Characterization of the immune responses induced in the initial stages of human immunodeficiency virus type 1 (HIV-1

Detection of asymptomatic or subclinical novel human coronavirus SARS-CoV-2 infection is critical for understanding the overall prevalence and infection potential of COVID-19. To estimate the cumulative prevalence of SARS-CoV-2 infection in China, we evaluated the host serologic response, measured by the levels of immunoglobulins M and G in 17,368 individuals, in the city of Wuhan, the epicenter of the COVID-19 pandemic in China, and geographic regions in the country, during the period from 9 March 2020 to 10 April 2020. In our cohorts, the seropositivity in Wuhan varied between 3.2% and 3.8% in different subcohorts. Seroposivity progressively decreased in other cities as the distance to the epicenter increased. Patients who visited a hospital for maintenance hemodialysis and healthcare workers also had a higher seroprevalence of 3.3% (51 of 1,542, 2.5-4.3%, 95% confidence interval (CI)) and 1.8% (81 of 4,384, 1.5-2.3%, 95% CI), respectively. More studies are needed to determine whether these results are generalizable to other populations and geographic locations, as well as to determine at what rate seroprevalence is increasing with the progress of the COVID-19 pandemic. Serologic surveillance has the potential to provide a more faithful cumulative viral attack rate for the first season of this novel SARS-CoV-2 infection.The novel human coronavirus SARS-CoV-2 is a highly contagious virus, and its disease, COVID-19, can lead to significant morbidity and mortality in a proportion of patients 1-3 . On 12 March 2020, the World Health Organization declared it a global pandemic 4 . As of 12 May 2020, there were more than 4.2 million confirmed infections globally in more than 180 countries with over 290,000 deaths (https://www.arcgis.com/apps/opsdashboard/index.html#/ bda7594740fd40299423467b48e9ecf6).Detection of SARS-CoV-2 in asymptomatic individuals 5,6 suggests that subclinical active infection might be an important contributor to this outbreak. Currently, reported cases of COVID-19 are mainly limited to symptomatic individuals, those having close

A soluble molecular analogue of photoresponsive Co/Fe Prussian blues is described within this report. As judged via a variety of spectroscopic, magnetic, and crystallographic methods, electron transfer within the octanuclear complex (below 250 K) converts paramagnetic red crystals into green diamagnetic ones. The color and magnetic changes are associated with the transformation of FeIIILS-CN-CoIIHS units into FeIILS-CN-CoIIILS fragments in manner that is identical to that found for the An[Co(OH2)(6-6m)][Fe(CN)6]m.xH2O (An = alkali metal cation) family of three-dimensional Prussian blues. Moreover, this intramolecular electron transfer can be quantitatively circumvented via rapid thermal quenching and reversed via simple white light irradiation at low temperatures. Remarkably the data suggests that thermally or photoinduced paramagnetic metastable phases are identical and exhibit long relaxation times that approach 10 years at 120 K.

During primary infection, the number of HIV-1 particles in plasma increases rapidly, reaches a peak, and then declines until it reaches a set point level. Understanding the kinetics of primary infection, and its effect on the establishment of chronic infection, is important in defining the early pathogenesis of HIV. We studied the viral dynamics of very early HIV-1 infection in 47 subjects identified through plasma donation screening. We calculated how fast the viral load increases and how variable this parameter is among individuals. We also estimated the basic reproductive ratio, the number of new infected cells generated by an infectious cell at the start of infection when target cells are not limiting. The initial viral doubling time had a median of 0.65 days with an interquartile range of 0.56 to 0.91 days. The median basic reproductive ratio was 8.0 with an interquartile range of 4.9 to 11. In 15 patients, we also observed the postpeak decay of plasma virus and found that the virus decay occurred at a median rate of 0.60 day ؊1 , corresponding to a half-life of 1.2 days. The median peak viral load was 5.8 log 10 HIV-1 RNA copies/ml, and it was reached 14 days after the virus was quantifiable with an assay, with a lower limit of detection of 50 copies/ml. These results characterize the early plasma viral dynamics in acute HIV infection better than it has been possible thus far. They also better define the challenge that the immune response (or therapeutic intervention) has to overcome to defeat HIV at this early stage.

Co/Fe Prussian Blue analogues are known to display both thermally and light induced electron transfer attributed to the switching between diamagnetic {Fe(II)LS(μ-CN)Co(III)LS} and paramagnetic {Fe(III)LS(μ-CN)Co(II)HS} pairs (LS = low spin; HS = high spin). In this work, a dinuclear cyanido-bridged Co/Fe complex, the smallest {Fe(μ-CN)Co} moiety at the origin of the remarkable physical properties of these systems, has been designed by a rational building-block approach. Combined structural, spectroscopic, magnetic and photomagnetic studies reveal that a metal-to-metal electron transfer that can be triggered in solid state by light, temperature and solvent contents, is observed for the first time in a dinuclear complex.

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4 pagesInternational audienceFlip to be square: Structural, spectroscopic, magnetic, and photomagnetic studies conclusively demonstrate that a tetranuclear cyanometalate {Fe2Co2} complex undergoes reversible thermally and light-induced changes in optical and magnetic properties. This instability is induced by an intramolecular electron transfer, as oberved in three-dimensional Co/Fe Prussian blue compounds (see picture)

Abstract. Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well.