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Human brain organoids embodied within virtual learning environments provide a novel experimental system for mechanistic studies of learning and memory. Yet these forms of synthetic biological or organoid intelligence raise moral and ethical concerns. Bioethicists argue that if biocomputers become conscious, they have ‘moral status’, which places limits on permissible research. Yet previous empirical studies of public opinion suggest that creation of conscious brain organoids may be essential to modeling features of the human brain relevant to medical advancement. We conducted a nationally representative experimental study that investigated factors that shape public attitudes toward brain organoids integrated with computer hardware and software, herein referred to as embodied brain organoids or ‘biocomputers’. We report several key findings. First, the public’s tendency to attribute consciousness to biocomputers is positively correlated with attribution of other cognitive traits and increased ethical concerns, but also the perceived benefits and overall support for research. Second, support for research is largely unaffected by applications of biocomputers to medicine or development of information technology. Third, support is highest for biocomputers that are described as having moral status-conferring abilities, such as awareness of other biocomputers. Finally, attribution of consciousness to biocomputers and support for research are lowest among people who endorse a ‘foundational distinction’ between humans and embodied organoid systems. The negative impact of these foundational distinctions on perceived benefits can be mitigated, however, by how the technology is applied toward societal benefit. Our findings provide key insights into factors that shape moral judgements about brain organoids integrated with conventional computing systems to perform cognitive-like tasks. 

Adhesion of bacteria to oral implant surfaces can lead to oral infections, and the prevention of strong biofilm adherence to implant surfaces can assist in the prevention of these infections like peri-implantitis. In prior studies, single species biofilm adhesion has been quantitatively measured via the laser spallation technique. However, colonizing oral biofilms rarely consists of a single bacteria species. Multiple early colonizer species, including several strains of Streptococci, dominate initial oral biofilm formation. This study aims to characterize the adhesion of a multi-species oral biofilm consisting of S. oralis, S. sanguinis, and S. gordonii on titanium, a common implant material, using the laser spallation technique. Previous work has established these specific Streptococci strains as a multi-species periodontal biofilm model. This study is the first to provide a quantitative adhesion measurement of this multi-species model onto a dental implant surface. First, adhesion strength of the multi-species model is compared to adhesion strength of the single-species streptococci constituents. Fluorescent staining and imaging by fluorescent microscopy are used to identify individual bacteria species within the biofilm. The multi-species biofilm presented in this study provides a more representative model of in vivo early biofilms and provides a more accurate metric for understanding biocompatibility on implant surfaces. 

Abstract Data preservation is a mandatory specification for any present and future experimental facility and it is a cost-effective way of doing fundamental research by exploiting unique data sets in the light of the continuously increasing theoretical understanding. This document summarizes the status of data preservation in high energy physics. The paradigms and the methodological advances are discussed from a perspective of more than ten years of experience with a structured effort at international level. The status and the scientific return related to the preservation of data accumulated at large collider experiments are presented, together with an account of ongoing efforts to ensure long-term analysis capabilities for ongoing and future experiments. Transverse projects aimed at generic solutions, most of which are specifically inspired by open science and FAIR principles, are presented as well. A prospective and an action plan are also indicated. 

The brain is arguably the most powerful computation system known. It is extremely efficient in processing large amounts of information and can discern signals from noise, adapt, and filter faulty information all while running on only 20 watts of power. The human brain's processing efficiency, progressive learning, and plasticity are unmatched by any computer system. Recent advances in stem cell technology have elevated the field of cell culture to higher levels of complexity, such as the development of three-dimensional (3D) brain organoids that recapitulate human brain functionality better than traditional monolayer cell systems. Organoid Intelligence (OI) aims to harness the innate biological capabilities of brain organoids for biocomputing and synthetic intelligence by interfacing them with computer technology. With the latest strides in stem cell technology, bioengineering, and machine learning, we can explore the ability of brain organoids to compute, and store given information (input), execute a task (output), and study how this affects the structural and functional connections in the organoids themselves. Furthermore, understanding how learning generates and changes patterns of connectivity in organoids can shed light on the early stages of cognition in the human brain. Investigating and understanding these concepts is an enormous, multidisciplinary endeavor that necessitates the engagement of both the scientific community and the public. Thus, on Feb 22–24 of 2022, the Johns Hopkins University held the first Organoid Intelligence Workshop to form an OI Community and to lay out the groundwork for the establishment of OI as a new scientific discipline. The potential of OI to revolutionize computing, neurological research, and drug development was discussed, along with a vision and roadmap for its development over the coming decade. 

Abstract The FASER experiment is a new small and inexpensive experiment that is placed 480 meters downstream of the ATLAS experiment at the CERN LHC. FASER is designed to capture decays of new long-lived particles, produced outside of the ATLAS detector acceptance. These rare particles can decay in the FASER detector together with about 500–1000 Hz of other particles originating from the ATLAS interaction point. A very high efficiency trigger and data acquisition system is required to ensure that the physics events of interest will be recorded. This paper describes the trigger and data acquisition system of the FASER experiment and presents performance results of the system acquired during initial commissioning. 

This paper presents a search for massive, charged, long-lived particles with the ATLAS detector at the Large Hadron Collider using an integrated luminosity of $$140~fb^{−1}$$ of proton-proton collisions at $$\sqrt{s}=13$$~TeV. These particles are expected to move significantly slower than the speed of light. In this paper, two signal regions provide complementary sensitivity. In one region, events are selected with at least one charged-particle track with high transverse momentum, large specific ionisation measured in the pixel detector, and time of flight to the hadronic calorimeter inconsistent with the speed of light. In the other region, events are selected with at least two tracks of opposite charge which both have a high transverse momentum and an anomalously large specific ionisation. The search is sensitive to particles with lifetimes greater than about 3 ns with masses ranging from 200 GeV to 3 TeV. The results are interpreted to set constraints on the supersymmetric pair production of long-lived R-hadrons, charginos and staus, with mass limits extending beyond those from previous searches in broad ranges of lifetime