This work addresses the challenge of building an accurate and generalizable periocular recognition model with a small number of learnable parameters. Deeper (larger) models are typically more capable of learning complex information. For this reason, knowledge distillation (kd) was previously proposed to carry this knowledge from a large model (teacher) into a small model (student). Conventional KD optimizes the student output to be similar to the teacher output (commonly classification output). In biometrics, comparison (verification) and storage operations are conducted on biometric templates, extracted from pre-classification layers. In this work, we propose a novel template-driven KD approach that optimizes the distillation process so that the student model learns to produce templates similar to those produced by the teacher model. We demonstrate our approach on intra- and cross-device periocular verification. Our results demonstrate the superiority of our proposed approach over a network trained without KD and networks trained with conventional (vanilla) KD. For example, the targeted small model achieved an equal error rate (EER) value of 22.2% on cross-device verification without KD. The same model achieved an EER of 21.9% with the conventional KD, and only 14.7% EER when using our proposed template-driven KD. Biometric deployment on smartphones and different embedded devices enables novel applications for identity verification or service personalization. Facial characteristics are one of the commonly used biometric traits for identity recognition. This is driven by the high user acceptance  and the Many biometric applications resort to deep learning models that are trained as identity classifiers. However, these models are used as template (feature) extractors by sampling pre-classification network layers ,25. ThisThe present work addresses this issue by proposing its main contribution, a novel template-driven KD for periocular recognition that transfers the optimal template extraction knowledge from a deeper network into a network with a significantly lower number of trainable parameters. The proposed approach is designed so that it can be adapted to any suitable student/teacher architecture combinations. We extensively demonstrate the applicability of our proposed approach for periocular verification, both for intra-device and cross-device scenarios, to show the effectiveness of our proposed template-driven KD. To demonstrate the generalizability of the proposed concept, we train our template-driven KD network with two different loss functions, mean squared error (MSE) loss and Cosine loss. Further, we compare the performance of the resulting student network with the same network when trained from scratch, as well as two different larger teacher networks to validate our assertion. Our results are further benchmarked against the conventional KD optimized for classification, consistently showing the benefits of our proposed solution. These achievements are demonstrated on a periocular dataset of 152 unique identities consisting of 6682 images captured with 2 different smartphones (iPhone 5S and Nokia Lumia 1020) to demonstrate generalizability across capture devices. We demonstrate that our template-driven KD solution successfully and consistently reduced the verification error rates under cross-device experimental settings. Our template-driven KD solution successfully reduced the cross-device false non-match rate (FNMR) at a false match rate (FMR) of 10% of the same efficient network structure from 39.45% when trained from scratch and 23.2% when conventional KD is applied, to 20.1%.In the remainder of this article, we present a set of related works in Periocular recognition is preferred when capturing the full face consistently is a challenge ,11,15, fAs noted by Rattani et al. , the higIn order to address the constraints of compact model requirements, earlier works have minimized deep-learning-model size using mainly one of three methods, parametric pruning , quantizWhile this seems a feasible solution, biometric applications need to extract identity information  in order to achieve better performance ,42. PerfIn this work we present a template-driven KD framework to enhance the accuracy and generalizability of a compact CNN model for periocular verification under a smartphone verification scenario. We employed the widely used ResNet model  as a basf were extracted from the last layer, with dimension In this work, we deployed ResNet-34 and ResNet-18 as teacher networks and the compact ResNet-110 as a student, as well as a stand-alone baseline. In order to adopt these models for periocular verification, we modified the number of classes in the classification layer to match the number of identities in our training dataset. We applied transfer learning on the models ResNet-34 and ResNet-18 pretrained on the ImageNet dataset  by fine-f were extracted from the last fully connected layer. Using the exact training setups, we trained and evaluated the ResNet-110 model with the conventional (vanilla) KD approach. Finally, we trained and evaluated the ResNet-110 model with our template-driven KD.The number features in the last layer of the ResNet-110 model is T, student model S, and training dataset X is the training images and Y is their class labels. The output of the teacher model for any input i is computed by comparing KD is a technique to improve the performance and generalizability of smaller models by transferring the knowledge learned by a cumbersome model (teacher) to a single small model (student). The key idea is to guide the student model to learn the relationship between different classes discovered by the teacher model that contains more information beyond the ground-truth labels . Supposei is given as:This probability distribution will have a high probability value of n et al.  proposedS can produce a soft class probability distribution using the temperature parameter t, Y is the ground-truth label, t, Similarly, the student model n et al. .n is the size of the template. The final template KD loss, in this case, for the student model can be defined as:Recently, a number of approaches have been proposed to improve the conventional vanilla KD method by either introducing an additional loss function to the KD loss  or by foEquation . In addiThe student loss using cosine distance for template KD can be defined as follows:T and compact student model S, a batch of n training samples The training paradigm of the proposed template-driven KD approach can be described in a step-wise algorithmic manner as follows: (1) Given a high-performing teacher model Equation  for the This section presents the implementation details and evaluation settings used in this work.We evaluated our proposed approach on a semi-public internal dataset of periocular images captured using two different smartphones—iPhone 5S and Nokia Lumia 1020. We chose this database as it provides a realistic cross-device evaluation protocol that reflects the real use of such technology where the sensor that captures the reference might be different from the one capturing the probe. The database contains 152 unique periocular identities captured captured under mixed illumination settings with external illumination from sunlight and illumination from artificial room light in day-light. The periocular images were captured using the rear camera of the smartphones in a semi-cooperative manner (assisted capture setting). For each identity, a number of multiple samples were captured, resulting in a total set of 6642 images  and non-uniform illumination. The images also present a set of degradation due to motion blur and eye blinking, as encountered in everyday situations. Given that the images were captured in cross-illumination setting and with different cameras (iPhone 5S with 12 Mp and Nokia Lumia 1020 with 41 Mp), we first localized the periocular region using a Haar-cascade-based eye region detector. Further, the data were manually curated to eliminate any inconsistent segmentation in order to have only the periocular region. Given the nature of the images as illustrated in For the sake of the experimental evaluation, we further divided the whole dataset of 152 unique periocular identities as a disjoint set of training and training with no identities in common. We chose the first 100 identities for the training dataset and the rest of 52 unique identities as a testing set to report our experimental results in the rest of this work.The presented models in this work use ResNet  as baselWe followed the common choice for the knowledge distillation hyper-parameters ,48,49 wiWe evaluated the verification performance with cosine distance for comparison. The result is reported first for the models ResNet-34, ResNet-18, and ResNet-110, without applying KD. Additionally, we report the result of the conventional KD approach with KLD loss using the ResNet-110 model as the student with either ResNet-34 or ResNet-18 as the teacher, denoted as ResNet-110_KD34 and ResNet-110_KD18 respectively. Moreover, we demonstrate the performance of the proposed template-driven KD by reporting the results of using the ResNet-110 model as the student with either ResNet-34 or ResNet-18 as the teacher. These are denoted as ResNet-110_KD34MSE and ResNet-110_KD18MSE when the MSE loss is used, and as ResNet-110_KD34COS and ResNet-110_KD18COS when the cosine embedding loss is used.iPhone verification scenario: The reference and the probe images were acquired using the camera of the iPhone smartphone.Nokia verification scenario: Similar to the previous scenario, the reference and the probe images were acquired using a Nokia smartphone.Cross-smartphone verification scenario: The reference and probe images were captured using two different smartphone cameras, where the reference images were captured using an iPhone camera and the probe images were captured using a Nokia camera.For each of the settings, we investigated the verification performance under three different evaluation scenarios, defined as follows:The verification performance is reported using receiver operating characteristic (ROC) curves, area under the curve (AUC), FNMR at fixed FMR , and equal error rate (EER).The verification performances of the different experimental settings are presented in The first three rows of The achieved results by the conventional KD approach are presented in The results presented in In comparison to the recent works that focused on providing compact periocular recognition models, our proposed model (1.8 m parameters) outperformed the DenseNet-20 (1.1 m parameters) proposed by  on the mThis work was motivated by the need for compact biometric deep learning models for deployment on resource-critical devices. Such models aim to have the capability to extract highly distinctive templates, as the larger models (more learned parameters) do. KD has been used to map such knowledge by teaching a smaller student model to produce a similar output as a larger teacher model. However, given that KD is commonly optimized on the classification output of such models, the knowledge of extracting biometric templates from pre-classification layers might not be optimally transferred to the student model. Therefore, we proposed a novel template-driven KD approach that aims at teaching the template extraction knowledge to the student model. The proposed approach was evaluated on smartphone periocular verification in intra- and cross-device settings. The achieved results showed that when the targeted small model was trained with our template-driven KD approach, it consistently outperformed similar models trained without KD or with the conventional KD approach.


Serological assays for measuring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies have crucial applications in the control and surveillance of the current COVID-19 pandemic. A large number of such assays have been developed and are now commercially available. However, there are limited studies evaluating the performance of these tests. We evaluated the performances of the following six commercially available serological assays for detecting SARS-CoV-2 antibodies: (i) Genscript cPass surrogate virus neutralization test (Genscript cPass), (ii) Diasorin-SARS-CoV-2 S1/S2 IgG detection (Diasorin-S1/S2 IgG), (iii) Alinity SARS-CoV-2 IgG II (Alinity IgG II), (iv) Diasorin-SARS-CoV-2 TrimericS IgG (Diasorin-TrimericS IgG), (v) Roche Elecsys anti-SARS-CoV-2-cobas (Roche Elecsys), and (vi) AESKU enzyme linked immunosorbent assay (AESKULISA). The results of these tests were compared against the gold standard plaque reduction neutralization test (PRNT). Roche Elecsys had the highest sensitivity, and the Genscript cPass had the highest specificity. Diasorin-TrimericS IgG had the best overall performance with the highest agreement with the PRNT results. Parallel testing of Genscript cPass with Diasorin-TrimericS IgG and Diasorin-S1/S2 IgG had the optimum performance. Based on the receiver operating characteristic (ROC) curve, lowering the cutoff from 30% to 20% in the Genscript cPass significantly increased the sensitivity and the overall agreement with the PRNT results. Commercially available serological assays are good alternatives to the standard PRNT. However, further studies on larger sample numbers are required for optimization of the assay cutoff values and for evaluation of cost effectiveness.IMPORTANCE Commercial serological assays for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are now widely available. This study adds new knowledge regarding the optimization of these assays for evaluating postvaccination antibodies status. It highlights the positive and negative aspects of each assay in terms of sensitivity, specificity, and positive and negative predictive values, compared to the gold standard neutralization test. When using serological assays to assess postvaccine immune status, a balance of all parameters needs to be considered and not simply the high specificity. This balance is particularly relevant in the current situation where countries are aiming to mass vaccinate their populations and bring this pandemic under control. Assays with good sensitivity will have a lower percentage of false negatives and thus provide confidence for vaccination. Understanding the strengths and limitations of commercially available serological assays is important, not only for better application of these tests but also to understand the immune response and the duration of protection postvaccination. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the current COVID-19 pandemic, was first identified in a small group of patients in Wuhan, China, in December 2019 . By 30 J4–8–2 measuring antibodies to the S protein is of particular importance in preventing disease manifestation , followed by Alinity IgG II (98.1%) and AESKULISA (92.3%). Genscript cPass had the highest specificity (94.6%), followed by Diasorin-S1/S2 IgG detection (94.1%) and Diasorin-Trimeric S IgG (90%) .Genscript cPass surrogate virus neutralization test being a functional test was combined with a quantitative test in series testing, it showed that Genscript cPass in parallel testing with Diasorin-S1/S2 IgG or Diasorin-SARS-CoV-2 TrimericS IgG T2 (Diasorin-TrimericS IgG) had the optimum sensitivity (92.8%–95.6%) and specificity (85.1%–89.1%) and the results can be obtained in 3 hours . GenscriROC curves demonstrating the best ability to differentiate positive and negative results in comparison to the PRNT results were plotted for all the serological assays and the area under the curve (AUC) was largest for Diasorin-TrimericS IgG test (0.953), followed by Genscript cPass (0.939) and Diasorin S1/S2 IgG (0.935) assays .The ROC curve showed 20% as an optimum cut-off for Genscript cPass. 20 samples that were reported positive by PRNT were reported negative by Genscript cPass. 13 out of these 20 samples (65%) had inhibition percentage of ≥ 20%. This demonstrates the fact that 65% of the positive samples are missed by Genscript cPass by increasing the cut-off to 30% .Similarly, in Diasorin-TrimericS IgG by raising the cut-off to 40 BAU/ml, 3 out of 13 (23%) samples that showed discrepnacies with the PRNT reports were correctly reported as negative, therby increasing the specificity and overall agreement with PRNT.In Roche Elecsys by raising the cut-off from 0.8 to 5U/ml, our study found that 14 out of 28 (50%) samples that was initially reported as positive by the test were correctly reported as negative.Our results also showed that 2 serum samples that were identified as positive by all the serological assays were reported negative by the PRNT assay at <1:20 dilution; however, we did not find any sample with a positive PRNT result reported negative by all the other serology assays.Cohen’s kappa coefficient was calculated to see the agreement between the assays and PRNT and we found that Diasorin-TrimericS IgG had the maximum kappa coefficient value (0.750), showing highest agreement with PRNT (Table S5).Receiver operating characteristic (ROC) curves demonstrating the best ability to differentiate positive and negative results in comparison to the PRNT results were plotted for all the serological assays and the AUC was largest for Diasorin-TrimericS IgG test (0.953), followed by Genscript cPass (0.939) and Diasorin S1/S2 IgG (0.935) assays  Table S.The ROC curve showed 20% as an optimum cut-off for Genscript cPass. 20 samples that were reported positive by PRNT were reported negative by Genscript cPass. 13 out of these 20 samples (65%) had inhibition percentage of ≥ 20%. This demonstrates the fact that 65% of the positive samples are missed by Genscript cPass by increasing the cut-off to 30%. .Similarly, in Diasorin-TrimericS IgG by raising the cut-off to 40 BAU/ml, 3 out of 13 (23%) samples that showed discrepnacies with the PRNT reports were correctly reported as negative, therby increasing the specificity and overall agreement with PRNT.In Roche Elecsys by raising the cut-off from 0.8 to 5U/ml, our study found that 14 out of 28 (50%) samples that was initially reported as positive by the test were correctly reported as negative.This study compared six immunoassays for the detection of IgG antibodies against SARS-CoV-2 with the standard viral neutralization test PRNT. Our findings indicated that the Genscript cPass test had the lowest sensitivity but a high specificity and positive predictive value (PPV). Other studies which have evaluated Genscript cPass reported the test to have high sensitivity , 26. ThiThe ROC curve-adapted cutoff for the Genscript cPass test was around 20% inhibition, and when this value was used as a cutoff, the sensitivity increased to 92.31% and the overall agreement with the PRNT assay increased to 90.4%. Using Chi square, the difference was statistically significant, suggesting 20% inhibition as a more optimum cutoff for the test. Another study that evaluated the Genscript cPass test also suggested that the test might require specific cutoffs with respect to patient ethnicity, geographical background, and prevalence of COVID-19 infection. It also stated that the introduction of an equivocal range with repeat testing within the range of 18% to 22% can reduce the false-positive results .However, according to the ROC values, raising the cutoffs for Diasorin-TrimericS IgG and Roche Elecsys tests increased the agreement with the PRNT results, but the difference was not significant.Studies suggest the necessity for a revision of cutoff values provided by manufacturers, as most of the assay validations are done on a small sample size and among specific ethnic or regional groups . TherefoRoche Elecsys and Alinity IgG II demonstrated high sensitivity in this study. This agrees with the other studies, although, in contrast to our findings, these studies also reported high specificity –33. A me–Alinity IgG II was evaluated in a study which compared antibodies in postvaccination patients to those in prepandemic serum samples, and they reported high sensitivity and a specificity of 100% . While oDiasorin-TrimericS IgG had good sensitivity and specificity with the highest agreement with the PRNT results, which is in agreement with another study that evaluated the detection of circulating antibodies to SARS-CoV-2 using Diasorin . When a The CDC recommends serological tests with high sensitivity and specificity and tests detecting IgG or both IgG and IgM. This recommendation is because, currently, serological tests are recommended by CDC to only identify persons with previous infections or to identify resolving infections and to better understand the epidemiology of SARS-CoV-2 . HoweverThe WHO, in collaboration with Coalition for Epidemic Preparedness Innovations (CEPI) and the National Institute for Biological Standards and Control (NIBSC), has come up with the international standards for anti-SARS-CoV-2 immunoglobulins. These standards are crucial since vaccine developers have been using various immunoassays with different measuring units, which makes comparisons of immunogenicity difficult. Hence, with these recommendations, future studies can make the comparison of immunogenicity more standardized and reliable .Most COVID-19 vaccinations target the virus spike protein S, which contains the receptor-binding domain (RBD) that binds to the host receptor ACE2 to gain entry into the cell . Thus, sMost evaluation studies compare serological assay results with RT-PCR reports. This study compares commercial serological assays using the same serum sample and by evaluating the results with the gold standard PRNT assay. Thus, this study reduces the biases and provides a standard comparison.The study did not consider demographic details of the study participants like age, sex, and comorbidities, which can influence antibody response to vaccination. The number of samples were limited and additional factors such, days post vaccination and the type of vaccine were not taken into consideration in this study, which could have provided further insights on the serological assay performances. This study was also limited by the sample ppavailability to evaluate every samples with all six assays alongside the gold standard PRNT.Serological assays that are commercially available are efficient and show good agreement with the gold standard PRNT results. We recommend further studies on these serological assays with a large number of samples to help comprehensively evaluate the performance of these assays. Moreover, performing two tests in parallel can improve the sensitivity and provide a better alternative to the conventional PRNT assay. However, cost-effective evaluations will be required before drafting any recommendation. We also suggest optimizing cutoff values for these serological assays, based on prevalence, age, ethnic, and geographical variations. Recommendations based on the balance of all performance indicators, rather than just sensitivity or specificity, will help in the application of these serological assays in assessing postvaccination status.The study evaluated six different methods for the detection of antibodies against SARS-CoV-2 in postvaccinated individuals and compared the results against the gold standard PRNT. The six different methods were as follows: (i) Genscript SARS-CoV-2 surrogate virus neutralization test (Genscript cPass), (ii) Diasorin-SARS-CoV-2 S1/S2 IgG detection (Diasorin-S1/S2 IgG), (iii) Alinity SARS-CoV-2 IgG II (Alinity IgGII), (iv) Diasorin-SARS-CoV-2 TrimericS IgG (Diasorin-TrimericS IgG), (v) Roche Elecsys anti-SARS-CoV-2-cobas (Roche Elecsys), and (vi) AESKU enzyme-linked immunosorbent assay (AESKULISA).The PRNT is a gold standard serological test, which utilizes the ability of a specific antibody to neutralize a virus and, hence, prevent the virus from forming plaques in a cell monolayer. For this study, Vero E6 cells were grown to a confluent monolayer in a 6 well plate and for the positive control, serum sample collected from 60 patients 14-to-28 days postvaccination were pooled. For negative control, the viral stock dilution media, Dulbecco’s modification of Eagle medium (DMEM), was used. DMEM was preferred as a negative control over a negative serum sample to avoid interference from non-specific antibodies that may have been present in the serum. Results for each of the assays were read by comparing the plaque count in relation to the negative control and the interpretation of PRNT results were based on the dilution of serum identified at 50% reduction of the total plaque (virions) count in the negative control.Optimizations of the viral culture and plaque assays were performed in the lab during which conditions, such as days of cell seeding before use in the PRNT, working viral dilution, volume of inoculum, time for the infection of cell monolayer with serum/virus mixture, percentage of agarose medium, and days post-incubation, were assessed. Confirmation of the viral strain was accomplished using the polymerase chain reaction (PCR) and sequencing platforms before and after culture. The cut-off for the positive/protective serum was determined at the sample dilution of 1:20 based on previous studies and from the observations on comparative testing done in the lab , 45.ROC curves were constructed for the various assays and the AUC was calculated. The higher the AUC, the better the performance of the test at distinguishing between the positive and negative reports. The ROC curves for the assays were plotted with PRNT results as the state variable. From the constructed ROC curves, best threshold values were obtained and the sensitivity and specificity based on the optimal cut-offs were calculated.The study used 125 stored, de-identified excess serum samples from the lab. Samples were randomly chosen from stored samples that were collected for an ongoing, IRB-approved study on antibody production postvaccination. Since the study used de-identified samples, there was no access to associated patient demographics. The 69 positive samples were chosen randomly from samples collected from patients 14-to-28 days postvaccination who were tested for antibodies against SARS-CoV-2. The 56 negative serum samples were selected randomly from persons who tested negative by PCR test for SARS-CoV-2 and who did a baseline antibody test prior to vaccination and had no documented history of previous COVID-19 infection or PCR-positive report. All serum samples were separated, aliquoted and stored at −80 degrees Celsius. Each individual serum sample was tested for antibodies using the PRNT method and using all six different serological assays. The results of each method were compared to the gold standard PRNT reference method.Despite PRNT being the gold standard for detection of neutralizing antibodies, as mentioned earlier it is a complex sophisticated procedure therefore, in our study we considered combination testing of the commercial assays, which are relatively simpler and rapid with a quick TAT. To assess the performance of these assays when applied in combination, we considered parallel and series testing of the commercial assays. In parallel testing, two different assays are conducted alongside at the same time; in series testing, the tests are performed sequentially, when the first applied test shows positive.The Genscript cPass surrogate virus neutralization test is the only FDA-approved neutralizing antibody test. It measures the ability of the antibodies to block the virus attachment to the ACE2 receptors. It is a ELISA-based method where the absorbance of the sample is inversely related to the titer of the anti-SARS-CoV-2 neutralizing antibodies. The reports are given as percentage inhibition and, based on the recent FDA recommendation, ≥30% inhibition is considered positive for SARS-CoV-2 neutralizing antibody. Therefore, Genscript cPass surrogate virus neutralization test was used in series and parallel testing with the other serological assays to evaluate whether applying a functional  along with the other quantitative assays improves the performances of these assays in detecting neutralizing antibodies.All serological assays were carried out according to the manufacturers’ instructions and recommendations. Table S1 in the supplemental material shows the details of the various serological assays evaluated in this study.Ethics approval was obtained from Department of Health (DOH) institutional review board (IRB), Abu Dhabi. All methods were carried out in accordance with relevant guidelines and regulations.Informed consent was waived by the Department of Health (DOH) institutional review board (IRB), Abu Dhabi.Sensitivity, specificity, PPV, and negative predictive value, along with the 95% confidence interval (CI) were calculated to determine the performance of each serological assays. AUC was calculated from the ROC curves for each test. Chi-square tests were used for comparing the change in sensitivity, specificity, and overall agreement using the ROC-based cut-offs. Cohen’s kappa coefficient was used to identify percentage agreement between individual assays and PRNT reports. All data were analyzed using the SPSS version 28 statistical software.All the data are available from the corresponding author S.A.M., Director of Biogenix G42 lab, and can be produced on request.


Background: Increasing social interactions through communication technologies could offer a cost-effective prevention approach that slows cognitive decline and delays the onset of Alzheimer's disease. This paper describes the protocol of an active project named “Internet-based conversational engagement clinical trial (I-CONECT)” . The COVID-19 pandemic related protocol modifications are also addressed in the current paper.Methods: I-CONECT is a multi-site, assessor-blind, randomized controlled behavioral intervention trial (RCT). We aim to randomize 320 socially isolated adults 75+ years old  recruited from the community to either the video chat intervention group or the control group . Those in the video chat group receive a computer and Internet service for the duration of the study, which they use to video chat with study staff for 30 min/day 4×/week for 6 months (high dose), and then 2×/week for an additional 6 months (maintenance dose). Both video chat and control groups have a brief (about 10 min) telephone check-in with study staff once per week. The primary outcome is the change in global cognitive function measured by Montreal Cognitive Assessment (MoCA) from baseline to 6 months. Secondary outcomes include changes in cognition in memory and executive function domains, emotional well-being measured by NIH Toolbox emotional battery, and daily functional abilities assessed with the Revised Observed Tasks of Daily Living (OTDL-R). Eligible participants have MRIs at baseline and 6 months. Participants contribute saliva for genetic testing , and all video chats, weekly check-in calls and neuropsychological assessment sessions are recorded for speech and language analysis. The pandemic halted research activities and resulted in protocol modifications, including replacing in-person assessment with remote assessment, remote deployment of study equipment, and revised targeted sample size.Discussion: This trial provides user-friendly hardware for the conversational-based intervention that can be easily provided at participants' homes. The trial aspires to use age and culture-specific conversational materials and a related platform developed in this trial for enhancing cognitive reserve and improving cognitive function. It is well-established that social isolation  and loneliness  can lead to adverse health outcomes , 2, inclClinicalTrials.gov: NCT01571427) demonstrated high adherence in an older adult population (mean age 80 years) and efficacy in improving language-based executive functions among those with normal cognition and mild cognitive impairment (MCI), and psychomotor speed among those with MCI, suggesting that this intervention could be promising for delaying cognitive decline  funded pilot randomized controlled behavioral clinical trial, we developed a conversation-based social interaction cognitive stimulation protocol delivered by trained interviewers. In the study, we hypothesized that conversational interactions, i.e., the core component of social interactions, could enhance cognitive resrve, and thereby improve or sustain cognitive functions. Daily 30 min face-to-face communications with trained conversational staff were conducted over a 6-week trial period in the intervention group using personal computers, webcams, and a user-friendly interactive Internet interface, while control participants received only weekly phone contacts . A largeIn the ongoing RCT, we target socially isolated and lonely older adults because cognitive function can be enhanced further when activities are novel to individuals . Sociallwww.i-conect.org” , a multi-site, assessor-blind, randomized controlled behavioral intervention trial. Increasing daily social contact through communication technologies could offer a cost-effective home-based prevention approach that slows cognitive decline and delays the onset of Alzheimer's Disease and Related Dementias (ADRD), thereby reducing the overall societal burden of dementia.In this paper, we introduce the protocol of the active project called “Internet-based conversational engagement clinical trial (I-CONECT), The I-CONECT trial started recruitment and data collection in July 2018. During the second year of study recruitment, the COVID-19 pandemic hit the USA. The duration of trial before the pandemic was about 19 months, from July 2018 to March 2020. The pandemic halted research activities and resulted in protocol modifications, including replacing in-person assessment with remote assessment, remote deployment of study equipment, pausing the MRI assessments, and revising sample size for the main analysis. These COVID-19 pandemic related protocol modifications are also addressed in the current paper.The main objective of the I-CONECT trial is to investigate the extent to which online conversational interactions can prevent cognitive decline among socially isolated older adults with normal cognition or MCI. ClinicalTrials.gov: NCT02871921) aims to examine whether conversations through user-friendly video chats with multiple trained conversational staff can enhance cognitive functions and emotional well-being. This study is an extension of the previous pilot trial . We aim to recruit 50:50 split between those with MCI and normal cognition.We originally targeted those aged 80 and older, but due to a significant challenge in recruiting African American subjects in this age group in Detroit, we lowered the age criterion to 75 after 6 months from the start of the recruitment. Additionally, answering “often” to at least one question on the 3-item UCLA Loneliness Scale was added as a criterion after 10 months from the start of the recruitment to include those who failed to meet the social isolation criteria using LSNS-6 and frequency of conversations, but still expressed isolation  . Social https://mcuaaar.org/cores/community-liaison-and-recruitment-core/participant-resource-pool/) to facilitate recruitment of African American participants. Study advertisements are also used, including flyers distributed and/or posted in the community, the www.I-CONECT.org study website, write-ups for publication , and social media postings and online advertisements (Facebook).This study recruits via direct outreach to potential subjects . Potential subjects may be identified for direct outreach from publicly available data , word-of-mouth referrals, or through relationships with community partners, including Meals on Wheels People, a non-profit organization that delivers meals to low income or home-bound seniors and Area Agency on Aging (AAA) which provides various services to home-bound seniors. The study also collaborate with the Healthier Black Elders Registry in Detroit, which is maintained by the Michigan Center for Urban African American Aging Research (MCUAAAR)  is included in the consent form to participate in the study, but genotype assessment (information on APOE ε4) is made optional to alleviate participant privacy concerns. Six Data and Safety Monitoring Board (DSMB) members were selected before the data who are not in the same institutions as the PIs and Co-investigators of the project and included two members from the funding source .Randomization is conducted by a data manager who are not analyze study outcomes. Participants are randomized in a 1:1 ratio to the experimental or control conditions balancing the following factors: age , sex, years of education (high school completion vs. below high school education), and cognitive status  and MoCA score (MoCA score: 23 and above vs. lower than 23). A minimization algorithm is used for randomization . This meIn the first face-to-face interview, we use the MoCA  to obtain a rough estimate of cognitive status and exclude those with suspected dementia , 24. We The assessors are blind to participants' group assignments. We ensure blindness by having a total separation between assessors and staff who engage in conversational sessions. Assessors are not allowed to discuss any issues with participants that reveal whether the subject is in an experimental or control group. Experienced and trained research associates conduct baseline, post-trial (6 month), and 1 year follow-up assessments.All participants who consent to participate receive $50 at screening. Individuals who consent to be screened yet do not meet the inclusion criteria also receive the compensation. Those who receive MRI testing receive $100 for the first MRI visit and $100 for the second MRI visit. Participants receive $50 if they complete testing at the Month 6 time point. Participants receive $75 if they complete testing at the Month 12 time point. The compensations are not pro-rated in the event that a subject discontinues the study early for any reason. In total, a participant who completes the study could receive either $175 (no MRI) or $375 (two MRI visits). These amounts are carefully determined so that the compensation does not constitute undue inducement and also allow participants to retain federal and state income assistance, if applicable.Briefly, the experimental group engages in 30 minutes semi-structured conversations with trained and standardized conversational staff 4 days per week for 6 months (high dose), followed by twice per week for an additional 6 months (maintenance dose). Both intervention and control groups receive a phone call once per week (~10 min) to assess their changes in health and monitor the level of social interaction. This weekly phone call also serves to retain control group participation. Conversational sessions (both audio and video) and weekly calls (audio) are recorded and stored on HIPPA compliant servers at OHSU. We examine the efficacy during the first 6 months (high dose period) and the following 6 months (maintenance dose period) separately. During the maintenance dose period, we aim to examine whether we can maintain gains or improve primary and secondary outcomes beyond those obtained during the high dose period. However, due to the COVID-19 pandemic, the additional follow-ups beyond the first 6 months have been discontinued for the majority of participants (discussed later in detail).In our previous project, we developed a systematic standardization approach which includes (1) practice with other conversational staff and senior volunteers before the actual trial (at least 40 h of training), (2) recording conversational sessions and listening to them for quality control and analyzing word counts generated by interviewer/interviewee. Additionally (3) every Monday, we assess negative and positive affects using International Positive and Negative Affect Schedule Short Form (PANAS) immediately before and after the 30 min conversation and compare the change in scores across interviewers . We examPC usage was a significant predictor of whether subjects were willing to volunteer in our previous study using communication technologies . Thus, aThe conversation is semi-structured with pre-specified topics with crafted questions to promote natural conversations. Over 150 themes were developed, including topics like historical events, philosophical ideas, social issues, leisure activities and travels. During every session, conversational staff shows three topics under one theme . Allowing the participant to select a topic of the day provides him/her a sense of autonomy, which improves motivation to participate in the conversation.In order to recruit African American participants, all the study flyers and advertisement as well as conversational picture stimuli were modified from those used for the white participants to reflect their cultural tradition and heritages. It is well-known that the recruitment of minority groups requires trust and established relationship for successful recruitment. We provided a number of lectures discussing brain health at local community events and circulated study flyers in advance before starting official recruitment.https://naccdata.org/) Uniform Data Set version 3 (UDS V3)  for within-project cross validation. Both NACC UDS V3 and NIH-Toolbox are developed to assess changes in cognition associated with normal aging as well as ADRD (Alzheimer's Disease and Related Dementias). NIH Toolbox also includes a comprehensive emotion battery that assesses psychological well-being, which will be used to test the hypothesized mediation relationships.A thorough cognitive assessment with standard neuropsychological tests is administered at baseline, 6 months (end point of high dose) and 12 months (end point of maintenance dose) visits, using the National Alzheimer's Coordinating Center (NACC) ((UDS V3) , 36. We Secondary outcomes include language-based executive function measured by Category Fluency  (CAT)  and episOther secondary outcomes: Other secondary outcomes include changes in (1) psychological well-being measured by the NIH Toolbox emotion battery, (2) daily functioning  measured by the Revised Observed Tasks of Daily Living (OTDL-R) , and (3)The NIH toolbox Emotion battery is used to assess changes in psychological well-being. It includes four general domains: negative affect , psychological well-being , stress and self-efficacy (perceived stress and self-efficacy), and social relationships . The total scores are used to assess changes. We hypothesize that the efficacy shown on cognitive functions through this intervention is partly mediated by improved psychological well-being.Revised Observed Tasks of Daily Living (OTDL-R) is a performance-based test of everyday problem solving . The meaStudies have shown cross-sectional associations between social network size and brain structure , 44. To Examining functional connectivity using resting-state fMRI, we specifically focus on large-scale brain networks, which support a broad array of cognitive processes including social function . We hypoAdditionally, we measure brain perfusion using arterial spin labeling (ASL). Blood perfusion is critical for delivery of oxygen and glucose throughout the brain. Reduced cerebrovascular health, common in aging, can affect perfusion and have downstream effects on cognitive performance . TherefoOther exploratory outcomes include changes in speech utterances and characteristics (acoustic and linguistic features) and medication adherence measured by an electronic pillbox that stamps time when the medication is taken . All parHumans are heterogeneous with different preferences, and we do not assume one intervention can work for all. It is also important that subjects can sustain the intervention effects after the RCT is over and to maintain the behavior changes imposed during the trials. Therefore, it is of considerable importance to identify characteristics of subjects who can adhere to our intervention and benefit from it. For example, in our previous pilot project, we found different levels of efficacy depend on aspects of participants' personality . AdditioThe following variables will be considered for identifying the characteristics of those who gained: APOE ε4 genotype , personality (NEO-5 factor inventory) , GeriatrOur protocol has two stages: one under the high-dose period and another under the maintenance dose period. During the maintenance dose period, we will examine whether maintenance dose (30 min conversation twice per week for 6 months) will lead to further improvement in primary and secondary outcomes among the intervention group or at least sustain the improvement observed during the high dose period. Since the maintenance dose requires only half the conversational frequency, if this dose is proven to be sufficient to maintain the gained effect attained during the high does period, it serves as a cost-effective maintenance dose.We will conduct modified intention to treat (ITT) analysis where those with at least one follow-up assessment under each dose are included in the analysis. We will use changes in primary and secondary outcomes for two separate hypotheses, one for high dose (from baseline to 6 months assessment) and another for low dose (from 6 months assessment to 12 months) using linear regression models where dependent variables are outcomes specified above at month 6 (M6) (controlling for the outcome at baseline) and at month 12 (M12) (controlling for the outcome at M6). For non-continuous outcome variables , we will use generalized linear mixed effects model to estimate the occurrence of events over time. Per protocol analysis (PPT) will be conducted by using the participants who completed at least 80% of the expected sessions under each stage.d = 0.53 when comparing pre-post changes in language-based executive function measured by the Category Fluency test between the control and experimental groups among participants with CDR = 0 and CDR = 0.5 combined. CDR = 0 group showed even stronger efficacy (Cohen's d = 0.68) . In the previous pilot study, the Mini Mental State Exam (MMSE) was used as an indicator of global cognitive function. In the current study, we use the MoCA for the primary outcome, which has been shown to be more sensitive to subtle cognitive decline associated with MCI than the MMSE  based on the pilot study result. We aim to randomize 160 subjects with MCI and 160 subjects with normal cognition (targeted 160 Caucasian and 160 African American participants). Given 20% drop out (10% drop out each between baseline and M6 and between M6 and M12), we expect 128 completers each for those with MCI and normal cognition. With this sample size , we would have 80% power to detect Cohen's d of at least 0.5 at alpha = 0.05 (two-tailed test) within each diagnostic group.Our previous pilot study (based on a 6-week intervention) yielded a Cohen's  = 0.68)  , we were required to process several protocol modifications.https://naccdata.org/ for details) upon adaptation the T-COG by the National Alzheimer's Coordinating Center as an alternative to the Uniform Data set version 3 (UDS V3). The T-COG includes the blind MoCA which eliminates visual items from the original MoCA test (https://www.mocatest.org/paper-tests/moca-test-blind/). The modality change is expected to affect cognitive test scores and therefore it is not feasible to examine pre-post changes in certain cognitive tests including MoCA (our primary outcome) and Trail Making Test B (one of secondary outcomes tapping executive functions). Additionally, before switching to remote assessments, participants approaching either M6 or M12 were offered the option to continue in the study at their current level of participation, rather than decreasing chat frequency to maintenance dose or ending chats and/or calls. This extension allowed us to collect assessment data within a reasonable time frame of a subject's final intervention dose, once assessments were able to resume. Although individual participation duration was carefully monitored and will be adjusted for in the statistical analysis, these two major modifications in the study protocol  dictated that we limit our main analysis of this trial to those who completed the end point assessments before March 18, 2020 so that we could: (1) use the outcomes collected with the same modality between baseline and follow-up assessments and (2) avoid contaminated effects of COVID-19 on their emotional status, such as amplified efficacy among the experimental group under the COVID-19-induced social isolation experienced by many older adults in the USA. This decision was suggested by the research team and approved by the DSMB members in September of 2020.First, we switched our assessment protocol from in-person assessments to complete remote assessments in late June (~3 months after the stay-at-home order). This includes sanitizing all the equipment before delivering to participants, developing color coded easy-to-follow instructions for setting up the equipment, and dropping them off at participants' homes (contactless delivery at the door of the home) and observing from distance to ensure equipment is successfully collected by the participant. Along with changes in logistics of delivering and collecting video chat equipment, we switched to telephone cognitive testing  will be imputed with the best approach available and used for assessing pre-post changes in the primary outcome. Supplemental analyses will be conducted after main analyses which use only those who completed the assessments before March 18, 2020.Additional protocol changes include as follows: (1) MRI assessments will be discontinued until COVID-19 related mandatory social distance is lifted. As of 1/1/2021, no MRI assessments have been conducted. (2) the NIH toolbox cognitive battery and grip strength and OTDL-R are no longer administered after COVID-19 due to challenges in standardizing administration. However, the NIH Toolbox Emotional battery is still being collected by delivering a hard copy and asking participants to mail the survey back using pre-stamped envelopes. Follow-up calls are being made to avoid prolonged delay in administration of the survey if participants are late in returning the survey. (3) Due to the cost and lapsed time incurred to maintain staff during the COVID-19 pandemic when no new recruitment and endpoint assessments could occur, it was determined not feasible to continue interventions beyond M6. Although the original proposal aimed to provide reduced dose of intervention (twice per week) for additional 6 months to examine the efficacy of maintenance dose, the study team and DSMB determined that we need to end the trial at 6 months to provide sufficient time to perform data clean up and analyses. This arrangement is also preferred because of assessment modality changes to be faced by the majority of our participants between baseline and M12 assessments. (4) Subjects are required to provide their own, preexisting Internet connection in order to participate in the trial after March 18, 2020, as we cannot safely have Internet vendors enter the homes to install new connections. An Internet stipend is provided.d of 0.8 with 80% power, alpha = 0.05 (two tailed). In the pilot R01 trial , we found Cohen's d of 0.53 after 6 weeks of daily video chats. In our current experiment, the experimental group will experience 6 months of video chats. Therefore, the effect size of 0.8 is not necessarily an overestimation of our trial effect.As discussed above, our main analyses utilize participants who completed M6 assessments before March 18, 2020. Fifty five participants completed both baseline and 6 months assessments before COVID-19 pandemic and these participants will be used for the main analysis. Among the 55 participants, 46 self-identified as non-Hispanic White and 9 identified as non-Hispanic Black. Twenty four participants had normal cognition and 31 were diagnosed with MCI. This sample size is powered to detect Cohen's We plan to unblind the data for the participants who completed the intervention before March 18, 2020 upon acceptance of this protocol paper. The remaining data will be blinded until we complete all the M6 assessments for all participants. The reports for the main analyses based on the pre-COVID participants and supplemental analyses based on the post-COVID participants are expected to be available by the end of 2021 and 2022, respectively.Based upon a successful pilot project completed in 2014 , the curThere are some limitations in the study design. It would be ideal to have a larger sample size for each racial group since there might be cultural differences that differentiate the levels of efficacy. Even though we aim to recruit 50% African American participants, once we stratify the analysis by cognitive status , the sample size within each strata diminishes quickly. Duration of follow-up is also limited up to 1 year (and modified as up to 6 months after COVID-19 pandemic) even though most epidemiological studies that provided evidence in the past for the association between social engagement and cognition had been following participants from mid-life to late-life. That said, this RCT can test whether enhancing social interactions through video chat can improve cognitive function at later life and open a new venue for cost-effective home-based behavioral interventions in the community.The studies involving human participants were reviewed and approved by Institutional Review Board at the Oregon Health & Science University. The patients/participants provided their written informed consent to participate in this study.HD, KW, KP, BH JK, LMS, PP, PL, EA, JL, MM, and LCS contributed to the conception and design of the study. KY and HD contributed to draft and edit the full manuscript. HD received the funding. All authors contributed to manuscript revision, read, and approved the submitted version.MM was employed by company Syneos Health. The remaining 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.


This unique molecular machine couples electron transfer from quinol to O2 with the generation of a proton motive force without proton pumping. Apart from energy conservation, the bd enzyme plays an additional key role in the microbial cell, being involved in the response to different environmental stressors. Cytochrome bd promotes virulence in a number of pathogenic species that makes it a suitable molecular drug target candidate. This review focuses on recent advances in understanding the structure of cytochrome bd and the development of its selective inhibitors.Cytochrome  T. Tb558 rbd oxidases further differ in the heme arrangement within the triangle  into TM helix 3 of CydA, resulting in its stronger curvature. This generates a voluminous cavity for the binding of dioxygen at the axial position of heme d, which is enclosed by hydrophobic isoleucine and phenylalanine residues (d (see below).Unexpectedly, the heme orthogonally placed to heme rificans , is hemeructures ,13,14,15es methyl]-1-ethyl-indole-2-carboxylic acid (MQL-Hing site . The comectively . SimilarN-(4-(4-(trifluoromethyl)phenoxy)phenyl)quinazolin-4-amine (ND-011992), that seems to target cytochrome bd pyrimidin-4-amine (named compound 19) appeared to be the most potent compound, having ATP IC50 of 6 to 18 μM against all three strains in the presence of Q203. Based on their results, the authors will aim to develop new members of this class of compounds with improved potency and acceptable pharmacokinetics to warrant in vivo evaluation [Hopfner et al. found that another class of compounds, thieno[3,2-bacteria . The aut of Q203 . None ofaluation .bd should be very helpful for the design and discovery of new antibacterial agents which do not affect the energy metabolism of human organs and tissues. The studies reviewed above show that at present the main efforts are concentrated on the search for inhibitors specific to the cytochrome bd quinol oxidation site. We assume that other structural segments of the enzyme, such as the oxygen-binding site, specific intraprotein oxygen channels, and proton transfer pathways, should also be considered as promising targets when creating effective inhibitors of the bd oxidase. In general, the development of next-generation antibiotics targeting the respiratory chain enzymes including cytochrome bd will contribute to overcoming a great global public health challenge—antibiotic resistance.New structural information on cytochrome


Following publication of the original article , the autThe following two paragraphs (paragraph 9 and 10 in the Discussion section) have been corrected to reflect citation  respectively.“Thus, aging-related characteristics, which have been proposed to explain susceptibility to SARS-CoV-2 infection and progression to COVID-19 [32], could be the main reasons related to the fatal COVID-19 outcome observed in our cohort.Many of the differences we have observed between the first and second wave were totally unexpected, also considering that no available data have been delivered on this aspect yet. One plausible reason could be the different approaches to the disease in the two waves, such as the patient management skills, the training and experience acquired by healthcare personnel, the increased number of beds reserved for COVID-19 patients, with dedicated staff and adequate equipment, as well as the scientific knowledge acquired on this newly discovered viral infection. The explanation cannot be the onset of viral variants that although present since August 2020 [33] have become the prevalent SARS-CoV-2 strain in Brescia only after February 2021. It might be interesting to investigate further patients hospitalized after this date.”The references can be accessed in the original article  which ha


Fibers used as reinforcement can increase the mechanical characteristics of engineering cementitious composites (ECC), but their frost resistance has received less attention. The mechanical properties of various fiber cementitious materials under the dual factors of freeze-thaw action and fiber dose are yet to be determined. This study examines the performance change patterns of cementitious composites, which contain carbon fiber, glass fiber, and polyvinyl alcohol (PVA) fiber at 0%, 0.5%, and 1% volume admixture in freeze-thaw tests. Three fiber cement-based materials are selected to do the compression and bending testing, and ABAQUS finite element modeling is used to assess the performance of fiber cement-based composite materials. The microscopic observation results show that the dispersion of glass and PVA fibers is higher than that of carbon fibers. As a result, the mechanical characteristics of the fiber-doped cementitious composites increase dramatically after freeze-thaw with increasing dosage. The compression test results show the frost resistance of carbon fiber > PVA fiber > glass fiber. In addition, the bending test results show the frost resistance of carbon fiber > glass fiber > PVA fiber. The 3D surface plots of the strength changes are established to observe the mechanical property changes under the coupling effect of admixture and freeze-thaw times. ABAQUS modeling is used to predict the strength of the cementitious composites under various admixtures and freeze-thaw cycles. The bending strength numerical equation is presented, and the bending and compressive strengths of three different fiber-cement matrix materials are accurately predicted. Engineering fiber-reinforced cement-based composites (ECC) are used to improve the initial mechanical performance of traditional cement-based materials by doping fibers to overcome the inadequacies of traditional cement-based materials. The impact of various types of fibers  on the performance of cement-based composites are now the focus of the main study under various doses, lengths, and modification treatments. Furthermore, a few types of research focus on the ideal mix ratio by examining various fiber kinds and sizes. Other studies are focused on using fiber to improve the durability of cement-based products, such as fire resistance, high-temperature resistance, and frost resistance.To investigate various fibers on the performance of cement-based materials, some scholars primarily conduct mechanical experiments to study the performance improvement of various fiber types, dosages, length dimensions, and modification treatments ,2,3. NiuOther scholars’ research discussed the hybrid fiber mix ratio and size in cementitious composites. Zhang et al.  studied In addition, some studies found that fibers have a specific effect on the durability of cement-based composites at a certain amount of fiber ,27,28. NFibers have greatly improved the different properties of cementitious products. However, few studies have compared the properties and finite element simulations of various fiber-cement matrix composites. Therefore, this paper uses several fiber-cement matrix composites to investigate cementitious composites’ mechanical properties and frost resistance. This study aims to investigate the extent of the effect of different fibers on the properties of cementitious materials under the effect of both doping and freeze-thaw factors. The strength under these factors is well predicted by finite element modeling and software fitting of mechanical data equations. Microscopic techniques are used to observe damaged surfaces under freeze-thaw action. The study combines mechanical testing machine tests and computerized ABAQUS finite elements for mechanical tests, a relatively new approach. Strength predictions are made using Origin and Curve 3D software, and the data were fitted to obtain surface equations. As a result, it can effectively predict the flexural strength of the material, which helps to establish theoretical conditions for engineering practice with other fiber cement-based materials and provides a basis for determining the mix ratio of fiber cement-based composites.Fiber cement-based materials mainly included cement, fibers, fine aggregates, water, and additives. The performance of cement-based materials is improved by adding fibers. Cement: Swan brand P·O 42.5-grade cement is used in this study. All indicators of cement meet the requirements of GB 175-2007 ‘General Portland Cement’. The material property of Portland cement is displayed in Three types of fibers with a length of 10 mm were selected, including carbon fiber, glass fiber, and PVA fiber . The carThis study investigated different fibers that affect the mechanical characteristics of cement-based composites. The water-cement ratio was 0.5 for this test’s control group and incorporated carbon fiber, glass fiber, and PVA fiber. The experimental test included seven groups of bending and compressive specimens. Each group of specimens had three identical specimens to guarantee data consistency. According to the code of ‘Standard for Long-term Performance and Durability Test Methods of Ordinary Concrete (GB/T 50082-2009)’, the freeze-thaw test uses the rapid freeze-thaw method. As shown in The bending and compression tests were carried out according to the ‘Cement Mortar Strength Test Method (ISO) (GB/T 17671-2020)’. The loading speed of the bending tester met 50 N/s ± 10 N/s. The load was evenly distributed along the width of the specimen without generating torsional stress. After the bending strength test was completed, two half-section specimens were taken out, and the compressive strength test was carried out on the side of the half-section specimens. In the compressive test, the load was uniformly distributed at a rate of 2000 N/s ± 200 N/s until failure. The size of the bending test piece was 40 mm (height) × 40 mm (width) × 160 mm (length), and the effective size of the compression test piece was 40 mm (height) × 40 mm (width) × 40 mm (length).The specimen’s surface was scraped flat after it had been vibrated.The mold was disassembled and placed in water at intervals for curing at 20 ± 1 °C; after 24 h.The bending and compressive tests were carried out.The YAW-300H testing machine produced by Jinan Hengruijin was used for the test, as shown in In order to further study the different fibers’ mechanisms, a sample was selected from the bending test. A GAOSUO digital microscope was used to examine the cross-sections of several fiber cement-based material specimens microscopically (observation magnification was 400 times), as shown in Magnification: 1×~500×Imaging distance: manual adjustment 0~infinityImage resolution: standard 640 × 480Fixed base: universal metal base GAOSUO digital microscope specifications are as follows:Each group has three test pieces with the same conditions. By observing the failure section of the bending test, the carbon fiber cement-based composite material under different freezing and thawing times is shown in The glass fiber’s dispersion effect in the cement-based composite material section is better than PVA fiber and carbon fiber. As the number of freezing and thawing increased, the slippage of fibers and cement-based materials increased. However, carbon fibers ensure lower slippage with cement-based materials than other fibers after freezing and thawing. The main reason is that the carbon fiber has a higher elastic modulus and reduced the deformation under freezing and thawing.The compression test results are shown in Compressive strength can be increased by incorporating a certain volume percentage of fibers, but it will significantly decrease beyond a limit percentage. It can be explained that fibers in cement-based materials have a unique bridging effect, limiting the expansion range of internal cracks. Uneven dispersion of internal fiber clusters and voids will result from an excessive volume fraction. On the other hand, an increase in voids degrades the cement body’s compactness and strength. First, different fibers in cement have a bridging effect determined by their elastic modulus and tensile strength. Second, the effect is proportional to the friction force between the cement bases. Carbon fiber’s elastic modulus is significantly greater than that of glass and PVA fiber, making it the best material for this application. A microscopic examination revealed that PVA fiber had the highest dispersibility and exhibited no slippage when mixed with cement. As a result, carbon fiber has a more significant reinforcing effect at a specific dosage than PVA or glass fiber. The compressive strength of fiber-doped cement-based materials was substantially more significant than that of the control group after 50 and 100 freeze-thaw cycles. This demonstrates that the fiber’s frost resistance to the cement-based composite material has increased dramatically. The rationale for this is that fibers can help prevent fractures in cement-based composites induced by thermal expansion and contraction during freeze-thaw. Additionally, the fiber cement composite expands and contracts continuously and provides energy for the fiber with increasing freezing and thawing times. As a result, the absorbed energy of fiber reaches a limit level, losing the enhancement effect. This demonstrates that carbon fibers store the most energy of the three fibers and withstand greater loads. It can be determined that the strength of carbon fiber cement-based composites declines the slowest with increasing freeze-thaw periods, followed by PVA fiber and glass fiber, at 0.5% and 1% volume content. Carbon fibers have a high modulus of elasticity  and relaThe test suggests that fiber doping can boost bending strength considerably. This is due to the fiber’s unique bridging properties within the cement-based substance. The bottom of the cement-based composite material gets stretched when the higher end is crushed. Because the cement-based substance has a low tensile strength, it can help to prevent bottom fractures from developing. The bending strength of the fiber-doped cement-based material was considerably higher than that of the control group after 50 and 100 freeze-thaw cycles, demonstrating that the fiber is helpful to the cement-based composite material’s frost resistance. Carbon fiber had the most prominent influence on the bending strength of cement and materials, followed by glass fiber and PVA fiber. It can be concluded that the strength of the carbon fiber cementitious material decreases the slowest with an increasing number of freeze-thaw cycles, followed by PVA fiber and glass fiber. Carbon fibers have a high modulus of elasticity that helps cement composites resist deformation and crack formation during freeze-thaw cycles. In the bending test, the mortar’s tensile strength and ultimate strain were small. The bending strength mainly depends on the fiber bearing capacity, and the effect of the fiber is noticeable. Carbon fibers with higher tensile strength and less elongation had the greater load-bearing capacity in bending tests. Additionally, carbon fibers store more energy than other fibers, protecting them from premature failure during freeze-thaw cycles. Thus, carbon fiber exerts a better frost resistance than other fibers in the bending test, followed by glass and PVA fiber.The bending-compressive strength ratio can be used to reflect the crack resistance of the material, and it can also characterize the ductility and toughness of cement-based composites. Under the circumstances of 0, 50, and 100 times freeze-thaw, the bending-compressive strength ratio of PVA, glass, and carbon fiber composite materials is determined at 0%, 0.5%, and 1% volume fraction, respectively. The bending-compressive strength ratio results are shown in The bending-compressive strength ratio of 1% carbon fiber composite material was as high as 18.25% without freeze-thaw cycles. When the number of freeze-thaw cycles was 50, the bending-compressive strength ratio of 1% glass fiber composites was as high as 19.1%. The maximum bending-compressive strength ratio of 1% volume fraction of carbon fiber composites was 19.08% under the action of 100 freeze-thaw cycles. Compared with the control group, the bending-compressive strength ratio of cement-based composites continued to increase with fiber content, and 1% was more significant than 0.5% and greater than 0. The three fiber composite materials’ bending-compressive strength ratio showed an upward trend. PVA fiber composite materials increased marginally, whereas carbon fiber and glass fiber composite materials significantly increased.The fiber volume is positively related to the more excellent bending-compressive strength ratio. As a result, fiber can significantly increase the sample’s toughness and ductility. The bending-compressive strength ratio is influenced differently depending on the kind of fiber, with glass fiber being greater than carbon fiber and PVA fiber. The impacts of freeze-thaw will exacerbate this discrepancy.The 3D surface maps for different fiber types are established for fiber cement-based composites under the two variables of freeze-thaw duration and dose. For example, The three fiber composites’ compressive strength grew initially and declined when the freeze-thaw cycles remained constant. The compressive strength diminished as the number of freeze-thaw cycles rose while the fiber content remained constant. The shaded portion of the projected area at 0.5% volume fraction and 0 freeze-thaw cycles was observed. The strength of the doped carbon fiber composites decreased toward the periphery and were relatively symmetrical at about 0.5% volume fraction. The difference in compressive strength between 0 percent and 0.5 percent volume fractions was not apparent in the glass fiber composite, and the influence of the dose was modest. However, the strength dropped sharply when the dosage was more significant than 0.5%. The strength of the glass fiber composite material reached the lowest when the freeze-thaw cycle was 100 times and the volume fraction was 1%. The change rule of PVA fiber composite material in the figure was similar to that of carbon fiber composite material. The two fiber composite materials were symmetrical at about 0.5%, but the compressive strength of PVA fiber composite materials was lower than that of carbon fiber at any point. Coordinate points  signify the following: volume fraction, freeze-thaw times, compressive strength. The highest point of carbon fiber composite materials reached the maximum compressive strength at O1 , while the lowest point reached the lowest compressive strength at P1 . The highest point of glass fiber composite material reached the maximum compressive strength at O2 , while the lowest point reached the lowest compressive strength at P2 . The highest point of PVA fiber composite material reached the maximum compressive strength at O3 , while the lowest point reached the lowest compressive strength at P3 . The amount and number of freeze-thaw cycles corresponding to the highest strength point were the same for the three fiber composites, but the amount and number of freeze-thaw cycles corresponding to the lowest strength point were different.The appropriate amount of fiber cement-based composite material can significantly improve its frost resistance, thereby increasing the compressive strength of the cement-based material. However, excessive fiber will result in insufficient internal compactness of cement-based materials and an increase in voids, which will cause the frost resistance to decrease again. In addition, the compressive strength decreases with the increase in the number of freeze-thaw cycles. The compressive strength was the highest when the dosage was 0.5%, and the freeze-thaw cycle was 0 times. Therefore, the compressive strength presents a triangular pyramid-shaped space surface.A 3D space map was created for several fiber types in fiber cement-based composites under two variables: freeze-thaw timeframes and content. The bending strength of the three fiber composites increased sharply from 0 to 0.5% by volume fraction when the number of freeze-thaw cycles was constant. However, the bending strength increased slowly between 0.5% and 1% by volume fraction. The bending strength decreased slowly as the number of freeze-thaw cycles increased as the fiber content was constant. It shows that the fiber can improve the bending strength of cement-based materials after freeze-thaw. The bending strength has a tilted contour map distribution when seen from the perspective of the 3D projection surface. Coordinate points x, y, and z2 respectively represent dosage, freeze-thaw times, and bending strength. The highest point of carbon fiber composite materials reached the maximum bending strength at M1 , and the lowest point reached the lowest bending strength at N1 . The highest point of glass fiber composite material reached the maximum bending strength at M2 , and the lowest point reached the lowest at N2 . The highest point of PVA fiber composite material reached the maximum bending strength at M3 , and the lowest point reached the lowest bending strength at N3 . The volume fractions and freeze-thaw times corresponding to the highest and lowest points of the three fiber composite materials were the same. The more fiber content, the greater the bending strength, and the more freeze-thaw cycles, the lower the bending strength.The test results show that the fiber works when the cement-based material is bent at a low fiber content, preventing bottom cracks, thereby significantly improving the bending strength. However, if the volume fraction exceeded 0.5%, the material’s bending strength decreased. At the same time, the bending strength decreased with the increase of freeze-thaw times. Therefore, the bending strength reaches its highest when the volume fraction is 1% with 0 freeze-thaw cycles and exhibits a spatially folded surface. Carbon fiber, glass fiber, and PVA fiber composites exhibit relatively consistent patterns with different strength magnitudes. This has to do with the fiber’s physical characteristics and its interaction with cement-based materials.The bending strength of fiber cement-based composites exhibits the same equation under the action of different fiber volume fractions and freeze-thaw cycles. On this foundation, a formula model for the bending strength of cement-based composites with different fiber doping under the action of volume fraction and freeze-thaw is proposed.0 is 0% volume fraction and 0 freeze-thaw bending strength; B, C, D, E and F are constant parameters; x is fiber volume fraction; y is freeze-thaw times. The main function of the POWER function is to return the power of a given number. Parameter a represents the base, and parameter b represents the exponent.After determining the type of fiber, we request to optimize a performance parameter or multiple performance parameters. We present a mathematical method for this type of binary parameter optimization. For practical problems, a particular performance parameter usually first increases and then decreases with the increase of the independent variable (or first decreases and then increases). Therefore, we assume that this is a binary function describing the problem. 0, for example, indicates the bending strength under zero content and zero freeze-thaw cycles. The parameter values B, C, D, E, and F impact the bending strength of various fibers under varied content x and freeze-thaw periods y. The greater the value of B, C, F, F parameters, the greater the strength, indicating the better the material properties. There is no apparent link between the size of C and D and the strength.The six parameters of various fibers are shown in The optimal volume fraction may be estimated using this experimental approach under the known freeze-thaw periods of a specific fiber composite material. This method verifies that the fiber improves cement-based materials’ mechanical properties and frost resistance. It can reveal the law of bending strength of different kinds of fiber cement-based materials with the number of freeze-thaw cycles and volume fraction. This approach is appropriate since the strength of cement-based materials is discrete. The equation’s freeze-thaw times and mixing amount should be selected according to particular engineering experience, and the corresponding parameters have the correct range of variation.Firstly, the simulation utilizes Python to generate fiber code. The principle of the Python code is first to determine the three-dimensional parameters of the specimen, such as 4*4*16 cm, and then import the fiber diameter, length, and volume content. The coordinates of the fibers appeared randomly within the three-dimensional parameters of the specimen. It was imported into ABAQUS to build uniformly distributed and discrete fiber parts. Then, concrete parts were built, and the C40 concrete damage plasticity parameters were given to the cement-based composites under 0, 50, and 100 freeze-thaw cycles. Moreover, the performance indicators such as elasticity, plasticity, and density of the glass fiber were given to the fiber. Finally, the contact between the fibers and the cement-based material was done in a “built-in” fashion. The mesh accuracy of the fiber part is four times that of the concrete mesh. The composite model was subjected to multiple compression and bending simulation tests, and the following results were finally obtained.A 4 cm cube fiber cement-based composite was obtained by ABAQUS modeling to simulate the compression test. Compared with A 4 × 4 × 16 cm fiber–cement composite was created using ABAQUS modeling to simulate a bending test, where two arcuate rigid bodies support the bottom and the upper half is the test piece. The ABAQUS simulated stress cloud diagram shows that stress concentration occurs at the two points at the bottom and the contact point where the force is applied at the top, as shown in It can be concluded that the fibers present a uniform and non-cross distribution under the simulation inside the cement base. However, the measured strength is somewhat lower than the predicted value due to irregular local fiber dispersion during the actual test. The findings of the finite element modeling and the actual test results show that the fiber may improve the mechanical characteristics of cement-based composites after freeze-thaw, regardless of whether the test is bending or compressive. Through the established finite element model, the compressive and bending strengths of carbon fiber cement-based materials, glass fiber cement-based composite materials, and PVA fiber cement-based composite materials can be predicted within the range of 1% volume fraction and 100 freeze-thaw cycles. For example, the bending strength of carbon fiber cement-based materials can be predicted at a volume fraction of 0.6% after 30 freeze-thaw cycles. The strength of fiber cement-based materials is successfully estimated with a volume percentage of more than 1% and 100 freeze-thaw cycles.i is sample mean ; i is sample mean ; This paper uses the well-known ‘Pearson’s correlation coefficient’ to study the relationship between strength, freeze-thaw cycles, and volume percent obtained under simulated experiments. The Pearson correlation coefficient is often used in the natural sciences to evaluate the degree of connection between two variables, and its value ranges from −1 to 1. The calculation Formula (5) is as follows:Four columns of data include fiber content, number of freeze-thaw cycles, and simulated flexural and compressive strengths for each set of samples. The determined fiber content and the number of freeze-thaw cycles determine the flexural and compressive strengths. In addition, Pearson’s correlation analysis was performed on the four columns of data. Finally, all data in This paper studies the strength under the two factors of dosage and freeze-thaw times. The columns and rows represent the correlation between the fiber content and the number of freeze-thaw factors and the strength. The Pearson correlation coefficient of carbon fiber cement-based composite materials is shown in It is concluded that the compressive strength of the three fiber cement-based materials have a significant correlation with the number of freeze-thaw cycles, and the bending strength has a significant correlation with the volume fraction. However, the Pearson coefficients of the three fibers are different. Therefore, the fiber has different effects in the compression and bending tests and can play a better role in the bending test. Therefore, the dosage correlates with the flexural strength and a low correlation with the compressive strength. The freeze-thaw effect is mainly on the cement base, so the freeze-thaw has a high correlation with the compressive strength and a low correlation with the flexural strength.The two factors are stacked to provide a comprehensive strength prediction method that predicts the 0 percent to 1 percent volume fraction and strength under 0 to 100 freeze-thaw cycles. Each model is calculated once for the fiber content of 0%, 0.5%, and 1% and freeze-thaw cycles of 0, 50, and 100 times, yielding nine strength values that linearly regress to a surface. The fitting was performed with the formula of Curve 3D software itself, and the formula of the fitting surface with the minimum error was obtained, as shown in Equation (6):In this paper, the compressive, bending, and microscope observation tests of three fiber cement-based composite materials were mainly carried out by fiber volume fraction and freeze-thaw times. Its mechanical properties were analyzed under different volume fractions and freeze-thaw cycles, and its bivariate numerical model was established. The findings of finite element modeling were compared with actual testing using ABAQUS, and the failure mechanism was investigated.1. Based on microscopic observation, the interface between carbon fiber and cement base is good, but the dispersion effect is not ideal. Glass fiber and PVA fiber are evenly dispersed and have better adaptability to the cement base, but the interface effect is relatively weak.2. The compressive strength of the three fiber cement-based composites showed the law of first increasing and then decreasing with the volume fraction. For example, the highest compressive strength was 46.8 MPa for a 0.5% volume fraction of carbon fiber composite material. The bending strength follows a pattern of a quick rise initially, then gradually increasing as the volume fraction increases. The maximum bending strength was 7.6 MPa for a 1% carbon fiber composite. The compression test observed the frost resistance performance of carbon fiber > PVA fiber > glass fiber, and the bending test found the frost resistance performance of carbon fiber > glass fiber > PVA fiber.3. The fiber’s frost resistance to the cement-based composite material has been much increased. As a result, the strength is much higher than the control group in both bending and compressive tests. Carbon fiber is better than PVA fiber, and PVA fiber is better than glass fiber. After 50 freeze-thaw cycles, the compressive strength of carbon fiber decreased by 3.8%, and the bending strength decreased by 3.9%. The compressive strength after 100 freeze-thaw cycles decreased by 9.6%, and the bending strength decreased by 7.9%.4. The test findings demonstrate that the bending-compressive strength ratio increases as the fiber volume increases. As a result, fiber can significantly increase the sample’s toughness and ductility. However, the bending-compressive strength ratio has a different effect depending on the fiber. This discrepancy becomes more pronounced as the number of freeze-thaw cycles increases. Glass fiber is more significant than carbon fiber and PVA fiber.5. According to the test results, a mathematical method for studying the bending strength of the three fibers under the combined action of different volume fractions and freeze-thaw cycles is proposed. This mathematical approach can accurately forecast all bending strengths in the range of 0–1% volume fraction and 0–100 freeze-thaw cycles, and it is also commonly used in engineering to obtain binary issue calculation answers.6. Cloud images were simulated by analyzing ABAQUS. The fiber received very little force during compression, and the maximum stress at failure was 39 MPa. The fiber cement-based composite material was mostly shattered near the cement matrix’s bottom, with a maximum stress of 7.0 MPa. The limit load variation law of simulation and actual measurement results are relatively consistent, and the simulation results are slightly larger than the actual test values. The finite element model can accurately forecast bending and compressive strengths with a volume fraction greater than 1% and 100 freeze-thaw cycles.