Search

The United Nations Secretary-General Mechanism (UNSGM) for investigation of the alleged use of chemical and biological weapons is the only established international mechanism of this type under the UN. The UNGSM may launch an international investigation, relying on a roster of expert consultants, qualified experts, and analytical laboratories nominated by the member states. Under the framework of the UNSGM, we organized an external quality assurance exercise for nominated laboratories, named the Disease X Test, to improve the ability to discover and identify new pathogens that may cause possible epidemics and to determine their animal origin. The “what-if” scenario was to identify the etiological agent responsible for an outbreak that has tested negative for many known pathogens, including viruses and bacteria. Three microbes were added to the samples, Dabie bandavirus, Mammarenavirus, and Gemella spp., of which the last two have not been taxonomically named or published. The animal samples were from Rattus norvegicus, Marmota himalayana, New Zealand white rabbit, and the tick Haemaphysalis longicornis. Of the 11 international laboratories that participated in this activity, six accurately identified pathogen X as a new Mammarenavirus, and five correctly identified the animal origin as R. norvegicus. These results showed that many laboratories under the UNSGM have the capacity and ability to identify a new virus during a possible international investigation of a suspected biological event. The technical details are discussed in this report.

ABSTRACT Coronavirus disease 2019 (COVID-19) is a respiratory infectious disease responsible for many infections worldwide. Differences in respiratory microbiota may correlate with disease severity. Samples were collected from 20 severe and 51 mild COVID-19 patients. High-throughput sequencing of the 16S rRNA gene was used to analyze the bacterial community composition of the upper and lower respiratory tracts. The indices of diversity were analyzed. When one genus accounted for >50% of reads from a sample, it was defined as a super dominant pathobiontic bacterial genus (SDPG). In the upper respiratory tract, uniformity indices were significantly higher in the mild group than in the severe group (P < 0.001). In the lower respiratory tract, uniformity indices, richness indices, and the abundance-based coverage estimator were significantly higher in the mild group than in the severe group (P < 0.001). In patients with severe COVID-19, SDPGs were detected in 40.7% of upper and 63.2% of lower respiratory tract samples. In patients with mild COVID-19, only 10.8% of upper and 8.5% of lower respiratory tract samples yielded SDPGs. SDPGs were present in both upper and lower tracts in seven patients (35.0%), among which six (30.0%) patients possessed the same SDPG in the upper and lower tracts. However, no patients with mild infections had an SDPG in both tracts. Staphylococcus, Corynebacterium, and Acinetobacter were the main SDPGs. The number of SDPGs identified differed significantly between patients with mild and severe COVID-19 (P < 0.001). SDPGs in nasopharyngeal microbiota cause secondary bacterial infection in COVID-19 patients and aggravate pneumonia. IMPORTANCE The nasopharyngeal microbiota is composed of a variety of not only the true commensal bacterial species but also the two-face pathobionts, which are one a harmless commensal bacterial species and the other a highly invasive and deadly pathogen. In a previous study, we found that the diversity of nasopharyngeal microbiota was lost in severe influenza patients. We named the genus that accounted for over 50% of microbiota abundance as super dominant pathobiontic genus, which could invade to cause severe pneumonia, leading to high fatality. Similar phenomena were found here for SARS-CoV-2 infection. The diversity of nasopharyngeal microbiota was lost in severe COVID-19 infection patients. SDPGs in nasopharyngeal microbiota were frequently detected in severe COVID-19 patients. Therefore, the SDPGs in nasopharynx microbiota might invade into low respiratory and be responsible for secondary bacterial pneumonia in patients with SARS-CoV-2 infection.

In a changing climate, ship speed optimization plays an important role in energy conservation and emission reduction. In order to establish a dual-objective optimization model of minimizing ship operating costs and reducing carbon emissions, fuel costs, berthing costs, emission costs and fixed cost during sailing cycles, the emission reduction strategies of ships using MGO in emission control areas and the AMP in ports are taken into account. The PSO algorithm is adopted to find the Pareto solution set, and the TOPSIS algorithm is used to screen the optimal compromise solution, while Yang Ming, a trans-Pacific route, is selected to verify the applicability of the model. The result shows that the optimization model can effectively reduce the operating cost during sailing cycles and control carbon emissions, which can provide references for ship operation decision-making to achieve carbon peaking and carbon neutrality.