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Jonathan Sebat and colleagues report the association of microduplication on chromosome 16p11.2 with schizophrenia, while the reciprocal microdeletion was associated with autism and developmental disorders. Recurrent microdeletions and microduplications of a 600-kb genomic region of chromosome 16p11.2 have been implicated in childhood-onset developmental disorders 1 , 2 , 3 . We report the association of 16p11.2 microduplications with schizophrenia in two large cohorts. The microduplication was detected in 12/1,906 (0.63%) cases and 1/3,971 (0.03%) controls ( P = 1.2 × 10 −5 , OR = 25.8) from the initial cohort, and in 9/2,645 (0.34%) cases and 1/2,420 (0.04%) controls ( P = 0.022, OR = 8.3) of the replication cohort. The 16p11.2 microduplication was associated with a 14.5-fold increased risk of schizophrenia (95% CI (3.3, 62)) in the combined sample. A meta-analysis of datasets for multiple psychiatric disorders showed a significant association of the microduplication with schizophrenia ( P = 4.8 × 10 −7 ), bipolar disorder ( P = 0.017) and autism ( P = 1.9 × 10 −7 ). In contrast, the reciprocal microdeletion was associated only with autism and developmental disorders ( P = 2.3 × 10 −13 ). Head circumference was larger in patients with the microdeletion than in patients with the microduplication ( P = 0.0007).

AimsAtypical lymphocytes circulating in blood have been reported in COVID-19 patients. This study aims to (1) analyse if patients with reactive lymphocytes (COVID-19 RL) show clinical or biological characteristics related to outcome; (2) develop an automatic system to recognise them in an objective way and (3) study their immunophenotype.MethodsClinical and laboratory findings in 36 COVID-19 patients were compared between those showing COVID-19 RL in blood (18) and those without (18). Blood samples were analysed in Advia2120i and stained with May Grünwald-Giemsa. Digital images were acquired in CellaVisionDM96. Convolutional neural networks (CNNs) were used to accurately recognise COVID-19 RL. Immunophenotypic study was performed throughflow cytometry.ResultsNeutrophils, D-dimer, procalcitonin, glomerular filtration rate and total protein values were higher in patients without COVID-19 RL (p<0.05) and four of these patients died. Haemoglobin and lymphocyte counts were higher (p<0.02) and no patients died in the group showing COVID-19 RL. COVID-19 RL showed a distinct deep blue cytoplasm with nucleus mostly in eccentric position. Through two sequential CNNs, they were automatically distinguished from normal lymphocytes and classical RL with sensitivity, specificity and overall accuracy values of 90.5%, 99.4% and 98.7%, respectively. Immunophenotypic analysis revealed COVID-19 RL are mostly activated effector memory CD4 and CD8 T cells.ConclusionWe found that COVID-19 RL are related to a better evolution and prognosis. They can be detected by morphology in the smear review, being the computerised approach proposed useful to enhance a more objective recognition. Their presence suggests an abundant production of virus-specific T cells, thus explaining the better outcome of patients showing these cells circulating in blood.

The recent emergence of advanced information technologies such as cloud computing, artificial intelligence, and data science has improved and optimized various processes in acoustics with potential real-world applications. Noise monitoring tasks on large terrains can be captured using an array of sound level meters. However, current monitoring systems only rely on the knowledge of a singular measured value related to the acoustic energy of the captured signal, leaving aside spatial aspects that complement the perception of noise by the human being. This project presents a system that performs binaural measurements according to subjective human perception. The acoustic characterization in an anechoic chamber is presented, as well as acoustic indicators obtained in the field initially for a short period of time. The main contribution of this work is the construction of a binaural prototype that resembles the human head and which transmits and processes acoustical data on the cloud. The above allows noise level monitoring via binaural hearing rather than a singular capturing device. Likewise, it can be highlighted that the system allows for obtaining spatial acoustic indicators based on the interaural cross-correlation function (IACF), as well as detecting the location of the source on the azimuthal plane.

Abstract Health and fitness are contributing factors to physical resilience, or the ability to resist or recover from functional decline following health stressors. Accelerometer based activity monitors have been used in both the in-patient and outpatient setting to monitor mobility. While using sensors to track mobility is increasing, most clinical settings rely on patient reported outcomes. These measures often under or overestimate movement. The lack of a clinically meaningful way to measure mobility in the in-patient setting is a barrier to improving the mobility of hospitalized individuals. This is especially important when considering that over one-third of hospitalized older adults are discharged with a major new functional disability in performing activities of daily living. Our goal was to automatically determine if the subject is laying, reclining, sitting, standing, and walking to better reflect actual activity. Other platforms and studies indicate the ability to determine a difference in activity vs. inactivity or laying and reclining vs. standing and walking, but not all five phases of movement defined here. The aim of this study was to use accelerometer data to train a machine learning algorithm to automatically classify the postural changes (i.e. laying, reclining, sitting, standing, and walking). Preliminary results demonstrate that our trained algorithm is overall 95% accurate in determining each position from unlabeled data from the subject population. Additionally, this algorithm will be applied to in-patient hospitalized older adults for tracking of positions throughout the day.

We review the current and future threats to duck populations that breed, stage, moult and/or winter in the Nordic countries. Migratory duck species are sensitive indicators of their changing environment, and their societal value confirms the need to translate signals from changes in their distribution, status and abundance into a better understanding of changes occurring in their wetland environments. We used expert opinion to highlight 25 major areas of anthropogenic change (and touch briefly on potential mitigation measures through nature restoration and reserve management projects) that we consider key issues likely to influence Nordic duck populations now and in the near future to stimulate debate, discussion and further research. We believe such reviews are essential in contributing to development of successful management policy as well as stimulating specific research to support the maintenance of duck species in favourable future conservation status in the face of multiple population pressures and drivers.

The existence of non-zero neutrino masses points to the likely existence of multiple SM neutral fermions. When such states are heavy enough that they cannot be produced in oscillations, they are referred to as Heavy Neutral Leptons (HNLs). In this white paper we discuss the present experimental status of HNLs including colliders, beta decay, accelerators, as well as astrophysical and cosmological impacts. We discuss the importance of continuing to search for HNLs, and its potential impact on our understanding on key fundamental questions, and additionally we outline the future prospects for next-generation future experiments or upcoming accelerator run scenarios.