Understanding the drivers of spatially and temporally correlated (synchronous) fluctuations in abundance is a fundamental challenge in ecology and conservation. Synchronous fluctuations in different demographic rates can potentially drive or dampen abundance synchrony, but demographic synchrony is generally poorly understood. Using long-term count and demographic data for breeding land-birds at sites across Europe, we show that the strength and scale of synchrony are greatest in productivity, followed by adult survival rates and then counts. However, count fluctuations are more synchronous with survival than with productivity. Despite migratory and resident species having similar periodicities of count synchrony, synchrony in both demographic rates was more common over long-timescales in resident species and short-timescales in migrant species. These findings suggest local impacts of synchronous fluctuations in adult survival rates on count synchrony, potentially dampening effects of large-scale synchrony in productivity, and that the environmental drivers of synchrony may differ between residents and migrants.

Theoretical models of the evolution of discrete phenotypes show that the most evolvable populations are composed of genotypes with intermediate levels of phenotypic robustness. This has been attributed to a special kind of epistasis, the analog of which for complex quantitative traits might not readily apparent. Here, with simulation models, I show that a variety of plausible kinds of quantitative genetic epistasis will do; as long as it increases cryptic genetic diversity and expected allele effect sizes are not too large. In fact, epistasis is not necessary, since cryptic genetic diversity can also accumulate via phenotypic plasticity. But with phenotypic plasticity, the mapping of phenotypic robustness to evolvability is sensitive to the nature and predictability of environmental variation. So, just as for discrete-traits, the robustness of quantitative traits can have complex effects on evolvability, and this depends on exactly how genetic diversity is hidden and revealed.

Flagship species are used to promote conservation and tourism. Africa’s famous ‘Big Five’, have become marketing flagships that fundraisers and tourism promoters emulate on other continents, choosing regional groups of species for marketing campaigns. Selections can be based on characteristics identified as appealing: colour, size, or behaviour, but this approach may overlook unique flagships or homogenise seelctions. Polling the public to reveal existing preferences for animals may identify suitable species more directly. We used questionnaires with tourists in the Peruvian Amazon to identify existing biases for species suitable for tourism and conservation marketing. Without a species list, preferences were expressed at inconsistent taxonomic levels. The response ‘monkeys’ (infraorder Simiiformes) was highest ranked, followed by ‘jaguar’ (Panthera onca), ‘Amazon dolphin’ (Inia geoffrensis), ‘sloths’ (suborder Folivora), ‘caiman’ (subfamily Caimaninae) and ‘birds’ (class Aves). When ranking species from a preselected shortlist, jaguar, Amazon dolphins, and sloths (represented by Bradypus variegatus) remained popular, while vote splitting within higher taxonomic levels, in particular monkeys, made room in the top rankings for green-winged macaw (Ara chloropterus) and anaconda (Eunectes murinus). When asked about their willingness to pay for excursions or donate to conservation, tourists were overwhelmingly more likely to quote larger figures to see or conserve jaguars than any other species, but results for other species were more homogenous. Important species for tourism in rainforest regions are often from diverse taxonomic groups; monkeys may be represented by 8-14 species at single sites in Amazonia, birds by several hundred species. A big five strategy obscures this diversity. Similarly, using physical characteristics as selection criteria underplays diversity and can overlook popular taxa. A strategy of polling the public to identify regional flagships more directly identifies salient species for marketing and is especially useful where budgets are limited, but diversity may trump the Big five approach in megadiverse areas.

The nuclear genome is crucial for cells, and increasing evidence shows that nuclear DNA content alone can alter cell and organismal characteristics. However, the extent of genome size variation and its ecological and evolutionary consequences are not well understood, especially in microorganisms. We used flow cytometry to estimate genome size and GC content for 53 evolutionary lineages of the microalgal genus Synura (Chrysophyceae, Stramenopiles). Genome size evolution was reconstructed in a phylogenetic framework using molecular markers. A set of genomic, morphological, and ecogeographic variables characterizing Synura lineages were evaluated and tested as predictors of genome size variation in phylogeny-corrected statistical models. Both genome size and GC content varied widely in Synura, ranging from 0.19 to 3.70 pg DNA and from 34.0% to 49.3%, respectively. Genome size variation was mainly associated with cell size, less with the size of the silica scales covering the cell surface, and not at all with the phylogenetically conserved ultrastructure of the silica scales. The ecological requirements of Synura lineages were significantly affected by genome size variation, suggesting larger genomes are associated with habitats with higher soil nitrogen content, higher latitudes, and lower mean temperatures. The evolution of genome size in Synura suggests potential dynamism. Unlike genome size decreases, increases were mostly restricted to short terminal branches, indicating lower macroevolutionary stability. Lineages with larger genomes exhibited a narrower range of suitable ecological conditions, likely due to evolutionary constraints on upper genome size limits.

The major histocompatibility complex (MHC) plays a critical role in the immune response against pathogens. Its high polymorphism is thought to be mainly the consequence of host-pathogen co-evolution, but elucidating the mechanism(s) driving MHC evolution remains challenging for natural populations. We investigated the diversity of MHC class II genes in a wild population of pied flycatchers Ficedula hypoleuca, and tested its associations with two key components of individual fitness: lifetime reproductive success and survival. Among 180 breeding adults in our study population, we found 182 unique MHC class II exon 2 alleles. The alleles showed a strong signal of positive selection and grouped into 9 functional supertypes based on physicochemical properties at the inferred antigen-binding sites. Three supertypes were found in > 98% of the sampled individuals, indicating that they are nearly fixed in the population. We found no rare supertypes in the population, as all supertypes were present in >70% of individuals. Three supertypes were related to different components of individual fitness: two were associated with lower offspring production over time, while the third was positively associated with survival. Overall, the substantial allelic and functional diversity and the relationship between specific supertypes and fitness is in accordance with the notion that balancing selection maintains MHC class II diversity in the study population, possibly with fluctuating selection as the underlying mechanism. The absence of rare supertypes in the population suggests that the balancing selection is not driven by rare-allele advantage.

1 IntroductionSubacute combined degeneration (SCD) is a neurodegenerative demyelinating disease that affects the posterior and lateral columns of the cervical and upper dorsal parts of the spinal cord and is caused primarily by vitamin B12 deficiency. In addition to SCD, vitamin B12 deficiency produces various other neurological and psychiatric symptoms as well as a wide spectrum of hematological abnormalities [1]. Chronic vitamin B12 deficiency has been identified in the majority of patients with hyperhomocysteinaemia (HHcy), which promotes venous thrombosis and atherosclerosis via various mechanisms [2]. One such case report of a young patient demonstrated severe HHcy secondary to vitamin B12 deficiency as a cause of pulmonary embolism and myocardial infarction [3]. Although the association of deep venous thrombosis with HHcy has been well established [4, 5], its presentation with SCD is not reported in literature to our best of knowledge. The current report describes a 68 years old female who presented with SCD of the spinal cord coexisting with deep venous thrombosis (DVT).

Ramaria has been a remarkable taxon throughout the history of macrofungi. However, there is a lack of information on these macrofungi. This study determined the order of nucleotides in the mitochondrial genomes (mitogenomes) of three Ramaria species, followed by a detailed investigation of the obtained genetic information. Circular mitogenomes of Ramaria brunnecliacina, R. ichnusensis, and R. flavescens had sizes of 78,960, 61,851, and 81,282 bp, respectively. The genomes exhibited variations in genetic content, gene length, tRNA, and codon usage. Ramaria mitogenomes demonstrated variable evolutionary rates across several protein-coding genes. Results revealed significant gene rearrangements in Ramaria mitogenomes, including gene displacement and tRNA duplication. Bayesian inference and maximum likelihood methods, yielded well-supported phylogenetic trees for Basidiomycota. Phylogenetic analysis, using a comprehensive collection of conserved mitochondrial proteins, yielded a strong and reliable phylogenetic tree for Basidiomycetes, which clarified the evolutionary connections between Ramaria and other Basidiomycetes members. R. brunnecliacina and R. flavescens were identified as closely related species. Species of Ramaria, a paraphyletic group, exhibited a close genetic relationship with other species belonging to the subclass Phallomycetidae. This study presents a basic structure for understanding the evolutionary dynamics, genetic makeup, and taxonomy categorization of this significant fungal community.

Introduction: The transdiagnostic approach has recently emerged in psychopathology and offers a new understanding of psychiatric disorders. However, such conceptualization has mainly focused on anxiety and depression. To fill this gap, researchers recently developed the Symptomatic Transdiagnostic Test (S2T). This tool offers a broader evaluation of patients, more than the assessment of anxiety and depression. However, this scale is time-consuming, and its psychometric properties have not been assessed within a clinical population. This study aims to develop a short version of such tool and to assess its psychometric properties within a clinical sample. Method: We recruited 2 741 participants from the general population and 252 patients suffering from psychiatric disorders. They undertook the s-S2T and the Transdiagnostic Skills Scale. We assessed the factorial structure of the scale, its internal consistency, its concurrent validity and ecological validity. Results: We found good fit indexes for a B-ESEM model composed of a general psychopathological factor and 10 specific factors. We found good internal consistency and concurrent validity with the transdiagnostic skills scale. Such results were found within both general and clinical population. Finally, we demonstrated higher levels of symptoms among the clinical population than among the general population except for addictive and eating disorder symptoms. Conclusion: The s-S2T has good psychometric properties within both general population and clinical population. It can be quickly administrated and offers a broad evaluation of psychopathology for clinicians and researchers.

Lithium metal batteries are the most promising next-generation energy storage technologies due to their high energy density. However, their practical application is impeded by serious interfacial side reactions and dendrite growth of lithium metal anode (LMA). Herein, copper 2,4,5-trifluorophenylacetate (CuTFPAA) is synthesized and used to stabilize LMA by in-situ constructing a dense and mixed-conductive interfacial protective layer. The in-situ formed passivated layer not only significantly inhibits interfacial side reactions by avoiding direct contact between LMA and electrolyte but also effectively suppresses lithium dendrite growth due to its high mechanical strength. As a result, the CuTFPAA-treated LMAs show greatly improved cycle stability under both high current density and high areal deposition capacity. Notably, the assembled liquid symmetrical cells with CuTFPAA-treated LMAs can stably work for more than 3000, 5000, and 4800 h at 1.0 mA cm‒2‒1.0 mAh cm‒2, 2.0 mA cm‒2‒5.0 mAh cm‒2, and 10 mA cm‒2‒5.0 mAh cm‒2, respectively. Furthermore, the assembled liquid full cell with a high LiFePO4 loading (~ 16.9 mg cm‒2) shows a significantly enhanced cycle life of 250 cycles with stable Coulombic efficiencies (> 99.1%). Moreover, the assembled all-solid-state lithium metal battery with a high LiNi0.6Co0.2Mn0.2O2 loading (~ 5.0 mg cm‒2) also exhibits improved cycle stability. These findings underline that the CuTFPAA-treated LMAs show great promise for high-performance lithium metal batteries.

The Healthy Context Paradox suggests that victims of peer-bullying experience greater psychological adjustments difficulties in environments with low victimization norms. This study aims to explain this phenomenon from a cognitive perspective guided by Beck’s Model of Depression. We examined the clique-level effects of cognitive bias between victimization and depression with the data collected from two junior-high schools, a total of 2091 students (54.3% boys, Mage = 13.26 years, SD = 0.66), in Shaanxi province. The Social Cognitive Map was conducted to identify cliques. And the results showed that cognitive biases play a partial mediating role between bullying victimization and depression. Clique victimization norms moderated the impact of peer-victimization on cognitive biases but had no significant effect on depression, and in cliques with low victimization norms, peer-victimization predicts a greater impact on cognitive biases. Peer-victimization predicted cognitive bias within all-girl cliques only with low victimization norms, but this effect was found in all-boy cliques with both high and low victimization norms. And the moderating mediating effects for depression were non-significant in all-boy cliques and mixed gender cliques. These results indicated a possible combination of the Healthy Context Paradox and Beck’s depression Model and provide a practical guidance for the intervention of preventing depression, especially in an environment where bullying victimization is common.

Background: For future health technology assessment, an assessment of the utility value of influenza-like illnesses (ILIs) is crucial. Therefore, the objective of this study was to evaluate the impact of ILIs on utility value in a Japanese population. Methods: We conducted an online survey between March and June 2024 to evaluate the impacts of ILIs on health-related quality of life, using a Japanese version of the EuroQol 5 Dimensions 5 Levels (EQ-5D-5L) and EuroQol visual analog scale (EQ-VAS). Participants were children and adults aged <80 years who experienced ILI symptoms or required home isolation due to a respiratory infection. A follow-up survey was conducted 2–3 weeks after the first survey to assess recovery. For children, we asked their parents or guardians to answer as the child’s proxy. A generalized linear model was used to assess the impact of patient demographics on disutility. Results: In total, 264 participants answered the follow-up survey (134 adult participants and 130 parents or guardians). The mean differences in the utility value and VAS score between the first and follow-up surveys were −0.067 and −14.2, respectively. In the generalized linear model, symptom onset within 7 days in the first survey was significantly associated with disutility value (coefficient −0.049 [95% confidence interval [CI] −0.086 to −0.012], p=0.010). However, none of the patient demographics were significantly associated with disutility value. Conclusions: Utility values were lower during the symptomatic phase compared with the recovery phase. Our results are useful for disease burden assessment, health technology assessment, and cost-effectiveness analysis, which can support national decision-making on the preventive and therapeutic management of respiratory infections.

This paper investigated the effect of automation processes in an industrial company engineering complex cyber-physical systems. The authors used industry-as-laboratory as research method, exploring an ongoing development project. The automation efforts focused on four areas, being 1) test setup, 2) test execution, 3) test result analysis, and 4) documentation. All four areas showed promising results on increased effectiveness and/or efficiency. Especially automation of test result analysis will help the industrial company, KONGSBERG, reduce their main bottleneck in the test process, as well as reduce the risk of costly project delays. An automated system integration test process, facilitating iterative regression testing, will leverage the efficiency of the verification test process.

Background With the rapid development of artificial intelligence technology, highly intelligent and unmanned factories have become an important trend. In the complex environments of smart factories, the long-term tracking and inspection of specified targets such as operators and special goods, as well as comprehensive visual recognition and decision-making capabilities throughout the whole production process, are critical components of automated unmanned factories. It is inevitable that issues such as target occlusion and disappearance will occur, thus exacerbating the long-term tracking challenge. Currently, there are no long-term tracking studies specifically addressing occlusions in these environments. Methods We first construct three new benchmark datasets in the complex workshop environment of a smart factory (referred to as SF-Complex3 data), which include challenging conditions such as complete occlusion and partial occlusion of targets. Next, utilising a brain memory inspired approach, we determine uncertainty estimation parameters: confidence, peak-to-sidelobe ratio (PSR), and average peak to correlation energy (APCE), to derive a continual learning based adaptive model update method. Additionally, we design a lightweight target detection model to automatically detect and locate targets in the initial frame and during re-detection. Finally, we integrate the algorithm with ground mobile robots and UAV-based imaging and processing equipment to build a new visual detection and tracking framework, termed SFC-RT (Smart Factory Complex Tracking and Identification). Results We conducted extensive tests on the benchmark UAV20L and SF-Complex3 datasets. Compared to state-of-the-art tracking algorithms, our proposed algorithm demonstrates an average performance improvement of 6% when facing key challenging attributes. Moreover, it can smoothly run on embedded platforms, including mobile robots and UAVs, at a real-time speed of 36.4 fps. Conclusions The proposed SFC-RT framework is shown to efficiently and accurately address the challenges of target loss and occlusion for long-term tracking within complex smart factory environments. It meets the requirements of real-time performance, robustness, and lightweight design.

Main ManuscriptKey Clinical Messages: This case report underlines the diagnostic problems of bilateral lung involvement caused by popcorn kernel aspiration in a 13-month-old infant. It emphasizes the importance of comprehensive clinical evaluation, chest x-ray interpretation, and understanding of the hazards of popcorn aspiration in young children to avoid misdiagnosis and complications.

Saline agricultural soils in the Senegal River delta, which are predominantly used for rice cultivation, require preliminary washing of the plots, the addition of mineral fertilizers, and several passes of agricultural machinery for various tillage operations, treatments, and harvesting. Consequently, the natural state of compaction is altered, which may be affecting the productivity of the plots due to surface compaction, with the formation of a hardpan, or deeper compaction, leading to poor drainage. For this study on soil compaction in agriculture, several plots from the Ross-Béthio GIE Cooperative, located in the Senegal River valley, Saint Louis region, were selected with different moisture levels after fallow. A soil sample was taken from different subsamples of the plots for a complete physicochemical analysis. This analysis was performed at the Centre de Recherches Agricoles in Saint-Louis of the Senegalese Agricultural Research Institute (ISRA). With a manual Eijkelkamp cone penetrometer, force measurements were taken every 5 cm in depth, from 0 to 100 cm, to later determine the compaction expressed as a Cone Index. To determine soil moisture, an IMKO TRIME-PICO 64/32 probe was used. The data correspond to the water volume fraction in soil volume (m 3/m 3). A result of pH 6.32 was determined, corresponding to a low acid pH, the analyzed soil has excessive salinity, very low nitrogen content, and very low organic matter content. The state of soil compaction in the rice fields of the Senegal River delta is characterized by a superficial crust in the first 5 cm with a Cone Index ranging from 0.38 to 1.02 MPa. From there, the surface compaction values range from 1.82 to 2.32 MPa up to 50 and 55 cm. Deep layer compaction, up to 65 and 70 cm, can reach 3.00 MPa. It can be considered that the hardpan appears at 50 cm deep with 2.32 MPa. These results occur without significant influence of the soil water content during the tests. On the other hand, from the behavior of the Cone Index in the superficial and deep layers, it is observed that the water content has a negligible influence, that the tillage system and the number of passes correspond to traditional tillage techniques. In addition, the increase in the Cone Index in the deep layers is due to the high salt content and the low organic matter content.

An agricultural experimental setup has been constructed with the aim of assessing the impact of soil conservation practices on surface runoff and water quality. The site is located at Saint-Lambert-de-Lauzon (near Québec City, Canada) and is composed by twelve 624 m 2 catchments for which surface and tillage runoffs, water quality (suspended matter, phosphorus, nitrate-nitrite, dissolved metals), soil physical and chemical properties, and crop yields are monitored. The experimental design allows the comparison of four agricultural treatments: two compaction treatments (with and without soil compaction) and two conservation regimes (conventional and soil conservation agricultural practices), each regime being duplicated three times. Generalized Additive Mixed Model (GAMM) highlighted significant relations between the conservation regimes and suspended matter charges, and surface runoff. In other words, conservation practices allow a significant short-term reduction of suspended matter at the field scale. On the other hand, they appear to favour an increase of surface runoff in the springtime. Since only one three-year rotation cycle has been conducted, no effect was observed on soil properties, and crop yields. Long term impacts of soil conservation practices were estimated by implementing a physically based hydrologic model SWAT over each catchment. Restored soil properties were scenarized using measurements conducted over surrounding unperturbed sites. Modelling results suggest that a restoration of soil physical properties would translate into a moderate decrease surface runoff (-5%) at the field scale. The study brings an advanced and multidimensional understanding of the field-scale processes driving soil health, quantitative hydrology, and water quality. It also quantifies potential long-term benefits of implementing soil conservation practices.

After the COVID-19 pandemic, several companies and organisations are actively developing, scaling up and optimising mRNA manufacturing processes to produce their vaccines and therapeutics. Herein, we evaluated tangential flow filtration (TFF) for high-recovery, and high-purity separation of mRNA from unreacted NTPs in the IVT reaction mixture. For the first time, the membrane fouling behaviour caused by mRNA and separation of nucleoside triphosphates (NTPs) was mathematically modelled. The mRNA membrane fouling model is necessary for optimising mRNA separation processes, designing a suitable strategy for membrane clean-up, estimating the end of production life and reducing the process cost. Recovery greater than 70% mRNA without degradation was obtained by implementing a capacity load of ~19 g/m2, <2.5 psi transmembrane pressure (TMP) and feed flux of 300 LMH. This approach enables the purification of multiple RNA drug substance sequences for the prevention and treatment of a wide range of diseases.

Federated Distillation (FD) offers an innovative approach to distributed machine learning, leveraging knowledge distillation for efficient and flexible cross-device knowledge transfer without necessitating the upload of extensive model parameters to a central server. While FD has gained popularity, its vulnerability to poisoning attacks remains underexplored. To address this gap, we previously introduced FDLA (Federated Distillation Logits Attack), a method that manipulates logits communication to mislead and degrade the performance of client models. However, the impact of FDLA on participants with different identities and the effects of malicious modifications at various stages of knowledge transfer remain unexplored. To this end, we present PCFDLA (Peak-Controlled Federated Distillation Logits Attack), an advanced and more stealthy logits poisoning attack method for FD. PCFDLA enhances the effectiveness of FDLA by carefully controlling the peak values of logits to create highly misleading yet inconspicuous modifications. Furthermore, we introduce a novel metric for better evaluating attack efficacy, demonstrating that PCFDLA maintains stealth while being significantly more disruptive to victim models compared to its predecessors. Experimental results across various datasets confirm the superior impact of PCFDLA on model accuracy, solidifying its potential threat in federated distillation systems.

Neuroinflammation is a hallmark of various neurodegenerative disorders, yet effective treatments remain limited. This study investigates the neuroprotective potential of a Cannabis sativa L. (C.S) extract rich in cannabidiol (CBD) and containing THC, CBG, THCV, and β-caryophyllene in a lipopolysaccharide (LPS)-induced neuroinflammation Male mice model. The effects on anxiety-like behavior, cognitive function, and locomotor activity were assessed using behavioral tests (open field, elevated plus maze, novel object recognition, and Morris water maze). Antioxidant activity was measured by assaying glutathione (GSH) levels and lipid peroxidation by-products (TBARs). Anti-inflammatory properties were evaluated using quantitative reverse transcription polymerase chain reaction (QRt-PCR) for proinflammatory cytokines (IL-6 and TNF-α), glial fibrillary acidic protein (GFAP), and cannabinoid receptor 1 (CB1) mRNAs in the prefrontal cortex (PFC). Additionally, the metabolic activity of cortical astrocytes was assessed. Compared to synthetic CBD, the C.S extract (20.0 mg/kg) demonstrated superior efficacy in attenuating LPS-induced anxiety-like behavior, cognitive decline, and locomotor impairments. It also significantly mitigated oxidative stress (increased GSH, reduced TBARs) and suppressed pro-inflammatory cytokines and GFAP mRNAs, indicating potent anti-inflammatory properties. The extract modulated CB1 receptor expression and preserved metabolic homeostasis in cortical astrocytes, preventing their shift from glycolysis to oxidative phosphorylation under neuroinflammatory conditions. Deep learning approaches revealed that Delta-9-THC’s conformational change at residues Phe200 and Trp356 in the CB1 receptor synergistically enhances CBD’s effect. These findings highlight the therapeutic potential of C.S extract for managing neuroinflammatory disorders, warranting further investigation into its clinical applications and underlying mechanisms

The Internet of Things (IoT) generation has made sizeable improvements in diverse domains, including healthcare, transportation, agriculture, manufacturing, supply chain management, smart grid or electricity creation, and smart homes. IoT has dramatically altered how gadgets and networks communicate and interact, leading to endless advantages in these domains. However, the enormous adoption of IoT technology has also raised large protection and privacy concerns. This review paper affords a top-level view of IoT systems and networks’ safety and privacy challenges. It examines the susceptibilities that threaten data integrity, confidentiality, and availability in IoT ecosystems. Furthermore, it analyzes the inherent dangers associated with unauthorized entry, data breaches, identification theft, and surveillance. The paper explores possible solutions which include Cryptographic Algorithms, Machine Learning and Deep Learning, Blockchain algorithm privacy-improving technology, and Fog Computing technology designed to mitigate security and privacy concerns in IoT systems. Additionally, it discusses a multi-layered approach and best practices for ensuring sturdy security features in IoT deployments. By synthesizing current research findings in this subject matter from numerous sources consisting of educational literature and enterprise reports, this review intends to contribute to a higher understanding of the complicated outlook of the security and privacy issues in IoT.

As of today, TCP remains the de-facto transport protocol in the Internet. However, TCP may incur high delays, especially when retransmitting lost packets as they have to be retransmitted only by the source and after a timeout that is roughly equal to a round trip time. To reduce such delay, recent work [1–3] proposed to deploy a special network function, called Transport Assistant (TA), that is able to detect and retransmit lost TCP packets from inside the network rather than the source, and thereby, reduces retransmission delays. Unfortunately, there is no study on the impact of the placement of the TA on its performance benefits in terms of packet delivery delay. In this paper, we focus on the TA placement problem. We discuss the trade-offs and parameters to be considered to select the best placement for the TA. We first mathematically model the TCP packet delivery delay, i.e., the time needed to deliver TCP packets, when the TA is deployed. We also formulate, as an Integer Linear Program (ILP), the problem of placing multiple TAs in order to reduce TCP packet delivery delays while minimizing their deployment costs. We consider use-cases, one where a TA could handle a single flow and another where a TA could handle multiple flows. We then propose two heuristics to solve the problem with minimal execution time. Through experiments, we demonstrated that the deployment of TAs could reduce TCP packet delivery delays by up to 30% and could be leveraged to guide routing and load balancing. Moreover, we show that using the proposed heuristics for placing TAs could lead to performance that is close to optimal solutions obtained with the ILP but with lower execution time.

A novel strain of the genus Pseudomonas was isolated from a high-altitude volcano in Toluca, Mexico. This isolate, initially named Ps4, could grow well at 4 °C making it a psychrotolerant bacterium. Previous characterization of Ps4 revealed interesting characteristics related to plant growth promotion, including auxin production, siderophores, and phosphate solubilization capacity. In this work, the ability of Ps4 to stimulate the early development of Capsicum annuum plants without signs of phytopathogenicity was proven. A thorough whole genome analysis determined that isolate Ps4 belongs to the Pseudomonas fluorescens species complex, and specifically to the species P. marginalis with a Mash distance of 0.0118927 to the closest reference genome (RefSeq: GCF_007858175.1). The phylogenomic evidence allowed us to establish that it is a new strain denoted as P. marginalis BMH-2007. Noticeably, its genomic features were consistent with plant-growth promotion traits, we observed coding sequences for IAA biosynthesis, iron and phosphate transport, among others. As it is a species associated with phytopathogenicity, the virulence factors encoded by the genome were analyzed, which revealed the presence of a type VI secretion system, associated both with biological control and plant-growth promotion.

SARS-CoV-2, a positive-strand RNA virus, utilizes both genomic replication and subgenomic mRNA transcription. Whole genome sequencing (WGS) from clinical samples can estimate viral gene expression levels. We conducted WGS on 529 SARS-CoV-2 positive clinical samples from Assam and northeastern India to track viral emergence and assess gene expression patterns. Our results reveal differential expression across structural, non-structural, and accessory genes, with notable upregulation of the M gene, especially in the Omicron variant, followed by E and ORF6. The mean Transcript Per Million (TPM) expression levels of the M gene were significantly higher in Omicron variants (175611±46921), peaking in the KP.1/KP.2 sublineage (220493±34917), compared to the Delta variant (129717±33773). The relative fold change of M gene expression between Delta and Omicron 2024 subvariants showed a 1.6-fold change. Variant-wise gene expression analysis suggests a correlation between gene expression and viral mutation, impacting replication and transmission. As anticipated, the expression levels of genes surge with the increase in the virus mutation. The Chi-square trend for average substitution count vs. average TPM of the M gene was highly significant (72.78, p<0.0001). The M gene’s high expression and low mutation rate make it an ideal target for designing a real-time RT-PCR kit assay. These findings highlight the need for continuous surveillance and understanding of viral gene expression dynamics for effective COVID-19 management. Further studies are necessary to elucidate the significance of these observations in viral pathogenesis and transmission dynamics.

Aims: The primary objective was an evaluation of the correlation between the lymphocyte-to-C-reactive protein ratio (LCR) and the prevalence of overactive bladder (OAB) within the adult United States population. Methods: Data was collected from the National Health and Nutrition Examination Survey (2005‒2010), and involved a non-pregnant cohort aged ≥20 years of age, with available LCR index and OAB information. We employed weighted univariate and multivariate logistic regression to assess the link between LCR and OAB risk. Subgroup, examinations of interaction, and restricted cubic spline analyses were also performed. Results: In all, 13723 subjects were recruited for analysis. Among them, 14.99% exhibited OAB and the mean LCR value was 2.61±0.07. The OAB risk decreased with higher quartiles of LCR. In model 2, subjects in the highest LCR quartiles showed 48% (OR: 0.52, 95%CI: 0.38–0.71, P < 0.001) reductions in the OAB risk, relative to those in the lowest LCR quartiles. These negative associations were found to be independent of various factors, shown in the subgroup analyses and interaction tests. Additionally, based on the restricted cubic spline analyses, there was a negative, non-linear relationship between LCR and OAB risk.