Accessible Domains Research Articles

Free-energy transduction mechanisms shape the flux space of metabolic networks

The transduction of free energy in metabolic networks represents a thermodynamic mechanism by which the free energy derived from nutrients is converted to drive nonspontaneous, energy-requiring metabolic reactions. This transduction is typically observed in processes that generate energy-rich molecules such as ATP and NAD(P)H, which, in turn, power specific reactions, particularly biosynthetic reactions. This property establishes a pivotal connection between the intricate topology of metabolic networks and their ability to redirect energy for functional purposes. The present study proposes a dedicated framework aimed at exploring the relationship between free-energy dissipation, network topology, and metabolic objectives. The starting point is that, regardless of the network topology, nonequilibrium chemostatting conditions impose stringent thermodynamic constraints on the feasible flux steady states to satisfy energy and entropy balances. An analysis of randomly sampled reaction networks shows that the network topology imposes additional constraints that restrict the accessible flux solution space, depending on key structural features such as the reaction’s molecularity, reaction cycles, and conservation laws. Notably, topologies featuring multimolecular reactions that implement free-energy transduction mechanisms tend to extend the accessible flux domains, facilitating the achievement of metabolic objectives such as anabolic flux maximization or flux rerouting capacity. This approach is applied to a coarse-grained model of carbohydrate metabolism, highlighting the structural requirements for optimal biomass yield.

Isolation and chemical immobilization of E. coli‐specific bacteriophage with NH2‐MIL‐101(Fe) MOF, a high photoluminescence rod‐shaped microcrystals for low‐level bacteria detection

As concern raised by the World Health Organization (WHO) of antibiotic‐resistant and bio‐defensive bacteria, a metal–organic framework (MOF) based optical biosensor came into consideration for precise, quick, and sensitive detection of Escherichia coli (E. coli) (ATCC 10799) using bacteriophage as a bio‐recognition element. In the present study, amine‐functionalized Fe‐based MOF, i.e., NH2‐MIL‐101(Fe), was synthesized by the solvothermal method (approx. 531–1106 nm in size and 20 mV zeta potentials by DLS) and further characterized by SEM, XRD, ATR‐FTIR, UV–VIS, and photoluminescent (PL) spectroscopy. The lytic bacteriophage was isolated from a sewage sample, purified, and concentrated using the ultra‐centrifugation method and achieved a high titer of 7.3 × 1012 PFU/ml. The concentration, stability, and accessible receptor binding domains (RBDs) of the biorecognition element for binding with their analytes play an important role in developing sensitive and specific biosensor systems. To fulfill the mentioned criteria, optimized glutaraldehyde concentration was estimated at 0.25%, at 30 °C for conjugating maximum bacteriophage titer of 8.6 × 105 PFU/ml for each 1 mg amine functionalized iron‐based MOF. The synthesized detection probe has shown excellent photoluminescence and antibacterial activity and achieved a detection limit of 652 CFU/ml over a bacterial detection concentration range from 5.78 × 101 to 5.78 × 106 CFU/ml for E. coli with 10–12 min of response time, high specificity, and long‐term stability even at room temperature. Therefore, it can be inferred that this MOF‐based strategy can be helpful in the specific and sensitive detection of various bacterial pathogens using bacteriophage as a bio‐recognition element.

Liquid phase oxidation of cyclopentanone over metal-free carbon catalysts

Oxidation of cyclopentanone (CPO) was studied over metal-free carbon catalysts in solvent-free conditions. Norit SX plus (900 m2 g−1) and Vulcan XC72 (240 m2 g−1) were used as pristine materials. Catalysts were prepared by activation in nitrogen and by treatment with urea, calcined in nitrogen at 500 °C and 950 °C. SEM, EDX, XPS, X-ray powder diffraction, nitrogen adsorption–desorption isotherms, acid–base titration, FTIR and Raman techniques were used for characterization. Catalytic tests carried out at 0.45 MPa and 80–110 °C enabled to find the best results: 30% selectivity to glutaric acid (GA) and 3% to succinic acid at 25% conversion of CPO, over Vulcan type catalysts. A low activity, selectivity and strong deactivation of Norit type catalysts can be ascribed to a higher specific surface in a disordered arrangement providing a high concentration of unselective catalytic sites. A higher extent of side reactions can yield in higher molar mass products, which stick on the surface and block catalytic sites. Optimum amount of accessible planar domains, represented by graphene and/or graphitic plates with some disorders present in Vulcan type catalysts classified them superior to Norit catalysts. A redox reaction scheme of oxidation of CPO to GA is also proposed.Graphical abstract

Applied Artificial Intelligence and Prospect of Internet of Everything (IoE) for the Detection of Tuberculosis

ABSTRACT Mycobacterium tuberculosis is a bacterium that causes disease known as Tuberculosis. Tuberculosis is highly contagious and can result in high mortality rate if left untreated. In order to screen individuals suspected of the disease, medical expert relies on several conventional approaches which are hindered by several limitations which include time consuming, high workload, false positive results, etc. This calls for the need to develop smart and automatic approaches that can address these challenges. Majority of existing studies reported the use of 1 or 2 pretrained models and the use of SoftMax as the classifier. Moreover, majority of the studies trained models using a single type of dataset which are mostly curated from public accessible domains. Thus, this study addressed these challenges by: (1) The use of several pretrained models (2) The use of 2 classifiers which include SVM and KNN and (3) Training and validating pretrained models fused with classifiers on microscopic slide and chest X-ray images. The result achieved in this study highlights the prospect of computer-assisted techniques in triaging and screening of TB. The integration of Internet of Everything (IoE) in medical diagnosis has the potential to increase healthcare outcome, boost productivity, and reduce workload.

Teaching vocabulary through invoking conceptually accessible epistemic domains

ABSTRACT While existing research into L2 classroom discourse has highlighted how knowledge and knowledge positions are negotiated, no attention seems to have been paid to what might be called conceptual epistemic domains. Drawing on 22 hours of video-recorded data from Iranian EFL classrooms, our conversation analytic study illustrates how invoking conceptually accessible epistemic domains creates interactional space for teachers to introduce conditionally relevant vocabulary. We define conceptual epistemic domains as areas of world knowledge to which learners have ostensible access. The bottom-up analysis of our data showcases that such practices serve two pivotal pedagogical functions: (1) providing contingent assistance; (2) initiating and maintaining warm-up activities. In the former, the teachers invoke the conceptually accessible epistemic domain facing learners’ claims of insufficient knowledge to elicit relevant further talk. In the latter, the teachers sequentially build up a shared conceptual access to conduct a warm-up activity. Both cases unfold interactional space where relevant vocabulary items emerge as learnables. We argue that by bringing to the fore what is conceptually accessible to the learners, teachers orient to relevant vocabulary items as teachable objects. Our findings thus shine light on an interactional resource through which the need for vocabulary teaching is talked into being.

Candidate Biomarkers for Targeting in Type 1 Diabetes; A Bioinformatic Analysis of Pancreatic Cell Surface Antigens.

Type 1 diabetes (T1Ds) is an autoimmune disease in which the immune system invades and destroys insulin-producing cells. Nevertheless, at the time of diagnosis, about 30-40% of pancreatic beta cells are healthy and capable of producing insulin. Bi-specific antibodies, chimeric antigen receptor regulatory T cells (CAR-Treg cells), and labeled antibodies could be a new emerging option for the treatment or diagnosis of type I diabetic patients. The aim of the study is to choose appropriate cell surface antigens in the pancreas tissue for generating an antibody for type I diabetic patients. In this bioinformatics study, we extracted pancreas-specific proteins from two large databases; the Human Protein Atlas (HPA) and Genotype-Tissue Expression (GTEx) Portal. Pancreatic-enriched genes were chosen and narrowed down by Protter software for the investigation of accessible extracellular domains. The immunohistochemistry (IHC) data of the protein atlas database were used to evaluate the protein expression of selected antigens. We explored the function of candidate antigens by using the GeneCards database to evaluate the potential dysfunction or activation/hyperactivation of antigens after antibody binding. The results showed 429 genes are highly expressed in the pancreas tissue. Also, eighteen genes encoded plasma membrane proteins that have high expression in the microarray (GEO) dataset. Our results introduced four structural proteins, including NPHS1, KIRREL2, GP2, and CUZD1, among all seventeen candidate proteins. The presented antigens can potentially be used to produce specific pancreatic antibodies that guide CARTreg, bi-specific, or labeling molecules to the pancreas for treatment, detection, or other molecular targeted therapy scopes for type I diabetes.

The Unpronounced Support: Wives’ Engagement in Sustaining Fishers’ Households Enterprises in the Fishery Challenging Space in Kilwa Kisiwani, Tanzania

In contrast to convenient and easily accessible productive domains such as agriculture, where women’s participation is deemed indispensable and visible, their engagement in households’ fishing enterprises has been grossly undervalued. However, the 2021 Kilwa Kisiwani study found that fishermen’s wives were vital to their families’ fishing businesses in the face of declining fish stocks and government moratoriums. This cross-sectional study that involved 221 respondents targeted fishing-dependent households where both husbands and wives were involved, with the latter making a substantial amount. Data gathering, analysis, interpretation and discussion employed qualitative and quantitative methods. The findings indicate that most households’ fishing enterprises survived because spouses provided labour, worked outside the home or supported the enterprise from home. The paper cautions against the notion that male dominance invariably determines the socioeconomic trajectory of households while women are relegated to subordinate roles. This perspective is deemed an oversimplification and a distortion of reality.

Heat kernels and Hardy spaces on non-tangentially accessible domains with applications to global regularity of inhomogeneous Dirichlet problems

Let n ≥ 2 n\ge 2 and Ω \Omega be a bounded non-tangentially accessible domain (for short, NTA domain) of R n \mathbb {R}^n . Assume that L D L_D is a second-order divergence form elliptic operator having real-valued, bounded, measurable coefficients on L 2 ( Ω ) L^2(\Omega ) with the Dirichlet boundary condition. The main aim of this article is threefold. First, the authors prove that the heat kernels { K t L D } t > 0 \{K_t^{L_D}\}_{t>0} generated by L D L_D are Hölder continuous. Second, for any p ∈ ( 0 , 1 ] p\in (0,1] , the authors introduce the ‘geometrical’ Hardy space H r p ( Ω ) H^p_r(\Omega ) by restricting any element of the Hardy space H p ( R n ) H^p(\mathbb {R}^n) to Ω \Omega and the authors show that, when p ∈ ( n n + δ 0 , 1 ] p\in (\frac {n}{n+\delta _0},1] , H r p ( Ω ) = H p ( Ω ) = H L D p ( Ω ) H^p_r(\Omega )=H^p(\Omega )=H^p_{L_D}(\Omega ) with equivalent quasi-norms, where H p ( Ω ) H^p(\Omega ) and H L D p ( Ω ) H^p_{L_D}(\Omega ) respectively denote the Hardy space on Ω \Omega and the Hardy space associated with L D L_D and where δ 0 ∈ ( 0 , 1 ] \delta _0\in (0,1] is the critical index of the Hölder continuity for the kernels { K t L D } t > 0 \{K_t^{L_D}\}_{t>0} . Third, as applications, the authors obtain the global gradient estimates in both L p ( Ω ) L^p(\Omega ) , with p ∈ ( 1 , p 0 ) p\in (1,p_0) , and H z p ( Ω ) H^p_z(\Omega ) , with p ∈ ( n n + 1 , 1 ] p\in (\frac {n}{n+1},1] , for the inhomogeneous Dirichlet problem of second-order divergence form elliptic equations on bounded NTA domains, where p 0 ∈ ( 2 , ∞ ) p_0\in (2,\infty ) is a constant depending only on n n , Ω \Omega , and the coefficient matrix of L D L_D . Here, the ‘geometrical’ Hardy space H z p ( Ω ) H^p_z(\Omega ) is defined by restricting any element of the Hardy space H p ( R n ) H^p(\mathbb {R}^n) supported in Ω ¯ \overline {\Omega } to Ω \Omega , where Ω ¯ \overline {\Omega } denotes the closure of Ω \Omega in R n \mathbb {R}^n . It is worth pointing out that the range p ∈ ( 1 , p 0 ) p\in (1,p_0) for the global gradient estimate in the scale of Lebesgue spaces L p ( Ω ) L^p(\Omega ) is sharp and the above results are established without any additional assumptions on both the coefficient matrix of L D L_D and the domain Ω \Omega .

Cost-effective framework for gradual domain adaptation with multifidelity

In domain adaptation, when there is a large distance between the source and target domains, the prediction performance will degrade. Gradual domain adaptation is one of the solutions to such an issue, assuming that we have access to intermediate domains, which shift gradually from the source to the target domain. In previous works, it was assumed that the number of samples in the intermediate domains was sufficiently large; hence, self-training was possible without the need for labeled data. If the number of accessible intermediate domains is restricted, the distances between domains become large, and self-training will fail. Practically, the cost of samples in intermediate domains will vary, and it is natural to consider that the closer an intermediate domain is to the target domain, the higher the cost of obtaining samples from the intermediate domain is. To solve the trade-off between cost and accuracy, we propose a framework that combines multifidelity and active domain adaptation. The effectiveness of the proposed method is evaluated by experiments with real-world datasets.

Unique Device Identification–Based Linkage of Hierarchically Accessible Data Domains in Prospective Surgical Hospital Data Ecosystems: User-Centered Design Approach

The electronic health record (EHR) targets systematized collection of patient-specific, electronically stored health data. The EHR is an evolving concept driven by ongoing developments and open or unclear legal issues concerning medical technologies, cross-domain data integration, and unclear access roles. Consequently, an interdisciplinary discourse based on representative pilot scenarios is required to connect previously unconnected domains. We address cross-domain data integration including access control using the specific example of a unique device identification (UDI)-expanded hip implant. In fact, the integration of technical focus data into the hospital information system (HIS) is considered based on surgically relevant information. Moreover, the acquisition of social focus data based on mobile health (mHealth) is addressed, covering data integration and networking with therapeutic intervention and acute diagnostics data. In addition to the additive manufacturing of a hip implant with the integration of a UDI, we built a database that combines database technology and a wrapper layer known from extract, transform, load systems and brings it into a SQL database, WEB application programming interface (API) layer (back end), interface layer (rest API), and front end. It also provides semantic integration through connection mechanisms between data elements. A hip implant is approached by design, production, and verification while linking operation-relevant specifics like implant-bone fit by merging patient-specific image material (computed tomography, magnetic resonance imaging, or a biomodel) and the digital implant twin for well-founded selection pairing. This decision-facilitating linkage, which improves surgical planning, relates to patient-specific postoperative influencing factors during the healing phase. A unique product identification approach is presented, allowing a postoperative read-out with state-of-the-art hospital technology while enabling future access scenarios for patient and implant data. The latter was considered from the manufacturing perspective using the process manufacturing chain for a (patient-specific) implant to identify quality-relevant data for later access. In addition, sensor concepts were identified to use to monitor the patient-implant interaction during the healing phase using wearables, for example. A data aggregation and integration concept for heterogeneous data sources from the considered focus domains is also presented. Finally, a hierarchical data access concept is shown, protecting sensitive patient data from misuse using existing scenarios. Personalized medicine requires cross-domain linkage of data, which, in turn, require an appropriate data infrastructure and adequate hierarchical data access solutions in a shared and federated data space. The hip implant is used as an example for the usefulness of cross-domain data linkage since it bundles social, medical, and technical aspects of the implantation. It is necessary to open existing databases using interfaces for secure integration of data from end devices and to assure availability through suitable access models while guaranteeing long-term, independent data persistence. A suitable strategy requires the combination of technical solutions from the areas of identity and trust, federated data storage, cryptographic procedures, and software engineering as well as organizational changes.

Perturbation of elliptic operators in 1-sided NTA domains satisfying the capacity density condition

Abstract Let Ω ⊂ ℝ n + 1 {\Omega\subset\mathbb{R}^{n+1}} , n ≥ 2 {n\geq 2} , be a 1-sided non-tangentially accessible domain (aka uniform domain), that is, Ω satisfies the interior Corkscrew and Harnack chain conditions, which are respectively scale-invariant/quantitative versions of openness and path-connectedness. Let us assume also that Ω satisfies the so-called capacity density condition, a quantitative version of the fact that all boundary points are Wiener regular. Consider L 0 ⁢ u = - div ⁢ ( A 0 ⁢ ∇ ⁡ u ) {L_{0}u=-\mathrm{div}(A_{0}\nabla u)} , L ⁢ u = - div ⁢ ( A ⁢ ∇ ⁡ u ) {Lu=-\mathrm{div}(A\nabla u)} , two real (non-necessarily symmetric) uniformly elliptic operators in Ω, and write ω L 0 {\omega_{L_{0}}} , ω L {\omega_{L}} for the respective associated elliptic measures. The goal of this program is to find sufficient conditions guaranteeing that ω L {\omega_{L}} satisfies an A ∞ {A_{\infty}} -condition or a RH q {\mathrm{RH}_{q}} -condition with respect to ω L 0 {\omega_{L_{0}}} . In this paper we establish that if the discrepancy of the two matrices satisfies a natural Carleson measure condition with respect to ω L 0 {\omega_{L_{0}}} , then ω L ∈ A ∞ ⁢ ( ω L 0 ) {\omega_{L}\in A_{\infty}(\omega_{L_{0}})} . Additionally, we can prove that ω L ∈ RH q ⁢ ( ω L 0 ) {\omega_{L}\in\mathrm{RH}_{q}(\omega_{L_{0}})} for some specific q ∈ ( 1 , ∞ ) {q\in(1,\infty)} , by assuming that such Carleson condition holds with a sufficiently small constant. This “small constant” case extends previous work of Fefferman–Kenig–Pipher and Milakis–Pipher together with the last author of the present paper who considered symmetric operators in Lipschitz and bounded chord-arc domains, respectively. Here we go beyond those settings, our domains satisfy a capacity density condition which is much weaker than the existence of exterior Corkscrew balls. Moreover, their boundaries need not be Ahlfors regular and the restriction of the n-dimensional Hausdorff measure to the boundary could be even locally infinite. The “large constant” case, that is, the one on which we just assume that the discrepancy of the two matrices satisfies a Carleson measure condition, is new even in the case of nice domains (such as the unit ball, the upper-half space, or non-tangentially accessible domains) and in the case of symmetric operators. We emphasize that our results hold in the absence of a nice surface measure: all the analysis is done with the underlying measure ω L 0 {\omega_{L_{0}}} , which behaves well in the scenarios we are considering. When particularized to the setting of Lipschitz, chord-arc, or 1-sided chord-arc domains, our methods allow us to immediately recover a number of existing perturbation results as well as extend some of them.

Variable Hardy Spaces on Non-tangentially Accessible Domains and Their Applications to Div-Curl Lemma

Variable Hardy Spaces on Non-tangentially Accessible Domains and Their Applications to Div-Curl Lemma

Multibranch Adversarial Regression for Domain Adaptative Hand Pose Estimation

Although hand pose estimation has achieved a great success in recent years, there are still challenges with RGB-based estimation tasks, the most significant of which is the absence of labeled training data. At present, the synthetic dataset has plenty of images with accurate annotation, but the difference from real-world datasets affects generalization. Therefore, a transfer learning strategy, which tries to transfer knowledge from a labeled source domain to an unlabeled target domain, is a frequent solution. Existing methods such as mean-teacher, Cyclegan, and MCD will train models with the help of some easily accessible domains such as synthetic data. However, these methods are not guaranteed to operate well in real-world settings due to the domain shift. In this paper, we design a new unsupervised domain adaptation method named Multi-branch Adversarial Regressors (MarsDA) in hand pose estimation, where it could be better for feature migration. Specifically, we first generate pseudo-labels for unlabeled target domain data. Then, the new adversarial training loss between multiple regression branches we designed for hand pose estimation is introduced to narrow the domain gap. In this way, our model can reduce the noise of pseudo labels caused by the domain gap and improve the accuracy of pseudo labels. We evaluate our method on two publicly available real-world datasets, H3D and STB. Experimental results show that our method outperforms existing methods by a large margin.

The inhomogeneous boundary Harnack principle for fully nonlinear and $p$-Laplace equations

We prove a boundary Harnack principle in Lipschitz domains with small constant for fully nonlinear and p -Laplace-type equations with a right-hand side, as well as for the Laplace equation on nontangentially accessible domains under extra conditions. The approach is completely new and gives a systematic approach for proving similar results for a variety of equations and geometries.

New mechanism of chromatin compartmentalization by BRD2

The molecular mechanism underlying 3D genome compartmentalization remains a mystery. Xie et al. found that the bromodomain and extraterminal (BET) family scaffold protein BRD2 promotes the compartmentalization of the accessible chromatin domains after cohesin loss. An antagonistic interplay between loop extrusion and compartmentalization modulates chromatin folding.

High-performance large-scale simulation of multi-stable metastructures

In this paper, we have developed a solver based on the message-passing interface (MPI) to enable rapid large-scale simulation of generic metastructures composed of bi- or multi-stable elements. The in-house solver has been thoroughly validated against a commercial numerical solver (Abaqus) and the well-established serial codes from the previous studies. We can achieve up to 4th-order solution accuracy with fully explicit Runge-Kutta (RK) methods, exceeding what many commercial structural analysis tools provide. With our parallel code dedicated to solving specific problem types, the absolute computational speed can be improved by three orders of magnitude, enabling the investigation of a large parameter space. More importantly, the in-house implementation enables an effective distribution of the computational load following the intrinsic structural periodicity, thus achieving efficient parallel scalability. To demonstrate our code's capability to handle massively large problems previously unattainable with existing solvers, we investigate the amplitude-dependent energy transmissibility of bi-stable metabeams and the stability of the transition wave's propagation speed. The achieved numerical and computational performance gains drastically expand the accessible analysis domains of general nonlinear metamaterial and metastructure architectures, thus opening up the potential to uncover new dynamics and enable practical implementations. Program summaryProgram Title:NM̂3 (Nonlinear MetaMaterials MPI) solverCPC Library link to program files:https://doi.org/10.17632/8f4n99jccf.1Developer's repository link:https://github.com/wonnie87/NMCubeLicensing provisions: MITProgramming language: FortranNature of problem:NM̂3 enables massively parallel simulations of strongly nonlinear metamaterials and metastructures, including 1D multi-stable lattice with coupled pendula (discrete sine-Gordon model), 1D lattice with quartic on-site potentials (discrete ϕ-4 model), and metabeam with a bi-stable microstructure.Solution method: Up to the 4th-order explicit Runge-Kutta (RK) methods are implemented in NM̂3. The Newmark-β (implicit) method with constant average acceleration is also available if unconditional numerical stability is desired.Additional comments including restrictions and unusual features: Running NM̂3 requires installation of Python (with NumPy library), MPI, and HDF5. A Python script is used to generate input files. The code use MPI system calls to allow a massive parallelization among the compute processes. The code uses HDF5 file format for data storage.

Evaluation of Spike Protein Epitopes by Assessing the Dynamics of Humoral Immune Responses in Moderate COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic is caused by a novel coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spike protein (S) of SARS-CoV-2 is a major target for diagnosis and vaccine development because of its essential role in viral infection and host immunity. Currently, time-dependent responses of humoral immune system against various S protein epitopes are poorly understood. In this study, enzyme-linked immunosorbent assay (ELISA), peptide microarray, and antibody binding epitope mapping (AbMap) techniques were used to systematically analyze the dynamic changes of humoral immune responses against the S protein in a small cohort of moderate COVID-19 patients who were hospitalized for approximately two months after symptom onset. Recombinant truncated S proteins, target S peptides, and random peptides were used as antigens in the analyses. The assays demonstrated the dynamic IgM- and IgG recognition and reactivity against various S protein epitopes with patient-dependent patterns. Comprehensive analysis of epitope distribution along the spike gene sequence and spatial structure of the homotrimer S protein demonstrated that most IgM- and IgG-reactive peptides were clustered into similar genomic regions and were located at accessible domains. Seven S peptides were generally recognized by IgG antibodies derived from serum samples of all COVID-19 patients. The dynamic immune recognition signals from these seven S peptides were comparable to those of the entire S protein or truncated S1 protein. This suggested that the humoral immune system recognized few conserved S protein epitopes in most COVID-19 patients during the entire duration of humoral immune response after symptom onset. Furthermore, in this cohort, individual patients demonstrated stable immune recognition to certain S protein epitopes throughout their hospitalization period. Therefore, the dynamic characteristics of humoral immune responses to S protein have provided valuable information for accurate diagnosis and immunotherapy of COVID-19 patients.

Conserved Targets to Prevent Emerging Coronaviruses.

Novel coronaviruses emerged as zoonotic outbreaks in humans in 2003 (SARS), 2012 (MERS), and notably in 2019 (SARS2), which resulted in the COVID-19 pandemic, causing worldwide health and economic disaster. Vaccines provide the best protection against disease but cannot be developed and engineered quickly enough to prevent emerging viruses, zoonotic outbreaks, and pandemics. Antivirals are the best first line of therapeutic defense against novel emerging viruses. Coronaviruses are plus sense, single stranded, RNA genome viruses that undergo frequent genetic mutation and recombination, allowing for the emergence of novel coronavirus strains and variants. The molecular life cycle of the coronavirus family offers many conserved activities to be exploited as targets for antivirals. Here, we review the molecular life cycle of coronaviruses and consider antiviral therapies, approved and under development, that target the conserved activities of coronaviruses. To identify additional targets to inhibit emerging coronaviruses, we carried out in silico sequence and structure analysis of coronavirus proteins isolated from bat and human hosts. We highlight conserved and accessible viral protein domains and residues as possible targets for the development of viral inhibitors. Devising multiple antiviral therapies that target conserved viral features to be used in combination is the best first line of therapeutic defense to prevent emerging viruses from developing into outbreaks and pandemics.

Impact of actinide-induced crystal defects on REE-bearing titanite reactivity

Titanite is a widespread accessory silicate which, in some deposits, hosts rare earth elements (REEs) at levels where it can be considered an important carrier. As titanite also accommodates Th and U at concentrations up to 2000 ppm, evidence of metamitctization, a process where radioactive decay of the actinides progressively damages the crystalline structure, is often observed. The current study aims to investigate how the extent of metamictization within a natural titanite specimen may impact its reactivity, principally in the context of REE recovery. Detailed structural characterization revealed the presence of crystalline domains coexisting with sectors having a dominant amorphous component. Compositionally, the density of irradiation-induced defects, leading to amorphization, shows a strong positive correlation not only with the abundance of actinides, but also with the REE concentration. Heat treatment of titanite performed at temperatures ranging from 400 to 800 °C indicates that, while the compositional zonation is maintained, thermal annealing of the defects results in progressive recovery of the crystalline structure. After treatment in sulfuric acid at 200 °C, the habit of reacted titanite grains is highly irregular, indicating distinct decomposition rates for specific sectors, where the more refractory regions coincide with the REE-depleted crystalline domains. A more detailed visualization of the impact of crystal properties on titanite reactivity, obtained by exposing polished grains to a 50% NaOH solution at 50 °C, reveals that, for pristine titanite, the extent of etching observed on crystal surfaces is controlled by the density of the actinide-induced defects, mainly concentrated in the REEs- and Th-enriched amorphous domains. On the other hand, for grains that were initially heat-treated, the etching features are much weaker, emphasizing that thermal annealing renders the crystals less reactive. From these observations, selective REE recovery, where only the more accessible amorphous titanite domains need to be decomposed, seems possible as long as the ore pre-treatment does not involve a high-temperature stage.

The SARS-CoV-2 spike reversibly samples an open-trimer conformation exposing novel epitopes.

Current COVID-19 vaccines and many clinical diagnostics are based on the structure and function of the SARS-CoV-2 spike ectodomain. Using hydrogen-deuterium exchange monitored by mass spectrometry, we have uncovered that, in addition to the prefusion structure determined by cryo-electron microscopy, this protein adopts an alternative conformation that interconverts slowly with the canonical prefusion structure. This new conformation-an open trimer-contains easily accessible receptor-binding domains. It exposes the conserved trimer interface buried in the prefusion conformation, thus exposing potential epitopes for pan-coronavirus antibody and ligand recognition. The population of this state and kinetics of interconversion are modulated by temperature, receptor binding, antibody binding, and sequence variants observed in the natural population. Knowledge of the structure and populations of this conformation will help improve existing diagnostics, therapeutics, and vaccines.