Miracle-dz/UniFAR-FTUs huggingface dataset:
Projections et distances discrètes

Le travail se situe dans le domaine de la geometrie discrete. La tomographie discrete sera abordee sous l'angle de ses liens avec la theorie de l'information, illustres par l'application de la transformation Mojette et de la "Finite Radon Transform" au codage redondant d'information pour la transmission et le stockage distribue. Les distances discretes seront exposees selon les points de vue theorique (avec une nouvelle classe de distances construites par des chemins a poids variables) et algorithmique (transformation en distance, axe median, granulometrie) en particulier par des methodes en un balayage d'image (en "streaming"). Le lien avec les sequences d'entiers non-decroissantes et l'inverse de Lambek-Moser sera mis en avant.
Comparative in vitro activities of tigecycline and 11 other antimicrobial agents against 215 epidemiologically defined multidrug-resistant Acinetobacter baumannii isolates.

J Antimicrob Chemother 2006; 57: 373–83. 3. Pournaras S, Markogiannakis A, Ikonomidis A et al. Outbreak of multiple clones of imipenem-resistant Acinetobacter baumannii isolates expressing OXA-58 carbapenemase in an intensive care unit. J Antimicrob Chemother 2006; 57: 557–61. 4. Tsakris A, Ikonomidis A, Pournaras S et al. VIM-1 metallo-blactamase in Acinetobacter baumannii. Emerg Infect Dis 2006; 12: 981–3. 5. Turton JF, Ward ME, Woodford N et al. The role of ISAba1 in expression of OXA carbapenemase genes in Acinetobacter baumannii. FEMS Microbiol Lett 2006; 258: 72–7. 6. Poirel L, Nordmann P. Genetic structures at the origin of acquisition and expression of the carbapenem-hydrolyzing oxacillinase gene blaOXA-58 in Acinetobacter baumannii. Antimicrob Agents Chemother 2006; 50: 1442–8. 7. Héritier C, Poirel L, Fournier PE et al. Characterization of the naturally occurring oxacillinase of Acinetobacter baumannii. Antimicrob Agents Chemother 2005; 49: 4174–9.
Cross-National Differences in Gain-Domain Risk Preferences among Older Populations: Judgment and Decision-Making Behaviors in Rapidly Aging Countries

This research explores the systematic, cross-national differences in choice-inferred risk preferences between American and South Korean (“Korean�? ) elders. A total of four different sample groups including – the American elders , the Korean elders, the American young adults, and the Korean young adults – were surveyed. All four groups were asked to partake in a two-part questionnaire: one pertaining to their background information and the other consisting of a set of gain-domain choice questions based on hypothetical lottery situations. The result of the study highlights three statistically significant (α=0.05) findings among the groups surveyed: (1) The Korean elderly group tend to be more risk-averse than the American elderly group, (2) the American elderly group tend to be more risk-seeking than the American young adult group, (3) and the Korean elderly group tend to be more risk-averse than the Korean young adult group.
Optimization of the Water Alternating Gas Injection Strategy in an Oil Reservoir Using Evolutionary Algorithms

The majority of the countries use oil as the main source of their energetic matrices. Techniques of Enhanced Oil Recovery (EOR) have been widely used with the aim of increasing oil and gas recovery trapped in oil reservoir, as 011 as improving the sweep and displacement efficiency. The use of tertiary (or advanced) recovery techniques such as Water Alternating Gas (WAG) injection has been considered promising to attend the expectations of improving the oil recovery, at the same it contributes with changes in some chemical properties of the reservoir that facilitate its exploitation. This paper describes the optimization of a water alternating gas injection strategy in an oil reservoir taking into account techniques of Evolutionary Computing used for maximizing the Net Present Value (NPV) of the field in analysis. It was created a program, developed in JAVA, that performs the optimization through the use of the custom evolutionary algorithm implemented and also uses the commercial software $\mathbf{GEM}^{\bigcirc\! \! \! \text{R}}$, which executes the numerical simulation and deliver important data to the optimizer.
MANAGING DRUG USE IN THE ELDERLY. General practitioners’ adherence to guidelines and patients’ conceptions of medication

Introduction: Older patients’ knowledge about their medicines is generally poor. Patients are entitled to be informed on an individual and adequate level. Evidence-based guidelines should, in most cases, be followed also in the treatment of elderly to avoid suboptimal treatment, for example for cardiovascular conditions, or risky prescriptions in the case of renal impairment. Objectives: 1. To describe frail elderly patients’ knowledge about and attitudes towards their medicines and to explore their experiences of receiving information about their medications. 2. To describe how well general practitioners (GPs) adhere to guidelines when using renal risk drugs in frail elderly patients and when treating elderly with cardio¬¬vascular disease and to evaluate local education of GPs as a tool for improvement. Methods: (Paper I, IV) For each prescribed medicine, the patient was asked about the indication and possible adverse effects. Attitudes were investigated with the Beliefs about Medicines Questionnaire. Semi-structured interviews covering questions on information about medication were analysed qualitatively. (Paper II) Medications for elderly patients in nursing homes were registered and the use of renal risk drugs was compared to renal function, estimated from s-creatinine and s-cystatin C. (Paper III) Data about GPs adherence to guidelines was collected from the medical records of patients aged ≥ 65 with a cardiovascular diagnosis. Local education was organized, followed by repeated measurements. Results: The knowledge about indication was satisfying but the knowledge about possible adverse effects was poor. The patients believed strongly in the benefits of their medication. The elderly felt comfortable with information when they trusted their physician or their medication, when they received enough information from the prescriber or when they knew how to find out sufficient information by themselves. They felt insecure if they were anxious, if the availability of medical care was poor or if they did not receive enough information. Reduced renal function was common (53%) in nursing home patients but s-creatinine was often normal. Renal risk drugs were rarely prescribed. The adherence to guidelines about cardiovascular diseases was low among GPs, but educational efforts appeared to increase the adherence. Conclusions: To receive an adequate drug therapy in the elderly, GPs should pay attention to patients’ need for suitable information on their medication and follow evidence-based guidelines about when to initiate and when to avoid certain drugs.
Soybean Rust Resistance Derived from Glycine tomentella in Amphiploid Hybrid Lines

Soybean rust (SBR), caused by the fungal pathogen Phakopsora pachyrhizi Syd. , has the potential to cause significant economic yield loss in U.S. soybean production. Four single dominant resistance genes have been identified in soybean [Glycine max (L.) Merr. ] that only confer specific resistance to a few rust isolates that have been tested. Additional resistance genes have been identified in wild perennial relatives, including G. tomentella Hayata (accession PI 483218, 2n 5 78). Intersubgeneric hybrids have been created between G. max (cv. Altona) and this G. tomentella accession. Amphiploid hybrid lines (2n 5 118) were the result of this hybridization and when further backcrossed to G. max (cv. Clark 63), derived fertile lines (2n 5 40) were also generated. Both sets of progeny were screened at the USDA-ARS facility at Ft. Detrick, MD, to determine if the resistance to SBR was inherited in the subsequent populations. The amphiploid hybrid clones still retained the genetic SBR resistance that was found in the G. tomentella parent. However, the derived fertile lines were susceptible. These lines were not screened for SBR resistance following each backcross, which may explain this occurrence. Reinstituting the backcross procedure, while testing for SBR resistance at every generation, could move the SBR resistance gene(s) from G. tomentella to the cultivated soybean G. max.
Sensor‐based nitrogen fertilization for midseason rice production in southeast Missouri

Sensor-based nitrogen (N) decision management tools have shown potential to improve N fertilizer efficiency, yield, and profitability. Small-plot experiments were conducted to evaluate the use of sensor-based management in guiding midseason N application decisions. Treatments were two rice (Oryza sativa L.) cultivars (Jupiter, a medium grain, and Roy J, a long grain) and eight N treatments: (i) check (no N fertilizer); (ii) 60 lb N/acre applied preflood + midseason N applied based on sensor reading (sensor-based management); (iii) 90 lb N/acre applied preflood + midseason N applied based on sensor reading (sensor-based management); (iv) 120 lb N/acre applied preflood; (v) 120 lb N/acre applied preflood + 30 lb N/acre applied at midseason (traditional practice); (vi) 120 lb N/acre applied preflood + midseason N applied based on sensor reading (sensor-based management); (vii) 150 lb N/acre applied preflood; and (viii) 180 lb N/acre applied preflood (N reference strip). Grain yield and nitrogen use efficiency (NUE) were compared among N treatments. Results showed sensor-based management for midseason application recommended lower total N application rate by 15 to 90 lb N/acre over the traditional practice. These results indicate that N fertilization made by a sensor-based midseason recommendation can optimize yield and NUE in southeast Missouri. The optimal amount of N fertilizer required for rice crops may change dramatically from year to year. This is due to varying level of plant-available N in soil system through N turnovers from N-fixing organisms, mineralization of organic matter in the soil, and decomposition crop residues. In fact, most producers are aware that their yield levels change significantly, but they are not aware that the yield response to additional N changes as well. Nitrogen responsiveness and yield levels dictate precisely how much N should be applied. Thus, what farmers need to embrace is that their NUE changes each year as well. In southeast Missouri, the common N management practice is to apply most of the N (70– 120 lb N/acre) preflood (Dunn et al., 2008). Then a permanent flood is immediately established. The remaining N is applied based on the plant-N status at midseason. Determining the plant status can be very challenging for producers. To ensure the plant has adequate N to optimize yield, most producers often apply 30 to 45 lb N/acre at midseason via aerial application, Published online August 17, 2017
Robust online learning in neuromorphic systems with spike-based, distributed synapses

Neuromorphic Very Large Scale Integration (VLSI) hardware offers a low-power and compact electronic substrate for implementing distributed plastic synapses for artificial systems. However, the technology used for constructing analog neuromorphic circuits has limited resolution and high intrinsic variability, leading to large circuit mismatch. Consequently, neuromorphic synapse circuits are imprecise and thus the mapping of pre-defined models onto neural processing systems built with such components is practically unfeasible. This problem of variability can be avoided by off-loading learning to ad-hoc digital resources external to the synapse but this work-around introduces communication bottlenecks and so compromises compactness and power efficiency. Here I propose a more direct solution using a system composed of aggregated classifiers and distributed, stochastic learning to exploit the intrinsic variability and mismatch of the substrate. The system consists of a feed-forward neural network with one hidden layer of randomly connected neurons with fixed weights and a pool of binary classifiers with stochastic learning. To demonstrate the system, I developed software procedures to configure the hardware classifier and used them to test its ability to learn to identify a target class. The method follows earlier works where the neuron circuit parameters are first configured to obtain the desired behavior and then the plastic synapses are characterized by imposing determined preand postsynaptic mean activities. The classifier is then included in a simulated environment to compare the complete system to theoretical and computational models. In particular, a well-known dataset consisting of images of hand-written digits has been used as benchmark to demonstrate with experimental results that by aggregating the responses of imprecise classifiers trained with the feed-forward network it is possible to achieve a performance which is comparable to state-of-the-art. The intrinsic noise present in the neuromorphic hardware and the type of distributed plastic synapses used result in an effective combination of classifiers, as described by recent machine learning techniques. This work is a crucial step towards enabling online learning through synaptic plasticity on imprecise hardware, thus providing a novel, resource efficient substrate for machine learning. It demonstrates how a “bottom-up” approach that exploits the properties of the substrate to obtain a functioning neuromorphic system can elegantly overcome the complications due to the calibration of imprecise circuits with limited configurability, thus extending the valuability and feasibility of analog VLSI techniques. The biological plausibility of the system links to relevant issues in neuroscience, such as the role of intrinsic variability in probabilistic computation in the brain, and suggests possible design strategies for future emerging technologies.
Detection of brinjal little leaf phytoplasma in situ by light and fluorescence microscopy

*National Research Centre for Citrus, PO Box 464,Nagpur 440 010, E-mail: dasashiskumar@hotmail.com ‘Little leaf’ is one of the most important diseases of brinjal (Solanum melongana L.) in India (7). The disease is transmitted by grafting and through leaf hopper vector Hishimonus phycitis Dist. The infected plants are characterized by severe stunting and profuse branching, smalling of the leaves and phyllody of flowers. In early infection, no fruiting takes place and the loss is total. In late infection, fruits become malformed and seeds are shrivelled. The causative agent of this disease, phytoplasma (formerly called mycoplasma-like organism, MLO), a non culturable plant mollicute observed exclusively in the phloem tissues, was first detected in 1969 by electron microscopy (12). Since then, number of staining techniques have been tested to improve the detection of phytoplasma in plants and to reduce dependency on electron microscope for its identification. In recent years, more simple techniques have been developed for confirming phytoplasmas in plant tissue, using either the chromatic Dienes’ stain or the DNAbinding fluorochrome, 4′-6-diamidino-2-phenylindole (DAPI) with the aid of light or fluorescence microscopy. Dienes’ stain is widely used to detect animal mycoplasmas, plant spiroplasmas in culture and phytoploasmas in plant tissue sections (1,6). DAPI is a fluorochrome which binds to AT-specific double stranded DNA, having an excitation wavelength of 372 nm and emission wavelength of 456 nm (5). It was first used to detect mycoplasma contamination in animal tissue culture by Russel et al.(8). Since then it has been employed to detect phytoplasma in several plants like Catharanthus roseus (4), peach and chokecherry (3), white ash and lilac (9), sandal (10). The purpose of this study was to detect phytoplasma in tissues of little leaf disease affected brinjal plant using DAPI stain and to compare its efficiency with Dienes’ stain.
A Deep Collaborative Framework for Face Photo–Sketch Synthesis

Great breakthroughs have been made in the accuracy and speed of face photo–sketch synthesis in recent years. Regression-based methods have gained increasing attention, which benefit from deeper and faster end-to-end convolutional neural networks. However, most of these models typically formulate the mapping from photo domain <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> to sketch domain <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> as a unidirectional feedforward mapping, <inline-formula> <tex-math notation="LaTeX">$G: X \to Y$ </tex-math></inline-formula>, and vice versa, <inline-formula> <tex-math notation="LaTeX">$F: Y \to X$ </tex-math></inline-formula>; thus, the utilization of mutual interaction between two opposite mappings is lacking. Therefore, we proposed a collaborative framework for face photo–sketch synthesis. The concept behind our model was that a middle latent domain <inline-formula> <tex-math notation="LaTeX">$\widetilde {Z}$ </tex-math></inline-formula> between the photo domain <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> and the sketch domain <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> can be learned during the learning procedure of <inline-formula> <tex-math notation="LaTeX">$G: X \to Y$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$F: Y \to X$ </tex-math></inline-formula> by introducing a collaborative loss that makes full use of two opposite mappings. This strategy can constrain the two opposite mappings and make them more symmetrical, thus making the network more suitable for the photo–sketch synthesis task and obtaining higher quality generated images. Qualitative and quantitative experiments demonstrated the superior performance of our model in comparison with the existing state-of-the-art solutions.
MICROSATELLIT MARKERS BAT25 AND BAT26 IN SUBJECTS WITH COLORECTAL CANCER IN SENEGAL.

A Ndiaye, B Kénémé, F Mbaye, F Diallo, A Samba, S Sanni, F Cisse, S Thiam, D Doupa, M Seck, I Thiam, C M M Dial, P S Diop, N D Sall, M Toure and M Sembène. 1. Laboratory of Molecular Biochemistry-Biology Cheikh Anta Diop University of Dakar, (Senegal). 2. Department of Animal Biology, Faculty of Science and Technology, Cheikh Anta Diop University, Dakar (Senegal). 3. Laboratory of Molecular Biochemistry-Biology Training and Research Unit (UFR), Faculty of Health Sciences, Gaston Berger University, Saint Louis (Senegal). 4. Department of general surgery, Aristide Le Dantec Hospital Dakar (Senegal). 5. Laboratory of anatomopathology, Aristide Le Dantec Hospital Dakar (Senegal). 6. Laboratory of anatomopathology, Grand Yoff Hospital, Dakar (Senegal). 7. Department of digestive surgery and hepatobiliary-Cancerology, Grand Yoff Hospital, Dakar (Senegal). Manuscript Info Abstract . Manuscript History
The Definitive Guide to How Computers Do Math : Featuring the Virtual DIY Calculator

Laboratories. Do You Speak Martian Chapter 1. Why This Book Is So Cool. Chapter 2. Introducing Binary and Hexadecimal Numbers. Chapter 3. Computers and Calculators. Chapter 4. Subroutines and Other Stuff. Chapter 5. Creating an Integer Calculator. Chapter 6. More Functions and Experiments. Interactive Laboratories. Appendix A: Installing Your DIY Calculator. Appendix B: Addressing Modes. Appendix C: Instruction Set Summary. Appendix D: Additional Resources. About the Authors. Acknowledgments. Index.
Curvature Adaptive 3D Scanning Transformation Calculation

Three-dimensional objects can be scanned by 3D laser scanners that use active triangulation. These scanners create three-dimensional point clouds from the scanned objects. The laser line is identified in the images, which are captured at given transformations by the camera, and the point cloud can be calculated from these. The hardest challenge is to construct these transformations so that most of the surface can be captured. The result of a scanning may have missing parts because either not the best transformations were used or because some parts of the object cannot be scanned. Based on the results of the previous scans, a better transformation plan can be created, with which the next scan can be performed. In this paper, a method is proposed for transforming a special 3D scanner into a position from where the scanned point can be seen from an ideal angle. A method is described for estimating this transformation in real-time, so these can be calculated for every point of a previous scan to set up a next improved scan.
Percent amplitude of fluctuation: A simple measure for resting-state fMRI signal at single voxel level

The amplitude of low-frequency fluctuation (ALFF) measures resting-state functional magnetic resonance imaging (RS-fMRI) signal of each voxel. However, the unit of blood oxygenation level-dependent (BOLD) signal is arbitrary and hence ALFF is sensitive to the scale of raw signal. A well-accepted standardization procedure is to divide each voxel’s ALFF by the global mean ALFF. However, this makes the individual voxel’s ALFF dependent on the global mean. Although Fractional ALFF (fALFF), proposed as a ratio of the ALFF to the total amplitude within the full frequency band, offers possible solution of the standardization, it actually mixes with the fluctuation power within the full frequency band and thus cannot reveal the true amplitude characteristics of a given frequency band. We proposed a new standardized, stand-alone, single-voxel metrics for RS-fMRI, namely percent amplitude of fluctuation (PerAF). PerAF is an analog to the percent signal change that has been widely used in the task fMRI communities, which allows it to be a straightforward measurement of BOLD signal fluctuations during resting state. We further conducted a test-retest reliability analysis comparing the relevant metrics, which indicated that PerAF was generally more reliable than the ALFF and fALFF. In a real RS-fMRI application, we further demonstrated that with and without standardization by global mean PerAF yielded prominently different results when comparing eyes open with eyes closed resting conditions, suggesting that future study should provide both with and without global mean standardization. The above results suggest that PerAF is a more reliable, straightforward and promising measurement for voxelwise brain activity-based RS-fMRI studies. For prompting future application of PerAF, we also implemented this method into a user-friendly toolbox REST-PerAF.
Response to reviewers

I think the abstract could be somewhat condensed, and (i) more quantitative information could be given; (ii) the text switches back and forth between amplitude, phase, ozone, water and temperature (e.g. around Lines 6-12), which is confusing. The abstract has been revised to discuss the ozone annual cycle first then the water vapour. P1/L7,8: “. and out of phase with the observed annual cycle of the tracer mixing ratio. ” P1/L8: Suggest: “The ozone contribution calculated here is . ” P1/L9: Suggest to replace ”This difference” with ”The difference”; but see general comment on Abstract above. These suggestions have been taken into account in the revised text. P2/L20: Suggest (the tropopause temperature is important for stratospheric water, not dehydration per se.) “Therefore the temperatures at 90hPa and 100hPa shown in Figs 1(c/d) are more directly relevant to water entering the stratosphere than the temperature at 70hPa. ” This is a good point and the sentence has been changed. P4/L26: Quantitative details for H2O missing in previous work. Fueglistaler et al. state that their calculations “show that the temperature adjustment for the annual cycle in water vapour around 70 hPa is about an order of magnitude smaller than that for ozone. ” At the time of writing Fueglistaler et al., this seemed to be sufficient information. However, in the context of your ozone effect being substantially larger than theirs, it is interesting to note that their water vapor impact (around 0.2K) is also smaller than your water vapor impact (your Figure 5b gives 0.6K). I think it would be great if your paper could resolve these