There is an unmet need for noninvasive imaging markers that can help identify the aggressive subtype(s) of pancreatic ductal adenocarcinoma (PDAC) at diagnosis and at an earlier time point, and evaluate the efficacy of therapy prior to tumor reduction. In the past few years, there have been two major developments with potential for a significant impact in establishing imaging biomarkers for PDAC and pancreatic cancer premalignancy: (1) hyperpolarized metabolic (HP)-magnetic resonance (MR), which increases the sensitivity of conventional MR by over 10,000-fold, enabling real-time metabolic measurements; and (2) applications of artificial intelligence (AI).Our objective of this review was to discuss these two exciting but independent developments (HP-MR and AI) in the realm of PDAC imaging and detection from the available literature to date.A systematic review following the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines was performed. Studies addressing the utilization of HP-MR and/or AI for early detection, assessment of aggressiveness, and interrogating the early efficacy of therapy in patients with PDAC cited in recent clinical guidelines were extracted from the PubMed and Google Scholar databases. The studies were reviewed following predefined exclusion and inclusion criteria, and grouped based on the utilization of HP-MR and/or AI in PDAC diagnosis.Part of the goal of this review was to highlight the knowledge gap of early detection in pancreatic cancer by any imaging modality, and to emphasize how AI and HP-MR can address this critical gap. We reviewed every paper published on HP-MR applications in PDAC, including six preclinical studies and one clinical trial. We also reviewed several HP-MR–related articles describing new probes with many functional applications in PDAC. On the AI side, we reviewed all existing papers that met our inclusion criteria on AI applications for evaluating computed tomography (CT) and MR images in PDAC. With the emergence of AI and its unique capability to learn across multimodal data, along with sensitive metabolic imaging using HP-MR, this knowledge gap in PDAC can be adequately addressed. CT is an accessible and widespread imaging modality worldwide as it is affordable; because of this reason alone, most of the data discussed are based on CT imaging datasets. Although there were relatively few MR-related papers included in this review, we believe that with rapid adoption of MR imaging and HP-MR, more clinical data on pancreatic cancer imaging will be available in the near future.Integration of AI, HP-MR, and multimodal imaging information in pancreatic cancer may lead to the development of real-time biomarkers of early detection, assessing aggressiveness, and interrogating early efficacy of therapy in PDAC. There is an unmet need for noninvasive surrogate markers that can help to identify the aggressive subtype(s) in a pancreatic lesion at an early time point . In cont13C-labeled probes that can monitor enzymatic conversions through key biochemical pathways -pyruvate is the most common HP probe for determining glycolytic flux in cancer. Another key point is that pyruvate is taken up rapidly by monocarboxylate transporters  pyruvate with HP-MR  pyruvate. They used genetically engineered mice with K-Ras and p53 mutations or with K-Ras mutation only, which developed PanIN spontaneously. In addition, wild-type mice treated with caerulein injections to induce acute pancreatitis that developed into PanIN over time were included. The mice were imaged using HP [1-13C] pyruvate at different stages of development from PanIN to PDAC precursor lesions, and the metabolic fluxes from [1-13C] pyruvate to lactate and alanine were measured  pyruvate MRI in PDAC patients, and no adverse effect was observed after bolus injection of pyruvate  pyruvate has only been studied in preclinical models to date; however, the preclinical data illustrate the ability of HP-MR to assist clinical trials by providing a framework for personalized medicine. HP-MR can provide information about the efficacy of drugs at an early stage that can lead to changes in clinical management, enabling the clinician to change the drug for a nonresponding patient to a more effective drug at an early stage.One of the promising utilities of HP-MR is its ability to assess treatment response early during the regimen; this has been established for solid tumors characterized by “aggression correlated with increased glycolysis.” This technique can thus complement the standard fluorodeoxyglucose-positron emission tomography imaging, which can only detect changes in tumor size (rather than intracellular metabolic changes) once it shrinks in response to a long-term regimen of chemotherapy or radiation therapy. 13C] pyruvate) on three subcutaneous  patient-derived xenografts of PDAC in mice. HP-MR using [1-13C] pyruvate was employed to evaluate the metabolic phenotypes of Hs766t, MIAPaCa-2, and SU.86.86 PDAC cell line xenografts. The Hs766t and MIAPaCa-2 xenografts showed higher lactate-to-pyruvate ratios and more hypoxia. However, the SU.86.86 xenograft was resistant to the TH302 hypoxic prodrug because it was less hypoxic. The mice were treated for 2 weeks at a rate of five times a week and tumor sizes were measured at regular intervals with calipers to determine the treatment efficacy. The Hs766t and MIAPaCa-2 groups showed an excellent response with TH302 compared to the SU.86.86 group [Wojtkowiak et al  Table 2Table 2 s86 group .13C] pyruvate, which was injected into the mice prior to the start of treatment and 7 days after treatment, prior to any changes in tumor volume. Mice responding to the treatment showed a decreased lactate-to-pyruvate ratio after FX11 administration, whereas nonresponders showed an increased HP lactate-to-pyruvate ratio after the treatment. This result demonstrates the strength of the noninvasive HP-MR modality to predict treatment efficacy prior to tumor size reduction.Rajeshkumar et al  Table 2Table 2 tThe β-lapachone chemotherapeutic drug acts on the quinone oxidoreductase 1 (NQO1)-mediated redox cycle, resulting in elevated superoxide and peroxide formation and in turn nicotinamide adenine dinucleotide (NAD+) depletion due to DNA damage and hyperactivation of poly(ADP-ribose) polymerase. Silvers et al  Table 2Table 2 slactate-to-ADCpyruvate ratio was observed in viable PDAC compared to MCF-7 breast carcinoma cells. The ADC measurements of metabolites could complement the HP lactate-to-pyruvate and HP fumarate-to-malate ratios to determine cell necrosis. This technique can be extended to in vivo measurements to determine the necrotic areas and evaluate the therapeutic response in PDAC patients.Feuerecker et al  Table 2Table 2 tSeveral studies have shown that early responses to radiation therapy can be assessed using molecular imaging. Ionizing radiation generates reactive oxygen species in tumor tissues . DetermiME2) with homozygous deletion of the neighboring SMAD4 in many cases of PDAC. This makes ME3 inhibition a useful drug target because ME2 and ME3 are paralogous isoforms involved in NADPH regeneration and thus redox balance. The downstream effect of ME3 inhibition entails a reduction in the levels of branched-chain amino acid aminotransferase (BCAT) (encoded by BCAT2) via AMP-activated protein kinase–mediated mechanisms  glutamine. The feasibility of this approach depends on several factors. First, the decay time for HP 13C glutamine must be considerably longer than the uptake of glutamine by PDAC cells, and the time of conversion to glutamate and then to aspartate. Additionally, there needs to be preferential uptake in PDAC cells compared to the cells of the normal pancreas. HP glutamine has already been used to study cancer cells from other tumor types [Another possible approach for the early detection of PDAC is using HP on et al  describeor types  (Table 1Pancreatitis (inflammation of the pancreas) and PDAC are characterized by acidic microenvironments. The interstitial pH of the pancreas is reduced in patients with chronic pancreatitis -74. The Deep learning has shown robust and extraordinary performance in medical image analysis. Many previous studies have explored the applications of AI, especially deep learning, for diagnosing and detecting various diseases, including pancreatic cancer, from different imaging modalities ,77. LeveMost of the innovative applications of deep learning in biomedical imaging were triggered by convolutional neural networks (CNNs) , a powerA convolutional layer includes a set of filters with learnable parameters. Each filter is slid across the width and height of the input, and the dot product of the filter and input at every special position is calculated and goes through an activation function. A nonlinear activation function, typically rectified linear units (ReLUs), expands the potential in approximation of any nonlinear function . The outSeveral variants of CNNs with innovative architectures have been proposed to achieve better performance on specific tasks or types of data. VGG  introducTo review the previous studies on using AI for PDAC, we grouped the 17 selected papers  meeting Tumor growth, especially for pancreatic neuroendocrine tumors, is related to cancer cell properties and relies on the dynamic interaction between cells and the microenvironment. Swanson et al  proposedFu et al  discusseAlternatively, to implement multiorgan segmentation, especially on abdominal CT in the pancreas, Gibson et al  modifiedAs another example of an attempt to improve pancreas segmentation performance in CT scans, Boers et al  developeLiu et al  presenteBesides CT images, investigators using T1-MRI proposed several innovative approaches to segment the pancreas. Liang et al  introducTo implement preoperative prediction of pancreatic neuroendocrine neoplasms (pNENs) grading by CT, Luo et al  applied For the prediction of clinically relevant postoperative pancreatic fistula using CT images, Mu et al  utilizedHussein et al  utilizedTo implement diagnosis of PDAC from CT images, Liu et al  utilizedMa et al  utilizedZhang et al  proposedStereotactic body radiotherapy (SBRT) has shown more success in patients with locally advanced pancreatic cancer compared to conventional radiotherapy. To overcome interference due to motion in the breathing cycle and patient weight loss , cone-beTo test the CT data collection quality, Park et al  proposedSuman et al  used CNN7 parameters requiring 1010 floating point operations for a single classification task, making it almost impossible to be traced or explained by humans [In this review, we have discussed how two different techniques, HP-MR and AI, are revealing exciting information about PDAC and PanINs that was not accessible by diagnostic imaging even a few years ago. Deep-learning models eliminate the requirement of domain knowledge for feature engineering that is necessary for conventional machine learning models by learning from raw data. Deep-learning models are capable of learning features from the raw data and apply nonlinear transformations to map the input data to high-dimensional representations trivializing classification or regression. These models are uniquely able to transform multiple modalities into common latent space to synthesize features across all modalities to improve classification performance. However, there is no free lunch, and the flexibility and high accuracy resulting from millions of parameters comes with a requirement of a huge training dataset in comparison with other machine-learning techniques. Moreover, these models suffer from lack of interpretability and uncertainty measurement. In machine-learning algorithms, there is a tradeoff between interpretability and accuracy. When the prediction accuracy grows with more complex (increase in the number of trainable parameters) deep-learning models, the interpretability decreases. For instance, ResNet contains 5×10y humans ,111. Las1 that allows for rapid acquisition of dynamic metabolic flux in the organ of interest. Studies on HP-MR have demonstrated that this modality can detect metabolic changes at very early stages of lesion formation in the pancreas ; however, this is more of a spectroscopic technique than an imaging modality. Furthermore, the signal from HP compounds lasts no more than a few minutes depending on the TTo take advantage of the strengths of AI, MRI, and HP-MR, and mitigate their weaknesses, we propose the following pipeline as illustrated in The training process of our pipeline is as follows: axial, sagittal, and coronal MR images in the T1 and T2 modalities are annotated to highlight the pancreas area by radiologists to train a deep-learning semantic segmentation network developed by our team. We extract the ROIs from MR images . The extracted ROIs with metabolic information from HP-MR are the inputs for our multimodal deep-learning model to predict pancreatic cancer status. The appropriate combination of MRI and HP-MR as complementary modalities improves the classification performance. Therefore, the ground truth for our second deep-learning model is the presence of early stages of PDAC established by pathology reports and electronic health records of the patients. The training path is shown with the dashed lines and the inference path is shown with the solid lines in Leveraging AI and HP-MR applications together may lead to the development of real-time actionable biomarkers of early detection, assessing aggressiveness, and interrogating the early efficacy of therapy in PDAC. For example, multimodal AI can learn features from both HP-MR, as well as anatomical MRI and CT imaging modalities, to yield “hybrid biomarkers” and reduce the time required to detect PDAC evolution in three key areas of tumor progression: initial development of the tumor, its regression following therapy, and the eventual recurrence of the tumor. This innovative synthesis of these techniques may result in a more sensitive readout of tumor progression that can be readily translated and significantly impact how PDAC patients, as well as patients at high risk of developing this deadly disease, are currently managed in the clinic.


The purpose of this mini-review is to focus on the main evidence on DC involvement in IPF pathogenesis. Unanswered questions and opportunities for future research ranging from a better understanding of their contribution to diagnosis and prognosis to personalized DC-based therapies will be explored.Idiopathic pulmonary fibrosis (IPF) is the most devastating progressive interstitial lung disease that remains refractory to treatment. Pathogenesis of IPF relies on the aberrant cross-talk between injured alveolar cells and myofibroblasts, which ultimately leads to an aberrant fibrous reaction. The contribution of the immune system to IPF remains not fully explored. Recent evidence suggests that both innate and adaptive immune responses may participate in the fibrotic process. Dendritic cells (DCs) are the most potent professional antigen-presenting cells that bridge innate and adaptive immunity. Also, they exert a crucial role in the immune surveillance of the lung, where they are strategically placed in the airway epithelium and interstitium. Immature DCs accumulate in the IPF lung close to areas of epithelial hyperplasia and fibrosis. Conversely, mature DCs are concentrated in well-organized lymphoid follicles along with T and B cells and bronchoalveolar lavage of IPF patients. We have recently shown that all sub-types of peripheral blood DCs  are severely depleted in therapy naïve IPF patients. Also, the low frequency of conventional CD1c Idiopathic pulmonary fibrosis (IPF) is a progressive and devastating fatal lung disease that usually remains refractory to treatment –3, with Notably, DCs encompass a heterogeneous family of bone marrow-derived cells recognized as the most specialized and potent antigen-presenting cells (APCs) of the immune system , 16. DCsThe present review aims to offer an overview including the most relevant contributions in the field of IPF to focus on the emerging evidence addressing the role of DCs in disease pathogenesis and clinical behavior and potentially in immune-targeted therapy development.DCs originate from bone marrow progenitors through hematopoiesis, a finely regulated development process that involves several cellular and molecular events. Recent studies have identified a common DC precursor, the human granulocyte-monocyte-DC progenitor (GMDP), which supports the development of all the three major human DC subtypes . The GMD+cDCs, that are CD1c+ or CD141+ cells, and CD11c- pDCs, including CD123+ or CD303+ cells. Conventional DCs, previously termed type-1 (CD1c+) and type-2 (CD141+) myeloid DCs (mDCs), have recently reclassified as cDC2 and cDC1, respectively . Evidence shows that mo-DCs arise from monocytes recruited to the inflammatory site and express CD11c, CD1c, CD1a, Fccontexts . A synopMature DCs display phenotypic and functional profiles distinct from their naïve (immature) counterparts. Immature DCs express low levels of major histocompatibility complex (MHC) and co-stimulatory molecules and are usually found in peripheral tissues where they play as sentinels for immune monitoring. These cells can endocytose and process antigens but are poorly effective in generating peptide-MHC complexes to ensure optimal antigen presentation and efficient T-cell activation –45. TissDue to their anatomy and function, the lungs are vital organs constantly exposed to the external environment. Consequently, inhaled particles of different nature and origin and potential pathogens need to be efficiently counteracted by a finely adjusted immune response to preserve lung health . The act+, cDC1 CD141+, and mo-DCs  and obstructive sleep apnea –68. We h+ DCs, reducing lung fibrosis in wild-type (WT) mice exposed to AdTGF-beta1 (In the light of the above evidence, DCs appear to play a role in the fibrotic process and, more specifically, in IPF pathogenesis. IPF notably shares many similarities with lung cancer, ranging from genetics to clinical behavior . It is aGF-beta1 .IPF remains, for the most part, an unexplored field due to the non-recognition of the trigger cause. Perturbations of the lung microbiome and viral infections have been hypothesized to have a potential link with the development of IPF –86. TherInterpreting the involvement of the immune response in the pathogenesis of IPF has become a prosperous field of investigation only in recent years. New reports reveal expanding potential pathogenic roles for DCs in lung fibrosis. These findings promise to open new scenarios to understand better the cause and the biological mechanisms underlying the disease. Further efforts and challenges will be to evaluate their potential in terms of easy to perform biomarkers predictive of clinical behavior and targets of immune-based treatments. In analogy with cancer, combination therapy strategies with anti-fibrotic drugs could optimistically represent a milestone shortly.All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Lipid domains less than 200 nm in size may form a scaffold, enabling the concerted function of plasma membrane proteins. The size-regulating mechanism is under debate. We tested the hypotheses that large values of spontaneous monolayer curvature are incompatible with micrometer-sized domains. Here, we used the transition of photoswitchable lipids from their cylindrical conformation to a conical conformation to increase the negative curvature of a bilayer-forming lipid mixture. In contrast to the hypothesis, pre-existing micrometer-sized domains did not dissipate in our planar bilayers, as indicated by fluorescence images and domain mobility measurements. Elasticity theory supports the observation by predicting the zero free energy gain for splitting large domains into smaller ones. It also indicates an alternative size-determining mechanism: The cone-shaped photolipids reduce the line tension associated with lipid deformations at the phase boundary and thus slow down the kinetics of domain fusion. The competing influence of two approaching domains on the deformation of the intervening lipids is responsible for the kinetic fusion trap. Our experiments indicate that the resulting local energy barrier may restrict the domain size in a dynamic system. Phase separation in biological membranes has significant functional implications. Liquid-ordered lipid domains (LODs) and liquid-disordered domains (LDDs) exhibit different permeabilities to small molecules and dissimilar mechanical properties ,2. Proteα, spontaneous monolayer curvature J0 is the primary determinant of domain size )0 values  of LODs


American Indians (AI) have the highest smoking rates and lowest quit rates of any racial/ethnic group in the U.S. Researchers and community members from the American Indian Health Research and Education Alliance (AIHREA) created and evaluated a culturally-tailored smoking cessation program, All Nations Breath of Life (ANBL) as a recruitment tool for smoking cessation programs among AI. To increase enrollment in ANBL, AI smokers were approached at cultural events and asked to attend a 30-minute educational session . Tele-video (30%) and in-person (9%) session participants were recruited into ANBL. Pre- and post-tests showed participants in both sessions demonstrated increased motivation and confidence to quit smoking but significant differences were present in both sessions (p < 0.0001). Results indicate that theoretically guided and culturally tailored education sessions are viable approaches to educate and recruit underserved populations into programs that promote smoking cessation. Commercial tobacco usage rates among American Indians (AI) are the highest when compared to all other racial and ethnic groups in the U.S. . AI haveTo better understand how to bolster AI participation into smoking cessation programs, we explored the utility of culturally tailored educational sessions as a recruitment tool. Theoretically guided and culturally tailored tobacco education sessions delivered by AI research staff provided participating AI communities with health information about the consequences of cigarette smoking. The team subsequently used the tobacco education sessions to recruit participants based on community engagement principles and Health Belief Model (HBM) constructs. Information gathered through this process provided a framework to influence and motivate individuals to quit smoking through a culturally appropriate and relevant program.From August 2011 to November 2012 American Indian Health Research Education Alliance (AIHREA) team members recruited AI smokers, 18 years of age or older N=276) for participation in tobacco education sessions (All Nations Breath of Life) [ for partRecruitment for the in-person education sessions was conducted on-site by AI team members through announcements at event venues, word of mouth, and personal invitation. These methods yielded 179 participants for 12 in-person sessions. Recruitment for the tele-video sessions was conducted in a similar fashion but incorporated the use of AI specific listservs and flyers and yielded 97 participants for the 12 tele-video sessions.An AI team member led the sessions (n=24) which lasted for 30 minutes. Prior to the start of each session, participants answered demographic and health-related questions, as well as a 12- question pre-test about their recreational smoking health knowledge. At the end of the session, participants took a 10-question post-test that mirrored the pre-test . ParticiThe education sessions were interactive; participants were encouraged to ask questions and to make comments throughout the presentation. The educational material was delivered orally, a format preferred by AI participants in previous research conducted by the team. Each presenter was given a detailed outline of information to cover and then follow during all sessions. Given that the sessions were interactive and participants could ask questions and make comments, there were some differences among the discussions; however, the educational material was presented uniformly across sessions.AI members of the research team conducted tobacco education sessions throughout regional AI community and cultural events. Throughout the sessions the team relayed information about health consequences of cigarette smoking, environmental tobacco smoke, and third hand smoke. The facts provided were specific and culturally tailored to AI populations and guided by components of the Health Belief Model . HBM conp < 0.01). Participants attending the in-person sessions had more significant partners/spouses who “currently smoke” . Participants in the in-person sessions smoked less days in the prior 30 days  and had been smoking for less years at their current rate  than participants in the tele-video sessions. Participants in the in-person sessions also had lower dependence on nicotine, measured by the Fagerstrom Test for Nicotine Dependence , but were more likely to use other forms of tobacco . Participants in both groups were similar across other variables (The average age of participants for the in-person session was 32 compared to 41 for tele-video sessions (ariables .(p < 0.01). Further, participants who attended either session demonstrated significant increased confidence to quit smoking (p=0.04 for in-person participants and p < 0.01 for tele-video participants). Knowledge scores of tobacco use and side effects of tobacco usage significantly increased from pre- to post-tests in both in-person (p < 0.01) and tele-video (p < 0.01) sessions.Pre- and post-test data for motivation and confidence to quit, and tobacco knowledge, were collected during both the in-person and tele-video sessions. Similar results were found in both arms. Participants who attended either session demonstrated an increased motivation to quit smoking; this increase was significant among in-person session participants p < 0.01), but not for motivation or confidence. Similarly, overall pre- and post-test score comparisons were significant, including motivation to quit smoking , confidence to quit smoking , and knowledge .Significant differences were found in total knowledge gains pre-test to post-test for in-person participants compared to tele-video participants  after attending one of the education sessions. More than 9 % of the participants who attended an in-person session and 30% of the participants who attended a tele-video session were recruited into ANBL, even though all participants entered the sessions not planning to enroll in a quit smoking program.Results suggest that education sessions theoretically guided by the HBM and community engagement principles were an effective way to inform AI smokers about smoking-related health outcomes and other tobacco facts specific to AI. This strategy also shows promise as a successful recruitment tool for AI programs similar to the All Nations Breath of Life (ANBL) smoking cessation program in the Midwest. While participants in both sessions experienced an increase in knowledge, the in-person sessions showed a greater impact on improved total knowledge. There was a significant increase in motivation and confidence to quit smoking for both sessions. Here, theoretically guided education sessions helped increase motivation and confidence to quit smoking among AI in this sample. This suggests that education sessions based on CBPR principles and guided by constructs of the HBM may be tools to use in recruitment and tailoring specific information. There is utility in applying theoretically driven health education that accounts for individual perceptions and beliefs. This approach may prove beneficial in demystifying interventions and health promotion programs targeted to minority, underserved and vulnerable populations. Here too, CBPR principles where members of AI were involved helped to strengthen recruitment efforts into smoking cessation programs.In this study, the team was able to compare in-person versus tele-video tobacco education sessions to determine which was more successful at 1) improving participants’ tobacco knowledge and motivation to quit smoking, and 2) enrolling participants into the ANBL culturally tailored smoking cessation program. Present findings indicate that age, smoking status of significant partner/spouse, number of children living with a participant, number of days cigarettes were smoked in the past 30 days, years smoked at current smoking rate, nicotine dependence, and recreational use of other tobacco products may be important correlates of whether an individual participated in an in-person or tele-video tobacco education session. It is also possible that recruitment methods or locations for each type of session determined the type of individual to participate in that type of session.Participants from both types of education sessions were recruited into the ANBL smoking cessation program . AI smokers historically have lower quit rates and are less likely to enroll in smoking cessation programs than members of other racial/ethnic groups. Fu and colleagues .Other studies have reported factors that influence the success of study recruitment and retention, such as building rapport  increasiThere were some limitations with the study that included sample selection process and managing recruitment of individuals into education sessions near the closing of the ANBL program. All participants included in the study self-identified as smokers however it is possible that not all participants were smokers. Participants also were recruited for the sessions (in-person vs. tele-video) by team members and were not randomized into sessions. In addition, the participants were recruited throughout the region and thus there may be variation in other AI communities where geographical, historical, and sociocultural characteristics differ. The sample did represent, however, a heterogeneous mix of tribes and localities  and team members conducted purposive recruitment among AI community members to achieve a representative number in both in-person and tele-video sessions. Finally, recruitment for the ANBL effectiveness trial ended before the completion of the planned education sessions. The team chose to finish the scheduled education sessions although recruitment for the ANBL program had ended and offered these individuals an opportunity to participate in future programs. Therefore, it is possible that additional participants could have been recruited into the program. Future studies are needed to further test tobacco education sessions as a recruitment tool and the impact on health behavior . GatheriA major strength of this study was the use of education sessions (both in-person and tele-video) in recruiting for a culturally tailored quit smoking program. This study explored the impact of education sessions on motivation, confidence, knowledge and the utility in recruiting AI into a smoking cessation program. Pinpointing these individual-belief factors among AI members increases the potential to identify motivators and facilitators for smoking cessation behavior and also cues to action. These factors also may help health promoters to innovatively shape education via recruitment processes that lead to increased beneficial health promotion programming among AI populations.


Routine scalp EEG is essential in the clinical diagnosis and management of epilepsy. However, a normal scalp EEG  recorded from a patient with epilepsy can cause delays in diagnosis and clinical care delivery. Here, we investigated whether normal EEGs might contain subtle electrophysiological clues of epilepsy. Specifically, we investigated (i) whether there are indicators of abnormal brain electrophysiology in normal EEGs of epilepsy patients, and (ii) whether such abnormalities are modulated by the side of the brain generating seizures in focal epilepsy. We analysed awake scalp EEG recordings of age-matched groups of 144 healthy individuals and 48 individuals with drug-resistant focal epilepsy who had normal scalp EEGs. After preprocessing, using a bipolar montage of eight channels, we extracted the fraction of spectral power in the alpha band (8–13 Hz) relative to a wide band of 0.5–40 Hz within 10-s windows. We analysed the extracted features for (i) the extent to which people with drug-resistant focal epilepsy differed from healthy subjects, and (ii) whether differences within the drug-resistant focal epilepsy patients were related to the hemisphere generating seizures. We then used those differences to classify whether an EEG is likely to have been recorded from a person with drug-resistant focal epilepsy, and if so, the epileptogenic hemisphere. Furthermore, we tested the significance of these differences while controlling for confounders, such as acquisition system, age and medications. We found that the fraction of alpha power is generally reduced (i) in drug-resistant focal epilepsy compared to healthy controls, and (ii) in right-handed drug-resistant focal epilepsy subjects with left hemispheric seizures compared to those with right hemispheric seizures, and that the differences are most prominent in the frontal and temporal regions. The fraction of alpha power yielded area under curve values of 0.83 in distinguishing drug-resistant focal epilepsy from healthy and 0.77 in identifying the epileptic hemisphere in drug-resistant focal epilepsy patients. Furthermore, our results suggest that the differences in alpha power are greater when compared with differences attributable to acquisition system differences, age and medications. Our findings support that EEG-based measures of normal brain function, such as the normalized spectral power of alpha activity, may help identify patients with epilepsy even when an EEG does not contain any epileptiform activity, recorded seizures or other abnormalities. Although alpha abnormalities are unlikely to be disease-specific, we propose that such abnormalities may provide a higher pre-test probability for epilepsy when an individual being screened for epilepsy has a normal EEG on visual assessment. Varatharajah et al. report that subtle abnormalities in EEG measures such as the spectral power of alpha activity may help identify (i) patients with epilepsy, and (ii) the seizure generating hemisphere in focal epilepsy, even when a routine scalp EEG is free of any epileptiform activity or other visible abnormalities.  Additionally, there is evidence that people with epilepsy who have seizures originating from their dominant hemisphere can experience relatively more disruptions in their normal brain function compared to those with seizure foci in their non-dominant hemispheres.Our primary analyses utilized the scalp EEG data of 144 healthy individuals age-matched to 48 patients with DRFE. We also utilized the normal scalp EEG data of 104 patients with psychogenic non-epileptic seizures (PNES) to analyse the effect of different EEG acquisition systems on our findings. We obtained the data on healthy individuals from the publicly available LEMON datasetyoung and old groups with age ranges 20–35 and 59–77, respectively. The EEGs were recorded using the BrainAmp MR plus recording system with the ActiCAP electrodes , including 62 channels according to the standard 10–10 localization system.The LEMON dataset consisted of scalp EEG recordings from 203 healthy individuals .We obtained scalp-EEG recordings from 48 individuals with DRFE  and 104 individuals with PNES  that were performed as part of their clinical evaluations. Our study was approved by the Mayo Clinic Institutional Review Board, and patients provided informed consent. The EEGs were recorded using the XLTEK EMU40EX headbox  with the WaveGuard Original EEG cap  including 31 channels according to the extended 10–20 localization systemOur first analysis focussed on differences between epilepsy patients and healthy individuals. We utilized the data of all 144 healthy individuals from the LEMON study and 48 Mayo patients for this analysis. Our second analysis focussed on differences based on the seizure-generating hemisphere of the brain in epilepsy patients. In addition to the EEG features, we utilized handedness (right or left) to determine the dominant hemisphere. Of the 48 epilepsy patients, 43 were right-handed, and 5 were left-handed. Furthermore, although there is evidence that the dominant hemisphere of right-handed individuals is generally deterministic (i.e. the left side), the dominant hemisphere of left-handed individuals is non-deterministic (can be the left or the right side).,We compared the effect of age against the group differences between healthy and DRFE individuals. We divided the healthy population into young  and old  populations and compared them with the DRFE patients. To analyse whether the group differences between healthy and DRFE individuals were distinct from acquisition system differences, we performed a three-way comparison between all healthy, PNES and DRFE individuals. Here, the EEGs of PNES and DRFE patients were recorded using the same acquisition system. Because both the DRFE and PNES populations were heterogeneous with respect to medications, as an extension of the previous comparison, we performed a second comparison by limiting the two patient populations to those taking ASMs only (27 PNES patients and 43 DRFE patients). We then compared the effects of ASMs in three ways: (i) differences between DRFE patients who were not taking any ASMs (5 patients) compared to those who did (43 patients); (ii) differences between PNES patients who were not taking any ASMs (44 patients) compared to those who did (27 patients); and (iii) differences between DRFE patients who were taking either levetiracetam (25 patients) or lamotrigine (19 patients), the most common ASMs in this study. Note that we did not include any patients who were receiving antipsychotics or selective serotonin reuptake inhibitor drugs and patients with other significant comorbidities in the aforementioned groups comparing the effects of ASMs. See Further preprocessing was done in EEGLAB for MATLAB.R2 > 0.90). More details on the tool’s implementation can be found in the original manuscript.Eye blinks were detected and counted using an automated tool available as part of the EEGLAB software.Because the EEG data of healthy individuals were recorded using a 62-channel 10–10 system, we selected a subset of channels that matched the 10–20 system used to record the EEG data from epilepsy patients. Within the EEG data of selected channels from the healthy and patient populations, we selected four bipolar pairs of electrodes from each hemisphere, producing eight channels of EEG data for each participant. z-scored) each channel separately within each segment and divided each segment into 10-s non-overlapping windows  of the log-transformed features in the healthy population to characterize normal brain function. An example of this is pictorially illustrated in To combine the values of all 16 features in an EC window, we assume that the individual estimations can be independently combined. By applying the independence assumption, we now derive a combined probability-of-normality, as shown below.Note that we use the geometric mean of the product of the individual feature-based estimates as the combined estimate because the multiplication of 16 fractional numbers will produce a very small probability value. Furthermore, we calculated normal, we used a maximum likelihood estimation based on the window-level probability-of-normality values. We model the window-level estimates of a participant To estimate whether a participant’s EEG is In order to characterize the group differences based on the CDFs, we utilized the Wasserstein distance (or earth mover’s distance) metric.We performed the following operations to generate boxplots. First, we computed the CDFs of the 16 features  using the data of all 144 healthy individuals. Then, we calculated the window-level probability-of-normality estimates for the groups of individuals we were interested in comparing, using the computed CDFs (see previous sections for more information). Those values were then divided among the comparison groups to generate the boxplots. For reference, we also plot the probability-of-normality estimates for the same healthy individuals whose CDFs are used to generate those estimates.In order to test the statistical significance of the differences between the two distributions, we used the Wilcoxon rank-sum test.Headplots illustrating the spatial distributions of features of interest were plotted using the headplot function in the EEGLAB toolbox.We first plotted receiver operating characteristic (ROC) curves and calculated the area under the ROC curve (AUC) to compare model performances. An optimal threshold on the ROC curve was selected using the convex hull method.https://gitlab.engr.illinois.edu/varatha2/epilepsy_alpha_rhythm.git .The data of healthy controls is already publicly available. The deidentified spectral features of healthy individuals and patients and the software used to perform statistical analyses are available at We used the frequency domain features based on the alpha rhythm power to characterize normal brain function. P < 0.05 based on the Wilcoxon rank-sum test).The primary goal of this study is to understand whether there are subtle abnormalities in the visually classified normal EEGs of DRFE patients. To study this, we analysed how the distributions of the log-transformed alpha-power fractions in the healthy and DRFE individuals differed using (i) their CDFs and (ii) the probability-of-normality values. P < 0.05, Wilcoxon rank-sum test).To understand the contribution of the seizure focus to disruptions in brain activity, we analysed how the distributions of the log-transformed alpha-power fractions within the right-handed DRFE patients differed based on the hemisphere generating seizures. As described previously, the left side is generally the dominant hemisphere in right-handed DRFE patients. Next, to evaluate the potential clinical usefulness of alpha activity abnormalities and lateralize the hemisphere of the seizure focus, we performed two classification experiments: (i) classifying healthy and DRFE individuals, and (ii) classifying the seizure-generating side of the brain, both using the classification framework described previously. Finally, we sought to distinguish the contribution of chronic DRE in the observed spectral changes of the alpha rhythm from the contributions of age and ASMs. In these evaluations, we calculated probability-of-normality estimates using the normalized alpha power features extracted from all brain regions and both low and high alpha frequency bands.Effects ofageingversusDRFE: In this analysis, we used the alpha features of healthy-young individuals  for characterizing normal brain function based on CDFs. Then, using those CDFs, we computed the window-level probability-of-normality estimates (as described in Methods) for healthy-old individuals  and DRFE patients (N = 48). P < 0.01). We also observed that the mean window-level probability-of-normality of DRFE patients was significantly lower than the means of the other two groups.Effects of acquisition system differences versusDRFE: In this analysis, we performed a three-way comparison between the healthy (N = 144), PNES (N = 104) and DRFE (N = 48) populations. Note that the EEGs of PNES and DRFE patients were recorded using the same acquisition system . P < 0.01). Interestingly, we also found a difference between PNES and normal controls (P < 0.01). We also observed that the mean window-level probability-of-normality of PNES patients was significantly lower than those of healthy individuals, and the mean probability-of-normality of DRFE patients was significantly lower than the means of both healthy and PNES groups.N = 27) and DRFE (N = 43) populations. Noteworthy is that these two populations share similar characteristics with respect to acquisition system, ASMs, and age range . P < 0.01) when all confounders are controlled for.We performed another comparison to further homogenize the PNES and DRFE populations. We selected subgroups who were taking ASMs within PNES  for characterizing normal brain function. Then, using those CDFs, we computed the window-level probability-of-normality values for two groups of DRFE patients: patients not taking any ASMs (N = 5) and patients taking ASMs (N = 43). P < 0.05). However, the mean probability-of-normality of DRFE individuals was still lower than that of the healthy individuals, regardless of whether or not they took ASMs. Furthermore, the variance of the probability-of-normality values of the DRFE patients not taking ASMs was notably large because of the small sample size (N = 5).Effects of ASMs in PNES: We performed a similar comparison within the PNES population to address the sample size limitations. We identified 44 PNES patients who were not taking any ASMs and 27 taking ASMs and computed the window-level probability-of-normality values with respect to the healthy population. The results of multiple comparisons between those groups are illustrated in P = 0.19).Comparison between major ASMs: In this analysis, we divided the DRFE population based on the consumption of two ASMs: levetiracetam (N = 25) or lamotrigine (N = 19). We computed the window-level probability-of-normality values for those two groups of DRFE patients based on the CDFs obtained from the healthy population. P = 0.25).Comparison between arousal states: We utilized the rate of eye blinks as a surrogate measure for drowsiness and compared the number of eye blinks within a minute between healthy and DRFE populations. R2 > 0.90) was used to identify robust eye blinks. We found that the two populations were not significantly different with respect to the rate of eye blinks (P = 0.89). A comparison between all potential eye blinks is provided in P = 0.23).In this study, we investigated whether visually classified normal EEGs of patients with epilepsy contain subtle abnormalities that may have diagnostic and clinical value. We conducted this retrospective study using the scalp EEG recordings of 48 patients with DRFE that were visually classified as normal EEG by expert review and the scalp EEGs of 144 age-matched healthy individuals. We extracted alpha power-related measures from eyes-closed (EC), awake segments in the EEGs of the healthy population and used them to represent normal brain activity. We then analysed how the same alpha power-related measures in the DRFE population differed from the healthy controls. Our analyses indicated that (i) alpha power is significantly reduced in DRFE compared to healthy controls and (ii) alpha power of right-handed DRFE patients with left hemispheric seizures is significantly lower compared to those with right hemispheric seizures. We also utilized these findings in a classification framework to classify (i) whether an EEG was recorded from an epilepsy patient and (ii) if so, the seizure generating side of the patient's brain by using EEG recordings that do not contain any epileptiform activity. A 10-fold cross-validation approach achieved mean AUC values of 0.83 and 0.77 for the respective classification tasks . These findings suggest that EEG measures representing normal brain function may be useful in the diagnosis of epilepsy even when the EEG is free of epileptiform activity. This finding is significant because the ability to diagnose epilepsy at the earliest possible time can prevent significant delays in treatment and can support more efficient triage of patients to costly in-hospital monitoring studies. In that context, our study presents a promising research direction in the treatment of epilepsy.We note that several previous studies have reported alpha spectral abnormalities in epilepsy and suggested the potential diagnostic value in routine EEG studies.Our results showed significant abnormalities in the normalized spectral power of the alpha rhythm in the frontal and temporal regions of DRFE patients . This obOur findings also agree with previously described slowing of the alpha rhythm observed in the presence of neurological diseases, including epilepsy.EEG spectral abnormalities related to neurological diseases are typically confounded by age-related changes and changes induced by certain medications.,Noteworthy is that all variables including, acquisition system/environment, age and ASMs, were similar in the second experiment between PNES and DRFE patients . A signiWe developed a probabilistic approach to characterize normal brain function using EEG features based on the alpha rhythm extracted from eight bipolar channels. This involves computing the CDFs of log-transformed alpha power features in the individual channels of 10-s EEG windows and using those CDFs to assign a window-level probability-of-normality estimate to new windows. We combined the individual channel-based values to obtain a combined probability-of-normality estimate for each window. Furthermore, we applied a maximum likelihood approach to aggregate the window-level probability-of-normality values of a participant's entire EEG recording to obtain a probability-of-normality estimate for the participant. These values were then used for classification purposes. Our approach can be considered as an anomaly detection technique wherein we estimate whether a new sample is anomalous compared to the reference population. An advantage of this approach is that it can be developed using the data of the reference population alone without requiring data from anomalous samples. Furthermore, our approach presents a general paradigm for EEG-based anomaly detection, which can be beneficial for other EEG applications such as seizure forecasting.Our participants were limited to healthy individuals and DRFE and PNES patients. However, a normal EEG could be recorded from a patient with any number of other neurologic or psychiatric diseases. As such, although our study establishes the feasibility of using alpha spectral features in aiding the clinical diagnosis of epilepsy,Another limitation of our study is that the EEGs of two populations, healthy and DRFE, were acquired using different systems under different conditions. To address this limitation as best we could, (i) we undertook the same preprocessing steps for both the EEG datasets, and (ii) we used the fraction of alpha power within the wideband of 0.5–40 Hz to mask any subject-specific differences in total signal power. In addition, we also demonstrated using a population of PNES patients that the differences between healthy and DRFE populations are not entirely due to acquisition system/environment differences. However, EEGs of both controls and patients recorded using the same acquisition system are necessary to confirm our results without this confounder. Our future efforts will investigate this possibility.Finally, there is substantial evidence in the literature suggesting the value of EEG functional connectivity measures in increasing the diagnostic value of normal EEG segments.Upon further validation using population-level datasets and sufficient automation of the proposed approach, we envision that a clinical decision support tool will be developed and deployed for prospective evaluation. This tool will provide probability-of-normality estimates at a patient level to guide decisions regarding costly inpatient EEG studies in the absence of known epileptiform abnormalities. This could prove particularly useful in obtaining payer authorizations for prolonged EEG studies in the case of normal routine EEGs. Furthermore, this tool can also help in the lateralization of focal epilepsy and can potentially impact presurgical evaluations. Although there is a possibility of misclassification, the risk associated with such errors is minimal because prolonged EEG studies can only improve the diagnostic yield. As such, we believe that the proposed approach and the findings have the potential to directly impact clinical practice and therefore warrant further investigation.EEG-based diagnosis of epilepsy, which is the gold-standard approach, relies on visual identification of epileptiform activity. However, epileptiform activity may not be recorded in a short EEG recording session, and that can cause delays in the delivery of clinical care. Unfortunately, such scenarios are common in the clinic; approximately 50% of the EEGs recorded from patients with seizures are deemed normal based on expert visual review. In this study, we investigated the possibilities of diagnosing DRFE and lateralizing seizure focus based on normal EEGs using a semi-automated approach. Our results support the hypothesis that EEG-based measures of normal brain function, based on the alpha rhythm, can help diagnose DRFE and lateralize seizure focus when an EEG does not contain any epileptiform activity, recorded seizures, or other non-specific abnormalities. Based on these findings, we further hypothesize that such findings in a normal EEG can suggest a higher pre-test probability for epilepsy when an individual is screened for epilepsy for the first time. In addition, our findings also suggest that automated analyses of scalp EEG can help in developing scalable and cost-effective approaches for advancing the current state of clinical electrophysiology. However, prospective studies and addressing the identified limitations of our work are necessary to fully understand the clinical value of these hypotheses. Going forward, our efforts will focus on expanding the study to population-level datasets, including other biomarkers of normal brain function, developing fully automated methods, and addressing systemic biases introduced by EEG acquisition systems.Brain Communications online.fcab102_Supplementary_DataClick here for additional data file.


Background: To investigate whether serial morphometric measurements of the brainstem using high resolution trans-foramen-magnum ultrasound (US) in premature neonates correlate with neurological outcomes.Methods: Serial brain ultrasound scans were performed in 36 consecutive preterm infants born at <34 weeks of gestation from birth until term-equivalent age. Two-dimensional brainstem measurements of the pons and medulla oblongata were compared with those in a cohort of 67 healthy full-term newborns. Neurologic assessment of the premature infants was assessed at 5 years of age.Results: Of the 36 preterm infants born between 25 and 34 weeks of gestation, eight had significantly delayed growth profiles in both the pons and medulla and developed neurological sequelae by 5 years of age.Conclusions: Morphometric measurements of the developing brainstem using high resolution trans-foramen-magnum ultrasound (US) may help predict neurological outcome in high-risk neonates, particularly in those who are born extremely premature. The incidence of severe neurologic and developmental disabilities is high in children who were born as extremely preterm infants . StudiesStudies have shown that better visualization of the neonatal midbrain and posterior fossa can be achieved when sonography is performed through alternative acoustic windows such as the posterior fontanelle, mastoid fontanelle, the temporal suture and the cisterna magnum –7.Sonographic measurements of the cerebellum including transverse cerebellar diameter, the central vermian area, anterior-posterior distance and superior-inferior distance of the vermis have been assessed in preterm and term neonates with or without growth restriction –11. FurtIn this observational cohort study, total of 67 healthy full-term neonates  were prospectively enrolled to determinate the morphometric reference values of the pons and medulla oblongata. In addition, we enrolled 36 premature neonates  to investigate whether the developmental curve of these regions could predict neurologic outcome. The 36 premature neonates were divided into two groups, namely a group of subjects with normal neurological development and a group of subjects with abnormal neurological consequences (five boys and three girls). Gestational age was determined according to the date of the mother's last menstrual period and/or the date on which sonography showed definitive pregnancy. All neonates were recruited with appropriate for gestational age and without small for gestational age nor fetal growth restriction. Apgar score are normal (≥7) at 1 and 5 min in term neonates after delivery and normal at 5 min in premature neonates. In addition, premature neonates who needed resuscitation (NRP) immediately after birth are excluded from this study. Infants were excluded from enrollment if there was clinical evidence of congenital infection or congenital malformation or if there was sonographic evidence of severe intraventricular hemorrhage (IVH) (grade III–IV). Infants were studied only after voluntary informed consent was obtained from the parents or guardians. The design of this study was approved by the institutional review board of Tri-Service General Hospital (IRB/REC-41948).Cranial sonography was performed within 48 h after birth in normal full-term neonates and within the first 3 days of life in stable premature babies. Repeated scans were performed in premature infants once/2 weeks until discharge. All studies were performed with a high-resolution electronically focused real-time system  using a 7.5-MHz linear transducer. All scans were obtained in a midline sagittal view focused on the brainstem region through either the anterior fontanelle (AF) or foramen magnum (FM) as viewing acoustic windows. For performing trans-FM sonography, newborn was put in supine position with head gently bent forward similar to positioning lumbar puncture. We demonstrate the landmark of diagrammatic sonography via convex transducer in The anterior-posterior (AP) diameters of the midpons (including the diameter of basis part and tegmentum part) and the oliver-shape area pons were measured in the mid-sagittal plane through either AF or FM acoustic windows  and determined at 5 years of age in all premature infants by pediatric neurologists.P ≤ 0.05.Data were analyzed with SigmaPlot 11 statistical software . Calculations of average values were made for each item including both standard references (data collected either from of AF or from of FM) in healthy term children and the kinetic results of the developmental curves in premature babies with or without neurologic sequelae. Statistical results were significant at a level of n = 2) or severe infection with bacteremia and meningitis during the period of investigation (n = 1). Therefore, 67 healthy full-term neonates  were studied to determinate the morphometric reference values of the pons and medulla oblongata. Besides, 36 premature neonates    were stu6 girls) . At foll6 girls) . Among t2 and 139.7 ± 18.4 mm2, respectively  via the FM approach . We obseThe structures in the posterior cranial fossa in neonates and infants can be visualized ultrasonographically via the anterior fontanelle; however, posterior fontanelle ultrasound allows for better comprehensive delineation of occipital lobe structure . PreviouPosterior fontanelle imaging demonstrates subtle differences in echogenicity between clots and the choroid glomus and can depict clots extending into the occipital and temporal horns. This scanning method also allows for visualization of subarachnoid blood and clots obstructing the outlet of the fourth ventricle . MastoidTo the best of our knowledge, however, few studies have investigated the potential effects of volume of brain regions on neurobehavioral outcome in children who were born extremely premature. Nevertheless, some limitations are emerged in this study. Restrictedly, we enrolled premature cases with normal Apgar scare at 5 min without resuscitation after birth. Due to few cases of extreme prematurity are enrolled, we cannot confirm the correlation of Apgar score and abnormal neurologic outcome of premature group in this study. In addition, we cannot detect apparent vision of medulla oblongata via traditional AF window to evaluate the exact size of the width of medulla oblongata as comparisons of detection via FM window. For ergonomic limitation, the linear scan transducer is not easily to handle when perform sonography through FM window in extreme premature babies younger than 27 corresponding gestational ages. Moreover, cases born at 25 GA are under high risk of hypothermia and/or desaturation during this procedure via FM window, leading us to collect data since the corresponding GA of 27 weeks. Due to limited case numbers of premature groups were enrolled leading to difficulty of statistical analysis. Besides, for the data merged at corresponding GA, we only showed the growth tendency but not the individual growth curve of each case precisely. As well, we did not report comprehensively for Apgar score, classification of weight in comparisons with biometric measurements of brainstem and neurologic development. Thus, a well-designed and extend prospective study should be conducted to improve these restrictions, to provide more informations.In summary, in this study, we observed a rapid growth curve beginning at 31 weeks of GA in well-developed preterm newborns but only a steady slow growth curve in these measurements of the pons in preterm infants who subsequently developed neurologic problems . Our finThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Institutional Review Board, Tri-service General Hospital. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.S-JC performed ultrasonography, data collection, data analysis, and writing of the manuscript. C-FH and C-HT follow up the cases and clinical evaluation a part and assist the collection of data. C-YC designed the study, defined the landmark of brainstem ultrasonography, and guide ultrasonography as well as instructed performing ultrasonography. All authors contributed to the article and approved the submitted version.This work was supported by grants from the MOST-109-2314B-016-023, TSGH-C01-109014, 801GB110034, CTH-101-2C01, and TSGH-C-110031 to S-JC.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Arabidopsis seedlings, but also the glabra 1 mutant, which has a different composition of the cuticle. Both lines had GFP-labeled microtubules (MTs), allowing us to observe their arrangement. To quantify MT dynamics, we developed a new microscopic method based on the FRAP technique. The number and growth rate of MTs decreased significantly after AgNPs, similarly in both lines. However, the layer above the plasma membrane thickened significantly in wild-type plants. The levels of three major stress phytohormone derivatives—jasmonic, abscisic, and salicylic acids—after AgNP (with concomitant Ag+) treatment increased significantly (particularly in mutant plants) and to some extent resembled the plant response after mechanical stress. The profile of phytohormones helped us to estimate the mechanism of response to AgNPs and also to understand the broader physiological context of the observed changes in MT structure and dynamics.The superior properties of silver nanoparticles (AgNPs) has resulted in their broad utilization worldwide, but also the risk of irreversible environment infestation. The plant cuticle and cell wall can trap a large part of the nanoparticles and thus protect the internal cell structures, where the cytoskeleton, for example, reacts very quickly to the threat, and defense signaling is subsequently triggered. We therefore used not only wild-type  Nanotechnology has been one of the most rapidly advancing fields of human activity in the past decade. The unsurpassed physical and chemical properties of nanoparticles (NPs) and their relatively low cost have promoted their infiltration into each area of human life. The high stability of, e.g., metal NPs has resulted in their low biodegradability and potential high risk for ecosystems in the future. Some reports have described the benefits of NPs for plants. Engineered nanovectors can deliver bioactive compounds encapsulated in biopolymers inside cells in the plant body, for example . Carbon,+)  JA, [2H6] ABA, and 20 pmol of [2H4] SA  per sample. The extracts were purified using Oasis HLB columns  and then evaporated to dryness under a stream of nitrogen. Jasmonate levels were quantified by ultra-high performance liquid chromatography-electrospray tandem mass spectrometry  using stable isotope-labelled internal standards as a reference.Endogenous levels of jasmonates  were determined in 10 mg (around 50 seedlings) of plant material according to the method described by Flokova et al. . All expStatistical significance was tested via one-way or multifactorial ANOVA followed by Tukey’s post-hoc HSD test. When normality and/or homoskedasticity (the Shapiro-Wilks’s test) were violated , log-transformation highly improved the distribution (Gaussianity) and those log-transformed data were tested.MT dynamics data were processed and plots created using the RStudio development environment for the R programming language and open-source packages .


South Eastern Bantu-speaking (SEB) groups constitute more than 80% of the population in South Africa. Despite clear linguistic and geographic diversity, the genetic differences between these groups have not been systematically investigated. Based on genome-wide data of over 5000 individuals, representing eight major SEB groups, we provide strong evidence for fine-scale population structure that broadly aligns with geographic distribution and is also congruent with linguistic phylogeny . Although differential Khoe-San admixture plays a key role, the structure persists after Khoe-San ancestry-masking. The timing of admixture, levels of sex-biased gene flow and population size dynamics also highlight differences in the demographic histories of individual groups. The comparisons with five Iron Age farmer genomes further support genetic continuity over ~400 years in certain regions of the country. Simulated trait genome-wide association studies further show that the observed population structure could have major implications for biomedical genomics research in South Africa. Despite linguistic and geographic diversity in South Eastern Bantu-speaking (SEB) groups of South Africa, genetic variation in these groups has not been investigated in depth. Here, the authors analyse genome-wide data from 5056 individuals, providing insights into demographic history across SEB groups. Three sets of migration events have dramatically reshaped the genetic landscape of this geographic region in the last two millennia. The first of these was a relatively small scale migration of East African pastoralists, who introduced pastoralism to Southern Africa ~2 kya7. This population was subsequently assimilated by local Southern African San hunter-gatherer groups, forming a new population that was ancestral to the Khoekhoe herder populations12. Today, Southern African Khoe and San populations collectively refer to hunter-gatherer (San) and herder (Khoekhoe) communities. While Khoe-San groups are distributed over a large geographic area today , these groups are scattered, small, and marginalised14.The sarchaeological record and rock art evidence trace the presence of San-like hunter-gatherer culture in Southern Africa to at least 20–40 thousand years ago (kya)17. The earliest communities spread along the East coast to reach the KwaZulu-Natal South coast by the mid-fifth century AD while the final major episode of settlement is estimated to be around AD 135019. These archaeologically distinct groups gradually spread across present-day South Africa, interacting to various degrees with the Khoe-San groups, eventually giving rise to South Africa’s diverse BS communities. The third major movement into Southern Africa was during the colonial era in the last four centuries when European colonists settled the area. During this period slave trade introduced additional intercontinental gene flow giving rise to complex genomic admixture patterns in current-day Southern African populations23.The introduction of pastoralism in the region was closely followed by the arrival of the second set of migrants i.e., the Bantu-speaking (BS) agro-pastoralists. The archaeological record suggests that ancestors of the current-day BS populations undertook different waves of migration instead of a single large-scale movement27. A new lexicon-based linguistic phylogeny included in this study  groups has not been systematically investigated. One of the very early studies based on the Y-chromosome and a few autosomal markers, which included almost all the main SEB groups and covered most of the provinces from South Africa, indicated the possibility of genetic structure within the SEB populations29. However, many subsequent studies using genome-wide datasets did not investigate genetic differentiation or population structure within SEB groups, which consequently led to its consideration as a group without clear internal substructure30. Moreover, studies including multiple SEB groups were often limited in terms of sample size or SEB group diversity32.South Africa has 11 official languages of which nine are Bantu languages belonging to this family’s South-Eastern branch. Within these nine languages two large subclusters are traditionally distinguished: Nguni  and Sotho-Tswana . Venda and Tsonga tend to be seen as independent linguistic entitiesHere we describe a systematic study of genetic diversity of South African SEB groups based on an analysis of 5056 individuals (AWI-Gen study) genotyped on the Illumina H3A-genotyping array (~2.3 M SNPs). Although the eight SEB groups have very specific geographic distributions of linguistic majority areas (LMAs) within the country, for our study they were sampled at three sites; Soweto (SWT) in Gauteng, Agincourt (AGT) in Mpumalanga, and Dikgale (DKG) in Limpopo province Table .Table 1DThis study reports a fine-scale population structure among SEB groups that parallels both linguistic affinities and geographic distribution in the country. Simulated association studies demonstrate that the fine-scale structure has a potential to influence genetic association in cohorts that include multiple SEB groups and/or SEB groups from multiple study sites within South Africa.The principal component analysis (PCA) of 4319 unrelated SEB participants (AWI-S2 dataset) reflects the linguistic phylogeny with partial separation of Tsonga, Sotho-Tswana  and Nguni (Zulu and Xhosa) speakers Fig.  suggesti33. These recent admixtures could result in incomplete boundaries observed between the SEB groups in the PC plot. In order to minimize the effect of such recent admixture on population structure detection, we analysed 2702 SEB participants   and geographical distances between the SEB groups also show a moderate correlation . Procrustes transformation analysis further highlights the correlation between PC and geography   Fig.  and lingST) Fig.  of the S32, we estimated the level of Khoe-San ancestry proportions in each SEB group (based on the Merged dataset 2-EC downsized) using an unsupervised clustering approach37. ADMIXTURE analysis at K = 3 highlights the separation of BS, Khoe-San and Eurasian ancestry   Fig. . The varies Fig. . To inveies Fig. .Fig. 2GeADMIXTURE analysis on the full set of unrelated samples . Ancestry-specific PCA after masking haploid genomes shows that the core differences within SEB groups, although reduced, persist even after accounting for differential Khoe-San gene flow Fig. . The obs38 . Moreover, for all SEB groups, a single admixture event model was detected to be the best-guess conclusion by fastGLOBETROTTER  onto the genetic variation of present-day SEB individuals  of each SEB group over the last 100 generations by analysing the sharing patterns of identity-by-descent (IBD) segments using IBDNe50. As depicted in Fig. Ne for all the SEB groups was very similar for the 100th to the 40th generations before present. It needs to be noted that most of the present-day SEB groups did not exist, as such, more than 50 generations ago and the older estimates here correspond to possible ancestral populations of these groups. The period of around 40 generations ago also corresponds to the estimated time scale for the oldest Khoe-San admixture dates  when Nguni-speakers first began to move North-west into the interior, becoming the first BS in South Africa to occupy grasslands51. The comparison of Sotho and Zulu Ne profiles between our study and samples from a previous study32 shows a high concordance, demonstrating an overall robustness in these estimates  to the edi Fig. . MoreoveAPOL1 variant rs73885319 among Sotho and Xhosa . Similarly, alleles in genes such as HERC2 (associated to skin colour), PCSK9 and SLC22A1 (associated to lipid level phenotype) also showed three-fold or higher allele frequency differences between SEB groups  between the SEB groups. The comparison Fig.  shows alups Fig. . A detaiPopulation structure accompanied by high allele frequency differences could have major implications for genome-wide association studies (GWAS). Therefore, to assess the extent to which the observed structure could bias association results, we conducted four categories of simulated traits GWASs (binary trait) using study sites  and/or ethno-linguistic labels as ‘trait-proxies’ (see Methods). Category 1 was aimed at stimulating a scenario where cases and controls are sampled from different study sites. The Fig. P-value threshold of 5 × 10−8. While the genomic inflation normalizes with homogenization of the dataset, GWASs for category 3 and to a lesser extent category 4 generated false positives in a few simulations . The addition of rare variants (MAF < 0.05) in a real GWAS, as well as increasing this dataset size by imputation, as is commonly performed in GWASs, could further increase false positives. Many of the signals from the simulated-trait GWASs have been previously reported as trait/disease genetic associations in the GWAS catalogSYT1, PAH, CAPN2, and SLC8A3 genes) to reach outlier threshold in some of the SEB groups but not in others, suggesting that the fine-scale structure can also influence evolutionary analyses. A population branch statistics (PBS) approach further detected SNPs showing high differentiation between Tswana and Tsonga  and Pedi (around ZBTB20 gene) also harboured selection outliers hinting at possible post-admixture selection scenarios , and also reflects the geographic distribution of LMAs to a large extent. The resolution of this structure within the SEB groups was enhanced considerably by taking ethno-linguistic concordance of individuals and their geographic locations into account. However, it needs to be noted that self-identity itself is complex, with about one third of the participants having more than one parent or grand-parent with a different ethnic self-identity. Moreover, while the PCA and PCA-UMAP shows clear population structure, there are exceptions highlighting the fluidity of cultural identity. Thus, self-selected group-identity encompasses significant group-related genetic variability, and it is important to emphasise that cultural identity and genetic variation are not necessarily aligned. Studies on population structure in South Africa should not be seen as justifying the ethnic nationalism generated by the country’s colonial and apartheid past. Our aim was to explore the role of genetic diversity in explaining population history and in health research. We recognise, and our study shows, that self-identity can involve considerable fluidity and that biological reductionist approaches pose dangers for the interpretation of our findings.32, our data also shows that differential Khoe-San gene flow plays a major role in the population structure of SEB groups. However, the persistence of the structure even after accounting for differential Khoe-San admixture suggests the contribution of other demographic factors in the genetic differentiation of these groups. The SEB groups start to show clear divergence in population size dynamics from about 40 generations ago. This timeframe converges with the earliest dates of Khoe-San admixture and probably points at the initiation of migration events that gradually separated these groups. On the other hand, a rather wide variation in Khoe-San admixture dates (spanning ~20 generations) among SEB groups possibly reflects the complexity of the settlement of different parts of the country by the ancestral BS populations. Comparison of present-day SEB groups with Iron-Age farmer genomes provided evidence for genetic continuity in a geographic region in Central-East South Africa for at least the last 300–500 years. Our results, while attesting to the well-known pattern of Khoe-San female-biased gene flow, showed notable differences in the extent of this bias among different SEB groups demonstrating that the nature of interaction between Khoe-San and BS could have varied temporally and geographically.In alignment with results from previous studiesThe dataset we generated for this study has provided a much better contextualization for previously sequenced Iron-Age genomes from Southern Africa. The SEB are unique in Africa, as being among the very few populations that contain considerable gene flow from the Khoe-San. These data therefore are of major importance in terms of understanding the interaction between the Khoe-San and other Southern African populations. They will play an important role in providing insights through comparative analyses once more genetic data from hunter-gatherers and ancient genomes from this geographic region become available.Our analyses including allele frequency comparisons, genome-wide scans for selection and Khoe-San ancestry distribution show the SEB groups to be highly diverged at certain genomic regions. Based on simulated-trait GWAS, we further illustrate that the fine-scale population structure within the SEB groups could impact a GWAS by introducing a large number of false positives. A combination of cautious study design to minimize geographic and ethno-linguistic biases and stringent measures for population structure correction is therefore recommended for GWASs involving SEB groups. Moreover, while GWAS can address the false positives introduced due to population structure using genomic control, PC or other approaches, it is impossible to identify and control for population structure in candidate gene studies. Therefore, utmost care should be taken during study design to ethnically and geographically homogenise samples in order to control for false positives in association studies using limited markers.A major limitation of our study is that the sampling sites do not cover the full geographic spread of SEB groups in the country, possibly causing some of the groups to be suboptimally represented in our dataset. Nevertheless, our results suggest that we are at a critical point in history where the population structure is still observable with efficient sampling and in-depth ethno-linguistic characterization, even if it is gradually diminishing due to migration and intermingling between different SEB groups. We hope that our findings will motivate studies with larger sample sizes and wider geographic representation to help unravel the demographic events that contributed to the peopling of South Africa.56. Of these SWT is urban, whereas DKG and AGT are rural/semi-urban sites. The study included a total of 5268 individuals (mostly within the age range of 40–60 years) representing eight major South African SEB groups: Tsonga, Pedi, Venda, Sotho, Tswana, Swazi, Zulu, and Xhosa. This study was approved by the Human Research Ethics Committee  of the University of the Witwatersrand (Wits) (protocol number M121029), and renewed in 2017 (protocol number M170880). In addition, research at the Dikgale Study Centre was approved by the Medunsa Research and Ethics Committee of the University of Limpopo (MREC/HS/195/2014:CR). Community engagement preceded sample collection and all participants provided broad consent for medical and population genetic studies. Details of community engagement, written informed consent, and genomic DNA extraction from blood samples have been described elsewhere57. In brief, the community engagement was tailored for each of the study sites according to their setting in a rural or urban area. It involved meetings with community leaders and elders, providing an opportunity to ask questions about the study and the potential benefits for the communities. The opportunity was also used to raise awareness of the rise in cardiometabolic diseases, as the AWI-Gen study aims to identify environmental and genetic risk factors. At the end of the first round of data collection there was community level and individual level feedback and further discussions. The outcomes of this population genetics study will be shared in future community interactions. The samples were genotyped on the H3Africa array (~2.3 M SNPs) using the Illumina FastTrack Sequencing Service2. The default Illumina pipeline was used for the genotype calling (build GRCh37/hg19).The volunteers included in this study were sampled across three study sites Fig. ; Agincouhttps://en.wikipedia.org/wiki/Languages_of_South_Africa#/media/File:South_Africa_2011_dominant_language_map.svg. Attribution: “the user acknowledges Stats SA as the source of the basic data wherever they process, apply, utilise, publish or distribute the data, and also that they specify that the relevant application and analysis (where applicable) result from their own processing of the data”. The language centroid points were calculated for this study by the author of the original map, Adrian Frith. The geometric medians of the population of speakers of each language  were calculated. Since the large population sizes of the cosmopolitan Gauteng Province  will distort the picture, geometric medians excluding Gauteng have been calculated.The map in Fig. 58 and involved removal of duplicate SNPs, multi-allelic SNPs, INDELs and SNPs with a missingness >0.05, MAF < 0.01 and SNPs that failed HWE test . Individuals with missingness >0.05, discordant sex information and lacking self-reported ethnicity information were also removed. The genotype dataset post-QC consists of 5056 samples and 1,733,001 autosomal SNPs (AWI-S1)  using PLINK. The same parameters for LD-pruning were used for the datasets described below.Quality control (QC) on the AWI-Gen genotype dataset was performed using PLINK v1.9).958 and 59 and GENESIS60 and PC-Relate option for plotting. After these QC-steps, no first-degree or second-degree relatives were found in the dataset used for the analyses below  segments for each sample pair, based on the LD-pruned AWI-S1 dataset. For each pair of related individuals (PIHAT > 0.18), the sample with higher missingness was dropped, resulting in the removal of 737 SEB participants in the process leading to AWI-S2 dataset  groups as well as between SEB and non-SEB groups has been common in recent South African history. Since admixture events could influence fine-scale comparisons between SEB groups, we identified the participants that were ethno-linguistically concordant (EC), i.e., they have reported the same ethnicity for themselves, both parents and the four grandparents . This set of 2,702 EC participants was defined as AWI-S3 dataset, details are listed in Table The representation of various SEB groups in the AWI-Gen study was notably skewed toward Tsonga, Pedi, and Zulu  (Table 21), 1000 Genomes Project Phase 3 (KGP)61, and African Genome Variation Project (AGVP)32 using PLINK (hereafter Merged dataset 1), and only the SNPs that overlapped between all datasets were retained . This layered merging was performed to retain the maximum number of SNPs possible for each analysis.For comparison of our population with previously studied populations, the AWI-S2 data  was merged with additional worldwide datasets from 34. Lastly, to better discern fine-scale population structure among SEB groups we performed haplotype-based clustering analysis using ChromoPainter jointly with fineSTRUCTURE35  was performed on the basis of the LD-pruned AWI-S2 dataset using the program smartPCA implemented in the EIGENSOFT suiteST statistics (Weir and Cockerham 1984) between pairwise SEB populations included in the Merged dataset 2 (EC downsized) using PLINK. The relationship between the SEB groups based on pairwise FST values was represented with a UPGMA tree using the program MEGA X64.To investigate genetic affinities between the different SEB groups, we estimated Weir and Cockerham’s F65. The lexical data were binary recorded in 1304 partial cognate sets (form-meaning associations). The resulting matrix was analysed with Bayesian inference methods as implemented in MrBayes (v3.2.7)66 using a restriction-site model67.The linguistic phylogeny is based on lexical data for 100 concepts in 69 Bantu varieties, 34 of them part of South-Eastern Bantu languages and 35 outgroup languages belonging to different major Bantu brancheshttp://www.or.uni-bonn.de/~vygen/files/fl.pdf), and considered them as the midpoints of each group. The great circle geographic distance between each midpoint was estimated using an online tool . All individuals belonging to a particular SEB group were assigned to the same geographic location (geometric median), and the Procrustes transformation analysis was performed using the R package MCMCpack68. The correlations between PCA (PC1-PC2) results and geographic location of each SEB group was estimated using R package vegan69 (9999 permutations). For the Mantel test, the genetic distance matrix was based on weighted mean FST estimates for each pair of SEB groups. This once again was performed using the R package vegan69, using 9999 permutations to test the correlations between the geographic distances and FSTbased genetic distances.Procrustes transformation analysis and Mantel tests were implemented to investigate possible relationships between the geographic and genetic distances between the SEB groups. As many of the groups were sampled from sites that are quite distant to their native geography, such as Zulu and Xhosa, we calculated geometric medians of the population of speakers for each language using Weiszfeld’s algorithm (37 on the Merged dataset 2 (EC downsized). The number of K-groups analysed varied from K = 3 to K = 8, and 50 independent runs with a random seed for each K-group was performed. The K-group with the lowest cross-validation error was considered “optimal”. PONG70 was used for merging and visualizing the clustering outputs of all the runs from the ADMIXTURE analysis, and major modes were used for the ADMIXTURE plots. To compare the differential contributions of the main ancestries , the average admixture proportions of each ancestry was computed from the ADMIXTURE results at K = 3 for each SEB group in the Merged dataset 1. For each study site, we further estimated the average admixture proportion for each ancestry at K = 3 in each SEB group. We applied a t-test to compare whether there are significant differences in Khoe-San ancestry proportion across the three sites for a given SEB group.For global ancestry inference, we used an unsupervised clustering algorithm implemented in ADMIXTURE (v1.3)71, on the basis of the Merged dataset 1 (EC). As reference panels, we selected: YRI for Central-West African ancestry; CEU for Eurasian ancestry; and combined Ju|’hoansi, /Gui //Gana, and Karretjie21 for Khoe-San ancestry. The merged dataset was first phased using SHAPEIT272 with a reference panel of worldwide haplotypes61, and then analysed with two runs of expectation maximization (EM = 2), forward-backward and PopPhased options. The genetic map from HapMap Phase 2 build GRCh37/hg19 was used for the analysis.For local ancestry inference, we used RFMix (v1.54)73.To investigate whether the differential Khoe-San gene flow is the only factor leading to the observed population structure, SEB haploid genomes were masked for regions of Khoe-San and European ancestries identified using RFMix. We then analysed haploid regions with more than 50% Bantu-related ancestry using the ancestry-specific PCA approach38, MALDER (v1.0)43, and MOSAIC (v1.3.7)44. The details for each method is described in Supplementary Note To reconstruct the timeframe of admixture events between the major ancestry components in SEB populations, we used three admixture dating methods, fastGLOBETROTTER, the recent implementation of GLOBETROTTER10, and one 700-years-old sample from Pemba, Tanzania (AWI-AG dataset)11. We used smartPCA to project the ancient samples onto the modern samples . To better visualize genetic affinities between ancient and modern samples, we performed the PCA-UMAP analysis using the PC coordinates for the first ten PCs and UMAP tool for the analysis 34, and a custom Python script for the plotting.To compare the genetic affinities of modern SEB groups to Iron-age Bantu-related samples from Southern Africa, we analysed the AWI-S3 dataset together with five ancient samples: four associated with Iron-Age (300–500 year old) farmers in South AfricaplotY tool (https://github.com/shaze/ymthaplotools), based on a modified version of the tree and mutations table of AMY-tree74. The results were then validated using SNAPPY75. MtDNA haplotyping was performed using Haplogrep 276, using Phylotree mtDNA tree build 17rsrs-RSRS77. The details for mtDNA and Y-haplogroup detection are described in Supplementary Note Y-haplogroup analysis was carried out using our new in-house 78. To investigate the extent of sex-bias in the contributions of different ancestral populations to admixed SEB groups, the AWI-S3 and YRI and CEU from KGP were merged with available data62, consisting of the 33 Khoe-San samples. The ancestry proportions for each were estimated using ADMIXTURE at K = 3 for three datasets: the X-chromosome dataset, the autosomal dataset, and the merged autosome-X-chromosome dataset. Admixture difference (ΔAdmix) ratios were then calculated using the method proposed by (ref. 79). A positive ΔAdmix ratio indicates an excess of female-specific admixture contributions, while a negative value indicates an excess of male-specific admixture. To test statistical significance of the difference between the ΔAdmix for each ancestry between pairs of populations, we used the Wilcoxon rank-sum test.Recent literature has suggested the comparison of X-chromosome and autosomal contributions from the two source population as a robust method to test for possible sex-biased admixture80 for each group (with default parameters). The output was then used as input for the program IBDNe50, which computes the effective population size (Ne) for each SEB group for the last few hundred generations. To avoid the conflation effect of short IBD segments81, only IBD segments longer than 4 cM were retained for the Ne estimation, and the remaining parameters were set as default. Ancestry-specific effective population size (ancestry-specific IBDNe)50 was estimated for the different SEB groups to identify the contribution of Khoe-San and BS ancestries to the Ne dynamics of various SEB groups. This analysis was performed on the dataset that was used to estimate the overall Ne. We followed the pipeline provided by the authors, which implements both IBD and local ancestry information from the genotype data. The first step in this approach was to phase the data using Beagle (v5)82, and then IBD segments were detected using RefinedIBD and local ancestry information was inferred with RFMix . Finally, IBDNe was used to estimate the ancestry-specific Ne from the detected IBD segments and the ancestry blocks inferred from the local ancestry analysis.To estimate and compare the variation in recent population size of the different SEB groups, IBD segments were detected from the downsized Merged dataset 1 using the program IBDseq55 (accessed on 19 April 2020), in the six major SEB groups . Standard error for allele frequencies was estimated using 50 bootstrap iterations in a subset of 30 individuals from each SEB group.We used PLINK to estimate allele frequencies of all SNPs in our dataset that are included in the GWAS catalogn = 800) are a mixture of samples from AGT and SWT and the controls (n = 800) are sampled from SWT only. Three sets of cases with varying proportions of AGT and SWT representation  were generated and 50 iterations were performed for each set. The third category of trait simulation was aimed at studying the impact of ethno-linguistic stratification within a sampling site, SWT. For two sets  generated in this category, the assignment was done in a way in which one of the ethno-linguistic groups (Tswana in set 1 and Tsonga in set 2) was absent in cases but present in controls. The fourth category was generated by randomly assigning case and control labels to the samples from a single site at a time. GWAS for each of the case-control pairs in all the sets under the four categories were conducted using the association testing function in PLINK. Genomic inflation scores were recorded for each run, and signals at a genome-wide significance threshold of P-value = 5 × 10−8 were identified, as well as a less stringent suggestive significance threshold . To assess the extent of population structure correction possible with a genomic control based approach, for each run the inbuilt correction testing function was implemented using the “adjust” flag in PLINK. To assess the impact of PC-based correction, for each of the case-control iterations, PCs for the dataset were estimated using PLINK and the first three principal components were used as covariates in logistic regression based association testing in PLINK. QQ plots were generated using a custom R script. Possible phenotypic roles of the associations detected in these simulated-trait GWASs were assessed using the GWAS catalog55.To simulate various possible scenarios for genetic association studies using ethno-linguistically and geographically mixed sets of SEB participants, four categories of artificial “case-control” trait simulations were performed. In the first category, the sampling site was used as the basis for assigning the case and control status. Here, the “case” label was assigned to 800 randomly sampled individuals from one of the three sites and the “control” label assigned to 800 randomly sampled individuals from a different site. Independent comparisons, 50 iterations each for AGT-DKG; DKG-SWT; AGT-SWT were performed. The second category corresponds to a scenario in which cases . The AWI-S4 dataset was used for this analysis, and only SNPs with MAF < 0.05 were considered. We included six SEB groups, and we removed Venda and Swazi samples due to their small sample size. For each SEB group, the raw iHS were normalized across 40 frequency bins. A random sampling of scores across populations was performed to assess P-values for various score cutoffs. Based on this |iHS| > 4 was considered as outliers . The mapping of SNPs to genes was performed based on information retrieved from Ensembl Biomart . We also used the population branch statistics (PBS) analysis84 to identify SNPs under positive selection. PBS is a summary statistic that utilizes pairwise Fst values among three populations to quantify genetic differentiation along each branch of their corresponding three-population tree. Since the overall genetic distance between the SEB groups is not very high, we considered only two groups from our study: the one with the highest Khoe-San ancestry (Tswana), and the other with the lowest Khoe-San ancestry (Tsonga). CHB from KGP was selected as the outlier population for this study. For each exonic SNP (identified using Ensembl Biomart as mentioned above) with MAF > 0.01, FST values were estimated between the three pairs of the populations  using VCFtools85. PBS scores were then estimated in Tswana-Tsonga-CHB and Tsonga-Tswana-CHB comparisons using the method described in (ref. 84).To identify SNPs under positive selection, we calculated the integrated haplotype homozygosity scores (iHS)To identify genomic regions showing enrichment of Khoe-San ancestry in the SEB groups, local ancestry inference was performed using RFMIX as described above. To avoid statistical noise, regions around centromeres and telomeres (2 Mb from each side) for each chromosome were excluded from the analysis. Only SNPs with a high confidence value for Khoe-San ancestry  were retained for the analysis. Ancestry regions (containing at least 3 SNPs) exceeding the average genome-wide Khoe-San ancestry estimate by at least +3SDs were considered as candidates for preferential Khoe-San gene flow. We then investigated whether there are regions of adaptive introgression in the genomes by overlapping the regions under positive selection (as described above) and regions showing Khoe-San enrichment.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary