Management of Abnormal Visual Developments

When human beings recognize the external world, more than 80% of the information come from visual function and visual system. Normal visual development and normal binocularity are the fundamental of good visual acuity and visual functions. Any abnormal visual experience would cause abnormality, such as refractive error, strabismus, amblyopia and other diseases. The patients with abnormal visual developments were reported to have abnormal, lonely, and other psycho problems. In this chapter, we will describe the normal developmental of visual function, summarize the abnormal developments and the correction or treatment

Non-invasive prediction of preeclampsia using the maternal plasma cell-free DNA profile and clinical risk factors

BackgroundPreeclampsia (PE) is a pregnancy complication defined by new onset hypertension and proteinuria or other maternal organ damage after 20 weeks of gestation. Although non-invasive prenatal testing (NIPT) has been widely used to detect fetal chromosomal abnormalities during pregnancy, its performance in combination with maternal risk factors to screen for PE has not been extensively validated. Our aim was to develop and validate classifiers that predict early- or late-onset PE using the maternal plasma cell-free DNA (cfDNA) profile and clinical risk factors.MethodsWe retrospectively collected and analyzed NIPT data of 2,727 pregnant women aged 24–45 years from four hospitals in China, which had previously been used to screen for fetal aneuploidy at 12 + 0 ~ 22 + 6 weeks of gestation. According to the diagnostic criteria for PE and the time of diagnosis (34 weeks of gestation), a total of 143 early-, 580 late-onset PE samples and 2,004 healthy controls were included. The wilcoxon rank sum test was used to identify the cfDNA profile for PE prediction. The Fisher’s exact test and Mann–Whitney U-test were used to compare categorical and continuous variables of clinical risk factors between PE samples and healthy controls, respectively. Machine learning methods were performed to develop and validate PE classifiers based on the cfDNA profile and clinical risk factors.ResultsBy using NIPT data to analyze cfDNA coverages in promoter regions, we found the cfDNA profile, which was differential cfDNA coverages in gene promoter regions between PE and healthy controls, could be used to predict early- and late-onset PE. Maternal age, body mass index, parity, past medical histories and method of conception were significantly differential between PE and healthy pregnant women. With a false positive rate of 10%, the classifiers based on the combination of the cfDNA profile and clinical risk factors predicted early- and late-onset PE in four datasets with an average accuracy of 89 and 80% and an average sensitivity of 63 and 48%, respectively.ConclusionIncorporating cfDNA profiles in classifiers might reduce performance variations in PE models based only on clinical risk factors, potentially expanding the application of NIPT in PE screening in the future

A Robust Feedback Path Tracking Control Algorithm for an Indoor Carrier Robot Considering Energy Optimization

This work develops an indoor carrier robot for people with disabilities, where the precise tracking of designated route is crucial. The parameter uncertainties and disturbances of the robot impose significant challenges for tracking. The present paper first investigates the dynamic of mechanical structure and modeling of actuator motors and constructs a new dynamic model by considering all main parameter uncertainties and disturbances. A novel robust feedback tracking controller considering both the optimization of path tracking and the minimization of the power consumption energy is proposed. It is proved that the tracking errors e and e ˙ satisfy a H∞ performance indicator while the energy consumption is minimum. A simulation example was performed and the results show that this novel algorithm can effectively reduce the tracking error from 0.2 m to 0.006 m while guaranteeing the minimum energy consumption. Furthermore, the effectiveness of the proposed method was validated by experiment compared with the non-robust one

Severe graft‐versus‐host disease post allogeneic hematopoietic stem cell transplantation due to loss of HLA heterozygosity in recipient lymphocytes after full graft rejection

Abstract Germ cell tumors complicated by hematological malignancy (HM) are a rare clinical phenomenon. Allogeneic hematopoietic stem cell transplantation (allo‐HSCT) is a potentially effective therapy, but graft‐versus‐host disease (GVHD) is a life‐threatening complication. We report a case of a 13‐year‐old female patient diagnosed with germ cell tumors followed by acute lymphoblastic leukemia. After chemotherapy, she received allo‐HSCT and her chimerism rate decreased rapidly to near zero by 6 months without evidence of HM recurrence. However, she developed severe, multiorgan GVHD‐like manifestations. DNA analysis revealed the pathogenesis of GVHD to be loss of HLA heterozygosity in recipient hematopoietic cells

Mechanism of LEF1-AS1 regulating HUVEC cells by targeting miR-489-3p/S100A11 axis

Background The venous malformation is the most common congenital vascular malformation and exhibits the characteristics of local invasion and lifelong progressive development. Long noncoding RNA (lncRNA) regulates endothelial cells, vascular smooth muscle cells, macrophages, vascular inflammation, and metabolism and also affects the development of venous malformations. This study aimed to elucidate the role of the lncRNA LEF1-AS1 in the development of venous malformations and examine the interaction among LEF1-AS1, miR-489-3p, and S100A11 in HUVEC cells. Methods Venous malformation tissues, corresponding normal venous tissues, and HUVEC cells were used. Agilent human lncRNA microarray gene chip was used to screen differential genes, RNA expression was detected using quantitative reverse transcription PCR, and protein expression was detected using Western blotting. The proliferation, migration, and angiogenesis of HUVEC cells were assessed using CCK8, transwell, and in vitro angiogenesis tests. Results A total of 1,651 lncRNAs were screened using gene chip analysis, of which 1015 were upregulated and 636 were downregulated. The lncRNA LEF1-AS1 was upregulated with an obvious difference multiple, and the fold-change value was 11.03273. The results of the analysis performed using the StarBase bioinformatics prediction website showed that LEF1-AS1 and miR-489-3p possessed complementary binding sites and that miR-489-3p and S100A11 also had complementary binding sites. The findings of tissue experiments revealed that the expressions of LEF1-AS1 and S100A11 were higher in tissues with venous malformations than in normal tissues, whereas the expression of miR-489-3p was lower in venous malformations than in normal tissues. Cell culture experiments indicated that LEF1-AS1 promoted the proliferation, migration, and angiogenesis of HUVEC cells. In these cells, LEF1-AS1 targeted miR-489-3p, which in turn targeted S100A11. LEF1-AS1 acted as a competitive endogenous RNA and promoted the expression of S100A11 by competitively binding to miR-489-3p and enhancing the proliferation, migration, and angiogenesis of HUVEC cells. Thus, LEF1-AS1 participated in the occurrence and development of venous malformation. Conclusions The expression of LEF1-AS1 was upregulated in venous malformations, and the expression of S100A11 was increased by the adsorption of miR-489-3p to venous endothelial cells, thus enhancing the proliferation, migration, and angiogenesis of HUVEC cells. In conclusion, LEF1-AS1 is involved in the occurrence and development of venous malformations by regulating the miR-489-3p/S100A11 axis, which provides valuable insights into the pathogenesis of this disease and opens new avenues for its treatment

A Novel Stacking Heterogeneous Ensemble Model with Hybrid Wrapper-Based Feature Selection for Reservoir Productivity Predictions

Acid fracturing is the most important stimulation method in the carbonate reservoir. Due to the high cost and high risk of acid fracturing, it is necessary to predict the reservoir productivity before acid fracturing, which can provide support to optimize the parameters of acid fracturing. However, the productivity of a single well is affected by various construction parameters and geological conditions. Overfitting can occur when performing productivity prediction tasks on the high-dimension, small-sized reservoir, and acid fracturing dataset. Therefore, this study developed a stacking heterogeneous ensemble model with a hybrid wrapper-based feature selection strategy to forecast reservoir productivity, resolve the overfitting problem, and improve productivity prediction. Compared to other baseline models, the proposed model was found to have the best predictive performances on the test set and effectively deal with the overfitting. The results proved that the hybrid wrapper-based feature selection strategy introduced in this study reduced data acquisition costs and improved model comprehensibility without reducing model performance

Non-destructive prediction of anthocyanin concentration in whole eggplant peel using hyperspectral imaging

Accurately detecting the anthocyanin content in eggplant peel is essential for effective eggplant breeding. The present study aims to present a method that combines hyperspectral imaging with advanced computational analysis to rapidly, non-destructively, and precisely measure anthocyanin content in eggplant fruit. For this purpose, hyperspectral images of the fruits of 20 varieties with diverse colors were collected, and the content of the anthocyanin were detected using high performance liquid chromatography (HPLC) methods. In order to minimize background noise in the hyperspectral images, five preprocessing algorithms were utilized on average reflectance spectra: standard normalized variate (SNV), autoscales (AUT), normalization (NOR), Savitzky–Golay convolutional smoothing (SG), and mean centering (MC). Additionally, the competitive adaptive reweighted sampling (CARS) method was employed to reduce the dimensionality of the high-dimensional hyperspectral data. In order to predict the cyanidin, petunidin, delphinidin, and total anthocyanin content of eggplant fruit, two models were constructed: partial least squares regression (PLSR) and least squares support vector machine (LS-SVM). The HPLC results showed that eggplant peel primarily contains three types of anthocyanins. Furthermore, there were significant differences in the average reflectance rates between 400–750 nm wavelength ranges for different colors of eggplant peel. The prediction model results indicated that the model based on NOR CARS LS-SVM achieved the best performance, with a squared coefficient of determination (R2) greater than 0.98, RMSEP and RMSEC less than 0.03 for cyanidin, petunidin, delphinidin, and total anthocyanin predication. These results suggest that hyperspectral imaging is a rapid and non-destructive technique for assessing the anthocyanin content of eggplant peel. This approach holds promise for facilitating the more effective eggplant breeding

Optimizing Observation Plans for Identifying Faxon Fir (<i>Abies fargesii <i>var.</i> Faxoniana</i>) Using Monthly Unmanned Aerial Vehicle Imagery

Faxon fir (Abies fargesii var. faxoniana), as a dominant tree species in the subalpine coniferous forest of Southwest China, has strict requirements regarding the temperature and humidity of the growing environment. Therefore, the dynamic and continuous monitoring of Faxon fir distribution is very important to protect this highly sensitive ecological environment. Here, we combined unmanned aerial vehicle (UAV) imagery and convolutional neural networks (CNNs) to identify Faxon fir and explored the identification capabilities of multispectral (five bands) and red-green-blue (RGB) imagery under different months. For a case study area in Wanglang Nature Reserve, Southwest China, we acquired monthly RGB and multispectral images on six occasions over the growing season. We found that the accuracy of RGB imagery varied considerably (the highest intersection over union (IoU), 83.72%, was in April and the lowest, 76.81%, was in June), while the accuracy of multispectral imagery was consistently high (IoU > 81%). In April and October, the accuracy of the RGB imagery was slightly higher than that of multispectral imagery, but for the other months, multispectral imagery was more accurate (IoU was nearly 6% higher than those of the RGB imagery for June). Adding vegetation indices (VIs) improved the accuracy of the RGB models during summer, but there was still a gap to the multispectral model. Hence, our results indicate that the optimized time of the year for identifying Faxon fir using UAV imagery is during the peak of the growing season when using a multispectral imagery. During the non-growing season, RGB imagery was no worse or even slightly better than multispectral imagery for Faxon fir identification. Our study can provide guidance for optimizing observation plans regarding data collection time and UAV loads and could further help enhance the utility of UAVs in forestry and ecological research