Changes in sexual behavior among MSM during the spread of COVID-19 in China: results of an online survey (preprint)

Background Sexual behavior drives the transmission of sexually transmitted infections, especially among men who have sex with men (MSM). This study aims to evaluate the sexual behavior changed among MSM during the COVID-19.Methods An online survey was conducted to collect socio-demographic, sexual behavioral, and HIV testing information before and during the COVID-19. Chi-square was used to determine the behavior differences before and during the COVID-19. We identified the factors associated with sexual behavior among MSM using logistic regression.Results Totally 506 MSM participated in the survey. Compared with participants who didn't reduce the number of sexual partners (193, 38.1%), participants who reduced sexual partners (313, 61.9%) had higher values of multiple sexual partners, causal sexual partners, used condoms with causal sexual partners, and sought sexual partners using apps. Participants who reduced the number of sexual partners than before COVID-19, reported having a higher proportion of causal sexual behavior (χ2 = 21.047, p < 0.001), which means engaged in casual sex in the last three months.Conclusions The lockdown measures significantly impacted the sexual behavior of MSM. After the epidemic is over, however, we need to increase health education for MSM to reduce their high-risk sexual behaviors and protect them from STDs.

Programming the scalable optical learning operator with spatial-spectral optimization (preprint)

Electronic computers have evolved drastically over the past years with an ever-growing demand for improved performance. However, the transfer of information from memory and high energy consumption have emerged as issues that require solutions. Optical techniques are considered promising solutions to these problems with higher speed than their electronic counterparts and with reduced energy consumption. Here, we use the optical reservoir computing framework we have previously described (Scalable Optical Learning Operator or SOLO) to program the spatial-spectral output of the light after nonlinear propagation in a multimode fiber. The novelty in the current paper is that the system is programmed through an output sampling scheme, similar to that used in hyperspectral imaging in astronomy. Linear and nonlinear computations are performed by light in the multimode fiber and the high dimensional spatial-spectral information at the fiber output is optically programmed before it reaches the camera. We then used a digital computer to classify the programmed output of the multi-mode fiber using a simple, single layer network. When combining front-end programming and the proposed spatial-spectral programming, we were able to achieve 89.9% classification accuracy on the dataset consisting of chest X-ray images from COVID-19 patients. At the same time, we obtained a decrease of 99% in the number of tunable parameters compared to an equivalently performing digital neural network. These results show that the performance of programmed SOLO is comparable with cutting-edge electronic computing platforms, albeit with a much-reduced number of electronic operations.

Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks.

Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors' intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets. The developed models were applied to prospective validations of recruiting experienced blood donors during two COVID-19 pandemics, while the routine method was used as a control. Overall, a total of 95,476 recruitments via SMS and their donation results were enrolled in our modelling study. The strongest predictor features for the donation of experienced donors were blood donation interval, age, and donation frequency. Among the seven baseline models, the eXtreme Gradient Boosting (XGBoost) and Support vector machine models (SVM) achieved the best performance: mean (95%CI) with the highest AUC: 0.809 (0.806-0.811), accuracy: 0.815 (0.812-0.818), precision: 0.840 (0.835-0.845), and F1 score of XGBoost: 0.843 (0.840-0.845) and recall of SVM: 0.991 (0.988-0.994). The hit rate of the XGBoost model alone and the combined XGBoost and SVM models were 1.25 and 1.80 times higher than that of the conventional method as a control in 2 recruitments respectively, and the hit rate of the high willingness to donate group was 1.96 times higher than that of the low willingness to donate group. Our results suggested that the machine learning models could predict and determine the experienced donors with a strong willingness to donate blood by a ranking score based on personalized donation data and demographical details, significantly improve the recruitment rate of blood donors and help blood agencies to maintain the blood supply in emergencies.

Prolonged Viral Shedding in Three Young Adult Cases of COVID-19

Severe acute respiratory syndrome coronavirus-2 infection is usually self-limited, with a short duration for viral shedding within several weeks. However, prolonged viral shedding has been observed in severe or immune-compromised coronavirus disease 2019 (COVID-19) cases. Here, we reported that three young adult cases of COVID-19 patients, who were either immunosuppressed nor severe, showed prolonged viral RNA shedding from the upper respiratory tract for 58, 81, and 137 days since initial diagnosis. To our knowledge, this is the longest duration of viral shedding reported to date in young adult patients. Further studies on factors relevant to prolonged viral positivity, as well as the correlation between viral positivity and transmission risk are needed for the optimal management of COVID-19 patients with prolonged nucleic acid positive.

Stock Market Reaction to the Announcement of the COVID-19 Lockdown: Evidence from Healthcare Companies in Malaysia

The sudden global pandemic of COVID-19 occurred in Malaysia at the beginning of new 2020, which increased the uncertainty of the economy. As a highly demanded industry during diseases, COVID-19-related news had a mixed influence on investors' confidence in the healthcare industry, so the short-term market reaction of the Malaysian healthcare industry is investigated during this unfolding event. This paper examines whether the “lockdown” suppressed the influence of COVID-19 pandemic on stock performance in 12 listed healthcare companies in Malaysia. We consider the “lockdown” order has different impacts on samples. The hardest hit among the four events is the first announcement of lockdown, whose cumulative average abnormal return (CAAR) is negative (CAAR<0), for its strict movement control. However, the impacts of the following three lockdown events are positive and less severe as the market gradually digest these kinds of news and the deregulation of movement control. Previous studies have justified the influence of disease outbreaks on the stock market;however, this study compensates for other studies by employing the event study methodology (ESM) approach to provide the first empirical evidence of the unprecedented influence of “lockdown” on Malaysian healthcare stock market. This study has practical implications for Malaysian financial markets that the lockdown orders matter for the Malaysian healthcare industry. The empirical results show that the stock market has positively affected the lockdown announcement after the first event. In turn, the policymakers could draw on these results related to stock performance to modify the regulations in the healthcare industry.

Influenza vaccine uptake among children and older adults in China: A secondary analysis of a quasi-experimental study (preprint)

Background: Influenza vaccination is the key to reducing the influenza-related disease burden, especially among high-risk populations. However, influenza vaccine uptake is low in China. This secondary analysis of a quasi-experimental trial in Guangdong Province aimed to understand factors associated with influenza vaccine uptake among children and older people stratified by funding context. Methods: : A total of 225 children (aged 0.5-8 years) and 225 older people (aged 60 years or above) were recruited from three clinics (rural, suburban, urban). Participants were allocated into two groups based on different funding contexts: self-paid group (N=150, including 75 children and 75 older adults) in which participants paid full market price for their vaccination; and subsidized group (N=300, including 150 children and 150 older adults) in which varying levels of financial support was provided. Univariable and multivariable logistic regressions were conducted stratified by funding contexts. Results: : Overall, 75.0% (225/300) of participants in the subsidized group and 36.7% (55/150) in the self-paid group got vaccinated. Older adults had lower vaccination rates than children in both funding groups, while both age groups showed much higher uptake in the subsidized group than in the self-paid group (86.7% vs 53.3% among children; 63.3% vs 20.0% among older people). In the self-paid group, participants living with children (aOR:2.61, 95%CI: 1.06-6.42) or older people (aOR:4.76, 95%CI: 1.08-20.90) having prior influenza vaccination in the same household were more likely to be vaccinated; trust in providers’ advice (aOR=4.95, 95%CI:1.99, 12.43) or effectiveness of the vaccine (aOR: 12.18, 95%CI: 5.21-28.50), and experienced influenza-like illnesses in the family (aOR=46.52, 4.10, 533.78) were associated with higher vaccine uptake in the subsidized group. Conclusions: Older people had suboptimal vaccine uptake compared to children in both contexts and need more attention in future efforts to enhance influenza vaccination. Tailoring interventions to different vaccine funding contexts may help improve influenza vaccine uptake: In self-paid context, measures to motivate people to accept their first ever influenza vaccination may be a promising strategy. In subsidized context, strategies to improve public confidence in vaccine effectiveness and providers’ advice would be useful. Trial registration: ChiCTR2000040048. Registered on November 19, 2020.

Changes in sexual behavior among men who have sex with men during the spread of COVID-19 in China: results of an online survey (preprint)

Background: Sexual behavior drives the transmission of sexually transmitted infections, especially among men who have sex with men (MSM). This study aims to evaluate the sexual behavior changed among MSM during the outbreak of COVID-19.Material and Methods: An online survey was conducted to collect sociodemographic, sexual behavioral, and HIV testing information before and during the COVID-19 epidemic. Chi-square was used to determine the behavior differences before and during the COVID-19 pandemic. We identified the factors associated with casual sexual behavior among MSM using bivariate and multivariable logistic regression.Results: A total of 506 MSM participated in the online survey. The 506 MSM were individuals born biologically as a male, self-identified as a male, aged 18 or over, and ever engaged in sex with a man (in the past three month). Compared with participants who didn't reduce the number of sexual partners (n = 193, 38.1%), participants who reduced sexual partners (n = 313, 61.9%) had higher values of multiple sexual partners, causal sexual partners, used a condom with causal sexual partners, and sought sexual partners using apps (all p< 0.05). Participants who reduced the number of sexual partners than before COVID-19, reported having a higher proportion of causal sexual behavior (66.77% vs 46.11%, c2 = 21.047, p< 0.001), which means engaged in casual sex in the last three months. But at-risk sexual behavior, which means unprotected anal sex with a casual partner without using condom protection, did not change significantly between the two groups during COVID-19 (p>0.05). Multiple factors are related to casual sexual behavior and at-risk sexual behavior.Conclusions: During the COVID-19 pandemic, the lockdown measures significantly impacted the sexual behavior of Chinese MSM. Sustain behavior interventions are still needed during the COVID-19 pandemic.

Willingness to receive the COVID-19 vaccine among HIV positive men who have sex with men in China: a cross-sectional study (preprint)

Background：People living with HIV(PLWH) are deemed more vulnerable to the SARS-CoV-2 infection than the uninfected population. Vaccination is an effective measure for COVID-19 control, yet, little knowledge exists about the willingness of men who have sex with men (MSM) living with HIV in China to be vaccinated. Methods：This cross-sectional study evaluated the willingness of MSM living with HIV to receive COVID-19 vaccination in six cities of Guangdong, China, from July to September 2020. Factors associated with willingness to receive COVID-19 vaccination using multivariable logistic regression. Results：In total, we recruited 944 HIV-positive MSM with a mean age of 29.2±7.7 years. Of all participants, 92.4% of them were willing to receive the COVID-19 vaccine. Participants who were separated, divorced, or widowed (OR: 5.29, 95%CI: 1.02-27.48), had an annual income higher than 9,000 USD (OR: 1.70, 95%CI: 1.01-2.86), had ever taken an HIV self-test (OR: 1.78, 95%CI: 1.07-2.95), had ever disclosed sexual orientation to a doctor/nurse (OR: 3.16, 95%CI: 1.33-7.50), had ever disclosed sexual orientation to others besides their male partners (OR: 2.18, 95%CI: 1.29-3.69) were more willing to receive the vaccine. Sex with a female partner in the past six months decreased the likelihood of willingness to receive the vaccine (OR: 0.40, 95%CI: 0.17-0.95). Economic burden, worry that my health condition could not bear the risk of receiving COVID-19 vaccines, and concern that the vaccination would affect the immune status and antiretroviral therapy were the main reasons for unwillingness to receive vaccination. Conclusion: Our study showed that HIV-positive MSM had a high willingness to receive the COVID-19 vaccination. Targeted interventions such as health education should be conducted among MSM with HIV infection to enhance COVID-19 vaccine uptake.

Genetic Diversity and Evolutionary Convergence of Cryptic SARS-CoV-2 Lineages Detected Via Wastewater Sequencing (preprint)

Wastewater-based epidemiology (WBE) is an effective way of tracking the appearance and spread of SARS-COV-2 lineages through communities. Beginning in early 2021, we implemented a targeted approach to amplify and sequence the receptor binding domain (RBD) of SARS-COV-2 to characterize viral lineages present in sewersheds. Over the course of 2021, we reproducibly detected multiple SARS-COV-2 RBD lineages that have never been observed in patient samples in 9 sewersheds located in 3 states in the USA. These cryptic lineages contained between 4 to 24 amino acid substitutions in the RBD and were observed intermittently in the sewersheds in which they were found for as long as 14 months. Many of the amino acid substitutions in these lineages occurred at residues also mutated in the Omicron variant of concern (VOC), often with the same substitution. One of the sewersheds contained a lineage that appeared to be derived from the Alpha VOC, but the majority of the lineages appeared to be derived from pre-VOC SARS-COV-2 lineages. Specifically, several of the cryptic lineages from New York City appeared to be derived from a common ancestor that most likely diverged in early 2020. While the source of these cryptic lineages has not been resolved, it seems increasingly likely that they were derived from immunocompromised patients or animal reservoirs. Our findings demonstrate that SARS-COV-2 genetic diversity is greater than what is commonly observed through routine SARS-CoV-2 surveillance. Wastewater sampling may more fully capture SARS-CoV-2 genetic diversity than patient sampling and could reveal new VOCs before they emerge in the wider human population. Author Summary During the COVID-19 pandemic, wastewater-based epidemiology has become an effective public health tool. Because many infected individuals shed SARS-CoV-2 in feces, wastewater has been monitored to reveal infection trends in the sewersheds from which the samples were derived. Here we report novel SARS-CoV-2 lineages in wastewater samples obtained from 3 different states in the USA. These lineages appeared in specific sewersheds intermittently over periods of up to 14 months, but generally have not been detected beyond the sewersheds in which they were initially found. Many of these lineages may have diverged in early 2020. Although these lineages share considerable overlap with each other, they have never been observed in patients anywhere in the world. While the wastewater lineages have similarities with lineages observed in long-term infections of immunocompromised patients, animal reservoirs cannot be ruled out as a potential source.

The Contagion Effect of Jump Risk across Asian Stock Markets during the Covid-19 Pandemic

This paper tests the market jump contagion hypothesis in the context of the Covid-19 pandemic. We first use a nonparametric approach to identify jumps by decomposing the realized volatility into continuous and jump components, and we use the threshold autoregressive model to describe the jump interdependency structure between different markets. We empirically investigate the contagion effect across several major Asian equity markets (Mainland China, Hong Kong, Japan, South Korea, Singapore, Thailand, and Taiwan) using the 5-minute high frequency data. Some key findings emerge: jump behaviors occur frequently and make an important contribution to the total realized volatility;jump dynamics exhibit significant nonlinearity, asymmetry, and the feature of structural breaks, which can be effectively captured by the threshold autoregressive model;jump contagion effects are obviously detected and this effect varies depending on the regime.

Clinical Applicable AI System Based on Deep Learning Algorithm for Differentiation of Pulmonary Infectious Disease.

Objective: To assess the performance of a novel deep learning (DL)-based artificial intelligence (AI) system in classifying computed tomography (CT) scans of pneumonia patients into different groups, as well as to present an effective clinically relevant machine learning (ML) system based on medical image identification and clinical feature interpretation to assist radiologists in triage and diagnosis. Methods: The 3,463 CT images of pneumonia used in this multi-center retrospective study were divided into four categories: bacterial pneumonia (n = 507), fungal pneumonia (n = 126), common viral pneumonia (n = 777), and COVID-19 (n = 2,053). We used DL methods based on images to distinguish pulmonary infections. A machine learning (ML) model for risk interpretation was developed using key imaging (learned from the DL methods) and clinical features. The algorithms were evaluated using the areas under the receiver operating characteristic curves (AUCs). Results: The median AUC of DL models for differentiating pulmonary infection was 99.5% (COVID-19), 98.6% (viral pneumonia), 98.4% (bacterial pneumonia), 99.1% (fungal pneumonia), respectively. By combining chest CT results and clinical symptoms, the ML model performed well, with an AUC of 99.7% for SARS-CoV-2, 99.4% for common virus, 98.9% for bacteria, and 99.6% for fungus. Regarding clinical features interpreting, the model revealed distinctive CT characteristics associated with specific pneumonia: in COVID-19, ground-glass opacity (GGO) [92.5%; odds ratio (OR), 1.76; 95% confidence interval (CI): 1.71-1.86]; larger lesions in the right upper lung (75.0%; OR, 1.12; 95% CI: 1.03-1.25) with viral pneumonia; older age (57.0 years ± 14.2, OR, 1.84; 95% CI: 1.73-1.99) with bacterial pneumonia; and consolidation (95.8%, OR, 1.29; 95% CI: 1.05-1.40) with fungal pneumonia. Conclusion: For classifying common types of pneumonia and assessing the influential factors for triage, our AI system has shown promising results. Our ultimate goal is to assist clinicians in making quick and accurate diagnoses, resulting in the potential for early therapeutic intervention.

In vitro and in vivo preclinical studies predict REGEN-COV protection against emergence of viral escape in humans (preprint)

Monoclonal antibodies against SARS-CoV-2 are a clinically validated therapeutic option against COVID-19. As rapidly emerging virus mutants are becoming the next major concern in the fight against the global pandemic, it is imperative that these therapeutic treatments provide coverage against circulating variants and do not contribute to development of treatment emergent resistance. To this end, we investigated the sequence diversity of the spike protein and monitored emergence of minor virus variants in SARS-COV-2 isolates found in nature or identified from preclinical in vitro and in vivo studies and in the clinic. This study demonstrates that a combination of non-competing antibodies not only provides full coverage against currently circulating variants but also protects against emergence of new such variants and their potential seeding into the population in a clinical setting.

Clinical Features and Death Risk Factors in COVID-19 Patients with Cancer: A Retrospective Study (preprint)

Purpose: Coronavirus disease 2019 (COVID-19) has spread around the world. This retrospective study aims to analyze the clinical features of COVID-19 patients with cancer and identify death outcome related risk factors.Methods: From February 10th to April 15th, 2020, 103 COVID-19 patients with cancer were enrolled. Difference analyses were performed between severe and non-severe patients. A propensity score matching analysis, including 103 COVID-19 patients with cancer and 206 matched non-cancer COVID-19 patients were performed. Next, we identified death related risk factors and developed a nomogram for predicting the probability.Results: In 103 COVID-19 patients with cancer, the main cancer categories were breast cancer, lung cancer and bladder cancer. Compared to non-severe patients, severe patients had a higher median age, and a higher proportion of smokers, diabetes, heart disease and dyspnea. In addition, most of the laboratory results between two groups were significant different. PSM analysis found that the proportion of dyspnea was much higher in COVID-19 patients with cancer. The severity incidence in two groups were similar, while a much higher mortality was found in COVID-19 patients with cancer compared to that in COVID-19 patients without cancer (11.7% vs. 4.4%, P = 0.028). Furthermore, we found that neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were related to death outcome. And a nomogram based on the factors was developed.Conclusion: In COVID-19 patients with cancer, the clinical features and laboratory results between severe group and non-severe group were significant different. NLR and CRP were the risk factors that could predict death outcome.

Development and Validation of a Novel Predictive Nomogram for Disease Progression of COVID-19: A Multicenter Retrospective Cohort Study of 4,086 Cases in China (preprint)

Aim: Coronavirus disease 2019 (COVID-19) has caused an unprecedented healthcare crisis. We aim to develop and validate a nomogram for predicting disease progression based on a large cohort of hospitalized COVID-19 patients. Methods: This is a multicenter retrospective cohort study, with a total of 4,086 hospitalized COVID-19 patients enrolled from two hospitals in Wuhan, China between February 3rd and Apr 10th. Nomogram was developed based on a cohort of 3, 022 patients from one hospital, and externally validated in another cohort of 1,064 patients from the other hospital. The calibration was assessed by a calibration plot and the HL test to evaluate the goodness of fit, and the Area under the ROC Curve (AUROC) as a measure of discriminative ability.Results: Six independent predictors, including age, dyspnea, platelet count, lactate dehydrogenase, D-dimer and cardiovascular disease, were finally identified for construction of the nomogram for predicting disease progression of COVID-19 patients during hospitalization. The AUROC was 0.877 and 0.817 for development cohort and validation cohort, respectively. The calibration plots AND Hosmer-Lemeshow test showed optimal agreement between nomogram prediction and actual observation. The decision curve analysis showed the performance of the nomograms were better than all univariable models, and had greater net benefit. Next, a predictive nomogram for disease severity on admission was formulated and the six independent factors used were similar to that of the nomogram for disease progression, which indicates that those factors play important roles in determining disease severity and the risk of disease progression. Conclusion: In the current study, a nomogram was developed based on generally readily available variables at hospital admission to help predict disease progression of COVID-19.

Lower Circulating Interferon-Gamma Is a Risk Factor for Lung Fibrosis in COVID-19 Patients.

Cytokine storm resulting from SARS-CoV-2 infection is one of the leading causes of acute respiratory distress syndrome (ARDS) and lung fibrosis. We investigated the effect of inflammatory molecules to identify any marker that is related to lung fibrosis in coronavirus disease 2019 (COVID-19). Seventy-six COVID-19 patients who were admitted to Youan Hospital between January 21 and March 20, 2020 and recovered were recruited for this study. Pulmonary fibrosis, represented as fibrotic volume on chest CT images, was computed by an artificial intelligence (AI)-assisted program. Plasma samples were collected from the participants shortly after admission, to measure the basal inflammatory molecules levels. At discharge, fibrosis was present in 46 (60.5%) patients whose plasma interferon-γ (IFN-γ) levels were twofold lower than those without fibrosis (p > 0.05). The multivariate-adjusted logistic regression analysis demonstrated the inverse association risk of having lung fibrosis and basal circulating IFN-γ levels with an estimate of 0.43 (p = 0.02). Per the 1-SD increase of basal IFN-γ level in circulation, the fibrosis volume decreased by 0.070% (p = 0.04) at the discharge of participants. The basal circulating IFN-γ levels were comparable with c-reactive protein in the discrimination of the occurrence of lung fibrosis among COVID-19 patients at discharge, unlike circulating IL-6 levels. In conclusion, these data indicate that decreased circulating IFN-γ is a risk factor of lung fibrosis in COVID-19.

SARS-CoV-2 Quasispecies provides insight into its genetic dynamics during infection (preprint)

A novel coronavirus disease (COVID-19) caused by SARS-CoV-2 has been pandemic worldwide. The genetic dynamics of quasispecies afford RNA viruses a great fitness on cell tropism and host range. However, no quasispecies data of SARS-CoV-2 have been reported yet. To explore quasispecies haplotypes and its transmission characteristics, we carried out single-molecule real-time (SMRT) sequencing of the full-length of SARS-CoV-2 spike gene within 14 RNA samples from 2 infection clusters, covering first-to third-generation infected-patients. We observed a special quasispecies structure of SARS-CoV-2 (modeled as One-King): one dominant haplotype (mean abundance ~70.15%) followed by numerous minor haplotypes (mean abundance < 0.10%). We not only discovered a novel dominant haplotype of F1040 but also realized that minor quasispecies were also worthy of attention. Notably, some minor haplotypes (like F1040 and currently pandemic one G614) could potentially reveal adaptive and converse into the dominant one. However, minor haplotypes exhibited a high transmission bottleneck (~6% could be stably transmitted), and the new adaptive/dominant haplotypes were likely originated from genetic variations within a host rather than transmission. The evolutionary rate was estimated as 2.68-3.86 x 10-3 per site per year, which was larger than the estimation at consensus genome level. The One-King model and conversion event expanded our understanding of the genetic dynamics of SARS-CoV-2, and explained the incomprehensible phenomenon at the consensus genome level, such as limited cumulative mutations and low evolutionary rate. Moreover, our findings suggested the epidemic strains may be multi-host origin and future traceability would face huge difficulties.

Tailoring steroids in the treatment of COVID-19 pneumonia assisted by CT scans: three case reports.

In this article, we analyze and report cases of three patients who were admitted to Renmin Hospital, Wuhan University, China, for treating COVID-19 pneumonia in February 2020 and were unresponsive to initial treatment of steroids. They were then received titrated steroids treatment based on the assessment of computed tomography (CT) images augmented and analyzed with the artificial intelligence (AI) tool and output. Three patients were finally recovered and discharged. The result indicated that sufficient steroids may be effective in treating the COVID-19 patients after frequent evaluation and timely adjustment according to the disease severity assessed based on the quantitative analysis of the images of serial CT scans.