Biotech

Deep convolutional networks (DCNNs) are achieving previously unseen performance in object classification, raising questions about whether DCNNs operate similarly to human vision. In biological vision, shape is arguably the most important cue for recognition. We tested the role of shape information in DCNNs trained to recognize objects. In Experiment 1, we presented a trained DCNN with object silhouettes that preserved overall shape but were filled with surface texture taken from other objects. S

BACKGROUND: Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis may be caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data. RESULTS: In this study, we pr

World Economic Forum Founder and Executive Chairman Klaus Schwab offers a practical companion and field guide to his previous book, The Fourth Industrial Revolution. Today, technology is changing everything--how we relate to one another, the way we work, how our economies and goverments function, and even what it means to be human. One need not look hard to see how the incredible advances in artificial intelligence, cryptocurrencies, biotechnologies, and the internet of things are transfor

BACKGROUND: Protein secondary structure can be regarded as an information bridge that links the primary sequence and tertiary structure. Accurate 8-state secondary structure prediction can significantly give more precise and high resolution on structure-based properties analysis. RESULTS: We present a novel deep learning architecture which exploits an integrative synergy of prediction by a convolutional neural network, residual network, and bidirectional recurrent neural network to improve the p

A PubMed query run in June 2018 using the keyword 'blockchain' retrieved 40 indexed papers, a reflection of the growing interest in blockchain among the medical and healthcare research and practice communities. Blockchain's foundations of decentralisation, cryptographic security and immutability make it a strong contender in reshaping the healthcare landscape worldwide. Blockchain solutions are currently being explored for: (1) securing patient and provider identities; (2) managing pharmaceutica

A new emerging frontier in the evolution of the digitalisation and the 4 th industrial revolution (Industry 4.0) is considered to be that of "Biologicalisation in Manufacturing". This has been defined by the authors to be "The use and integration of biological and bio-inspired principles, materials, functions, structures and resources for intelligent and sustainable manufacturing technologies and systems with the aim of achieving their full potential." In this White Paper, detailed consideration

While new generations of implantable brain computer interface (BCI) devices are being developed, evidence in the literature about their impact on the patient experience is lagging. In this article, we address this knowledge gap by analysing data from the first-in-human clinical trial to study patients with implanted BCI advisory devices. We explored perceptions of self-change across six patients who volunteered to be implanted with artificially intelligent BCI devices. We used qualitative method

BACKGROUND: Target identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene-disease association data from the Open Targets platform is

Synthetic biology is an emerging domain that combines biological and engineering concepts and which has seen rapid growth in research, innovation, and policy interest in recent years. This paper contributes to efforts to delineate this emerging domain by presenting a newly constructed bibliometric definition of synthetic biology. Our approach is dimensioned from a core set of papers in synthetic biology, using procedures to obtain benchmark synthetic biology publication records, extract keywords

The Portuguese population enjoys good health and increasing life expectancy, though at lower levels than other western European countries. All residents in Portugal have access to health care provided by the National Health Service (NHS), financed mainly through taxation. Co-payments have been increasing over time, and the level of cost-sharing is highest for pharmaceutical products. Approximately one-fifth to a quarter of the population enjoys a second (or more) layer of health insurance covera

The ability to estimate the age of the donor from recovered biological material at a crime scene can be of substantial value in forensic investigations. Aging can be complex and is associated with various molecular modifications in cells that accumulate over a person’s lifetime including epigenetic patterns. The aim of this study was to use age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age using data from whole blood. In total, 45 age-ass

BACKGROUND: Biclustering techniques are capable of simultaneously clustering rows and columns of a data matrix. These techniques became very popular for the analysis of gene expression data, since a gene can take part of multiple biological pathways which in turn can be active only under specific experimental conditions. Several biclustering algorithms have been developed in the past recent years. In order to provide guidance regarding their choice, a few comparative studies were conducted and r

BACKGROUND: The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and theref

// Ramiro Vázquez 1 , María E. Riveiro 2 , Lucile Astorgues-Xerri 2 , Elodie Odore 2, 3 , Keyvan Rezai 3 , Eugenio Erba 1 , Nicolò Panini 1 , Andrea Rinaldi 4 , Ivo Kwee 4, 5, 6 , Luca Beltrame 1 , Mohamed Bekradda 2 , Esteban Cvitkovic 2, 7 , Francesco Bertoni 4, 8 , Roberta Frapolli 1 , Maurizio D'Incalci 1 1 Laboratory of Anti-tumor Pharmacology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy 2 Oncology Therapeutic Development, Clichy, France 3 Radiop

Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans the biological, physical, and social sciences.

// Maria E. Riveiro 1 , Lucile Astorgues-Xerri 1 , Ramiro Vazquez 2 , Roberta Frapolli 2 , Ivo Kwee 3, 5 , Andrea Rinaldi 3 , Elodie Odore 6 , Keyvan Rezai 6 , Mohamed Bekradda 1 , Giorgio Inghirami 7, 9 , Maurizio D’Incalci 2 , Kay Noel 10 , Esteban Cvitkovic 1, 10 , Eric Raymond 11 , Francesco Bertoni 3, 12 1 Oncology Therapeutic Development, Clichy, France 2 IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy 3 Lymphoma and Genomics Research Program, IOR Institute of Onc

In this paper, we argue for a theoretical separation of the free-energy principle from Helmholtzian accounts of the predictive brain. The free-energy principle is a theoretical framework capturing the imperative for biological self-organization in information-theoretic terms. The free-energy principle has typically been connected with a Bayesian theory of predictive coding, and the latter is often taken to support a Helmholtzian theory of perception as unconscious inference. If our interpretatio

Over recent decades it has become widely accepted that managing fisheries resources means managing human behaviour, and so understanding social and economic dynamics is just as important as understanding species biology and ecology. Until recently, fisheries managers and researchers have struggled to develop effective methods and data for social and economic analysis that can integrate with the predominantly biological approaches to fisheries management. The field is now growing fast, however, a

// Hong Zheng 1 , Jiansong Ji 2 , Liangcai Zhao 1 , Minjiang Chen 1, 2 , An Shi 3 , Linlin Pan 1 , Yiran Huang 3 , Huajie Zhang 1 , Baijun Dong 3 , Hongchang Gao 1 1 School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China 2 Lishui Central Hospital, The Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, 323000, China 3 Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China Correspondence to: Hongc

Invasive lobular carcinoma (ILC) is the second most frequently occurring histological breast cancer subtype after invasive ductal carcinoma (IDC), accounting for around 10% of all breast cancers. The molecular processes that drive the development of ILC are still largely unknown. We have performed a comprehensive genomic, transcriptomic and proteomic analysis of a large ILC patient cohort and present here an integrated molecular portrait of ILC. Mutations in CDH1 and in the PI3K pathway are the

The prevention and control of peri-implantitis is a challenge in dental implant surgery. Dental implants with sustained antimicrobial coating are an ideal way of preventing peri-implantitis. This study reports development of a non- immunotoxicity multilayered coating on a titanium surface that had sustained antimicrobial activity and limited early biofilm formation. In this study, the broad spectrum AMP, Tet213, was linked to collagen IV through sulfo-SMPB and has been renamed as AMPCol. The mul

BACKGROUND: Bevacizumab is an exogenous inhibitor which inhibits the biological activity of human VEGF. Several studies have investigated the effectiveness of bevacizumab therapy according to different cancer types but these days there is an intense debate on its utility. We have investigated different methods to find the best tumor volume estimation since it creates the possibility for precise and effective drug administration with a much lower dose than in the protocol. MATERIALS AND METHODS:

In this study, we investigated the effect of five feature selection approaches on the performance of a mixed model (G-BLUP) and a Bayesian (Bayes C) prediction method. We predicted height, high density lipoprotein cholesterol (HDL) and body mass index (BMI) within 2,186 Croatian and into 810 UK individuals using genome-wide SNP data. Using all SNP information Bayes C and G-BLUP had similar predictive performance across all traits within the Croatian data, and for the highly polygenic traits heig

Modern technologies are capable of generating enormous amounts of data that measure complex biological systems. Computational biologists and bioinformatics scientists are increasingly being asked to use these data to reveal key systems-level properties. We review the extent to which curricula are changing in the era of big data. We identify key competencies that scientists dealing with big data are expected to possess across fields, and we use this information to propose courses to meet these gr

OBJECTIVE: The combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. This study aims to explore whether pre-treating frontal quantitative EEG (QEEG) cordance is associated with response to rTMS treatment among MDD patients by using an artificial intelligence approach, artificial neural netwo