Feature selection becomes prominent, especially in the data sets with many variables and features. It will eliminate unimportant variables and improve the accuracy as well as the performance of ...classification. Random Forest has emerged as a quite useful algorithm that can handle the feature selection issue even with a higher number of variables. In this paper, we use three popular datasets with a higher number of variables (Bank Marketing, Car Evaluation Database, Human Activity Recognition Using Smartphones) to conduct the experiment. There are four main reasons why feature selection is essential. First, to simplify the model by reducing the number of parameters, next to decrease the training time, to reduce overfilling by enhancing generalization, and to avoid the curse of dimensionality. Besides, we evaluate and compare each accuracy and performance of the classification model, such as Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA). The highest accuracy of the model is the best classifier. Practically, this paper adopts Random Forest to select the important feature in classification. Our experiments clearly show the comparative study of the RF algorithm from different perspectives. Furthermore, we compare the result of the dataset with and without essential features selection by RF methods
varImp(),
Boruta, and Recursive Feature Elimination (RFE) to get the best percentage accuracy and kappa. Experimental results demonstrate that Random Forest achieves a better performance in all experiment groups.
Full text
Available for:
CEKLJ, NUK, ODKLJ, UL, UM, UPUK
CELLO2GO (http://cello.life.nctu.edu.tw/cello2go/) is a publicly available, web-based system for screening various properties of a targeted protein and its subcellular localization. Herein, we ...describe how this platform is used to obtain a brief or detailed gene ontology (GO)-type categories, including subcellular localization(s), for the queried proteins by combining the CELLO localization-predicting and BLAST homology-searching approaches. Given a query protein sequence, CELLO2GO uses BLAST to search for homologous sequences that are GO annotated in an in-house database derived from the UniProt KnowledgeBase database. At the same time, CELLO attempts predict at least one subcellular localization on the basis of the species in which the protein is found. When homologs for the query sequence have been identified, the number of terms found for each of their GO categories, i.e., cellular compartment, molecular function, and biological process, are summed and presented as pie charts representing possible functional annotations for the queried protein. Although the experimental subcellular localization of a protein may not be known, and thus not annotated, CELLO can confidentially suggest a subcellular localization. CELLO2GO should be a useful tool for research involving complex subcellular systems because it combines CELLO and BLAST into one platform and its output is easily manipulated such that the user-specific questions may be readily addressed.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The multispectral imaging technique is considered a reformation of hyperspectral imaging. It can be employed to noninvasively and rapidly evaluate food quality. Even though several imaging or ...sensor‐based techniques have been conducted for the quality assessment of various food products, the rise of multispectral imaging has been more promising. This paper presents a comprehensive review of the use of the multispectral sensor in the quality assessment of plant foods (such as cereals, legumes, tubers, fruits, and vegetables). Different quality parameters (such as physicochemical and microbiological aspects) of plant‐based foods that were determined and visualized by the combination of modeling methods and feature wavelength selection approaches are summarized. Based on the literature, the most frequently used wavelength selection methods are the successive projection algorithm (SPA) and the regression coefficient (RC). The most effective models developed for analyzing plant food products are the partial least squares regression (PLSR), least square support vector machine (LS‐SVM), support vector machine (SVM), partial least squares discriminant analysis (PLSDA), and multiple linear regression (MLR). This article concludes with a discussion of challenges, potential uses, and future trends of this flourishing technique that is now also being applied to plant foods.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Although 5-methylcytosine (m
C) is a widespread modification in RNAs, its regulation and biological role in pathological conditions (such as cancer) remain unknown. Here, we provide the ...single-nucleotide resolution landscape of messenger RNA m
C modifications in human urothelial carcinoma of the bladder (UCB). We identify numerous oncogene RNAs with hypermethylated m
C sites causally linked to their upregulation in UCBs and further demonstrate YBX1 as an m
C 'reader' recognizing m
C-modified mRNAs through the indole ring of W65 in its cold-shock domain. YBX1 maintains the stability of its target mRNA by recruiting ELAVL1. Moreover, NSUN2 and YBX1 are demonstrated to drive UCB pathogenesis by targeting the m
C methylation site in the HDGF 3' untranslated region. Clinically, a high coexpression of NUSN2, YBX1 and HDGF predicts the poorest survival. Our findings reveal an unprecedented mechanism of RNA m
C-regulated oncogene activation, providing a potential therapeutic strategy for UCB.
Full text
Available for:
EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Radiotherapy is an important therapeutic strategy for cancer treatment through direct damage to cancer cells and augmentation of antitumor immune responses. However, the efficacy of radiotherapy is ...limited by hypoxia-mediated radioresistance and immunosuppression in tumor microenvironment. Here, we construct a stabilized theranostic nanoprobe based on quantum dots emitting in the near-infrared IIb (NIR-IIb, 1,500-1,700 nm) window modified by catalase, arginine-glycine-aspartate peptides and poly(ethylene glycol). We demonstrate that the nanoprobes effectively aggregate in the tumor site to locate the tumor region, thereby realizing precision radiotherapy with few side-effects. In addition, nanoprobes relieve intratumoral hypoxia and reduce the tumor infiltration of immunosuppressive cells. Moreover, the nanoprobes promote the immunogenic cell death of cancer cells to trigger the activation of dendritic cells and enhance T cell-mediated antitumor immunity to inhibit tumor metastasis. Collectively, the nanoprobe-mediated immunogenic radiotherapy can boost the abscopal effect to inhibit tumor metastasis and prolong survival.
Near-infrared (NIR) and mid-infrared (MIR) hyperspectral techniques in tandem with chemometric analyses were employed for developing multispectral real-time systems allowing the food industry to ...monitor moisture content (MC) in tubers including various potato and sweet potato products during drying. Multivariate models were established by partial least-squares regression (PLSR), support vector machine regression (SVMR), locally weighted partial least square regression (LWPLSR), and back propagation artificial neural network (BPANN) using full spectral ranges of 10372-6105 cm
−1
(Spectral Set I), 3996-600 cm
−1
(Spectral Set II), and 1700-900 cm
−1
(Spectral Set III). The LWPLSR from Spectral Set I and BPANN from Spectral Set II and III, obtained the highest accuracies for tuber MC prediction. Then, both regression coefficient (RC) and successive projection algorithm (SPA) were respectively used for the selection of feature wavelengths in Spectral Set I, II and III. Instead of choosing many groups of characteristic variables for different varieties of potatoes and sweet potatoes, one set of feature variables for all tubers was selected from each spectral region for the convenience of industrial application. Eventually, six sets of feature wavelengths chosen from Spectral Set I, II and III were used to optimize models. The simplified SPA-LWPLSR from Spectral Set II and SPA-BPANN from Spectral Set III acquired good model performances for the tuber MC prediction, with determination coefficients in prediction (R
2
P
) of 0.950 and 0.904, respectively. The RC-BPANN model from Spectral Set I achieved the highest R
2
P
of 0.965. Such accuracies were comparable to that of full spectral models. The results reveal that hyperspectral techniques have great potential in the food industry for real-time measurement of tuber MC.
Full text
Available for:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
7.
Adipose‐derived stem cells for wound healing Hassanshahi, Alireza; Hassanshahi, Mohammadhossein; Khabbazi, Samira ...
Journal of cellular physiology,
June 2019, Volume:
234, Issue:
6
Journal Article
Peer reviewed
Wound healing is a complex but a fine‐tuned biological process in which human skin has the ability to regenerate itself following damage. However, in particular conditions such as deep burn or ...diabetes the process of wound healing is compromised. Despite investigations on the potency of a wide variety of stem cells for wound healing, adipose‐derived stem cells (ASCs) seem to possess the least limitations for clinical applications, and literature showed that ASCs can improve the process of wound healing very likely by promoting angiogenesis and/or vascularisation, modulating immune response, and inducing epithelialization in the wound. In the present review, advantages and disadvantages of various stem cells which can be used for promoting wound healing are discussed. In addition, potential mechanisms of action by which ASCs may accelerate wound healing are summarised. Finally, clinical studies applying ASCs for wound healing and the associated limitations are reviewed.
Despite investigations on the potency of a wide variety of stem cells for wound healing, adipose‐derived stem cells (ASCs) seem to possess the least clinical limitations for clinical applications, and our review showed that ASCs can improve the process of wound healing very likely by promoting angiogenesis and/or vascularisation, modulating immune response, and inducing epithelialization in the wound.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
When considering food security and huge market interest, a high-efficiency method to ensure the authenticity of the food product is necessary. For this goal, spectral imaging was explored for ...quantitative detection of Irish organic wheat flour (OWF) adulterated with common wheat flour (WF), cassava flour (CaF) and corn flour (CoF). Hyperspectral images (900–1700 nm) of OWF samples with a series of adulteration percentages were collected. The acquired spectra were pre-processed by second derivative (2nd Der) and standard normal variate (SNV) before modelling. Then partial least squares regression (PLSR) and principal component regression (PCR) were employed for quantitative analysis of adulteration proportion of CoF, CaF and WF in OWF. To develop more effective simplified models, three groups of feature wavelengths were selected from the loading plots of principal component analysis (PCA), and first-derivative and mean centering iteration algorithm (FMCIA). The models developed using FMCIA were better than PCA. After, the corresponding feature wavelengths were further reduced based on model regression coefficients (RC). The optimal result of admixture detection was emerged by the RC-FMCIA-PLSR model, with a determination coefficient of prediction (R2P) of 0.973 and a root mean square error of prediction (RMSEP) of 0.036 for OWF adulterated with CoF, R2P of 0.986 and RMSEP of 0.026 for OWF adulterated with CaF, and R2P of 0.971 and RMSEP of 0.038 for OWF adulterated with WF. Visualization maps were generated by calculating the spectral response of each pixel on flour samples. This result indicates that spectral imaging integrated with multivariate analysis has the potential to authenticate the admixtures in specific wheat flour in the range of 3–75% (w/w).
•Qualitative measure of flour adulterants was explored by discriminant analysis.•Regression models were used to quantitative analysis of flour adulteration.•A new method for characteristic wavelength selection was tested.•Optimal models were developed based on the selected feature wavelengths.•Visualization maps were depicted using image processing algorithms.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK, ZRSKP
Light emission from biased tunnel junctions has recently gained much attention owing to its unique potential to create ultracompact optical sources with terahertz modulation bandwidth1–5. The ...emission originates from an inelastic electron tunnelling process in which electronic energy is transferred to surface plasmon polaritons and subsequently converted to radiation photons by an optical antenna. Because most of the electrons tunnel elastically, the emission efficiency is typically about 10−5–10−4. Here, we demonstrate efficient light generation from enhanced inelastic tunnelling using nanocrystals assembled into metal–insulator–metal junctions. The colour of the emitted light is determined by the optical antenna and thus can be tuned by the geometry of the junction structures. The efficiency of far-field free-space light generation reaches ~2%, showing an improvement of two orders of magnitude over previous work3,4. This brings on-chip ultrafast and ultracompact light sources one step closer to reality.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, SBMB, UL, UM, UPUK
Crop productivity is readily reduced by competition from weeds. It is particularly important to control weeds early to prevent yield losses. Limited herbicide choices and increasing costs of weed ...management are threatening the profitability of crops. Smart agriculture can use intelligent technology to accurately measure the distribution of weeds in the field and perform weed control tasks in selected areas, which cannot only improve the effectiveness of pesticides, but also increase the economic benefits of agricultural products. The most important thing for an automatic system to remove weeds within crop rows is to utilize reliable sensing technology to achieve accurate differentiation of weeds and crops at specific locations in the field. In recent years, there have been many significant achievements involving the differentiation of crops and weeds. These studies are related to the development of rapid and non-destructive sensors, as well as the analysis methods for the data obtained. This paper presents a review of the use of three sensing methods including spectroscopy, color imaging, and hyperspectral imaging in the discrimination of crops and weeds. Several algorithms of machine learning have been employed for data analysis such as convolutional neural network (CNN), artificial neural network (ANN), and support vector machine (SVM). Successful applications include the weed detection in grain crops (such as maize, wheat, and soybean), vegetable crops (such as tomato, lettuce, and radish), and fiber crops (such as cotton) with unsupervised or supervised learning. This review gives a brief introduction into proposed sensing and machine learning methods, then provides an overview of instructive examples of these techniques for weed/crop discrimination. The discussion describes the recent progress made in the development of automated technology for accurate plant identification as well as the challenges and future prospects. It is believed that this review is of great significance to those who study automatic plant care in crops using intelligent technology.
PDF
