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Figure 2. Sample images from the three different versions of the PlantVillage dataset used in various experimental configurations. (A) Leaf 1 color, (B) Leaf 1 grayscale, (C) Leaf 1 segmented, (D) Leaf 2 color, (E) Leaf 2 gray-scale, (F) Leaf 2 segmented.Our approach is based on recent work Krizhevsky et al. (2012) which showed for the first time that end-to-end supervised training using a deep convolutional neural network architecture is a practical possibility even for image classification problems with a very large number of classes, beating the traditional approaches using hand-engineered features by a substantial margin in standard benchmarks. The absence of the labor-intensive phase of feature engineering and the generalizability of the solution makes them a very promising candidate for a practical and scaleable approach for computational inference of plant diseases. In the present study the properties of the JLCM are assessed by Monte-Carlo simulations. Simulations focus on the general model properties, on the model robustness to the number of individuals and the number of events, and on the quality of class separation. We analyze 54,306 images of plant leaves, which have a spread of 38 class labels assigned to them.
Modern technologies have given human society the ability to produce enough food to meet the demand of more than 7 billion people. However, food security remains threatened by a number of factors including climate change (Tai et al. , 2014), the decline in pollinators (Report of the Plenary of the Intergovernmental Science-PolicyPlatform on Biodiversity Ecosystem and Services on the work of its fourth session, 2016), plant diseases (Strange and Scott, 2005), and others. Plant diseases are not only a threat to food security at the global scale, but can also have disastrous consequences for smallholder farmers whose livelihoods depend on healthy crops. In the developing world, more than 80 percent of the agricultural production is generated by smallholder farmers (UNEP, 2013), and reports of yield loss of more than 50 due to pests and diseases are common (Harvey et al. , 2014). Furthermore, the largest fraction of hungry people (50) live in smallholder farming households (Sanchez and Swaminathan, 2005), making smallholder farmers a group that's particularly vulnerable to pathogen-derived disruptions in food supply.
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All patients were examined at inclusion and every 3 months thereafter for a maximum of 18 months for clinical, biochemical and hematological parameters. The disease-specific functional rating scale, revised ALSFRS (ALSFRS-R), was also assessed 1 month post-inclusion and then every 3 months until 18 months maximum. Survival time was defined as the duration between the date of disease onset and the date of a composite end-point: ALS-related death, tracheotomy, beginning of the non-invasive positive pressure ventilation (NIPPV) over 23 hours per day for 14 consecutive days or the date when last known to be alive.
Muthén and Shedden [4] jointly studied longitudinal data with a binary outcome. Lin et al. [5] developed the joint latent class model (JLCM) replacing the binary outcome by a time-to-event. The JLCM allows firstly to account for the dependency between a longitudinal biomarker and a time-to-event by distinguishing between different profiles of biomarker progression associated with the risk of event. Secondly, it allows to analyze different profiles of longitudinal biomarker process censored by the event occurrence. Finally, the JLCM provides predictions for the risk of event conditional on the biomarker progression. The obtained simulations results can be summarized as follows: in general the MLE properties of the model parameters are impacted by the number of individuals as well as by the number of observed events and the number of longitudinal observations, which are both governed by the censoring rate. Note that the frequency of longitudinal marker observations also determines the number of observed measures, although this parameter is left fixed in the present study. Deep neural networks have recently been successfully applied in many diverse domains as examples of end to end learning.
As for the absolute values, in the high separation setting (Fig. 6 and Table1), the RB is the most important for two parameters of class 2: 1) the survival sub-model Weibull shape parameter (RB over 10 for small number of individuals) and 2) the mixed sub-model slope parameter (RB varies from 10 to 120 depending on number of individuals and on the censoring rate, the mean number of longitudinal markers in the worse case (100 patients and a censor of 50) is 5. 1). For the remaining parameters the RB does not exceed 10. The trend is quite similar for the low separation setting (Fig. 6 and Table2), but to a higher extent: the RB varies from over 30 to 530 in the worst setting (small n and high τ). Connie Tomaino is one of music therapy's pioneers. More than 37 years ago, she walked into a dementia unit carrying her guitar and looked at the patients. "Many were overmedicated. Half of them were catatonic and had feeding tubes.
According to the author, "Are you a laboratory scientist par excellence with international research ambitions? As my tip list draws on common stereotypes, I'll presume that you are a high-income country scientist with low-income country research aspirations. " Among her twenty hot tips for success, Dr. Okeke has this handy advice: "Pick a Partner Country. Avoid locations where good research in the area has previously been conducted. Instead prioritize countries with pristine beaches, a game reserve or some other 'must see'. Holiday brochures could be helpful at this stage. " Write a short paper identifying the risk factors for the disease. Chronic Kidney Disease According to the BIC, 4 latent classes were retained for the model without covariates (BIC=15110 for 1 latent class, 14974 for 2 classes, 14911 for 3 latent classes and 14901 for 4 latent classes)and 2 latent classes for the model with covariates (BIC=14517 for 1 latent class, 14408 for 2 classes, 14410 for 3 latent classes and 14420 for 4 latent classes). Estimation results are presented in Table6 and in Table7 for the two models respectively. Models without and with covariates using the complete cases sample included 511 and 497 patients respectively. The difference in the number of patients is caused by missing covariates.
The disease duration spanned between 6 and 36 months. Patients were treated with 50mgriluzole twice a day for at least one month and had a baseline slow vital capacity (SVC) of 70. An action research paper documents a "cycle of inquiry," in which the writer evaluates a problem and develops a strategy of reform. Educators and educational administrators typically use this writing format to foster continual improvement in teaching or organizational methods. Action research papers include several predefined steps, including problem identification, data collection, interpretation of varying theories, proposed resolution and implementation plans. Rather than analyzing problems objectively, the goal is for writers to assess their own roles in promoting progress. Mixture models are widely used in medical research. Different extensions allowing to account for the potential heterogeneity in population were proposed. Verbeke and Lesaffre [3] extended the mixture model to longitudinal data, assuming a latent profile of the biomarker progression (growth mixture model GMM).
Data collection usually involves actionable fieldwork, enabling researchers to tailor their analyses to their own environments. Toward the conclusion, the paper reports findings and presents a plan to take action to implement a proven, repeatable method. Class 1 is the largest (92. 6 of patients), is characterized by a moderate ALSFRS progression (-2. 3 point by months) and by a better survival prognosis (over 20 months median survival compared to around 8 months for class 2, for a patient with the average covariates vector). Scenes like this are being repeated in nursing facilities and homes across America. New research is confirming and expanding an idea long held by those who work with dementia patients: Music can not only improve the mood of people with neurological diseases, it can boost cognitive skills and reduce the need for antipsychotic drugs.
Each class label is a crop-disease pair, and we make an attempt to predict the crop-disease pair given just the image of the plant leaf. Figure 1 shows one example each from every crop-disease pair from the PlantVillage dataset. In all the approaches described in this paper, we resize the images to 256 256 pixels, and we perform both the model optimization and predictions on these downscaled images. The data were collected in the framework of the Trophos prospective cohort study (TRO19622), a multicenter, randomized, placebo controlled, phase II/III clinical trial, which showed no efficacy of olesoxime in ALS [27]. The cohort consisted of 512 patients recruited across 15 European centres during the three-years period (20092011). The study time scale is the time since inclusion. The mean age of patients was 56 (sd=11. 2) years at inclusion and 55 (sd=11. 2) years at symptoms onset, with 331 (64. 6) men and 181 (35. 4) women. The diagnosis was definite in 107 patients (20. 9) and probable in 404 patients (79. 1) [28]; 101 (19. 8) patients suffered from bulbar form.
In terms of the relative bias, the trends are more complex. The parameters of the survival sub-model are also more impacted, especially for a small sample size n. The large number of individuals does not compensate for heavy censoring, as it was the case for normality. There is no particular trend in terms of n, except for the survival sub-model parameters, whose bias is considerably increased for n=100. The bias decreases quasi linearly for almost all parameters with increasing number of observed events (decreasing censoring rate). The estimations in the low separation case are less robust to the sample size and to the censoring rate than in the high separation case. Based on thorough investigative research, the opening sections of an action paper evaluate existing theories and values from other experts alongside the writers' proposed beliefs. Another important element is the problem statement, which identifies the focus, research questions and challenges the writer faces in developing an effective strategy. The body of the paper addresses the writer's methods of data collection and analysis of their impact.
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Class 1 is the largest (92.6 of patients), is characterized by a moderate ALSFRS progression (-2.3 point by months) and by a better survival prognosis (over 20 months median survival compared to around 8 months for class 2, for a patient with the average covariates vector).
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Class 2 is composed only of 37 patients (7.4) and describes a specific patient profile, worsening and dying very quickly.
The ones that were agitated had mitts on their hands and were tied to wheelchairs," she says. "I just started singing 'Let Me Call You Sweetheart. ' Many of the people who were considered to be catatonic lifted up their heads and looked at me. And the people who were agitated stopped being upset. Most of them started singing the words to the song. " Simulations results: relative bias of the class 1 parameter estimations according to the censoring rate τ and to the number of individuals n, high separation setting. Results for mixed model intercept, mixed model slope, Weibull scale and Weibull shape, (β01,β11,ζ11,ζ21, respectively form Eq. (8)) are presented. Same vertical scale is used for the four figures As expected, departures from normality decrease with increasing number of individuals (see Fig. 3 for the Weibull scale and shape parameters, heavy censoring) regardless of heavy censoring. Note that most of papers dealing with asymptotic properties of survival models are focused on the regression few papers focus on the Weibull distribution parameters. Sirvanci and Yang [23] derives the asymptotic normality of the Weibull model parameters for Type I censoring data (fixed length of follow-up).
For Alzheimer's patients, music can be good medicine
Neural networks provide a mapping between an inputsuch as an image of a diseased plantto an outputsuch as a cropdisease pair. The nodes in a neural network are mathematical functions that take numerical inputs from the incoming edges, and provide a numerical output as an outgoing edge. Deep neural networks are simply mapping the input layer to the output layer over a series of stacked layers of nodes. The challenge is to create a deep network in such a way that both the structure of the network as well as the functions (nodes) and edge weights correctly map the input to the output. Deep neural networks are trained by tuning the network parameters in such a way that the mapping improves during the training process. This process is computationally challenging and has in recent times been improved dramatically by a number of both conceptual and engineering breakthroughs (LeCun et al. , 2015; Schmidhuber, 2015). The real parameters were chosen to mimic the real data, described in Stamenic et al.
paper [15], dealing with a prognostic tool for individualized prediction of graft failure risk within ten years after kidney transplantation, using serum creatinine progression as a longitudinal marker. Following Eqs. (1 - 4), the generated data were governed by the following general model: The coverage rate is globally satisfactory (refer to Tables3 and 4 for the 95 coverage rates in the high and the low separation settings respectively). However, the large sample size in terms of the number of individuals results in smaller confidence intervals, entailing lower empirical coverage rate. This trend is especially visible for heavy censoring. Departures from normality already mentioned for these settings can also be a cause of this phenomenon. To summarize, the departures from normality are particularly present for the survival sub-model parameters, and these departures disappear for a large enough number of observed events (small censoring rate) and/or large enough sample size (from 500 individuals normality is generally respected even for heavy censoring).
In addition, before sending your paper to you, we check it for plagiarism to make sure it has no copy-pasted parts. Action research papers provide a valuable inquiry process for settings in which a group of professionals need ongoing reform to deliver the best results. The topic may involve fixing an existing problem, such as student absenteeism, or learning about a subject that seems promising, such as blending learning models. As action research papers are informal and intended for an organizational audience, the format varies while incorporating staple elements. The paper may be written in first person and include an abstract. When establishing partnerships with research counterparts in under-resourced countries, global health researchers often deploy discourse that claims to focus on sustainability and capacity building. But how is this viewed from the other side of the partnership? A satirical opinion article publishing September 30th in PLOS Biology by Iruka Okeke of University of Ibadan, Nigeria, highlights the extractive and exploitive aspects of health research partnerships between biomedical researchers in high-income countries and their counterparts in low-income, disease-endemic countries.
Simulations results: relative bias of the class 2 parameter estimations according to the censoring rate τ and to the number of individuals n, low separation setting. Results for mixed model intercept, mixed model slope, Weibull scale and Weibull shape, (β02,β12,ζ12,ζ22, respectively form Eq. (8)) are presented. A specific vertical scale is used for each figure Simulation study reveals some departures from normality of the model for survival sub-model parameters. The censoring rate and the number of individuals impact the relative bias of parameters, especially when the classes are weakly distinguished. In real-data application the observed heterogeneity on individual profiles in terms of a longitudinal marker evolution and of the event occurrence remains after adjusting to clinically relevant and available covariates; The deadline is close and you still have no idea how to write your essay, research, or article review? With us, you can get a well-researched and professionally prepared paper overnight or even within 8 hours if you are pressed for time.
However, in our study, empirically the departures from normality are reported for small sample size in terms of the number of events and/or the number of individuals (simulation results not presented here); in this sense, the normality problem is not specific to the joint latent class model, but is rather inherited from survival analysis. The JLCM properties have been evaluated. We have illustrated the discovery in practice and highlights the usefulness of the joint models with latent classes in this kind of data even with pre-specified factors. We made some recommendations for the use of this model and for future research. Further work is needed to assess the role of covariates, their place in different sub-models of the JLCM, and the impact of their omission on parameter estimations and class membership identification. Also, precise recommendations concerning a minimum number of events or individuals needed to obtain satisfactory results within the JLCM can be formulated. Impact of longitudinal observation frequency on parameter estimations and latent classes identification can also be study considered in further work. We complete all assignments from scratch, which are not connected to any essay databases. This means we do not resell any paper.
Joint models for longitudinal and time-to-event data are now widespread due to large cohort studies allowing collection of repeated measures of biomarkers and clinical events times [1]. The most popular way to analyze this kind of combined data are the shared random effects models, proposed by Wulfsohn and Tsiatis [2], where a function of random effects, issued from the model for longitudinal marker, is included as a covariate into the survival model. This approach allows to explain the relation between a longitudinal parameter and a time-to-event, assuming a homogeneous population. However, for certain diseases, the homogeneity assumption is not met and existence of different profiles of biomarker progression and/or of the time to-event should be accounted for in the model.