Miracle-dz/UniFAR-FTUs huggingface dataset:
Occurrence of Multiple Glyphosate-Resistant Weeds in Brazilian Citrus Orchards

Glyphosate is the most widely used herbicide for weed control in citrus orchards in Brazil; therefore, it is likely that several species have gained resistance to this herbicide and that more than one resistant species can be found in the same orchard. The objective was to identify weeds resistant to glyphosate in citrus orchards from different regions of the São Paulo State (SP) and determine how many resistant species are present within the same orchard. Seeds of Amaranthus deflexus, A. hybridus, Bidens pilosa, Chloris elata, Conyza bonariensis, Digitaria insularis, Solanum Americanum, and Tridax procumbens, which, as reported by growers, are suspected to be resistant to glyphosate, were collected from plants that survived the last application of this herbicide (>720 g of acid equivalent [ae] ha–1) in sweet orange and Tahiti acid lime orchards. Based on dose–response and shikimic acid accumulation assays, all populations of A. deflexus, A. hybridus, B. pilosa, and T. procumbens were sensitive to glyphosate. However, populations of B. pilosa from the Olimpia region (R-NS, R-PT and R-OdA) showed signs of resistance based on plant mortality rates by 50% within a population (LD50 = 355–460 g ae ha−1). All populations of C. bonariensis, C. elata, and D. insularis were resistant to glyphosate, presenting resistance ratios from 1.9 to 27.6 and low shikimate accumulation rates. Solanum americanum also showed resistance, with resistance ratios ranging from 4.3 to 25.4. Most of the citrus orchards sampled presented the occurrence of more than one species resistant to glyphosate: Nossa Senhora—one species; Olhos D’agua and Passatempo—two species; Araras—four species; and Cordeiropolis and Mogi-Mirim—up to five species. The results reported in this paper provide evidence of multiple species in citrus orchards from São Paulo that have exhibited resistance to glyphosate. This underscores the difficulties in managing glyphosate-resistant weeds which are prevalent throughout the country, such as C. bonariensis and D. insularis. The presence of these resistant species further complicates the control of susceptible species that may also develop resistance. In addition, the glyphosate resistance of S. americanum was identified for the first time.
Health reform in Nigeria: the politics of primary health care and universal health coverage

Abstract Over the past decade, Nigeria has seen major attempts to strengthen primary health care, through the Saving One Million Lives (SOML) initiative, and to move towards universal health care, through the National Health Act. Both initiatives were successfully adopted, but faced political and institutional challenges in implementation and sustainability. We analyse these programmes from a political economy perspective, examining barriers to and facilitators of adoption and implementation throughout the policy cycle, and drawing on political settlement analysis (PSA) to identify structural challenges which both programmes faced. The SOML began in 2012 and was expanded in 2015. However, the programme’s champion left government in 2013, a key funding source was eliminated in 2015, and the programme did not continue after external funding elapsed in 2021. The National Health Act passed in 2014 after over a decade of advocacy by proponents. However, the Act’s governance reforms led to conflict between health sector agencies, about both reform content and process. Nine years after the Act’s passage, disbursements have been sporadic, and implementation remains incomplete. Both programmes show the promise of major health reforms in Nigeria, but also the political and institutional challenges they face. In both cases, health leaders crafted evidence-based policies and managed stakeholders to achieve policy adoption. Yet political and institutional challenges hindered implementation. Institutionally, horizontal and vertical fragmentation of authority within the sector impeded coordination. Politically, electoral cycles led to frequent turnover of sectoral leadership, while senior politicians did not intervene to support fundamental institutional reforms. Using PSA, we identify these as features of a ‘competitive clientelist’ political settlement, in which attempts to shift from clientelist to programmatic policies generate powerful opposition. Nonetheless, we highlight that some policymakers sought to use health reforms to change institutions at the margin, suggesting future avenues for governance-oriented health reforms.
Hybrid strategies for stand-alone surgical ablation of atrial fibrillation

Atrial fibrillation (AF) has been reported as a major cause of cardiac morbidity and mortality, and significantly reduces the quality of life in symptomatic patients. Current guidelines recommend antiarrhythmic drugs and catheter ablation (CA) as first-line therapy. Despite CA showed to be associated with lower incidence of peri-procedural complications, rhythm outcomes are far from optimal. Indeed, patients undergoing CA frequently require multiple AF ablation procedures, especially in those with persistent and long-standing persistent AF. While surgical ablation can provide transmural lesions, surgical invasiveness has limited the widespread use of this approach due to the increased perioperative complications. The development of minimally invasive thoracoscopic approaches has renewed the interest towards surgical ablation, thus favoring more simplified ablation sets. Therefore, the concept of “hybrid” ablation has emerged in order to theoretically enhance advantages of both minimally invasive and CA procedures while seeking to improve rhythm outcomes and reduce invasiveness and incidence of perioperative complications. On one hand, it provides the effectiveness of a surgical ablation, on the other, electrical mapping during CA can identify and treat any ablation gap or provide additional ablation lines, thus improving the chance of a stable sinus rhythm restoration at long-term follow-up. Three main thoracoscopic strategies are currently available. All of them can be performed in conjunction with the “catheter ablation procedure”: the “Fusion” technique, the bipolar clamp technique, and the most recent “convergent” technique. CA can be performed either simultaneously or with a staged approach after a blanking period in order to allow the ablation lesion to stabilize. Excellent results of the hybrid procedures have been reported in terms of rhythm outcomes and incidence of perioperative complications. This narrative review aims to discuss the rationale behind the concept of hybrid ablation for the treatment of AF regarding different available strategies, results and expert opinions.
Impact of frame rate on automatic speech-text alignment for corpus-based phonetic studies

Phonetic segmentation is the basis for many phonetic and linguistic studies. As manual segmentation is a lengthy and tedious task, automatic procedures have been developed over the years. They rely on acoustic Hidden Markov Models. Many studies have been conducted, and refinements developed for corpus based speech synthesis, where the technology is mainly used in a speaker-dependent context and applied on good quality speech signals. In a different research direction, automatic speech-text alignment is also used for phonetic and linguistic studies on large speech corpora. In this case, speaker independent acoustic models are mandatory, and the speech quality may not be so good. The speech models rely on 10 ms shift between acoustic frames, and their topology leads to strong minimum duration constraints. This paper focuses on the acoustic analysis frame rate, and gives a first insight on the impact of the frame rate on corpus-based phonetic studies.
Spatial dispersion in solids and plasmas

Preface. Basic concepts and formalism of spatial dispersion (R. Fuchs and P. Halevi). 1. Microscopic approach to the dielectric response of metallic surfaces (R.R. Gerhardts). 2. Spatial dispersion and surface electromagnetic absorption in metals (E.A. Kaner, A.A. Krokhin and N.M. Makarov). 3. Experimental aspects of spatial dispersion in thin films and small particles (T. Lopez-Rios). 4. Bulk and surface plasma waves in semiconducting superlattices (J.J. Quinn, G. Eliasson and P. Hawrylak). 5. Theory of spatial dispersion in small particles (R. Fuchs). 6. Exciton polaritons and optical properties of direct gap semiconductors (P. Halevi). 7. Effect of a DC current on electromagnetic surface waves (R.F. Wallis and B.G. Martin). 8. Low-frequency magnetoplasma waves in metals (V.T. Petrashov). 9. Magnetohydrodynamic surface waves (N.F. Cramer and I.J. Donnelly). 10. Nonlinear surface modes (A. Shivarova). 11. Non-local effects in laser plasmas (M.G. Haines). Author index. Subject index.
Improving MapReduce Performance Using Smart Speculative Execution Strategy

MapReduce is a widely used parallel computing framework for large scale data processing. The two major performance metrics in MapReduce are job execution time and cluster throughput. They can be seriously impacted by straggler machines-machines on which tasks take an unusually long time to finish. Speculative execution is a common approach for dealing with the straggler problem by simply backing up those slow running tasks on alternative machines. Multiple speculative execution strategies have been proposed, but they have some pitfalls: (i) Use average progress rate to identify slow tasks while in reality the progress rate can be unstable and misleading, (ii) Cannot appropriately handle the situation when there exists data skew among the tasks, (iii) Do not consider whether backup tasks can finish earlier when choosing backup worker nodes. In this paper, we first present a detailed analysis of scenarios where existing strategies cannot work well. Then we develop a new strategy, maximum cost performance (MCP), which improves the effectiveness of speculative execution significantly. To accurately and promptly identify stragglers, we provide the following methods in MCP: (i) Use both the progress rate and the process bandwidth within a phase to select slow tasks, (ii) Use exponentially weighted moving average (EWMA) to predict process speed and calculate a task's remaining time, (iii) Determine which task to backup based on the load of a cluster using a cost-benefit model. To choose proper worker nodes for backup tasks, we take both data locality and data skew into consideration. We evaluate MCP in a cluster of 101 virtual machines running a variety of applications on 30 physical servers. Experiment results show that MCP can run jobs up to 39 percent faster and improve the cluster throughput by up to 44 percent compared to Hadoop-0.21.
Overcoming in Known-Component Reconstruction of Cochlear Implants

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Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology

Significance Causes of intersubject variability in electrophysiological activity are unknown. We describe a methodology to unravel the ionic determinants of variability exhibited in experimental cardiac action potential recordings, based on the construction and calibration of populations of models. We show that 213 of 10,000 candidate models are consistent with the control experimental dataset. Ionic properties across the model population cover a wide range of values, and particular combinations of ionic properties determine shape, amplitude, and rate dependence of specific action potentials. Finally, we demonstrate that the calibrated model population quantitatively predicts effects caused by four concentrations of a potassium channel blocker. Cellular and ionic causes of variability in the electrophysiological activity of hearts from individuals of the same species are unknown. However, improved understanding of this variability is key to enable prediction of the response of specific hearts to disease and therapies. Limitations of current mathematical modeling and experimental techniques hamper our ability to provide insight into variability. Here, we describe a methodology to unravel the ionic determinants of intersubject variability exhibited in experimental recordings, based on the construction and calibration of populations of models. We illustrate the methodology through its application to rabbit Purkinje preparations, because of their importance in arrhythmias and safety pharmacology assessment. We consider a set of equations describing the biophysical processes underlying rabbit Purkinje electrophysiology, and we construct a population of over 10,000 models by randomly assigning specific parameter values corresponding to ionic current conductances and kinetics. We calibrate the model population by closely comparing simulation output and experimental recordings at three pacing frequencies. We show that 213 of the 10,000 candidate models are fully consistent with the experimental dataset. Ionic properties in the 213 models cover a wide range of values, including differences up to ±100% in several conductances. Partial correlation analysis shows that particular combinations of ionic properties determine the precise shape, amplitude, and rate dependence of specific action potentials. Finally, we demonstrate that the population of models calibrated using data obtained under physiological conditions quantitatively predicts the action potential duration prolongation caused by exposure to four concentrations of the potassium channel blocker dofetilide.
Role of Neuroimaging as a Biomarker for Neurodegenerative Diseases

It has recently been recognized that neurodegenerative diseases are caused by common cellular and molecular mechanisms including protein aggregation and inclusion body formation. Each type of neurodegenerative disease is characterized by the specific protein that aggregates. In these days, the pathway involved in protein aggregation has been elucidated. These are leading to approaches toward disease-modifying therapies. Neurodegenerative diseases are fundamentally diagnosed pathologically. Therefore, autopsy is essential for a definitive diagnosis of a neurodegenerative disease. However, recently, the development of various molecular brain imaging techniques have enabled pathological changes in the brain to be inferred even without autopsy. Some molecular imaging techniques are described as biomarker in diagnostic criteria of neurodegenerative disease. Magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), positron emission tomography (PET), and amyloid imaging are described in the diagnostic guidelines for Alzheimer’s disease in the National Institute on Aging-Alzheimer’s Association. MRI, dopamine transporter (DAT) imaging, and 123I-metaiodobenzyl-guanidine (MIBG) myocardial scintigraphy listed in the guidelines for consensus clinical diagnostic criteria for dementia with Lewy bodies are described as potential biomarkers. The Movement Disorder Society Progressive Supranuclear Palsy Study Group defined MRI, SPECT/PET, DAT imaging, and tau imaging as biomarkers. Other diagnostic criteria for neurodegenerative disease described neuroimaging findings as only characteristic finding, not as biomarker. In this review, we describe the role of neuroimaging as a potential biomarker for neurodegenerative diseases.
A Probabilistic Model of Redundancy in Information Extraction

Unsupervised Information Extraction (UIE) is the task of extracting knowledge from text without using hand-tagged training examples. A fundamental problem for both UIE and supervised IE is assessing the probability that extracted information is correct. In massive corpora such as the Web, the same extraction is found repeatedly in different documents. How does this redundancy impact the probability of correctness? This paper introduces a combinatorial "balls-andurns" model that computes the impact of sample size, redundancy, and corroboration from multiple distinct extraction rules on the probability that an extraction is correct. We describe methods for estimating the model's parameters in practice and demonstrate experimentally that for UIE the model's log likelihoods are 15 times better, on average, than those obtained by Pointwise Mutual Information (PMI) and the noisy-or model used in previous work. For supervised IE, the model's performance is comparable to that of Support Vector Machines, and Logistic Regression.
Points on curves in small boxes en applications

We introduce several new methods to obtain upper bounds on the number of solutions of the congruences $f(x) \equiv y \pmod p$ and $f(x) \equiv y^2 \pmod p,$ with a prime $p$ and a polynomial $f$, where $(x,y)$ belongs to an arbitrary square with side length $M$. We use these results and methods to derive non-trivial upper bounds for the number of hyperelliptic curves $Y^2=X^{2g+1} + a_{2g-1}X^{2g-1} +. + a_1X+a_0$ over the finite field $\F_p$ of $p$ elements, with coefficients in a $2g$-dimensional cube $ (a_0,., a_{2g-1})\in [R_0+1,R_0+M]\times. \times [R_{2g-1}+1,R_{2g-1}+M]$ that are isomorphic to a given curve and give an almost sharp lower bound on the number of non-isomorphic hyperelliptic curves with coefficients in that cube. Furthermore, we study the size of the smallest box that contain a partial trajectory of a polynomial dynamical system over $\F_p$.
Neural Mechanisms Underlying the Computation of Hierarchical Tree Structures in Mathematics

Whether mathematical and linguistic processes share the same neural mechanisms has been a matter of controversy. By examining various sentence structures, we recently demonstrated that activations in the left inferior frontal gyrus (L. IFG) and left supramarginal gyrus (L. SMG) were modulated by the Degree of Merger (DoM), a measure for the complexity of tree structures. In the present study, we hypothesize that the DoM is also critical in mathematical calculations, and clarify whether the DoM in the hierarchical tree structures modulates activations in these regions. We tested an arithmetic task that involved linear and quadratic sequences with recursive computation. Using functional magnetic resonance imaging, we found significant activation in the L. IFG, L. SMG, bilateral intraparietal sulcus (IPS), and precuneus selectively among the tested conditions. We also confirmed that activations in the L. IFG and L. SMG were free from memory-related factors, and that activations in the bilateral IPS and precuneus were independent from other possible factors. Moreover, by fitting parametric models of eight factors, we found that the model of DoM in the hierarchical tree structures was the best to explain the modulation of activations in these five regions. Using dynamic causal modeling, we showed that the model with a modulatory effect for the connection from the L. IPS to the L. IFG, and with driving inputs into the L. IFG, was highly probable. The intrinsic, i.e., task-independent, connection from the L. IFG to the L. IPS, as well as that from the L. IPS to the R. IPS, would provide a feedforward signal, together with negative feedback connections. We indicate that mathematics and language share the network of the L. IFG and L. IPS/SMG for the computation of hierarchical tree structures, and that mathematics recruits the additional network of the L. IPS and R. IPS.
Xylella fastidiosa: Host Range and Advance in Molecular Identification Techniques

In the never ending struggle against plant pathogenic bacteria, a major goal is the early identification and classification of infecting microorganisms. Xylella fastidiosa, a Gram-negative bacterium belonging to the family Xanthmonadaceae, is no exception as this pathogen showed a broad range of vectors and host plants, many of which may carry the pathogen for a long time without showing any symptom. Till the last years, most of the diseases caused by X. fastidiosa have been reported from North and South America, but recently a widespread infection of olive quick decline syndrome caused by this fastidious pathogen appeared in Apulia (south-eastern Italy), and several cases of X. fastidiosa infection have been reported in other European Countries. At least five different subspecies of X. fastidiosa have been reported and classified: fastidiosa, multiplex, pauca, sandyi, and tashke. A sixth subspecies (morus) has been recently proposed. Therefore, it is vital to develop fast and reliable methods that allow the pathogen detection during the very early stages of infection, in order to prevent further spreading of this dangerous bacterium. To this purpose, the classical immunological methods such as ELISA and immunofluorescence are not always sensitive enough. However, PCR-based methods exploiting specific primers for the amplification of target regions of genomic DNA have been developed and are becoming a powerful tool for the detection and identification of many species of bacteria. The aim of this review is to illustrate the application of the most commonly used PCR approaches to X. fastidiosa study, ranging from classical PCR, to several PCR-based detection methods: random amplified polymorphic DNA (RAPD), quantitative real-time PCR (qRT-PCR), nested-PCR (N-PCR), immunocapture PCR (IC-PCR), short sequence repeats (SSRs, also called VNTR), single nucleotide polymorphisms (SNPs) and multilocus sequence typing (MLST). Amplification and sequence analysis of specific targets is also mentioned. The fast progresses achieved during the last years in the DNA-based classification of this pathogen are described and discussed and specific primers designed for the different methods are listed, in order to provide a concise and useful tool to all the researchers working in the field.