Simulated wrapped phase optimizes phase retrieval in phase-shifting interferometry.

Phase retrieval is crucial in phase-shifting interferometry and other phase measurement techniques. However, in noisy wrapped phase maps with high steepness, discontinuities arise and cause phase unwrapping errors. To solve this problem, this Letter presents a phase retrieval method based on a simulated wrapped phase. By establishing the correspondence between the simulated and measured interferograms, the difference in wrapped phases between them can be obtained. The difference in wrapped phase map, which has sparse and wide interference fringes, has a higher reliability of phase unwrapping. The proposed method not only possesses high phase retrieval accuracy but it also simplifies the processing of interferograms. Furthermore, the layout of all interferometric systems, the parameters of optical components, and the model of the measured object are known, so the proposed method can be used as a reference for phase retrieval.

Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan.

AIMS: We aimed at giving a preliminary analysis of the weakness of a current test strategy, and proposing a data-driven strategy that was self-adaptive to the dynamic change of pandemic. The effect of driven-data selection over time and space was also within the deep concern. METHODS: A mathematical definition of the test strategy were given. With the real COVID-19 test data from March to July collected in Lahore, a significance analysis of the possible features was conducted. A machine learning method based on logistic regression and priority ranking were proposed for the data-driven test strategy. With performance assessed by the area under the receiver operating characteristic curve (AUC), time series analysis and spatial cross-test were conducted. RESULTS: The transition of risk factors accounted for the failure of the current test strategy. The proposed data-driven strategy could enhance the positive detection rate from 2.54% to 28.18%, and the recall rate from 8.05% to 89.35% under strictly limited test capacity. Much more optimal utilization of test resources could be realized where 89.35% of total positive cases could be detected with merely 48.17% of the original test amount. The strategy showed self-adaptability with the development of pandemic, while the strategy driven by local data was proved to be optimal. CONCLUSIONS: We recommended a generalization of such a data-driven test strategy for a better response to the global developing pandemic. Besides, the construction of the COVID-19 data system should be more refined on space for local applications.

The diagnostic value and pathogenetic role of lncRNA-ATB in patients with osteoarthritis.

BACKGROUND: In view of the roles of long non-coding RNAs (lncRNAs) in human diseases and the high incidence of osteoarthritis, we investigated the role of long non-coding RNA activated by transforming growth factor-ß (lncRNA-ATB) in osteoarthritis and explored its diagnostic value for this disease. METHODS: The study involved 98 patients with osteoarthritis and 76 healthy subjects. Blood was extracted from each participant and the expression of lncRNA-ATB in the serum was detected using quantitative Real Time -PCR. ROC curve analysis was performed to evaluate the diagnostic value of lncRNA-ATB for osteoarthritis. Based on the median serum level of lncRNA-ATB, patients were divided into a high-level group and a low-level group. Correlations between the serum levels of lncRNA-ATB and basic information about the patients were analyzed using the chi-square test. LncRNA-ATB overexpression in human chondrocyte cell line CHON-001 (ATCC CRL-2846) was established to study the effects on chondrocyte proliferation (using the CCK-8 assay) and viability. RESULTS: LncRNA-ATB expression was significantly downregulated in the serum of osteoarthritis patients compared with the healthy controls, meaning this downregulation effectively distinguished osteoarthritis patients from healthy subjects. LncRNA-ATB expression in the serum was not significantly affected by the patients' gender, age or habits, including smoking and alcohol consumption. LncRNA-ATB overexpression activated Akt signaling, promoted proliferation and increased the viability of the chondrocytes. CONCLUSION: We conclude that downregulation of lncRNA-ATB in serum is a reliable diagnostic marker for osteoarthritis and that this lncRNA participates in the pathogenesis of osteoarthritis by regulating the proliferation and viability of chondrocytes through the activation of Akt signaling.

Review of the application of neural network approaches in pedestrian dynamics studies.

In recent years, artificial intelligence methods have been widely used in the study of pedestrian dynamics and crowd evacuation. Different neural network models have been proposed and tested using publicly available pedestrian datasets. These studies have shown that different neural network models present large performance differences for different crowd scenarios. To help future research select more appropriate models, this article presents a review of the application of neural network methods in pedestrian dynamics studies. The studies are classified into two categories: pedestrian trajectory prediction and pedestrian behavior prediction. Both categories are discussed in detail from a conceptual perspective, as well as from the viewpoints of methodology, measurement, and results. The review found that the mainstream method of pedestrian trajectory prediction is currently the LSTM-based method, which has adequate accuracy for short-term predictions. Furthermore, the deep neural network is the most popular method for pedestrian behavior prediction. This method can emulate the decision-making process in a complex environment, and it has the potential to revolutionize the study of pedestrian dynamics. Overall, it is found that new methods and datasets are still required to systemize the study of pedestrian dynamics and eventually ensure its wide-scale application in industry.

Prognosis and Risk Factors Influencing Recurrence in Surgery-treated Patients with Primary Sacral Tumors.

BACKGROUND: We aimed to explore the prognosis and risk factors influencing tumor recurrence in surgery-treated patients with primary sacral tumors. METHODS: Fifty-six patients between February 2011 and December 2016 in Yishui Central Hospital with primary sacral tumors were selected and treated with radical surgeries. The perioperative outcomes and postoperative neurological functions were observed. After postoperative follow-up, the overall survival time (OS), disease-free survival time (DFS), and recurrence were recorded to analyze the potential risk factors influencing tumor recurrence. RESULTS: The average surgical duration and intraoperative hemorrhagic volume were 3.92 ± 1.46 h and 2, 348.21 ± 813.67 ml, respectively. The postoperative short-term complications included three patients with infection from obstructed drainage and two with skin flap necrosis-induced infection, who recovered after anti-infection therapies; nine with incision-edge necrosis; two with calf muscle venous thrombosis; and one with an endorhachis cerebrospinal fluid fistula, who recovered after conventional treatment. Among patients, the 1-, 2- and 3-year survival rates were 91.07% (51/56), 82.14% (46/56), and 75.00% (42/56) while the 1-, 2- and 3-year DFS rates were 89.29% (50/56), 78.57% (44/56) and 71.43% (40/56), respectively. Of the 56 patients, 16 had recurrence after surgery, with recurrence rate of 28.57%. It was predicated that surgical methods and local infiltration were the independent risk factors influencing tumor recurrence (P<0.01). CONCLUSION: The reservation of bilateral S3 or > unilateral S3 nerves can improve quality of life of patients. Surgical methods and local infiltration are the independent risk factors influencing tumor recurrence, and extensive resection can effectively control the recurrence rate.