Memories of the future

The year is 2020. Sheffield University’s MSc in Electronic & Digital Library Management has been running for 10 years. What paths have its graduates’ careers taken

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network\u27s prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine

Autoantibodies neutralizing type I IFNs underlie West Nile virus encephalitis in ∼40% of patients

Mosquito-borne West Nile virus (WNV) infection is benign in most individuals but can cause encephalitis in \u3c1% of infected individuals. We show that ∼35% of patients hospitalized for WNV disease (WNVD) in six independent cohorts from the EU and USA carry auto-Abs neutralizing IFN-α and/or -ω. The prevalence of these antibodies is highest in patients with encephalitis (∼40%), and that in individuals with silent WNV infection is as low as that in the general population. The odds ratios for WNVD in individuals with these auto-Abs relative to those without them in the general population range from 19.0 (95% CI 15.0-24.0, P value \u3c10-15) for auto-Abs neutralizing only 100 pg/ml IFN-α and/or IFN-ω to 127.4 (CI 87.1-186.4, P value \u3c10-15) for auto-Abs neutralizing both IFN-α and IFN-ω at a concentration of 10 ng/ml. These antibodies block the protective effect of IFN-α in Vero cells infected with WNV in vitro. Auto-Abs neutralizing IFN-α and/or IFN-ω underlie ∼40% of cases of WNV encephalitis

Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness

Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges

Numerical performance of healthy processing for HMF content in honey

The objective of this study is to develop a kinetic model correlating the effect of heating temperature and the duration of thermal treatment on HMF formation for different types of honey from different geographical locations. In this study, the experimental data from previous re-search papers for European and Asian honey was collected from year 1999 to 2012. The data was analysed and performed visually in graphical representation to draw the relationship between the factors. Then, a descriptive mathematical model was developed by using Math Work to correlate the parameters and the model was validated based on the data from Malaysian and European honey samples. The study showed that both heating temperature and duration could accelerate the production of HMF content in honey. The formation of HMF con-tent is proportionally increased with the increase of heating temperature and duration

Evaluation and characterisation of Citrullus colocynthis (L.) Schrad seed oil: comparison with Helianthus annuus (sunflower) seed oil.

The physicochemical properties, fatty acid, tocopherol, thermal properties, 1H NMR, FTIR and profiles of non-conventional oil extracted from Citrullus colocynthis (L.) Schrad seeds were evaluated and compared with conventional sunflower seed oil. In addition, the antioxidant properties of C. colocynthis seed oil were also evaluated. The oil content of the C. colocynthis seeds was 23.16%. The main fatty acids in the oil were linoleic acid (66.73%) followed by oleic acid (14.78%), palmitic acid (9.74%), and stearic acid (7.37%). The tocopherol content was 121.85 mg/100 g with γ-tocopherol as the major one (95.49%). The thermogravimetric analysis showed that the oil was thermally stable up to 286.57 °C, and then began to decompose in four stages namely at 377.4 °C, 408.4 °C, 434.9 °C and 559.2 °C. The present study showed that this non-conventional C. colocynthis seed oil can be used for food and non-food applications to supplement or replace some of the conventional oils

Real-time monitoring of magnetic drug targeting using fibered confocal fluorescence microscopy

Magnetic drug targeting has been proposed as means of concentrating therapeutic agents at a target site and the success of this approach has been demonstrated in a number of studies. However, the behavior of magnetic carriers in blood vessels and tumor microcirculation still remains unclear. In this work, we utilized polymeric magnetic nanocapsules (m-NCs) for magnetic targeting in tumors and dynamically visualized them within blood vessels and tumor tissues before, during and after magnetic field exposure using fibered confocal fluorescence microscopy (FCFM). Our results suggested that the distribution of m-NCs within tumor vasculature changed dramatically, but in a reversible way, upon application and removal of a magnetic field. The m-NCs were concentrated and stayed as clusters near a blood vessel wall when tumors were exposed to a magnetic field but without rupturing the blood vessel. The obtained FCFM images provided in vivo in situ microvascular observations of m-NCs upon magnetic targeting with high spatial resolution but minimally invasive surgical procedures. This proof-of-concept descriptive study in mice is envisaged to track and quantify nanoparticles in vivo in a non-invasive manner at microscopic resolution

Identification of outer membrane Porin D as a vitronectin-binding factor in cystic fibrosis clinical isolates of Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a pathogen that frequently colonizes patients with cystic fibrosis (CF) or chronic obstructive pulmonary disease (COPD). Several pathogens are known to bind vitronectin to increase their virulence. Vitronectin has been shown to enhance P. aeruginosa adhesion to host epithelial cells

A Multiple Dependent State Repetitive Sampling Plan Based on Performance Index for Lifetime Data with Type II Censoring

In this paper, a multiple dependent state repetitive (MDSR) sampling plan based on the lifetime performance index C-L is proposed for lifetime data with type II censoring when the lifetime of a product follows the exponential distribution or Weibull distribution. The optimal parameters of the proposed plan are determined by minimizing the average sample number while satisfying the producer's risk and consumer's risk at corresponding quality levels. Besides, the performance of the proposed plan is compared with that of the existing lifetime sampling plan in terms of the average sample number and operating characteristic curve. Two illustrative examples are given for the demonstration of the proposed plan.11Ysciescopu

2-{[5-(Adamantan-1-yl)-4-methyl-4H-1,2,4-triazol-3-yl]sulfan­yl}-N,N-dimethyl­ethanamine

In the title compound, C17H28N4S, the 1,2,4-triazole ring is nearly planar [maximum deviation = 0.005 (2) Å]. There are no significant hydrogen bonds observed in the crystal structure. The crystal studied was a non-merohedral twin, the refined ratio of twin components being 0.281 (3):0.719 (3)