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Abstract Quantum computing offers new heuristics for combinatorial problems. With small- and intermediate-scale quantum devices becoming available, it is possible to implement and test these heuristics on small-size problems. A candidate for such combinatorial problems is the heterogeneous vehicle routing problem (HVRP): the problem of finding the optimal set of routes, given a heterogeneous fleet of vehicles with varying loading capacities, to deliver goods to a given set of customers. In this work, we investigate the potential use of a quantum computer to find approximate solutions to the HVRP using the quantum approximate optimization algorithm (QAOA). For this purpose we formulate a mapping of the HVRP to an Ising Hamiltonian and simulate the algorithm on problem instances of up to 21 qubits. We show that the number of qubits needed for this mapping scales quadratically with the number of customers. We compare the performance of different classical optimizers in the QAOA for varying problem size of the HVRP, finding a trade-off between optimizer performance and runtime.

Abstract The gut microbiota (GM) can regulate bone mass, but its association with incident fractures is unknown. We used Cox regression models to determine whether the GM composition is associated with incident fractures in the large FINRISK 2002 cohort (n = 7043, 1092 incident fracture cases, median follow-up time 18 years) with information on GM composition and functionality from shotgun metagenome sequencing. Higher alpha diversity was associated with decreased fracture risk (hazard ratio [HR] 0.92 per standard deviation increase in Shannon index, 95% confidence interval 0.87–0.96). For beta diversity, the first principal component was associated with fracture risk (Aitchison distance, HR 0.90, 0.85–0.96). In predefined phyla analyses, we observed that the relative abundance of Proteobacteria was associated with increased fracture risk (HR 1.14, 1.07–1.20), while the relative abundance of Tenericutes was associated with decreased fracture risk (HR 0.90, 0.85–0.96). Explorative sub-analyses within the Proteobacteria phylum showed that higher relative abundance of Gammaproteobacteria was associated with increased fracture risk. Functionality analyses showed that pathways related to amino acid metabolism and lipopolysaccharide biosynthesis associated with fracture risk. The relative abundance of Proteobacteria correlated with pathways for amino acid metabolism, while the relative abundance of Tenericutes correlated with pathways for butyrate synthesis. In conclusion, the overall GM composition was associated with incident fractures. The relative abundance of Proteobacteria, especially Gammaproteobacteria, was associated with increased fracture risk, while the relative abundance of Tenericutes was associated with decreased fracture risk. Functionality analyses demonstrated that pathways known to regulate bone health may underlie these associations.

ABSTRACT The environmental DNA (eDNA) approach is an emerging tool for monitoring marine biodiversity. However, the sampling effort needs optimization according to the site characteristics and target taxonomic groups. In this study, we optimized the eDNA sampling effort in terms of sample volume and number of replicates to monitor the diversity of marine vertebrates (mainly fish) in Hong Kong's subtropical waters that show a gradient of estuarine to oceanic waters. To maximize detection, we used three pairs of metabarcoding primers (12S‐v5, MiFish‐U, and MiFish‐E). We compared vertebrate diversity in 78 water samples, ranging from 1 to 10 L, collected from oceanic and estuarine sites. Metabarcoding yielded a total of 140 vertebrate species, of which 18 were unique to the estuarine site, 66 unique to the oceanic site, and 56 shared between both sites. The detected species were predominantly ray‐finned fish (136 species), and the three primer pairs exhibited differential sensitivity toward different taxa, especially cartilaginous fish and cetaceans. Increasing sampling volume per replicate generally increased the total detected species, average species per replicate, and species coverage, and sampling 3 or 4 × 4 L represented the most efficient sampling effort for the estuarine and oceanic sites, respectively. The diversity analysis revealed that sampling >2 L per replicate reduced variability and improved diversity analysis. The results also showed that a larger sampling volume per replicate increased the probability of detecting endangered, indicator, invasive, and elusive species, with 4 L representing the most efficient volume. This study recommended sampling 4 L per replicate and 3 replicates for estuarine and 4 for oceanic sites, respectively for effectively monitoring marine fish in subtropical waters using the eDNA approach.

Antibiotic resistance genes (ARGs) are prevalent in the infant gut microbiota and make up the intestinal resistome, representing a community ARG reservoir. This study focuses on the dynamics and persistence of ARGs in the early gut microbiota, and the effect of early exposures therein. We leveraged 2,328 stool metagenomes from 475 children in the HELMi cohort and the available parental samples to study the diversity, dynamics, and intra-familial sharing of the resistome during the first two years of life. We found higher within-family similarity of the gut resistome composition and ARG load in infant-mother pairs, and between spouses, but not in father-infant pairs. Early gut microbiota composition and development correlated with the ARG load; Bacteroides correlated positively and Bifidobacterium negatively with the load, reflecting the typical resistance levels in these taxa. Caesarean delivered infants harbored lower ARG loads, partly reflecting the scarcity of Bacteroides compared to vaginally delivered. Exposure to intrapartum or post-natal antibiotics showed only modest associations with the ARG load and composition, mainly before 12 months. Our results indicate that the resistome is strongly driven by the normal development of the microbiota in early life, and suggest importance of longer evolution of ARGs over effects of recent antibiotic exposure.

ABSTRACTThe gut microbiota is vital for human body development and function. Its development in early life is influenced by various environmental factors. In this randomized controlled trial, the gut microbiota was obtained as a secondary outcome measure in a study on the effects of one hour of daily skin-to-skin contact (SSC) for five weeks in healthy full-term infants. Specifically, we studied the effects on alpha/beta diversity, volatility, microbiota maturation, and bacterial and gut-brain-axis-related functional abundances in microbiota assessed thrice in the first year. Pregnant Dutch women (n = 116) were randomly assigned to the SSC or care-as-usual groups. The SSC group participants engaged in one hour of daily SSC from birth to five weeks of age. Stool samples were collected at two, five, and 52 weeks and the V4 region was sequenced. We observed significant differences in the microbiota composition, bacterial abundances, and predicted functional pathways between the groups. The SSC group exhibited lower microbiota volatility during early infancy. Microbiota maturation was slower in the SSC group during the first year and our results suggested that breastfeeding duration may have partially mediated this relation. Our findings provide evidence that postpartum SSC may influence microbiota development. Replication is necessary to validate and generalize these results. Future studies should include direct stress measurements and extend microbiota sampling beyond the first year to investigate stress as a mechanism and research SSC’s impact on long-term microbiota maturation trajectories.

ABSTRACTBackground Gingivitis, i.e. inflammation of the gums, is often induced by dental plaque. However, its exact link to the oral microbiota remains unclear.Methods In a case-control study involving 120 participants, comprising 60 cases and 60 controls (mean age (SD) 36.6 (7.6) years; 50% males), nested within a prospective multicentre cohort study, we examined the oral microbiome composition of gingivitis patients and their controls using shotgun metagenomic sequencing of saliva samples. Participants underwent clinical and radiographic oral health examinations, including bleeding on probing (BOP), at six tooth sites. BOP ≥33% was considered ‘generalized gingivitis/initial periodontitis’(GG/IP), and BOP

Abstract Manipulative behaviour that consists of touching or close contact with ears or tails of pen mates is common in pigs and can become damaging. Manipulative behaviour was analysed from video recordings of 45-day-old pigs, and 15 manipulator-control pairs (n = 30) were formed. Controls neither received nor performed manipulative behaviour. Rectal faecal samples of manipulators and controls were compared. 16S PCR was used to identify Lactobacillaceae species and 16S amplicon sequencing to determine faecal microbiota composition. Seven culturable Lactobacillaceae species were identified in control pigs and four in manipulator pigs. Manipulators (p = 0.02) and females (p = 0.005) expressed higher Lactobacillus amylovorus, and a significant interaction was seen (sex * status: p = 0.005) with this sex difference being more marked in controls. Females (p = 0.08) and manipulator pigs (p = 0.07) tended to express higher total Lactobacillaceae. A tendency for an interaction was seen in Limosilactobacillus reuteri (sex * status: p = 0.09). Results suggest a link between observed low diversity in Lactobacillaceae and the development of manipulative behaviour.

Executive summary Microbes are all pervasive in their distribution and influence on the functioning and well‐being of humans, life in general and the planet. Microbially‐based technologies contribute hugely to the supply of important goods and services we depend upon, such as the provision of food, medicines and clean water. They also offer mechanisms and strategies to mitigate and solve a wide range of problems and crises facing humanity at all levels, including those encapsulated in the sustainable development goals (SDGs) formulated by the United Nations. For example, microbial technologies can contribute in multiple ways to decarbonisation and hence confronting global warming, provide sanitation and clean water to the billions of people lacking them, improve soil fertility and hence food production and develop vaccines and other medicines to reduce and in some cases eliminate deadly infections. They are the foundation of biotechnology, an increasingly important and growing business sector and source of employment, and the centre of the bioeconomy, Green Deal, etc. But, because microbes are largely invisible, they are not familiar to most people, so opportunities they offer to effectively prevent and solve problems are often missed by decision‐makers, with the negative consequences this entrains. To correct this lack of vital knowledge, the International Microbiology Literacy Initiative–the IMiLI–is recruiting from the global microbiology community and making freely available, teaching resources for a curriculum in societally relevant microbiology that can be used at all levels of learning. Its goal is the development of a society that is literate in relevant microbiology and, as a consequence, able to take full advantage of the potential of microbes and minimise the consequences of their negative activities. In addition to teaching about microbes, almost every lesson discusses the influence they have on sustainability and the SDGs and their ability to solve pressing problems of societal inequalities. The curriculum thus teaches about sustainability, societal needs and global citizenship. The lessons also reveal the impacts microbes and their activities have on our daily lives at the personal, family, community, national and global levels and their relevance for decisions at all levels. And, because effective, evidence‐based decisions require not only relevant information but also critical and systems thinking, the resources also teach about these key generic aspects of deliberation. The IMiLI teaching resources are learner‐centric, not academic microbiology‐centric and deal with the microbiology of everyday issues. These span topics as diverse as owning and caring for a companion animal, the vast range of everyday foods that are produced via microbial processes, impressive geological formations created by microbes, childhood illnesses and how they are managed and how to reduce waste and pollution. They also leverage the exceptional excitement of exploration and discovery that typifies much progress in microbiology to capture the interest, inspire and motivate educators and learners alike. The IMiLI is establishing Regional Centres to translate the teaching resources into regional languages and adapt them to regional cultures, and to promote their use and assist educators employing them. Two of these are now operational. The Regional Centres constitute the interface between resource creators and educators–learners. As such, they will collect and analyse feedback from the end‐users and transmit this to the resource creators so that teaching materials can be improved and refined, and new resources added in response to demand: educators and learners will thereby be directly involved in evolution of the teaching resources. The interactions between educators–learners and resource creators mediated by the Regional Centres will establish dynamic and synergistic relationships–a global societally relevant microbiology education ecosystem–in which creators also become learners, teaching resources are optimised and all players/stakeholders are empowered and their motivation increased. The IMiLI concept thus embraces the principle of teaching societally relevant microbiology embedded in the wider context of societal, biosphere and planetary needs, inequalities, the range of crises that confront us and the need for improved decisioning, which should ultimately lead to better citizenship and a humanity that is more sustainable and resilient. Abstract The biosphere of planet Earth is a microbial world: a vast reactor of countless microbially driven chemical transformations and energy transfers that push and pull many planetary geochemical processes, including the cycling of the elements of life, mitigate or amplify climate change (e.g., Nature Reviews Microbiology, 2019, 17, 569) and impact the well‐being and activities of all organisms, including humans. Microbes are both our ancestors and creators of the planetary chemistry that allowed us to evolve (e.g., Life's engines: How microbes made earth habitable, 2023). To understand how the biosphere functions, how humans can influence its development and live more sustainably with the other organisms sharing it, we need to understand the microbes. In a recent editorial (Environmental Microbiology, 2019, 21, 1513), we advocated for improved microbiology literacy in society. Our concept of microbiology literacy is not based on knowledge of the academic subject of microbiology, with its multitude of component topics, plus the growing number of additional topics from other disciplines that become vitally important elements of current microbiology. Rather it is focused on microbial activities that impact us–individuals/communities/nations/the human world–and the biosphere and that are key to reaching informed decisions on a multitude of issues that regularly confront us, ranging from personal issues to crises of global importance. In other words, it is knowledge and understanding essential for adulthood and the transition to it, knowledge and understanding that must be acquired early in life in school. The 2019 Editorial marked the launch of the International Microbiology Literacy Initiative, the IMiLI. Here, we present our concept of how microbiology literacy may be achieved and the rationale underpinning it; the type of teaching resources being created to realise the concept and the framing of microbial activities treated in these resources in the context of sustainability, societal needs and responsibilities and decision‐making; and the key role of Regional Centres that will translate the teaching resources into local languages, adapt them according to local cultural needs, interface with regional educators and develop and serve as hubs of microbiology literacy education networks. The topics featuring in teaching resources are learner‐centric and have been selected for their inherent relevance, interest and ability to excite and engage. Importantly, the resources coherently integrate and emphasise the overarching issues of sustainability, stewardship and critical thinking and the pervasive interdependencies of processes. More broadly, the concept emphasises how the multifarious applications of microbial activities can be leveraged to promote human/animal, plant, environmental and planetary health, improve social equity, alleviate humanitarian deficits and causes of conflicts among peoples and increase understanding between peoples (Microbial Biotechnology, 2023, 16(6), 1091–1111). Importantly, although the primary target of the freely available (CC BY‐NC 4.0) IMiLI teaching resources is schoolchildren and their educators, they and the teaching philosophy are intended for all ages, abilities and cultural spectra of learners worldwide: in university education, lifelong learning, curiosity‐driven, web‐based knowledge acquisition and public outreach. The IMiLI teaching resources aim to promote development of a global microbiology education ecosystem that democratises microbiology knowledge.

Abstract Background Reports regarding the presence of bacteria in the fetal environment remain limited and controversial. Recently, extracellular vesicles secreted by the human gut microbiota have emerged as a novel mechanism for host-microbiota interaction. We aimed to investigate the presence of bacterial extracellular vesicles in the fetal environment during healthy pregnancies and determine whether extracellular vesicles derived from the gut microbiota can cross biological barriers to reach the fetus. Results Bacterial extracellular vesicles were detectable in the amniotic fluid of healthy pregnant women, exhibiting similarities to extracellular vesicles found in the maternal gut microbiota. In pregnant mice, extracellular vesicles derived from human maternal gut microbiota were found to reach the intra-amniotic space. Conclusions Our findings reveal maternal microbiota-derived extracellular vesicles as an interaction mechanism between the maternal microbiota and fetus, potentially playing a pivotal role in priming the prenatal immune system for gut colonization after birth. Video Abstract

Abstract Background The maternal microbiota modulates fetal development, but the mechanisms of these earliest host-microbe interactions are unclear. To investigate the developmental impacts of maternal microbial metabolites, we compared full-term fetuses from germ-free and specific pathogen-free mouse dams by gene expression profiling and non-targeted metabolomics. Results In the fetal intestine, critical genes mediating host-microbe interactions, innate immunity, and epithelial barrier were differentially expressed. Interferon and inflammatory signaling genes were downregulated in the intestines and brains of the fetuses from germ-free dams. The expression of genes related to neural system development and function, translation and RNA metabolism, and regulation of energy metabolism were significantly affected. The gene coding for the insulin-degrading enzyme (Ide) was most significantly downregulated in all tissues. In the placenta, genes coding for prolactin and other essential regulators of pregnancy were downregulated in germ-free dams. These impacts on gene expression were strongly associated with microbially modulated metabolite concentrations in the fetal tissues. Aryl sulfates and other aryl hydrocarbon receptor ligands, the trimethylated compounds TMAO and 5-AVAB, Glu-Trp and other dipeptides, fatty acid derivatives, and the tRNA nucleobase queuine were among the compounds strongly associated with gene expression differences. A sex difference was observed in the fetal responses to maternal microbial status: more genes were differentially regulated in male fetuses than in females. Conclusions The maternal microbiota has a major impact on the developing fetus, with male fetuses potentially more susceptible to microbial modulation. The expression of genes important for the immune system, neurophysiology, translation, and energy metabolism are strongly affected by the maternal microbial status already before birth. These impacts are associated with microbially modulated metabolites. We identified several microbial metabolites which have not been previously observed in this context. Many of the potentially important metabolites remain to be identified.

Obesity and sedentarism are associated with increased liver and pancreatic fat content (LFC and PFC, respectively) as well as impaired organ metabolism. Exercise training is known to decrease organ ectopic fat but its effects on organ metabolism are unclear. Genetic background affects susceptibility to obesity and the response to training. We studied the effects of regular exercise training on LFC, PFC, and metabolism in monozygotic twin pairs discordant for BMI. We recruited 12 BMI-discordant monozygotic twin pairs (age 40.4, SD 4.5 years; BMI 32.9, SD 7.6, 8 female pairs). Ten pairs completed six months of training intervention. We measured hepatic insulin-stimulated glucose uptake using [18F]FDG-PET and fat content using magnetic resonance spectroscopy before and after the intervention. At baseline LFC, PFC, gamma-glutamyl transferase (GT), and hepatic glucose uptake were significantly higher in the heavier twins compared to the leaner co-twins (p = 0.018, p = 0.02 and p = 0.01, respectively). Response to training in liver glucose uptake and GT differed between the twins (Time*group p = 0.04 and p = 0.004, respectively). Liver glucose uptake tended to decrease, and GT decreased only in the heavier twins (p = 0.032). In BMI-discordant twins, heavier twins showed higher LFC and PFC, which may underlie the observed increase in liver glucose uptake and GT. These alterations were mitigated by exercise. The small number of participants makes the results preliminary, and future research with a larger pool of participants is warranted.

Abstract Microbiomes never stop changing. Their compositions and functions are shaped by the complex interplay of intrinsic and extrinsic drivers, such as growth and migration rates, species interactions, available nutrients and environmental conditions. Mathematical models help us make sense of these complex drivers and intuitively explain how, why and when specific microbiome states are reached while others are not. To make simulations of microbiome dynamics intuitive and accessible, we present miaSim. miaSim provides users with a wide range of possibilities to match their specific assumptions and scenarios, starting from a core implementation of four widely used models (namely the stochastic logistic model, MacArthur's consumer‐resource model, Hubbell's neutral model and the generalized Lotka‐Volterra model) and several of their derivations. The diverse model implementations share the same data structures and, whenever possible, share state variables, which significantly facilitates cross‐model combinations and comparisons. We combined and simulated some published examples of microbiome models in miaSim and performed cross‐model comparisons and tested diverse model assumptions. Our examples illustrate the reliability, robustness and user‐friendliness of the package. In addition, miaSim is accompanied by miaSimShiny, which allows users to explore the parameter space of their models in real‐time in an intuitive graphical interface. miaSim is fully compatible with the ‘miaverse’, an R/Bioconductor framework for microbiome data science, allowing users to combine and compare model simulations with microbiome datasets. The stable version of miaSim is available through Bioconductor 3.15, and the version for future development is available at https://github.com/microbiome/miaSim. miaSimShiny is available at https://github.com/gaoyu19920914/miaSimShiny. We anticipate that miaSim will significantly facilitate the task of simulating microbiome dynamics, highlighting the role of ecological simulations as important tools in microbiome data science.

The early gut microbiota composition is fundamentally important for piglet health, affecting long-term microbiome development and immunity. In this study, the gut microbiota of postparturient dams was compared with that of their offspring in three Finnish pig farms at three growth phases. The differences in fecal microbiota of three study development groups (Good, Poorly, and PrematureDeath) were analyzed at birth (initial exposure phase), weaning (transitional phase), and before slaughter (stable phase). Dam Lactobacillaceae abundance was lower than in piglets at birth. Limosilactobacillus reuteri and Lactobacillus amylovorus were dominantly expressed in dams and their offspring. Altogether 17 piglets (68%) were identified with Lactobacillaceae at the initial exposure phase, divided unevenly among the development groups: 85% of Good, 37.5% of Poorly, and 75% of PrematureDeath pigs. The development group Good was identified with the highest microbial diversity, whereas the development group PrematureDeath had the lowest diversity. After weaning, the abundance and versatility of Lactobacillaceae in piglets diminished, shifting towards the microbiome of the dam. In conclusion, the fecal microbiota of pigs tends to develop towards a similar alpha and beta diversity despite development group and rearing environment.

Distinct seasonality of export pathways from the different pumps in the Pacific Southern Ocean are revealed using year-round robotic profiler observations, contributing to understanding of particle export into the oceans’ interior.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

The notions of change, such as birth, death, growth, evolution and longevity, extend across reality, including biological, cultural and societal phenomena. Patterns of change describe how success and composition of every entity, from species to societies, vary across time. Languages develop into new languages, music and fashion continuously evolve, economies rise and decline, ecological and societal crises come and go. A common way to perceive and analyse change processes is through patterns of rise and decline, the ubiquitous, often distinctively unimodal trajectories describing life histories of various entities. These patterns come in different shapes and are measured according to varying definitions. Depending on how they are measured, patterns of rise and decline can reveal, emphasize, mask or obscure important dynamics in natural and cultural phenomena. Importantly, the variations of how dynamics are measured can be vast, making it impossible to directly compare patterns of rise and decline across fields of science. Standardized analysis of these patterns has the potential to uncover important but overlooked commonalities across natural phenomena and potentially help us catch the onset of dramatic shifts in entities' state, from catastrophic crashes in success to gradual emergence of new entities. We provide a framework for standardized recognizing, characterizing and comparing patterns of change by combining understanding of dynamics across fields of science. Our toolkit aims at enhancing understanding of the most general tendencies of change, through two complementary perspectives: dynamics of emergence and dynamics of success. We gather comparable cases and data from different research fields and summarize open research questions that can help us understand the universal principles, perception-biases and field-specific tendencies in patterns of rise and decline of entities in nature.

Standardised tests are often used to determine the ecotoxicity of chemicals and focus mainly on one or a few generic endpoints (e.g. mortality, growth), but information on the sub-cellular processes leading to these effects remain usually partial or missing. Flow cytometry (FCM) can be a practical tool to study the physiological responses of individual cells (such as microalgae) exposed to a stress via the use of fluorochromes and their morphology and natural autofluorescence. This work aimed to assess the effects of five chlorine-based disinfection by-products (DBPs) taken individually on growth and sub-cellular endpoints of the green microalgae Raphidocelis subcapitata. These five DBPs, characteristic of a chlorinated effluent, are the following monochloroacetic acid (MCAA), dichloroacetic acid (DCAA), trichloroacetic acid (TCAA), bromochloroacetic acid (BCAA) and 1,1-dichloropropan-2-one (1,1-DCP). Results showed that 1,1-DCP had the strongest effect on growth inhibition (EC50 = 1.8 mg.L-1), followed by MCAA, TCAA, BCAA and DCAA (EC50 of 10.1, 15.7, 27.3 and 64.5 mg.L-1 respectively). Neutral lipid content, reactive oxygen species (ROS) formation, red autofluorescence, green autofluorescence, size and intracellular complexity were significantly affected by the exposure to the five DBPs. Only mitochondrial membrane potential did not show any variation. Important cellular damages (>10%) were observed for only two of the chemicals (BCAA and 1,1-DCP) and were probably due to ROS formation. The most sensitive and informative sub-lethal parameter studied was metabolic activity (esterase activity), for which three types of response were observed. Combining all this information, an adverse outcome pathways framework was proposed to explain the effect of the targeted chemicals on R. subcapitata. Based on these results, both FCM sub-cellular analysis and conventional endpoint of algal toxicity were found to be complementary approaches.

Most signaling molecules are involved in inter-or intra-species communication, and signaling involving cross-kingdom cell-to-cell communication is limited. Howerver, algae and bacteria exchange nutrients and information in a range of interactions in marine environments. Multiple signaling molecules exist between algae and bacteria, including quorum-sensing molecules, nitric oxide, and volatile organic compounds. Recently, indole-3-acetic acid (IAA), an auxin hormone that is a well-studied signaling molecule in terrestrial ecosystems, was found to act as a cue in cross-kingdom communication between algae and bacteria in aquatic environments. To increase understanding of the roles of IAA in the phycosphere, the latest evidence regarding the ecological functions of IAA in cross-kingdom communication between algae and bacteria has been compiled in this review. The pathways of IAA biosynthesis, effects of IAA on algal growth & reproduction, and potential mechanisms at phenotypic and molecular levels are summarized. It is proposed that IAA is an important molecule regulating algal–bacterial interactions and acts as an invisible driving force in the formation of algal blooms.

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

Deposition of Siberian wildfire aerosols, which contained nitrogen, enhanced phytoplankton growth in the eastern Eurasian Basin of the Arctic Ocean in summer 2014, suggest satellite-based ocean color data and atmospheric transport modeling.

M-type stars are the most abundant stars in our Galaxy. Their visible spectra are characterized by strong TiO and VO absorption bands with occasional ScO bands near 6000–6100 Å. This region corresponds to the A ^2 Π–X ^2 Σ ^+ band system and can be used to measure the abundance of ScO. In this work, a new Doppler-limited resolution spectrum of ScO obtained in a hollow-cathode experiment is presented. The A ^2 Π–X ^2 Σ ^+ electronic transition is analyzed with special care dedicated to fitting the ${{\rm{A}}}^{{\prime} 2}{\rm{\Delta }}(v+2)$ ∼ A ^2 Π( v ) perturbation. Spectroscopic constants of the A ^2 Π( v ) states are reported for v ≤ 9, and a partial fit of spectroscopic constants is made for the ${{\rm{A}}}^{{\prime} 2}{\rm{\Delta }}(v)$ states 2 ≤ v ≤ 6. The modeled interaction of the two states reproduces the experimental data remarkably well. New equilibrium constants are extracted from the fit. Band strengths are calculated with the help of a scaled transition dipole moment function, previously calculated with ab initio methods. Finally, a line list of the A ^2 Π–X ^2 Σ ^+ band system is provided, including Einstein A coefficients and oscillator strengths. Using this line list, the spectrum of ScO can be simulated for different temperature and pressure conditions in stellar atmospheres.

Cool red giant stars exhibit absorption bands of calcium monochloride (CaCl) in the optical region. In this work, the bands of CaCl corresponding to the A ^2 Π − X ^2 Σ ^+ transition with ${v}^{{\prime} }\leqslant $ 4 and v ″ ≤ 4, and B ^2 Σ ^+ − X ^2 Σ ^+ transition with ${v}^{{\prime} }\leqslant $ 2 and v ″ ≤ 2 are rotationally analyzed with PGOPHER, a multipurpose program for simulating and fitting molecular spectra. All bands were analyzed using high-resolution absorption spectra collected at the National Solar Observatory in 1985, calibrated with atomic lines from a high-resolution emission spectrum obtained in a separate hollow cathode experiment. The results are compared with previous laser-induced fluorescence measurements. Overall, the accuracy of spectroscopic constants for the A ^2 Π and B ^2 Σ ^+ states is improved. Scaled transition dipole moment calculations were used to obtain line strengths. Line lists are provided with Einstein A coefficients and oscillator strengths, which can be used to determine CaCl stellar abundances. Additionally, isotope splittings allowed us to estimate band origins for the ^40 Ca ^37 Cl isotopologue based on band-head positions.

Summary: Background: Atrial fibrillation (AF) is an important heart rhythm disorder in aging populations. The gut microbiome composition has been previously related to cardiovascular disease risk factors. Whether the gut microbial profile is also associated with the risk of AF remains unknown. Methods: We examined the associations of prevalent and incident AF with gut microbiota in the FINRISK 2002 study, a random population sample of 6763 individuals. We replicated our findings in an independent case–control cohort of 138 individuals in Hamburg, Germany. Findings: Multivariable-adjusted regression models revealed that prevalent AF (N = 116) was associated with nine microbial genera. Incident AF (N = 539) over a median follow-up of 15 years was associated with eight microbial genera with false discovery rate (FDR)-corrected P

A new record of Hippocampus mohnikei Bleeker, 1853 was confirmed in Hong Kong through morphological and molecular identification. It is highly possible that there is an established population of H. mohnikei in Hong Kong due to continuous sightings by citizen scientists in the past nine years. The record is significant from a biogeographical perspective as it connects the distributions of known populations in northern China and Japan to those spanning Thailand to India. This further affirms the status of Hong Kong as a hotspot for seahorse biodiversity and conservation in China.

AbstractObjective Examine characteristics and time trends of respiratory tract infection (RTI) consultations in Norwegian primary care and compare consultations in daytime general practice and out-of-hours (OOH) services.Design Registry-based study using reimbursement claims data.Setting All in-person primary care consultations during 2006–2015.Patients All patients visiting primary care during the study period.Main outcome measures The main outcome variable was RTI consultations. Differences regarding service type (general practice or OOH services) and changes over time were investigated. We report associations with patient age and sex, season, point-of-care C-reactive protein (CRP) test use, and sickness certificate issuing.Results RTI consultations (n = 16 304 777) represented 11.6% of all consultations (N = 140 199 637) in primary care over the ten-year period. The annual number of RTI consultations per 1000 inhabitants decreased from 335 to 314, while the number of consultations for any reason increased. Of RTI consultations, 83.2% occurred in general practice. OOH services had a higher proportion of RTI consultations (21.4%) compared with general practice (10.6%). Young children (0–4 years) represented 18.9% of all patients in RTI consultations. CRP testing was used in 56.2% of RTI consultations, and use increased over time. Sickness certificates were issued in 31.9% of RTI consultations with patients of working age (20–67 years).Conclusion Most RTI consultations occurred in general practice, although the proportion was higher in OOH services. Laboratory testing and/or issuing of sickness certificates were part of most consultations. This could be an important reason for seeking health care. Key PointsPatients with a respiratory tract infection (RTI) are mostly managed in primary care, where they represent much of the workload.Most consultations for RTIs took place in daytime general practice, but out-of-hours services had a higher proportion of RTI consultations.RTIs were the dominating reason for encounter among young children both in out-of-hours services and daytime general practice.CRP tests were used in over half of RTI consultations, and their use expanded over time.

Microbial interactions including competition, mutualism, commensalism, parasitism, and predation, which can be triggered by nutrient acquisition and chemical communication, are universal phenomena in the marine ecosystem. The interactions may influence the microbial population density, metabolism, and even their environmental functions. Herein, we investigated the interaction between a heterotrophic bicosoecid flagellate, Pseudobodo sp. (Bicoecea), and a dinoflagellate, Gambierdiscus balechii (Dinophyceae), which is a well-known ciguatera food poisoning (CFP) culprit. The presence of Pseudobodo sp. inhibited the algal proliferation and decreased the cardiotoxicity of zebrafish in the algal extract exposure experiment. Moreover, a significant difference in microbiome abundance was observed in algal cultures with and without Pseudobodo sp. Chemical analysis targeting toxins was performed by using liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with molecular networking (MN), showing a significant alteration in the cellular production of gambierone analogs and some super-carbon chain compounds. Taken together, our results demonstrated the impact of heterotrophic flagellate on the photosynthetic dinoflagellates, revealing the complex dynamics of algal toxin production and the ecological relationships related to dinoflagellates in the marine environment.

Abstract Ecological communities and other complex systems can undergo abrupt and long‐lasting reorganization, a regime shift, when deterministic or stochastic factors bring them to the vicinity of a tipping point between alternative states. Such changes can be large and often arise unexpectedly. However, theoretical and experimental analyses have shown that changes in correlation structure, variance, and other standard indicators of biomass, abundance, or other descriptive variables are often observed prior to a state shift, providing early warnings of an anticipated transition. Natural systems manifest unknown mixtures of ecological and environmental processes, hampered by noise and limited observations. As data quality often cannot be improved, it is important to choose the best modeling tools available for the analysis. We investigate three autoregressive models and analyze their theoretical differences and practical performance. We formulate a novel probabilistic method for early warning signal detection and demonstrate performance improvements compared to nonprobabilistic alternatives based on simulation and publicly available experimental time series. The probabilistic formulation provides a novel approach to early warning signal detection and analysis, with enhanced robustness and treatment of uncertainties. In real experimental time series, the new probabilistic method produces results that are consistent with previously reported findings. Robustness to uncertainties is instrumental in the common scenario where mechanistic understanding of the complex system dynamics is not available. The probabilistic approach provides a new family of robust methods for early warning signal detection that can be naturally extended to incorporate variable modeling assumptions and prior knowledge.

Ecological memory refers to the influence of past events on the response of an ecosystem to exogenous or endogenous changes. Memory has been widely recognized as a key contributor to the dynamics of ecosystems and other complex systems, yet quantitative community models often ignore memory and its implications. Recent modeling studies have shown how interactions between community members can lead to the emergence of resilience and multistability under environmental perturbations. We demonstrate how memory can be introduced in such models using the framework of fractional calculus. We study how the dynamics of a well-characterized interaction model is affected by gradual increases in ecological memory under varying initial conditions, perturbations, and stochasticity. Our results highlight the implications of memory on several key aspects of community dynamics. In general, memory introduces inertia into the dynamics. This favors species coexistence under perturbation, enhances system resistance to state shifts, mitigates hysteresis, and can affect system resilience both ways depending on the time scale considered. Memory also promotes long transient dynamics, such as long-standing oscillations and delayed regime shifts, and contributes to the emergence and persistence of alternative stable states. Our study highlights the fundamental role of memory in communities, and provides quantitative tools to introduce it in ecological models and analyse its impact under varying conditions.

Marine snow is a major route through which photosynthetically fixed carbon is transported to the deep ocean, but the factors affecting flux are largely unknown. Here the authors use high frequency imaging of marine snow particles collected during phytoplankton blooms to categorize and quantify transport.

Gut microbiome composition has a role in health and disease. Here the authors show that microbiome signatures related to the Enterobacteriaceae family are associated with cause-specific mortality risk in a well phenotyped Finish population over a 15-year follow-up.

Abstract Background Gastric adenocarcinoma is associated with H. pylori infection and inflammation that can result in the dysbiosis of gastric microbiota. The association of intestinal microbiota with gastric adenocarcinoma subtypes or with gastric gastrointestinal stromal tumors (GIST) is however not well known. Therefore, we performed 16S rRNA gene sequencing on DNA isolated from stool samples of Finnish patients and controls to study differences in microbiota among different histological subtypes of gastric adenocarcinoma, gastric GIST and healthy controls. Results We found that gut microbiota alpha diversity was lowest in diffuse adenocarcinoma patients, followed by intestinal type and GIST patients, although the differences were not significant compared to controls. Beta-diversity analysis however showed significant differences in microbiota composition for all subtypes compared to controls. Significantly higher abundance of Enterobacteriaceae was observed in both adenocarcinoma subtypes, whereas lower abundance of Bifidobacteriaceae was seen only in diffuse adenocarcinoma and of Oscillibacter in intestinal adenocarcinoma. Both GIST and adenocarcinoma patients had higher abundance of Enterobacteriaceae and lower abundance of Lactobacillaceae and Oscillibacter while lower abundance of Lachnoclostridium, Bifidobacterium, Parabacteroides and Barnesiella was seen only in the adenocarcinoma patients. Conclusions Our analysis shows association of higher Enterobacteriaceae abundance with all types of gastric tumors. Therefore it could be potentially useful as a marker of gastric malignancies. Lower gut microbiota diversity might be indicative of poorly differentiated, invasive, advanced or aggressive tumors and could possibly be a prognostic marker for gastric tumors.

Abstract Background Obesity and physical inactivity are major global public health concerns, both of which increase the risk of insulin resistance and type 2 diabetes. Regulation of glucose homeostasis involves cross-talk between the central nervous system, peripheral tissues, and gut microbiota, and is affected by genetics. Systemic cross-talk between brain, gut, and peripheral tissues in glucose homeostasis: effects of exercise training (CROSSYS) aims to gain new systems-level understanding of the central metabolism in human body, and how exercise training affects this cross-talk. Methods CROSSYS is an exercise training intervention, in which participants are monozygotic twins from pairs discordant for body mass index (BMI) and within a pair at least the other is overweight. Twins are recruited from three population-based longitudinal Finnish twin studies, including twins born in 1983–1987, 1975–1979, and 1945–1958. The participants undergo 6-month-long exercise intervention period, exercising four times a week (including endurance, strength, and high-intensity training). Before and after the exercise intervention, comprehensive measurements are performed in Turku PET Centre, Turku, Finland. The measurements include: two positron emission tomography studies (insulin-stimulated whole-body and tissue-specific glucose uptake and neuroinflammation), magnetic resonance imaging (brain morphology and function, quantification of body fat masses and organ volumes), magnetic resonance spectroscopy (quantification of fat within heart, pancreas, liver and tibialis anterior muscle), echocardiography, skeletal muscle and adipose tissue biopsies, a neuropsychological test battery as well as biosamples from blood, urine and stool. The participants also perform a maximal exercise capacity test and tests of muscular strength. Discussion This study addresses the major public health problems related to modern lifestyle, obesity, and physical inactivity. An eminent strength of this project is the possibility to study monozygotic twin pairs that share the genome at the sequence level but are discordant for BMI that is a risk factor for metabolic impairments such as insulin resistance. Thus, this exercise training intervention elucidates the effects of obesity on metabolism and whether regular exercise training is able to reverse obesity-related impairments in metabolism in the absence of the confounding effects of genetic factors. Trial registration ClinicalTrials.gov , NCT03730610 . Prospectively registered 5 November 2018.

The establishment of symbiotic interaction between Aeschynomene evenia and photosynthetic bradyrhizobia doesn’t involve the canonical Nod factors and infection threads. Here, the authors assemble the draft genome of A. evenia and identify a receptor-like kinase in mediating the symbiotic interaction.

New means to stabilize the microbial balance during pregnancy could benefit maternal health. Our objectives were to investigate in overweight/obese pregnant women 1) the impact of long-chain polyunsaturated fatty acids (fish oil) and/or probiotics on the vaginal microbiota, 2) its relation to gestational diabetes mellitus (GDM) and 3) its interaction with vaginal active matrix metalloproteinase-8 (aMMP-8) and serum high sensitivity C-reactive protein (hsCRP) and phosphorylated insulin-like growth factor-binding protein-1 (phIGFBP-1), IGFBP-1 and aMMP-8.The women were allocated to fish oil + placebo, probiotics + placebo, fish oil + probiotics and placebo + placebo-groups, from early pregnancy onwards (fish oil: 1.9 g docosahexaenoic acid and 0.22 g eicosapentaenoic acid; probiotics: Lacticaseibacillus rhamnosus HN001 (formerly Lactobacillus rhamnosus HN001) and Bifidobacterium animalis ssp. lactis 420, 1010 colony-forming units each). Vaginal and serum samples (early pregnancy, n = 112; late pregnancy, n = 116), were analyzed for vaginal microbiota using 16S rRNA gene amplicon sequencing and vaginal aMMP-8 and serum hsCRP, aMMP-8, phIGFBP-1 and IGFBP-1 by immunoassays. GDM was diagnosed from a 2-h 75 g OGTT. ClinicalTrials.gov, NCT01922791.The intervention exerted effects on many low-abundant bacteria. Compared to the placebo-group, there was a lower abundance of potential pathobionts, namely Ureaplasma urealyticum in the fish oil-group, Ureaplasma, U. urealyticum and Prevotella disiens in the probiotics-group, Dialister invisus and Prevotella timonensis in the fish oil + probiotics-group. Moreover, probiotics decreased the abundance of a few potential pathobionts during pregnancy. Many bacteria were related to GDM. The vaginal aMMP-8 level correlated significantly with α-diversity and inversely with two Lactobacillus species.Dietary interventions, especially probiotics, may have beneficial effects on the vaginal microbiota during pregnancy.

ABSTRACT We introduce the operational genomic unit (OGU) method, a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent of taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance, and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldom applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome data sets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project data set and more accurate prediction of human age by the gut microbiomes of Finnish individuals included in the FINRISK 2002 cohort. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate adoption of the OGU method in future metagenomics studies. IMPORTANCE Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution. To solve these challenges, we introduce operational genomic units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition and (ii) permitting use of phylogeny-aware tools. Our analysis of real-world data sets shows that it is advantageous over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGUs as an effective practice in metagenomic studies.

ABSTRACT Diet and gut microbiota are known to modulate metabolic health. Our aim was to apply a metagenomics approach to investigate whether the diet-gut microbiota-metabolism and inflammation relationships differ in pregnant overweight and obese women. This cross-sectional study was conducted in overweight (n = 234) and obese (n = 152) women during early pregnancy. Dietary quality was measured by a validated index of diet quality (IDQ). Gut microbiota taxonomic composition and species diversity were assessed by metagenomic profiling (Illumina HiSeq platform). Markers for glucose metabolism (glucose, insulin) and low-grade inflammation (high sensitivity C-reactive protein [hsCRP], glycoprotein acetylation [GlycA]) were analyzed from blood samples. Higher IDQ scores were positively associated with a higher gut microbiota species diversity (r = 0.273, P = 0.007) in obese women, but not in overweight women. Community composition (beta diversity) was associated with the GlycA level in the overweight women (P = 0.04) but not in the obese. Further analysis at the species level revealed a positive association between the abundance of species Alistipes finegoldii and the GlycA level in overweight women (logfold change = 4.74, P = 0.04). This study has been registered at ClinicalTrials.gov under registration no. NCT01922791 (https://clinicaltrials.gov/ct2/show/NCT01922791). IMPORTANCE We observed partially distinct diet-gut microbiota-metabolism and inflammation responses in overweight and obese pregnant women. In overweight women, gut microbiota community composition and the relative abundance of A. finegoldii were associated with an inflammatory status. In obese women, a higher dietary quality was related to a higher gut microbiota diversity and a healthy inflammatory status.

Background Human skin harbors complex transient and resident microbial communities that show intra- & inter-individual variation due to various environmental and host-associated factors such as skin site, diet, age, gender, genetics, or the type and use of cosmetics. This variation remains largely uncharacterized in the Indian population; hence, the present study aims to characterize the variation in skin microbiota among individuals of Indian origin and quantify associations with age, diet, and geography. Methods Axillary sweat samples from genetically unrelated individuals (N = 58) residing in the three geographical locations of Maharashtra, India, were collected using a sterile cotton swab. Bacterial DNA was extracted using a standard protocol and checked for quality. Variable regions (V3–V4) of the 16S rRNA gene were sequenced using the Illumina platform. We used standard methods from microbiota bioinformatics, including alpha and beta diversity, community typing, and differential abundance, to quantify the association of skin microbiota with age, diet, and geographical location. Results Our study indicated the prevalence of phyla- Firmicutes, Proteobacteria, and Actinobacteria, consistent with previous reports on skin microbiota composition of the world population level. The alpha diversity (Shannon index) was significantly associated with the age group (Kruskal–Wallis test, p = 0.02), but not with geography (p = 0.62) or diet (p = 0.74). The overall skin microbiota community composition was significantly associated with geographical location based on Community State Types (CST) analysis and PERMANOVA (R2 = 0.07, p = 0.01). Differential abundance analysis at the genus level indicated a distinctively high abundance of Staphylococcus and Corynebacterium among individuals of the Pune district. Pseudomonas and Anaerococcus were abundant in individuals from Ahmednagar whereas, Paenibacillus, Geobacillus, Virgibacillus, Jeotgalicoccus, Pullulanibacillus, Delsulfosporomusa, Citinovibrio, and Calditerricola were abundant in individuals from Nashik district. Conclusion Our work provides one of the first characterizations of skin microbiota variation in different sub-populations in India. The analysis quantifies the level of individuality, as contrasted to the other factors of age, geography, and diet, thus helping to evaluate the applicability of skin microbiota profiles as a potential biomarker to stratify individuals.

Abstract Internal tandem duplication of Fms‐like tyrosine kinase 3 (FLT3/ITD) occurs in about 30% of acute myeloid leukemia (AML) and is associated with poor response to conventional treatment and adverse outcome. Here, we reported that human FLT3/ITD expression led to axis duplication and dorsalization in about 50% of zebrafish embryos. The morphologic phenotype was accompanied by ectopic expression of a morphogen follistatin (fst) during early embryonic development. Increase in fst expression also occurred in adult FLT3/ITD‐transgenic zebrafish, Flt3/ITD knock‐in mice, and human FLT3/ITD AML cells. Overexpression of human FST317 and FST344 isoforms enhanced clonogenicity and leukemia engraftment in xenotransplantation model via RET, IL2RA, and CCL5 upregulation. Specific targeting of FST by shRNA, CRISPR/Cas9, or antisense oligo inhibited leukemic growth in vitro and in vivo. Importantly, serum FST positively correlated with leukemia engraftment in FLT3/ITD AML patient‐derived xenograft mice and leukemia blast percentage in primary AML patients. In FLT3/ITD AML patients treated with FLT3 inhibitor quizartinib, serum FST levels correlated with clinical response. These observations supported FST as a novel therapeutic target and biomarker in FLT3/ITD AML.

This paper presents nonlinear mathematical models of one- and two-track multitrailer vehicles. We derive nonlinear equations of motion in the form of a system of implicit ordinary differential equations (ODEs) by using Lagrangian mechanics. The system of ODEs has the minimum number of states and equations that enables efficient computations yet maintains the most important nonlinear vehicle dynamic behavior and allows actuator coordination and energy consumption evaluation. As examples, we build different models of a 4-unit long combination vehicle, i.e., two-track 11-axle and single-track 6-axle nonlinear models as well as a linear single-track 6-axle model. We compare the performance of these models to experimental data of different driving maneuvers. The nonlinear single-track model demonstrates close dynamic behavior to the experiment, which makes it an efficient alternative to the two-track model. The vehicle equations can be generated automatically by using the code provided in this paper and subsequently used for conducting frequency analysis, evaluating energy consumption, deriving performance measures from simulations, and facilitating optimal control applications that involve combined steering, braking and propulsion control.

Dysfunctional autophagy is associated with various human diseases, e.g., cancer. The discovery of small molecules modulating autophagy with therapeutic potential could be significant. To this end, we screened the ability of a series of metabolites isolated from marine microorganisms to modulate autophagy. Anhydrodebromoaplysiatoxin (ADAT), a metabolite yielded by the marine red algae Gracilaria coronopifolia, inhibited autophagosome-lysosome fusion in mammalian cells, thereby inducing the accumulation of autophagosomes. Treatment of cells with ADAT alkalinized lysosomal pH. Interestingly, ADAT also activated the mTOR/p70S6K/FoxO3a signaling pathway, likely leading to the inhibition of autophagy induction. ADAT had little effect on apoptosis. Our results suggest that ADAT is a dichotomic autophagy inhibitor that inhibits both late-stage (autophagosome-lysosome fusion) and early-stage (autophagy induction) autophagy.