Primary immunodeficiency

Primary immunodeficiency disorder (PID) refers to a heterogeneous group of over 130 disorders that result from defects in immune system development and/or function. PIDs are broadly classified as disorders of adaptive immunity (i.e., T-cell, B-cell or combined immunodeficiencies) or of innate immunity (e.g., phagocyte and complement disorders). Although the clinical manifestations of PIDs are highly variable, most disorders involve at least an increased susceptibility to infection. Early diagnosis and treatment are imperative for preventing significant disease-associated morbidity and, therefore, consultation with a clinical immunologist is essential. PIDs should be suspected in patients with: recurrent sinus or ear infections or pneumonias within a 1 year period; failure to thrive; poor response to prolonged use of antibiotics; persistent thrush or skin abscesses; or a family history of PID. Patients with multiple autoimmune diseases should also be evaluated. Diagnostic testing often involves lymphocyte proliferation assays, flow cytometry, measurement of serum immunoglobulin (Ig) levels, assessment of serum specific antibody titers in response to vaccine antigens, neutrophil function assays, stimulation assays for cytokine responses, and complement studies. The treatment of PIDs is complex and generally requires both supportive and definitive strategies. Ig replacement therapy is the mainstay of therapy for B-cell disorders, and is also an important supportive treatment for many patients with combined immunodeficiency disorders. The heterogeneous group of disorders involving the T-cell arm of the adaptive system, such as severe combined immunodeficiency (SCID), require immune reconstitution as soon as possible. The treatment of innate immunodeficiency disorders varies depending on the type of defect, but may involve antifungal and antibiotic prophylaxis, cytokine replacement, vaccinations and bone marrow transplantation. This article provides a detailed overview of the major categories of PIDs and strategies for the appropriate diagnosis and management of these rare disorders

Inferring adaptive codon preference to understand sources of selection shaping codon usage bias.

Alternative synonymous codons are often used at unequal frequencies. Classically, studies of such codon usage bias (CUB) attempted to separate the impact of neutral from selective forces by assuming that deviations from a predicted neutral equilibrium capture selection. However, GC-biased gene conversion (gBGC) can also cause deviation from a neutral null. Alternatively, selection has been inferred from CUB in highly expressed genes, but the accuracy of this approach has not been extensively tested, and gBGC can interfere with such extrapolations (e.g., if expression and gene conversion rates covary). It is therefore critical to examine deviations from a mutational null in a species with no gBGC. To achieve this goal, we implement such an analysis in the highly AT rich genome of Dictyostelium discoideum, where we find no evidence of gBGC. We infer neutral CUB under mutational equilibrium to quantify “adaptive codon preference,” a nontautologous genome wide quantitative measure of the relative selection strength driving CUB. We observe signatures of purifying selection consistent with selection favoring adaptive codon preference. Preferred codons are not GC rich, underscoring the independence from gBGC. Expression-associated “preference” largely matches adaptive codon preference but does not wholly capture the influence of selection shaping patterns across all genes, suggesting selective constraints associated specifically with high expression. We observe patterns consistent with effects on mRNA translation and stability shaping adaptive codon preference. Thus, our approach to quantifying adaptive codon preference provides a framework for inferring the sources of selection that shape CUB across different contexts within the genome

How Climate Shapes the Functioning of Tropical Montane Cloud Forests

This is the author accepted manuscript. The final version is available from Springer via the DOI in this recordPurpose of Review: Tropical Montane Cloud Forest (TMCF) is a highly vulnerable ecosystem, which occurs at higher elevations in tropical mountains. Many aspects of TMCF vegetation functioning are poorly understood, making it difficult to quantify and project TMCF vulnerability to global change. We compile functional traits data to provide an overview of TMCF functional ecology. We use numerical models to understand the consequences of TMCF functional composition with respect to its responses to climate and link the traits of TMCF to its environmental conditions. Recent Findings: TMCF leaves are small and have low SLA but high Rubisco content per leaf area. This implies that TMCF maximum net leaf carbon assimilation (An) is high but often limited by low temperature and leaf wetting. Cloud immersion provides important water and potentially nutrient inputs to TMCF plants. TMCF species possess low sapwood specific conductivity, which is compensated with a lower tree height and higher sapwood to leaf area ratio. These traits associated with a more conservative stomatal regulation results in a higher hydraulic safety margin than nearby forests not affected by clouds. The architecture of TMCF trees including its proportionally thicker trunks and large root systems increases tree mechanical stability. Summary: The TMCF functional traits can be conceptually linked to its colder and cloudy environment limiting An, growth, water transport and nutrient availability. A hotter climate would drastically affect the abiotic filters shaping TMCF communities and potentially facilitate the invasion of TMCF by more productive lowland species.Newton FundNatural Environment Research Council (NERC)FAPES

Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach

Background: Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and intervention programs to reduce the mental health burden worldwide during COVID-19. // Objective: The present study aimed to apply a machine learning approach to predict depression and PTSD symptoms based on psychometric questions that assessed: (1) the level of stress due to being isolated from one's family; (2) professional recognition before and during the pandemic; and (3) altruistic acceptance of risk during the COVID-19 pandemic among healthcare workers. // Methods: A total of 437 healthcare workers who experienced some level of isolation at the time of the pandemic participated in the study. Data were collected using a web survey conducted between June 12, 2020, and September 19, 2020. We trained two regression models to predict PTSD and depression symptoms. Pattern regression analyses consisted of a linear epsilon-insensitive support vector machine (ε-SVM). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r), the coefficient of determination (r2), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels. // Results: Results were significant using two different cross-validation strategies to significantly decode both PTSD and depression symptoms. For all of the models, the stress due to social isolation and professional recognition were the variables with the greatest contributions to the predictive function. Interestingly, professional recognition had a negative predictive value, indicating an inverse relationship with PTSD and depression symptoms. // Conclusions: Our findings emphasize the protective role of professional recognition and the vulnerability role of the level of stress due to social isolation in the severity of posttraumatic stress and depression symptoms. The insights gleaned from the current study will advance efforts in terms of intervention programs and public health messaging

Consórcios de caupi e milho em cultivo orgânico para produção de grãos e espigas verdes.

No período de outono-inverno-primavera de 2007, foi conduzido um estudo em Seropédica, Região Metropolitana do estado do Rio de Janeiro (Baixada Fluminense), com o objetivo de avaliar diferentes tipos de consórcio entre caupi (cv. Mauá) e milho (cv. AG-1051), em sistema orgânico de produção. O experimento foi instalado em área de Argissolo Vermelho-Amarelo no delineamento de blocos ao acaso, com quatro repetições. Os tratamentos constaram de diferentes épocas ou intervalos de tempo de semeadura do caupi em relação à do milho, a saber: (E1) 21 dias antes do milho; (E2) 14 dias antes do milho; (E3) 7 dias antes do milho; e (E4) no mesmo dia do milho. Tratamentos correspondentes aos cultivos solteiros do caupi e do milho foram incluídos, ambos semeados na data do tratamento E4. O cultivo consorciado com o caupi não interferiu na produtividade do milho em espigas verdes e também em termos de comprimento e diâmetro basal dessas espigas, independentemente do intervalo entre semeaduras. Com referência ao caupi, a produtividade em grãos verdes no cultivo solteiro foi superior à dos consórcios com o milho. Os valores obtidos para os Índices de Equivalência de Área (IEA), foram todos acima de 1,0, indicando que os consórcios foram eficientes quanto ao desempenho agronômico/biológico. Considerando, ainda a produtividade de cada cultura participante do consórcio, a semeadura do caupi antecipada de 21 dias em relação à do milho afigura-se mais adequada ao manejo orgânico adotado e às condições edafoclimáticas da região