Relatively preserved functional immune capacity with standard COVID-19 vaccine regimen in people living with HIV.

Introduction: People living with HIV (PLWH) are at a higher risk of severe disease with SARS-CoV-2 virus infection. COVID-19 vaccines are effective in most PLWH. However, suboptimal immune responses to the standard two-shot regimen are a concern, especially for those with moderate to severe immunodeficiency. An additional dose is recommended as part of the extended primary series in Taiwan. Herein, we study the efficacy of this additional shot in PLWH with mild immunodeficiency compared to that in healthy non-HIV people. Methods: In total, 72 PLWH that were asymptomatic or with mild immunodeficiency (CD4 counts ≥200/mm3) and suppressed virology, and 362 healthcare workers of our hospital were enrolled. None of the participants had a history of SARS-CoV-2 infection. They received mRNA-1273 and ChAdOx1 vaccines. Anti-SARS-CoV-2 neutralizing and anti-Spike IgG antibodies, and SARS-CoV-2-specific T cell responses were evaluated. Results: The standard two-shot regimen elicited lower responses in PLWH than the healthcare workers without HIV infection, although the difference was statistically insignificant. They had comparable levels of neutralizing and anti-Spike antibodies and comparable effector CD4+ and CD8+ T cell responses. The third shot boosted the SARS-CoV-2 immunity significantly more with better antibody responses and higher IFN-γ and IL-2 responses of the CD4+ and CD8+ T cells in PLWH compared to those without HIV. Upon in vitro stimulation with extracted Wuhan strain SARS-CoV-2 proteins, CD8+ T cells from PLWH after 3 shots had more durable effector responses than the non-HIV controls with extended time of stimulation. Conclusion: This subtle difference between PLWH and non-HIV people implied immune exhaustion with two shots in non-HIV people. Slightly compromised immunity in PLWH indeed preserved the functional capacity for further response to the third shot or natural infection.

Using 3D photogrammetry to quantify the subtle differences of coral reefs under the impacts of marine activities.

Marine activities may cause the degradation of coral reefs. The composition of benthic communities and seawater quality have been commonly used as the proxies to assess the impacts of marine activities. However, these proxies may not be able to detect the subtle differences within homogeneous environment. We used photogrammetry to quantify the subtle differences of structural complexity between heavily and lightly trafficked sites at Wanlitong, southern Taiwan. Our study demonstrated that the impacts of marine activities can be detected within tens of meters through quantifying structural complexity of coral reefs. Vector ruggedness measure (VRM) is a more suitable metric than conventional linear rugosity to detect such impacts. The correlations between structural complexity and coral cover have variances while comparing with previous studies. The results show that using photogrammetry to quantify the structure of coral reefs can provide a novel aspect to evaluate the subtle differences caused by marine activities.

Predicting gram-positive bacterial protein subcellular localization based on localization motifs.

The subcellular localization of proteins is closely related to their functions. In this work, we propose a novel approach based on localization motifs to improve the accuracy of predicting subcellular localization of Gram-positive bacterial proteins. Our approach performed well on a five-fold cross validation with an overall success rate of 89.5%. Besides, the overall success rate of an independent testing dataset was 97.7%. Moreover, our approach was tested using a new experimentally-determined set of Gram-positive bacteria proteins and achieved an overall success rate of 96.3%.

Using predicted shape string to enhance the accuracy of γ-turn prediction.

Numerous methods for predicting γ-turns in proteins have been developed. However, the results they generally provided are not very good, with a Matthews correlation coefficient (MCC)≤0.18. Here, an attempt has been made to develop a method to improve the accuracy of γ-turn prediction. First, we employ the geometric mean metric as optimal criterion to evaluate the performance of support vector machine for the highly imbalanced γ-turn dataset. This metric tries to maximize both the sensitivity and the specificity while keeping them balanced. Second, a predictor to generate protein shape string by structure alignment against the protein structure database has been designed and the predicted shape string is introduced as new variable for γ-turn prediction. Based on this perception, we have developed a new method for γ-turn prediction. After training and testing the benchmark dataset of 320 non-homologous protein chains using a fivefold cross-validation technique, the present method achieves excellent performance. The overall prediction accuracy Qtotal can achieve 92.2% and the MCC is 0.38, which outperform the existing γ-turn prediction methods. Our results indicate that the protein shape string is useful for predicting protein tight turns and it is reasonable to use the dihedral angle information as a variable for machine learning to predict protein folding. The dataset used in this work and the software to generate predicted shape string from structure database can be obtained from anonymous ftp site ftp://cheminfo.tongji.edu.cn/GammaTurnPrediction/ freely.

Improving the performance of ß-turn prediction using predicted shape strings and a two-layer support vector machine model.

BACKGROUND: The ß-turn is a secondary protein structure type that plays an important role in protein configuration and function. Development of accurate prediction methods to identify ß-turns in protein sequences is valuable. Several methods for ß-turn prediction have been developed; however, the prediction quality is still a challenge and there is substantial room for improvement. Innovations of the proposed method focus on discovering effective features, and constructing a new architectural model. RESULTS: We utilized predicted secondary structures, predicted shape strings and the position-specific scoring matrix (PSSM) as input features, and proposed a novel two-layer model to enhance the prediction. We achieved the highest values according to four evaluation measures, i.e. Q(total) = 87.2%, MCC = 0.66, Q(observed) = 75.9%, and Q(predicted) = 73.8% on the BT426 dataset. The results show that our proposed two-layer model discriminates better between ß-turns and non-ß-turns than the single model due to obtaining higher Q(predicted). Moreover, the predicted shape strings based on the structural alignment approach greatly improve the performance, and the same improvements were observed on BT547 and BT823 datasets as well. CONCLUSION: In this article, we present a comprehensive method for the prediction of ß-turns. Experiments show that the proposed method constitutes a great improvement over the competing prediction methods.