Robust and prototypical immune responses toward COVID-19 vaccine in First Nations peoples are impacted by comorbidities.

High-risk groups, including Indigenous people, are at risk of severe COVID-19. Here we found that Australian First Nations peoples elicit effective immune responses to COVID-19 BNT162b2 vaccination, including neutralizing antibodies, receptor-binding domain (RBD) antibodies, SARS-CoV-2 spike-specific B cells, and CD4+ and CD8+ T cells. In First Nations participants, RBD IgG antibody titers were correlated with body mass index and negatively correlated with age. Reduced RBD antibodies, spike-specific B cells and follicular helper T cells were found in vaccinated participants with chronic conditions (diabetes, renal disease) and were strongly associated with altered glycosylation of IgG and increased interleukin-18 levels in the plasma. These immune perturbations were also found in non-Indigenous people with comorbidities, indicating that they were related to comorbidities rather than ethnicity. However, our study is of a great importance to First Nations peoples who have disproportionate rates of chronic comorbidities and provides evidence of robust immune responses after COVID-19 vaccination in Indigenous people.

SARS-CoV-2-specific T cell memory with common TCRαß motifs is established in unvaccinated children who seroconvert after infection.

As the establishment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific T cell memory in children remains largely unexplored, we recruited convalescent COVID-19 children and adults to define their circulating memory SARS-CoV-2-specific CD4+ and CD8+ T cells prior to vaccination. We analyzed epitope-specific T cells directly ex vivo using seven HLA class I and class II tetramers presenting SARS-CoV-2 epitopes, together with Spike-specific B cells. Unvaccinated children who seroconverted had comparable Spike-specific but lower ORF1a- and N-specific memory T cell responses compared with adults. This agreed with our TCR sequencing data showing reduced clonal expansion in children. A strong stem cell memory phenotype and common T cell receptor motifs were detected within tetramer-specific T cells in seroconverted children. Conversely, children who did not seroconvert had tetramer-specific T cells of predominantly naive phenotypes and diverse TCRαß repertoires. Our study demonstrates the generation of SARS-CoV-2-specific T cell memory with common TCRαß motifs in unvaccinated seroconverted children after their first virus encounter.

CD8+ T cells specific for an immunodominant SARS-CoV-2 nucleocapsid epitope display high naive precursor frequency and TCR promiscuity.

To better understand primary and recall T cell responses during coronavirus disease 2019 (COVID-19), it is important to examine unmanipulated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific T cells. By using peptide-human leukocyte antigen (HLA) tetramers for direct ex vivo analysis, we characterized CD8+ T cells specific for SARS-CoV-2 epitopes in COVID-19 patients and unexposed individuals. Unlike CD8+ T cells directed toward subdominant epitopes (B7/N257, A2/S269, and A24/S1,208) CD8+ T cells specific for the immunodominant B7/N105 epitope were detected at high frequencies in pre-pandemic samples and at increased frequencies during acute COVID-19 and convalescence. SARS-CoV-2-specific CD8+ T cells in pre-pandemic samples from children, adults, and elderly individuals predominantly displayed a naive phenotype, indicating a lack of previous cross-reactive exposures. T cell receptor (TCR) analyses revealed diverse TCRαß repertoires and promiscuous αß-TCR pairing within B7/N105+CD8+ T cells. Our study demonstrates high naive precursor frequency and TCRαß diversity within immunodominant B7/N105-specific CD8+ T cells and provides insight into SARS-CoV-2-specific T cell origins and subsequent responses.

SARS-CoV-2-specific CD8+ T cells from people with long COVID establish and maintain effector phenotype and key TCR signatures over 2 years.

Long COVID occurs in a small but important minority of patients following COVID-19, reducing quality of life and contributing to healthcare burden. Although research into underlying mechanisms is evolving, immunity is understudied. SARS-CoV-2-specific T cell responses are of key importance for viral clearance and COVID-19 recovery. However, in long COVID, the establishment and persistence of SARS-CoV-2-specific T cells are far from clear, especially beyond 12 mo postinfection and postvaccination. We defined ex vivo antigen-specific B cell and T cell responses and their T cell receptors (TCR) repertoires across 2 y postinfection in people with long COVID. Using 13 SARS-CoV-2 peptide-HLA tetramers, spanning 11 HLA allotypes, as well as spike and nucleocapsid probes, we tracked SARS-CoV-2-specific CD8+ and CD4+ T cells and B-cells in individuals from their first SARS-CoV-2 infection through primary vaccination over 24 mo. The frequencies of ORF1a- and nucleocapsid-specific T cells and B cells remained stable over 24 mo. Spike-specific CD8+ and CD4+ T cells and B cells were boosted by SARS-CoV-2 vaccination, indicating immunization, in fully recovered and people with long COVID, altered the immunodominance hierarchy of SARS-CoV-2 T cell epitopes. Meanwhile, influenza-specific CD8+ T cells were stable across 24 mo, suggesting no bystander-activation. Compared to total T cell populations, SARS-CoV-2-specific T cells were enriched for central memory phenotype, although the proportion of central memory T cells decreased following acute illness. Importantly, TCR repertoire composition was maintained throughout long COVID, including postvaccination, to 2 y postinfection. Overall, we defined ex vivo SARS-CoV-2-specific B cells and T cells to understand primary and recall responses, providing key insights into antigen-specific responses in people with long COVID.

Broad spectrum SARS-CoV-2-specific immunity in hospitalized First Nations peoples recovering from COVID-19.

Indigenous peoples globally are at increased risk of COVID-19-associated morbidity and mortality. However, data that describe immune responses to SARS-CoV-2 infection in Indigenous populations are lacking. We evaluated immune responses in Australian First Nations peoples hospitalized with COVID-19. Our work comprehensively mapped out inflammatory, humoral and adaptive immune responses following SARS-CoV-2 infection. Patients were recruited early following the lifting of strict public health measures in the Northern Territory, Australia, between November 2021 and May 2022. Australian First Nations peoples recovering from COVID-19 showed increased levels of MCP-1 and IL-8 cytokines, IgG-antibodies against Delta-RBD and memory SARS-CoV-2-specific T cell responses prior to hospital discharge in comparison with hospital admission, with resolution of hyperactivated HLA-DR+ CD38+ T cells. SARS-CoV-2 infection elicited coordinated ASC, Tfh and CD8+ T cell responses in concert with CD4+ T cell responses. Delta and Omicron RBD-IgG, as well as Ancestral N-IgG antibodies, strongly correlated with Ancestral RBD-IgG antibodies and Spike-specific memory B cells. We provide evidence of broad and robust immune responses following SARS-CoV-2 infection in Indigenous peoples, resembling those of non-Indigenous COVID-19 hospitalized patients.

Suboptimal SARS-CoV-2-specific CD8+ T cell response associated with the prominent HLA-A*02:01 phenotype.

An improved understanding of human T cell-mediated immunity in COVID-19 is important for optimizing therapeutic and vaccine strategies. Experience with influenza shows that infection primes CD8+ T cell memory to peptides presented by common HLA types like HLA-A2, which enhances recovery and diminishes clinical severity upon reinfection. Stimulating peripheral blood mononuclear cells from COVID-19 convalescent patients with overlapping peptides from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to the clonal expansion of SARS-CoV-2-specific CD8+ and CD4+ T cells in vitro, with CD4+ T cells being robust. We identified two HLA-A*02:01-restricted SARS-CoV-2-specfic CD8+ T cell epitopes, A2/S269-277 and A2/Orf1ab3183-3191 Using peptide-HLA tetramer enrichment, direct ex vivo assessment of A2/S269+CD8+ and A2/Orf1ab3183+CD8+ populations indicated that A2/S269+CD8+ T cells were detected at comparable frequencies (∼1.3 × 10-5) in acute and convalescent HLA-A*02:01+ patients. These frequencies were higher than those found in uninfected HLA-A*02:01+ donors (∼2.5 × 10-6), but low when compared to frequencies for influenza-specific (A2/M158) and Epstein-Barr virus (EBV)-specific (A2/BMLF1280) (∼1.38 × 10-4) populations. Phenotyping A2/S269+CD8+ T cells from COVID-19 convalescents ex vivo showed that A2/S269+CD8+ T cells were predominantly negative for CD38, HLA-DR, PD-1, and CD71 activation markers, although the majority of total CD8+ T cells expressed granzymes and/or perforin. Furthermore, the bias toward naïve, stem cell memory and central memory A2/S269+CD8+ T cells rather than effector memory populations suggests that SARS-CoV-2 infection may be compromising CD8+ T cell activation. Priming with appropriate vaccines may thus be beneficial for optimizing CD8+ T cell immunity in COVID-19.

A Novel Hybrid Convolutional Neural Network- and Gated Recurrent Unit-Based Paradigm for IoT Network Traffic Attack Detection in Smart Cities.

Internet of Things (IoT) devices within smart cities, require innovative detection methods. This paper addresses this critical challenge by introducing a deep learning-based approach for the detection of network traffic attacks in IoT ecosystems. Leveraging the Kaggle dataset, our model integrates Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to capture both spatial and sequential features in network traffic data. We trained and evaluated our model over ten epochs, achieving an impressive overall accuracy rate of 99%. The classification report reveals the model's proficiency in distinguishing various attack categories, including 'Normal', 'DoS' (Denial of Service), 'Probe', 'U2R' (User to Root), and 'Sybil'. Additionally, the confusion matrix offers valuable insights into the model's performance across these attack types. In terms of overall accuracy, our model achieves an impressive accuracy rate of 99% across all attack categories. The weighted- average F1-score is also 99%, showcasing the model's robust performance in classifying network traffic attacks in IoT devices for smart cities. This advanced architecture exhibits the potential to fortify IoT device security in the complex landscape of smart cities, effectively contributing to the safeguarding of critical infrastructure.

A Study of Maternal and Umbilical Cord Blood Lead Levels in Pregnant Women.

Lead is a highly toxic element which can cross the placental barrier and enter the fetus during pregnancy. Parental lead exposure has adverse effect on infant as well as on maternal health. As part of our program to investigate the lead poisoning in human population we investigated the maternal blood lead levels (MBLL) and umbilical cord blood lead (UBLL) levels in 200 pregnant women and collected their socio-demographic details. In the study we found high lead levels in both maternal and umbilical cord blood samples. The results showed 47.5% maternal blood (n = 95) detected with lead while 38.5% umbilical cord blood (n = 77) samples had lead concentration higher than that of reference range of ≤ 5 µg/dL. We also found that the Spearman's correlation coefficient (rs) revealed a strong positive correlation between the MBLL and UBLL (rs = 0.63). The results from socio-demographic questionnaire demonstrated that the recent home painting (p = 0.002) and residing close proximity to traffic congestion (p = 0.05) were significantly associated with MBLL. Education, mother age, fuel and water sources were not significantly associated with MBLL. Iron and calcium deficiency along with tiredness, lethargy, abdominal pain were also reported in women having high lead level > 5 µg/dL. Concludingly, on the basis of results obtained it may be stated that we found elevated BLLs in both pregnant women as well as in umbilical cord blood. The prevalence of elevated lead levels in mothers will expose the fetus to lead through placental barriers mobilization and it can have long term adverse effects on the developing fetus. Therefore, it is recommended that screening of blood lead levels be carried out in high-risk women based on their social, occupational, environmental, and individual factors. In addition, stringent regulations on lead-based products are also required from government agencies/authorities to reduce environmental lead burden and toxicity. Moreover, public awareness programs should be organized on hazardous effect of lead.

Structural basis of biased T cell receptor recognition of an immunodominant HLA-A2 epitope of the SARS-CoV-2 spike protein.

CD8+ T cells play an important role in vaccination and immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although numerous SARS-CoV-2 CD8+ T cell epitopes have been identified, the molecular basis underpinning T cell receptor (TCR) recognition of SARS-CoV-2-specific T cells remains unknown. The T cell response directed toward SARS-CoV-2 spike protein-derived S269-277 peptide presented by the human leukocyte antigen (HLA)-A∗02:01 allomorph (hereafter the HLA-A2S269-277 epitope) is, to date, the most immunodominant SARS-CoV-2 epitope found in individuals bearing this allele. As HLA-A2S269-277-specific CD8+ T cells utilize biased TRAV12 gene usage within the TCR α-chain, we sought to understand the molecular basis underpinning this TRAV12 dominance. We expressed four TRAV12+ TCRs which bound the HLA-A2S269-277 complex with low micromolar affinity and determined the crystal structure of the HLA-A2S269-277 binary complex, and subsequently a ternary structure of the TRAV12+ TCR complexed to HLA-A2S269-277. We found that the TCR made extensive contacts along the entire length of the S269-277 peptide, suggesting that the TRAV12+ TCRs would be sensitive to sequence variation within this epitope. To examine this, we investigated cross-reactivity toward analogous peptides from existing SARS-CoV-2 variants and closely related coronaviruses. We show via surface plasmon resonance and tetramer studies that the TRAV12+ T cell repertoire cross-reacts poorly with these analogous epitopes. Overall, we defined the structural basis underpinning biased TCR recognition of CD8+ T cells directed at an immunodominant epitope and provide a framework for understanding TCR cross-reactivity toward viral variants within the S269-277 peptide.

A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images.

Breast cancer is one of the most fatal diseases leading to the death of several women across the world. But early diagnosis of breast cancer can help to reduce the mortality rate. So an efficient multi-task learning approach is proposed in this work for the automatic segmentation and classification of breast tumors from ultrasound images. The proposed learning approach consists of an encoder, decoder, and bridge blocks for segmentation and a dense branch for the classification of tumors. For efficient classification, multi-scale features from different levels of the network are used. Experimental results show that the proposed approach is able to enhance the accuracy and recall of segmentation by 1.08%, 4.13%, and classification by 1.16%, 2.34%, respectively than the methods available in the literature.

Modelling mobile-based technology adoption among people with dementia.

The work described in this paper builds upon our previous research on adoption modelling and aims to identify the best subset of features that could offer a better understanding of technology adoption. The current work is based on the analysis and fusion of two datasets that provide detailed information on background, psychosocial, and medical history of the subjects. In the process of modelling adoption, feature selection is carried out followed by empirical analysis to identify the best classification models. With a more detailed set of features including psychosocial and medical history information, the developed adoption model, using kNN algorithm, achieved a prediction accuracy of 99.41% when tested on 173 participants. The second-best algorithm built, using NN, achieved 94.08% accuracy. Both these results have improved accuracy in comparison to the best accuracy achieved (92.48%) in our previous work, based on psychosocial and self-reported health data for the same cohort. It has been found that psychosocial data is better than medical data for predicting technology adoption. However, for the best results, we should use a combination of psychosocial and medical data where it is preferable that the latter is provided from reliable medical sources, rather than self-reported.

SARS-CoV-2-specific CD8+ T-cell responses and TCR signatures in the context of a prominent HLA-A*24:02 allomorph.

In-depth understanding of human T-cell-mediated immunity in coronavirus disease 2019 (COVID-19) is needed if we are to optimize vaccine strategies and immunotherapies. Identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) T-cell epitopes and generation of peptide-human leukocyte antigen (peptide-HLA) tetramers facilitate direct ex vivo analyses of SARS-CoV-2-specific T cells and their T-cell receptor (TCR) repertoires. We utilized a combination of peptide prediction and in vitro peptide stimulation to validate novel SARS-CoV-2 epitopes restricted by HLA-A*24:02, one of the most prominent HLA class I alleles, especially in Indigenous and Asian populations. Of the peptides screened, three spike-derived peptides generated CD8+ IFNγ+ responses above background, S1208-1216 (QYIKWPWYI), S448-456 (NYNYLYRLF) and S193-201 (VFKNIDGYF), with S1208 generating immunodominant CD8+ IFNγ+ responses. Using peptide-HLA-I tetramers, we performed direct ex vivo tetramer enrichment for HLA-A*24:02-restricted CD8+ T cells in COVID-19 patients and prepandemic controls. The precursor frequencies for HLA-A*24:02-restricted epitopes were within the range previously observed for other SARS-CoV-2 epitopes for both COVID-19 patients and prepandemic individuals. Naïve A24/SARS-CoV-2-specific CD8+ T cells increased nearly 7.5-fold above the average precursor frequency during COVID-19, gaining effector and memory phenotypes. Ex vivo single-cell analyses of TCRαß repertoires found that the A24/S448+ CD8+ T-cell TCRαß repertoire was driven by a common TCRß chain motif, whereas the A24/S1208+ CD8+ TCRαß repertoire was diverse across COVID-19 patients. Our study provides an in depth characterization and important insights into SARS-CoV-2-specific CD8+ T-cell responses associated with a prominent HLA-A*24:02 allomorph. This contributes to our knowledge on adaptive immune responses during primary COVID-19 and could be exploited in vaccine or immunotherapeutic approaches.

Modelling assistive technology adoption for people with dementia.

PURPOSE: Assistive technologies have been identified as a potential solution for the provision of elderly care. Such technologies have in general the capacity to enhance the quality of life and increase the level of independence among their users. Nevertheless, the acceptance of these technologies is crucial to their success. Generally speaking, the elderly are not well-disposed to technologies and have limited experience; these factors contribute towards limiting the widespread acceptance of technology. It is therefore important to evaluate the potential success of technologies prior to their deployment. MATERIALS AND METHODS: The research described in this paper builds upon our previous work on modelling adoption of assistive technology, in the form of cognitive prosthetics such as reminder apps and aims at identifying a refined sub-set of features which offer improved accuracy in predicting technology adoption. Consequently, in this paper, an adoption model is built using a set of features extracted from a user's background to minimise the likelihood of non-adoption. The work is based on analysis of data from the Cache County Study on Memory and Aging (CCSMA) with 31 features covering a range of age, gender, education and details of health condition. In the process of modelling adoption, feature selection and feature reduction is carried out followed by identifying the best classification models. FINDINGS: With the reduced set of labelled features the technology adoption model built achieved an average prediction accuracy of 92.48% when tested on 173 participants. CONCLUSIONS: We conclude that modelling user adoption from a range of parameters such as physical, environmental and social perspectives is beneficial in recommending a technology to a particular user based on their profile.

CD8+ T-cell responses towards conserved influenza B virus epitopes across anatomical sites and age.

Influenza B viruses (IBVs) cause substantive morbidity and mortality, and yet immunity towards IBVs remains understudied. CD8+ T-cells provide broadly cross-reactive immunity and alleviate disease severity by recognizing conserved epitopes. Despite the IBV burden, only 18 IBV-specific T-cell epitopes restricted by 5 HLAs have been identified currently. A broader array of conserved IBV T-cell epitopes is needed to develop effective cross-reactive T-cell based IBV vaccines. Here we identify 9 highly conserved IBV CD8+ T-cell epitopes restricted to HLA-B*07:02, HLA-B*08:01 and HLA-B*35:01. Memory IBV-specific tetramer+CD8+ T-cells are present within blood and tissues. Frequencies of IBV-specific CD8+ T-cells decline with age, but maintain a central memory phenotype. HLA-B*07:02 and HLA-B*08:01-restricted NP30-38 epitope-specific T-cells have distinct T-cell receptor repertoires. We provide structural basis for the IBV HLA-B*07:02-restricted NS1196-206 (11-mer) and HLA-B*07:02-restricted NP30-38 epitope presentation. Our study increases the number of IBV CD8+ T-cell epitopes, and defines IBV-specific CD8+ T-cells at cellular and molecular levels, across tissues and age.

Automated lead toxicity prediction using computational modelling framework.

Background: Lead, an environmental toxicant, accounts for 0.6% of the global burden of disease, with the highest burden in developing countries. Lead poisoning is very much preventable with adequate and timely action. Therefore, it is important to identify factors that contribute to maternal BLL and minimise them to reduce the transfer to the foetus. Literacy and awareness related to its impact are low and the clinical establishment for biological monitoring of blood lead level (BLL) is low, costly, and time-consuming. A significant contribution to an infant's BLL load is caused by maternal lead transfer during pregnancy. This acts as the first pathway to the infant's lead exposure. The social and demographic information that includes lifestyle and environmental factors are key to maternal lead exposure. Results: We propose a novel approach to build a computational model framework that can predict lead toxicity levels in maternal blood using a set of sociodemographic features. To illustrate our proposed approach, maternal data comprising socio-demographic features and blood samples from the pregnant woman is collected, analysed, and modelled. The computational model is built that learns from the maternal data and then predicts lead level in a pregnant woman using a set of questionnaires that relate to the maternal's social and demographic information as the first point of testing. The range of features identified in the built models can estimate the underlying function and provide an understanding of the toxicity level. Following feature selection methods, the 12-feature set obtained from the Boruta algorithm gave better prediction results (kNN = 76.84%, DT = 74.70%, and NN = 73.99%). Conclusion: The built prediction model can be beneficial in improving the point of care and hence reducing the cost and the risk involved. It is envisaged that in future, the proposed methodology will become a part of a screening process to assist healthcare experts at the point of evaluating the lead toxicity level in pregnant women. Women screened positive could be given a range of facilities including preliminary counselling to being referred to the health centre for further diagnosis. Steps could be taken to reduce maternal lead exposure; hence, it could also be possible to mitigate the infant's lead exposure by reducing transfer from the pregnant woman.

Robust SARS-CoV-2 T cell responses with common TCRαß motifs toward COVID-19 vaccines in patients with hematological malignancy impacting B cells.

Immunocompromised hematology patients are vulnerable to severe COVID-19 and respond poorly to vaccination. Relative deficits in immunity are, however, unclear, especially after 3 vaccine doses. We evaluated immune responses in hematology patients across three COVID-19 vaccination doses. Seropositivity was low after a first dose of BNT162b2 and ChAdOx1 (∼26%), increased to 59%-75% after a second dose, and increased to 85% after a third dose. While prototypical antibody-secreting cells (ASCs) and T follicular helper (Tfh) cell responses were elicited in healthy participants, hematology patients showed prolonged ASCs and skewed Tfh2/17 responses. Importantly, vaccine-induced expansions of spike-specific and peptide-HLA tetramer-specific CD4+/CD8+ T cells, together with their T cell receptor (TCR) repertoires, were robust in hematology patients, irrespective of B cell numbers, and comparable to healthy participants. Vaccinated patients with breakthrough infections developed higher antibody responses, while T cell responses were comparable to healthy groups. COVID-19 vaccination induces robust T cell immunity in hematology patients of varying diseases and treatments irrespective of B cell numbers and antibody response.

Effect of Mindfulness Meditation on Intraocular Pressure and Trabecular Meshwork Gene Expression: A Randomized Controlled Trial.

PURPOSE: To evaluate the effect of mindfulness meditation (MM) on intraocular pressure (IOP) and trabecular meshwork (TM) gene expression in patients with medically uncontrolled primary open angle glaucoma (POAG). DESIGN: Parallel arm, single-masked, randomized controlled trial. METHODS: Sixty POAG patients with IOP ≥21 mm Hg taking maximal topical medication and scheduled for trabeculectomy were included in this study at a tertiary eye care center in India. Thirty patients (Group 1) underwent 3 weeks of 45-minute daily MM sessions in addition to medical therapy while Group 2 continued medical therapy only. Primary outcome was change in IOP (ΔIOP) after 3 weeks of MM. Secondary outcomes were probability of success, percentage of reduction in IOP, effect on diurnal variations of IOP, changes in quality of life (QoL), and changes in gene expression patterns in TM. RESULTS: At 3 weeks, a significant decrease in IOP was seen in Group 1 (20.16 ± 3.3 to 15.05 ± 2.4mm Hg; P = .001), compared to Group 2 (21.2 ± 5.6 to 20.0 ± 5.8mm Hg; P = .38). ΔIOP was significantly higher in Group 1 than in Group 2 (5.0 ± 1.80 vs. 0.20 ± 3.03mm Hg; P = .001). Analysis of gene expression revealed significant upregulation of nitric oxide synthetase (NOS1 and NOS3) and neuroprotective genes with downregulation of proinflammatory genes in Group 1 in comparison to Group 2 (P = .001). CONCLUSIONS: MM was associated with significant decrease in IOP and changes in TM gene expression, indicating its direct impact on ocular tissues.

Bent conformation of a backbone pilin N-terminal domain supports a three-stage pilus assembly mechanism.

Effective colonization of host cells by some Gram-positive bacteria often involves using lengthy, adhesive macromolecular structures called sortase-dependent pili. Among commensals, the gut-adapted Lactobacillus rhamnosus GG strain encodes the operons for two varieties of these pili (SpaCBA and SpaFED), with each structure consisting of backbone, tip, and basal pilin subunits. Although the tertiary structure was recently solved for the backbone subunit (SpaA) of the SpaCBA pilus, no structural information exists for its counterpart in the SpaFED pilus. Here, we report several crystal structures for the SpaD backbone pilin, two of which capture the N-terminal domain in either the closed (linear) or open (bent) conformation. To our knowledge, this is the first observation of the bent conformation in Gram-positive pilin structures. Based on this bent conformation, we suggest a three-stage model, which we call the expose-ligate-seal mechanism, for the docking and assembly of backbone pilins into the sortase-dependent pilus.

New insights about pilus formation in gut-adapted Lactobacillus rhamnosus GG from the crystal structure of the SpaA backbone-pilin subunit.

Thus far, all solved structures of pilin-proteins comprising sortase-assembled pili are from pathogenic genera and species. Here, we present the first crystal structure of a pilin subunit (SpaA) from a non-pathogen host (Lactobacillus rhamnosus GG). SpaA consists of two tandem CnaB-type domains, each with an isopeptide bond and E-box motif. Intriguingly, while the isopeptide bond in the N-terminal domain forms between lysine and asparagine, the one in the C-terminal domain atypically involves aspartate. We also solved crystal structures of mutant proteins where residues implicated in forming isopeptide bonds were replaced. Expectedly, the E-box-substituted E139A mutant lacks an isopeptide bond in the N-terminal domain. However, the C-terminal E269A substitution gave two structures; one of both domains with their isopeptide bonds present, and another of only the N-terminal domain, but with an unformed isopeptide bond and significant conformational changes. This latter crystal structure has never been observed for any other Gram-positive pilin. Notably, the C-terminal isopeptide bond still forms in D295N-substituted SpaA, irrespective of E269 being present or absent. Although E-box mutations affect SpaA proteolytic and thermal stability, a cumulative effect perturbing normal pilus polymerization was unobserved. A model showing the polymerized arrangement of SpaA within the SpaCBA pilus is proposed.

Technology adoption and prediction tools for everyday technologies aimed at people with dementia.

A wide range of assistive technologies have been developed to support the elderly population with the goal of promoting independent living. The adoption of these technology based solutions is, however, critical to their overarching success. In our previous research we addressed the significance of modelling user adoption to reminding technologies based on a range of physical, environmental and social factors. In our current work we build upon our initial modeling through considering a wider range of computational approaches and identify a reduced set of relevant features that can aid the medical professionals to make an informed choice of whether to recommend the technology or not. The adoption models produced were evaluated on a multi-criterion basis: in terms of prediction performance, robustness and bias in relation to two types of errors. The effects of data imbalance on prediction performance was also considered. With handling the imbalance in the dataset, a 16 feature-subset was evaluated consisting of 173 instances, resulting in the ability to differentiate between adopters and non-adopters with an overall accuracy of 99.42 %.