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om placebo for LEM5 and LEM15 (p < 0.01 and p = 0.002, respectively). There were no serious treatment-emergent adverse events or worsening of cognitive function, as assessed by the MMSE and ADAS-Cog. Trametinib Lemborexant was well tolerated. No subjects discontinued treatment.

This study provides preliminary evidence of the potential utility of lemborexant as a treatment to address both nighttime and daytime symptoms in patients with ISWRD and AD-D.

This study provides preliminary evidence of the potential utility of lemborexant as a treatment to address both nighttime and daytime symptoms in patients with ISWRD and AD-D.Previous findings from the positron emission tomography (PET) substudy of the SCarlet RoAD and Marguerite RoAD open-label extension (OLE) showed gantenerumab doses up to 1200 mg every 4 weeks administered subcutaneously resulted in robust beta-amyloid (Aβ) plaque removal over 24 months in people with prodromal-to-moderate Alzheimer's disease (AD). In this 36-month update, we demonstrate continued reduction, with mean (standard error) centiloid values at 36 months of -4.3 (7.5), 0.8 (6.7), and 4.7 (8.0) in the SCarlet RoAD (double-blind pooled placebo and active groups), Marguerite RoAD double-blind placebo, and Marguerite RoAD double-blind active groups respectively, representing a change of -57.0 (10.3), -90.3 (9.0), and -74.9 (10.5) centiloids respectively. These results demonstrate that prolonged gantenerumab treatment, at doses up to 1200 mg, reduces amyloid plaque levels below the amyloid positivity threshold. The ongoing GRADUATE Phase III trials will evaluate potential clinical benefits associated with gantenerumab-induced amyloid-lowering in people with early (prodromal-to-mild) AD.The terahertz (THz) spectrum of dl-alanine has been measured for the first time at cryogenic temperatures and with a pure sample. Several sharp absorptions are observed, over a wide frequency range (0.8-4.8 THz), at 8 K. The sample structure and purity were confirmed with both Raman spectroscopy and X-ray diffraction. Temperature dependent spectra revealed redshifting, with increasing temperature, for all modes except one at 2.70 THz. This mode exhibits blueshifting until ≈120 K, where it starts to redshift. A Bose-Einstein distribution has been used to model the frequency shift with temperature for the four lowest energy modes. Strong correlations between the fits and data indicate that these modes are caused by phonon excitation in an anharmonic potential. Density functional theory has also been used to identify the origin of these low frequency modes. They are attributed to large scale molecular vibrations.

Several candidate vaccines to prevent COVID-19 disease have entered large-scale phase 3 placebo-controlled randomized clinical trials and some have demonstrated substantial short-term efficacy. Efficacious vaccines should, at some point, be offered to placebo participants, which will occur before long-term efficacy and safety are known.

Following vaccination of the placebo group, we show that placebo-controlled vaccine efficacy can be derived by assuming the benefit of vaccination over time has the same profile for the original vaccine recipients and the placebo crossovers. This reconstruction allows estimation of both vaccine durability and potential vaccine-associated enhanced disease.

Post-crossover estimates of vaccine efficacy can provide insights about durability, identify waning efficacy, and identify late enhancement of disease, but are less reliable estimates than those obtained by a standard trial where the placebo cohort is maintained. As vaccine efficacy estimates for post-crossover periods acebo, yet still allows important insights about immunological and clinical effectiveness over time.Radiology reports contain important clinical information about patients which are often tied through spatial expressions. Spatial expressions (or triggers) are mainly used to describe the positioning of radiographic findings or medical devices with respect to some anatomical structures. As the expressions result from the mental visualization of the radiologist's interpretations, they are varied and complex. The focus of this work is to automatically identify the spatial expression terms from three different radiology sub-domains. We propose a hybrid deep learning-based NLP method that includes - 1) generating a set of candidate spatial triggers by exact match with the known trigger terms from the training data, 2) applying domain-specific constraints to filter the candidate triggers, and 3) utilizing a BERT-based classifier to predict whether a candidate trigger is a true spatial trigger or not. The results are promising, with an improvement of 24 points in the average F1 measure compared to a standard BERT-based sequence labeler.We present a novel method for global diffeomorphic phase alignment of time-series data from resting-state functional magnetic resonance imaging (rsfMRI) signals. Additionally, we propose a multidimensional, continuous, invariant functional representation of brain time-series data and solve a general global cost function that brings both the temporal rotations and phase reparameterizations in alignment. We define a family of cost functions for spatiotemporal warping and compare time-series warps across them. This method achieves direct alignment of time-series, allows population analysis by aligning time-series activity across subjects and shows improved global correlation maps, as well as z-scores from independent component analysis (ICA), while showing new information exploited by phase alignment that was not previously recoverable.Individuals with spinal cord injury (SCI) have a significantly increased risk for cognitive impairment that is associated with cerebrovascular remodeling and endothelial dysfunction. The sub-acute stage following high thoracic SCI is characterized by increased fibrosis and stiffness of cerebral arteries. However, a more prolonged duration after SCI exacerbates cerebrovascular injury by damaging endothelium. Endothelial dysfunction is associated with reduced expression of transient receptor potential cation channel 4 that mediates the production of nitric oxide and epoxyeicosatrienoic acids following shear stress and the response to carbachol and other endothelium-dependent vasodilators. Reduced expression of CD31 in cerebral arteries also suggests the loss of endothelial cell integrity following chronic SCI. Repetitively transient hypertension and intermittent hypotension contribute to cerebrovascular endothelial dysfunction in the animals with a sub-acute stage of high thoracic SCI. The increase in vascular remodeling and endothelial dysfunction ultimately reduce cerebral blood flow, which promotes cerebral hypoperfusion and cognitive dysfunction in the chronic phase of SCI. In conclusion, the duration and magnitude of fluctuations in blood pressure after SCI play a vital role in the onset and progress of cerebrovascular dysfunction, which promotes the development of cognitive impairment.[This corrects the article DOI 10.1016/j.patter.2020.100080.].The International Space Station (ISS) is a world-class laboratory in low Earth orbit, supporting research investigations in biological sciences, physical sciences, human health, Earth and space science, technology development, and educational engagement. Data from many investigations are available in open-source science databases, providing unprecedented opportunity to leverage the unique environment.As humanity explores the Solar System, the further our spacecraft get from Earth the further their data signals have to travel. We look at some of the biggest obstacles that come up when attempting to transfer data billions of kilometers across space using a power- and weight-limited spacecraft.The FAIR principles need to be applied in context. To do that, we need to understand both the needs of data users and the characteristics of the data to be shared. This Opinion introduces ten different dataset archetypes that can be used to inform plans for how data are to be accessed, used, and shared.In the absence of direct measurements of state-level household gun ownership (GO), the quality and accuracy of proxy measures for this variable are essential for firearm-related research and policy development. In this work, we develop two highly accurate proxy measures of GO using traditional regression analysis and deep learning, the former accounting for non-linearities in the covariates (portion of suicides committed with a firearm [FS/S] and hunting license rates) and their statistical interactions. We subject the proxies to extensive model diagnostics and validation. Both our regression-based and deep-learning proxy measures provide highly accurate models of GO with training R2 of 96% and 98%, respectively, along with other desirable qualities-stark improvements over the prevalent FS/S proxy (R2 = 0.68). Model diagnostics reveal this widely used FS/S proxy is highly biased and inadequate; we recommend that it no longer be used to represent state-level household gun ownership in firearm-related studies.We have learned from the debate on diversity and inclusion that archiving is not neutral or unbiased even though it is presented in this way. Seen from the perspective of cultural humility, we need to keep learning and challenge power imbalances from both the individual and the organizational level. This article discusses what this means for digital preservation concepts.Space agencies have announced plans for human missions to the Moon to prepare for Mars. However, the space environment presents stressors that include radiation, microgravity, and isolation. Understanding how these factors affect biology is crucial for safe and effective crewed space exploration. There is a need to develop countermeasures, to adapt plants and microbes for nutrient sources and bioregenerative life support, and to limit pathogen infection. Scientists across the world are conducting space omics experiments on model organisms and, more recently, on humans. Optimal extraction of actionable scientific discoveries from these precious datasets will only occur at the collective level with improved standardization. To address this shortcoming, we established ISSOP (International Standards for Space Omics Processing), an international consortium of scientists who aim to enhance standard guidelines between space biologists at a global level. Here we introduce our consortium and share past lessons learned and future challenges related to spaceflight omics.Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields, including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design. We dissect the emerging approaches using deep learning techniques for protein structural modeling and discuss advances and challenges that must be addressed. We argue for the central importance of structure, following the "sequence → structure → function" paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.