Presentation_1_mRNA vaccines against SARS-CoV-2 induce comparably low long-term IgG Fc galactosylation and sialylation levels but increasing long-term IgG4 responses compared to an... by Jana Sophia Buhre (12295230)

“…</p>Results<p>We show that the initially high mRNA vaccine-induced blood and salivary anti-S IgG levels, particularly IgG1, markedly decrease over time and approach the lower levels induced with the adenovirus-based vaccine. …”

Straightforward and Ultrastable Surface Modification of Microfluidic Chips with Norepinephrine Bitartrate Improves Performance in Immunoassays by Haiying Shen (4906882)

“…Surface modification is stable for at least 2.5 years, allowing for autoinjection of aqueous solution into the channels. …”

<i>Hnrnph1</i> Is A Quantitative Trait Gene for Methamphetamine Sensitivity by Neema Yazdani (835533)

“…We used interval-specific congenic mouse lines carrying various segments of chromosome 11 from the DBA/2J strain on an isogenic C57BL/6J background to positionally clone a 206 kb QTL (50,185,512–50,391,845 bp) that was causally associated with a reduction in the locomotor stimulant response to methamphetamine (2 mg/kg, i.p.; DBA/2J < C57BL/6J)—a non-contingent, drug-induced behavior that is associated with stimulation of the dopaminergic reward circuitry. …”

Treatment with a TRPV agonist reduces the ability of mf to establish infection in mosquitoes. by Nicolas J. Wheeler (8947214)

“…<p>(A) NAM added to blood at up to 50 mM increases the proportion of blood-fed mosquitoes when allowed to feed to repletion but reduces mosquito blood feeding at concentrations greater than 50 mM. …”

Tuning the Performance of Metalloporphyrin-Based Pressure-Sensitive Paints through the Nature of the Central Metal Ion by Elliott J. Nunn (20445750)

“…This new insight into luminophore design can be used to optimize PSPs for a desired application.…”

Tuning the Performance of Metalloporphyrin-Based Pressure-Sensitive Paints through the Nature of the Central Metal Ion by Elliott J. Nunn (20445750)

“…This new insight into luminophore design can be used to optimize PSPs for a desired application.…”

Tuning the Performance of Metalloporphyrin-Based Pressure-Sensitive Paints through the Nature of the Central Metal Ion by Elliott J. Nunn (20445750)

“…This new insight into luminophore design can be used to optimize PSPs for a desired application.…”

Characteristics of treatment modality. by Adekunle Emmanuel Sajo (15285520)

“…The annual average age decreased from 56 years to as low as 40 years, a mean difference of 16 years, p 0.012.…”

Demographic and clinical characteristics. by Adekunle Emmanuel Sajo (15285520)

“…The annual average age decreased from 56 years to as low as 40 years, a mean difference of 16 years, p 0.012.…”

Scanning electron micrographs of the tegument of <i>S</i>. <i>japonicum</i> males in the control group. by Jing Xu (15337)

Group average ocular blood flow in DBA/2J mice. by Eric R. Muir (11819939)

Threshold test of LCID. by Guoge Yang (19251000)

Structure diagram of ensemble model. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Synonymous Co-Variation across the E1/E2 Gene Junction of Hepatitis C Virus Defines Virion Fitness by Brendan A. Palmer (3361331)

“…Overtime, the number of codons actively mutating decreases for all virus groupings. We identify strong synonymous co-variation between codon sites in a group of sequences harbouring a 3 bp in-frame insertion and propose that synonymous mutation acts to stabilize the RNA structural backbone.…”

Fitting formula parameter table. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Test plan. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting surface parameters. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Relevant epidemiological metrics in England over the course of the pandemic. by Siyu Chen (278663)

Model generalisation validation error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Empirical model prediction error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”