A diagnostic multiplex PCR scheme for identification of plant-associated bacteria of the genus Pantoea.

Unrefereed reprintThe genus Pantoea forms a complex of more than 25 species, among which several cause diseases of several crop plants, including rice. Notably, strains of Pantoea ananatis and Pantoea stewartii have been found to cause bacterial leaf blight of rice in Togo and Benin, while other authors have observed that Pantoea agglomerans can also cause bacterial leaf blight of rice. The contribution of these and perhaps other species of Pantoea to plant diseases and yield losses of crop plants is currently not well documented, partly due to the lack of efficient diagnostic tools

A Review of Literature on Trust in Online Platforms- A Conceptual Unified Framework and Future Research Directions

There has been a lot of empirical work on trust research in online platforms in the past two decades. Due to great diversity in the underlying theories, methodologies, variables, and relationships in this field and a “confusing potpourri” of conceptualizations and operationalizations of the constructs, prior researchers have called for a need to synthesize the field knowledge in a meaningful way to build a cumulative tradition. With this as the underlying motivation, this review paper analyzes 106 empirical articles on trust in online platforms published in the past 20 years to synthesize the field knowledge and provide a state of art picture of the field. This paper also aims to provide a conceptual unifying framework that establishes the relationships among various constructs studied in the literature, along with some insights into existing research gaps and potential future opportunities

Impact of Feature Extraction Combined with Data Sampling Methods on Heartbeat Categorization

Dealing with class-imbalanced datasets in data analytics poses challenges, especially when faced with high-dimensional data. In order to handle this issue, researchers often utilize preprocessed methods like feature selection. Feature selection attempts to create a more informative and condensed feature set, while data sampling helps alleviate class imbalance. In our study, aim is to explore the effectiveness of data sampling preprocessed techniques combined with feature extraction using a dataset on ECG Heartbeat. We evaluate ensemble classifiers: Decision Tree; Random Forests (RF), Gradient-Boosted Trees (GBT) for feature extraction. In terms of data sampling, we assess the effectiveness of two methods: Random Under sampling (RUS) and Synthetic Minority Oversampling (SMOTE). The performance of this feature extraction is measured using the sensitivity and the specificity, two important metrics used for accuracy. Our findings depict that the combination of the RUS and GBT method yields the highest performance for ECG Heartbeat detection

In vitro binding of single-stranded RNA by human Dicer

AbstractWhile Dicer alone has been shown to form stable complexes with double-stranded RNAs and short interfering RNAs, its interactions with single-stranded RNAs (ssRNAs) have not been characterized. Here, we show that recombinant human Dicer alone can bind 21-nt ssRNAs in vitro, independent of their sequence and structure. We also demonstrate that Dicer binds ssRNAs having a 5′-phosphate with greater affinity versus those with a 5′-hydroxyl. In addition, 3′-biotinylated ssRNAs are bound by Dicer with lower affinity than 3′-hydroxyl ssRNAs. The stability of ssRNA–Dicer complexes was found to depend on divalent cations. Together, our results suggest a role for the PAZ domain of Dicer in binding ssRNAs and may indicate roles for Dicer in cellular function beyond those currently known

Acute Burkitt\u27s Leukemia: Case Report and Literature Review

The occurrence of leukemia in Burkitt\u27s lymphoma, with or without visceral or nodal tumefaction is uncommon, and its initial presentation as leukemia is even more unusual. Because it has a poor chemotherapeutic response and a grave prognosis, it is important to recognize this unusual leukemia correctly. Our report describes the clinical and pathologic findings of Burkitt\u27s lymphoma cell leukemia in a five-year-old white boy who presented with abdominal distension, hepatosplenomegaly, and lymphadenopathy. Blood examination revealed normocytic normochromic anemia, erythroblastosis, slight leukocytosis, and the presence of numerous (24%) blasts. A diagnosis of Burkitt\u27s lymphoma was established on the basis of morphologic, cytochemical, and immunologic studies performed on the blasts. When the chemotherapy protocol for the lymphoma was administered, the patient responded well initially but suffered uric acid nephropathy, which was successfully treated. However, within two weeks he had a rapid relapse of leukemia and died four months after admission

Paradise Isle: How Hollywood Created An Imaginary Hawaiʻi

M.A

Techniques of deep learning and image processing in plant leaf disease detection: a review

Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold the ophthalmic functionality of the human eye. In agriculture, leaf examination is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated

Performance Evaluation of Homogeneous Charge Compression Ignition Combustion Engine â A Review

The development of HCCI combustion technology has been drawing a great deal of attention from researchers. This survey explains ongoing research methodologies and results. HCCI combustion, other than conventional combustion, is purely based on chemical kinetics. At present the automobile sector faces the problem of emissions and needs to develop clean technologies. However, HCCI operation still has issues such as ignition control, combustion phasing control, operating range control, cold start, and UHC (unburned hydrocarbon) and CO (carbon monoxide) emissions. The challenge is to overcome these problems without compromising other engine parameters and performance. For HCCI, the mixture preparation is especially important, while the compression ratio, IVC (inlet valve closure) timing, inlet pressure, inlet temperature and EGR play a very prominent role in controlling it. This paper will go through a detailed discussion of all the above conditions