AI models and the future of genomic research and medicine: True sons of knowledge? Artificial intelligence needs to be integrated with causal conceptions in biomedicine to harness its societal benefits for the field

The increasing availability of large-scale, complex data has made research into how human genomes determine physiology in health and disease, as well as its application to drug development and medicine, an attractive field for artificial intelligence (AI) approaches. Looking at recent developments, we explore how such approaches interconnect and may conflict with needs for and notions of causal knowledge in molecular genetics and genomic medicine. We provide reasons to suggest that—while capable of generating predictive knowledge at unprecedented pace and scale—if and how these approaches will be integrated with prevailing causal concepts will not only determine the future of scientific understanding and self-conceptions in these fields. But these questions will also be key to develop differentiated policies, such as for education and regulation, in order to harness societal benefits of AI for genomic research and medicine

Fission barriers and asymmetric ground states in the relativistic mean field theory

The symmetric and asymmetric fission path for 240Pu, 232Th, and 226Ra is investigated within the relativistic mean field model. Standard parametrizations which are well fitted to nuclear ground state properties are found to deliver reasonable qualitative and quantitative features of fission, comparable to similar nonrelativstic calculations. Furthermore, stable octupole deformations in the ground states of Radium isotopes are investigated. They are found in a series of isotopes, qualitatively in agreement with nonrelativistic models. But the quantitative details differ amongst the models and between the various relativsitic parametrizations.Comment: 30 pages RevTeX, 7 tables, 12 low resolution Gif figures (high resolution PostScript versions are available at http://www.th.physik.uni-frankfurt.de/~bender/nucl_struct_publications.html or at ftp://th.physik.uni-frankfurt.de/pub/bender

Potential up-scaling of inkjet-printed devices for logical circuits in flexible electronics

Inkjet Technology is often mis-believed to be a deposition/patterning technology which is not meant for high fabrication throughput in the field of printed and flexible electronics. In this work, we report on the 1) printing, 2) fabrication yield and 3) characterization of exemplary simple devices e.g. capacitors, organic transistors etc. which are the basic building blocks for logical circuits. For this purpose, printing is performed first with a Proof of concept Inkjet printing system Dimatix Material Printer 2831 (DMP 2831) using 10 pL small print-heads and then with Dimatix Material Printer 3000 (DMP 3000) using 35 pL industrial print-heads (from Fujifilm Dimatix). Printing at DMP 3000 using industrial print-heads (in Sheet-to-sheet) paves the path towards industrialization which can be defined by printing in Roll-to-Roll format using industrial print-heads. This pavement can be termed as "Bridging Platform". This transfer to "Bridging Platform" from 10 pL small print-heads to 35 pL industrial print-heads help the inkjet-printed devices to evolve on the basis of functionality and also in form of up-scaled quantities. The high printed quantities and yield of inkjet-printed devices justify the deposition reliability and potential to print circuits. This reliability is very much desired when it comes to printing of circuits e.g. inverters, ring oscillator and any other planned complex logical circuits which require devices e.g. organic transistors which needs to get connected in different staged levels. Also, the up-scaled inkjet-printed devices are characterized and they reflect a domain under which they can work to their optimal status. This status is much wanted for predicting the real device functionality and integration of them into a planned circuit

Inkjet printed metal insulator semiconductor (MIS) diodes for organic and flexible electronic application

All inkjet printed rectifying diodes based on a metal-insulator-semiconductor (MIS) layer stack are presented. The rectifying properties were optimized by careful selection of the insulator interlayer thickness and the layout structure. The different diode architectures based on the following materials are investigated: (1) silver/ poly (methylmethacrylate-methacrylic acid)/ polytriarylamine/ silver, (2) silver/ polytriarylamine/ poly (methylmethacrylate-methacrylic acid)/ silver, and (3) silver/ poly (methylmethacrylate-methacrylic acid)/ poly-triarylamine/ poly(3,4-ethylenedioxythiophene) poly (styrenesulfonate). The MIS diodes show an averaged rectification ratio of 200 and reasonable forward current density reaching 40 mA cm -2. They are suitable for a number of applications in flexible printed organic electronics.EU [287682

A Cross-Sectional, Abattoir-Based Study

Abstract Toxigenic Escherichia coli (E. coli) are an important cause of gastroenteritis in developing countries. In Ethiopia, gastroenteritis due to food-borne disease is a leading cause of death. Yet, there is no surveillance for E. coli O157 and little is known about the carriage of this pathogen in Ethiopia’s livestock. This study aimed to assess the prevalence and levels of antimicrobial resistance of E. coli O157 in goat meat, feces, and environmental samples collected at a large abattoir in the Somali region of Ethiopia. The samples were enriched in modified tryptone broth containing novobiocin, and plated onto sorbitol MacConkey agar. Isolates were confirmed using indole test and latex agglutination. Antimicrobial susceptibility testing was conducted using the disk diffusion method. A total of 235 samples, including 93 goat carcass swabs, 93 cecal contents, 14 water, 20 hand, and 15 knife swabs were collected. Overall, six (2.5%) samples were contaminated with E. coli O157 of which two (2.1%) were isolated from cecal contents, three (3.2%) from carcass swabs, and one (7.1%) from water. All isolates were resistant to at least two of the 18 antimicrobials tested. Two isolates (33.3%) were resistant to more than five antimicrobials. Abattoir facilities and slaughter techniques were conducive to carcass contamination. This study highlights how poor hygiene and slaughter practice can result in contaminated meat, which is especially risky in Ethiopia because of the common practice of eating raw meat. We detect multi-resistance to drugs not used in goats, suggesting that drugs used to treat human infections may be the originators of antimicrobial resistance in livestock in this ecosystem. The isolation of multidrug-resistant E. coli O157 from goats from a remote pastoralist system highlights the need for global action on regulating and monitoring antimicrobial use in both human and animal populations

Artificial intelligence in human genomics and biomedicine - Dynamics, potentials and challenges

The increasing availability of extensive and complex data has made human genomics and its applications in (bio)medicine an at­ tractive domain for artificial intelligence (AI) in the form of advanced machine learning (ML) methods. These methods are linked not only to the hope of improving diagnosis and drug development. Rather, they may also advance key issues in biomedicine, e. g. understanding how individual differences in the human genome may cause specific traits or diseases. We analyze the increasing convergence of AI and genom­ics, the emergence of a corresponding innovation system, and how these associative AI methods relate to the need for causal knowledge in biomedical research and development (R&D) and in medical prac­tice. Finally, we look at the opportunities and challenges for clinical practice and the implications for governance issues arising from this convergence.Die zunehmende Verfügbarkeit umfangreicher und komplexer Daten hat die Humangenomik und ihre Anwendungsbereiche in der (Bio-)Medizin zu einem attraktiven Bereich für künstliche Intelligenz (KI) vor allem in Form von fortgeschrittenen Methoden des maschinellen Lernens (ML) gemacht. Diese Methoden sind nicht nur mit der Hoffnung verbunden, Diagnosen und die Medikamentenentwicklung zu verbessern. Sie könnten auch darum, Kernthemen in der Biomedizin voranzubringen, z. B. zu verstehen, wie individuelle Unterschiede im menschlichen Genom bestimmte Merkmale oder Krankheiten verursachen können. Wir analysieren die zunehmende Konvergenz von KI und Genomik, das Entstehen eines entsprechenden Innovationssystems und wie diese assoziativen KI‑Methoden mit dem Bedarf an kausalem Wissen in der biomedizinischen Forschung und Entwicklung und in der medizinischen Praxis zusammenhängen. Schließlich betrachten wir die Potenziale und Herausforderungen für die klinische Praxis und die sich aus dieser Konvergenz ergebenden Implikationen für Governance-Fragen