Digital Terrain Models Generation from Airborne LiDAR Point Clouds Using A Multi-scale Terrain Filtering Method

A Digital Terrain Model (DTM) is an important topographic product, required in many applications. Data needed to create a DTM was traditionally obtained via land surveying, however this method can be costly and time consuming depending on the size of the geographic area. Over time, the land surveying was partially replaced by photogrammetry. Today, airborne Light Detection and Ranging (LiDAR) has become another powerful alternative that collect 3D point clouds for digital surface models (DSM) acquisition. LiDAR is especially useful when dealing with heavily vegetated areas using a canopy penetration feature of laser pulse. Nowadays, LiDAR plays an important role in DTM generation. This thesis presents a hierarchical recovery method to generate DTMs from a cloud of 3D points composed of “single returns” and “multiple returns” from laser pulses using the idea of layering. The proposed method will begin by registering the last return points, then layering them. The layering is done by dividing the points into different height layers and assigning layer numbers to each point. The layer numbers are used as a comparison feature in a later identification process. Then a series of rasterized pyramid levels, which consists of the lowest points in each cell, are generated. After layering, outliers are removed; cells in the top level are assumed as terrain points and used as references for identifying cells in the second level. The identification process will identify the cells of the second level into terrain cells and off-terrain cells, and an interpolation will then occur in the cells which identified as off-terrain. The interpolated level will be used as references for the next level and the same process is then repeated for each level that comes after. Once this process has been completed for the bottom level, the proposed method adjusts the results based on the first return feedback, followed by another interpolation. As a result, the final DTM is produced. The developed method is data driven, and does not assume a prior knowledge about the scene complexity. The proposed method was tested with three airborne LiDAR datasets, covering different terrain types and filtering difficulties. Results illustrated that the proposed method can perform well for areas of flat terrain or gentle slope A comparative study was conducted over existing filters and showed that results of the proposed method has similar accuracy in above mentioned area and faster speed than two comparing algorithms

The association between sleep duration, respiratory symptoms, asthma, and COPD in adults

IntroductionThe association between sleep duration and cough, wheezing, and dyspnea was unclear. This research aimed to test this relationship.MethodsResearch data were obtained from people who participated in the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2012. We used weighted logistic regression analysis and fitted curves to explore the association between sleep and respiratory symptoms. In addition, we investigated the association between sleep duration, chronic obstructive pulmonary disease (COPD), and asthma. The stratified analysis is used to analyze inflection points and specific populations.ResultsThe 14,742 subjects are weighted to reflect the 45,678,491 population across the United States. Weighted logistic regression and fitted curves show a U-shaped relationship between sleep duration and cough and dyspnea. This U-shaped relationship remained in people without COPD and asthma. The stratified analysis confirmed that sleep duration before 7.5 h was negatively associated with cough (HR 0.80, 95% CI 0.73–0.87) and dyspnea (HR 0.82, 95% CI 0.77–0.88). In contrast, it was positively associated with cough and (HR 1.30, 95% CI 1.14–1.48) dyspnea (HR 1.12, 95% CI 1.00–1.26) when sleep duration was >7.5 h. In addition, short sleep duration is associated with wheezing, asthma, and COPD.ConclusionBoth long and short sleep duration are associated with cough and dyspnea. And short sleep duration is also an independent risk factor for wheezing, asthma, and COPD. This finding provides new insights into the management of respiratory symptoms and diseases

Modified nusinersen intrathecal injection method: inclusion of a septal needle-free closed infusion connector

ObjectiveNusinersen, an extremely expensive biologic drug (around 100,000 US$ per dose) that needs to be administered intrathecally, is approved for the treatment of 5q-spinal muscular atrophy (SMA). Because of the low muscle tone of the back muscles of pediatric SMA patients, especially type 1 SMA patients, the safe, effective, and fast execution of sheath injection is needed. Therefore, a modified intrathecal injection method was developed accordingly. This paper aims to describe the applicability and safety of this modified method.MethodsThe modified intrathecal injection method (MIIM) mainly includes a septal needle-free closed infusion connector between the lumbar puncture needle and the syringe, besides the procedures of routine lumbar puncture. Its applicability and safety were evaluated through clinical observation.ResultsA total of 92 children with SMA have successfully received nusinersen treatment at our hospital using the modified method since 2019 without obvious adverse events related to the modified injection method. Based on the clinical feedback of operators, the advantages of the modified method include successfully injecting the total dose of nusinersen with constant injection rate and a more stable fixation of the puncture needle, as well as making the operator more relaxed. However, compared with the routine method, the procedure of the modified method has additional steps.ConclusionThe modified intrathecal injection method is an effective and safe method to inject nusinersen when weighing the pros and cons, and it may also be used for administering intrathecal injections of other expensive medicines or for patients with other strict requirements for intrathecal injection

GWAS and WGCNA uncover hub genes controlling salt tolerance in maize (Zea mays L.) seedlings

Salt stress influences maize growth and development. To decode the genetic basis and hub genes controlling salt tolerance is a meaningful exploration for cultivating salt-tolerant maize varieties. Herein, we used an association panel consisting of 305 lines to identify the genetic loci responsible for Na+- and K+-related traits in maize seedlings. Under the salt stress, seven significant single nucleotide polymorphisms were identified using a genome-wide association study, and 120 genes were obtained by scanning the linkage disequilibrium regions of these loci. According to the transcriptome data of the above 120 genes under salinity treatment, we conducted a weighted gene co-expression network analysis. Combined the gene annotations, two SNaC/SKC (shoot Na+ content/shoot K+ content)-associated genes GRMZM2G075104 and GRMZM2G333183 were finally identified as the hub genes involved in salt tolerance. Subsequently, these two genes were verified to affect salt tolerance of maize seedlings by candidate gene association analysis. Haplotypes TTGTCCG-CT and CTT were determined as favorable/salt-tolerance haplotypes for GRMZM2G075104 and GRMZM2G333183, respectively. These findings provide novel insights into genetic architectures underlying maize salt tolerance and contribute to the cultivation of salt-tolerant varieties in maize

A Mental Examination---Using Personality to predict Happiness, Altruism and Health

According to professor Jokela, psychologists can know the social functioning of a person only by assessing their Personality traits. However, empirical studies have been focused on building linear regressions between only one facet of personality and Life Satisfaction, Altruism and Health accordingly; also, the accuracy of the prediction remained debatable. In practice, scales online help researchers to get data measurements of participants’ information needed in the study. Gradient descent works by building the optimized multiple linear regression to model the relationship of a lot of inputs and a single output; python programs enable researchers to test the accuracy of the predicted output of the regressions. The data was from a preparing study by another group of graduated students from Cambridge University, and it contained information of 1769 participants. By splitting the sample into testing sample (33%) and training sample (67%), three multiple linear regressions were built to model the relationship between 120 Personality items and an average Life Satisfaction score, Altruism score and Health score using the training sample; then, the accuracy of the models was tested using the testing sample. According to the small p-values of correlation between the y-reported and y-predicted for all the three predictions, the probability of getting extreme values was very small, which ensured the reliability of these prediction. According to Cohen’s conventions about effect size of correlation in Psychology and another authorized peer research, the Pearson-correlation value of Personality & Life Satisfaction regression shows a very high accuracy of using Personality to predict Life Satisfaction; also, the correlation values for Personality & Altruism and Personality & Health are also above moderate, which indicate nice and acceptable predictability for two regressions

Inequality in benefit distribution of reducing the outpatient cost-sharing: evidence from the outpatient pooling scheme in China

ObjectiveThe implementation of the outpatient pooling scheme in China has substantially elevated the compensation levels for outpatient expenses. This study aims to assess whether socioeconomically disadvantaged enrollees benefit proportionally compared to their non-disadvantaged counterparts.MethodA cohort comprising 14,581 Urban and Rural Resident Basic Medical Insurance (URRBMI) enrollees and 830 Urban Employee Basic Medical Insurance (UEBMI) enrollees was derived from the China Health and Retirement Longitudinal Study 2018. Outpatient pooling scheme benefits were evaluated based on two metrics: the probability of obtaining benefits and the magnitude of benefits (reimbursement amounts and ratios). Two-part models were employed to adjust outpatient benefits for healthcare needs. Inequality in benefit distribution was assessed using the concentration curve and concentration index (CI).ResultsFollowing adjustments for healthcare needs, the CI for the probability of receiving outpatient benefits for URRBMI and UEBMI enrollees were − 0.0760 and − 0.0514, respectively, indicating an evident pro-poor pattern under the outpatient pooling scheme. However, the CIs of reimbursement amounts (0.0708) and ratio (0.0761) for URRBMI recipients were positive, signifying a discernible pro-rich inequality in the degree of benefits. Conversely, socioeconomically disadvantaged UEBMI enrollees received higher reimbursement amounts and ratios.ConclusionDespite a higher likelihood of socioeconomically disadvantaged groups receiving outpatient benefits, a pro-rich inequality persists in the degree of benefits under the outpatient pooling scheme in China. Comprehensive strategies, including expanding outpatient financial benefits, adopting distinct reimbursement standards, and enhancing the accessibility of outpatient care, need to be implemented to achieve equity in benefits distribution

The role of cognitive impairment in pain care in the emergency department for patients from residential aged care facilities: A retrospective, case-control study

Background: Patients with cognitive impairment are at risk of substantial delays to analgesic medication when presenting to the emergency department in pain. Aim: To identify if patients from residential aged care facilities with cognitive impairment experience the same delays to analgesic medication are reported in the general emergency department population. Methods: This study is a retrospective case-control review of patients presenting to one emergency department with pain as a presenting complaint from residential aged care, with and without cognitive impairment. Results: Patients without cognitive impairment experience delays in time to first analgesic medication (175 min vs 98 min, p = 0.006) compared to cognitively impaired patients from residential aged care facilities. Both cohorts of patients waited more than three times the national benchmark for analgesic medication and 66% of all patients in this study did not have pain assessment completed. Conclusion: Patients presenting from residential caged care facilities in this study without cogitative impairment wait longer for analgesia then patients who present with cogitative impairment, contrary to previously described relationships. Pain assessment and treatment are open to cogitative bias, and in the absence of pain assessment cogitative bias may prevail leading to poor pain care and discrepancies between patients with and without cogitative impairment.</p