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This paper studies the relationship between mutual fund management's direct experience with air pollution and the fund's engagements with portfolio companies on environmental issues. We find that higher air pollution in a mutual fund's headquarter county increases the propensity of the fund to vote in support of shareholders' environmental proposals and to liquidate its holdings of portfolio firms with lower environmental ratings. We also find that greater air pollution exposure of a mutual fund predicts better environmental performance at its portfolio companies. To establish causality, we use elevated air pollution produced by large wildfires in a mutual fund's headquarter county as an instrument and identify similar effects on the outcome variables. These results suggest that a mutual fund's direct exposure to pollution can inform its environmental engagements at its portfolio companies.

Background Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. Methods In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among multiple objects presented in TPGLDA model. Results The experimental results showed that our proposed method achieved a reliable performance with Area Under Roc Curve (AUC) and Area Under Precision-Recall Curve (AUPR) values of 0.9788 and 0.9373, respectively, under fivefold-cross-validation experiments. It outperformed than some other previous methods such as DCSMDA and TPGLDA. Furthermore, it demonstrated the ability to derive new associations between miRNAs and diseases among 8, 19 and 14 new associations out of top 40 predicted associations in case studies of Prostatic Neoplasms, Heart Failure, and Glioma diseases, respectively. All of these new predicted associations have been confirmed by recent literatures. Besides, it could discover new associations for new diseases (or miRNAs) without any known associations as demonstrated in the case study of Open-angle glaucoma disease. Conclusion With the reliable performance to infer new associations between miRNAs and diseases as well as to discover new associations for new diseases (or miRNAs) without any known associations, our proposed method can be considered as a powerful tool to infer miRNA-disease associations.

Purpose This study aims to examine the linear and non-linear effects of corporate social responsibility (CSR) engagement on trade receivables of listed firms in China. Furthermore, this paper analyzes whether CSR explains the provision for doubtful trade receivables. Design/methodology/approach The authors use a sample of listed firms in China over the period from 2008 to 2015. System generalized method of moments is used to estimate dynamic panel models. Findings CSR is positively related to trade receivables, in line with previous studies in this field. Nonetheless, the investigation of the non-linear effect of CSR reveals that CSR has an inverted U-shaped relationship with trade receivables. This implies that at low levels, CSR is more likely to be a tool to mitigate risk and/or build a trusting relationship between suppliers and buyers; whereas, at high levels, CSR is more prone to be subject to agency cost. The authors further find that CSR has a U-shaped relationship with the provision for bad trade receivables, which substantiates the above link between CSR and trade receivables. Originality/value Previous studies have extensively examined the link between trade credit extension and firm performance and determinants of trade credit. CSR can be connected to trade receivables in some ways, but very little effort has been exerted in verifying this relationship. In addition, CSR is linearly linked to trade receivables in previous literature, but theoretically, it can be expected to have a non-linear relationship with trade receivables. Furthermore, CSR has not been examined as a determinant of the provision for doubtful trade receivables. The authors aim to void the gaps here by using a sample of listed firms in China.

(1) Background: Exhaled nitric oxide (NO) has been considered as a biomarker of airway inflammation. The measurement of fractional exhaled NO (FENO) is a valuable test for assessing local inflammation in subjects with allergic rhinitis (AR). (2) Objective: To evaluate (a) the correlation between nasal FENO with anthropometric characteristics, symptoms of AR and nasal peak flows in children without and with AR, and (b) the cut-off of nasal FENO for diagnosis of AR in symptomatic children. (3) Methods: The study was a descriptive and cross-sectional study in subjects with and without AR &lt, 18 years old. All clinical and functional characteristics of the study subjects were recorded for analysis. They were divided into healthy subjects for the control group and subjects with AR who met all inclusion criteria. (4) Results: 100 subjects (14 ± 3 years) were included, including 32 control subjects and 68 patients with AR. Nasal FENO in AR patients was significantly higher than in control subjects: 985 ± 232 ppb vs. 229 ± 65 ppb (p &lt, 0.001). In control subjects, nasal FENO was not correlated with anthropometric characteristics and nasal inspiratory or expiratory peak flows (IPF or EPF) (p &gt, 0.05). There was a correlation between nasal FENO and AR symptoms in AR patients and nasal IPF and EPF (p = 0.001 and 0.0001, respectively). The cut-off of nasal FENO for positive AR diagnosis with the highest specificity and sensitivity was ≥794 ppb (96.7% and 92.6%, respectively). (5) Conclusion: The use of nasal FENO as a biomarker of AR provides a useful tool and additional armamentarium in the management of allergic rhinitis.

Vietnam is a rabies-endemic country where eating dog meat is customary. However, the risks of rabies transmission to dog slaughtering and processing workers have not been identified. This study aimed to determine the rabies neutralizing antibody (NTA) and risk factors in dog slaughterers to propose appropriate intervention methods for this occupational group. In 2016, a cross-sectional study on NTA against rabies virus and related factors was conducted among 406 professional dog slaughterers in Vietnam. The participants were interviewed using a structured questionnaire, and their sera were tested for rabies NTA by a rapid focus fluorescence inhibition test. Statistical algorithms were used to analyze the data. The results showed that most of the professional dog butchers (344/406 subjects, 84.7%) had no rabies NTA. Interestingly, 7.8% (29/373) had NTA without a rabies vaccination history. Over 5 years of experience as a dog butcher was positively associated with the presence of NTA in unvaccinated individuals (OR = 6.16, P = 0.001). The NTA in vaccinated butchers was present in higher titer and for longer persistence to those of other previously reported professionals, which is possibly as a result of multiple exposures to low levels of rabies virus antigens during dog slaughtering. Our study demonstrated that professional dog butchers in Vietnam are at a high risk of rabies virus infection, apart from those with common bite experiences. In countries where dog meat consumption is customary, rabies control and prevention activities should focus on safety during dog trading and slaughtering.

Type 2 Diabetes (T2DM) makes up about 90% of diabetes cases, as well as tough restriction on continuous monitoring and detecting become one of key aspects in T2DM. This research aims to develop an ensemble of several machine learning and deep learning models for early detection of T2DM with high accuracy. With high diversity of models, the ensemble provides more excessive performance than single models. Methodology. The proposed system is modified enhanced ensemble of machine learning models for T2DM prediction. It is composed of Logistic Regression, Random Forest, SVM, and Deep Neural Network models to generate a modified ensemble model. Results. The output of each model in the modified ensemble is used to figure out the final output of the system. The datasets being used for these models include Practice Fusion HER, Pima Indians diabetic’s data, UCI AIM94 Dataset and CA Diabetes Prevalence 2014. In comparison to the previous solutions, the proposed ensemble model solution exposes the effectiveness of accuracy, sensitivity, and specificity. It provides an accuracy of 87.5 from 83.51% in average, sensitivity of 35.8 from 29.59% as well as specificity of 98.9 from 96.27%. The processing time of the proposed model solution with 96.6 ms is faster than the state-of-the-art with 97.5 ms. Conclusion. The proposed modified enhanced system in this work improves the overall prediction capability of T2DM using an ensemble of several machine learning and deep learning models. A majority voting scheme utilizes the output from several models to make the final accurate prediction. Regularization function in this work is modified in order to include the regularization of all the models in ensemble, that helps prevent the overfitting and encourages the generalization capacity of the proposed system.

Paroxysmal kinesigenic dyskinesia (PKD) is a rare condition characterized by abnormal involuntary movements that are precipitated by a sudden movement. PKD is often misdiagnosed with psychogenic movement disorders. Carbamazepine is usually the first choice of medication due to its well-established evidence but could induce Stevens-Johnson syndrome. We report a 21-year-old male patient with PKD referred to our movement disorders clinic after being misdiagnosed with conversion syndrome. PRRT2 gene testing using next-generation sequencing revealed a mutation in c.649dupC p. (Arg217fs). The patient responded well to carbamazepine but had to withdraw the treatment due to carbamazepine-induced Stevens-Johnson syndrome after 3 weeks of medication. Our patient did not respond to trials of levetiracetam and phenytoin but finally responded well to oxcarbazepine. The patient was followed up for 4 years, during which he had no attacks and no side effects. Here, we present a PKD case with carbamazepine-induced Stevens-Johnson syndrome successfully treated with oxcarbazepine despite the risk of cross-reactive skin eruption between these antiepileptics. Careful history taking and examining patient’s attacks are crucial to accurate diagnosis and treatment in PKD patients.

Objective: The aim of the present study was to measure the correlation between the level of exhaled Nitric Oxide (NO) and Obstructive Sleep Apnea (OSA) severity and to evaluate its modification after Continuous Positive Airway Pressure (CPAP) treatment in severe OSA patients. Methods: It was a descriptive and cross-sectional study. Subjects were classified by mild-moderate or severe OSA. CPAP was used for severe OSA patients and followed up for 3 months. Exhaled NO was measured in the morning after the previous night of Respiratory Polygraphy (RPG) recording. Results: This study recruited 123 subjects, including 40 mild-moderate and 83 severe OSA patients. The level of maximum bronchial NO production (J’awNO) in patients with severe OSA was significantly higher than mild-moderate OSA [36.2 (6.1–92.2) vs. 19.6 (1.6-73.0) ppb; p=0.001)]. The level of concentration of NO in the gas phase of the alveolar (CANO) in patients with severe OSA was lower than mild-moderate OSA [5.2 (1.2-12.7) vs. 6.9 (0.8-14.0) ppb; p=0.002). The total flux of NO in the conducting airway compartment (J’awNO) and CANO were correlated with an apnea-hypopnea index (AHI) (rho=0.25 and p=0.02; rho= 0.18 and p=0.04, respectively). There was a weak significant correlation between J’awNO and nadir SpO2 in patients with OSA (rho = -0.22 and p= 0.023). After 3-month treatment with CPAP, the level of J’awNO was significantly reduced in patients with severe OSA [23.4 (12.9-44.5) vs. 33.1 (21.2-55.0); p Conclusion: J’awNO is proportionally increased in patients with severe OSA and reduced after treatment with CPAP. Thus, J’awNO may be used as a relevant and surrogate biomarker of severe OSA.

Prior studies confirm that poor leadership is a key driver of employee turnover. This study tests the influence of 10 leadership competencies toward job satisfaction and turnover intentions in New ...

OBJECTIVE In the hospital settings, buprenorphine is used for the treatment of patients with neonatal abstinence syndrome. It is extemporaneously compounded and stored in oral plastic syringes. However, limited information exists about the stability of buprenorphine and its compounded formulations when stored under specific conditions. Hence, we developed a stability-indicating high-performance liquid chromatography–mass spectrometry (LC-MS) method to analyze the stability of buprenorphine over time. METHODS A stability-indicating LC-MS method was developed to map the potential degradation peaks of buprenorphine when exposed to acidic, basic, and oxidative conditions. This method was used to study the stability of compounded buprenorphine oral syringes stored under refrigeration (2°C–8°C) and room temperature (25°C ± 2°C with 60% relative humidity). Syringes from each storage condition were assessed for stability using pH meter and stability-indicating LC-MS assay for 30 days. RESULTS Buprenorphine gets completely degraded in the presence of acid at the end of 1 hour of exposure. Various degradation peaks were identified using LC-MS assay for buprenorphine under acidic, basic, and peroxide conditions. Stability study of oral buprenorphine syringes showed no precipitation, cloudiness, or color change during this study at all storage conditions. The LC-MS assay revealed that buprenorphine oral syringes retained greater than 90% of the initial concentrations for 30 days. CONCLUSIONS Highly sensitive stability-indicating LC-MS method was developed for studying the stability of extemporaneously compounded buprenorphine oral syringes. This study demonstrates that buprenorphine extemporaneous formulation prepared according to the manufacturers' recommendations is stable under refrigerated or room temperature conditions for 30 days in oral plastic syringes.

The aim of this study is to evaluate and compare the performances of 5 machine learning (ML) techniques for predicting the spatial probability of landslide at Atsuma, Japan, and Mt. Umyeon, Korea. 5 ML models used are Naive Bayes (NB), ensemble learning (random forest (RF) and adaboost (AB)), and deep learning (multilayer perceptron (MLP) and convolutional neural network (CNN)) models. Real landslide events at the study areas are randomly separated to the training set for landslide mapping and the validation set for assessing performance. To assess the performance of the used models, the resulting models are validated using receiver operating characteristic (ROC) curve. The success rate curves show that the areas under the curve (AUC) for the NB, RF, AB, MLP, and CNN are 85.1, 88.8, 88.6, 87.5, and 95.0%, respectively, at Atsuma and 68.7, 85.6, 90.5, 81.6, and 92.0%, respectively, at Mt. Umyeon. Similarly, the validation results show that the areas under the curve for the NB, RF, AB, MLP, and CNN are 84.3, 87.1, 87.1, 86.7, and 89.7%, respectively, at Atsuma and 64.9, 85.5, 83.9, 84.7, and 90.5%, respectively, at Mt. Umyeon. In addition, statistical tests such as Chi-square test and difference of proportions test show that all classified landslide susceptibility maps have statistical significance and the significant difference in classified landslide susceptibility maps from different ML models. The comparison results among 5 ML models show that the CNN model had the best performance and NB model had the worst performance in both study areas.

In this study, the effect of dimethyl ether enriched air (DMEA) on pollutants and performance characteristics a diesel engine was evaluated. The combustion parameters have been evaluated by simulat...

Sleep stage classification is important to accurately predict and diagnose patients with sleep disorders. Though various deep learning approaches have been implemented to classify sleep classes, these consist limitations that impact the accuracy and processing time of the classification model. The aim of this research is to enhance the accuracy and minimize the training time of the deep learning classification model. The proposed system consists of One Dimensional Convolutional Neural Network (CNN) with enhanced loss function to improve the accuracy of scoring of five different sleep classes. Preprocessing, Feature Extraction and Classification are the main components of the proposed system. Initially, EEG signals are fed to an adaptive filter for preprocessing, in order to remove any noise in signal. Thereafter, feature is extracted through multiple convolutional and pooling layers, and finally the classification is done by fully connected layer using softmax activation with enhanced loss function. The proposed solution is tested on data samples from multiple datasets with five classes of Sleep classification. Based on the obtained results, the proposed solution has found to achieve an accuracy of 96.26% which is almost 4.2% higher than the state-of-the-art solution which is 92.76%. Furthermore, the processing time has been reduced by 11 milliseconds against the state-of-the-art solution. The proposed system focused on classifying sleep stages in five classes using EEG signals with deep learning approach. It enhances the loss function in order to minimize errors in the prediction of sleep classes and improves the accuracy of the model. Furthermore, the training speed of the model has also been reduced by applying batch normalization techniques inside the model. In the future, larger datasets of different sleep disorder patients with varying features can be used for training and implementing the proposed solution. The datasets can also be pre-processed using additional techniques to refine the data before feeding to the neural network model.

PurposeThis research investigates the link between corporate social responsibility (CSR) activities and bank risk-taking in Vietnam and introduces the constraint factor to see whether this link alters with different levels of constraint.Design/methodology/approachUsing a sample of commercial banks in Vietnam from 2008 to 2017, this study employs two-step system generalized method of moments (Sys GMM) with a finite sample correction mechanism to estimate the models.FindingsThe results suggest that CSR activities reduce bank risk-taking, and this relationship is only present in the case of financially constrained banks. Unconstrained banks, on the other hand, are more likely to invest in unnecessary CSR, thus reducing bank performance and increasing bank risk-taking.Research limitations/implicationsThe first implication from this study is that CSR activities might be considered as a risk-mitigating tool and should be invested in that respect. Secondly, regulatory units and investors should be more cautious about CSR expenditures since this type of spending could increase default risk, especially for banks with easy access to external financing. One particular limitation of this study is the low number of observations available for banks in Vietnam. Future studies could use texture analysis to expand the sample or consider macro-level governance characteristics to examine which factors might modify the relationship between CSR and bank risk.Originality/valueVery limited studies discussed the link between corporate social responsibility and bank performance and bank risk. There are even fewer papers examining the relationship between CSR and risk, and most of these papers deal with advanced economies. Furthermore, no studies investigate the interaction effect of CSR and financial constraint, which should be prevalent in developing countries on bank risk. As a consequence, the current study seeks to verify the impact of financial constraints on the link between CSR and bank risk.

Background: It remains controversial if intravenous thrombolysis (IVT) prior to mechanical thrombectomy (MTE) is superior to MTE alone in patients with acute ischemic stroke caused by large vessel occlusion. We aim to compare functional outcomes, mortality, reperfusion, and intracranial hemorrhage rates in bridging therapy (IVT prior thrombectomy) and MTE alone groups within 6 h from symptom onset. Materials and Methods: Consecutive hospitalized patients (September 2017 and July 2018) with acute large artery occlusion within the anterior cerebral circulation eligible for MTE with or without prior IVT were included. A modified Rankin Scale score of 0 to 2 was considered as good functional outcome at 90 days. Successful reperfusion was defined as a Thrombolysis in Cerebral Infarction scale of 2b to 3. Results: Of the 124 patients included, 56 (45.2%) received bridging therapy and 68 (54.8%) received MTE alone. Patients receiving bridging therapy were younger (median, 56 vs 63, P = .045) and had shorter onset-to-groin time (median, 270 vs 370 min, P < .001) than those receiving MTE alone. Successful reperfusion rate was significantly greater in the bridging therapy group (87.5% vs 72.1%, P = 0.03). There were no statistically significant differences between the 2 groups in functional independence (bridging 58.9% vs 75.0%, P = 0.07), mortality at 90 days (bridging 14.3% vs 7.4%, P = 0.22), parenchymal hematoma type 2 (bridging 3.6% vs 2.9%, P > .99), and any hemorrhage (bridging 42.3% vs 26.5%, P = 0.07). Conclusion: Compared to MTE alone, bridging therapy with IVT improved the reperfusion rate but not other outcomes. Further clinical trials are needed to confirm our findings.

Importance of early prediction of Sudden Cardiac Deaths (SCD) has been rising as a large percentage of mortality of patients with cardiovascular diseases. Various deep learning methodologies has been developed to predict the onset of SCDs, Their key limitation is either classification accuracy or the processing time. This research tries to improve the classification accuracy and decrease the processing time. A Convolutional Neural Network (CNN) is combined with a Recurrence Complex Network (RCN) along with Dropout Regularization to enhance the accuracy of SCD classification. Initially, the synchronization feature of individual heartbeat of the electrocardiogram (ECG) signal is constructed by RCN. The recurrence matrix from the (RCN) will generate Eigen values. Then, CNN will be employed to extract features and detect SCD by analysing the Eigen values. Finally, the performance of the classification is improved by the developing a voting algorithm for the SCD detection. MIT-BIH SCD database is used to evaluate the proposed system. The average accuracy and processing time for MIT-BIH Arrhythmia dataset is 93.24% and 21 epochs, MIT-BIH SCD Holter dataset is 90.60% and 11.5 epochs, and Apnoea-ECG dataset is 92.13% and 13.5 epochs. The average processing time has also been reduced to 20.77 milliseconds against the current processing time of 32.96 milliseconds. The proposed system enhances the classification accuracy and the processing time of the prediction system. The study eradicates the issue of gradient saturation during the training of the CNN by proposing a new activation function as well as eliminates the risk of overfitting by implementing dropout regularization in CNN.

Employing renewable energy sources for power generation have been developed in recent years due to their several benefits including low emission of greenhouse gases and their inexhaustible nature. Among the applicable renewable energy sources for electricity generation, geothermal energy is one of the most appropriate ones owing to its independent availability from the weather condition. Due to the acceptable performance of Organic Rankine cycles (ORCs) in generating power from low- or medium-temperature heat sources, these cycles are appropriate choices for utilization in geothermal power plants. There are several parameters that influence the rate of power production and the efficiency of geothermal-based ORCs. The impacts of influential parameters have been investigated in several studies by modeling and optimization of these cycles. This article provides a comprehensive review of the previous studies focused on modeling and optimization of the ORCs with different configurations and operating conditions, which would be useful for the researchers working in this field of science. Conclusions of the reviewed studies reveal that geothermal-based ORCs performance is significantly influenced by the applied operating fluid in the cycle, working condition in addition to configuration and architecture of the applied cycle. Moreover, studies that concentrated on the optimization of geothermal-based ORCs are also reviewed and their key conclusions are reflected as well. In the majority of the studies in this field, technical and economic objectives have been considered for optimization of the cycles. Depending on the characteristics of the case studies, the efficiency and cost of the generated power in optimized geothermal-based ORCs vary greatly. Finally, several recommendations are proposed for future studies in this field.

BACKGROUND AND PURPOSE: Various patterns of leukoencephalopathy have been described in coronavirus disease 2019 (COVID-19). In this article, we aimed to describe the clinical and imaging features of acute disseminated leukoencephalopathy in critically ill patients with COVID-19 and the imaging evolution during a short-term follow-up. MATERIALS AND METHODS: We identified and reviewed the clinical data, laboratory results, imaging findings, and outcomes for 8 critically ill patients with COVID-19 with acute disseminated leukoencephalopathy. RESULTS: All patients demonstrated multiple areas of white matter changes in both cerebral hemispheres; 87.5% (7/8) of patients had a posterior predilection. Four patients (50%) had short-term follow-up imaging within a median of 17 days after the first MR imaging; they developed brain atrophy, and their white matter lesions evolved into necrotizing cystic cavitations. All (8/8) patients had inflammatory cytokine release syndrome as demonstrated by elevated interleukin-6, D-dimer, lactate dehydrogenase, erythrocyte sedimentation rate, C-reactive protein, and ferritin levels. Most (7/8; 87.5%) patients were on prolonged ventilator support (median, 44.5 days; interquartile range, 20.5 days). These patients had poor functional outcomes (6/8 [75%] patients were discharged with mRS 5) and high mortality (2/8, 25%). CONCLUSIONS: Critically ill patients with COVID-19 can develop acute disseminated leukoencephalopathy that evolves into cystic degeneration of white matter lesions with brain atrophy during a short period, which we dubbed virus-associated necrotizing disseminated acute leukoencephalopathy. This may be the result of COVID-19-related endothelial injury, cytokine storm, or thrombotic microangiopathy.

Supplemental Digital Content is available in the text., Background and Purpose: We conducted this study to investigate the prevalence and distribution of cerebral microbleeds and leukoencephalopathy in hospitalized patients with coronavirus disease 2019 (COVID-19) and correlate with clinical, laboratory, and functional outcomes. Methods: We performed a retrospective chart review of 4131 COVID-19 positive adult patients who were admitted to 3tertiary care hospitals of an academic medical center at the epicenter of the COVID-19 pandemic in New York City from March 1, 2020, to May 10, 2020, to identify patients who had magnetic resonance imaging (MRI) of the brain. We evaluated the MRIs in detail, and identified a subset of patients with leukoencephalopathy and/or cerebral microbleeds. We compared clinical, laboratory, and functional outcomes for these patients to patients who had a brain MRI that did not show these findings. Results: Of 115 patients who had an MRI of the brain performed, 35 (30.4%) patients had leukoencephalopathy and/or cerebral microbleeds. Patients with leukoencephalopathy and/or cerebral microbleeds had neuroimaging performed later during the hospitalization course (27 versus10.6 days; P

Background. Resting heart rate (RHR) is considered as a strong predictor of total mortality and hospitalization due to heart failure in hypertension patients. Bisoprolol fumarate, a second-generation beta-adrenoreceptor blockers (β-blocker) is commonly prescribed drug to manage hypertension. The present study was to retrospectively evaluate changes in the average RHR and its association with cardiovascular outcomes in bisoprolol-treated coronary artery disease (CAD) patients from the CAD treated with bisoprolol (BISO-CAD) study who had comorbid hypertension. Methods. We performed ad-hoc analysis for hypertension sub-group of the BISO-CAD study (n = 866), which was a phase IV, multination, multi-center, single-arm, observational study carried out from October 2011 to July 2015 across China, South Korea, and Vietnam. Multivariate regression analysis was used to identify factors associated with incidence of composite cardiac clinical outcome (CCCO), the results were presented as adjusted odds ratio (OR) along with 95% confidence interval (CI) and adjusted P value. Results. A total of 681 patients (mean age: 64.77 ± 10.33 years) with hypertension from BISO-CAD study were included in the analysis. Bisoprolol improved CCCOs in CAD patients with comorbid hypertension, with RHR

Within New Zealand, the turnover rate within the hospitality sector is high, which can be expensive in terms of performance, recruitment and training costs. However, while research asserts an emplo...

Background. Multiple myeloma is overall the 14th most common malignancy but is rarely seen in those younger than 35 years. Those in the younger age group have been shown to have a more aggressive course but reportedly have had similar responses to treatment compared to older cohorts. Extramedullary plasmacytomas are discrete soft tissue masses of neoplastic monoclonal plasma cells that often occur in patients with multiple myeloma. Case. This case entails a young female with new diagnosis of multiple myeloma and multiple extramedullary plasmacytomas presenting with neurological symptoms. She was treated with CyBorD regimen but was refractory and progressed. Treatment was changed to DCEP regimen, and daratumumab was added for primary refractory myeloma. The patient improved rapidly. Conclusion. This is a unique case of multiple myeloma in a young female that had failed first-line treatment but responded to targeted therapy of daratumumab. It highlights that multiple myeloma may present atypically in young patients. More research is needed on appropriate initial diagnosis and treatment of patients in this age group.

Vietnam is one of the most popular tourist destinations in the Asia-Pacific region and its tourism industry has been growing unprecedentedly over the past few years. However, the Vietnamese tourism industry (VTI) is concerned with the weakness competitive factor. This paper aims to analyze Green Supply Chain Management Practice (GSCM) and its effect on the destination image of Vietnam as the way to improve the completeness. The paper seeks to examine internal environment management (IEM) practices; external environment (EE) and environmental regulation (ER), as possible drivers to GSCM practices and its destination image. The empirical analysis used data from 269 questionnaires which were valid for analysis,through applying the PLS- SEM method. The result of path analysis indicates that the path between internal environmental management and GSCM practice in the VTI was highly significant; internal environmental management (IEM) had a significant impact on the external environment and also had a significant impact on destination image in VTI. The external environment has also maintained a significant impact on the GSCM practices of the VTI and on destination image in VTI. There is also a positive relationship between GSCM and destination image in VTI. As a result, it can be concluded that all hypotheses are supported. This paper concludes with a discussion of the implications of the findings from the study for tourism managers.

International audience

We report on manufacturing outcomes for 41 autologous polyclonal regulatory T cell (PolyTreg) products for 7 different Phase 1 clinical trials over a 10-year period (2011-2020). Data on patient characteristics, manufacturing parameters, and manufacturing outcomes were collected from manufacturing batch records and entered into a secure database. Overall, 88% (36/41) of PolyTreg products met release criteria and 83% (34/41) of products were successfully infused into patients. Of the 7 not infused, 5 failed release criteria, and 2 were not infused because the patient became ineligible due to a change in clinical status. The median fold expansion over the 14-day manufacturing process was 434.8 -fold (range 29.8-2,232), resulting in a median post-expansion cell count of 1,841 x 106 (range 56.9-16,179 x 106). The main correlate of post-expansion cell number was starting cell number, which positively correlates with absolute circulating Treg cell count. Other parameters, including date of PolyTreg production, patient sex, and patient age did not significantly correlate with fold expansion of Treg during product manufacturing. In conclusion, PolyTreg manufacturing outcomes are consistent across trials and dates of production.

Migraine is a poorly understood neurological disorder and a leading cause of disability in young adults, particularly women. Migraines are characterized by recurring episodes of severe pulsating unilateral headache and usually visual symptoms. Currently there is some disagreement in the electrophysiological literature regarding the universality of all migraineurs exhibiting physiological visual impairments also during interictal periods (i.e., the symptom free period between migraines). Thus, this meta-analysis investigated the evidence for altered visual function as measured electrophysiologically via pattern-reversal visual evoked potential (VEP) amplitudes and habituation in adult migraineurs with or without visual aura and controls in the interictal period. Twenty-three studies were selected for random effects meta-analysis which demonstrated slightly diminished VEP amplitudes in the early fast conducting P100 component but not in N135, and substantially reduced habituation in the P100 and the N135 in migraineurs with and without visual aura symptoms compared to controls. No statistical differences were found between migraineurs with and without aura, possibly due to inadequate studies. Overall, insufficient published data and substantial heterogeneity between studies was observed for all latency components of pattern-reversal VEP, highlighting the need for further electrophysiological experimentation and more targeted temporal analysis of visual function, in episodic migraineurs.

With 214 source vocabularies, the construction and maintenance process of the UMLS (Unified Medical Language System) Metathesaurus terminology integration system is costly, time-consuming, and error-prone as it primarily relies on (1) lexical and semantic processing for suggesting groupings of synonymous terms, and (2) the expertise of UMLS editors for curating these synonymy predictions. This paper aims to improve the UMLS Metathesaurus construction process by developing a novel supervised learning approach for improving the task of suggesting synonymous pairs that can scale to the size and diversity of the UMLS source vocabularies. We evaluate this deep learning (DL) approach against a rule-based approach (RBA) that approximates the current UMLS Metathesaurus construction process. The key to the generalizability of our approach is the use of various degrees of lexical similarity in negative pairs during the training process. Our initial experiments demonstrate the strong performance across multiple datasets of our DL approach in terms of recall (91-92%), precision (88-99%), and F1 score (89-95%). Our DL approach largely outperforms the RBA method in recall (+23%), precision (+2.4%), and F1 score (+14.1%). This novel approach has great potential for improving the UMLS Metathesaurus construction process by providing better synonymy suggestions to the UMLS editors.

The fourth chapter presents the results of numerical and simulation modeling of the amount of robotic technology for processing agricultural land. The experiments were carried out in the developed program AgrobotModeling, which provides the calculation of the number of unmanned aerial vehicles and ground-based service mobile platforms involved in the processing of agricultural land of a given area. The program also simulates the functioning of a selected number of robotic equipment, as well as calculates a multi-criteria assessment based on a linear combination of three main criteria: total processing time, total consumed energy, total cost of the equipment involved.

The structure of the developed software system AgroGripModeling, which provides modeling and multicriteria optimization of the robotic gripper configuration, is described. The configuration of the developed robotic gripper for picking tomatoes, which consists of a four-fingered mechatronic system, a vacuum suction nozzle and differs from existing analogues by using a linear actuator to move the vacuum nozzle simultaneously with the movements of the four-fingered mechanism, is described. The results of modeling and optimization of the configuration of a robotic gripper using a posteriori algorithms MOGWO, NSGA-II, MOPSO are presented. A generalized algorithm for controlling the cycle of operation of software and hardware modules for robotic gripping when removing a fetus is presented. The results of testing a robotic gripper with a vacuum bellows for picking tomatoes are discussed.

This chapter presents a formal statement of the problem of interaction with heterogeneous agricultural robots, describes the developed model-algorithmic support for controlling the interaction of heterogeneous robotic systems during UAV service during group agricultural tasks.

The chapter provides an overview of the tasks of control technological and robotic operations in agricultural production. The relevance of the research on robotization of agriculture problem is justified by the possibility of improving the quality of fresh fruits and vegetables, reducing production costs, reducing the labor shortage through the development and implementation of agricultural robots. A classification of agricultural grippers, which are installed on robotic agricultural equipment and are used for weed control and harvesting, has been compiled. Also the tasks of directional weeds spraying and pruning of branches and leaves, in which manipulators are involved, are mentioned. Examples of existing research agricultural robots equipped with combined grippers according to the proposed classification, related to various types are given: vacuum gripper with a video camera for grabbing tomatoes, a six-fingered pneumatic gripper with a video camera, a two-fingered grip with pressure and collision sensors for picking apples, a three-fingered gripper with a video camera for citrus fruits, eggplant grippers and others. Also the relevance of the joint interaction of a group of heterogeneous ground-based and air robots was noted, when performing agricultural tasks in an autonomous mode.

The third chapter describes the developed method and system for assessing and supporting decision-making on the composition and number of heterogeneous agricultural complexes required to process a given area of land, weather conditions and other aspects that affect the cost and speed of work. First, a method for multi-criteria assessment of the number and composition of heterogeneous equipment for processing agricultural land is described, then a graph model for calculating the efficiency of servicing UAV batteries is considered, and a recommendation software system AgrobotModeling is presented.

Here, we describe a rare case of Aerococcus endocarditis causing aortic insufficiency and paravalvular abscess presenting as complete heart block and shock. A 76-year-old man with diabetes mellitus presented to the emergency department with fever and dyspnea. His temperature was 102.4°F, heart rate 59 beats per minute, blood pressure 105/44 mmHg, and oxygen saturation was 98% on 6L oxygen. Examination revealed bounding carotid pulses, a 2/6 early blowing diastolic murmur at the left lower sternal border, and diminished lung sounds at the bases. Laboratory data showed leukocytosis of 19.65 k/µL, blood urea nitrogen 72 mg/dL, creatinine 2.92 mg/dL, lactic acid 3.1 mmol/L, pro-B-type natriuretic peptide 15,342 pg/mL, high-sensitivity troponin 136 ng/L, aspartate aminotransferase 129 U/L, and alanine aminotransferase of 115 U/L. An electrocardiogram showed complete heart block, and a transvenous pacemaker was placed. A transesophageal echocardiogram revealed an aortic root abscess and severe aortic insufficiency secondary to Aerococcus urinae. Ventricular pacing was used to decrease aortic insufficiency and optimize computed tomography with gating to view the coronary arteries due to wall motion abnormalities seen on the transthoracic echocardiogram. His aortic valve was replaced, and a pacemaker was planned. Aortic valve Aerococcus endocarditis is rare and can lead to complete heart block and aortic insufficiency. Cardiac pacing improves hemodynamics by increasing heart rate and decreasing left ventricular end-diastolic pressure.

STATCOM is a FACTS device installed in power systems for damping stability and increasing power transmission capacity. The algorithms used to control the STATCOM often use a PID controller. However, the PID controller is not robust for its stability control in highly nonlinear and complex systems. This paper presents the application firefly algorithm for improving the STATCOM controller to enhance the stability in a network connected to a wind energy system. The wind energy system is represented as a Doubly Fed Induction Generator (DFIG) driven by a Wind Turbine (WT). The power grid is simulated by an IEEE standard 14-bus system encompassing 14 nodes, 5 sources, and 11 loads. The studied wind power system is one of 5 sources that supply power to the network. Simulation results are performed with different disturbance conditions for testing the stability of the studied system. It can be concluded from simulation results that the proposed STATCOM controller helps improve the studied system's stability.

On the trail progressing towards the era of whirlwind technological advances in the field of Information Technology (IT) and media during Industry 4.0, never before has the Internet been integrated into such an essential component in our society, and along with the decrease in the cost of telecommunication equipment, Internet of Things (IoT) has become the mainstay of future development. Therefore, the installation of 3G modem and lighting cables for over 1000 switchgear as well as 110 kV and 22 kV substation has been initiated across Vietnam with a view of conducting distant monitor and information gathering. After a procedure of real-time information gathering, the quality and information storage considered to be essential as a huge amount of data are collected for analyzing useful information on customers. In order to take advantage of this plentiful data resource, an automatic tool for analysis is prerequisite. A procedure of electrical system basing on improved parallel neuron network with smart identification, quick diagnosis, and great stability will be elaborated in this research paper. This study has successfully developed an improved model of parallel neuron network for the stable identification problem. The suggested model has achieved the main target of improving the layers’ accuracy.

Dizziness and vertigo are common symptoms in the primary care and emergency settings, resulting in a significant decrease in quality of life and a high cost burden to the US health care system. The etiology of these symptoms is difficult to elucidate owing to a wide range of diseases with overlapping manifestations. The broad differential diagnosis based on whether the disease process is central or peripheral is showcased. Each differential will be categorized into neoplastic, infectious or inflammatory, structural, traumatic, and iatrogenic causes. Computed tomography scans, MRI, and vascular imaging are frequently complimentary in providing diagnoses and guidance in management.

Video streaming is a multimedia service that continuously transmits the data over the Internet and presents the content on user screens without predownloading the entire video. Augmented video streaming is an advanced version of video streaming, where the video is enriched with additional embedded information in video frames. These additional data aim to provide a better user experience. Using QR code is one of the efficient approaches to incorporate information into video streams in this context. However, receiving the data in the embedded QR code is considered a challenging task owing to video quality and view angles. This paper proposes a lightweight two-stage QR code decoder for augmented video streaming using deep learning technologies. In the first stage, the position of the embedded QR code is detected using an online object detection algorithm. In the second stage, the detected region of the QR code is fed into a QR code reader to extract the embedded data. The experimental results show that the proposed decoder achieves high performances in terms of response time and decoding accuracy while being very lightweight, which is promising to be implemented in smartphones.

Background: Rates of early initiation of breastfeeding are low in Southeast Asia, despite evidence that increased initiation of early breastfeeding would lead to better long-term infant and child health and decrease inequities in long-term health and well-being. In response, a novel performance-based, baby-friendly hospital program designates hospitals that adhere to evidence-based early essential newborn care (EENC) and breastfeeding interventions as Centers of Excellence for Breastfeeding (COE). This study examined whether hospital participation in the program was associated with better breastfeeding outcomes. Methods: Hospitals (n = 28) were invited into the program in December 2018. Hospitals developed an improvement plan for promoting a breastfeeding-friendly environment and meeting the standards of the COE accreditation process and were enrolled on a rolling basis over the course of a year. Post-partum surveys were conducted with parents (n = 9585) from January 2019 through April 2020 to assess their breastfeeding and post-partum experience. Segmented regression models were used to assess how breastfeeding outcomes evolved before and after hospital enrollment in the COE program. Results: Enrollment was associated with a 6 percentage-point (95% CI: 3, 9) increase in the level of early initiation of breastfeeding, which continued to increase in the post-enrollment period, and a 5 percentage-point (95% CI: 2, 9) increase in the level of exclusive breastfeeding during hospital stay. We did not observe evidence that enrollment was immediately associated with receipt of lactation counseling or exclusive breastfeeding at survey time. Conclusion: The prevalence of early and exclusive breastfeeding increased after enrollment in the COE program, suggesting that the program has the potential to improve breastfeeding initiation rates and longer-term child health and well-being. Further research should be conducted to examine whether the program has an impact on the overall duration of breastfeeding.

This paper explores a new perspective of finding the best tournament structures by focusing on two aspects (apart from the number of matches): competitiveness development and ranking precision. Competitiveness development emphasizes the importance of participants’ motivation while keeping the matches exciting whereas ranking precision reflects the persuasiveness of tournament result to its participants. To address competitiveness development, this paper proposes a new method that visualizes tournament structures using a tree-like graphical model called progress tree. In addition, ranking precision is addressed by considering the similarities and qualities of the ranking process by the sorting algorithm during the ranking process. With respect to these two aspects, several well-known tournament structures such as the single elimination, double elimination, Round-Robin, and Swiss systems were analyzed. Although each tournament has its own pros and cons, none of them can thoroughly convince all participants of the tournament results while keeping the matches strongly motivating. As such, a new tournament structure called Reaper tournament system with its variants, are proposed. The Reaper tournament system with its variants were evaluated on simulated football matches and validated with real-world data of previous football tournaments. Thence, practical insights into tournament structures are obtained and possible future works are discussed.

International audience; Wired electrocardiogram monitors are an important component of current perioperative monitoring. Wireless monitoring units could help reduce the number of cables attached to patients and thus improve anesthesia ergonomics and patient management. However, there is concern that electromagnetic interference generated by electrosurgical units may prevent effective wireless signals in the operating room. To evaluate the extent of this problem, we developed a Bluetooth electrocardiogram prototype monitor and compared its electrocardiogram traces to those captured with a standard wired electrocardiogram monitor in our operating room. METHODS: Bluetooth electrocardiogram and standard electrocardiogram traces captured from 10 patients undergoing surgical procedures that required use of an electrosurgical unit were compared by analysis of the durations of the P wave, QRS complex, and T wave and the position of the ST segment from the isoelectric line. The impact of the electrosurgical units on the Bluetooth electrocardiogram and S-electrocardiogram recordings was also assessed. RESULTS: There were no clinically relevant differences in P wave, QRS complex, or T-wave durations (0.006, 0.004, and 0.017 seconds, respectively) between Bluetooth electrocardiogram and standard electrocardiogram or in the position of the ST segment from the isoelectric line (0.02 mV). Mean differences were near zero, and Bland-Altman limits of agreement for individual differences were narrow (-0.035 to 0.047, -0.03 to 0.038, and -0.112 to 0.078 seconds for P wave, QRS complex, and T-wave durations, respectively, and -0.13 to 0.17 mV for ST segment position). Electrosurgical units use electrically disrupted Bluetooth electrocardiogram and standard electrocardiogram signals, but there was no electromagnetic interference effect on the Bluetooth electrocardiogram signals. CONCLUSIONS: Wireless electrocardiogram using Bluetooth can be reliably used in the operating room. The electrosurgical unit induces electric rather than electromagnetic artifacts, thus affecting wired and wireless electrocardiogram in a similar fashion.

Reduction of environmental pollution and fossil fuel dependence are two worldwide concerned problems. Many research projects have addressed many solutions for these issues, in which, the trend of using bio-fuel has been widely studied and applied. Ethanol, a kind of biofuel has been used on motorcycle engines, which use carburetor system, to replace conventional gasoline. Ethanol is characterized by a higher heat of vaporization value than gasoline, thus the reduction of the intake manifold temperature which effects engine performance especially at cold starting and idling conditions. In this study, a certain amount of exhaust gas was recirculated to heat the intake manifold, thereby increasing intake mixture temperature, to improve the combustion process of the engine, results in improvement of engine performances. In experiment, an exhaust gas heating transfer system was designed to recirculate a partial mass of exhaust gas to transfer heat energy to the intake manifold of the test motorcycle fueled with ethanol. The experimental results show that, with the exhaust gas heating transfer system, the specific fuel consumption of the test engine improve remarkably and some gaseous emissions tend to be reduced; in addition, hot starting ability of the test engine with the heating transfer system improves significantly compared to the case of without the system.