Number Of Classes Research Articles

Automated mapping of electronic data capture fields to SDTM

Objective The goal of this work is to reduce the amount of manual work required to go from data capture to regulatory submission. It will be shown that the use of Siamese networks will allow for the generation of embeddings that can be used by traditional machine learning classifiers to perform the classification at much higher levels of accuracy than standard approaches. Methods Siamese networks are a method for training data embeddings such that data within the same class are closer with respect to a given distance metric than they are to data points in another class. Because they are designed to learn similarity within pairs of data points, they work well in situations where the number of classes is relatively large compared to the number of training samples. In this work, we will show that embeddings generated via a Siamese network from metadata associated with electronic data capture forms can be used to predict the associated SDTM field. Results With a relatively simple network coupled with a basic classification algorithm, the proposed method can achieve accuracies greater than 90%, which is significantly higher than what has been achieved with traditional methods, with many of the inaccurate mappings due to a lack of training data. In many cases, there is a 15% increase in accuracy vs. more traditional methods. Conclusion Leveraging Siamese networks, it is possible to generate embeddings that efficiently represent data fields in a lower dimensional space. This allows the creation of a system that can automatically map between data schemas at high levels of accuracy. Such systems represent the first step in automating one of the many labor-intensive data management tasks associated with clinical trials.

Roodmus: a toolkit for benchmarking heterogeneous electron cryo-microscopy reconstructions.

Conformational heterogeneity of biological macromolecules is a challenge in single-particle averaging (SPA). Current standard practice is to employ classification and filtering methods that may allow a discrete number of conformational states to be reconstructed. However, the conformation space accessible to these molecules is continuous and, therefore, explored incompletely by a small number of discrete classes. Recently developed heterogeneous reconstruction algorithms (HRAs) to analyse continuous heterogeneity rely on machine-learning methods that employ low-dimensional latent space representations. The non-linear nature of many of these methods poses a challenge to their validation and interpretation and to identifying functionally relevant conformational trajectories. These methods would benefit from in-depth benchmarking using high-quality synthetic data and concomitant ground truth information. We present a framework for the simulation and subsequent analysis with respect to the ground truth of cryo-EM micrographs containing particles whose conformational heterogeneity is sourced from molecular dynamics simulations. These synthetic data can be processed as if they were experimental data, allowing aspects of standard SPA workflows as well as heterogeneous reconstruction methods to be compared with known ground truth using available utilities. The simulation and analysis of several such datasets are demonstrated and an initial investigation into HRAs is presented.

Filtering-Assisted Airborne Point Cloud Semantic Segmentation for Transmission Lines

Point cloud semantic segmentation is crucial for identifying and analyzing transmission lines. Due to the number of point clouds being huge, complex scenes, and unbalanced sample proportion, the mainstream machine learning methods of point cloud segmentation cannot provide high efficiency and accuracy when extending to transmission line scenes. This paper proposes a filter-assisted airborne point cloud semantic segmentation for transmission lines. First, a large number of ground point clouds is identified by introducing the well-developed cloth simulation filter to alleviate the impact of the imbalance of the target object proportion on the classifier’s performance. The multi-dimensional features are then defined, and the classification model is trained to achieve the multi-element semantic segmentation of the transmission line scene. The experimental results and analysis indicate that the proposed filter-assisted algorithm can significantly improve the semantic segmentation performance of the transmission line point cloud, enhancing both the point cloud segmentation efficiency and accuracy by more than 25.46% and 3.15%, respectively. The filter-assisted point cloud semantic segmentation method reduces the volume of sample data, the number of sample classes, and the sample imbalance index in power line scenarios to a certain extent, thereby improving the classification accuracy of classifiers and reducing time consumption. This research holds significant theoretical reference value and engineering application potential for scene reconstruction and intelligent understanding of airborne laser point cloud transmission lines.

Clinical diagnostic reference levels and image quality metrics for CT in oncology patients

Background: In CT, the volumetric CT dose index (CTDIvol), dose-length product (DLP) and patient’s size-specific dose estimates (SSDE) are used as diagnostic reference level (DRL) metrics.Objectives: To develop clinical local DRL values for CT chest-abdomen-pelvis (CAP) examinations using the CTDIvol, DLP and SSDE, and to determine the image quality achieved.Method: In total, 201 cancer patients were included in the study. The scanning parameters, dose metrics from the CT unit and participants’ body mass index (BMI) were documented. The local CT DRL values for CAP examinations were defined as the median and 75th percentiles of the dose distribution.Results: The local DRL values given in terms of median CTDIvol ranged between 8.4 mGy and 12.7 mGy for the different types of cancers. The median DLPs ranged from 848 to 1173.4 mGy.cm for the various cancers. Generally, the radiation dose was directly proportional to the BMI and number of scan phases. Significant differences were observed between the DRLs for the various size-related parameters, number of scan phases and BMI classifications. The image quality was clinically satisfactory.Conclusion: No baseline data for clinical DRL values were available for this medical oncology department. The achieved DRLs were similar to published size-specific DRLs. The image quality was maintained during CT imaging. Dose optimisation and image quality assessment should be implemented to ensure optimal scanning parameters for different cancers in CT CAP examinations.Contribution: The first size-specific local DRL values for CT CAP investigations carried out on oncology patients in South Africa have been established.

Effect of Health Education on Environmental Pollution as a Primary Factor in Sustainable Development

Environmental Pollution (EP) is an escalating worldwide issue affecting the entire planet. Environmental consciousness plays a crucial role in addressing EP's problems. Environmental Education (EE) is one of the methods used to instill these ideals in individuals. EE is a critical determinant of enhancing the condition of the ecosystem, and it is imperative to get instruction from a young age. Instructors influence children's interests, mindsets, and beliefs on environmental issues and their associated difficulties in Peru. The significance of EE is exceedingly elevated. This significantly impacts the nation's Economic Development (ED) and Public Health (PH) . The Sustainable Development Goals (SDGs) serve as a comprehensive plan for achieving a more prosperous and environmentally responsible future for everyone. The establishment of SDGs aims to eliminate poverty, protect against natural calamities, and provide universal prosperity and security. This study investigates educators' potential, perspectives, and opinions in instructing the subject of EP and PH. The research documents the correlation between EE and EP in Peru. The primary objective was to engage with instructors and ascertain their capacity to educate students about EP. The requisite information was acquired by a survey (240 participants) distributed to all pertinent elementary and secondary schools. The mean yearly number of classes taught is just 2.62, although the preferred number of lessons, as indicated by the instructors, is considerably more. Instructors find water and air contamination particularly intriguing among the various forms of pollution. When instructors mediate the curriculum, they prioritize respecting both living and inanimate nature, focusing on living nature for 19% of their efforts. Several instructors continue to depend on inadequate technological advancement, which is restricted by nature protection (2%). According to instructors, the conversation technique (34%) is a frequently used approach to engage students in learning about the issue of contamination. Studies have indicated that instructors believe that to address the concerns adequately, the number of classes devoted to this topic should be increased threefold. The collected results validate educators' endeavors in addressing environmental contamination, which is a positive trajectory.

Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning

The perception and recognition of objects around us empower environmental interaction. Harnessing the brain’s signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects’ responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models.

Predicting hardwood porosity domains: Toward cascading computer-vision wood identification models

Prior work on computer-vision wood identification (CVWID) for North American hardwoods yielded two independent deep learning models – a 22-class model for diffuse-porous woods and a 17-class model for ring-porous woods – but did not address semi-ring-porous woods nor provide a CVWID solution for an unknown specimen without a human first determining which model to deploy. As untrained human operators would lack the anatomical proficiency to differentiate among porosity domains, it is necessary to develop a consolidated model that can identify diffuse-, ring-, and semi-ring-porous woods. Previous research suggests that prediction accuracy might decrease as class number grows. A potential strategy to reduce the number of classes a CVWID system must consider at a time is to hierarchically deploy a cascade of models. In pursuit of a unified model that can cover North American hardwoods of all porosity types, this study compared the accuracies of a consolidated 39-class (ring- + diffuse-porous) model and a consolidated 42-class (ring- + diffuse- + semi-ring-porous) model with a two-tiered, cascading model scheme whereby images are first differentiated into three porosity domain classes and then again into only those taxonomic classes with that porosity. The results showed that the cascading model scheme can mitigate the accuracy reductions incurred by the 42-class model and nearly eliminate the occurrence of cross-domain misidentifications.

A GIS-Based Framework to Analyze the Behavior of Urban Greenery During Heatwaves Using Satellite Data

This work proposes a new unsupervised method to evaluate the behavior of urban green areas in the presence of heatwave scenarios by analyzing three indices extracted from satellite data: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST). The aim of this research is to analyze the behavior of urban vegetation types during heatwaves through the analysis of these three indices. To evaluate how these indices characterize urban green areas during heatwaves, an unsupervised classification method of the three indices is proposed that uses the Elbow method to determine the optimal number of classes and the Jenks classification algorithm. Each class is assigned a Gaussian fuzzy set and the green urban areas are classified using zonal statistics operators. The membership degree of the corresponding fuzzy set is calculated to assess the reliability of the classification. Finally, for each type of greenery, the frequencies of types of green areas belonging to NDVI, NDMI, and LST classes are analyzed to evaluate their behavior during heatwaves. The framework was tested in an urban area consisting of the city of Naples (Italy). The results show that some types of greenery, such as deciduous forests and olive groves, are more efficient, in terms of health status and cooling effect, than other types of urban green areas during heatwaves; they are classified with NDVI and NDMI values of mainly High and Medium High, and maximum LST values of Medium Low. Conversely, uncultivated areas show critical behaviors during heatwaves; they are classified with maximum NDVI and NDMI values of Medium Low and maximum LST values of Medium High. The research results represent a support to urban planners and local municipalities in designing effective strategies and nature-based solutions to deal with heat waves in urban settlements.

Nighttime blood pressure reductions after radiofrequency renal denervation through 24 months in patients with uncontrolled hypertension: pooled analysis from the SPYRAL HTN trials

Abstract Introduction Studies have shown that elevated nighttime and early morning systolic blood pressure (SBP) are associated with the highest risk for cardiovascular (CV) events. Radiofrequency renal denervation (RF RDN) is an interventional therapy to reduce BP in patients with uncontrolled hypertension (HTN). Although sustained, durable reductions in office and 24-h ambulatory systolic BP (SBP) after RDN have been observed, whether the therapy produces similar, durable nighttime and early morning SBP reductions requires further evaluation. Objective To assess whether RF RDN significantly reduces nighttime SBP through long-term follow-up, in addition to other times of the day. Methods We pooled data from patients with uncontrolled HTN undergoing RF RDN using the latest-generation Symplicity SpyralTM multi-electrode catheter from the published SPYRAL HTN-OFF and -ON MED clinical trials. Office and 24-h ambulatory SBP were evaluated from baseline through 24 months. We also assessed BP changes during specific, diurnal time ranges including nighttime (1am-6am), early morning (7am-9am), and daytime (9am-9pm). The number of antihypertensive drugs and drug burden (determined by number of classes and prescribed dosage) from urine and plasma drug testing were tabulated from baseline through 24 months. Results Outcomes from 388 patients undergoing RF RDN were pooled for this analysis. Patients were on average 54±10yrs old, 26.8% female, mean BMI was 31.3±6.0, 45.1% with >10yrs diagnosis of hypertension, 7.7% with type 2 diabetes, 9.8% with obstructive sleep apnea, 47.7% with a history of smoking. The baseline nighttime SBP was 139±11mmHg, morning SBP was 154±15mmHg, and daytime SBP was 156±9mmHg. At baseline, 92.5% of patients had nocturnal hypertension (BP ≥120/70 mmHg) and took 1.0±1.2 antihypertensive drugs. 12 months after RF RDN, 30.6% of patients no longer had nocturnal hypertension (p<0.0001). The mean nighttime SBP change at 12 months was -10.8±15.4mmHg, morning changed was -12.8±20.4mmHg, and daytime changed was -12.2±13.6mmHg (Figure). The mean 24-h ambulatory SBP change at 12 months was -11.9±12.4mmHg, and the office SBP change was -17.7±15.4mmHg . Patients took 1.8±1.1 antihypertensive drugs at 12 months. Results from 24 months, not currently available, will also be presented for the first time. Conclusions In this pooled cohort of RDN patients with uncontrolled hypertension, nocturnal hypertension was highly prevalent. Clinically meaningful reductions at 12 months throughout the 24-hr circadian cycle, including at nighttime and early morning SBP, are consistent with the "always on" effect of RDN. Results from 24 months will also be presented for the first time at ESC. As an adjunctive therapy to pharmacotherapy, RDN may help to reduce CV risk by lowering nighttime and morning SBP.

Predictors of social withdrawal among early adolescents in Korea: a latent growth class analysis

Abstract Background This study examines the trajectory of social withdrawal from the fourth grade of elementary school to the first grade of middle school in Korea. By classifying them, we identify individual differences such as individual, family, and school factors. Methods This study uses data from the Korean Youth Panel Survey (KYCPS) conducted by the Korea Youth Policy Research Institute, which includes data from the 1st-4th waves of KYCPS 2018, with a total of 2,607 participants (1,313 boys and 1,294 girls). A latent class growth analysis verifies the trajectory of youth social withdrawal, classifies latent classes, and identifies predictive. SPSS 24.0 was used to analyze the main variables’ characteristics, descriptive analysis, and reliability. Mplus 8.9 was used to test the research model. Results First, results indicated that three was the optimum number of latent classes. We identified three latent classes ‘high level of withdrawal continued (HL)’, ‘medium level of withdrawal increased (ML)’, and ‘low level of withdrawal increased (LL)’. Second, when compared the ‘LL’ and ‘HL’ groups, the higher the self-esteem, the higher the friendship, and the higher the dependence on smartphones, the more likely it was to be in the ‘HL’ group. Third, when comparing ‘LL’ and ‘ML’ groups, the more negative the parenting attitude, the more likely it was to be in the ‘ML’ group. On the other hand, higher self-esteem and more positive friend and teacher relationships were associated with a higher likelihood of being in the ‘LL’ group. Conclusions It is necessary to seek differentiated interventions to prevent social withdrawal among early adolescents. Parental education to understand the adolescent period and practical approaches to intervene in the negative psycho-emotional aspects of adolescents continuously are needed to enhance the healthy psychological development of early adolescents. Key messages • The higher the self-esteem, the higher the friendship, and the higher the dependence on smartphones, the more likely it was to be in the ‘high level of withdrawal continued’ group. • Higher self-esteem and more positive friend and teacher relationships were associated with a higher likelihood of being in the ‘low level of withdrawal increased’ group.

Blood pressure and renal function changes in patients with severe chronic kidney disease undergoing radiofrequency renal denervation: 12 months outcomes from the Global SYMPLICITY Registry DEFINE

Abstract Background Radiofrequency renal denervation (RF RDN) lowers blood pressure (BP) in patients with uncontrolled hypertension (HTN). The impact of renal function on safety and efficacy of RDN is unknown. Purpose To assess BP and renal function in patients with HTN following RDN. Methods All patients treated with RF RDN (n=3332) from the observational Global SYMPLICITY Registry Denervation Findings in Real World (GSR DEFINE) were included. Patients were categorized into CKD stages by the corresponding estimated glomerular filtration rate (eGFR; mL/min/1.73m2): <45 (CKD stage 3b and higher; n=339), 45-59 (CKD stage 3a; n=467), ≥60 (CKD stage 2 and lower; n=2334). BP (office, 24h ambulatory BP), eGFR changes from baseline through 12 months were compared between groups. Comparisons between groups were made after adjusting for baseline BP and change in number of antihypertensive drug classes from baseline. Results At baseline, office systolic SBP (OSBP) in eGFR groups <45, 45-59, and ≥60 ml/min/1.73 m2 was: 164±25, 163±26 and 165±25 mmHg, respectively (p=0.083). 24-h ambulatory systolic BP (ASBP) was: 156±19, 153±20 and 154±19 mmHg, respectively (p=0.082). Mean eGFR was: 32.5±11.2, 53.6±4.4, and 86.8±18.6 ml/min/1.73 m2, respectively. A total of 1.1% of patients were on dialysis. Patients in eGFR group <45 ml/min/1.73 m2 were significantly older, had higher rates of ischemic heart disease, heart failure, diabetes, on more antihypertensive drug classes compared to higher eGFR groups. Patients were on 5.0±1.3, 4.6±1.3, and 4.5±1.4 antihypertensive drugs in eGFR <45, 45-59 and ≥60 ml/min/1.73 m2 groups, respectively (p<0.001). At 12 months, all had significant OSBP drop from baseline within the different eGFR groups (p<0.001). OSBP changed by -10.8±27.8, -10.5±25.9 and -14.7±26.6 mmHg in eGFR <45, 45-59 and ≥60 ml/min/1.73 m2 groups, respectively (adjusted p=0.095; Figure 1A). Similarly at 12 months, all patients had significant ASBP reductions from baseline across the eGFR groups (p<0.01). ASBP changed by -5.2±18.0, -6.9±18.8, and -8.5±18.8 mmHg in eGFR <45, 45-59 and ≥60 ml/min/1.73 m2 subgroups, respectively (adjusted p=0.16). At 12 months, eGFR did not significantly change from baseline in eGFR <45 and 45-59 ml/min/1.73 m2 groups (Figure 1B): 0.1±14.6 (p=0.92) and -0.6±12.8 ml/min/1.73 m2 (p=0.45), respectively. Estimated GFR changed from baseline by -5.6±15.5 ml/min/1.73 m2 in eGFR ≥60 ml/min/1.73 m2 group (p<0.001). Between group differences in eGFR changes were statistically significant (adjusted p<0.001), but by a numerically small difference. The no. of antihypertensive drug classes changed by -0.2±1.3, -0.1±1.0 and -0.1±1.0 in eGFR <45 and 45-59 ml/min/1.73 m2 groups, respectively. Conclusion Patients with uncontrolled HTN with different CKD severity including those with eGFR <45 ml/min/1.73 m2 had significant SBP reductions from baseline through 12 months, whilst renal function was preserved.Figure 1

Effect of disabling biventricular pacing in patients with left ventricular assist device and cardiac resynchronization therapy

Abstract Introduction Taking into account the mechanism of the left ventricular assist device (LVAD) function, the cardiac resynchronization therapy (CRT) seems to be unnecessary and, according to the current reports, the left ventricular lead (LVL) should be disabled. However, observation of the patients suggests that deactivation of the left ventricle (LV) pacing leads to deterioration of the patients condition and progression of the heart failure (HF) symptoms. Purpose The study aimed to determine differences in survival of the patients with LVAD and maintained or disabled CRT, including patients subjected to heart transplantation, as well differences in left and right ventricle function, kidneys and liver condition, NTproBNP value, subjective exercise tolerance and frequency of HF exacerbation before and after CRT deactivation. Methods The study group involved 55 patients with LVAD and CRT devices, including 11 patients with disabled LV pacing. The statistical analysis concerned the comparison of groups with and without biventricular stimulation as well results within the group with disabled LVL before and after its deactivation. Analyzed data included laboratory results, echocardiographic parameters, number of HF exacerbation episodes and exercise tolerance by NYHA classification. Results Total CRT period did not differ in group with maintained and disabled CRT [24 (12-36)months vs. 30 IQR(12-48)months respectively; p=0,74]. The median period of CRT deactivation was 30 IQR(12-48) months after LVAD implantation. Time from LVAD implantation to heart transplantation differed significantly between patients with and without CRT [20 IQR(9-32)months vs. 49,5 IQR(46,5-62)months respectively; p=0,003]. LV disabling was connected with a significant increase of NTproBNP [3072 IQR(2287-6636) pg/ml vs. 2133 IQR(1618-3437) pg/ml before LVL deactivation; p=0,009), without an aggravation of the exercise intolerance by NYHA classification (p=0,11) or total number of HF exacerbation episodes (p=0,72). Lack of LV pacing was not related to change of concentration of creatinine (p=0,13), AST (p=0,89, ALT (p=0,5), bilirubin (p=0,06) and albumin (p=0,69) before and after LV deactivation. Comparison of echocardiographic parameters with and without CRT did not demonstrate significant differences in LV ejection fraction (p=0,37), LV end-diastolic diameter (p=0,33), TAPSE (p=0,72) and tricuspid regurgitation degree (p=0,39). Death-free survival from LVAD implantation has not differed in patients with and without CRT(p=0,08), similarly like death-free survival from heart transplantation (p=0,56), however with tendency to better outcome of patients with CRT till 12 months after the heart transplant. Conclusion CRT does not improve general health condition as well death-free survival in LVAD recipients, including patients subjected to heart transplantation, however with tendency to better outcome of patients with CRT till 12 months after the heart transplant.Death-free surival after LVADDeath-free survival after transplant

Variability in treatment of postoperative pain in children with severe neurologic impairment.

Treatment of postoperative pain for children with severe neurologic impairment (SNI) is challenging. We describe the type, number of classes, and duration of postoperative pain medications for procedures common among children with SNI, as well as the variability across children's hospitals in pain management with an emphasis on opioid prescribing. This retrospective cohort study included children with SNI ages 0-21 years old who underwent common procedures between January 1, 2019 and December 31, 2019 within 49 children's hospitals in the Pediatric Health Information System. We defined SNI using previously described high-intensity neurologic impairment diagnosis codes and identified six common procedures which included fracture treatment, tracheostomy, spinal fusion, ventriculoperitoneal shunt placement (VP shunt), colostomy, or heart valve repair. Medication classes included benzodiazepines, opioids, and other nonopioid pain medications. Acetaminophen and nonsteroidal anti-inflammatory drugs were excluded from analysis. All findings were summarized using bivariate statistics. A total of 7184 children with SNI underwent a procedure of interest. The median number of classes of pain medications administered varied by procedure (e.g., VP shunt: 0 (interquartile range [IQR] 0-1); tracheostomy: 3 (IQR 2-4)). Across all procedures, opioids and benzodiazepines were the most commonly prescribed pain medications (48.8% and 38.7%, respectively). We observed significant variability in the percentage of postoperative days with opioids across hospitals by procedure (all p < .001). There is substantial variability in the postoperative delivery of pain medications for children with SNI. A standardized approach may decrease the variability in postoperative pain control and enhance care for children with SNI.

The geometry of multi-curve interest rate models

We study the problems of consistency and the existence of finite-dimensional realizations for multi-curve interest rate models of Heath–Jarrow–Morton type, generalizing the geometric approach developed by T. Björk and co-authors for the classical single-curve setting. We characterize when a multi-curve interest rate model is consistent with a given parameterized family of forward curves and spreads and when a model can be realized by a finite-dimensional state process. We illustrate the general theory in a number of model classes and examples, providing explicit constructions of finite-dimensional realizations. Based on these theoretical results, we perform the calibration of a three-curve Hull–White model to market data and analyse the stability of the estimated parameters.

Effect of a Financial Incentive Scheme for Medication Review on Polypharmacy in Elderly Inpatients With Dementia: A Retrospective Before-and-After Study.

Polypharmacy is an important healthcare issue, especially in elderly patients with dementia. As an incentive to reduce polypharmacy, a health insurance reimbursement scheme was introduced in 2016 for medication review and the reduction of medications for inpatients in Japan. However, the effects of these incentive schemes were not evaluated. We identified 1,465,881 inpatients aged ≥65 years with dementia. An interrupted time-series analysis was conducted by fitting a Prais-Winsten linear regression model. The outcome measure was the number of classes of medications prescribed during discharge. No significant changes were observed in the average number of medication classes at discharge immediately after the introduction of the scheme (coefficient: -0.022, 95% confidence interval [CI]: -0.17 to 0.13). The slope change, representing the effect of the intervention over time, was also not significant (coefficient: -0.00053, 95% confidence interval: -0.0012 to 0.00018). The incentive scheme was not associated with a reduction in the number of medication classes at discharge among older inpatients with dementia.

DNA-metabarcoding of cyanobacteria and microalgae in chernozem soils of temperate continental climate of the forest-steppe zone of Eurasia under different degrees of agrotechnology intensification.

Chernozem soil is a valuable resource and contains a great diversity of microorganisms that play a global role in the process of soil formation, the species diversity of which has changed over the last five years under the influence of different agrotechnologies. For the first time, under the conditions of the Central Chernozem region, grain and fallow crop rotation, studies using the DNA-metabarcoding method were carried out to study the taxonomic structure of bacteria, fungi, cyanobacteria, and microalgae communities in the arable horizon of typical medium loamy chernozem under winter wheat cultivation. A comparative analysis of the composition of the genotypes showed significant differences in the presented level of mineral nutrition of the soil NPK (60) and NPK (100) compared with the control variant. After processing the 16S and 18S rRNA datasets, a similar trend of decreasing numbers of pro- and eukaryotic species was found from 6296 (control without MF) to 5310 with NPK (60) and to 4643 with NPK (100), respectively. The Chao1 index indicated that the expected diversity within the prokaryotic group was higher in the control without MF at 211, but decreased to 182 and 193 with NPK (60) and NPK (100) fertilizers, respectively. Analysis of the eukaryotic group revealed a 2.6- and 2.9-fold decrease in diversity by class and genus, respectively, depending on the nutritional levels in agrotechnologies, owing to the use of MF. In the prokaryotic community, Alphaproteobacteria microorganisms predominated at an amount of 14.20-14.46%, with Cyanophyceae accounting for 5.2-9.9%. The diversity of eukaryotes was smaller than the number of classes of prokaryotes; the main dominant were Zygnematophyceae 19.5-41%, Chlorophyceae occupied 10.4-15.8%. On the other hand, the doses of fertilizers used contributed to the emergence of dominant species adapted to high doses of mineral nutrients for plants.

Generative Adversarial Autoencoder Integration Voting Algorithm Based on Mass Spectrometry Data

Mass spectrometry technology is often used in disease prevention and diagnosis and treatment. However, the number of mass spectrometry data features is huge and the features vary greatly between different diseases, making multi-disease diagnosis. The task of judgment is complex and difficult. To address the above problems, this paper proposes a generative adversarial autoencoder integrated voting algorithm msDAGVote based on mass spectrometry data.The generative adversarial network based on dual autoencoders is used as the feature extraction framework of msDAGVote. After inputting mass spectrometry data for training, the generator subnetwork is used for feature extraction.Finally, the constructed features were screened by integrating the voting feature selection algorithm, and the optimal feature subset was used for multi-disease diagnosis.The results were evaluated on mass spectrometry datasets of different disease types. The experimental data showed that the features extracted by msDAGVote outperformed the comparison methods and significantly reduced the classification time.The required number of features and excellent disease classification and diagnosis capabilities are required. The classification AUC exceeds 0.98 on 6 datasets and The set exceeds 0.87.

Join Spaces and Lattices

Hypergroups represent a generalization of groups, introduced by Marty, that are rich in applications in several sectors of mathematics and in other fields. An important class of hypergroups called join spaces is presented in this paper, along with some connections to lattice theory, in particular, to modular and to distributive lattices. In particular, we study join spaces associated with chains through functions and we analyze when such join spaces are isomorphic. Moreover, a combinatorial problem is presented for a finite context, focusing on calculating the number of isomorphisms classes of join spaces.

Trends in Initial Antihypertensive Medication Prescribing Among >2.8 Million Veterans Newly Diagnosed With Hypertension, 2000 to 2019.

Among patients diagnosed with high blood pressure (BP), initial dual therapy has been recommended for patients with high pretreatment systolic BP (≥160 mm Hg) since 2003, and first-line β-blocker use without a compelling condition has fallen out of favor in US guidelines. This serial cross-sectional study of national Veterans Health Administration data included adult Veterans with incident hypertension initiating antihypertensive medication between January 1, 2000, and December 31, 2019. We assessed annual trends in initial regimens dispensed (index date: first antihypertensive dispense date) by number of classes and unique class combinations used overall and by pretreatment systolic BP (<140, 140 to <160, and ≥160 mm Hg), as well as trends in subgroups (age, sex, race and ethnicity, and comorbidities warranting β-blocker use). Among 2 832 684 eligible Veterans (average age 61 years, 95% men, 65% non-Hispanic White, and 8% with cardiovascular disease), from 2000-2004 to 2015-2019, initial monotherapy increased across all pretreatment systolic BP levels (<140 mm Hg: 62.1% to 66.4%; 140 to <160 mm Hg: 70.7% to 76.8%; ≥160 mm Hg: 64.2% to 69.7%). Initiation of dual therapy decreased across all pretreatment systolic BP levels (<140 mm Hg: 25.0% to 24.2%; 140 to <160 mm Hg: 20.4% to 17.6%; ≥160 mm Hg: 22.7% to 22.0%). Among 2 521 696 Veterans (89% of overall) without a β-blocker-indicated condition in 2015 to 2019, 20% initiated a β-blocker, most commonly as monotherapy. More than half of US Veterans diagnosed with hypertension with a pretreatment systolic BP ≥160 mm Hg were started on antihypertensive monotherapy. There are disparities between guideline-recommended first-line treatments and the actual regimens initiated for newly diagnosed Veterans with hypertension.

Patterns of Intimate Partner Violence Among Veterans: A Latent Class Analysis.

The majority of intimate partner violence (IPV) research is unidirectional, focusing on IPV use (i.e., perpetration) or experience (i.e., victimization). However, when IPV use and experience data are simultaneously included in analyses, bidirectional IPV often emerges as a common IPV pattern. The objective of this study was to examine patterns of IPV use and experience, risk factors that may be associated with these patterns, and potential gender differences within a sample of post-9/11 Veterans. This study included a national sample of post-9/11 Veterans (N = 1,150; 50.3% women) who completed self-report measures at two timepoints. We performed a latent class analysis (LCA) to determine the appropriate number of IPV classes, conducted sensitivity analyses, and examined factors potentially associated with IPV class membership. We identified three distinct classes of IPV: Low to no IPV, Bidirectional Psychological IPV, and Bidirectional Multiform IPV. Men and women reported similar rates of IPV use and experience, and there were no gender differences in the LCA model. However, race and ethnicity, employment status, children in the household, marital status, child abuse or witnessing family violence, lifetime physical assault, posttraumatic stress symptoms, and binge drinking were differentially associated with class membership. This study extends existing knowledge on patterns of IPV among Veterans and factors associated with these patterns. Bidirectional IPV was the most common IPV pattern, underscoring the importance of examining IPV use and experience concurrently within research and clinical samples, and developing comprehensive IPV screening and treatment strategies that incorporate bidirectional IPV in work to advance relationship health and safety among Veterans.