Barrett's esophagus (BE) is a premalignant condition that has an increased risk to turn into esophageal adenocarcinoma. Classification and staging of the different changes (BE in particular) in the esophageal mucosa are challenging since they have a very similar appearance. Confocal laser endomicroscopy (CLE) is one of the newest endoscopy tools that is commonly used to identify the pathology type of the suspected area of the esophageal mucosa. However, it requires a well-trained physician to classify the image obtained from CLE. An automatic stage classification of esophageal mucosa is presented. The proposed model enhances the internal features of CLE images using an image filter that combines fractional integration with differentiation. Various features are then extracted on a multiscale level, to classify the mucosal tissue into one of its four types: normal squamous (NS), gastric metaplasia (GM), intestinal metaplasia (IM or BE), and neoplasia. These sets of features are used to train two conventional classifiers: support vector machine (SVM) and random forest. The proposed method was evaluated on a dataset of 96 patients with 557 images of different histopathology types. The SVM classifier achieved the best performance with 96.05% accuracy based on a leave-one-patient-out cross-validation. Additionally, the dataset was divided into 60% training and 40% testing; the model achieved an accuracy of 93.72% for the testing data using the SVM. The presented model showed superior performance when compared with four state-of-the-art methods. Accurate classification is essential for the intestinal metaplasia grade, which most likely develops into esophageal cancer. Not only does our method come to the aid of physicians for more accurate diagnosis by acting as a second opinion, but it also acts as a training method for junior physicians who need practice in using CLE. Consequently, this work contributes to an automatic classification that facilitates early intervention and decreases samples of required biopsy.
In this work, sonication (20-kHz) was conducted to assist the biosorption of phenolics from blueberry pomace extracts by brewery waste yeast biomass. The adsorption capacity of yeast increased markedly under ultrasonic fields. After sonication at 394.2 W/L and 40 °C for 120 min, the adsorption capacity was increased by 62.7% compared with that under reciprocating shaking. An artificial neural network was used to model and visualize the effects of different parameters on yeast biosorption capacity. Both biosorption time and acoustic energy density had positive influences on yeast biosorption capacity, whereas no clear influence of temperature on biosorption process was observed. Regarding the mechanism of ultrasound-enhanced biosorption process, the amino and carboxyl groups in yeast were considered to be associated with the yeast biosorption property. Meanwhile, ultrasound promoted the decline of the structure order of yeast cells induced by phenolic uptake. The interactions between yeast cells and phenolics were also affected by the structures of phenolics. Moreover, the mass transfer process was simulated by a surface diffusional model considering the ultrasound-induced yeast cell disruption. The modeling results showed that the external mass transfer coefficient in liquid phase and the surface diffusion coefficient under sonication at 394.2 W/L and 40 °C were 128.5% and 74.3% higher than that under reciprocating shaking, respectively.
This work studied the influences of water blanching pretreatment (30 s), surface contacting ultrasound (492.3 and 1131.1 W/m2) assisted air drying, and their combination on drying kinetics and quality of white cabbage. Contacting sonication was performed by placing samples on an ultrasonic vibration plate, and the drying temperature was 60 °C. Through drying kinetic analysis and numerical simulation considering internal and external resistances and shrinkage, it was found that both blanching pretreatment and contacting sonication during drying intensified internal water diffusion and external water exchange to shorten cabbage drying time. Meanwhile, blanching pretreatment was more effective to enhance the drying process. The largest reduction of drying time (from 145 min to 24 min) was obtained when sequential blanching and contacting sonication at 1131.1 W/m2 were conducted. Dehydrated cabbages with blanching pretreatment were characterized by green color and high retention of vitamin C, while a severe loss of vitamin C was found in dried cabbages without blanching pretreatment. Moreover, although both blanching and contacting sonication shortened the drying time, the losses of phenolics, glucosinolates and resulting breakdown products were not alleviated. Contents of total phenolics, one glucosinolates (sinigrin) and one glucobrassicin breakdown product (indole-3-acetoritrile) in only air dried cabbages were significantly (p
Dengue is an arthropod-borne infectious disease caused by dengue virus (DENV) infection and transmitted byAedesmosquitoes. Approximately 50-100 million people are infected with DENV each year, resulting in a high economic burden on both governments and individuals. Here, we conducted a systematic review and meta-analysis to summarize information regarding the epidemiology, clinical characteristics, and serotype distribution and risk factors for global dengue outbreaks occurring from 1990 to 2015. We searched the PubMed, Embase and Web of Science databases through December 2016 using the term "dengue outbreak." In total, 3,853 studies were identified, of which 243 studies describing 262 dengue outbreaks met our inclusion criteria. The majority of outbreak-associated dengue cases were reported in the Western Pacific Region, particularly after the year 2010; these cases were primarily identified in China, Singapore and Malaysia. The pooled mean age of dengue-infected individuals was 30.1 years; of the included patients, 54.5% were male, 23.2% had DHF, 62.0% had secondary infections, and 1.3% died. The mean age of dengue patients reported after 2010 was older than that of patients reported before 2010 (34.0 vs. 27.2 years); however, the proportions of patients who had DHF, had secondary infections and died significantly decreased after 2010. Fever, malaise, headache, and asthenia were the most frequently reported clinical symptoms and signs among dengue patients. In addition, among the identified clinical symptoms and signs, positive tourniquet test (OR= 4.86), ascites (OR= 13.91) and shock (OR= 308.09) were identified as the best predictors of dengue infection, DHF and mortality, respectively (bothP< 0.05). The main risk factors for dengue infection, DHF and mortality were living with uncovered water container (OR= 1.65), suffering from hypotension (OR= 6.18) and suffering from diabetes mellitus (OR= 2.53), respectively (allP< 0.05). The serotype distribution varied with time and across WHO regions. Overall, co-infections were reported in 47.7% of the evaluated outbreaks, and the highest pooled mortality rate (2.0%) was identified in DENV-2 dominated outbreaks. Our study emphasizes the necessity of implementing programs focused on targeted prevention, early identification, and effective treatment.
To understand the source-to-sink of pollutants in the Kelantan River estuary and the adjacent shelf area in Malaysia, a total of 42 surface sediment samples were collected in the Kelantan River-estuary-shelf system to analyze for grain size, total organic carbon (TOC) content, Al and heavy metals (Cr, Ni, Cu, Zn, Cd and Pb). The surficial sediments were mainly composed of clayey silt and the TOC content in sediments decreased from the river to the shelf. The surficial sediments experienced Pb pollution; Cr only showed a certain level of pollution in the coastal area of the estuary but not in other areas, and Ni, Cu, Zn, and Cd showed no pollution. The heavy metals mainly originated from natural weathering and erosion of rocks and soils in the catchment and enriched near the river mouth. Total organic carbon can promote the enrichment of heavy metals in sediments.
Oil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as Ganoderma boninense. An infected tree shows few symptoms during early stage of infection, while potentially suffers an 80% lifetime yield loss and the tree may be dead within 2 years. Early detection of basal stem rot is crucial since disease control efforts can be done. Laboratory BSR detection methods are effective, but the methods have accuracy, biosafety, and cost concerns. This review article consists of scientific articles related to the oil palm tree disease, basal stem rot, Ganoderma Boninense, remote sensors and deep learning that are listed in the Web of Science since year 2012. About 110 scientific articles were found that is related to the index terms mentioned and 60 research articles were found to be related to the objective of this research thus included in this review article. From the review, it was found that the potential use of deep learning methods were rarely explored. Some research showed unsatisfactory results due to limitations on dataset. However, based on studies related to other plant diseases, deep learning in combination with data augmentation techniques showed great potentials, showing remarkable detection accuracy. Therefore, the feasibility of analyzing oil palm remote sensor data using deep learning models together with data augmentation techniques should be studied. On a commercial scale, deep learning used together with remote sensors and unmanned aerial vehicle technologies showed great potential in the detection of basal stem rot disease.
The first evidence for the Higgs boson decay to a Z boson and a photon is presented, with a statistical significance of 3.4 standard deviations. The result is derived from a combined analysis of the searches performed by the ATLAS and CMS Collaborations with proton-proton collision datasets collected at the CERN Large Hadron Collider (LHC) from 2015 to 2018. These correspond to integrated luminosities of around 140 fb^{-1} for each experiment, at a center-of-mass energy of 13 TeV. The measured signal yield is 2.2±0.7 times the standard model prediction, and agrees with the theoretical expectation within 1.9 standard deviations.