Vibrio alginolyticus ( V. alginolyticus) is a common pathogen that infects humans and animals. In addition to causing serious economic losses in aquaculture, it can also infect humans. The rapid detection of nucleic acids of V. alginolyticus with high sensitivity and specificity in the field is very important for the diagnosis and treatment of infection caused by V. alginolyticus. Here, we established a simple, fast and effective molecular method for the identification of V. alginolyticus that does not rely on expensive instruments and professionals. The method integrates RPA technology with CRISPR technology in a single PCR tube. Using this method, the results can be visualized by lateral flow dipstick in less than 50 minutes. The method was confirmed to achieve high specificity for the detection of V. alginolyticus with no cross-reactivity with similar Vibrio and common clinical pathogens. This diagnostic method shows high sensitivity; the detection limit of the RPA-CRISPR/Cas13a-LFD is 10 copies μL -1. The results for 55 wild strains were consistent with TaqMan-qPCR, and it can be concluded that the methods have 100% sensitivity and 100% specificity. In conclusion, RPA-CRISPR/Cas13a offers great potential as a useful tool for reliable and rapid diagnosis of V. alginolyticus infection, especially in limited conditions.

Laccases have shown to be efficient biocatalysts for the removal of recalcitrant pollutants from wastewater. Thus, they catalyse the oxidation of a wide variety of organic compounds by reducing molecular oxygen to water. However, the use of free laccases holds several drawbacks such as poor reusability, high cost, low stability and sensibility to different denaturing agents that may occur in wastewater. Such drawbacks can be circumvented by immobilising laccase enzymes in/on solid carriers. Hence, during the last decades different approaches considering various techniques and solid carriers to immobilise laccase enzymes have been developed and tested for the removal of pollutants from wastewater. To scale up wastewater treatment bioprocesses, the immobilised laccases are placed in different reactor configurations.

At present, the shortage of energy is becoming more and more serious, and the ecological environment is deteriorating. The proposal of the concept of park level multi energy complementary system (MECS) provides direction for achieving environmentally friendly and sustainable energy development. In recent years, how to set the capacity and scheduling methods of equipment to improve the economy and reliability of the system has become a hot research topic in this field. In this paper, a two-layer optimal scheduling strategy is proposed to allocate the capacity of various energy equipment in the park, considering the comprehensive energy self-sufficiency rate, comprehensive energy utilization rate and energy shortage expectation. The proposed capacity allocation scheme can effectively improve the economy of MECS in the park. Finally, the effectiveness and practicability of the algorithm are verified by simulation analysis.

The probability of silent cerebral infarcts (SCIs) is high among individuals with sickle cell anemia (SCA). The American Society of Hematology’s (ASH) guidelines recommend at least one non-sedated magnetic resonance image (MRI) in the SCA population. Implementing ASH guidelines in low-middle-income settings requires training non-neuroradiologists. We hypothesize that three general radiologists located in Nigeria will achieve a high level of agreement when assessed against a reference set of brain MRIs previously adjudicated for the presence of SCIs. Consensus evaluation utilizing MRIs from the axial and coronal planes by the radiologists revealed an excellent or substantial agreement with reference MRI scans. Thus, radiologists can detect SCIs comparable with board-certified radiologists.

Bioreactor scale-up is complicated by dynamic interactions between mixing, reaction, mass transfer, and biological phenomena, the effects of which are usually predicted with simple correlations or case-specific simulations. This two-part study investigated whether axial diffusion equations could be used to calculate mixing times and to model and characterize large-scale stirred bioreactors in a general and predictive manner without fitting the diffusivity parameter. In this first part, a resistances-in-series model analogous to basic heat transfer theory was developed to estimate the diffusivity such that only available hydrodynamic numbers and literature data were needed in calculations. For model validation, over 800 previously published experimentally determined mixing times were predicted with the transient axial diffusion equation. The collected data covered reactor sizes up to 160 m 3, single- and multi-impeller configurations, aerated and non-aerated operation in turbulent and transition flow regimes, and various mixing time quantification methods. The model performed excellently for typical multi-impeller configurations as long as flooding conditions were avoided. Mixing times for single-impeller and few non-standard bioreactors were not predicted equally well. The transient diffusion equation together with the developed transfer resistance analogy proved to be a convenient and predictive model of mixing in typical large-scale bioreactors.

Valeria A. Guinder1*, Carola Ferronato1, Ana I. Dogliotti2,3, Valeria Segura4, Vivian Lutz4,51Instituto Argentino de Oceanografía (IADO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Universidad Nacional del Sur (UNS), Bahía Blanca, Argentina.2Instituto de Astronomía y Física del Espacio (IAFE), Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales (CONICET -UBA), Buenos Aires, Argentina.3Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (UMI IFAECI/CNRS-CONICET-UBA), Buenos Aires, Argentina.4Instituto Nacional de Investigación y Desarrollo Pesquero (INIDEP), Mar del Plata, Argentina.5Instituto de Investigaciones Marinas y Costeras (IIMyC), Consejo Nacional de Investigaciones Científicas y Técnicas–Universidad Nacional de Mar del Plata (CONICET – UNMdP), Mar del Plata, Argentina.*Corresponding author: vguinder@criba.edu.arThis is a preprint of the following chapter: Guinder VA, Ferronato C, Dogliotti AI, Segura V, Lutz V. Chapter 3: The phytoplankton of the Patagonian Shelf-break Front. In: The Patagonian Shelf-break Front. Ecology, Fisheries, Wildlife Conservation. Acha M, Iribarne O, Piola A (Eds).  Springer Nature. The final authenticated version is available online at: http://dx.doi.org/[insert DOI]

Several theoretical models have been proposed as the underlying mechanisms behind occupancy frequency distribution (OFD) patterns. For instance, the metapopulation dynamic model predicts bimodal OFD pattern indicating the dominance of dispersal processes in structuring the assemblages, while the niche-based model predicts unimodal right-skewed OFD pattern, and thus assemblages are driven mostly by niche processes. However, it is well known that the observed OFD pattern reflects the interplay of several other factors (e.g., habitat heterogeneity, species specificity, sampling protocol parameters). It follows that the individual contribution of each factor to the OFD pattern is rather complicated to explore. Our main objective was to examine the role of the spatial extent of the sampling and the dispersal strategies of species in shaping OFD pattern. For this, we collected samples of stream insect assemblages inhabiting near-natural streams in the Pannon Ecoregion. We formed groups of species representing contrasting dispersal strategies (referred to as dispersal groups). Applying a computer program algorithm, we produced samples from different levels of a stream habitat hierarchy (reach, subbasin, basin, and regional) representing different spatial sample extents. We found that with increasing spatial extent, the OFD pattern changed from bimodal to unimodal for two dispersal groups. Insect groups with contrasting dispersal strategies differed in OFD patterns at reach, subbasin and basin levels. Dispersal groups also differed considering the change in OFD patterns with increasing spatial extent. Our results reflected the underlying changes in the niche and dispersal processes that structure assemblages with increasing spatial extent. We also concluded that the stream insect dispersal strategy influenced the relative role of dispersal and niche processes with increasing spatial extent. Based on our results, we could define spatial extents and dispersal strategies within which different metacommunity models (dispersal and niche processes) could be applied.

Distyly, a floral dimorphism that promotes outcrossing, is controlled by a hemizygous genomic region known as the S-locus. Disruptions of genes within the S-locus are responsible for the loss of distyly and the emergence of homostyly, a floral monomorphism that favors selfing. Using whole genome resequencing data of distylous and homostylous individuals from populations of Primula vulgaris and leveraging high-quality reference genomes of Primula we tested, for the first time, predictions about the evolutionary consequences of transitions to selfing on S-locus genes. Our results confirm the presence of previously reported homostyle-specific, loss-of-function mutations in the exons of the S-locus gene CYPᵀ, while also revealing a previously undetected structural rearrangement in CYPᵀ associated with the shift to homostyly. Additionally, we discovered that the promoter region of CYPᵀ in distylous and homostylous individuals is identical, suggesting that down-regulation of CYPᵀ via mutations in its promoter region is not a cause of shift to homostyly. Furthermore, we found that hemizygosity leads to reduced genetic diversity and less efficient purifying selection in S-locus genes compared to genes outside the S-locus, and that the shift to homostyly further lowers genetic diversity, as expected for mating-system shifts. Finally, we tested, for the first time, long-standing theoretical models of changes in S-locus genotypes during early stages of the transition to homostyly, supporting the assumption that two (diploid) copies of the S-locus might reduce homostyle viability.

Fire is increasingly driving loss and degradation of tropical habitats, but factors influencing biodiversity responses to fire are inadequately understood. We conduct a pan-tropical analysis of systematically collated data – 5257 observations of 1705 plant species (trees and shrubs, forbs, graminoids and climbers) in burnt and unburnt plots from 28 studies. We use model averaging of mixed effect models assessing how plant species richness and turnover (comparing burnt and unburnt communities) vary with time since fire, fire type, protected area status and biome type. More long-term studies are needed, but our analyses highlight three key findings. First, prescribed and non-prescribed burns have contrasting impacts on plant communities, the direction of which depends on focal life form and biome. Forb richness, for example, increases following non-prescribed (but not prescribed) burns in savannahs and flooded grasslands, but in moist broadleaved forest forb richness increases strongly following prescribed (but not non-prescribed) burns. Second, protected areas mitigate fire impacts on plant communities. Species richness of trees/shrubs increased (by ~50%) following fires in non-protected sites but tended to remain similar in protected sites. Similarly, ten years after a fire event graminoid community composition had recovered fully to resemble non-burnt communities in protected areas, but remained highly divergent in unprotected sites. Finally, this persistence in divergence of community composition following fire events occurs across a number of life forms. Composition of tree/shrub communities remained divergent from unburnt communities ten years after a fire, and composition of forb communities only returned to those of unburnt sites after ten years. Fire intervals are already less than ten years in some tropical locations, and future climate and land use change are predicted to further shorten these intervals. Plant communities across much of the tropics are thus likely to change substantially in response increased exposure to fire.

Honey bees play a vital role in providing essential ecosystem services and contributing to global agriculture. However, the potential effect of climate change on honey bee distribution is still not well understood. This study aims to identify the most influential bioclimatic and environmental variables, assess their impact on honey bee distribution, and predict future distribution. An ensemble modelling approach using the BIOMOD2 package in R was employed to develop three models, i.e., a climate-only model, an environment-only model, and a combined climate and environment model. The climate-only model focused on the bioclimatic factors: radiation of the wettest and driest quarters, and temperature seasonality. By utilizing bioclimatic data from 1990 to 2009, combined with observed honey bee presence and pseudo absence data, this model predicted honey bee distribution for two future time spans: 2020-2039 and 2060-2079. The climate-only model exhibited a True Skill Statistic (TSS) value of 0.85, underscoring the pivotal role of radiation and temperature seasonality in shaping honey bee distribution. The environment-only model incorporated environmental variables: proximity to regional ecosystems (floral resources), foliage projective cover, and elevation. This model demonstrated strong predictive performance, with a TSS of 0.88, emphasizing the significance of environmental variables in determining habitat suitability for honey bees. Remarkably, the combined model had a higher TSS of 0.96, indicating that the combination of climate and environmental variables enhances the model’s performance. Predictions for the 2060-2079 period revealed a concerning trend of 100% transition of highly suitable land into moderately (0.54%), marginally (17.56%) or not suitable areas (81.9%) for honey bees. These results emphasize the critical need for targeted conservation efforts and the implementation of policies aimed at safeguarding honey bees and the vital apiary industry.

Monoclonal antibodies, JB4 and SylH3, neutralize ricin toxin (RT) by inhibiting the galactose-specific lectin activity of the toxin’s B subunit (RTB), which is required for cell attachment and entry. It is not immediately apparent how the antibodies accomplish this feat, considering that RTB consists of two globular domains (D1, D2) each divided into three homologous sub-domains (a, b, g) with the two functional galactosyl-specific carbohydrate recognition domains (CRDs) situated on opposite poles (sub-domains 1a and 2g). Here we report the X-ray crystal structures of JB4 and SylH3 Fab fragments bound to RTB in the context of RT. The structures revealed that neither Fab obstructed or induced detectable conformational alterations in subdomains 1a or 2g. Rather, JB4 and SylH3 Fabs recognize nearly identical epitopes within an ancillary carbohydrate recognition pocket located in subdomain 1β. Despite limited amino acid sequence similarity between SylH3 and JB4 Fabs, each paratope inserts a Phe side chain from heavy (H) chain complementarity determining region (CDR3) into the 1β CRD pocket, resulting in local aromatic stacking interactions that potentially mimic a ligand interaction. Reconciling the fact that stoichiometric amounts of SylH3 and JB4 are sufficient to disarm RTB’s lectin activity without evidence of allostery, we propose that subdomain 1β functions as a “coreceptor” required to stabilize glycan interactions principally mediated by subdomains 1a and 2g. Further investigation into subdomain 1β will yield fundamental insights into the large family of R-type lectins and open novel avenues for countermeasures aimed at preventing toxin uptake into vulnerable tissues and cells.

Large-scale fermentation processes involve complex dynamic interactions between mixing, reaction, mass transfer, and the suspended biomass. Empirical correlations or case-specific computational simulations are usually used to predict and estimate the performance of large-scale bioreactors based on data acquired at bench scale. In this two-part-study, one-dimensional axial diffusion equations were studied as a general and predictive model of large-scale bioreactors. This second part focused on typical fed-batch operations where substrate gradients are known to occur, and characterized the profiles of substrate, pH, oxygen, carbon dioxide, and temperature. The physically grounded steady-state axial diffusion equations with first- and zeroth-order kinetics yielded analytical solutions to the relevant variables. The results were compared with large-scale Escherichia coli and Saccharomyces cerevisiae experiments and simulations from the literature, and good agreement was found in substrate profiles. The analytical profiles obtained for dissolved oxygen, temperature, pH, and CO 2 were also consistent with the available data. Distribution functions for the substrate were defined, and efficiency factors for biomass growth and oxygen uptake rate were derived. In conclusion, this study demonstrated that axial diffusion equations can be used to model the effects of mixing and reaction on the relevant variables of typical large-scale fed-batch fermentations.

In bioprocesses, the pH value is a critical process parameter that requires monitoring and control. For pH monitoring, potentiometric methods such as pH electrodes are state-of-the-art. Nevertheless, they are invasive and show measurement value drift. Spectroscopic pH monitoring is a non-invasive alternative to potentiometric methods avoiding this measurement value drift. In this study, we developed the Good pH probe, which is an approach for spectroscopic pH monitoring in bioprocesses with an effective working range between pH 6 and pH 8 that does not require the estimation of activity coefficients. The Good pH probe combines the Good buffer 3-( N-morpholino)propanesulfonic acid (MOPS) as pH indicator with Raman spectroscopy as spectroscopic technique, and Indirect Hard Modeling (IHM) for the spectral evaluation. During a detailed characterization, we proved that the Good pH probe is reversible, exhibits no temperature dependence between 15 and 40 °C, has low sensitivity to the ionic strength up to 1100 mM, and is applicable in more complex systems, in which other components significantly superimpose the spectral features of MOPS. Finally, the Good pH probe was successfully used for non-invasive pH in-line monitoring during an industrially relevant enzyme-catalyzed reaction with a root mean square error of prediction (RMSEP) of 0.04 pH levels.

A document by Nada Hamrouni. Click on the document to view its contents.

Early neutralising antibodies against Hepatitis C virus (HCV) and CD8+T cell effector responses can lead to viral clearance. However, these functions alone are not sufficient to protect patients against HCV infection, thus yet undefined additional anti-viral immune mechanisms are required. In recent years, Fc-receptor-dependent antibody effector functions particularly, antibody-dependent cellular phagocytosis (ADCP) was shown to offer immune protection against several RNA viruses. However, its development, and clinical role in patients with HCV infection remain unknown. In this study, we found that patients with chronic GT1a or GT3a HCV infection had significantly higher concentrations of anti-envelop 2 (E2) antibodies, predominantly IgG1 subclass, than patients that cleared the viruses while the latter had antibodies with higher affinities. 97% of the patients with HCV had measurable ADCP of whom patients with chronic disease showed significantly higher ADCP than those who naturally cleared the virus. Epitope mapping studies showed that patients with antibodies that target antigenic domains on the HCV E2 protein that are known to associate with neutralisation function also strongly associated with ADCP, suggesting antibodies with overlapping/dual functions. Correlation studies showed that ADCP significantly correlated with plasma anti-E2 antibody levels and neutralisation function regardless of clinical outcome and genotype of infecting virus while a significant correlation between ADCP and affinity was only evident in patients that cleared the virus. These results suggest ADCP was mostly driven by antibody titre in patients with chronic disease while maintained in clearers due the quality (affinity) of their anti-E2 antibodies despite having lower antibody titres.

A document by Muhammad Meeran Saleem. Click on the document to view its contents.

In the field of compressed sensing, the restricted block $\ell_1-\ell_2$ minimization model can recover the block sparse vector well. When solving the restricted block $\ell_1-\ell_2$ minimization model, it is often transformed into a unrestricted $\ell_1-\ell_2$ minimization model, and then the convex algorithm is used to solve the new model. Experiments have shown that this method is effective, but the theoretical results of the unrestricted $\ell_1-\ell_2$ minimization model being able to recover block sparse vectors have not yet been established. The main task of this paper is to establish sufficient conditions for the unrestricted $\ell_1-\ell_2$ minimization model to recover block sparse vectors based on the RIP condition, and to demonstrate the influence of parameter $\lambda$ in the unrestricted $\ell_1-\ell_2$ minimization model on the recovery of block sparse vectors through experimental methods.\\

In light of the challenges posed by groundwater contamination and the urgent need for accurate and efficient groundwater contaminated source estimation (GCSE), the present study proposes a novel approach for GCSE using a deep adaptive cycle generative adversarial neural network (DA-CGAN). Given the equifinality from different parameters (EFDP) often associated with GCSE, we leveraged a bidirectional adversarial training pattern involving a forward process and a recovery process to supervise the inverse mapping relationship. Once trained, the forward process can be utilized to provide estimation for GSCE. This bidirectional-training strategy mitigates EFDP, thereby effectively enhancing the reliability of GCSE. Moreover, the performance of DA-CGAN is closely related to the quality of the training samples. To address this, we introduced a significant enhancement through an adaptive sampling strategy. This substantially improves the quality of training samples and consequently increases the accuracy of the GCSE. Furthermore, the inherent data-driven attribute of the deep cycle GAN considerably reduces computational costs when conducting GCSE. The research unfolds in the contexts of both hypothetical and real-world scenarios, with the goal of providing an efficient, precise, and cost-effective solution for GCSE. The results demonstrate that the DA-CGAN, an innovative model in the hydrogeological domain, exhibits superior performance in both estimation accuracy (Average Relative Error (ARE) of 4.91% and R of 0.998) and computational efficiency (0.17 seconds per run). This is particularly notable when compared with typical inverse methods such as the genetic algorithm (GA) and the ensemble kalman filter (ENKF).

Himani Dhanze1, Balbir B. Singh2, Michael Walsh3, 4, 5, 6, M. Suman Kumar1, Amit Kumar1, K.N. Bhilegaonkar1, Victoria J. Brookes7*1Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India2Guru Angad Dev Veterinary and Animal, Sciences University (GADVASU), Ludhiana, Punjab, India3The University of Sydney, Faculty of Medicine and Health, Sydney School of Public Health, Camperdown, New South Wales, Australia4The University of Sydney, Faculty of Medicine and Health, Sydney Infectious Diseases Institute, Westmead, New South Wales, Australia5One Health Centre, The Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, India6The Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, India7Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Camperdown, New South Wales, Australia*Correspondence to:Victoria J. Brookes, Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Camperdown, 2006, New South Wales, Australia. Email: victoria.brookes@sydney.edu.au

Running Head: Spatio-temporal epidemiology of JE in pig populations in IndiaHimani Dhanze1, Balbir B. Singh2, Michael Walsh3, 4, 5, 6, M. Suman Kumar1, Amit Kumar1, K.N. Bhilegaonkar1, Victoria J. Brookes7,3*1Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India2Guru Angad Dev Veterinary and Animal, Sciences University (GADVASU), Ludhiana, Punjab, India3The University of Sydney, Faculty of Medicine and Health, Sydney School of Public Health, Camperdown, New South Wales, Australia4The University of Sydney, Faculty of Medicine and Health, Sydney Infectious Diseases Institute, Westmead, New South Wales, Australia5One Health Centre, The Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, India6The Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, India7Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Camperdown, New South Wales, Australia*Correspondence:Victoria J. Brookes, Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Camperdown, 2006, New South Wales, Australia. Email: victoria.brookes@sydney.edu.auInstitutions and places where this study was conducted:Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, IndiaSydney School of Veterinary Science, Faculty of Science, The University of Sydney, Camperdown, New South Wales, Australia

AbstractFanconi anemia (FA) is a rare disease (incidence of 1:300,000) primarily based on the inheritance of pathogenic variants in genes of the FA/BRCA (breast cancer) pathway. These variants ultimately reduce the functionality of different proteins involved in the repair of DNA interstrand crosslinks and DNA double-strand breaks. At birth, individuals with FA might present with typical malformations, particularly radial axis and renal malformations, as well as other physical abnormalities like skin pigmentation anomalies. During the first decade of life, FA mostly causes bone marrow failure due to reduced capacity and loss of the hematopoietic stem and progenitor cells. This often makes hematopoietic stem cell transplantation necessary, but this therapy increases the already intrinsic risk of developing squamous cell carcinoma (SCC) in early adult age. Due to the underlying genetic defect in FA, classical chemo-radiation-based treatment protocols cannot be applied. Therefore, detecting and treating the multi-step tumorigenesis process of SCC in an early stage, or even its progenitors, is the best option for prolonging the life of adult FA individuals. However, the small number of FA individuals makes classical evidence-based medicine approaches based on results from randomized clinical trials impossible. As an alternative, we introduce here the concept of multi-level dynamical modelling using large, longitudinally collected genome, proteome- and transcriptome-wide data sets from a small number of FA individuals. This mechanistic modelling approach is based on the “hallmarks of cancer in FA”, which we derive from our unique database of the clinical history of over 750 FA individuals. Multi-omic data from healthy and diseased tissue samples of FA individuals are to be used for training constituent models of a multi-level tumorigenesis model, which will then be used to make experimentally testable predictions. In this way, mechanistic models facilitate not only a descriptive but also a functional understanding of SCC in FA. This approach will provide the basis for detecting signatures of SCCs at early stages and their precursors so they can be efficiently treated or even prevented, leading to a better prognosis and quality of life for the FA individual.IntroductionRare diseases are disorders that affect less than one case in 2000 people, i.e., only a small percentage of the population. However, there are more than 6000 known rare diseases, affecting over 300 million people worldwide (1, 2). In 80% of the cases, the origin of a rare disease is one or multiple disadvantageous inherited variations of the genome (3). These are present in all cell types of the affected individual. Nevertheless, most rare diseases, which are not already prenatally lethal, are rather tissue specific. Pediatricians are more likely confronted with rare diseases than healthcare specialists from other disciplines, as those diseases frequently present symptoms early in life. Rare diseases are often referred to as “orphan diseases”, since in comparison to common non-communicable diseases, such as cardiovascular diseases and type 2 diabetes, there is less research and development of therapies for them. Fanconi anemia (FA) belongs to a small group of rare diseases that are investigated more intensively than most others (4). This is also the result of significant contributions from patient organizations in the United States, Germany and many other countries (5).In general, evidence-based medicine aims to make optimal medical decisions by integrating the experience of a clinician with data from the individual patient and available scientific information on the respective disease (6). The latter information often derives from randomized clinical trials involving large numbers of cases and controls. Those trials are the source for the construction of statistical models, i.e., to quantify mathematical relationships between non-random variables measured from the study participants (Fig. 1, left). For common diseases, there is no problem identifying a sufficiently large number of cases to achieve acceptable statistical power of the applied statistical model, e.g., reflected by the p-value. However, this approach cannot be used for rare diseases due to the small number of cases. An alternative approach is to study a few individuals in very high detail by collecting longitudinal samples for many biological parameters (7) (Fig. 1, right). For example, multi-omic analyses provide many thousands of data points per individual, such as genome-wide DNA methylation, histone modifications and gene expression. These data, together with mechanistic information on biochemical and regulatory pathways from public databases, such as KEGG (Kyoto Encyclopedia of Genes and Genomes) (8), Wikipathways (9) and SPOKE (Scalable Precision Medicine Open Knowledge Engine) (10), can then be used to construct multi-level dynamical computational models (11, 12). These models can act as virtual platforms for identifying novel therapeutic targets and designing treatment and preventative protocols to improve individual patient outcomes. In this Perspective article, we introduce the concept of mechanistic modelling as a clinical decision support tool in FA, using the example of the multi-step tumorigenesis of squamous cell carcinoma (SCC) in FA individuals.

Child malnutrition is a pressing concern in India, impacting physical and mental development and leading to socio-economic challenges. This study examines malnutrition prevalence, regional and social disparities, nutritional expenditure trends, and the impact of nutrition spending on children under 5, using secondary data from 1997-98 to 2021-22. Correlation and multiple linear regression are employed in a cross-sectional framework. The study shows improvements in nutritional status in low-income states like Madhya Pradesh, Jharkhand, Rajasthan, Odisha, and Uttar Pradesh. However, Bihar, Chhattisgarh, Jharkhand, and Madhya Pradesh still face challenges in reducing malnutrition. Rural areas have higher rates of stunting, wasting, and underweight. Per capita nutrition spending correlates negatively with stunting and underweight but positively with wasting and severe wasting. While nutrition spending alone does not reduce malnutrition, spending on ICDS and Poshan Abhiyan play a significant role. A one percent increase in per capita ICDS spending leads to a 9.15 percent reduction in underweight prevalence, and a one percent increase in per capita Poshan Abhiyan expenditure results in decreased stunting by 0.07 percent, wasting by 0.06 percent, and underweight by 0.16 percent. Other factors, like urbanization, SC-ST population, women's literacy, piped-water-availability, sanitation-facilities, and household-size are significant determinants of malnutrition among children under-5.

Habitat fragmentation is modifying landscapes and the distribution of floral resources, possibly shaping also pollinator resource acquisition. Here, using urban parks as field laboratories for the dramatic contrast around, we aimed to clarify how fragmentation and local flower availability shape bumblebee foraging dynamics by characterizing several components: the nutritional content and plant composition of collected pollen pellets, the foraging rate and the plant-nutrition association along a fragmentation gradient. We found mostly negative linear or non-linear relationships between nutritional quality and fragmentation, tight plant composition-nutrition associations interpretable as low access to alternative resources, and shorter foraging time in smaller green areas, showing behavioral limits by the landscape. Thus, fragmentation can constrain all aspects of bumblebee foraging by compromising resource accessibility. This study illuminates the link between landscape features and the nutritional ecology of pollinators, a key aspect for understanding pollinator foraging dynamics and even for outlining mitigation measures in urban contexts.

Marine invasive species (MIS) can cause irreparable change in new environments, though only 1 in 10 invasive species cause negative impacts to recipient ecosystems. Knowledge of which invasive species could have negative impacts helps ensure that limited resources available for MIS monitoring and management are deployed most effectively. This is particularly true in areas that are difficult to access, such as the Southern Ocean. We used an ensemble ecosystem model to explore the impact of potential future invasions by seven groups of marine invasive species on an Antarctic coastal marine food web. Although most simulations showed native species abundances only changed within 10% of the initial abundance, the establishment of a MIS could plausibly cause significant abundance declines to all native species. This analysis helps us to better understand the potential range of impacts on native species and aid the development of strategies to help prevent or manage their introduction.