Associations of Various Nighttime Noise Exposure Indicators with Objective Sleep Efficiency and Self-Reported Sleep Quality: A Field Study

It is unclear which noise exposure time window and noise characteristics during nighttime are most detrimental for sleep quality in real-life settings. We conducted a field study with 105 volunteers wearing a wrist actimeter to record their sleep during seven days, together with concurrent outdoor noise measurements at their bedroom window. Actimetry-recorded sleep latency increased by 5.6 min (95% confidence interval (CI): 1.6 to 9.6 min) per 10 dB(A) increase in noise exposure during the first hour after bedtime. Actimetry-assessed sleep efficiency was significantly reduced by 2%-3% per 10 dB(A) increase in measured outdoor noise (L; eq, 1h; ) for the last three hours of sleep. For self-reported sleepiness, noise exposure during the last hour prior to wake-up was most crucial, with an increase in the sleepiness score of 0.31 units (95% CI: 0.08 to 0.54) per 10 dB(A) L; eq,1h; . Associations for estimated indoor noise were not more pronounced than for outdoor noise. Taking noise events into consideration in addition to equivalent sound pressure levels (L; eq; ) only marginally improved the statistical models. Our study provides evidence that matching the nighttime noise exposure time window to the individual's diurnal sleep-wake pattern results in a better estimate of detrimental nighttime noise effects on sleep. We found that noise exposure at the beginning and the end of the sleep is most crucial for sleep quality

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

Importance Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. Objectives To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. Design, Setting, and Participants This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. Main Outcomes and Measures Accuracy and generalizability of prognostic systems. Results A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. Conclusions and RelevanceThese findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.Question Can a transition to psychosis be predicted in patients with clinical high-risk states or recent-onset depression by optimally integrating clinical, neurocognitive, neuroimaging, and genetic information with clinicians' prognostic estimates? Findings In this prognostic study of 334 patients and 334 control individuals, machine learning models sequentially combining clinical and biological data with clinicians' estimates correctly predicted disease transitions in 85.9% of cases across geographically distinct patient populations. The clinicians' lack of prognostic sensitivity, as measured by a false-negative rate of 38.5%, was reduced to 15.4% by the sequential prognostic model. Meaning These findings suggest that an individualized prognostic workflow integrating artificial and human intelligence may facilitate the personalized prevention of psychosis in young patients with clinical high-risk syndromes or recent-onset depression.</p

Genetic stability of Schmallenberg virus in vivo during an epidemic, and in vitro, when passaged in the highly susceptible porcine SK-6 cell line

Schmallenberg virus (SBV), an arthropod-borne orthobunyavirus was first detected in 2011 in cattle suffering from diarrhea and fever. The most severe impact of an SBV infection is the induction of malformations in newborns and abortions. Between 2011 and 2013 SBV spread throughout Europe in an unprecedented epidemic wave. SBV contains a tripartite genome consisting of the three negative-sense RNA segments L, M, and S. The virus is usually isolated from clinical samples by inoculation of KC (insect) or BHK-21 (mammalian) cells. Several virus passages are required to allow adaptation of SBV to cells in vitro. In the present study, the porcine SK-6 cell line was used for isolation and passaging of SBV. SK-6 cells proved to be more sensitive to SBV infection and allowed to produce higher titers more rapidly as in BHK-21 cells after just one passage. No adaptation was required. In order to determine the in vivo genetic stability of SBV during an epidemic spread of the virus the nucleotide sequence of the genome from seven SBV field isolates collected in summer 2012 in Switzerland was determined and compared to other SBV sequences available in GenBank. A total of 101 mutations, mostly transitions randomly dispersed along the L and M segment were found when the Swiss isolates were compared to the first SBV isolated late 2011 in Germany. However, when these mutations were studied in detail, a previously described hypervariable region in the M segment was identified. The S segment was completely conserved among all sequenced SBV isolates. To assess the in vitro genetic stability of SBV, three isolates were passage 10 times in SK-6 cells and sequenced before and after passaging. Between two and five nt exchanges per genome were found. This low in vitro mutation rate further demonstrates the suitability of SK-6 cells for SBV propagation

Complete Genome Sequences of Three Border Disease Virus Strains of the Same Subgenotype, BDSwiss, Isolated from Sheep, Cattle, and Pigs in Switzerland.

We report here the complete genome sequences of three border disease virus (BDV) strains of the same subgenotype isolated in Switzerland from a sheep, a cow, and a pig, respectively. This is the first report of full-length sequences of a tentatively new subgenotype isolated from three different species of cloven-hoofed farm animals

Forum Choice for Terrorism Suspects

What forum should be used to adjudicate the status of persons suspected of involvement in terrorism? Recent clashes between Congress and the president as to whether the status of terrorism suspects should be determined via Article III courts or military commissions have revived the debate about this venue question. The problem is typically framed as a matter of legal doctrine, with statutory and doctrinal rules invoked as dispositive guides for sorting suspects into either civilian or military venues. This Article takes issue with the utility of that framing of the problem. It argues that the forum question can more profitably be analyzed through an institutional-design lens. A key institutional-design decision is whether and when to create jurisdictional redundancy. When, that is, should the existence of overlapping jurisdictions vest the government with a threshold choice of forums or an option to retry a suspect who has been acquitted in an initial process? Jurisdictional redundancy is pervasive. But conventional wisdom suggests that it is unwise. This Article demonstrates, however, that overlap among forums has complex direct and indirect effects on the accuracy and cost of terrorism-related adjudication. The Article presents a comprehensive framework for analyzing redundancy by exploring how redundancy influences error rates, system-maintenance costs, externalities, information production, and incentives. Applying this framework, I contend that the conventional wisdom is flawed. Pervasive redundancy has surprising merit in contrast to two leading reform proposals that would eliminate most jurisdictional overlap

MOESM2 of Heterogeneous antigenic properties of the porcine reproductive and respiratory syndrome virus nucleocapsid

Additional file 2. Comparative reactivity of N from various PRRSV strains with mAb SDOW17 and predicted antigenic regions of N. A In analogy to the data of Figure 6, the reactivity of the mAbs SDOW17 and SR30 was determined against the different PRRSV N proteins expressed transiently in BHK-21 cells using IF. The secondary antibody was alexa-488-conjugated (green) and the cell nuclei were counterstained with DAPI (blue). The N proteins of the different strains were grouped according to the flow cytometry results of Figure 6. Black, light grey and dark grey shading highlight the strains with less than 25%, between 25 and 75%, and more than 75% N detection by SDOW17 versus SR30, respectively. B All ORF7 amino acid sequences of the strains used in the experiment of panel A and in Figure 6 were aligned and compared to the IVI-1173 sequence using the Clone Manager software. The sequence names are highlighted in black, light grey and dark gray according to the grouping described above. The predicted SDOW17 epitope region mapped for LV is highlighted in yellow [33]. The SDOW17 epitope regions mapped with PA-8 are delimited by dark blue rectangles [34]. The region harboring the SR30 epitope in PA-8 is delimited by a light blue rectangle [34]. LV, VR-2332, PA-8 and PrimePac were included as reference strains. The amino acid differences between IV3140 and IVI-1173 are shown in bold red