Background: Omicron JN.1 has become the dominant SARS-CoV-2 variant in recent months. JN.1 has the highest number of amino acid mutations in its receptor binding domain (RBD) and has acquired a hallmark L455S mutation...Background: Omicron JN.1 has become the dominant SARS-CoV-2 variant in recent months. JN.1 has the highest number of amino acid mutations in its receptor binding domain (RBD) and has acquired a hallmark L455S mutation. The immune evasion capability of JN.1 is a subject of scientific investigation. The US CDC used SGTF of TaqPath COVID-19 Combo Kit RT-qPCR as proxy indicator of JN.1 infections for evaluation of the effectiveness of updated monovalent XBB.1.5 COVID-19 vaccines against JN.1 and recommended that all persons aged ≥ 6 months should receive an updated COVID-19 vaccine dose. Objective: Recommend Sanger sequencing instead of proxy indicator to diagnose JN.1 infections to generate the data based on which guidelines are made to direct vaccination policies. Methods: The RNA in nasopharyngeal swab specimens from patients with clinical respiratory infection was subjected to nested RT-PCR, targeting a 398-base segment of the N-gene and a 445-base segment of the RBD of SARS-CoV-2 for amplification. The nested PCR amplicons were sequenced. The DNA sequences were analyzed for amino acid mutations. Results: The N-gene sequence showed R203K, G204R and Q229K, the 3 mutations associated with Omicron BA.2.86 (+JN.1). The RBD sequence showed 24 of the 26 known amino acid mutations, including the hallmark L455S mutation for JN.1 and the V483del for BA.2.86 lineage. Conclusions: Sanger sequencing of a 445-base segment of the SARS-CoV-2 RBD is useful for accurate determination of emerging variants. The CDC may consider using Sanger sequencing of the RBD to diagnose JN.1 infections for statistical analysis in making vaccination policies.展开更多
2020年超长梅雨期内的持续强降雨,导致安徽省发生全域性洪涝灾害,为了快速、准确地提取洪涝淹没范围,为防汛救灾提供科学支撑,选取安徽境内巢湖流域和淮河流域的灾前和灾中Sentinel-1A数据,首先,在快速预处理基础上,采用双极化水体指数(...2020年超长梅雨期内的持续强降雨,导致安徽省发生全域性洪涝灾害,为了快速、准确地提取洪涝淹没范围,为防汛救灾提供科学支撑,选取安徽境内巢湖流域和淮河流域的灾前和灾中Sentinel-1A数据,首先,在快速预处理基础上,采用双极化水体指数(Sentinel-1A dual-polarized water index,SDWI)法,并结合地形因子对平原和山区分别提取水体信息,建立一套洪水淹没区监测流程;然后通过该流程利用灾前、灾中两期合成孔径雷达数据提取2020年7月27日巢湖流域、淮河流域行蓄洪区洪水淹没范围。结果显示:SDWI比直接用后向散射系数提取水体具有优势;7月27日巢湖流域洪水淹没区面积为524.8 km^(2),其中受洪灾较重的是白石天河子流域,西河子流域次之;淮河流域安徽境内行蓄洪区,沿淮的4个地市淹没面积从大到小依次为淮南市、阜阳市、六安市、蚌埠市。研究表明,基于Sentinel-1A数据,采用SDWI和地形因子建立的洪水淹没区监测流程对平原和山区都具有较好的准确性、适用性,且具有较高的时效性,便于及时开展洪水灾害监测。展开更多
文摘Background: Omicron JN.1 has become the dominant SARS-CoV-2 variant in recent months. JN.1 has the highest number of amino acid mutations in its receptor binding domain (RBD) and has acquired a hallmark L455S mutation. The immune evasion capability of JN.1 is a subject of scientific investigation. The US CDC used SGTF of TaqPath COVID-19 Combo Kit RT-qPCR as proxy indicator of JN.1 infections for evaluation of the effectiveness of updated monovalent XBB.1.5 COVID-19 vaccines against JN.1 and recommended that all persons aged ≥ 6 months should receive an updated COVID-19 vaccine dose. Objective: Recommend Sanger sequencing instead of proxy indicator to diagnose JN.1 infections to generate the data based on which guidelines are made to direct vaccination policies. Methods: The RNA in nasopharyngeal swab specimens from patients with clinical respiratory infection was subjected to nested RT-PCR, targeting a 398-base segment of the N-gene and a 445-base segment of the RBD of SARS-CoV-2 for amplification. The nested PCR amplicons were sequenced. The DNA sequences were analyzed for amino acid mutations. Results: The N-gene sequence showed R203K, G204R and Q229K, the 3 mutations associated with Omicron BA.2.86 (+JN.1). The RBD sequence showed 24 of the 26 known amino acid mutations, including the hallmark L455S mutation for JN.1 and the V483del for BA.2.86 lineage. Conclusions: Sanger sequencing of a 445-base segment of the SARS-CoV-2 RBD is useful for accurate determination of emerging variants. The CDC may consider using Sanger sequencing of the RBD to diagnose JN.1 infections for statistical analysis in making vaccination policies.
文摘2020年超长梅雨期内的持续强降雨,导致安徽省发生全域性洪涝灾害,为了快速、准确地提取洪涝淹没范围,为防汛救灾提供科学支撑,选取安徽境内巢湖流域和淮河流域的灾前和灾中Sentinel-1A数据,首先,在快速预处理基础上,采用双极化水体指数(Sentinel-1A dual-polarized water index,SDWI)法,并结合地形因子对平原和山区分别提取水体信息,建立一套洪水淹没区监测流程;然后通过该流程利用灾前、灾中两期合成孔径雷达数据提取2020年7月27日巢湖流域、淮河流域行蓄洪区洪水淹没范围。结果显示:SDWI比直接用后向散射系数提取水体具有优势;7月27日巢湖流域洪水淹没区面积为524.8 km^(2),其中受洪灾较重的是白石天河子流域,西河子流域次之;淮河流域安徽境内行蓄洪区,沿淮的4个地市淹没面积从大到小依次为淮南市、阜阳市、六安市、蚌埠市。研究表明,基于Sentinel-1A数据,采用SDWI和地形因子建立的洪水淹没区监测流程对平原和山区都具有较好的准确性、适用性,且具有较高的时效性,便于及时开展洪水灾害监测。
