Caenorhabditis elegans defective-pharynx and constipated mutants are resistant to Orsay virus infection.

C. elegans animals with a compromised pharynx accumulate bacteria in their intestinal lumen and activate a transcriptional response that includes anti-bacterial response genes. In this study, we demonstrate that animals with defective pharynxes are resistant to Orsay virus (OrV) infection. This resistance is observed for animals grown on Escherichia coli OP50 and on Comamonas BIGb0172, a bacterium naturally associated with C. elegans . The viral resistance observed in defective-pharynx mutants does not seem to result from constitutive transcriptional immune responses against viruses. OrV resistance is also observed in mutants with defective defecation, which share with the pharynx-defective perturbations in the regulation of their intestinal contents and altered lipid metabolism. The underlying mechanisms of viral resistance in pharynx- and defecation-defective mutants remain elusive.

Exact Results for Non-Newtonian Transport Properties in Sheared Granular Suspensions: Inelastic Maxwell Models and BGK-Type Kinetic Model.

The Boltzmann kinetic equation for dilute granular suspensions under simple (or uniform) shear flow (USF) is considered to determine the non-Newtonian transport properties of the system. In contrast to previous attempts based on a coarse-grained description, our suspension model accounts for the real collisions between grains and particles of the surrounding molecular gas. The latter is modeled as a bath (or thermostat) of elastic hard spheres at a given temperature. Two independent but complementary approaches are followed to reach exact expressions for the rheological properties. First, the Boltzmann equation for the so-called inelastic Maxwell models (IMM) is considered. The fact that the collision rate of IMM is independent of the relative velocity of the colliding spheres allows us to exactly compute the collisional moments of the Boltzmann operator without the knowledge of the distribution function. Thanks to this property, the transport properties of the sheared granular suspension can be exactly determined. As a second approach, a Bhatnagar-Gross-Krook (BGK)-type kinetic model adapted to granular suspensions is solved to compute the velocity moments and the velocity distribution function of the system. The theoretical results (which are given in terms of the coefficient of restitution, the reduced shear rate, the reduced background temperature, and the diameter and mass ratios) show, in general, a good agreement with the approximate analytical results derived for inelastic hard spheres (IHS) by means of Grad's moment method and with computer simulations performed in the Brownian limiting case (m/mg→∞, where mg and m are the masses of the particles of the molecular and granular gases, respectively). In addition, as expected, the IMM and BGK results show that the temperature and non-Newtonian viscosity exhibit an S shape in a plane of stress-strain rate (discontinuous shear thickening, DST). The DST effect becomes more pronounced as the mass ratio m/mg increases.

Microsurgical management of radicular cyst using guided tissue regeneration technique: A case report.

BACKGROUND: Radicular cyst is a lesion of odontogenic origin that arises from epithelial remains due to periapical periodontitis caused by inflammatory reactions generated at the apex of affected teeth with infected or necrotic pulps. The therapeutic management of radicular cysts is controversial. There is only one case report of enucleation of a radicular cyst managed with microsurgery and apicoectomy, but without the use of the guided tissue regeneration (GTR) technique in the same surgical procedure. The present clinical case describes the management of a radicular cyst with microsurgical approach, performance of an apicoectomy of the tooth associated with the entity, application of GTR technique, use of a resorbable membrane of type I bovine collagen, and bovine xenograft. CASE SUMMARY: A 68-year-old patient presented with a radicular cyst from an upper lateral incisor. The microsurgical management used was aimed at enucleating the chemical membrane, performing apicoectomy of the tooth along with careful and precise retrograde filling, and implementing GTR technique using a resorbable collagen membrane and bovine xenograft. The diagnosis of radicular cyst was confirmed using histopathological analysis. The patient underwent follow-up evaluations at 10 and 30 d postoperatively. At 4 months postoperative evaluation, she remained asymptomatic, and radiographs showed significant periapical healing with adequate bone formation. CONCLUSION: These results suggest that microsurgical management using the GTR technique with collagen membrane and xenograft, contributes to bone regeneration.

Barriers to early diagnosis and management of oral cancer in Latin America and the Caribbean.

OBJECTIVE: This study aimed to explore perceived barriers to early diagnosis and management of oral cancer, as well as potential pathways for improvement in Latin America and the Caribbean (LAC). METHODS: This cross-sectional study used a self-administered online questionnaire created via the Research Electronic Data Capture platform. The survey was distributed to health professionals trained in Oral Medicine, Oral Pathology, Oral and Maxillofacial Surgery, and Dentists with clinical and academic expertise in oral potentially malignant disorder (OPMD) and oral cancer. Data obtained were systematically organized and analyzed descriptively using Microsoft Excel. RESULTS: Twenty-three professionals from 21 LAC countries participated. Major barriers included the limited implementation of OPMD and oral cancer control plans (17.4%), low compulsory reporting for OPMD (8.7%) and oral cancer (34.8%), unclear referral pathways for OPMD (34.8%) and oral cancer (43.5%), and a shortage of trained professionals (8.7%). Participants endorsed the utility of online education (100%) and telemedicine (91.3%). CONCLUSION: The survey highlights major perceived barriers to early diagnosis and management of OPMD and oral cancer in LAC, as well as potential avenues for improvement.

Caenorhabditis elegans immune responses to microsporidia and viruses.

The model organism Caenorhabditis elegans is susceptible to infection by obligate intracellular pathogens, specifically microsporidia and viruses. These intracellular pathogens infect intestinal cells, or, for some microsporidia, epidermal cells. Strikingly, intestinal cell infections by viruses or microsporidia trigger a common transcriptional response, activated in part by the ZIP-1 transcription factor. Among the strongest activated genes in this response are ubiquitin-pathway members and members of the pals family, an intriguing gene family with cross-regulations of different members of genomic clusters. Some of the induced genes participate in host defense against the pathogens, for example through ubiquitin-mediated inhibition. Other mechanisms defend the host specifically against viral infections, including antiviral RNA interference and uridylation. These various immune responses are altered by environmental factors and by intraspecific genetic variation of the host. These pathogens were first isolated 15 years ago and much remains to be discovered using C. elegans genetics; also, other intracellular pathogens of C. elegans may yet to be discovered.

Proactive Decision Support for Glaucoma Treatment: Predicting Surgical Interventions with Clinically Available Data.

A longitudinal ophthalmic dataset was used to investigate multi-modal machine learning (ML) models incorporating patient demographics and history, clinical measurements, optical coherence tomography (OCT), and visual field (VF) testing in predicting glaucoma surgical interventions. The cohort included 369 patients who underwent glaucoma surgery and 592 patients who did not undergo surgery. The data types used for prediction included patient demographics, history of systemic conditions, medication history, ophthalmic measurements, 24-2 VF results, and thickness measurements from OCT imaging. The ML models were trained to predict surgical interventions and evaluated on independent data collected at a separate study site. The models were evaluated based on their ability to predict surgeries at varying lengths of time prior to surgical intervention. The highest performing predictions achieved an AUC of 0.93, 0.92, and 0.93 in predicting surgical intervention at 1 year, 2 years, and 3 years, respectively. The models were also able to achieve high sensitivity (0.89, 0.77, 0.86 at 1, 2, and 3 years, respectively) and specificity (0.85, 0.90, and 0.91 at 1, 2, and 3 years, respectively) at an 0.80 level of precision. The multi-modal models trained on a combination of data types predicted surgical interventions with high accuracy up to three years prior to surgery and could provide an important tool to predict the need for glaucoma intervention.

Efficacy of the use of video games on mood, anxiety and depression in stroke patients: preliminary findings of a randomised controlled trial.

BACKGROUND: In the different published studies, there is no consensus on the efficacy of virtual reality as an adjuvant treatment of mood states. AIM: The aim of this study is to evaluate the impact of no immersive virtual reality with the Nintendo Switch device in rehabilitation treatment on mood, anxiety and depression in stroke patients admitted to neurorehabilitation units. METHODS: Fifty-eight patients admitted to neurorehabilitation units underwent a 1:3 multicentre randomised clinical trial. The intervention group consisted of 17 patients and the control group of 41 patients. The intervention group performed 6 virtual reality sessions together with the conventional treatment, and the control group performed only the conventional rehabilitation sessions. Primary and secondary clinical outcomes were measured before and six weeks after the intervention. RESULTS: Comparing the intervention group and control group, the anxiety levels of the intervention group decreased compared to the results observed in the control group (p = 0.01), as did the dependence of the intervention group (0.015). On the other hand, the results obtained after the intervention by the control group for anxiety (0.479) and depression (0.292) were not statistically significant. CONCLUSION: Rehabilitation VR used as an adjuvant treatment to conventional treatment has a beneficial impact on the neurological status and state of anxiety of stroke patients admitted to neurorehabilitation units. TRIAL REGISTRATION: Registered in the https://clinicaltrials.gov/ repository (NTC NCT05143385). Protocol registration date 7 October 2021, retrospectively registered.

Genetic basis of Arabidopsis thaliana responses to infection by naïve and adapted isolates of turnip mosaic virus.

Plant viruses account for enormous agricultural losses worldwide, and the most effective way to combat them is to identify genetic material conferring plant resistance to these pathogens. Aiming to identify genetic associations with responses to infection, we screened a large panel of Arabidopsis thaliana natural inbred lines for four disease-related traits caused by infection by A. thaliana-naïve and -adapted isolates of the natural pathogen turnip mosaic virus (TuMV). We detected a strong, replicable association in a 1.5 Mb region on chromosome 2 with a 10-fold increase in relative risk of systemic necrosis. The region contains several plausible causal genes as well as abundant structural variation, including an insertion of a Copia transposon into a Toll/interleukin receptor (TIR-NBS-LRR) coding for a gene involved in defense, that could be either a driver or a consequence of the disease-resistance locus. When inoculated with TuMV, loss-of-function mutant plants of this gene exhibited different symptoms than wild-type plants. The direction and severity of symptom differences depended on the adaptation history of the virus. This increase in symptom severity was specific for infections with the adapted isolate. Necrosis-associated alleles are found worldwide, and their distribution is consistent with a trade-off between resistance during viral outbreaks and a cost of resistance otherwise, leading to negative frequency-dependent selection.

Increasing the accuracy of single-molecule data analysis using tMAVEN.

Time-dependent single-molecule experiments contain rich kinetic information about the functional dynamics of biomolecules. A key step in extracting this information is the application of kinetic models, such as hidden Markov models (HMMs), which characterize the molecular mechanism governing the experimental system. Unfortunately, researchers rarely know the physicochemical details of this molecular mechanism a priori, which raises questions about how to select the most appropriate kinetic model for a given single-molecule data set and what consequences arise if the wrong model is chosen. To address these questions, we have developed and used time-series modeling, analysis, and visualization environment (tMAVEN), a comprehensive, open-source, and extensible software platform. tMAVEN can perform each step of the single-molecule analysis pipeline, from preprocessing to kinetic modeling to plotting, and has been designed to enable the analysis of a single-molecule data set with multiple types of kinetic models. Using tMAVEN, we have systematically investigated mismatches between kinetic models and molecular mechanisms by analyzing simulated examples of prototypical single-molecule data sets exhibiting common experimental complications, such as molecular heterogeneity, with a series of different types of HMMs. Our results show that no single kinetic modeling strategy is mathematically appropriate for all experimental contexts. Indeed, HMMs only correctly capture the underlying molecular mechanism in the simplest of cases. As such, researchers must modify HMMs using physicochemical principles to avoid the risk of missing the significant biological and biophysical insights into molecular heterogeneity that their experiments provide. By enabling the facile, side-by-side application of multiple types of kinetic models to individual single-molecule data sets, tMAVEN allows researchers to carefully tailor their modeling approach to match the complexity of the underlying biomolecular dynamics and increase the accuracy of their single-molecule data analyses.

Insights into estrogen impact in oral health & microbiome in COVID-19.

BACKGROUND: COVID-19 emerged in late 2019 and has occasioned more than 765 millions cumulative cases and 6.9 millions of deaths globally. Notably, around 70% of patients with severe COVID-19 are men. Therefore, it is to be presumed that women have a hormonal protector factor in inflammation and ACE2 expression. On the other hand, oral health status, and local microbiome can be key factors to respiratory viral infections control. Nevertheless, it has been poorly investigated. In our study 20 premenopausal, 18 postmenopausal and 22 men with COVID-19 were included. Oral health status, viral load, lingual ACE2 expression, as well as microbiome, estrogens and cytokines in saliva were analyzed. RESULTS: Our results showed a lower expression of ACE2 in tongue cells of postmenopausal compared with premenopausal (p = 0.05), and a strong negative correlation between saliva estrogen and viral load (r = -0.76; p = 0.001). Respect to IFN-γ (p = 0.05), IL-1ß, TNF-α, IL-18, and IL-23 levels were increased in postmenopausal. Oral microbiome signature of premenopausal was characterized by Prevotella melaninogenica (Log2 = 26.68; p = 1.34e-10), Haemophilus (Log2 = 23.99; p = 2.96e-9), and Alloprevotella (Log2 = 7.92; p = 0.0001). On the other hand, Leptotrichia (Log2 = -18.74; p = 0.001), Tanerella (Log2 = -17.08; p = 0.004), and Clostridiales (Log2 = -2.88; p = 0.04) represented the poor oral health group compared with the adequate group which was enriched with the commensal microorganism Neisseria perflava (Log2 = 26.70; p = 1.74e-7). Furthermore, the high viral load group was characterized by Prevotella nanceiensis (Log2 = 19.60; p = 6.06e-8), Prevotella melaninogenica (Log2 = 21.45; p = 9.59e-6), Alloprevotella (Log2 = 23.50; p = 2.70e-7) and bacteria from the red complex Porphyromonas endodentalis (Log2 = 21.97; p = 1.38e-7). CONCLUSIONS: Postmenopausal and men have a poor oral health status which could be related to a detrimental progression of COVID-19 also linked to a lower expression of ACE2, lower saliva estrogen levels and oral dysbiosis. Nevertheless, functional studies are required for a deeper knowledge.

Naturally-associated bacteria modulate Orsay virus infection of Caenorhabditis elegans.

Microbes associated with an organism can significantly modulate its susceptibility to viral infections, but our understanding of the influence of individual microbes remains limited. The nematode Caenorhabditis elegans is a model organism that in nature inhabits environments rich in bacteria. Here, we examine the impact of 71 naturally associated bacteria on C. elegans susceptibility to its only known natural virus, the Orsay virus. Our findings reveal that viral infection of C. elegans is significantly influenced by monobacterial environments. Compared to an Escherichia coli environmental reference, the majority of tested bacteria reduced C. elegans susceptibility to viral infection. This reduction is not caused by virion degradation or poor animal nutrition by the bacteria. The repression of viral infection by the bacterial strains Chryseobacterium JUb44 and Sphingobacterium BIGb0172 does not require the RIG-I homolog DRH-1, which is known to activate antiviral responses such as RNA interference and transcriptional regulation. Our research highlights the necessity of considering natural biotic environments in viral infection studies and opens the way future research on host-microbe-virus interactions.

Diabetic individuals with COVID-19 exhibit reduced efficacy of gliptins in inhibiting dipeptidyl peptidase 4 (DPP4). A suggested explanation for increased COVID-19 susceptibility in patients with type 2 diabetes mellitus (T2DM).

AIMS: Dipeptidyl peptidase 4 (DPP4) has been proposed as a coreceptor for SARS-CoV-2 cellular entry. Considering that type 2 diabetes mellitus (T2DM) has been identified as the most important risk factor for SARS-CoV-2, and that gliptins (DPP4 inhibitors) are a prescribed diabetic treatment, this study aims to unravel the impact of DPP4 in the intersection of T2DM/COVID-19. MATERIALS AND METHODS: We analyzed 189 serum human samples, divided into six clinical groups (controls, T2DM, T2DM + gliptins, COVID-19, COVID-19 + T2DM, and COVID-19 + T2DM + gliptins), measuring DPP4 protein concentration and activity by Western blot, ELISA, and commercial activity kits. The obtained results were verified in Huh-7 cellular models. KEY FINDINGS: Both DPP4 concentration and activity were decreased in COVID-19 patients, and as in T2DM patients, compared to controls. Despite these lower levels, the ratio of DPP4 activity/concentration in COVID-19 sera was the highest (0.782 ± 0.289 µU/ng vs. 0.547 ± 0.050 µU/ng in controls, p < 0.0001), suggesting a compensating mechanism in these patients. Supernatants of Huh-7 cells incubated with COVID-19 serum showed a consistent and significantly lower DPP4 concentration and activity. Furthermore, COVID-19 + T2DM + gliptins patients showed a higher serum DPP4 concentration and activity than T2DM + gliptin subjects (p < 0.05), indicating that sera from COVID-19 convalescents interfere with gliptins. SIGNIFICANCE: Either SARS-CoV-2 or some metabolites present in the sera of COVID-19-convalescent patients interact with soluble DPP4 or even gliptins themselves since the inhibitory effect of gliptins on DPP4 activity is being prevented. The interactions between DPP4, gliptins, and SARS-CoV-2 should be further elucidated to reveal the mechanism of action for these interesting observations.

Increasing the accuracy of single-molecule data analysis using tMAVEN.

Time-dependent single-molecule experiments contain rich kinetic information about the functional dynamics of biomolecules. A key step in extracting this information is the application of kinetic models, such as hidden Markov models (HMMs), which characterize the molecular mechanism governing the experimental system. Unfortunately, researchers rarely know the physico-chemical details of this molecular mechanism a priori, which raises questions about how to select the most appropriate kinetic model for a given single-molecule dataset and what consequences arise if the wrong model is chosen. To address these questions, we have developed and used time-series Modeling, Analysis, and Visualization ENvironment (tMAVEN), a comprehensive, open-source, and extensible software platform. tMAVEN can perform each step of the single-molecule analysis pipeline, from pre-processing to kinetic modeling to plotting, and has been designed to enable the analysis of a single-molecule dataset with multiple types of kinetic models. Using tMAVEN, we have systematically investigated mismatches between kinetic models and molecular mechanisms by analyzing simulated examples of prototypical single-molecule datasets exhibiting common experimental complications, such as molecular heterogeneity, with a series of different types of HMMs. Our results show that no single kinetic modeling strategy is mathematically appropriate for all experimental contexts. Indeed, HMMs only correctly capture the underlying molecular mechanism in the simplest of cases. As such, researchers must modify HMMs using physico-chemical principles to avoid the risk of missing the significant biological and biophysical insights into molecular heterogeneity that their experiments provide. By enabling the facile, side-by-side application of multiple types of kinetic models to individual single-molecule datasets, tMAVEN allows researchers to carefully tailor their modeling approach to match the complexity of the underlying biomolecular dynamics and increase the accuracy of their single-molecule data analyses.

Impact of virtual reality-based therapy on post-stroke depression: A systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Post-stroke depression is the most common neuropsychiatric consequence and reduces rehabilitation effectiveness. However, the efficacy of virtual reality (VR) on mental health treatment for patients after a stroke is uncertain. AIMS: The aim of this study was to evaluate the efficacy of VR as a co-adjuvant form of treatment to reduce depression in stroke patients admitted to neurorehabilitation units. METHODS: We systematically searched medical databases including PubMed, CINAHL, PsycINFO, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov from inception to November 16, 2023. Clinical trials comparing the use of VR as an adjuvant form of treatment in stroke patients' rehabilitation with the usual treatment were included. Pooled standardized mean differences were calculated using a random-effects model. Subgroup analyses were performed according to type of stroke, VR characteristics, and the scale used to measure depression. Meta-regression analysis was performed for intervention duration and to determine the mean age of the participants. RESULTS: Eight studies and 388 stroke patients were included. The VR interventions were associated with a lower risk of depression in patients (ES = -0.69; 95% CI [-1.05, -0.33]; I2 = 57.6%; p ≤ .02). The estimates were not affected by the type of stroke, the type of VR used, the blinding process, the type of scale used to detect depression, the duration of the intervention (weeks and minutes), and the total number of sessions. Meta-regression shows that younger samples (p = .00; 95% CI [0.01, 0.08) and longer interventions (p = < .05; 95% CI [-0.00, -0.00) lead to a greater reduction in depression. LINKING EVIDENCE TO ACTION: This review provides an important basis for treating depression in patients after a stroke. Professionals working in stroke neurorehabilitation units should consider VR as a form of co-adjuvant treatment for depression in patients. SYSTEMATIC REVIEW REGISTRATION: CRD42022303968.

Oral Pyogenic Granuloma: A Narrative Review.

Pyogenic granuloma (PG) is a benign vascular lesion found predominantly in the oral cavity. Characterized by rapid growth and propensity to bleed, PG presents diagnostic challenges due to its similarity and alarming proliferation. This narrative review synthesizes current knowledge on the epidemiology, etiopathogenesis, clinical manifestations, and management of oral PG, with emphasis on recent advances in diagnostic and therapeutic approaches. The epidemiology of the injury is meticulously analyzed, revealing a higher incidence in women and a wide range of ages of onset. It delves into the etiopathogenesis, highlighting the uncertainty surrounding the exact causal factors, although historical attributions suggest an infectious origin. It exhaustively analyzes the clinical and histopathological aspects of oral PG, offering information on its various presentations and the importance of an accurate diagnosis to guide effective treatment. It details treatment strategies, emphasizing the personalized approach based on individual patient characteristics. This comprehensive review consolidates current knowledge on oral PG, highlighting the need for further research to clarify its pathogenesis and optimize treatment protocols.

Integrating clinical research in an operative screening and diagnostic breast imaging department: First experience, results and perspectives using microwave imaging.

Rationale and objectives: Clinical research is crucial for evaluating new medical procedures and devices. It is important for healthcare units and hospitals to minimize the disruptions caused by conducting clinical studies; however, complex clinical pathways require dedicated recruitment and study designs.This work presents the effective introduction of novel microwave breast imaging (MBI), via MammoWave apparatus, into the clinical routine of an operative screening and diagnostic breast imaging department for conducting a multicentric clinical study. Materials and methods: Microwave breast imaging, using MammoWave apparatus, was performed on volunteers coming from different clinical pathways. Clinical data, comprising demographics and conventional radiologic reports (used as reference standard), was collected; a satisfaction questionnaire was filled by every volunteer. Microwave images were analyzed by an automatic clinical decision support system, which quantified their corresponding features to discriminate between breasts with no relevant radiological findings (NF) and breasts with described findings (WF). Results: Conventional breast imaging (DBT, US, MRI) and MBI were performed and adapted to assure best clinical practices and optimum pathways. 180 volunteers, both symptomatic and asymptomatic, were enrolled in the study. After microwave images' quality assessment, 48 NF (15 dense) and 169 WF (88 dense) breasts were used for the prospective study; 48 (18 dense) breasts suffered from a histology-confirmed carcinoma. An overall sensitivity of 85.8 % in breasts lesions' detection was achieved by the microwave imaging apparatus. Conclusion: An optimum recruitment strategy was implemented to assess MBI. Future trials may show the clinical usefulness of microwave imaging, which may play an important role in breast screening.

The mechanism of mRNA activation.

During translation initiation, messenger RNA molecules must be identified and activated for loading into a ribosome. In this rate-limiting step, the heterotrimeric protein eukaryotic initiation factor eIF4F must recognize and productively interact with the 7-methylguanosine cap at the 5' end of the messenger RNA and subsequently activate the message. Despite its fundamental, regulatory role in gene expression, the molecular events underlying cap recognition and messenger RNA activation remain mysterious. Here, we generate a unique, single-molecule fluorescence imaging system to interrogate the dynamics with which eIF4F discriminates productive and non-productive locations on full-length, native messenger RNA molecules. At the single-molecule level, we observe stochastic sampling of eIF4F along the length of the messenger RNA and identify allosteric communication between the eIF4F subunits which ultimately drive cap-recognition and subsequent activation of the message. Our experiments uncover novel functions for each subunit of eIF4F and we conclude by presenting a model for messenger RNA activation which precisely defines the composition of the activated message. This model provides a general framework for understanding how messenger RNA molecules may be discriminated from one another, and how other RNA-binding proteins may control the efficiency of translation initiation.

Viral strain-dependent impact of plant developmental stages on the nestedness and modularity of plant-virus interaction matrices.

This study examines the specificity of adaptation of lineages of turnip mosaic virus that were experimentally evolved from naïve and preadapted strains to Arabidopsis thaliana plants at various plant developmental stages. We conducted a cross-infection experiment involving three plant developmental stages and assessed the progression of disease and symptoms. We found a significative interaction between the host developmental stage where the virus evolved and the host developmental stage in which the virus was tested. The analysis of the resulting interaction matrices revealed significant nestedness for viruses evolved from the naïve strain, but not for those originating from the preadapted one. Furthermore, there was an absence of modularity across all matrices. Our findings suggest that the past adaptation history of the ancestral strain influences its future evolution, and each plant developmental stage imposes unique selective constraints. The study highlights the complexity of host-parasite interactions and the potential influence of the host's developmental stage on viral adaptation.

Diffusion of intruders in granular suspensions: Enskog theory and random walk interpretation.

The Enskog kinetic theory is applied to compute the mean square displacement of impurities or intruders (modeled as smooth inelastic hard spheres) immersed in a granular gas of smooth inelastic hard spheres (grains). Both species (intruders and grains) are surrounded by an interstitial molecular gas (background) that plays the role of a thermal bath. The influence of the latter on the motion of intruders and grains is modeled via a standard viscous drag force supplemented by a stochastic Langevin-like force proportional to the background temperature. We solve the corresponding Enskog-Lorentz kinetic equation by means of the Chapman-Enskog expansion truncated to first order in the gradient of the intruder number density. The integral equation for the diffusion coefficient is solved by considering the first two Sonine approximations. To test these results, we also compute the diffusion coefficient from the numerical solution of the inelastic Enskog equation by means of the direct simulation Monte Carlo method. We find that the first Sonine approximation generally agrees well with the simulation results, although significant discrepancies arise when the intruders become lighter than the grains. Such discrepancies are largely mitigated by the use of the second Sonine approximation, in excellent agreement with computer simulations even for moderately strong inelasticities and/or dissimilar mass and diameter ratios. We invoke a random walk picture of the intruders' motion to shed light on the physics underlying the intricate dependence of the diffusion coefficient on the main system parameters. This approach, recently employed to study the case of an intruder immersed in a granular gas, also proves useful in the present case of a granular suspension. Finally, we discuss the applicability of our model to real systems in the self-diffusion case. We conclude that collisional effects may strongly impact the diffusion coefficient of the grains.

Thermal evaluation of plant biomass from the phytostabilisation of soils contaminated by potentially toxic elements.

The combination of phytoremediation of soils contaminated by potentially toxic elements with energy production by combustion of the generated biomass can be a sustainable land management option, combining the production of renewable bioenergy with soil restoration while minimising energy consumption and CO2 emission. In this work, plant biomass from phytoremediation of soils contaminated by potentially toxic elements was studied as solid biofuel for combustion by thermal analysis and biomass composition. Six plant species were grown in two soils with differing degrees of contamination: Brassica juncea, Cynara cardunculus, Atriplex halimus, Nicotiana glauca, Dittrichia viscosa, Retama sphaerocarpa and Salvia rosmarinus. The composition of the plant biomass was characterised chemically and thermogravimetric analyses were performed for the mass loss (TG), derivative curves of mass loss (DTG) and temperature difference (DTA) signal. The cellulose concentration correlated with the parameters of the thermal analysis in the low temperature range (150-350 °C), while lignin correlated with the thermal parameters of the second peak in the high temperature range. Salvia rosmarinus and R. sphaerocarpa showed the best combustion characteristics according to the thermal profile and mineral residue results. The accumulation of potentially toxic elements in B. juncea grown in heavily contaminated soil led to a higher amount of residue at 750 °C, with a global activation energy lower than the one obtained when this species was grown in a soil with lower contamination. Therefore, the most beneficial combination of soil phytoremediation and energy production (combustion) that can be suggested would depend on the level of soil contamination: in heavily contaminated soil, phytostabilisation using R. sphaerocarpa and S. rosmarinus; in slightly contaminated soil, B. juncea due to its high energy of activation, although the concentrations of potentially toxic elements in the residue must be controlled, as well as possible particulate matter emissions during combustion.