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Vascular function and cardiovascular risk in a HIV infected and HIV free cohort of African ancestry: baseline profile, rationale and methods of the longitudinal EndoAfrica-NWU study
Carla M. T. Fourie ORCID: orcid.org/0000-0003-1950-15531,2,
Shani Botha-Le Roux1,2,
Wayne Smith1,2,
Aletta E. Schutte1,2,3,
Yolandi Breet1,2,
Carina M. C. Mels1,2,
Lebo F. Gafane-Matemane1,2,
Leandi Lammertyn1,2,
Lisa Uys1,2,
Adele Burger1,2,
Jitcy S. Joseph4,
Nandu Goswami5,
Patrick De Boever6,7,8 &
Hans Strijdom9
People living with the Human Immunodeficiency Virus (PLHIV) have an increased susceptibility to develop non-communicable diseases such as cardiovascular disease (CVD). Infection with HIV contributes to the development of CVD independent of traditional risk factors, with endothelial dysfunction being the central physiological mechanism. While HIV-related mortality is declining due to antiretroviral treatment (ART), the number of deaths due to CVD is rising in South Africa - the country with the highest number of PLHIV and the world's largest ART programme.
The EndoAfrica study was developed to determine whether HIV infection and ART are associated with cardiovascular risk markers and changes in vascular structure and function over 18 months in adults from different provinces of South Africa. This paper describes the rationale, methodology and baseline cohort profile of the EndoAfrica study conducted in the North West Province, South Africa.
In this case-control study, conducted between August 2017 and June 2018, 382 volunteers of African descent (276 women; 106 men), comprising of 278 HIV infected and 104 HIV free individuals were included. We measured health behaviours, a detailed cardiovascular profile, and performed biomarker analyses. We compared baseline characteristics, blood pressure, vascular function and biochemical markers between those infected and HIV free.
At baseline, the HIV infected participants were older (43 vs 39 years), less were employed (21% vs 40%), less had a tertiary education (7% vs 16%) and their body mass index was lower (26 vs 29 kg/m2) than that of the HIV free participants. While the cardiovascular profile, flow-mediated dilation and pulse wave velocity did not differ, glycated haemoglobin was lower (p = 0.017) and total cholesterol, high density lipoprotein cholesterol, triglycerides, gamma-glutamyltransferase and tobacco use were higher (all p < 0.047) in PLHIV.
Despite PLHIV being older, preliminary cross-sectional analysis suggests that PLHIV being treated with ART do not have poorer endothelial or vascular function compared to the HIV free participants. More detailed analyses on the baseline and follow-up data will provide further clarity regarding the cardiovascular profile of South Africans living with HIV.
The global fight against the acquired immunodeficiency syndrome (AIDS) epidemic has largely been successful with the number of human immunodeficiency virus (HIV) related deaths declining faster than the global rate of new infections. However, much work is still required [1]. The number of people living with HIV (PLHIV) has grown to 37,9 million, and 800,000 of the 1,7 million new infections in 2018 were in eastern and southern Africa, which is home to 54,4% of the world's PLHIV [1]. South Africa had approximately 7,97 million PLHIV in 2019, which is the highest number globally, with an HIV prevalence rate of approximately 19% in adults (15–49 years) [2]. Consequently, South Africa also has the largest antiretroviral treatment (ART) programme in the world with 62% of the adult PLHIV receiving ART [1].
Apart from the high prevalence of PLHIV, the impact of cardiovascular disease (CVD) on the sub-Saharan African population [3] is placing additional strain on the public health care system. It is clear that ART has improved the morbidity and mortality associated with HIV, however, PLHIV are exposed to adverse cardiovascular events due to a complex interplay between HIV, ART and CVD [4]. Cardiovascular disease has become a common cause of death in PLHIV as the population grows older [5]. In South Africa, non-communicable disease death rates are higher than that of HIV [6, 7], with CVD being the most prominent.
One of the main risk factors for the development of CVD is vascular dysfunction. In PLHIV this is clearly evidenced by reports of impaired endothelial (reduced flow-mediated dilation (FMD)) [8], and vascular function (increased large artery stiffness and decreased distensibility) [9]. Furthermore, significant variations in the microvasculature, such as the retinal vascular beds [10], increased carotid wall thickness [11] and electrocardiogram (ECG) abnormalities, which were predictive of incident CVD [12], were reported in PLHIV.
Endothelial dysfunction is characterised by increased endothelial permeability, leading to increased inflammatory cytokine levels and adhesion molecule expression [13]. The mechanisms contributing to HIV-related endothelial and vascular dysfunction are multifaceted. This could include a combination of a HIV induced increase in reactive oxygen species and reduced nitric oxide bioavailability affecting endothelium-dependent vasodilatation, direct HIV infection of the endothelium, viral related endothelial activation, and vascular inflammatory effects due to systemic inflammatory cytokine or chemokine dysregulation [13,14,15]. Furthermore, recent studies indicate that the HIV viral protein, gp120, reduces nitric oxide, and the HIV Tat protein induces endothelial oxidative stress, activates apoptosis of both endothelial cells and cardiomyocytes, and upregulates the expression of cellular adhesion molecules [13].
It is well known that ART successfully lowers the viral load and decreases the mortality rate, however, it is unclear how ART affects endothelial and vascular function. Although treatment was associated with increased stiffness [9], Solages et al. demonstrated worse endothelial function among those with elevated levels of HIV replication [8]. Furthermore, most of the reported research was not done in African populations; we have shown in South Africa that HIV infection is related to increased endothelial activation [16]. The odds of having increased endothelial activation were more prominent in those without treatment than in their treated counterparts [16].
Our understanding of the complex interaction between HIV and ART in the progression of CVD has improved, however, more detailed exploration into the mechanisms of HIV-induced endothelial dysfunction is needed [13]. A better understanding of HIV-specific biomarkers may contribute to the development or improvement of guidelines, such as the European AIDS Clinical Society guidelines, to guide health care providers in their approach to prevent and treat CVD in PLHIV [13].
To enhance the knowledge base on HIV, ART, endothelial dysfunction and CVD in South Africa, Strijdom et al. commenced the parent EndoAfrica study titled "Vascular endothelial dysfunction: The putative interface of emerging cardiovascular risk factors affecting populations living with and without HIV in Sub-Saharan Africa" in April 2015 [17]. However, the parent EndoAfrica study executed in the Western Cape, South Africa, largely recruited patients of mixed ancestry; challenging the extrapolation of the findings to the broader South African population [17]. We expanded on the parent study by including participants of African ancestry from the North-West province. The North-West province leg of the EndoAfrica study, EndoAfrica-NWU study, thus now provides an opportunity to broaden the relevance of the findings to a larger proportion of the Sub-Sahara African population.
The EndoAfrica-NWU study aims to determine whether HIV and first-line ART regimens are associated with CVD risk factors and changes in cardiovascular structure and function in adults living in the North West province of South Africa. Analyses on macro vascular structure and micro vascular function and structure is ongoing. Therefore, in this study, we describe the rationale, methods and baseline characteristics, blood pressure, vascular function and biochemical markers of the EndoAfrica-NWU study.
The design of the parent EndoAfrica study was discussed in detail by Strijdom et al. [17]. The North West province leg of the study (EndoAfrica-NWU) will adhere to the same design as the parent study, but within a population of African descent. The rationale for the study is summarised in Fig. 1. The study design and setting will be discussed in subsequent sections.
Rational for measurements and analyses in the EndoAfrica-NWU study. HIV: human immunodeficiency virus; ART: antiretroviral treatment; FMD: flow-mediated dilation; PWV: pulse wave velocity; IMT: intima-media thickness; DVA: dynamic vessel analysis; ECG: electrocardiography; CRP: C-reactive protein; HbA1c: glycated hemoglobin; GGT: γ-glutamyltransferase; Hb: hemoglobin; ICAM: intercellular adhesion molecule; VCAM: vascular cell adhesion molecule; MPO: myeloperoxidase; GDF-15: growth differentiation factor-15.
For the EndoAfrica-NWU study, participants were recruited in and around Potchefstroom, North West province, South Africa (Fig. 2). In 2018, South Africa's population was estimated at 57,73 million people of diverse origins and cultures. Of the total South African population, 80,9% declared themselves to be black Africans (African ancestry), 8,8% coloured (mixed ancestry), 7,8% white and 2,5% Indian or Asian [2]. While the mixed ancestry and white South Africans make up the majority of the population in the Stellenbosch (Western Cape) area where the parent EndoAfrica study was executed, the majority of people living in the Potchefstroom area is black South Africans (77,1% based on the 2016 census data).
Data collection for the EndoAfrica-NWU study took place in Potchefstroom within the North West province, South Africa. Source of the editable map Servier Medical Art (https://smart.servier.com/)
Participant recruitment and eligibility
The study was approved by the Health Research Ethics Committee of the North-West University (NWU) (ethics number: NWU-00045-15-A1) and the North West Department of Health. Goodwill permission to visit the clinics was granted by the North West Province of Health sub-district and the Potchefstroom Patient Group of the local hospital gave permission to recruit from the clinics. Participants were recruited at seven clinics in and around Potchefstroom and at HIV support groups by circulating leaflets (English and Setswana) as well as by word-of-mouth.
Four hundred and forty-three volunteers (n = 443) showed interest in the study. Eight of the interested volunteers were excluded (Fig. 3) due to age, co-morbidities or being pregnant. Thus, 435 volunteers were invited to take part in the baseline data collection. Thirty-five of these volunteers did not attend the day of data collection and baseline data was collected from 400 volunteers.
Diagram illustrating participation during the baseline data collection of the EndoAfrica-NWU study
HIV: human immunodeficiency virus; ART: antiretroviral treatment.
Baseline data collection took place within the Hypertension Research and Training clinic on the Potchefstroom campus of the NWU from August 2017 to June 2018. Of the 400 volunteers, 382 (276 women and 106 men) met the eligibility criteria and were included in the study consecutively (Fig. 3). On the day of data collection participants with unknown HIV status and those who claimed to be HIV free were counselled and screened for HIV infection.
Three main study groups were planned for the EndoAfrica-NWU study based on the HIV and ART status of participants: (i) adults (18–60 years) with HIV, with no treatment or ≤ 4 weeks on first-line ART; (ii) adults with HIV on first-line ART for ≥4 weeks, and (iii) adults without HIV (controls). Of the 382 participants included in the baseline study, 278 were HIV infected and 104 were HIV free. Of the participants living with HIV, only nine participants were newly diagnosed as being HIV infected or used ART for less than 4 weeks as specified for group (i). The majority (n = 247) were on first-line ART (Table 1) on the day of data collection, while 22 discontinued their ART and were not using ART on the day of data collection. The first-line fixed dose combination ART consisted of 200 mg emtricitabine, a nucleoside reverse transcriptase inhibitor (NRTI), 300 mg tenofovir, also a NRTI, and 600 mg Efavirenze, a non-nucleoside reverse transcriptase inhibitor (NNRTI).
Table 1 Baseline profile of EndoAfrica-NWU participants
A policy of universal test-and-treat was introduced in South Africa on 1 September 2016, making ART available to all HIV infected individuals regardless of their cluster of differentiation 4 (CD4) T lymphocytes cell count. Same-day initiation, advocating ART initiation on the day of a patient's HIV diagnosis, came into effect on 1 September 2017. As the EndoAfrica-NWU study commenced in mid-August 2017, these changes in the ART policy might have affected the recruited number of newly diagnosed patients or those that were on ART for < 4 weeks. Receiving an HIV diagnosis and needing to initiate ART may lead to internalized stigma (endorsing negative beliefs and feelings about PLHIV), which is particularly high in the first weeks after the diagnosis. Internalized stigma was associated with non-initiation of ART [18], and might also have played a role in the recruitment of these individuals for the study.
During recruitment for the study we found that some participants were defaulting (discontinuing or interrupting) their ART. Of those on ART the day of data collection (n = 247), 10% formerly interrupted their treatment and 22 participants (8,2% of those that were on ART in the past) were defaulting on the day of data collection. These participants reported to "feel better" and stopped taking their medication (ART). Defaulting ART may also be due to the mistaken belief that they have been healed when their viral load became undetectable [19]. Non-adherence to ART is the most common reason for treatment failure and may result in drug resistant HIV. In the State of the Epidemic Joint United Nations Programme on HIV/AIDS (UNAIDS) data report 2019, 67% of PLHIV in eastern and southern Africa were on treatment, but only 58% were virally suppressed [1]. Research identified the main determinants for non-adherence to be food insecurity, use of traditional herbs and medicine, stigma, issues with access to healthcare and disruption of services and medication distribution in sub-Saharan Africa [20, 21].
Data collection procedures
As participants showed interest in the study and met the inclusion criteria they were scheduled for data collection consecutively. Six to eight participants were scheduled per day and they were asked to fast from 22:00 the evening before data collection. The participants were transported free of charge to the clinic and were familiarized with the research environment of the clinic upon arrival at approximately 08:00. Data was collected under controlled conditions. After written informed consent was obtained, blood and early morning spot urine samples were collected (Fig. 4). All blood samples were immediately taken to the on-site research laboratory where blood glucose levels were determined with One Touch® Select (Johnson & Johnson, South Africa) test strips. The blood was centrifuged to obtain plasma or serum and aliquoted into cryovials. After the urine was tested with a Combur test strip (Roche Diagnostics, South Africa), it was also taken to the laboratory and aliquoted into cryovials. All samples were stored at − 80 °C until biochemical analyses could be performed.
Flow diagram of the organizational procedures during data collection in the EndoAfrica-NWU study
FMD: flow-mediated dilation; BP: blood pressure; ECG: electrocardiogram; PWV: pulse wave velocity; IMT: carotid intima-media thickness.
In parallel, a questionnaire was completed, and anthropometric and cardiovascular measurements (Fig. 4) were performed. After data collection a meal was served. Individual feedback on the immediate test results and assessments were given by the registered research nurse and the participants were referred to a local clinic or doctor if required. The HIV status of the participants were determined with First response rapid HIV card test (Premier Medical Corporation Limited, Daman, India) and, if positive, confirmed with a Toyo rapid test (TurkLab, Turkey). The participants newly identified as HIV infected were referred to the clinic after counselling. After data collection (11:30–12:30 h), the participants were transported home. Blood samples of all the HIV infected participants were sent to the National Health Laboratory Service for viral load and CD4 cell count analyses. The results were taken to the participants during personal visits at their home address, in order for them to take it to their local clinic.
A structured medical history of participants and their families (such as heart disease, stroke, hypertension, cancer and high cholesterol), lifestyle (smoking, alcohol consumption and eating habits) and medication use were obtained from the questionnaire.
Anthropometric measurements
Anthropometric measurements were performed according to standardized procedures with calibrated instruments and included body height (SECA 213 Stadiometer, SECA, Hamburg, Germany), body weight (SECA 813 Electronic scale, SECA, Hamburg, Germany), and waist and hip circumference (Lufkin steel anthropometric tape, W606PM; Lufkin, Apex, USA).
Flow-mediated dilation
Flow-mediated dilation (FMD) of the brachial artery is a non-invasive measurement technique and considered as a gold standard to measure macrovascular endothelial function [22]. Flow-mediated dilation is determined by the change in brachial artery diameter in response to a blood flow stimulus. This stimulus is created by releasing an arm cuff that was inflated to a supra-systolic blood pressure level. In response, nitric oxide (NO) is released from the endothelial cells and mediates the relaxation of the smooth muscle cells with subsequent widening of the artery [23].
The FMD procedure was performed by a trained observer, in a darkened room, while participants were in a supine position, and after they had rested for at least five minutes. After the procedure was explained, participants were asked whether they exercised, had anything to eat or drink, took any medications, or smoked since the previous night. A blood pressure cuff was fitted around the right forearm and the brachial artery was located on the right upper arm at around heart level. A longitudinal segment of the right brachial artery was visualized in B-mode with a 12 MHz linear probe, fixed in a single axis precision probe holder, and an Esaote MyLab™ Five ultrasound system (Esaote, Florence, Italy). The pulse repetition frequency was set at 6.7 kHz, after which depth was increased to 3 cm. After switching to pulse wave mode, the angle of insonation was set to + 60°. Flow velocity and diameter were continuously measured with edge-detection and wall tracking using Cardiovascular Suite™ Ultrasound Edition version 2.8.1 (Quipu srl, Italy) software. Baseline diameter was examined for one minute, inflation diameter for five minutes and the post-deflation diameter for three minutes. The software then automatically calculated baseline diameter, maximum diameter, recovery diameter, percentage FMD (the difference between baseline and maximum diameter, expressed as a percentage of baseline diameter), baseline shear rate and maximum shear rate.
Blood pressure and electrocardiogram
Duplicate brachial systolic blood pressure (SBP), diastolic blood pressure (DBP) and heart rate readings were obtained from the left arm of each participant using a validated OMRON M6 automatic digital blood pressure monitor (Omron Healthcare, Kyoto, Japan). Resting 12-lead ECG measurements were recorded in the supine position using the PC-ECG 1200 (Norvav Medical Ltd., Kiryat Bailik, Israel) apparatus.
Pulse wave velocity
The SphygmoCor® XCEL device (AtCor Medical, Sydney, Australia) was used to perform pulse wave analysis and determine carotid-femoral pulse PWV on the right-hand side with the participant in a supine position [24]. Pulse wave analysis was performed to produce an arterial wave form that provided brachial pulse pressure, estimated central SBP and central pulse pressure via a built-in generalized function [25]. Pulse wave velocity was measured using the femoral artery and carotid arterial waveforms which were captured simultaneously. To obtain these waveforms, an appropriate sized femoral cuff was placed on the upper right thigh and a tonometer was placed on the neck where the carotid pulse was felt the strongest. To determine the pulse wave travel distance, 80% of the distance measured between the two pulsating points (carotid-to-cuff measured using an infantometer, and femoral-to-cuff via a tape measure) was used [26]. Measurements were performed in duplicate, and repeated if PWV differed by more than 0.5 m/s.
Carotid wall analyses
Carotid intima-media thickness was measured non-invasively with the General Electric Vivid E9 (GE Vingmed Ultrasound A/S, Horten, Norway). A 3-lead ECG was attached with two electrodes just below the clavicles (left and right) and the third attached near the iliac crest on the participant's left side. A transducer was used to obtain images and clips from two optimal angles, on both the left and right side of the participant. The images were then digitalized and imported into the carotid vessel analyser automated software (Vascular Research Tools 6, Medical imaging applications, Coralville, Iowa, USA). One investigator then determined carotid wall parameters, which included intima-media thickness and lumen diameter assessments. These parameters will be used in subsequent calculations to determine for instance cross-sectional wall area, carotid distensibility, Young's elastic modulus and Beta-stiffness.
Prior to retinal imaging local anaesthetic drop (Novasine Wander 0.4% Novartis) was inserted into both eyes and intraocular pressure was measured using a Tono-Pen Avia applanation tonometer (Reichert 7–0908, ISO 9001, New York, USA). Retinal imaging was not performed on those with a high intraocular pressure or with a history of epilepsy. Retinal vessel imaging/assessments were done using the Dynamic Retinal Vessel analyser (DVA); fitted with a Zeiss Fundus Camera FF-450 Plus (Imedos, Jena, Germany). Images were taken 15–20 min after a drop containing 1% tropicamide and bensalconiumchloride 0.01% (m/v) (MYDRIACYL® 1% ophthalmic solution, Alcon Laboratories, South Africa) was administered to induce mydriatic conditions in the right eye. A colour retinal fundus image was captured by using Visualis 2.81 software (Imedos, Jena, Germany), with the camera angle set at 50°. The images were exported as Tagged Image File (TIF) format and retinal vessel calibers and pattern features such as vessel tortuosity, fractal analysis and lacunarity were determined using semi-automated MONO REVA (VITO, Belgium) software, as previously described [27].
In a subgroup of patients, dynamic retinal vessel responses to light flicker provocation were assessed. A 350 s protocol was used as described elsewhere [28]. The primary location for these assessments were the temporal superior retinal arterioles and venules located 0.5–2.0 optic nerve head diameters from the border of the optic nerve head. The raw numerical data generated from the measurement was exported to an Excel template (Microsoft), with macros. Parameters describing the vessel dilation, constriction and recovery phases were calculated as previously described [28].
Biological biomarkers
An array of biomarkers was analysed on site to evaluate cardiovascular risk and endothelial function. Plasma glucose levels were determined with the enzymatic reference method with hexokinase, and glycated haemoglobin (HbA1c) with a turbidimetric inhibition immunoassay from haemolysed whole blood. Components of the lipid profile, including total cholesterol (TC), high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol and triglyceride (TG) levels were determined in serum with enzymatic colorimetric methods. Serum apolipoproteins A-1 and B were determined with immunoturbidimetric assays. Liver enzymes, including alkaline phosphatase (ALP), alanine (ALT) and aspartate aminotransferase (AST) were determined with colorimetric methods according to the International Federation of Clinical Chemistry (without pyridoxal-5′-phosphate). Gamma-glutamyltransferase (GGT) was analysed with an enzymatic colorimetric assay in serum samples. C-reactive protein was determined with the high-sensitivity particle enhance turdidimetric assay, albumin with an immunoturbidimetric assay and creatinine with a kinetic colorimetric assay, based on the Jaffé method, in serum samples. All of the above mentioned, as well as urinary albumin, creatinine and ions were performed on the Cobas Integra® 400plus (Roche, Basel Switzerland).
Estimated glomerular filtration rate (eGFR) was calculated with the Chronic Kidney Disease Epidemiology (CKD-EPI) formula. Intercellular adhesion molecule-1, vascular cell adhesion protein-1, P-selectin, myeloperoxidase and growth differentiation factor-15 were determined with the Human Cardiovascular Disease Magnetic Bead panel 2 (HCVDP2, 96-Well Plate Assay, Luminex xMAP technology) kit on the Luminex 200 system (EMD Millipore®, Merck, Missouri, USA).
The CD4 cell count and viral loads of the HIV infected participants were analysed by the National Health Laboratory Services. The CD4 cell counts were determined using flow cytometric analysis (Beckman Coulter FC500 MPL/CellMek, Miami, FL). The Panleucogating (PLG) method was used for CD4 T-cell enumeration and the protocol uses a sequential automated gating strategy to identify all CD45+ leucocytes in order to measure the CD4% of lymphocyte and absolute CD4 count. The viral loads were assessed by using COBAS® AmpliPrep/COBAS® TaqMan® HIV-1 test, version 2.0. This test is a nucleic acid amplification test for the quantification of HIV 1 ribonucleic acid (RNA) in human plasma. The COBAS® AmpliPrep/COBAS® TaqMan® HIV-1 test, version 2.0 is based on three vital steps: (i) Isolation of HIV1 RNA from specimen; (ii) reverse transcription of RNA to complementary deoxyribonucleic acid (cDNA); and (iii) Polymerase chain reaction (PCR) amplification of cDNA and detection of cleaved dual labelled oligonucleotide detection probe specific to the target.
Although carotid wall and retinal imaging analyses were not available at the time of publication and do not form part the baseline profile results in this manuscript, the assessment methods are reported here as it forms part of the rational of the longitudinal EndoAfrica-NWU study.
Follow-up data collection
A follow-up assessment was done 18 months after baseline data collection to determine progression or regression of the endpoints [17].
Data management and handling
All the data generated were captured into the Research Electronic Data Capture (REDCap) system [29]. The baseline EndoAfrica-NWU study included 382 participants of whom 278 were HIV infected and 104 HIV free. In a sensitivity analysis, using G*power v3.1.9.2 software [30], we calculated that with our set group sizes, using an alpha error of 0.05 and power of 0.95, this study should be able to detect a medium effect size of 0.4.
We inspected continuous variables for normality using Q-Q plots, skewness and kurtosis. All variables with a non-Gaussian distribution were logarithmically transformed. Descriptive statistical analyses were used for inter-group comparisons (independent t-tests, analysis of covariance and Chi-square tests). We used IBM®, SPSS® version 26 (IBM Corporation, Armonk, New York) software for statistical analyses and the level for statistical significance was set at ≤0.05.
Baseline profile
The baseline profile of the participants is indicated in Table 1. The majority of the participants were women, representing 73% of the HIV infected and 69% of the HIV free participants. The HIV infected participants (n = 278) were older (43 vs 39 years), less were employed (21% vs 40%), less had a tertiary education (9% vs 16%) and their body mass index (BMI) was lower (26 vs 29 kg/m2) than that of the HIV free participants (n = 104). More than half of the HIV infected participants (62%) were living with the virus for more than five years and 47% were on ART for more than five years.
The hypertension prevalence, SBP, DBP and heart rate did not differ between PLHIV and those free of HIV. Furthermore, after adjusting for SBP and age, FMD (7.44% vs 7.23%, p = 0.75) and PWV (7.81 m/s vs 7.72 m/s, p = 0.54) also did not differ between the participants with and without HIV. Basic biochemical analyses indicated higher TC, HDL, TG and GGT and lower HbA1c in the HIV infected group compared to the HIV free group (all p ≤ 0.047). The PLHIV reported higher tobacco use (p = 0.008) than those not infected with HIV.
The findings of the EndoAfrica-NWU study address the knowledge gaps with regards to HIV, endothelial function and cardiovascular risk markers in South Africa. Adults without HIV were included as a comparison group to determine whether HIV infection and the treatment thereof affects the cardiovascular disease risk markers and vascular function of PLHIV. We included traditional and novel biomarkers, as well as gold standard cardiovascular measurements.
Specific aspects of the baseline population profile of our population is noteworthy. More women than men were included as study participants, thereby a form of bias was introduced. However, of the total number of PLHIV older than 15 years in South Africa, an estimated 61% is women [1]. Epidemiological evidence also indicated that men in sub-Saharan Africa do not access HIV services as often as women [31], and are therefore also less inclined to partake in a HIV-related research study.
The average age of the study population was 42 years with PLHIV being somewhat older (43 vs 39 years). It is well known that the incidence and prevalence of CVD rise with age. This has also been reported previously in an HIV infected population where comorbidities seemed to develop earlier in PLHIV than in HIV free populations [32]. Despite that PLHIV in our study were older, and had higher TC and TG levels, their cardiovascular profile did not differ from those not infected. Even though the prevalence of hypertension also did not differ, elevated BP is well documented as a risk factor for the development of CVD, and we therefore adjusted for both age and SBP in the preliminary analyses of the endothelial and vascular function measurements. No difference in vascular function was observed, which was in line with another study where we indicated endothelial activation, but no increase in arterial stiffness in HIV infected compared to HIV free Africans [16]. In the latter study carotid dorsalis-pedis PWV was measured [16] and not the gold standard carotid-femoral PWV as in this study. Treatment did not seem to influence vascular function of the infected participants of whom 40% were on ART [16]. Nonetheless, further detailed analyses are needed to investigate the influence of HIV/ART on arterial stiffness in this study population with 79,9% of participants on ART. Whether the differences in cardiovascular risk markers, such as lipid- and glycaemic markers, GGT and tobacco use will affect the vascular measurements also remains to be seen.
The prevalence of HIV is indicated as 19% among adults 15–49 years old in South Africa, but the number is higher (22%) among women in this age range [2]. During recruitment for our study, young people did not volunteer and only 10% of the HIV infected participants in our study were younger than 30 years of age. This is in line with previous findings as historically, national AIDS programs struggled to persuade young people, who often underestimate their HIV risk, to periodically get tested for HIV [1]. Also, low numbers of young adults enter the ART program in South Africa, resulting in only a small percentage of young people being virally suppressed [33].
The increasing use of ART and the global obesity epidemic will likely accelerate the growing issues of weight gain, obesity, diabetes and CVD in PLHIV [34]. Although waist circumference did not differ between the HIV infected and HIV free participants in our study, the body mass index was lower (26 vs 29 kg/m2) among those infected (79.9% on ART). As in the case of the age differences, it remains to be determined whether adiposity will affect the cardiovascular outcomes of participants in this study.
A possible limitation of a study using FMD measurements to indicate endothelial function, is the low reproducibility of FMD measurements due to several influencing participant-related covariates and environmental factors [23]. However, studies have shown that the strict adherence to operating guidelines creates reliable FMD measurements for assessing endothelial function and cardiovascular risk [35,36,37]. We aimed for reproducible measurements and a low coefficient of variation for FMD by executing the standard operating procedures described here and in Strijdom et al. [17]. This included extensive training of the operators, regular quality control, and measuring the same brachial artery regions. We limited the number of FMD operators in the EndoAfrica-NWU study by using only two well trained researchers.
A further limitation of the study is that the recruitment of our participants was based on voluntary participation constituting a convenience sample. Despite efforts, we were unable to include more ART naïve, or individuals who were on ART for less than 4 weeks. In comparison to the high prevalence of HIV in South Africa, this is a relatively small study population. However, our study was performed under controlled conditions, approximately at the same time of day, and we used gold standard measurement techniques by a trained and experienced team of researchers in South Africa, the country with the biggest HIV epidemic in the world. Furthermore, our study reflects current health seeking behaviours of PLHIV in South Africa and will provide valuable insights which will contribute to the cardiovascular health of PLHIV in South Africa.
The baseline phase of the study has been completed and the 18-month follow-up data collection was completed in December 2019. The data analyses are ongoing, also on baseline carotid intima-media thickness and retinal imaging. The data is managed by a data manager using the RedCap system hosted at the Hypertension in Africa Research Team (HART), North West University, as well as the Department of Biomedical Sciences, Stellenbosch University (as part of the parent study).
The EndoAfrica-NWU study addresses the lack of, and need for, research with regards to a detailed cardiovascular risk profile among South Africans living with HIV, in the epicentre of the global epidemic. While detailed analyses are still ongoing, the baseline profile of current data indicates no difference in the cardiovascular profile, including endothelial and vascular function measurements, despite the fact that the PLHIV were older. This study will generate knowledge unique to the South African HIV infected population and has the potential to lead to novel strategies to promote the cardiovascular health of this vulnerable population throughout Sub-Saharan Africa.
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Potential collaborators are invited to apply to the corresponding author and to the principal investigator of the parent study [17] stating brief objectives of their project and analyses plan.
ALP:
ALT:
AST:
Aspartate aminotransferase
CD4:
Cluster of differentiation 4
CKD-EPI:
Chronic Kidney Disease Epidemiology
CRP:
C-reactive protein
CVD:
DBP:
Diastolic blood pressure
DNA:
Deoxyribonucleic acid
DVA:
Dynamic vessel analysis
ECG:
Electrocardiogram
eGFR:
Estimated glomerular filtration rate
EndoAfrica-NWU:
EndoAfrica-North-West University
FMD:
GDF-15:
Growth differentiation factor-15
GGT:
Y-glutamyltransferase
HART:
Hypertension in Africa Research Team
Hb:
HbA1c:
Glycated haemoglobin
HDL:
High-density lipoprotein
HREC:
Health Research Ethics Committee
ICAM:
Intercellular adhesion molecule
IMT:
Intima-media thickness
Low-density lipoprotein
MPO:
Myeloperoxidase
NNRTI:
Non-nucleoside reverse transcriptase inhibitor
NRTI:
Nucleoside reverse transcriptase inhibitor
NWU:
North-West University
PLG:
Panleucogating
PLHIV:
People living with the Human Immunodeficiency Virus
PWV:
REDCap:
Research Electronic Data Capture
SBP:
Systolic blood pressure
Total cholesterol
TG:
UNAIDS:
Joint United Nations Programme on HIV/AIDS
VCAM:
Vascular cell adhesion molecule
UNAIDS data 2019. https://www.unaids.org/sites/default/files/media_asset/2019-UNAIDS-data_en.pdf. Accessed 11 February 2020.
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This research would not have been possible without the contribution and dedication of the recruitment team, voluntary participants, research staff, postgraduate students and interns at the Hypertension Research and Training clinic and laboratory at the North-West University.
Figure 2, the map illustrating where the data collection took place, was produced using Servier Medical Art (https://smart.servier.com/).
The EndoAfrica-NWU study formed part of a broader consortium under the ERAfrica program of the European Union 7th Framework Program. Funding is disbursed via the Department of Science and Innovation in South Africa (contract number DST/CON 0077/2014). AES is funded by the South African National Research Foundation (SARChI GUN 86895) and South African Medical Research Council. PDB and NG received funding from the Belgian Science Policy in Belgium (Grant number BL/67/eranet03), and the Österreichische Agentur für internationale Mobilität und Kooperation in Bildung, Wissenschaft und Forschung, OeAD GmbH (ÖAD) in Austria (Grant number: KEF-Projekt P202). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Hypertension in Africa Research Team (HART), North-West University, Private Bag X1290, Potchefstroom, South Africa
Carla M. T. Fourie, Shani Botha-Le Roux, Wayne Smith, Aletta E. Schutte, Yolandi Breet, Carina M. C. Mels, Lebo F. Gafane-Matemane, Leandi Lammertyn, Lisa Uys & Adele Burger
South African Medical Research Council: Unit for Hypertension and Cardiovascular Disease, Faculty of Health Sciences, North-West University, Potchefstroom, South Africa
School of Public Health and Community Medicine, University of New South Wales, The George Institute for Global Health, Sydney, Australia
Aletta E. Schutte
Toxicology and Biochemistry Department, National Institute for Occupational Health, Johannesburg, South Africa
Jitcy S. Joseph
Gravitational Physiology and Medicine Research Unit, Division of Physiology, Medical University of Graz, Graz, Austria
Nandu Goswami
Health Unit, Flemish Institute for Technological Research (VITO), Mol, Belgium
Patrick De Boever
Centre for Environmental Sciences, Hasselt University, Diepenbeek, Belgium
Department of Biology, University of Antwerp, Wilrijk, Belgium
Centre for Cardiometabolic Research in Africa, Division of Medical Physiology, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
Hans Strijdom
Carla M. T. Fourie
Shani Botha-Le Roux
Yolandi Breet
Carina M. C. Mels
Lebo F. Gafane-Matemane
Leandi Lammertyn
Lisa Uys
Adele Burger
HS, PDB, NG wrote the original grant proposal and HS is the international project coordinator. CMTF, SB-LR, AES, WS, YB, CMCM, LG-M, LL, HS, PDB, NG contributed to the design of the EndoAfrica-NWU study. CMTF, SB-LR, WS, AES, YB, CMCM, LG-M, LL, LU, AB, JSJ contributed to the acquisition, analyses, and interpretation of the data. CMTF drafted the manuscript and CMTF and SB-LR contributed to the statistical analyses. All authors critically revised the manuscript, gave input and final approval, and agreed to be accountable for all aspects of the work to ensure integrity and accuracy.
Correspondence to Carla M. T. Fourie.
The EndoAfrica study has received ethics approval from the Health Research Ethics Committee (HREC) of Stellenbosch University (reference numbers: N13/05/064 S16/07/114), and the EndoAfrica-NWU study of HREC of the North-West University (reference number NWU-00045-15-A1). The study was conducted according to the ethical guidelines and principles of the international Declaration of Helsinki, South African Guidelines for Good Clinical Practice and the Medical Research Council of South Africa Ethical Guidelines for Research. All the participants signed the informed consent form after a detailed description of the study and planned measurements were conveyed to them in their native language. Participants were allowed to withdraw from the study at any time.
All authors declare that they have no competing interest.
Fourie, C.M.T., Botha-Le Roux, S., Smith, W. et al. Vascular function and cardiovascular risk in a HIV infected and HIV free cohort of African ancestry: baseline profile, rationale and methods of the longitudinal EndoAfrica-NWU study. BMC Infect Dis 20, 473 (2020). https://doi.org/10.1186/s12879-020-05173-6
Cardiovascular risk markers
Endothelial function
Vascular function
African ancestry


text:
\section{Introduction}

Over the past decades, electroencephalography (EEG) based emotion recognition gained significant interest towards developing affective neural-machine interfaces \cite{muhl2014survey}.
However, due to the costly data collection processes, large EEG datasets recorded under emotion eliciting experimental paradigms are not ubiquitous.
Several small scale affective EEG datasets were previously introduced, which however highly differ in their experimental setups (\textit{e.g.}, stimuli, emotion labels).
This introduces the well-known \textit{domain adaptation} problem, i.e., models trained to recognize emotions on one of these datasets commonly fails to solve the same task for another dataset.
In order to make EEG-based emotion recognition algorithms suitable for a variety of experiments and real-life scenarios, it is crucial to obtain a model that can tackle this task across multiple datasets.
One approach to achieve this from a deep learning perspective is to extract and exploit domain-invariant representations from multi-channel EEG data.

Recent work have shown significant promise in using invariant representation learning from time-series EEG data for cross-subject generalization \cite{ozdenizci2020learning,jeon2021mutual,rayatdoost2021subject}, while learning across multiple dataset sources remains an open question.
Notable methods successfully introduced adversarial censoring for domain invariance, widely known as DANN \cite{ganin2016domain}, in various EEG decoding tasks \cite{ozdenizci2020learning,jeon2021mutual}, including emotion recognition \cite{rayatdoost2021subject}.
Adversarial domain regularization with DANN \cite{ganin2016domain} considers a cross-domain shared encoder that extracts features from data of all subjects, and two classifiers: a task and a domain classifier.
During training the encoder is adversarially penalized on the domain classification loss, which enforces the model towards learning domain-invariant representations.
In this work we take a different approach to invariant EEG representation learning by further considering to preserve domain privacy that is of critical importance in clinical settings \cite{agarwal2019protecting,ju2020federated}.

We propose a multi-source learning framework for domain invariant representation learning from time-series signals such as multi-channel EEG recordings.
From a different perspective than adversarial learning methods, our framework consists of a private feature encoder per domain and a cross-domain shared classifier, where we utilize a maximum-mean-discrepancy (MMD)~\cite{gretton2012kernel} based domain alignment loss across private feature encoders to minimize domain-specific leakage within the learned representations.
Our contributions in this work are three-fold:
(1) We introduce a deep neural signal processing architecture where EEG time-series are privately encoded at the source end and only the learned representations are shared to a global classifier that enables decentralized cross-dataset learning by preserving domain privacy.
(2) We reveal large dataset domain specific variances in conventionally trained centralized pipelines, and demonstrate that regularizing latent representations via an MMD-based domain alignment loss enables dataset source independent representation learning.
(3) We show that the use of adaptive batch normalization layers in such multi-source settings prevent performance decrease at inference time.

\section{Materials and Methods}

\subsection{Experimental Data}

\textbf{Benchmark Affective EEG Datasets:} We used four publicly available EEG-based emotion recognition datasets: \textit{DEAP}\ ~\cite{koelstra2011deap}, \textit{DREAMER}\ ~\cite{katsigiannis2017dreamer}, \textit{SEED}\ ~\cite{duan2013differential, zheng2015investigating}, \textit{SEED-IV}\ ~\cite{zheng2018emotionmeter}.
During EEG recordings subjects were presented audio-visual stimuli that are expected to elicit distinct emotions.
Differences in the experimental setups lead to a variability in the structure of data samples and their labels.
\textit{SEED} and \textit{SEED-IV} contain $62$-channel EEG recordings sampled at \SI{200}{\hertz}, whereas \textit{DEAP} contains recordings from $32$ electrodes and \textit{DREAMER}\ from $14$, both sampled at \SI{128}{\hertz}.
Emotion labels of \textit{SEED}\ and \textit{SEED-IV}\ represents one out of three and four discrete emotions respectively, labeled by the stimuli that the subjects were presented.
For \textit{DEAP}\ and \textit{DREAMER}\, post-recording subject self-assessment ratings on the \textit{valence} and \textit{arousal} continuous scales were included as the labels.

\vspace{0.1cm}
\noindent\textbf{Label Transformation:} To realize our cross-dataset experimental analyses, we transformed the label spaces across all four datasets into a common set. We determined the three discrete emotions \textit{Negative}, \textit{Neutral}, and \textit{Positive}\ (as also used in the \textit{SEED}-dataset) as the common label-space. Using \textit{k-means} clustering in the two-dimensional label space of \textit{Valence} and \textit{Arousal} for \textit{DEAP}, and \textit{DREAMER}\ datasets, we determined four clusters corresponding to the four discrete emotions used as labels in the \textit{SEED-IV}\ dataset: \textit{Fear}, \textit{Sad}, \textit{Neutral}, \textit{Happy}. We merged the former two into being the \textit{Negative}\ class, and considered \textit{Happy} to be the \textit{Positive}\ class. Hence we obtained three class labels in a unified manner.

\subsection{Multi-Source EEG Processing for Private Encoding}

We will denote multiple datasets $\mathcal{D}_k$ consisting of time-series EEG signal epochs and corresponding emotion label of the experiment paradigm as pairs $(s_j,l_j)$.
We pre-process the signals to serve as input to our architecture as follows.
First, we perform baseline correction for signals $s_j$ using a three-second time-window and apply a \SI{4}-\SI{40}{\hertz} Butterworth band-pass filter.
Since the length of the available $s_j$ differ within each dataset, we used only the last $T$ seconds of each available signal with $T$ being the length of the shortest time-series in the dataset, i.e., 60, 64, 185, 50 for \textit{DEAP}, \textit{DREAMER}, \textit{SEED}\ and \textit{SEED-IV}\ respectively.
Then, we segment the signals into non-overlapping \SI{2}{\second} windows that will be used as inputs $x_i$ to our model.
We assign the label $l_{j_i}$ to each window $x_i$.
All pairs of $x_i$ and corresponding labels $y_i$ obtained from a dataset are considered as a processed data-source $\mathcal{X}_k$.

To balance the number of samples across data-sources and with respect to the label for each data-source individually, we applied stochastic undersampling while constructing our training set.
We split the data into the training ($60\%$), validation ($20\%$) and test ($20\%$) sets, while we assure that the stratification constraints hold for each subset individually.

\subsection{Multi-Source Learning Framework}

\textbf{Domain Aligned Private Encoders (DAPE):} Our model consist of one private encoder per data-source and a shared classifier.
While the encoder can be designed arbitrarily for each data-source, for proof of concept we used the \textit{DeepConvNet} architecture~\cite{schirrmeister2017deep} for our all data-sources.
We replaced the final pooling layer in \textit{DeepConvNet} by an adaptive average pooling layer, that introduces the flexibility to control the number of features $n_z$ in the latent representation $z$.
In our models we chose $n_z=50$ as the best results in terms of the balance between emotion classification and domain invariance were achieved.
One extension of our model towards achieving better domain adaptation, which we denote by adaptive DAPE (aDAPE), considers the use of adaptive batch normalization layers~\cite{li2016revisiting}. Such layers were also recently used in deep EEG analysis by~\cite{jimenez2020custom,jimenez2021standardization}, instead of conventional batch normalization.
By doing so, we ensure each layer of any given encoder to receive data from a similar distribution at test time as well, as samples are normalized by the statistics estimated from the utilized mini-batch.

During each forward pass, a batch of $B$ samples from $x_i\sim\mathcal{X}_i$ are drawn and used as input to each of the $M$ encoders, represented through the parameters $\theta_i, i \in \{1,\ldots,M\}$, with $M$ also being the number of data-sources.
We consider the output representations $z_{i} = f(\theta_i, x_i) \in \mathbb{R}^{B \times n_z}$ of each encoder as samples drawn from independent data-source distributions.
To overcome the problem of domain shift across encoders and regularize the encoders towards learning domain-invariant representations, we use an MMD~\cite{gretton2012kernel} based domain-alignment loss $\mathcal{L}_{\text{DA}}$ using Gaussian kernels with $\sigma_j\in\{10, 15, 20, 50\}$.
Rather than computing the MMD for all combinations of data-sources, we randomly sample $M$ pairs of distributions $\{(z_p^{(i)}, z_q^{(i)})\}_{i=1}^M$, $p,q \in \{1,\ldots,M\}$ such that $p \neq q$, calculate the MMD loss~\cite{gretton2012kernel} between them and accumulate in the domain alignment regularizer:
\begin{equation}\label{eq:lda}
    \mathcal{L}_{DA} = \sum_{j=1}^{\vert\sigma \vert}\sum_{i=1}^{M} \text{MMD}^2(z_{p}^{(i)}, z_{q}^{(i)}, \sigma_j),
\end{equation}
where $\vert\sigma\vert$ denotes the cardinality for the pre-defined set $\sigma_j$ is chosen from and $\text{MMD}^{2}(z_{p}^{(i)},z_{q}^{(i)},\sigma_j)$ is defined as follows:
\begin{align}
\text{MMD}^{2} =& \frac{1}{B(B-1)} \sum_{m=1}^B \sum_{\substack{n=1\\n\neq m}}^B \varphi(z_{p_m}^{(i)}, z_{p_n}^{(i)}, \sigma_j) \nonumber \\
& + \frac{1}{B(B-1)} \sum_{m=1}^B \sum_{\substack{n=1\\n\neq m}}^B \varphi(z_{q_m}^{(i)}, z_{q_n}^{(i)}, \sigma_j) \nonumber\\
& - \frac{2}{B^2} \sum_{m=1}^B \sum_{n=1}^B \varphi(z_{p_m}^{(i)}, z_{q_n}^{(i)}, \sigma_j) ,
\end{align}
with $\varphi(z_{p_m}^{(i)}, z_{q_n}^{(i)}, \sigma_j)$ being the Gaussian kernel function:
\begin{equation}
    \varphi(z_{p_m}^{(i)}, z_{q_n}^{(i)}, \sigma_j) = \exp \left(\frac{- \Vert z_{p_m}^{(i)} - z_{q_n}^{(i)} \Vert^{2}}{2\sigma_j^{2}}\right).
\end{equation}
All batches of representations $z_i$ are then stacked to one vector of representations $z\in\mathbb{R}^{(B*M) \times n_z}$, and used as input for the classifier with parameters $\theta_C$ to compute predicted emotions $\hat y$.
We train the network with a categorical cross-entropy loss function $\mathcal{L}_{CE}= -\sum_j^{M*B} y_j \log(\hat y_j)$.
Note that only the parameters $\theta_C$ and $\theta_i$ (parameters of the encoder that processed a sample $x_j$) contribute to the prediction of an individual sample $\hat y_j$, which ensures that only the classifier and the encoder $i$ are optimized based on the gradient of the loss.

\vspace{0.2cm}
\noindent\textbf{Multi-Objective Optimization:} Training of \textit{DAPE} can be considered as a multi-objective optimization.
On one hand, we want to minimize the cross-entropy loss for the classification task and one the other hand we want to minimize the domain-alignment loss $\mathcal{L}_{\text{DA}}$ for learning domain-invariant representations.
We assume that these goals are not contradictory and that the classification task gets more robust using domain-invariant representations of the samples.
To control the domain-invariance of representations we use a hyperparameter $\kappa$ as the tunable weight in the optimization problem:
\begin{equation}
   \theta_1^{*}, \ldots, \theta_M^{*}, \theta_C^{*} = \argmin_{\theta_1, \ldots, \theta_M, \theta_C}{\mathcal{L}_{CE} + \kappa \mathcal{L}_{DA}}.
\end{equation}
We use an annealing update scheme for $\kappa$ as proposed by~\cite{schonfeld2019generalized,bowman2015generating}.
To ensure that the domain alignment does not lead the encoders to learn task-irrelevant representations, we start the training with $\kappa=0$ and increase it with the beginning of the 5th epoch by a rate of $0.25$ per epoch.
To prevent the domain-alignment loss dominating the cross-entropy loss in the later epochs, we stop updating $\kappa$ by the 70th epoch and retain the value of $16.25$ for all subsequent epochs.

\subsection{Quantifying Domain-Invariance of Representations}

One of the motivating goals in our architecture is to preserve domain privacy at the data-source end, as well as ensuring that an attacker not being able to deduce the information concerning the data-source from latent representations.
To assess invariance of learned representations via DAPE, we firstly compute the latent representations $z_i$ of all samples in the test set.
We use $80\%$ of the samples to train a \textit{domain classifier} using the data-source ID as label.
We subsequently evaluate the domain-invariance of the representations by calculating the achieved accuracies of these domain classifiers on the remaining $20\%$ of the test set representations.
Note that a lower 4-class domain-classifier accuracy corresponds to a higher domain-invariance of the learned representations, which is favorable for multi-source learning.
We considered a multitude of learning machines as domain classifiers (e.g., support vector machines (SVM), quadratic discriminant analysis). Since all domain classifiers showed similar results we report our results with linear SVMs in Section~\ref{sec:invariance}.

\section{Experimental Results}

\subsection{Emotion Classification}

\autoref{tab:results} summarizes our results on the emotion classification task.
Here \textit{local models} indicate four independent networks processed on each data-source individually (i.e., \textit{DAPE} with $M=1$), which would be a baseline approach if one is not interested in domain-privacy or representation invariance.
To the contrary, the \textit{global model} indicates a baseline where one trains a single, unified model by pooling all data-source inputs into one unified training set, however constraining both the amount of training data and the encoder specifications to be uniform and shared across all data-sources.
DANN~\cite{ganin2016domain} depicts an invariant representation learning approach that was previously considered for EEG decoding in \cite{ozdenizci2020learning,rayatdoost2021subject}, where one can train a model adversarially by censoring the data-source ID relevant information from latent representations to impose invariance, without considering data-source privacy.

While the global model and DANN perform only slightly above chance level (33\%) in emotion classification, we observe that \textit{DAPE} and \textit{aDAPE} clearly outperform the baseline approaches that learns a unified model for emotion classification, while simultaneously ensuring data-source privacy.
Furthermore, we observe that \textit{aDAPE} is also only slightly below the average of the extensive local models (49.09\% vs. 51.91\%), although solving this task across multiple domains poses a more challenging problem than learning locally.

\subsection{Domain-Invariance of the Learned Representations}
\label{sec:invariance}

Domain-invariance of the learned representations estimated through the linear SVM are shown at the bottom row of \autoref{tab:results}.
We observe that \textit{aDAPE} shows the highest domain-invariance in the learned representations, where in all other models the linear SVM was able to deduce the data-source ID from the representations with $>60\%$ accuracy.
Achieved domain-invariance is $\sim14\%$ higher for \textit{aDAPE} than for \textit{DANN}, and $\sim47\%$ higher than for the local models.
Furthermore, it is important to note that the performance of the domain classifier is much higher ($\sim30\%$) for \textit{aDAPE} than for \textit{DAPE}, from an ablation study perspective.
This significant gap results in the use of adaptive batch normalization layers.
Overall emotion classification accuracy of \textit{aDAPE} is higher than of \textit{DAPE}, and achieved domain-invariance of representations is also stronger (while still being slightly above the chance-level), confirming our hypothesis that using domain-invariant representations in multi-source settings helps by making the classification task more robust.

\section{Conclusion}

We present \textit{Domain Aligned Private Encoders} as a multi-source learning framework for domain-invariant representation learning, and demonstrate its feasibility on an EEG-based emotion classification task using data from four publicly available datasets.
Different than state-of-the-art adversarial training approaches to learn invariant EEG representations \cite{ozdenizci2020learning,jeon2021mutual,rayatdoost2021subject}, we utilize an MMD based domain alignment loss \cite{jimenez2020custom,chen2021ms} across dataset-specific private feature encoders.
Proposed deep neural signal processing architecture encodes multi-channel EEG time-series signals privately at the source end and only the learned representations are shared to a global classifier.
Our decentralized invariant representation learning approach and use of adaptive batch normalization layers improved performance in our experimental analyses.

Accessible EEG data is generally present in small datasets, which are also distributed across various countries and/or laboratories.
Due to naturally occurring data privacy concerns, we believe that such architectures as proposed in this work make common use of private data representations more convenient and real-life applicable (e.g., clinical time-series monitoring \cite{ozdenizci2018time}, personalized BCI-based stroke rehabilitation protocols \cite{agarwal2019protecting,mastakouri2017personalized}), hence our work advances the area of cross-domain transfer learning for EEG signals in that sense.

\section*{Outline}
A fundamental problem of Deep Learning approaches is the lack of generalization capability to new data. Therefore, transfer learning methods are often used to apply a trained model to new data. However, transfer learning methods are laborious and suffer from problems such as catastrophic forgetting. We propose a new learning paradigm, multi-source learning. Thereby, we train a model to solve a task in different domains, i.e. on different datasets, simultaneously. To prevent overfitting on a subset of the data, we learn domain-invariant representations of the data using aligned private encoders. By using such representations, it is possible to combine different small data sets into one large data set, increase privacy, and increase the signal-to-noise ratio of the representation. We demonstrate that using our multi-source learning approach, it is possible to reduce the domain variance of the learned representations, while the performance in the learned task is only slightly behind that of a standard single-source model. We also highlight multi-source specific problems to be addressed in the future.
\\
\\
Many models and methods for emotion recognition based on EEG have been proposed, most of them worked well only in the scenario that the training and test data were from the same distribution or domain i.e. coming from one participant. However, such learning models fail to recognize emotions across domains due to the distribution mismatch of EEG features. Learning models that extract emotions across subjects have been proposed, however, learning across EEG datasets has not been proposed yet.

a model, that is capable of solving a Task across multiple domains. Such a multi-source learning approach allow to combine multiple small datasets for training a model. This omits the need of large therefore expensive datasets. In a addition, a model that uses domain-invariant features can increase the privacy as it gets harder to conclude from which data-source, \textit{e.g.} from which participant, a sample was drawn. Furthermore the adaption of such a model towards data of a new data-source gets very simple.
This has not been done in the past due to the challenging task of distribution aligning EEG data, however, if achieved offers the possibility to learn generalized emotion classifier models independent of hardware and experimental design. Additionally, private encoded representations offer BCI researchers to publish useful non-identifiable EEG extracts without granting full access to the dataset and thereby enable researchers to exchange EEG experimental data more effectively.

In this work we propose a multi-source learning framework, consisting of one shared classifier and multiple private encoders, regularized by a MMD-based domain-alignment loss. Using a non-trainable classifier we show, that we are successfully decreasing the domain-covariance of the learned representations. We evaluate our framework using EEG datasets with emotional label and compare our classification results against a encoder-classifier-model that was learned and tested on each dataset individually.

We show, that multi-source learning is a promising approach for learning domain invariant representations. However, this new field of research poses significant challenges, for example as the gap of the domain-alignment-loss between training and validation can not be explained by the state-of-the-art. In addition we show that Batchnormalization layers as well as Dropout layers seem to have yet unknown impact on the training of multi-source setups.

\subsection{Contribution Statement}
Our paper makes the following contributions:
\begin{itemize}
    \item proposition of learning framework and preprocessing steps for data-source-invariant representation learning for EEG data
    \item analysis of data-source domain invariance on multiple emotional EEG datasets
    \item definition of obstacles and solutions for achieving domain invariance in EEG data
\end{itemize}
We show that, DAPE learns EEG representations, that can predict emotions with reasonable accuracy of $50\%$ in effect $2\%$ lower than traditional baseline approaches across 4 different data source domains, while also reducing privacy leaking information of $-40\%$ (domain classifier). Thereby demonstrating the usefulness of regularizing the domain-leakage information in future EEG encoding applications while achieving similar classification accuracies.

\begin{figure}[h]
    \centering
    \includegraphics[]{images/DAPE_baseline_comparison.eps}
    \caption{Comparison of the multi-source learning Framework with a single-source baseline model}
    \label{fig:my_label}
\end{figure}

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redpajama_set_name: RedPajamaArXiv


Currently owned by the VW group and based in Sant'Agata Bolognese, Lamborghini is known to create some of the most provocative and outlandish design ever fitted to a chassis. Ferruccio Lamborghini, the founder of 'Automobili Lamborghini' started out making tractors and race boat engines. It wasn't until 1963 that the company assumed its automaker identity to become the brand we recognize today.
Some of Lamborghini's most Iconic models include the Miura, Countach, Diablo and the Murcielago, all of which are powered by thunderous V12s. Lamborghini has experimented with several types of engines including a V8 in the 1980 Jalpa and more recently a V10 in the Gallardo, the Murcielago's little sister.
Today, Lamborghini's cars represent the avant-garde nature of their design and performance. The Aventador, a V12 monster that will reach a top speed in excess of 350 km/h, and the Huracan, powered by a v10 and capable of 0-100 km/h in under 3 seconds, are both available with Elite Rent in their hard-top and convertible/roadster configuration. When it comes to presence and sound, the raging bull is always an excellent choice.


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text:
Richemont flags slow progress in watch sales
Luxury goods group Richemont struck a cautious note after reporting sales numbers hurt by moves to combat the grey market and efforts by the Chinese government to discourage consumers from spending overseas.
The company known for Cartier jewellery and IWC watches attributed a weak performance in Europe in the six months to Sept. 30 to the disposal of its Lancel bag business and a drop in watch sales to third-party retailers to avoid unsold stock ending up on online platforms at big discounts.
"We are clamping down on grey market activities in the watch trade," finance chief Burkhart Grund told reporters on Friday. "Is the worst over? Well, I hope so. The market will tell us."
Richemont shares, which have lost more than 16 percent of their value this year, were down 6 percent at 1033 GMT, against a 3.8 percent fall for rival Swatch Group.
Sales growth has slowed for Swiss luxury watchmakers of late, with exports to their biggest markets, Hong Kong and the United States, turning negative in September.
Grund said that Asia-Pacific sales had dipped in September because of factors including bad weather and a weakening of the renminbi currency that affected regional tourism, but group sales had bounced back in October to first-half levels.
Richemont had already reported five-month sales in September, so investors' focus on Friday was mostly on September and October trading and any comments about a possible slowdown in Chinese spending amid its trade war with the United States.
Rival luxury groups Hermes and Kering had already reported solid sales growth and played down fears of cooling demand in China.
Richemont, the world's second-biggest luxury group, has recently made adjustments to its portfolio to gear it more towards the online sector and last month announced a strategic partnership with Chinese e-commerce giant Alibaba.
Group sales grew 8 percent at constant currency in the six months to Sept. 30, down from 10 percent during the five-month period. Including the contribution of recently acquired online distributors Yoox Net-a-Porter (YNAP) and Watchfinder, first-half sales were up 24 percent. While sales in Asia Pacific, its biggest market, held up with high single-digit sales growth in mainland China and double-digit increases in Hong Kong, Macau and Korea, European sales rose by only 1 percent.
Profit more than doubled to 2.25 billion euros ($2.55 billion) in the first half, helped by a 1.38 billion euro non-cash gain related to Yoox Net-a-Porter.
Excluding the one-off gain, net profit fell 10 percent to 875 million euros, hit by acquisition and disposal-related charges.
P&G Hygiene and Health Care Q2 profit dips 2 percent to Rs 207 crore as costs surge
Raymond Ltd Q3 net profit down 4 percent to Rs 97 crore
Century Textiles Q3 net profit slumps 44 percent to Rs 7 crore
Chicnutrix steps into skincare, launches K-Skincare
Groyyo and Private Office of Sheikh Saeed bin Ahmed Al Maktoum join forces for Middle East expansion
Gucci's challenge: reigniting heat while boosting timeless appeal
LVMH, L'Oréal, Shiseido said to be vying to buy stake in Aesop
Union Budget must focus on policy alignment for retail sector: Arrow CEO

redpajama_set_name: RedPajamaCommonCrawl


text:
HEWETT AND FRIENDS. A Biography of Santa Fe's Vibrant Era.
Chauvenet, B.
Museum of New Mexico Press, Santa Fe: 1983. xii, 248 pages, 52 photographs. Cloth cover. This biography of Edgar Lee Hewitt (founder of the School of American Research and the Museum of New Mexico) is a colorful social history of Santa Fe from 1900-1950. More
A GOLDEN JOURNEY. Memoirs of an Archaeologist
Cressman, L.s., J. Howard (foreword)
Salt Lake City: 1988. xvii, 506 pages. Cloth cover. The enchanting memoirs of an archaelogoist (who, also, was Margaret Mead's first husband) who knows how to weave a wondrous story. More
A NAVAHO AUTOBIOGRAPHY
Dyk, W.
VIKING FUND PUBLICATIONS IN ANTHROPOLOGY, No. 8, 1947. 218 pages of text containing 1 map, plus 16 full-page photographs. More
Edward Sheriff Curtis, VISIONS OF A VANISHING RACE.
Graybill, F.C. V. Boesen, H. Curtis
New York: 1976, 1994. 211 pages, 95 full-page duotone photograhs, 87 large b/w photographs. cloth cover, small folio size (10"x14"). Mint condition. For this book, Curtis' daughter (F. Curtis Graybill) selected 182 of her father's finest photographs. She and V. Boesen have provided a detailed and moving biography of Curtis'..... More
CRY OF THE THUNDERBIRD. The American Indian's Own Story
Hamilton, C. (ed.)
New York: 1950. xiii, 293 pages of text plus 10 leaves of drawings and 1 map. Cloth cover. More
NAMPEYO AND HER POTTERY
Kramer, B.
Albuquerque: 1996. xiii, 224 pages, 14, color and 10 b/w photographs of pots, 25 b/w archival photographs, 10 pages of drawings of pots and designs, 5 maps. Cloth cover. This is the first comprehensive biography and stylistic analaysis of the work of Nampeyo, the great Hopi-Tewa pottery of the late..... More
THE BIOGRAPHICAL DIRECTORY OF NATIVE AMERICAN PAINTERS
Lester, P.d
Norman: 1995. xvii, 701 pages. Cloth cover. 9" x12" A monumental reference work, this volume contains biographical information on more than three thousand Native American painters from 1800 to the present. Typical entries list the artist's tribal affiliation and name, birth and death dates, residence, publications, exhibits, awards, and honors--along..... More
EARL MORRIS AND SOUTHWEST ARCHAEOLOGY
Lister, F.C. & R.H.
Albuquerque: 1968. xviii, 197 pages of text plus 16 pages of photographs. Cloth cover with dust jacket. Inscribed by both authors. More
FOOLS CROW. The Long-awaited Memoir of the Revered Ceremonial Chief of the Teton Sioux
Mails, T.E.
1979. 278 pages, 24 b/w photographs and drawings, 7 color drawings. Cloth cover. More
ROBERT H. LOWIE (A Biography)
Murphy, R.e
New York: Leaders of Modern Anthropology, 1972. viii, 179 pages. Cloth cover. More
DERIC WITH THE INDIANS
Nussbaum, D
New York: 1927 (1st ed., 2nd impression). x, 204 pages, 24 pages of photographs, 24 small drawings. Cloth cover. More
HIDDEN SCHOLARS. Women Anthropologists and the Native American Southwest
Parezo, N. (ed.)
Albuquerque: 1993. xxiii, 429 pages, 18 pages of photographs. Cloth cover. More
DENIZENS OF THE DESERT. A Tale in Word and Pictures of Life among the Navaho Indians. The letters of Elizabeth W. Forster/ Photographs by Laura Gilpin
Sandweiss, M. (intro. & Ed.)
Albuquerque: 1988. xii, 140 pages, 20 full-page photographs. Cloth cover. More
SUN CHIEF, The Autobiography of a Hopi Indian
Simmons, L. (ed.), Robert V. Hine (intro.)
New Haven: 1942, 1967 (7th printing). xx, 460 pages, 5 illustrations. Cloth cover. with dust jacket. Very good-fine condition. More
Simmons, L.w. (ed.)
New Haven: 1942. x, 460 pages, 5 illustrations. Cloth cover. More
WEALTH AND REBELLION. Elsie Clews Parsons, Anthropologist and Folklorist
Zumwalt, R., R. Abrahams (foreword).
Urbana: 1992. xxi, 360 pages, 14 pages of photographs. Cloth cover. A wonderful biography of the famed anthropologist, the author of numerous seminal studies on Pueblo peoples. More


text:
Q: SyntaxError: Invalid or unexpected token после того как пытаюсь запустить файл с помощью node После того как пробую запустить файл, выдает ошибку
D:\NewExpressProject\app.js:1
(function (exports, require, module, __filename, __dirname) { ��l
                                                              ^

SyntaxError: Invalid or unexpected token
at new Script (vm.js:79:7)
at createScript (vm.js:251:10)
at Object.runInThisContext (vm.js:303:10)
at Module._compile (internal/modules/cjs/loader.js:656:28)
at Object.Module._extensions..js (internal/modules/cjs/loader.js:699:10)
at Module.load (internal/modules/cjs/loader.js:598:32)
at tryModuleLoad (internal/modules/cjs/loader.js:537:12)
at Function.Module._load (internal/modules/cjs/loader.js:529:3)
at Function.Module.runMain (internal/modules/cjs/loader.js:741:12)
at startup (internal/bootstrap/node.js:285:19)

Код файла:
let express = require("express");
let app = express();

app.get("/", function(req, res) {
   res.send("home page");
});

app.listen(3000, function(){
   console.log("server has started");
});

Ноут на 10 винде.
Переустанавливал node не помогло.
Версия node.js - 10.12.0 , версия npm - 6.4.1
Пробовал версию node.js - 8.12.0 - та же проблема
ПК на 7 винде всё работает.
Версия node.js - 10.9.0 , версия npm - 6.2.0

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