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Collaborative robots (cobots) can improve productivity by flexibly assisting production workers. Nevertheless, due to missing use cases, cobots have yet to be widely adopted in manufacturing. Democratizing robot programming by introducing cobots in public fablabs (or makerspaces) could lead to new applications transferrable to industry thanks to the creativity of interested laypersons. Fablabs could also mitigate the risk of unemployment due to automation by training cobot programmers for manufacturing jobs. In support of the proposed approach this paper introduces a participatory three-layer programming model designed to enable people with little or no programming experience to become effective cobot operators. The model builds on task-oriented programming environments extending them by more versatile yet intuitive programming environments. The model has been successfully evaluated on the basis of a real human-robot assembly application.

The Fukushima nuclear accident from 2011 provided an occasion for the public display of radiation maps (or dose projections) generated using decision-support systems for nuclear emergency management. Such systems rely on computer models for simulating the atmospheric dispersion of radioactive materials and estimating potential doses in the event of a radioactive release from a nuclear reactor. In Germany, as in Japan, such systems are part of the national emergency response apparatus and, in case of accidents, they can be used by emergency task forces for planning radioprotection countermeasures. In this context, the paper addresses the epistemology of dose projections by critically analyzing some of the sources of epistemic opacity and non-knowledge (or ignorance) affecting them, and the different methods and practices used by German radioprotection experts to improve their trustworthiness and reliability. It will be argued that dose projections are part of an entire radioprotection regime or assemblage built around the belief that the effects of nuclear accidents can be effectively mitigated thanks to the simulation technologies underlying different protocols and practices of nuclear preparedness. And, as the Fukushima experience showed, some of these expectations will not be met in real emergencies due to the inherent uncertainties entailed by the use of dose projections when planning protective countermeasures.

Histone deacetylase 6 (HDAC6) is involved in multiple regulatory processes, ranging from cellular stress to intracellular transport. Inhibition of aberrant HDAC6 activity in several cancers and neurological diseases has been shown to be efficacious in both preclinical and clinical studies. While selective HDAC6 targeting has been pursued as an alternative to pan-HDAC drugs, identifying truly selective molecular templates has not been trivial. Herein, we report a structure-activity relationship study yielding TO-317, which potently binds HDAC6 catalytic domain 2 (Ki = 0.7 nM) and inhibits the enzyme function (IC50 = 2 nM). TO-317 exhibits 158-fold selectivity for HDAC6 over other HDAC isozymes by binding the catalytic Zn2+ and, uniquely, making a never seen before direct hydrogen bond with the Zn2+ coordinating residue, His614. This novel structural motif targeting the second-sphere His614 interaction, observed in a 1.84 A resolution crystal structure with drHDAC6 from zebrafish, can provide new pharmacophores for identifying enthalpically driven, high-affinity, HDAC6-selective inhibitors.

Epigenetic targeting has emerged as an efficacious therapy for hematological cancers. The rare and incurable T-cell prolymphocytic leukemia (T-PLL) is known for its aggressive clinical course. Current epigenetic agents such as histone deacetylase (HDAC) inhibitors are increasingly used for targeted therapy. Through a structure-activity relationship (SAR) study, we developed an HDAC6 inhibitor KT-531, which exhibited higher potency in T-PLL compared to other hematological cancers. KT-531 displayed strong HDAC6 inhibitory potency and selectivity, on-target biological activity, and a safe therapeutic window in nontransformed cell lines. In primary T-PLL patient cells, where HDAC6 was found to be overexpressed, KT-531 exhibited strong biological responses, and safety in healthy donor samples. Notably, combination studies in T-PLL patient samples demonstrated KT-531 synergizes with approved cancer drugs, bendamustine, idasanutlin, and venetoclax. Our work suggests HDAC inhibition in T-PLL could afford sufficient therapeutic windows to achieve durable remission either as standalone or in combination with targeted drugs.

The ongoing development of collaborative robots (cobots) bears great potential for assembly processes by enabling new possibilities of task allocation between humans and machines. A detailed, empirically based evaluation of how cobots can live up to these possibilities in industrial practice is still missing in the manufacturing literature. In this paper some of the existing shortcomings -referred to as "the productivity gap" in current assembly automation processes are discussed. To cope with this gap, adaptive human-cobot task sharing is proposed and implemented as a complementary task allocation model. Its potential to increase productivity and flexibility in new and existing assembly applications is evaluated.

Grounded in a social scientific research approach, the present case study traces the shift in the German nuclear regulatory culture from prevention to preparedness, the latter of which built upon decision support systems for nuclear emergency management. These systems integrate atmospheric dispersion models for tracing radioactive materials released accidentally from nuclear facilities. For atmospheric dispersion modelers and emergency managers, this article provides a critical historical perspective on the practical, epistemic, and organizational issues surrounding the use of decision support systems for nuclear emergency management. This perspective suggests that atmospheric dispersion models and technologies are embedded within an entire assemblage of institutions, technologies, and practices of preparedness, which are challenged by the uniqueness of each nuclear accident.

Due to safety and security concerns, industrial human-machine interfaces (HMIs) cannot be updated as frequently and easily as other software. This leads to an innovation gap in productive industrial environments, which is aggravated by the demand that innovation yield immediate gains in productivity and profitability. Hence, industrial HMIs often lag behind the state of the art, which may pose risks in terms of the safety and security of human-machine interactive systems, such as collaborative robots, while impeding their interoperability with machine vision and other novel technologies. In response, this paper explores the potential and limits of novel tools and techniques from the domain of graphical user interface (GUI) automation, which can be used to extend existing robot HMIs in a flexible and pragmatic way. In this sense, the paper introduces a novel architecture for HMI automation and evaluates it using five non-exhaustive use cases for collaborative robots. Our evaluation demonstrates the effectiveness of the approach in various industrial application contexts that require specific extensions to off-the-shelf robotic software.

Dysregulated Histone Deacetylase (HDAC) activity across multiple human pathologies have highlighted this family of epigenetic enzymes as critical druggable targets, amenable to small molecule intervention. While efficacious, current approaches using non-selective HDAC inhibitors (HDACi) have been shown to cause a range of undesirable clinical toxicities. To circumvent this, recent efforts have focused on the design of highly selective HDACi as a novel therapeutic strategy. Beyond roles in regulating transcription, the unique HDAC6 (with two catalytic domains) regulates the deacetylation of α -tubulin; promoting growth factor-controlled cell motility, cell division, and metastatic hallmarks. Recent studies have linked aberrant HDAC6 function in various hematological cancers including acute myeloid leukaemia and multiple myeloma. Herein, we report the discovery, in vitro characterization, and biological evaluation of PTG-0861 (JG-265), a novel HDAC6-selective inhibitor with strong isozyme-selectivity (∼36× ) and low nanomolar potency (IC50 = 5.92 nM) against HDAC6. This selectivity profile was rationalized via in silico docking studies and also observed in cellulo through cellular target engagement. Moreover, PTG-0861 achieved relevant potency against several blood cancer cell lines (e.g. MV4-11, MM1S), whilst showing limited cytotoxicity against non-malignant cells (e.g. NHF, HUVEC) and CD-1 mice. In examining compound stability and cellular permeability, PTG-0861 revealed a promising in vitro pharmacokinetic (PK) profile. Altogether, in this study we identified a novel and potent HDAC6-selective inhibitor (∼4× more selective than current clinical standards – citarinostat, ricolinostat), which achieves cellular target engagement, efficacy in hematological cancer cells with a promising safety profile and in vitro PK.

This paper introduces the notion of epistemic debt as an analytical tool for understanding and managing the effects of technical ignorance in smart manufacturing. Drawing on the concepts of technical and social debt from software engineering, the metaphor of epistemic debt refers to the implied long-term costs of rework (e.g., redesign, replacement, reconfiguration or systems and/or organizational structures) caused by a lack of understanding and/or means of knowing the internals of complex software-based manufacturing systems essential to the value chain and core business of an organization. After defining the concept, we identify three of its sources and propose strategies for coping with epistemic debt in manufacturing.

We present the architecture of the new ABR-KFUE decision-support system for nuclear emergency management used in Germany. Such systems assist decision makers in taking countermeasures in case of releases of radioactive materials into the environment. The specificity of these systems is that they use simulation software in a safety-critical application context. The new architecture of the system thus aims at fulfilling non-functional requirements for improved reliability, performance, availability, and maintainability. The proposed solutions are evaluated using a stimulus/response analysis.

When reusing software architectural knowledge, such as design patterns or design decisions, software architects need support for exploring architectural knowledge collections, e.g., for finding related items. While semantic-based architectural knowledge management tools are limited to supporting lookup-based tasks through faceted search and fall short of enabling exploration, semantic-based exploratory search systems primarily focus on web-scale knowledge graphs without having been adapted to enterprise-scale knowledge graphs (EKG). We investigate how and to what extent exploratory search can be supported on EKGs of architectural knowledge. We propose an approach for building exploratory search systems on EKGs and demonstrate its use within Siemens, which resulted in the STAR system used in practice by 200–300 software architects. We found that the EKG’s ontology allows making previously implicit organisational knowledge explicit and this knowledge informs the design of suitable relatedness metrics to support exploration. Yet, the performance of these metrics heavily depends on the characteristics of the EKG’s data. Therefore both statistical and user-based evaluations can be used to select the right metric before system implementation.

Chemosensors for the detection of phosphate-containing biological species are in high need. Detection of proximally phosphorylated sites of PPi and those found in peptides and proteins has been demonstrated using chemosensors containing pyrene, as a fluorescent reporter, and a Zn2+-chelate, as a phosphate-binding group. Using these sensors, detection of proximal phosphate groups is afforded by binding of at least two of the sensor molecules to the adjacent phosphates, via the Zn2+ centres, leading to excimer formation between the pyrene groups and the corresponding shift in emission from 376 to 476 nm. Although several reports of this chemosensor class have been made, no detailed studies of selectivity of these sensors among major phosphate targets have been reported. In this study, a library of this class of chemosensors, termed ProxyPhos, which contained various linkers and Zn2+-chelating groups (i.e. DPA, cyclen and cyclam), was prepared and the effects of structural variation on the sensing efficiency and selectivity were evaluated among proximally phosphorylated peptides, proteins, nucleotides, Pi and PPi. As a result of this study, we have identified ProxyPhos library members that are most suitable for the detection of proximally phosphorylated peptides, PPi, UTP, and a DpYD peptide motif, and have generally provided a foundation for the selection of ProxyPhos chemosensors for further development of specific biologically relevant assays. The broad utility of ProxyPhos is further supported by the demonstrated lack of these sensors' cytotoxicity, ability to rapidly permeate into live and fixed cells and compatibility with gel staining methods.

The timely discovery, sharing and integration of architectural knowledge (AK) have become critical aspects in enabling the software architects to make meaningful conceptual and technical design decisions and trade-offs. In large-scale organizations particular obstacles in making AK available to architects are a heterogeneous pool of internal and external knowledge sources, poor interoperability between AK management tools and limited support of computational AK reasoning. Therefore we introduce the Continuous Architectural Knowledge Integration (CAKI) approach that combines the continuous integration of internal and external AK sources together with enhanced semantic reasoning and personalization capabilities dedicated to large organizations. Preliminary evaluation results show that CAKI potentially reduces AK search effort by concurrently yielding more diverse and relevant results.