Tag: nevin manimala

SN Comput Sci. 2021;2(4):330. doi: 10.1007/s42979-021-00723-4. Epub 2021 Jun 7.

ABSTRACT

Machine translation is one of the applications of natural language processing which has been explored in different languages. Recently researchers started paying attention towards machine translation for resource-poor languages and closely related languages. A widespread and underlying problem for these machine translation systems is the linguistic difference and variation in orthographic conventions which causes many issues to traditional approaches. Two languages written in two different orthographies are not easily comparable but orthographic information can also be used to improve the machine translation system. This article offers a survey of research regarding orthography’s influence on machine translation of under-resourced languages. It introduces under-resourced languages in terms of machine translation and how orthographic information can be utilised to improve machine translation. We describe previous work in this area, discussing what underlying assumptions were made, and showing how orthographic knowledge improves the performance of machine translation of under-resourced languages. We discuss different types of machine translation and demonstrate a recent trend that seeks to link orthographic information with well-established machine translation methods. Considerable attention is given to current efforts using cognate information at different levels of machine translation and the lessons that can be drawn from this. Additionally, multilingual neural machine translation of closely related languages is given a particular focus in this survey. This article ends with a discussion of the way forward in machine translation with orthographic information, focusing on multilingual settings and bilingual lexicon induction.

PMID:34723204 | PMC:PMC8550410 | DOI:10.1007/s42979-021-00723-4

Front Artif Intell. 2021 Oct 15;4:757864. doi: 10.3389/frai.2021.757864. eCollection 2021.

ABSTRACT

This paper compares the predictive power of different models to forecast the real U.S. GDP. Using quarterly data from 1976 to 2020, we find that the machine learning K-Nearest Neighbour (KNN) model captures the self-predictive ability of the U.S. GDP and performs better than traditional time series analysis. We explore the inclusion of predictors such as the yield curve, its latent factors, and a set of macroeconomic variables in order to increase the level of forecasting accuracy. The predictions result to be improved only when considering long forecast horizons. The use of machine learning algorithm provides additional guidance for data-driven decision making.

PMID:34723174 | PMC:PMC8554645 | DOI:10.3389/frai.2021.757864

Front Sports Act Living. 2021 Oct 14;3:722305. doi: 10.3389/fspor.2021.722305. eCollection 2021.

ABSTRACT

The purpose of this study was to investigate the relationship between volume regulatory biomarkers and the estrogen to progesterone ratio (E:P) prior to and following varying methods and degrees of dehydration. Ten women (20 ± 1 year, 56.98 ± 7.25 kg, 164 ± 6 cm, 39.59 ± 2.96 mL•kg•min^-1) completed four intermittent exercise trials (1.5 h, 33.8 ± 1.3°C, 49.5 ± 4.3% relative humidity). Testing took place in two hydration conditions, dehydrated via 24-h fluid restriction (Dehy, USG > 1.020) and euhydrated (Euhy, USG ≤ 1.020), and in two phases of the menstrual cycle, the late follicular phase (days 10-13) and midluteal phase (days 18-22). Change in body mass (%BMΔ), serum copeptin concentration, and plasma osmolality (P_osm) were assessed before and after both dehydration stimuli (24-h fluid restriction and exercise heat stress). Serum estrogen and progesterone were analyzed pre-exercise only. Estrogen concentration did not differ between phases or hydration conditions. Progesterone was significantly elevated in luteal compared to follicular in both hydration conditions (Dehy-follicular: 1.156 ± 0.31, luteal: 5.190 ± 1.56 ng•mL^-1, P < 0.05; Euhy-follicular: 0.915 ± 0.18, luteal: 4.498 ± 1.38 ng·mL^-1, P < 0.05). As expected, E:P was significantly greater in the follicular phase compared to luteal in both hydration conditions (Dehy-F:138.94 ± 89.59, L: 64.22 ± 84.55, P < 0.01; Euhy-F:158.13 ± 70.15, L: 50.98 ± 39.69, P < 0.01, [all •10³]). Copeptin concentration was increased following 24-h fluid restriction and exercise heat stress (mean change: 18 ± 9.4, P < 0.01). We observed a possible relationship of lower E:P and higher copeptin concentration following 24-h fluid restriction (r = -0.35, P = 0.054). While these results did not reach the level of statistical significance, these data suggest that the differing E:P ratio may alter fluid volume regulation during low levels of dehydration but have no apparent impact after dehydrating exercise in the heat.

PMID:34723178 | PMC:PMC8551666 | DOI:10.3389/fspor.2021.722305

EClinicalMedicine. 2021 Nov;41:101172. doi: 10.1016/j.eclinm.2021.101172. Epub 2021 Oct 24.

ABSTRACT

BACKGROUND: By the end of July 2021 Zimbabwe, has reported over 100,000 SARS-CoV-2 infections. The true number of SARS-CoV-2 infections is likely to be much higher. We conducted a seroprevalence survey to estimate the prevalence of past SARS-CoV-2 in three high-density communities in Harare, Zimbabwe before and after the second wave of SARS-CoV-2.

METHODS: Between November 2020 and April 2021 we conducted a cross-sectional study of randomly selected households in three high-density communities (Budiriro, Highfield and Mbare) in Harare. Consenting participants answered a questionnaire and a dried blood spot sample was taken. Samples were tested for anti-SARS-CoV-2 nucleocapsid antibodies using the Roche e801 platform.

FINDINGS: A total of 2340 individuals participated in the study. SARS-CoV-2 antibody results were available for 70·1% (620/885) and 73·1% (1530/2093) of eligible participants in 2020 and 2021. The median age was 22 (IQR 10-37) years and 978 (45·5%) were men. SARS-CoV-2 seroprevalence was 19·0% (95% CI 15·1-23·5%) in 2020 and 53·0% (95% CI 49·6-56·4) in 2021. The prevalence ratio was 2·47 (95% CI 1·94-3·15) comparing 2020 with 2021 after adjusting for age, sex, and community. Almost half of all participants who tested positive reported no symptoms in the preceding six months.

INTERPRETATION: Following the second wave, one in two people had been infected with SARS-CoV-2 suggesting high levels of community transmission. Our results suggest that 184,800 (172,900-196,700) SARS-CoV-2 infections occurred in these three communities alone, greatly exceeding the reported number of cases for the whole city. Further seroprevalence surveys are needed to understand transmission during the current third wave despite high prevalence of past infections.

FUNDING: GCRF, Government of Canada, Wellcome Trust, Bavarian State Ministry of Sciences, Research, and the Arts.

PMID:34723165 | PMC:PMC8542175 | DOI:10.1016/j.eclinm.2021.101172

Front Artif Intell. 2021 Oct 14;4:681915. doi: 10.3389/frai.2021.681915. eCollection 2021.

ABSTRACT

Algorithmic scoring methods are widely used in the finance industry for several decades in order to prevent risk and to automate and optimize decisions. Regulatory requirements as given by the Basel Committee on Banking Supervision (BCBS) or the EU data protection regulations have led to an increasing interest and research activity on understanding black box machine learning models by means of explainable machine learning. Even though this is a step into a right direction, such methods are not able to guarantee for a fair scoring as machine learning models are not necessarily unbiased and may discriminate with respect to certain subpopulations such as a particular race, gender, or sexual orientation-even if the variable itself is not used for modeling. This is also true for white box methods like logistic regression. In this study, a framework is presented that allows analyzing and developing models with regard to fairness. The proposed methodology is based on techniques of causal inference and some of the methods can be linked to methods from explainable machine learning. A definition of counterfactual fairness is given together with an algorithm that results in a fair scoring model. The concepts are illustrated by means of a transparent simulation and a popular real-world example, the German Credit data using traditional scorecard models based on logistic regression and weight of evidence variable pre-transform. In contrast to previous studies in the field for our study, a corrected version of the data is presented and used. With the help of the simulation, the trade-off between fairness and predictive accuracy is analyzed. The results indicate that it is possible to remove unfairness without a strong performance decrease unless the correlation of the discriminative attributes on the other predictor variables in the model is not too strong. In addition, the challenge in explaining the resulting scoring model and the associated fairness implications to users is discussed.

PMID:34723172 | PMC:PMC8552888 | DOI:10.3389/frai.2021.681915

Complex Analysis Synerg. 2021;7(3-4):25. doi: 10.1007/s40627-021-00083-y. Epub 2021 Jul 15.

ABSTRACT

We consider compact Leviflat homogeneous Cauchy-Riemann (CR) manifolds. In this setting, the Levi-foliation exists and we show that all its leaves are homogeneous and biholomorphic. We analyze separately the structure of orbits in complex projective spaces and parallelizable homogeneous CR-manifolds in our context and then combine the projective and parallelizable cases. In codimensions one and two, we also give a classification.

PMID:34723129 | PMC:PMC8550170 | DOI:10.1007/s40627-021-00083-y

Nihon Koshu Eisei Zasshi. 2021 Oct 29. doi: 10.11236/jph.21-070. Online ahead of print.

ABSTRACT

Objectives In recent years, the importance of healthcare support for public assistance recipients has been recognized, and healthcare support measures have been implemented for them. This study aimed to investigate the expectations and problems of welfare offices, as well as their requests to the central government and prefectures about the healthcare management support program for public assistance recipients, which has been mandated since 2021.Methods In November 2019, snowball sampling was used to select 23 welfare offices for sending self-administered questionnaires about the healthcare management support program. Respondents were asked open-ended questions about their expectations and problems regarding the program, as well as their requests to the central government and prefectures. A subsequent interview survey was conducted from November 2019 to February 2020, gathering additional information on the questionnaire survey.Results We received consent for the questionnaire survey and interview survey from 16 welfare offices (response rate 69.6%). It was revealed that the staff in charge of the healthcare management support program at the welfare office expected the program to improve recipients’ health awareness and condition and for it to be applied to other residents in the community. They reported difficulty in developing the implementation system, setting up the indicators and target population, and retaining health professionals. They requested the central government and prefectures to clarify the indicators and the criteria for evaluation, provide reference materials, introduce precedents, communicate and coordinate with welfare offices and related organizations in the community, hold meetings to share information, and secure financial resources.Conclusion Findings from our study suggest a need to strengthen the cooperation between the central government, prefectures, and local governments and to establish a multilayered system to implement the healthcare management support program effectively in welfare offices.

PMID:34719539 | DOI:10.11236/jph.21-070

J Nutr Sci Vitaminol (Tokyo). 2021;67(5):266-272. doi: 10.3177/jnsv.67.266.

ABSTRACT

Low 25 hydroxyvitamin D (25(OH)D) levels are closely associated with the risk of cardiovascular disease. Vitamin D deficiency is more common in patients with type 2 diabetes mellitus than in the general population. In addition, vitamin D status is lower in patients with the metabolic syndrome than in those without the syndrome. Therefore, we examined the association between lipid profiles and 25(OH)D levels. In this case control study, 285 type 2 diabetic patients who attended the Manda Memorial Hospital from March to October 2017 were selected and 25(OH)D, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglyceride (TG) levels, were obtained. Multiple regression analysis revealed that the association between 25(OH)D concentrations and TG levels was statistically significant (p<0.01) after adjusting for age, sex, body mass index, estimated glomerular flow rate (eGFR), insulin use, duration of diabetes mellitus, glycosylated hemoglobin (HbA1c), alcohol consumption, current smoking, and sampling timing. The serum 25(OH)D level was inversely associated with the TG level after the adjustment for the characteristics of Japanese patients with type 2 diabetes mellitus.

PMID:34719611 | DOI:10.3177/jnsv.67.266

Nihon Koshu Eisei Zasshi. 2021 Oct 29. doi: 10.11236/jph.21-022. Online ahead of print.

ABSTRACT

Objectives The first aim of this study was to develop risk prediction models based on age, sex, and functional health to estimate the absolute risk of the 3-year incidence of long-term care certification and to evaluate its performance. The second aim was to produce risk charts showing the probability of the incident long-term care certification as a tool for prompting older adults to engage in healthy behaviors.Methods This study’s data was obtained from older adults, aged ≥65 years, without any disability (i.e., they did not certify≥care level 1) and residing in Yabu, Hyogo Prefecture, Japan (n=5,964). A risk prediction model was developed using a logistic regression model that incorporated age and the Kihon Checklist (KCL) score or the Kaigo-Yobo Checklist (KYCL) score for each sex. The 3-year absolute risk of incidence of the long-term care certification (here defined as≥care level 1) was then calculated. We evaluated the model’s discrimination and calibration abilities using the area under the receiver operating characteristic curves (AUC) and the Hosmer-Lemeshow goodness-of-fit test, respectively. For internal validity, the mean AUC was calculated using a 5-fold cross-validation method.Results After excluding participants with missing KCL (n=4) or KYCL (n=1,516) data, we included 5,960 for the KCL analysis and 4,448 for the KYCL analysis. We identified incident long-term care certification for men and women during the follow-up period: 207 (8.2%) and 390 (11.3%) for KCL analysis and 128 (6.6%) and 256 (10.2%) for KYCL analysis, respectively. For calibration, the χ² statistic for the risk prediction model using KCL and KYCL was: P=0.26 and P=0.44 in men and P=0.75 and P=0.20 in women, respectively. The AUC (mean AUC) in the KCL model was 0.86 (0.86) in men and 0.83 (0.83) in women. In the KYCL model, the AUC was 0.86 (0.85) in men and 0.85 (0.85) in women. The risk charts had six different colors, suggesting the predicted probability of incident long-term care certification.Conclusions The risk prediction model demonstrated good discrimination, calibration, and internal validity. The risk charts proposed in our study are easy to use and may help older adults in recognizing their disability risk. These charts may also support health promotion activities by facilitating the assessment and modification of the daily behaviors of older adults in community settings. Further studies with larger sample size and external validity verification are needed to promote the widespread use of risk charts.

PMID:34719536 | DOI:10.11236/jph.21-022

J Parkinsons Dis. 2021 Oct 23. doi: 10.3233/JPD-212840. Online ahead of print.

ABSTRACT

BACKGROUND: Movement execution is impaired in patients with Parkinson’s disease. Evolving neurodegeneration leads to altered connectivity between distinct regions of the brain and altered activity at interconnected areas. How connectivity alterations influence complex movements like drawing spirals in Parkinson’s disease patients remains largely unexplored.

OBJECTIVE: We investigated whether deteriorations in interregional connectivity relate to impaired execution of drawing.

METHODS: Twenty-nine Patients and 31 age-matched healthy control participants drew spirals with both hands on a digital graphics tablet, and the regularity of drawing execution was evaluated by sample entropy. We recorded resting-state fMRI and task-related EEG, and calculated the time-resolved partial directed coherence to estimate effective connectivity for both imaging modalities to determine the extent and directionality of interregional interactions.

RESULTS: Movement performance in Parkinson’s disease patients was characterized by increased sample entropy, corresponding to enhanced irregularities in task execution. Effective connectivity between the motor cortices of both hemispheres, derived from resting-state fMRI, was significantly reduced in PD patients in comparison to controls. The connectivity strength in the nondominant to dominant hemisphere direction in both modalities was inversely correlated with irregularities during drawing, but not with the clinical state.

CONCLUSION: Our findings suggest that interhemispheric connections are affected both at rest and during drawing movements by Parkinson’s disease. This provides novel evidence that disruptions of interhemispheric information exchange play a pivotal role for impairments of complex movement execution in Parkinson’s disease patients.

PMID:34719510 | DOI:10.3233/JPD-212840