A wide variety of control and surveillance programmes that are designed and implemented based on country-specific conditions exists for infectious cattle diseases that are not regulated. This heterogeneity renders difficult the comparison of probabilities of freedom from infection estimated from collected surveillance data. The objectives of this review were to outline the methodological and epidemiological considerations for the estimation of probabilities of freedom from infection from surveillance information and review state-of-the-art methods estimating the probabilities of freedom from infection from heterogeneous surveillance data. Substantiating freedom from infection consists in quantifying the evidence of absence from the absence of evidence. The quantification usually consists in estimating the probability of observing no positive test result, in a given sample, assuming that the infection is present at a chosen (low) prevalence, called the design prevalence. The usual surveillance outputs are the sensitivity of surveillance and the probability of freedom from infection. A variety of factors influencing the choice of a method are presented; disease prevalence context, performance of the tests used, risk factors of infection, structure of the surveillance programme and frequency of testing. The existing methods for estimating the probability of freedom from infection are scenario trees, Bayesian belief networks, simulation methods, Bayesian prevalence estimation methods and the STOC free model. Scenario trees analysis is the current reference method for proving freedom from infection and is widely used in countries that claim freedom. Bayesian belief networks and simulation methods are considered extensions of scenario trees. They can be applied to more complex surveillance schemes and represent complex infection dynamics. Bayesian prevalence estimation methods and the STOC free model allow freedom from infection estimation at the herd-level from longitudinal surveillance data, considering risk factor information and the structure of the population. Comparison of surveillance outputs from heterogeneous surveillance programmes for estimating the probability of freedom from infection is a difficult task. This paper is a ‘guide towards substantiating freedom from infection’ that describes both all assumptions-limitations and available methods that can be applied in different settings.

In robot manipulation, Reinforcement Learning (RL) often suffers from low sample efficiency and uncertain convergence, especially in large observation and action spaces. Foundation Models (FMs) offer an alternative, demonstrating promise in zero-shot and few-shot settings. However, they can be unreliable due to limited physical and spatial understanding. We introduce ExploRLLM, a method that combines the strengths of both paradigms. In our approach, FMs improve RL convergence by generating policy code and efficient representations, while a residual RL agent compensates for the FMs' limited physical understanding. We show that Explorllm outperforms both policies derived from FMs and RL baselines in table-top manipulation tasks. Additionally, real-world experiments show that the policies exhibit promising zero-shot sim-to-real transfer. Supplementary material is available at https://explorllm.github.io.

Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI. Accomplishing this goal requires learning to ground language in perception and embodied actions, in order to accomplish complex tasks. The Scalable, Instructable, Multiworld Agent (SIMA) project tackles this by training agents to follow free-form instructions across a diverse range of virtual 3D environments, including curated research environments as well as open-ended, commercial video games. Our goal is to develop an instructable agent that can accomplish anything a human can do in any simulated 3D environment. Our approach focuses on language-driven generality while imposing minimal assumptions. Our agents interact with environments in real-time using a generic, human-like interface: the inputs are image observations and language instructions and the outputs are keyboard-and-mouse actions. This general approach is challenging, but it allows agents to ground language across many visually complex and semantically rich environments while also allowing us to readily run agents in new environments. In this paper we describe our motivation and goal, the initial progress we have made, and promising preliminary results on several diverse research environments and a variety of commercial video games.

Intratumoral heterogeneity arises as a result of genetically distinct subclones emerging during tumor progression. These subclones are characterized by various types of somatic genomic aberrations, with single nucleotide variants (SNVs) and copy number aberrations (CNAs) being the most prominent. While single-cell sequencing provides powerful data for studying tumor progression, most existing and newly generated sequencing datasets are obtained through conventional bulk sequencing. Most of the available methods for studying tumor progression from multi-sample bulk sequencing data are either based on the use of SNVs from genomic loci not impacted by CNAs or designed to handle a small number of SNVs via enumerating their possible copy number trees. In this paper, we introduce DETOPT, a combinatorial optimization method for accurate tumor progression tree inference that places SNVs impacted by CNAs on trees of tumor progression with minimal distortion on their variant allele frequencies observed across available samples of a tumor. We show that on simulated data DETOPT provides more accurate tree placement of SNVs impacted by CNAs than the available alternatives. When applied to a set of multi-sample bulk exome-sequenced tumor metastases from a treatment-refractory, triple-positive metastatic breast cancer, DETOPT reports biologically plausible trees of tumor progression, identifying the tree placement of copy number state gains and losses impacting SNVs, including those in clinically significant genes.

The approach of evaluating the final scores of multi-criteria decision-making (MCDM) methods according to the strength of association with real-life rankings is interesting for comparing MCDM methods. This approach has recently been applied mostly to financial data. In these studies, where it is emphasized that some methods show more stable success, it would be useful to see the results that will emerge by testing the approach on different data structures more comprehensively. Moreover, not only the final MCDM results but also the performance of normalization techniques and data types (fuzzy or crisp), which are components of MCDM, can be compared using the same approach. These components also have the potential to affect MCDM results directly. In this direction, in our study, the economic performances of G-20 (Group of 20) countries, which have different data structures, were calculated over ten different periodic decision matrices. Ten different crisp-based MCDM methods (COPRAS, CODAS, MOORA, TOPSIS, MABAC, VIKOR (S, R, Q), FUCA, and ELECTRE III) with different capabilities were used to better visualize the big picture. The relationships between two different real-life reference anchors and MCDM methods were used as a basis for comparison. The CODAS method develops a high correlation with both anchors in most periods. The most appropriate normalization technique for CODAS was identified using these two anchors. Interestingly, the maximum normalization technique was the most successful among the alternatives (max, min–max, vector, sum, and alternative ranking-based). Moreover, we compared the two main data types by comparing the correlation results of crisp-based and fuzzy-based CODAS. The results were very consistent, and the “Maximum normalization-based fuzzy integrated CODAS procedure” was proposed to decision-makers to measure the economic performance of the countries.

A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or measures of family history (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses [ICE CRISTAL]). Inference is made from the changes in regression coefficients of relatives' PRSs or PRS and family history before and after adjusting for each other. We applied these approaches to two breast cancer PRSs and multiple studies and found that (a) for breast cancer diagnosed at a young age, for example, <50 years, there was no evidence that the PRSs were causal, while (b) for breast cancer diagnosed at later ages, there was consistent evidence for causation explaining increasing amounts of the PRS‐disease association. The genetic variants in the PRS might be in linkage disequilibrium with truly causal variants and not causal themselves. These PRSs cause minimal heritability of breast cancer at younger ages. There is also evidence for nongenetic factors shared by first‐degree relatives that explain breast cancer familial aggregation. Familial associations are not necessarily due to genes, and genetic associations are not necessarily causal.

Cellular manufacturing represents a production system arrangement in which machines, tools, workers, and devices are grouped to produce a single product or a group of products with similar production requirements. By implementing cellular manufacturing, it is possible to significantly influence the elimination of the seven types of Lean waste: transport, inventory, unnecessary motion, waiting, overproduction, overprocessing, and defects. There are various models of a cellular manufacturing organization, and some of the most widely used and studied include the Toyota Sewing System, Bucket Brigades, Working Balance, and Rabbit Chase. This paper aims to present different types of lean cellular manufacturing organizations. By reviewing the literature, the advantages and disadvantages of individual types of cellular manufacturing will be systematized. In the practical part of the paper, the theoretical assumptions will be confirmed, and the impact and robustness of individual types of cellular manufacturing will be explored in different situations. Simulations were performed in real conditions on real products to demonstrate the efficiency of individual types of cellular manufacturing organizations depending on the duration of technological operations. The goal was also to examine the robustness of individual types of cellular organization in case of the absence of certain operators or insufficiently trained operators. The criteria used to compare different cellular models were productivity, non-conformance, WIP inventory, time to deliver the first correct piece, and flow time. Simulations were performed for the Toyota Sewing System, Bucket Brigade, Working Balance, and Rabbit Chase cellular manufacturing concepts. The simulation results indicate significant differences in the performance of individual concepts, where the difference in some criteria can reach up to 100%.  

Posterior reversible encephalopathy syndrome (PRES) and reversible cerebral vasoconstriction syndrome (RCVS) may cause ischaemic stroke and intracranial haemorrhage. The aim of our study was to assess the frequency of the afore‐mentioned outcomes.

<p><strong>Introduction.</strong> Digital literacy includes things like being able to use information systems and supporting infrastructure. With the increasing use of technology in healthcare, it is important for healthcare staff to be digitally literate. The aim of the paper is to determine the attitudes of primary and secondary health care workers towards the use of computers in health care and to examine the influence of sociodemographic factors on the information literacy of health care workers.&nbsp;<br /><strong>Methods.</strong> The research was conducted according to the principle of a cross-sectional study. The research included 80 respondents, healthcare workers. Data analysis included methods of descriptive and inferential statistics. The data will be presented in the form of a table.<br /><strong>Results.</strong> The results showed that certain socio-demographic factors influenced the attitude of health workers towards the use of computers. The most significant factors were the level of education and previous IT education, but the time the respondents sopent working on the computer and whether they used the computer exclusively at work or at home also had an impact.<br /><strong>Conclusion. </strong>Healthcare workers showed a positive attitude towards the use of computers in healthcare. The most significant socio-demographic factors influencing knowledge of computer work are the level of education of the respondents and whether and where they received their education in information technology.</p>

This work introduces a survey questionnaire about adult perceptions of privacy, attitudes, and comfort with robots in different spaces in the home. Additionally, in the survey, adult comfort was considered with the preconception that children would share information with robots and other third parties. As for the structure of the survey, it includes likert-style questions, multiple choice, and open responses for qualitative explanations of participant comfort in different situations. In this paper, we give more details about the survey, preliminary qualitative results, and suggestions for further use. We hope this work brings light to the importance of studying privacy concerns in the home with all family stakeholders.

Socially assistive robots can be used as therapeutic technologies to address depression symptoms. Through three sets of workshops with individuals living with depression and clinicians, we developed design guidelines for a personalized therapeutic robot for adults living with depression. Building on the design of Therabot™, workshop participants discussed various aspects of the robot’s design, sensors, behaviors, and a robot connected mobile phone app. Similarities among participants and workshops included a preference for a soft textured exterior and natural colors and sounds. There were also differences - clinicians wanted the robot to be able to call for aid, while participants with depression differed in their degree of comfort in sharing data collected by the robot with clinicians.CCS CONCEPTS• Human-centered computing → HCI design and evaluation methods; • Social and professional topics → User characteristics.

Participatory robot design projects with older adults often use multiple sessions to encourage design feedback and active participation from users. Prior projects have, however, not analyzed the learning outcomes for older adults across co-design sessions and how they support constructive design feedback and meaningful participation. To bridge this gap, we examined the learning outcomes within a "longitudinal panel." This panel comprised seven co-design sessions with 11 older adults of varying cognitive abilities over six months, aimed at designing a robot to guide a photograph-based conversational activity. Using Nelson and Stolterman’s framework of the hierarchy of design-learning, we demonstrate how older adult panelists achieved multiple design-learning outcomes – capacity, confidence, capability, competence, courage, and connection – which allowed them to provide actionable design suggestions. We provide guidelines for conducting longitudinal panels that can enhance user design-learning and participation in robot design.CCS CONCEPTS• Human-centered computing → Participatory design; User centered design; • Computer systems organization → Robotics; • General and reference → Design.