Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating time to ESRD using kidney failure risk equations: results from the African American Study of Kidney Disease and Hypertension (AASK).

BACKGROUND Planning for renal replacement therapy, such as referral for arteriovenous fistula placement and transplantation, often is guided by level of estimated glomerular filtration rate (eGFR). The use of risk equations might enable more accurate estimation of time to end-stage renal disease (ESRD), thus improving patient care. STUDY DESIGN Prospective observational study. SETTING & PARTICIPANTS 1,094 participants in the African American Study of Kidney Disease and Hypertension (AASK) cohort. PREDICTOR Age, sex, urine protein-creatinine ratio ≥ 1g/g, APOL1 high-risk status, and 3-year antecedent eGFR decline. OUTCOME Cumulative incidence of ESRD from 5 different starting points: eGFR of 30 and 15mL/min/1.73m(2) and 1-year ESRD risk of 5%, 10%, and 20%, estimated by a published 4-variable kidney failure risk equation. RESULTS 566 participants developed eGFR of 30mL/min/1.73m(2), 244 developed eGFR of 15mL/min/1.73m(2), and 437, 336, and 259 developed 1-year ESRD risks of 5%, 10%, and 20%, respectively. The 1-year cumulative incidence of ESRD was 4.3% from eGFR of 30mL/min/1.73m(2), 49.0% from eGFR of 15mL/min/1.73m(2), 6.7% from 5% ESRD risk, 15.0% from 10% ESRD risk, and 29% from 20% ESRD risk. From eGFR of 30mL/min/1.73m(2), there were several risk factors that predicted ESRD risk. From eGFR of 15mL/min/1.73m(2), only level of proteinuria did; median time to ESRD was 9 and 19 months in those with higher and lower proteinuria, respectively. Median times were less variable from corresponding ESRD risk thresholds. For example, median times to ESRD from 20% ESRD risk were 22 and 25 months among those with higher and lower proteinuria, respectively. LIMITATIONS Relatively homogeneous population of African Americans with hypertensive kidney disease. CONCLUSIONS Results of the present study suggest the potential benefit of incorporating kidney failure risk equations into clinical care, with selection of a specific threshold guided by its intended use.
High-rate codes over space, time, and frequency

This paper investigates increasing space, time, and frequency diversity through linear dispersion codes (LDC) in MIMO-OFDM wireless fading channels. Two new types of block-based high-rate space-time-frequency (STF) codes: (1) double linear dispersion space-time-frequency-coding (DLD-STFC), and (2) linear dispersion space-time-frequency-coding (LD-STFC) are proposed. In addition to double LDC encoding, DLD-STFC uses three-stage LDC decoding. The LD-STFC, on the other hand, requires only one LDC procedure across multiple OFDM subcarriers, OFDM blocks and multiple antennas. Both DLD-STFC and LD-STFC are backwards compatible to uncoded MIMO-OFDM systems. Comparison to an extension of a recently proposed LDC-OFDM to MIMO systems, called MIMO-LDC-OFDM, is made in which a single LDC-OFDM codeword is mapped to one transmit antenna. This paper discusses diversity properties of these STF block based designs. An error union bound analysis provides further insights, including more restrictive LDC code design criteria. Compared to other methods of similar complexity, simulations reveal that the bit error rate (BER) performance of full-rate DLD-STFC offers superior performance
SYBYL Line Notation (SLN): A Versatile Language for Chemical Structure Representation

SYBYL Line Notation (SLN) is an ASCII language used to represent chemical structures, including common organic molecules, macromolecules, polymers, and combinatorial libraries. SLN is also used to express substructural (2D) queries and includes a complete facility for Markush representation. This concise language is ideal for database storage of chemical entities as well as for network communication of structures and queries.
Entre salon et laboratoire : l'utilisation du « Q-Sort » de Waters et Deane pour décrire la qualité de la relation d'attachement parent-enfant

Ce texte decrit une adaptation francophone du Q-Sort d'attachement de Waters et Deane. L 'ambition de cet instrument est de permettre d'evaluer la qualite de la relation d'attachement du jeune enfant a ses parents ; il s'agit donc d'une methode qui se veut une alternative a la fameuse Situation etrange. Un Q-Sort est un type particulier de questionnaire dont les items sont proposes sur des cartes, qu'il s'agit de serier selon leur degre de pertinence. Cet instrument est destine a etre rempli soit par l'un des parents, soit par toute autre personne proche de l'enfant, ou encore par un observateur exterieur. L'instrument est presente dans ce texte ; les resultats de l'etude de validation seront fournis dans un second texte (meme numero).
Hybrid Voltage and Current Control Approach for DG-Grid Interfacing Converters With LCL filters

This paper presents a hybrid voltage and current control method to improve the performance of interfacing converters in distributed generation (DG) units. In general, current-controlled methods have been widely adopted in grid-connected converters nowadays. Nevertheless, in an islanded system, the voltage control of DG units is desired to provide direct voltage support to the loads. Due to the absence of closed-loop line current controller, the voltage control scheme can hardly regulate the DG unit's line current harmonics. Furthermore, if not addressed properly, the transfer between the grid-connected operation and autonomous islanding operation will introduce nontrivial transient currents. To overcome the drawbacks of voltage- and current-controlled DG units, this paper develops a hybrid voltage and current control method (HCM). The proposed method allows the coordinated closed-loop control of the DG unit fundamental voltage and line harmonic currents. With the HCM, local harmonic loads of the DG unit can even be compensated without using harmonic current extraction. In addition, the HCM guarantees smooth transition during the grid-connected/islanding operation mode transfer. Simulated and experimental results are provided to verify the feasibility of the proposed approach.
Attention and Memory: An Integrated Framework

This book brings together and assesses past and present research on information processing, and formulates a new general model of this entire system. Throughout his discussion, Cowan emphasizes that memory and attention cannot be considered independently and presents new research in psychophysiology, visual perception, cognitive development, and individual differences to support his thesis. This book offers both a review of the literature and a new model, and it will appeal to both the student and the professional interested in memory, attention, and cognitive psychology.
The North-West University Bible corpus: A multilingual parallel corpus for South African languages

Abstract This article reports on a project on the development of language resources and linguistic tools for Afrikaans. In the first part of the article, the multilingual parallel corpus that was created in the project is described. The corpus consists of aligned translations of the Bible in three languages, English, Dutch and Afrikaans. The second part of the article discusses one of the applications of a multilingual parallel corpus: induction of linguistic tools for a resource-scarce language.
Duration of untreated psychosis and cognitive functioning in first-episode patients

Background The ‘toxicity’ model of duration of untreated psychosis (DUP) suggests that longer DUP will be associated with poorer performance on cognitive tests in first-episode patients. Aims To test this hypothesis on a sample of 113 patients in a community-based early intervention programme for psychosis. Method Information was collected concerning a number of possible predictors of cognitive functioning including DUP. These were examined for their relation to performance on an extensive battery of cognitive tests administered shortly after the patients' admission to the programme. Results Although several variables such as gender, premorbid adjustment, education and handedness predicted cognitive functioning, no relation was found between DUP and performance on any component of the test battery. Conclusions Findings do not provide support for a toxic effect of DUP on cognitive functioning. Other mechanisms through which DUP might affect outcome such as psychological engulfment, social support and adherence to medication are discussed.
Direct Image Registration without Region of Interest

Standard direct image registration consists in estimating the geometric warp between a source and a target images by maximizing the photometric similarity for the pixels of a Region of Interest (ROI). The ROI must be included in the real overlap between the images otherwise standard registration algorithms fail. Determining a proper ROI is a hard ‘chicken-and-egg’ problem since the overlap is only known after a successful registration. Almost all algorithms in the literature consider that the ROI is given. This is generally either inconvenient or unreliable. In this paper we propose a new method that registers two images without using a ROI. The key idea of our method is to consider the off-target pixels as outliers. We define the off-target pixels as those pixels of the source image mapped outside the target image by the current warp. We use the classical robust M-estimation framework to handle both the off-target pixels and the usual outliers caused, for instance, by occlusions. With our formulation, the true image overlap is defined as the set of inliers. Experiments on synthetic and real data with the homography and Free-Form Deformation show that our method outperforms standard approaches in terms of accuracy and robustness while precisely retrieving the overlap in the source and target images.
Learning the hidden human knowledge of UAV pilots when navigating in a cluttered environment for improving path planning

We propose in this work a new model of how the hidden human knowledge (HHK) of UAV pilots can be incorporated in the UAVs path planning generation. We intuitively know that human's pilots barely manage or even attempt to drive the UAV through a path that is optimal attending to some criteria as an optimal planner would suggest. Although human pilots might get close but not reach the optimal path proposed by some planner that optimizes over time or distance, the final effect of this differentiation could be not only surprisingly better, but also desirable. In the best scenario for optimality, the path that human pilots generate would deviate from the optimal path as much as the hidden knowledge that its perceives is injected into the path. The aim of our work is to use real human pilot paths to learn the hidden knowledge using repulsion fields and to incorporate this knowledge afterwards in the environment obstacles as cause of the deviation from optimality. We present a strategy of learning this knowledge based on attractor and repulsors, the learning method and a modified RRT* that can use this knowledge for path planning. Finally we do real-life tests and we compare the resulting paths with and without this knowledge.
On a recursive structure of binary Hamming codes

We show that binary Hamming codes can be constructed recursively from binary block repetition codes with a parity bit. The recursive structure can be used to obtain the input-output weight enumerator for any block-length whereas brute-force enumeration can only be used for short block-length. The input-output weight enumerator of binary Hamming codes can be also derived using the split-weight enumerator of dual codes and the generalized MacWilliams identity. Hence, the bit-error rate of binary Hamming codes with binary antipodal signaling and hard-decision demodulation used on additive white Gaussian noise channels can be evaluated exactly. Numerically computed coding gain of Hamming codes reveals a surprising fact that the coding gain is minimum for a particular small value of SNR and the minimum coding gain is always negative. This contradicts statements found in the literature. Finally, we obtain the input-output weight enumerator of extended binary Hamming codes and we show that these codes also meet the Hamming bound although they are not perfect.
A PBW BASIS FOR LUSZTIG'S FORM OF UNTWISTED AFFINE QUANTUM GROUPS

Let ^ g be an untwisted aane Kac-Moody algebra over the eld K, and let U q (^ g) be the associated quantum enveloping algebra; let U q (^ g) be the Lusztig's integer form of U q (^ g), generated by q-divided powers of Chevalley generators over a suitable subring R of K(q). We prove a Poincar e-Birkhoo-Witt like theorem for U q (^ g), yielding a basis over R made of ordered products of q{divided powers of suitable quantum root vectors.
Pyelocaliectasis and Intrarenal Artery Doppler Indices in Uncomplicated Pregnancies

The effect of pyelocaliectasis on intrarenal arterial Doppler indices was evaluated in healthy pregnant women with respect to nonpregnant controls. There was no significant difference between the pregnant and nonpregnant subjects regarding the systolodiastolic ratio, resistivity index and pulsatility index throughout the second and third trimesters (p for all > 0.05). There were 134 kidneys with grade 0, 38 kidneys with grade I and 24 cases with grade II pyelocaliectasis in the pregnant population. Grade II caliectasis was found only in the right kidneys. The nonpregnant women all had grade 0 caliectasis. Doppler indices were not significantly different in various grades of caliectasis, and right and left kidneys had similar Doppler indices. The results of this study suggest that pyelocaliectasis increases with advancing gestational age and is more frequent and prominent on the right side but has no effect on intrarenal Doppler indices in healthy pregnant women. In cases of prominent alterations in Doppler indices, renal pathological conditions should be sought.
Loss of Somatostatin Receptor Subtype 2 in Prostate Cancer Is Linked to an Aggressive Cancer Phenotype, High Tumor Cell Proliferation and Predicts Early Metastatic and Biochemical Relapse

Somatostatin receptor subtype 2 (SSTR2) is the most frequently expressed SSTR subtype in normal human tissues. SSTR2 expression is differentially regulated in various tumor types and therapeutic somatostatin analogs binding to SSTR2 are in clinical use. In prostate cancers highly contradictory results in terms of SSTR2 expression and its consequences have been published over the past years. The aim of this study was to clarify prevalence and clinical significance of SSTR2 expression in prostate cancer. Therefore, quantitative immunohistochemistry (IHC) using a tissue microarray containing samples from 3,261 prostate cancer patients with extensive clinical and molecular cancer characteristics and oncological follow-up data was performed. IHC data was compared to publicly available Gene Expression Omnibus datasets of human prostate cancer gene expression arrays. While membranous SSTR2 staining was always seen in normal prostate epithelium, SSTR2 staining was absent in more than half (56.1%) of 2,195 interpretable prostate cancer samples. About 13% of all analyzed prostate cancers showed moderate to strong cytoplasmic and membranous SSTR2 staining. Staining intensities were inversely correlated with high Gleason grade, advanced pT category, high tumor cell proliferation (p<0.0001 each), high pre-operative PSA levels, (p = 0.0011) and positive surgical margins (p = 0.006). In silico analysis confirmed lower SSTR2 gene expression in prostate cancers vs. normal adjacent tissue (p = 0.0424), prostate cancer metastases vs. primary cancers (p = 0.0011) and recurrent vs. non-recurrent prostate cancers (p = 0.0438). PSA-free survival gradually declined with SSTR2 staining intensity (p<0.0001). SSTR2-negative cancers were more likely to develop metastases over time (p<0.05). In conclusion, most prostate cancers are indeed SSTR2-negative and loss of SSTR2 strongly predicts an unfavorable tumor phenotype and poor prognosis. Therefore, SSTR2 expression seems an important factor in the pathogenesis of prostate cancer and re-introduction of the receptor in SSTR2-negative prostate cancers may feature a promising target for novel gene therapy approaches.
A volumetric representation for obstacle detection in vegetated terrain

This paper presents a method for 3-D based obstacle detection on autonomous vehicles navigating in vegetated environments. At its core three different methods processing the surrounding occupancy, taken at separate stages and volumetric resolutions, are combined to a reliable and broad solution. Geometric relationships are evaluated at a coarse, yet robust, volumetric representation to form an initial assessment on obstacles. Then, a more careful evaluation takes place, at finer resolutions, to determine which obstacles are part of the scene's vegetation, thus not real obstacles. Field experiments are shown to validate the method's applicability on two different autonomous vehicles: a water surface robot and a terrestrial four-wheeled one.
Efficient SAT solving: beyond supercubes

SAT (Boolean satisfiability) has become the primary Boolean reasoning engine for many EDA applications, so the efficiency of SAT solving is of great practical importance. Recently, Goldberg et al introduced supercubing, a different approach to search-space pruning, based on a theory that unifies many existing methods. Their implementation reduced the number of decisions, but no speedup was obtained. In this paper, we generalize beyond supercubes, creating a theory we call B-cubing, and show how to implement B-cubing in a practical solver. On extensive benchmark runs, using both real problems and synthetic benchmarks, the new technique is competitive on average with the newest version of ZChaff, is much faster in some cases, and is more robust.
Sort Optimization Algorithm of Median Filtering Based on FPGA

The traditional sorting algorithm of median filtering is optimized according to the hardware structure features of FPGA. FPGA is used to acquire the data parallelly for comparing the data of the same column in the median filtering window. Comparing results shared by adjacent filter window are saved temporarily to match the new round of median filtering by using FPGA internal resources. This method can reduce the comparing times from current 21 down to 13, and improve the algorithm efficiency nearly 40%. The experimental results prove that the optimized algorithm can filter a 1K×1K gray-level image in about 20ms, which ensure the proposed algorithm can be applied in the real-time image median filtering system.
Cost efficient datacenter selection for cloud services

Many cloud services nowadays are running on top of geographically distributed infrastructures for better reliability and performance. They need an effective way to direct the user requests to a suitable datacenter, in a cost efficient manner. Previous work focused mostly on the electricity cost of datacenters. The approaches favor datacenters at locations with cheaper electricity prices. In this paper, we augment the picture by considering another significant cost contributor: network bandwidth. We propose to utilize statistical multiplexing to strategically bundle demands at different locations. The anti-correlation between demands effectively smooths out the aggregated bandwidth usage, thereby saving the bandwidth cost calculated by burstable billing methods that charge the peak bandwidth usage. We present an optimization framework that models the realistic environment and practical constraints a cloud faces. We develop an efficient distributed algorithm based on dual decomposition and the subgradient method, and evaluate its effectiveness and practicality using real-world traffic traces and electricity costs.
Non-Invasive Diagnostic Imaging of Peripheral Arterial Disease

textabstractCTA could be improved by reducing the impact of vessel wall calcifications on image interpretation. With a 16-slice or recently introduced 64-slice MDCT scanner a higher resolution can be obtained compared to a 4-slice MDCT. This may result in less blooming of calcifications, which will improve image interpretation. However, the potential for radiation dose reduction with the 16- and 64-slice MDCT scanners, due to the differences in gantry geometry compared to a 4-slice MDCT, is lost when we obtain higher resolution images using thinner slices and lower pitch. Subtraction CTA is another approach to minimize the burden of vessel wall calcifications on image interpretation (55). However, this technique requires a high level of patient collaboration, is not feasible in 20% of the patients in spite of good collaboration, generates two times more images, and increases the radiation exposure. The possible solution for the calcification problem may be found in post-processing software or more likely in hardware improvements. A complete different solution lies not in the technique, but in selecting patients for whom CTA is contra-indicated due to extensive vessel wall calcifications. In this thesis we performed an initial evaluation of clinical predictors of vessel wall calcifications on CTA. A future study is needed to develop and validate a clinical prediction rule for this problem. The results from our study can help design such a future study and restrict the data collection to the most relevant variables. In summary, CTA is the optimal non-invasive imaging test for the initial evaluation of patients with PAD. In elderly patients, patients with diabetes mellitus, and those with cardiac disease a CTA is contra-indicated due to decreased clinical utility. In these patients a MRA should be considered as initial imaging test. With the current knowledge of the DIPAD trial it is not useful to perform another study on the costs and effects of non-invasive imaging test for PAD.
Video annotation by graph-based learning with neighborhood similarity

Graph-based semi-supervised learning methods have been proven effective in tackling the difficulty of training data insufficiency in many practical applications such as video annotation. These methods are all based on an assumption that the labels of similar samples are close. However, as a crucial factor of these algorithms, the estimation of pairwise similarity has not been sufficiently studied. Usually, the similarity of two samples is estimated based on the Euclidean distance between them. But we will show that similarities are not merely related to distances but also related to the structures around the samples. It is shown that distance-based similarity measure may lead to high classification error rates even on several simple datasets. In this paper we propose a novel neighborhood similarity measure, which simultaneously takes into account both thse distance between samples and the difference between the structures around the corresponding samples. Experiments on synthetic dataset and TRECVID benchmark demonstrate that the neighborhood similarity is superior to existing distance based similarity.