Abstract Background After the first COVID-19 wave caused by the ancestral lineage, the pandemic has been fueled from the continuous emergence of new SARS-CoV-2 variants. Understanding key time-to-event periods for each emerging variant of concern is critical as it can provide insights into the future trajectory of the virus and help inform outbreak preparedness and response planning. Here, we aim to examine how the incubation period, serial interval, and generation time have changed from the ancestral SARS-CoV-2 lineage to different variants of concern. Methods We conducted a systematic review and meta-analysis that synthesized the estimates of incubation period, serial interval, and generation time (both realized and intrinsic) for the ancestral lineage, Alpha, Beta, and Omicron variants of SARS-CoV-2. Results Our study included 280 records obtained from 147 household studies, contact tracing studies, or studies where epidemiological links were known. With each emerging variant, we found a progressive shortening of each of the analyzed key time-to-event periods, although we did not find statistically significant differences between the Omicron subvariants. We found that Omicron BA.1 had the shortest pooled estimates for the incubation period (3.49 days, 95% CI: 3.13–4.86 days), Omicron BA.5 for the serial interval (2.37 days, 95% CI: 1.71–3.04 days), and Omicron BA.1 for the realized generation time (2.99 days, 95% CI: 2.48–3.49 days). Only one estimate for the intrinsic generation time was available for Omicron subvariants: 6.84 days (95% CrI: 5.72–8.60 days) for Omicron BA.1. The ancestral lineage had the highest pooled estimates for each investigated key time-to-event period. We also observed shorter pooled estimates for the serial interval compared to the incubation period across the virus lineages. When pooling the estimates across different virus lineages, we found considerable heterogeneities ( I 2 &gt; 80%; I 2 refers to the percentage of total variation across studies that is due to heterogeneity rather than chance), possibly resulting from heterogeneities between the different study populations (e.g., deployed interventions, social behavior, demographic characteristics). Conclusions Our study supports the importance of conducting contact tracing and epidemiological investigations to monitor changes in SARS-CoV-2 transmission patterns. Our findings highlight a progressive shortening of the incubation period, serial interval, and generation time, which can lead to epidemics that spread faster, with larger peak incidence, and harder to control. We also consistently found a shorter serial interval than incubation period, suggesting that a key feature of SARS-CoV-2 is the potential for pre-symptomatic transmission. These observations are instrumental to plan for future COVID-19 waves. 

pdf/assessing-changes-in-incubation-period-serial-interval-and-4ikij46v4b.pdf

Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis

Background: After the first COVID-19 wave caused by the ancestral lineage, the pandemic has been fueled from the continuous emergence of new SARS-CoV-2 variants. Understanding key time-to-event periods for each emerging variant of concern is critical as it can provide insights into the future trajectory of the virus and help inform outbreak preparedness and response planning. Here, we aim to examine how the incubation period, serial interval, and generation time have changed from the ancestral SARS-CoV-2 lineage to different variants of concern. Methods: We conducted a systematic review and meta-analysis that synthesized the estimates of incubation period, serial interval, and generation time (both realized and intrinsic) for the ancestral lineage, Alpha, Beta, and Omicron variants of SARS-CoV-2. Results: Our study included 274 records obtained from 147 household studies, contact tracing studies or studies where epidemiological links were known. With each emerging variant, we found a progressive shortening of each of the analyzed key time-to-event periods. Specifically, we found that Omicron had the shortest pooled estimates for the incubation period (3.63 days, 95%CI: 3.25-4.02 days), serial interval (3.19 days, 95%CI: 2.95-3.43 days), and realized generation time (2.96 days, 95%CI: 2.54-3.38 days) whereas the ancestral lineage had the highest pooled estimates for each of them. We also observed shorter pooled estimates for the serial interval compared to the incubation period across the virus lineages. We found considerable heterogeneities (I2 > 80%) when pooling the estimates across different virus lineages, indicating potential unmeasured confounding from population factors (e.g., social behavior, deployed interventions). Conclusion: Our study supports the importance of conducting contact tracing and epidemiological investigations to monitor changes in SARS-CoV-2 transmission patterns. Our findings highlight a progressive shortening of the incubation period, serial interval, and generation time, which can lead to epidemics that spread faster, with larger peak incidence, and harder to control. We also consistently found a shorter serial interval than incubation period, suggesting that a key feature of SARS-CoV-2 is the potential for pre-symptomatic transmission. These observations are instrumental to plan for future COVID-19 waves. Keywords: COVID-19, variants of concern, incubation period, serial interval, realized generation time, intrinsic generation time, systematic review, meta-analysis

/pdf/assessing-changes-in-incubation-period-serial-interval-and-1tokysx0.pdf

PURPOSE OF REVIEW One-third of patients awaiting heart transplant are sensitized and 25-35% of heart allograft recipients develop de novo DSAs. Solid phase assays for DSA measurement have facilitated wider use of antibody monitoring and as such, our experience with DSAs is continuously evolving. RECENT FINDINGS DSAs continue to exhibit poor correlation with biopsy-proven rejection. Novel molecular technologies, such as cell-free DNA and the molecular microscope (MMDx, which detects rejection-associated intragraft mRNA transcripts), are emerging as more sensitive methods to capture subclinical graft injury. High-resolution typing techniques are providing insight into the differential immunogenicity of HLA classes through epitope and eplet analysis. As sensitization of the transplant population is continuing to rise, our repertoire of desensitization strategies is also expanding. However, there is an acute need of predictive algorithms to help forecast the responders and the durability of desensitization. Novel immunomodulatory therapies have allowed safely transplanting across a positive crossmatch with good short-term survival but reported greater degree of rejection and lower long-term graft survival. SUMMARY Our experience of outcomes as pertaining to DSAs still originates primarily from single-center studies. Our field is confronted with the challenge to establish common practice algorithms for the monitoring and treatment of DSAs.

Donor-specific antibodies in heart transplantation: can we afford the price or is it too steep to pay?

In communication signal recognition, there are problems such as a tedious feature extraction process and low applicability of extracted features. This paper simulates wireless communication channels and suggests an algorithm that uses nearest neighbor component analysis (NCA) along with convolutional neural networks (CNN) for classification. The algorithm chooses wavelet entropy (WE), wavelet approximate energy ratio (WAER), and the first 2–4 singular values as the core features. Eight different forms of modulations, including GFSK, CPFSK, B-FM, DSB-AM, SSB-AM, BPSK, QPSK and PAM4 would be automatically classified using the technique. According to the experiment results, the average recognition accuracy for the eight signals is 93.6% when the signal-to-noise ratio is 30dB. In addition, this paper also discusses the results and accuracy of the model to identify 6 and 10 types of signal modulation and studies the accuracy of the recognition under different signal-to-noise ratios, verifying the robustness of the model. 

Automatic modulation recognition of communication signal based on wavelet transform combined with singular value and NCA-CNN

In communication signal recognition, there are problems such as a tedious feature extraction process and low applicability of extracted features. This paper simulates wireless communication channels and suggests an algorithm that uses nearest neighbor component analysis (NCA) along with convolutional neural networks (CNN) for classification. The algorithm chooses wavelet entropy (WE), wavelet approximate energy ratio (WAER), and the first 2–4 singular values as the core features. Eight different forms of modulations, including GFSK, CPFSK, B-FM, DSB-AM, SSB-AM, BPSK, QPSK and PAM4 would be automatically classified using the technique. According to the experiment results, the average recognition accuracy for the eight signals is 93.6% when the signal-to-noise ratio is 30dB. In addition, this paper also discusses the results and accuracy of the model to identify 6 and 10 types of signal modulation and studies the accuracy of the recognition under different signal-to-noise ratios, verifying the robustness of the model.

The serial interval distribution is used to approximate the generation time distribution, an essential parameter to predict the effective reproductive number "R_t", a measure of transmissibility. However, serial interval distributions may change as an epidemic progresses rather than remaining constant. Here we show that serial intervals in Hong Kong varied over time, closely associated with the temporal variation in COVID-19 case profiles and public health and social measures that were implemented in response to surges in community transmission. Quantification of the variation over time in serial intervals led to improved estimation of R_t, and provided additional insights into the impact of public health measures on transmission of infections.

/pdf/insights-into-covid-19-epidemiology-and-control-from-27uu3n0l.pdf

Insights into COVID-19 epidemiology and control from temporal changes in serial interval distributions in Hong Kong

. This paper presents a new algorithm of the singular value decomposition (SVD) in the wavelet domain for ground penetrating radar (GPR) to remove direct coupled waves. In fact, direct coupled waves commonly disturb the reﬂecting waves from underground targets. Besides, the amplitude and energy of direct coupled waves are large, which reduces the resolution of the images to the targets and ad-versely aﬀects the subsequent image interpretation work. The GPR signal is decomposed into several levels by Wavelet to obtain approximation components and detailed components of each level. The information of targets is contained in big eigenvalues of detail components, while the direct coupled waves are contained in small ones. Therefore, the SVD in the wavelet domain can reduce the misjudgment of eﬀective signals and improve the signal to noise ratio (SNR) of GPR signals. The simulated and ﬁeld GPR data show that the SVD in the wavelet domain denoising method has better results for direct coupled wave removal than the traditional methods, which validates the eﬀectiveness of the proposed denoising method.

/pdf/direct-coupled-wave-removal-for-gpr-data-based-on-svd-in-the-18db04p8.pdf

Direct Coupled Wave Removal for GPR Data Based on SVD in the Wavelet Domain

Purpose We previously reported that heart transplant (HTx) recipients with pre-formed, anti-HLA antibodies at low titer had one-year survival that was comparable to patients without any anti-HLA antibodies. As a result, we defined unacceptable HLA antigens by MFI>1000 in serum at 1:16 dilution. The current study evaluates how pre-formed, low-titer, donor-specific antibodies (DSA) and positive crossmatch affect long term survival of HTx recipients. Methods Total 495 adult recipients transplanted at our center between Jan. 2007 and Dec. 2016 were studied. Prior to HTx, recipients had HLA antibody screening (FlowPRA) and specificity testing (LABScreen). Unacceptable HLA antigens were defined by MFI>1000 in serum at 1:16 dilution. Acceptable donors were selected by virtual crossmatch using the criteria above, and flow cytometry crossmatch (FCXM) was performed peri- or post-HTx. Recipients transplanted across low titer DSA, positive in undiluted serum but negative at 1:16 dilution, received peri-operative plasmapheresis. We used log-rank testing to evaluate survival in the following groups compared to their controls: (1) presence of any pre-formed HLA antibodies; (2) presence of pre-formed DSA; and (3) positive FCXM. Results Of 495 HTx recipients, 179 (36%) had pre-existing HLA antibodies, and their overall survival was not different from patients without HLA antibodies (p=0.883). However, median survival time was reduced in patients with pre-formed DSA (n=62), compared to DSA-negative recipients (n=117): 9.1 years (95% CI 3.52 to 14.65) vs. 10.9 years (95% CI 6.13 to 15.70); p=0.02. The survival rates for 1-, 3-, 5-year of DSA-negative recipients were 91.5%, 85.4% and 79.5%, and the DSA-positive recipients were 83.9%, 66.1% and 61.7%, respectively. Total 62 recipients had pre-HTx DSA. Among them, 26 had a positive FCXM (46%). The survival rates of FCXM-positive cases were lower than FCXM-negative cases (n=36) at 1, 3, and 5 years: 80.8%, 57.9%, 52.1% vs. 86.1%, 71.4%, 67.6%. Conclusion Our findings suggest that pre-formed DSA, even at low titer, negatively impacted HTx survival. Cases with positive FXCM had the worst outcomes. Avoiding all pre-formed DSA may improve survival for HTx patients, but may increase waitlist mortality. To improve the HTx survival, close post-transplant monitoring and proactive immunological management may be required when pre-formed DSA are present.

Pre-Formed Donor-Specific Antibodies Impact Long Term Survival of Heart Transplants

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Insights into COVID-19 epidemiology and control from temporal changes in serial interval distributions in Hong Kong

Direct Coupled Wave Removal for GPR Data Based on SVD in the Wavelet Domain

Pre-Formed Donor-Specific Antibodies Impact Long Term Survival of Heart Transplants