The spatial interpolation for soil texture does not necessarily satisfy the constant sum and nonnegativity constraints. Meanwhile, although numeric and categorical variables have been used as auxiliary variables to improve prediction accuracy of soil attributes such as soil organic matter, they (especially the categorical variables) are rarely used in spatial prediction of soil texture. The objective of our study was to comparing the performance of the methods for spatial prediction of soil texture with consideration of the characteristics of compositional data and auxiliary variables. These methods include the ordinary kriging with the symmetry logratio transform, regression kriging with the symmetry logratio transform, and compositional kriging (CK) approaches. The root mean squared error (RMSE), the relative improvement value of RMSE and Aitchison's distance (DA) were all utilized to assess the accuracy of prediction and the mean squared deviation ratio was used to evaluate the goodness of fit of the theoretical estimate of error. The results showed that the prediction methods utilized in this paper could enable interpolation results of soil texture to satisfy the constant sum and nonnegativity constraints. Prediction accuracy and model fitting effect of the CK approach were better, suggesting that the CK method was more appropriate for predicting soil texture. The CK method is directly interpolated on soil texture, which ensures that it is optimal unbiased estimator. If the environment variables are appropriately selected as auxiliary variables, spatial variability of soil texture can be predicted reasonably and accordingly the predicted results will be satisfied.

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Mobile cloud computing has emerged as a promising paradigm to facilitate computationintensive and delay-sensitive mobile applications. Computation offloading services at the edge mobile cloud environment are provided by small-scale cloud infrastructures such as cloudlets. While offloading tasks to in-proximity cloudlets enjoys benefits of lower latency and smaller energy consumption, new issues related to the cloudlets are rising. For instance, unbalanced task distribution and huge load gaps among heterogeneous mobile cloudlets are becoming more challenging, concerning the network dynamics and distributed task offloading. In this paper, we propose 'FairEdge', a Fairness-oriented computation offloading scheme to enable balanced task distribution for mobile Edge cloudlet networks. By integrating the balls-andbins theory with fairness index, our solution promotes effective load balancing with limited information at low computation cost. The evaluation results from extensive simulations and experiments with real-world datasets show that, FairEdge outperforms conventional task offloading methods, and it can achieve a network fairness up to 0.85 and reduce the unbalanced task offload by 50%. INDEX TERMS Mobile cloudlets, load balancing, edge computing, fair task offloading.

As the core component of agricultural robots, robotic grippers are widely used for plucking, picking, and harvesting fruits and vegetables. Secure grasping is a severe challenge in agricultural applications because of the variation in the shape and hardness of agricultural products during maturation, as well as their variety and delicacy. In this study, a fruit identification method utilizing an adaptive gripper with tactile sensing and machine learning algorithms is reported. An adaptive robotic gripper is designed and manufactured to perform adaptive grasping. A tactile sensing information acquisition circuit is built, and force and bending sensors are integrated into the robotic gripper to measure the contact force distribution on the contact surface and the deformation of the soft fingers. A robotic manipulator platform is developed to collect the tactile sensing data in the grasping process. The performance of the random forest (RF), k-nearest neighbor (KNN), support vector classification (SVC), naive Bayes (NB), linear discriminant analysis (LDA), and ridge regression (RR) classifiers in identifying and classifying five types of fruits using the adaptive gripper is evaluated and compared. The RF classifier achieves the highest accuracy of 98%, while the accuracies of the other classifiers vary from 74% to 97%. The experiment illustrates that efficient and accurate fruit identification can be realized with the adaptive gripper and machine learning classifiers, and that the proposed method can provide a reference for controlling the grasping force and planning the robotic motion in the plucking, picking, and harvesting of fruits and vegetables.

The circadian clock is reported to play a role in the ovaries in a variety of vertebrate species, including the domestic hen. However, the ovary is an organ that changes daily, and the laying hen maintains a strict follicular hierarchy. The aim of this study was to examine the spatial-temporal expression of several known canonical clock genes in the granulosa and theca layers of six hierarchy follicles. We demonstrated that the granulosa cells (GCs) of the F1-F3 follicles harbored intrinsic oscillatory mechanisms in vivo. In addition, cultured granulosa cells (GCs) from F1 follicles exposed to luteinizing hormone (LH) synchronization displayed Per2 mRNA oscillations, whereas, the less mature GCs (F5 plus F6) displayed no circadian change in Per2 mRNA levels. Cultures containing follicle-stimulating hormone (FSH) combined with LH expressed levels of Per2 mRNA that were 2.5-fold higher than those in cultures with LH or FSH alone. These results show that there is spatial specificity in the localization of clock cells in hen preovulatory follicles. In addition, our results support the hypothesis that gonadotropins provide a cue for the development of the functional cellular clock in immature GCs.

Circadian timing system controlled the rhythmic events, for example, ovulation and oviposition in chickens. However, how biological clock mediates eggshell formation remains obscure. Here, A 24-h mRNA transcriptome analysis was carried out in the uterus of 18 chickens with similar oviposition time points to identify the rhythmic genes and to reveal critical genes and biological pathways involved in the eggshell biomineralization. JTK_CYCLE analysis and real-time PCR revealed a total of 1,793 genes from the sequencing database with 23,513 genes (FPKM>1) were rhythmic genes regulating the rhythmic system and the expression of typical clock genes Per2, Cry1, Bmal1, Clock, Per3 , and Rev-erbβ were rhythmically expressed, which suggested that endogenous clock in uterus might control the eggshell mineralization. Time of peak expression of the rhythmic genes was analyzed based on their acrophase. The main phases clustered at the periods from Zeitgeber time 0 ( ZT0 ) to ZT4 (6:00–10:00) and from ZT10 to ZT14 (16:00-20:00). The rhythmic genes were annotated to the following Gene Ontology terms rhythmic process, lyase, ATP binding, cell membrane component. KEGG pathway enrichment analysis revealed the top 15 rhythmic genes were involved in vital biological pathways, including syndecan (1, 2, 3)-mediated signaling, post-translational regulation of adheres junction stability and disassembly, FoxO family signaling, TGF-β receptor and transport of small molecular pathways. 166 of total 1,235 genes (13.4%) were defined as rhythmic transfer factors ( TFs ) and they were investigated expression time distribution of cis-elements of circadian clock system D-box, E-box, B-site, and Y-Box within 24 h. Results indicated that rhythmic TFs at each phase are potential drivers of their circadian transcription activities. Compared with the control, the expression abundances of ion transport elements SCNN1G, CA2, SPP1 , and ATP1B1 were significantly decreased after the interference of Bmal1 gene in synchronized uterine tubular gland cells. Clock genes changed their expression along with the eggshell formation, indicating that there is circadian clock in the uterus of chicken and it regulates the expression of eggshell formation genes.

This study evaluated the effects of housing system (cage versus pen), sex and line cross (EMY1 and EMY2) on meat quality in meat-type chickens. Chickens (n ¼ 640) from each line cross (males: females ¼ 1:1) were housed in batteries from d 1 to 28. Then, half of them were transferred to indoor floor pens, and the others were raised in single cages. Meat quality traits of breast fillets were measured at 91 d of age. Percent lipid and histidine were higher, whereas % total protein and myofibre density (MDS) were lower in caged than penned chickens. Cross EMY1 had higher MDS, but lower lipid % and myofibre diameters (MDM) than EMY2 (p < .05). Males had redder and brighter muscles and higher MDM and contents of glycine and proline than the females (p < .05). Penned females had smaller MDM and higher MDS than their caged counterparts (p < .05). Generally, housing systems alone, or interacting with sex and genetic line, affected yellowness, myofibre characteristics, % protein, % lipid, myofibre density, and % His of breast muscle.

BACKGROUND A nomogram is a diagram that aggregates various predictive factors through multivariate regression analysis, which can be used to predict patient outcomes intuitively. Lymph node (LN) metastasis and tumor deposit (TD) conditions are two critical factors that affect the prognosis of patients with colorectal cancer (CRC) after surgery. At present, few effective tools have been established to predict the overall survival (OS) of CRC patients after surgery. AIM To screen out suitable risk factors and to develop a nomogram that predicts the postoperative OS of CRC patients. METHODS Data from a total of 3139 patients diagnosed with CRC who underwent surgical removal of tumors and LN resection from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results program. The data were divided into a training set ( n = 2092) and a validation set ( n = 1047) at random. The Harrell concordance index (C-index), Akaike information criterion (AIC), and area under the curve (AUC) were used to assess the predictive performance of the N stage from the American Joint Committee Cancer tumor-node-metastasis classification, LN ratio (LNR), and log odds of positive lymph nodes (LODDS). Univariate and multivariate analyses were utilized to screen out the risk factors significantly correlating with OS. The construction of the nomogram was based on Cox regression analysis. The C-index, receiver operating characteristic (ROC) curve, and calibration curve were employed to evaluate the discrimination and prediction abilities of the model. The likelihood ratio test was used to compare the sensitivity and specificity of the final model to the model with the N stage alone to evaluate LN metastasis. RESULTS The predictive efficacy of the LODDS was better than that of the LNR based on the C-index, AIC values, and AUC values of the ROC curve. Seven independent predictive factors, namely, race, age at diagnosis, T stage, M stage, LODDS, TD condition, and serum carcinoembryonic antigen level, were included in the nomogram. The C-index of the nomogram for OS prediction was 0.8002 (95%CI: 0.7839-0.8165) in the training set and 0.7864 (95%CI: 0.7604-0.8124) in the validation set. The AUC values of the ROC curve predicting the 1-, 3-, and 5-year OS were 0.846, 0.841, and 0.825, respectively, in the training set and 0.823, 0.817, and 0.835, respectively, in the validation test. Great consistency between the predicted and actual observed OS for the 1-, 3-, and 5-year OS in the training set and validation set was shown in the calibration curves. The final nomogram showed a better sensitivity and specificity than the nomogram with N stage alone for evaluating LN metastasis in both the training set (-4668.0 vs -4688.3, P < 0.001) and the validation set (-1919.5 vs -1919.8, P ...