pathway. A different function (e.g. regulation of other proteins, maintenance of the anionic homeostasis in the sensillar lymph) and a different role (e.g. involvement in the olfactory system development) cannot be excluded however.
Research on the algorithm of Load-balanced Hierarchical Topology Control for WSN

In terms of the characteristics of itself node number, density and topology dynamic change in wireless sensor network, according to the wireless transmission energy consumption model REDM, Load-balanced Hierarchical Topology Control Algorithm based on LEACH algorithm is designed. The algorithm still uses the concept of "round". Substitute cluster head and load evaluation method are introduced in view of frequent election of cluster head after cluster head failure. It can reduce cluster formation frequency, balance network energy consumption, extend the network lifetime and improve network management efficiency. Finally, the simulation results have verified this conclusion.
Influential factors on strategic management accounting application and its influence on the business: Empirical evidence from a developing market

The successful application of strategic management accounting (SMA) and its influence on business in a developing market are contingent on various factors. Oyewo (2022) emphasizes the impact of contextual factors such as organizational structure, information technology (IT) quality, business strategy, market orientation, competition, and environmental uncertainty on the usage intensity of SMA and its influence on competitive advantage. This study investigates the drivers behind adopting SMA and its consequential effects on the performance of listed Vietnamese firms. The research employed a mixed-methods approach, integrating qualitative and quantitative methodologies to delineate research models, formulate hypotheses, assess the determinants influencing SMA implementation, and evaluate the impact of SMA application on corporate performance. The authors employed the partial least squares structural equation modeling (PLS-SEM) framework to analyse data from 147 listed companies in Vietnam. The outcomes reveal that variables such as the utilisation of information technology in business operations, participative decision-making, accountant qualifications, perceived environmental uncertainty, and strategic orientation positively influence SMA implementation, subsequently yielding positive effects on the performance of listed companies in Vietnam. This research contributes valuable insights into the factors shaping SMA adoption in Vietnamese enterprises, enriching practical managerial knowledge and theoretical perspectives.
Control of a robotic gripper for grasping objects in no-gravity conditions

In space applications, it is conceivable that part of the robotic activities could involve the grasp and/or manipulation of free-floating objects in the absence of gravity. In this case, synchronous application of contacts seems to represent a basic feature in order to efficiently grasp the floating items. In this sense, an additional difficulty is that objects may have irregular shape and/or be non well positioned in the gripper workspace. These difficulties cannot be handled in a simple way with standard 2-jaw grippers, with one (or two) degrees of freedom. In the paper, an activity for designing and experimenting with a gripper for this type of operations is reported, and the first laboratory results are presented and discussed. The main features of the gripper are its kinematic configuration (3 fingers with 3 dof) and its sensorial equipment, features that improve the dexterity of this device if compared to more classical devices.
A Two-Stage Approach for Learning a Sparse Model with Sharp Excess Risk Analysis

This paper aims to provide a sharp excess risk guarantee for learning a sparse linear model without any assumptions about the strong convexity of the expected loss and the sparsity of the optimal solution in hindsight. Given a target level ε for the excess risk, an interesting question to ask is how many examples and how large the support set of the solution are enough for learning a good model with the target excess risk. To answer these questions, we present a two-stage algorithm that (i) in the first stage an epoch based stochastic optimization algorithm is exploited with an established O(1/ε) bound on the sample complexity; and (ii) in the second stage a distribution dependent randomized sparsification is presented with an O(1/ε) bound on the sparsity (referred to as support complexity) of the resulting model. Compared to previous works, our contributions lie at (i) we reduce the order of the sample complexity from O(1/ε2) to O(1/ε) without the strong convexity assumption; and (ii) we reduce the constant in O(1/ε) for the sparsity by exploring the distribution dependent sampling.
Predicting Urban Water Quality With Ubiquitous Data - A Data-Driven Approach

Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. However, predicting the urban water quality is a challenging task since the water quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, water usage patterns, and land uses. In this article, we forecast the water quality of a station over the next few hours from a data-driven perspective, using the water quality data, and water hydraulic data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, pipe networks, structure of road networks, and point of interests (POIs). First, we identify the influential factors that affect the urban water quality via extensive experiments. Second, we present a multi-task multi-view learning method to fuse those multiple datasets from different domains into an unified learning model. We evaluate our method with real-world datasets, and the extensive experiments verify the advantages of our method over other baselines and demonstrate the effectiveness of our approach.