Abstract

Since the discovery of lanthanides, the expanding range of applications and the growing demand for lanthanides in different aspects of life have escalated their dispersion in the environment, raising concerns about their impact on the living world. This review explores the interaction between lanthanides and different groups of living organisms (bacteria, algae, lichens, plants, invertebrates, and low vertebrates), reflecting the current state of scientific knowledge. We have aimed to provide a comprehensive overview of relevant studies, highlight existing gaps, and suggest potential areas for future research to enhance the understanding of this topic.

Abstract

Healthcare-associated infections (HAIs), previously known as nosocomial infections, represent a significant threat to healthcare systems worldwide, prolonging patient hospital stays and the duration of antimicrobial therapy. One of the most serious consequences of HAIs is the increase in the rate of antibiotic resistance (AR) generated by the prolonged, frequent, and sometimes incorrect use of antibiotics, which leads to the selection of resistant bacteria, making treatment difficult and expensive, with direct consequences for the safety of patients and healthcare personnel. Therefore, timely and accurate diagnosis of HAIs is mandatory to develop appropriate infection prevention and control practices (IPC) and new therapeutic strategies. This review aimed to present the prevalence, risk factors, current diagnosis, including artificial intelligence (AI) and machine learning approaches, future perspectives in combating HAIs causative bacteria (phage therapy, microbiome-based interventions, and vaccination), and HAIs surveillance strategies. Also, we discussed the latest findings regarding the relationships of AR with climate change and environmental pollution in the context of the One Health approach. Phage therapy is an emerging option that can offer an alternative to ineffective antibiotic treatments for antibiotic-resistant bacteria causing HAIs. Clinical trials dealing with vaccine development for resistant bacteria have yielded conflicting results. Two promising strategies, fecal microbiota transplantation and probiotic therapy, proved highly effective against recurrent Clostridium difficile infections and have been shown to reduce HAI incidence in hospitalized patients undergoing antibiotic therapy. Artificial intelligence and machine learning systems offer promising predictive capabilities in processing large volumes of clinical, microbiological, and patient data but require robust data integration. Our paper argues that HAIs are still a global challenge, requiring stringent IPC policies, computer vision, and AI-powered tools. Despite promising avenues like integrated One Health approaches, optimized phage therapy, microbiome-based interventions, and targeted vaccine development, several knowledge gaps in clinical efficacy, standardization, and pathogen complexity remain to be answered.

Abstract

Healthcare-associated infections (HAIs), previously known as nosocomial infections, represent a significant threat to healthcare systems worldwide, prolonging patient hospital stays and the duration of antimicrobial therapy. One of the most serious consequences of HAIs is the increase in the rate of antibiotic resistance (AR) generated by the prolonged, frequent, and sometimes incorrect use of antibiotics, which leads to the selection of resistant bacteria, making treatment difficult and expensive, with direct consequences for the safety of patients and healthcare personnel. Therefore, timely and accurate diagnosis of HAIs is mandatory to develop appropriate infection prevention and control practices (IPC) and new therapeutic strategies. This review aimed to present the prevalence, risk factors, current diagnosis, including artificial intelligence (AI) and machine learning approaches, future perspectives in combating HAIs causative bacteria (phage therapy, microbiome-based interventions, and vaccination), and HAIs surveillance strategies. Also, we discussed the latest findings regarding the relationships of AR with climate change and environmental pollution in the context of the One Health approach. Phage therapy is an emerging option that can offer an alternative to ineffective antibiotic treatments for antibiotic-resistant bacteria causing HAIs. Clinical trials dealing with vaccine development for resistant bacteria have yielded conflicting results. Two promising strategies, fecal microbiota transplantation and probiotic therapy, proved highly effective against recurrent Clostridium difficile infections and have been shown to reduce HAI incidence in hospitalized patients undergoing antibiotic therapy. Artificial intelligence and machine learning systems offer promising predictive capabilities in processing large volumes of clinical, microbiological, and patient data but require robust data integration. Our paper argues that HAIs are still a global challenge, requiring stringent IPC policies, computer vision, and AI-powered tools. Despite promising avenues like integrated One Health approaches, optimized phage therapy, microbiome-based interventions, and targeted vaccine development, several knowledge gaps in clinical efficacy, standardization, and pathogen complexity remain to be answered. © The Author(s) 2025.

Abstract

We investigate a dynamical complexity measure defined for finite automata with translucent letters (FAwtl). Roughly, this measure counts the minimal number of necessary jumps for such an automaton in order to accept an input. The model considered here is the deterministic finite automaton with translucent letters (DFAwtl). Unlike in the case of the nondeterministic variant, the function describing the jump complexity of any DFAwtl is either bounded by a constant or it is linear. We give a polynomial-time algorithm for deciding whether the jump complexity of a DFAwtl is constant-bounded or linear and we prove that the equivalence problem for DFAwtl of O(1) jump complexity is decidable. We also consider another fundamental problem for extensions of finite automata models, deciding whether the language accepted by a FAwtl is regular. We give a positive partial answer for DFAwtl over the binary alphabet, in contrast with the case of NFAwtl, where the problem is undecidable. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract

We propose a direct simulation of an arbitrary network of splicing processors by a network of splicing processors having an underlying path graph. This is in line with similar simulations where the target network has other widely used graph topologies: complete graph, lattice graph, star graph, wheel graph, etc. Along with the effective construction, we provide an analysis of the size and time complexity of the obtained network. Our construction may not be the most economic conversion in terms of number of nodes, hence further investigation to find more succinct networks are of (at least) theoretical interest.

Abstract

Abstract

Background Accurate assessments of current and future fertility-including overall trends and changing population age structures across countries and regions-are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios.Methods To estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10-54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in habitable areas, and under-5 mortality) were given equal weights, and analyses were conducted utilising the MR-BRT (meta-regression-Bayesian, regularised, trimmed) tool. To capture time-series trends in CCF50 not explained by these covariates, we used a first-order autoregressive model on the residual term. CCF50 as a proportion of each 5-year ASFR was predicted using a linear mixed-effects model with fixed-effects covariates ( female educational attainment and contraceptive met need) and random intercepts for geographical regions. Projected TFRs were then computed for each calendar year as the sum of single-year ASFRs across age groups. The reference forecast is our estimate of the most likely fertility future given the model, past fertility, forecasts of covariates, and historical relationships between covariates and fertility. We additionally produced forecasts for multiple alternative scenarios in each location: the UN Sustainable Development Goal (SDG) for education is achieved by 2030; the contraceptive met need SDG is achieved by 2030; pro-natal policies are enacted to create supportive environments for those who give birth; and the previous three scenarios combined. Uncertainty from past data inputs and model estimation was propagated throughout analyses by taking 1000 draws for past and present fertility estimates and 500 draws for future forecasts from the estimated distribution for each metric, with 95% uncertainty intervals (UIs) given as the 2 center dot 5 and 97 center dot 5 percentiles of the draws. To evaluate the forecasting performance of our model and others, we computed skill values-a metric assessing gain in forecasting accuracy-by comparing predicted versus observed ASFRs from the past 15 years (2007-21). A positive skill metric indicates that the model being evaluated performs better than the baseline model (here, a simplified model holding 2007 values constant in the future), and a negative metric indicates that the evaluated model performs worse than baseline.Findings During the period from 1950 to 2021, global TFR more than halved, from 4 center dot 84 (95% UI 4 center dot 63-5 center dot 06) to 2 center dot 23 (2 center dot 09-2 center dot 38). Global annual livebirths peaked in 2016 at 142 million (95% UI 137-147), declining to 129 million (121-138) in 2021. Fertility rates declined in all countries and territories since 1950, with TFR remaining above 2 center dot 1-canonically considered replacement-level fertility-in 94 (46 center dot 1%) countries and territories in 2021. This included 44 of 46 countries in sub-Saharan Africa, which was the super-region with the largest share of livebirths in 2021 (29 center dot 2% [28 center dot 7-29 center dot 6]). 47 countries and territories in which lowest estimated fertility between 1950 and 2021 was below replacement experienced one or more subsequent years with higher fertility; only three of these locations rebounded above replacement levels. Future fertility rates were projected to continue to decline worldwide, reaching a global TFR of 1 center dot 83 (1 center dot 59-2 center dot 08) in 2050 and 1 center dot 59 (1 center dot 25-1 center dot 96) in 2100 under the reference scenario. The number of countries and territories with fertility rates remaining above replacement was forecast to be 49 (24 center dot 0%) in 2050 and only six (2 center dot 9%) in 2100, with three of these six countries included in the 2021 World Bank-defined low-income group, all located in the GBD super-region of sub-Saharan Africa. The proportion of livebirths occurring in sub-Saharan Africa was forecast to increase to more than half of the world's livebirths in 2100, to 41 center dot 3% (39 center dot 6-43 center dot 1) in 2050 and 54 center dot 3% (47 center dot 1-59 center dot 5) in 2100. The share of livebirths was projected to decline between 2021 and 2100 in most of the six other super-regions-decreasing, for example, in south Asia from 24 center dot 8% (23 center dot 7-25 center dot 8) in 2021 to 16 center dot 7% (14 center dot 3-19 center dot 1) in 2050 and 7 center dot 1% (4 center dot 4-10 center dot 1) in 2100-but was forecast to increase modestly in the north Africa and Middle East and high-income super-regions. Forecast estimates for the alternative combined scenario suggest that meeting SDG targets for education and contraceptive met need, as well as implementing pro-natal policies, would result in global TFRs of 1 center dot 65 (1 center dot 40-1 center dot 92) in 2050 and 1 center dot 62 (1 center dot 35-1 center dot 95) in 2100. The forecasting skill metric values for the IHME model were positive across all age groups, indicating that the model is better than the constant prediction.Interpretation Fertility is declining globally, with rates in more than half of all countries and territories in 2021 below replacement level. Trends since 2000 show considerable heterogeneity in the steepness of declines, and only a small number of countries experienced even a slight fertility rebound after their lowest observed rate, with none reaching replacement level. Additionally, the distribution of livebirths across the globe is shifting, with a greater proportion occurring in the lowest-income countries. Future fertility rates will continue to decline worldwide and will remain low even under successful implementation of pro-natal policies. These changes will have far-reaching economic and societal consequences due to ageing populations and declining workforces in higher-income countries, combined with an increasing share of livebirths among the already poorest regions of the world.

Abstract

Motivated by the need of reducing the huge amount of data navigating simultaneously through a network of polarized splicing processors, we look to the possibility of introducing probabilities which theoretically could decrease this amount, at a price of some loss of certainty. We imagined two possible situations regarding the splicing step: to associate either fixed or dynamically computed probabilities with splicing rules in every node. Similarly to the splicing step, two situations could be considered for the communication step depending on the way the probabilities are associated: statically or dynamically. We believe that this new feature together with the communication protocol based on polarization might facilitate software simulations or hardware implementations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract

We define the jump complexity of a finite automaton with translucent letters as a function that computes the smallest upper bound on the number of jumps needed by the automaton in order to accept each word of length n, for any positive integer n. We prove that a sufficient condition for a finite automaton with translucent letters to accept a regular language is to have a jump complexity bounded by a constant. Along the same lines, we show that there are languages which require a jump complexity in Omega(n) of any finite automaton with translucent letters accepting one of these languages. We also show that there exist nondeterministic finite automata with translucent letters of jump complexity in O(log n) and O(root n) that accept non-regular languages. Several open problems and directions for further developments are finally discussed.

Abstract

We consider networks whose nodes host splicing processors, that is processors that are able to simulate the DNA recombination by splicing. Several topologies for the underlying graph of these networks are investigated. More precisely, we show that each network of splicing processors with some underlying graph can be directly converted into an equivalent network having an underlying graph of a different topology. Several common topologies are considered: full-mesh, star, grid, and wheel (ring-star). We also investigate the time and size complexity of each of these simulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.