The increase in soil salinity has a negative effect on the growth and yield of plants. Mitigating the negative effects of soil salinity is therefore a difficult task and different methods are being used to overcome the negative effects of salt stress on crop plants. One of the often-used approaches is seed priming that can increase plants’ vigor and resilience. In this paper, we tested the effects of hydropriming, proline priming, and salicylic acid priming on the mitigation of the negative effects of salt stress on two bell pepper varieties (Capsicum annuum L.): Herkules and Kurtovska kapija. Sweet bell pepper seeds were primed following desiccation to achieve the original water content, and subsequently cultivated in salt-supplemented medium. The positive effects on vigor (in the form of increased germination and seedling establishment) as well as on level of tolerance for salt stress were recorded for both cultivars. The positive effects varied between the priming treatments and pepper cultivar used. The results of germination, seedling performance, photosynthetic pigments, and osmolytes were measured for seedlings grown from unprimed and primed seeds with under 0, 25, and 50 mM of NaCl. Both cultivars demonstrated greater germination when primed with proline and salicylic acid, while the Herkules cultivar demonstrated a higher tolerance to salt when proline was used as the priming agent. Priming with salicylic acid and proline in the seed improved germination and seedling performance, which could be related to the increase in proline content in the seedlings.

In robot manipulation tasks with large observation and action spaces, reinforcement learning (RL) often suffers from low sample efficiency and uncertain convergence. As an alternative, foundation models have shown promise in zero-shot and few-shot applications. However, these models can be unreliable due to their limited reasoning and challenges in understanding physical and spatial contexts. This paper introduces ExploRLLM, a method that combines the commonsense reasoning of foundation models with the experiential learning capabilities of RL. We leverage the strengths of both paradigms by using foundation models to obtain a base policy, an efficient representation, and an exploration policy. A residual RL agent learns when and how to deviate from the base policy while its exploration is guided by the exploration policy. In table-top manipulation experiments, we demonstrate that ExploRLLM outperforms both baseline foundation model policies and baseline RL policies. Additionally, we show that this policy can be transferred to the real world without further training. Supplementary material is available at https://explorllm.github.io.

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.

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.

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.

<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>

The decisions made by autonomous robots hold substantial influence over how humans perceive their behavior. One way to alleviate potential negative impressions of such decisions by humans and enhance human comprehension of them is through explaining. We introduce visual and textual explanations integrated into robot navigation, considering the surrounding environmental context. To gauge the effectiveness of our approach, we conducted a comprehensive user study, assessing user satisfaction across different forms of explanation representation. Our empirical findings reveal a notable discrepancy in user satisfaction, with significantly higher levels observed for explanations that adopt a multimodal format, as opposed to those relying solely on unimodal representations.

Stroke is a leading cause of death and disability worldwide, and early diagnosis and prompt medical intervention are thus crucial. Frequent monitoring of stroke patients is also essential to assess treatment efficacy and detect complications earlier. While computed tomography (CT) and magnetic resonance imaging (MRI) are commonly used for stroke diagnosis, they cannot be easily used onsite, nor for frequent monitoring purposes. To meet those requirements, an electromagnetic imaging (EMI) device, which is portable, non-invasive, and non-ionizing, has been developed. It uses a headset with an antenna array that irradiates the head with a safe low-frequency EM field and captures scattered fields to map the brain using a complementary set of physics-based and data-driven algorithms, enabling quasi-real-time detection, two-dimensional localization, and classification of strokes. This study reports clinical findings from the first time the device was used on stroke patients. The clinical results on 50 patients indicate achieving an overall accuracy of 98% in classification and 80% in two-dimensional quadrant localization. With its lightweight design and potential for use by a single para-medical staff at the point of care, the device can be used in intensive care units, emergency departments, and by paramedics for onsite diagnosis.