Abstract: Cosmic-Ray Neutron Sensing (CRNS) has become a standard method for non-invasive soil moisture monitoring at the field scale. With most CRNS sensors being derivatives from scientific nuclear equipment, the development of instruments based on alternative neutron detection technologies is a major development goal for CRNS. We present a modular instrument family based on boron-10-lined proportional counters, specifically designed for long-term autonomous field operation. The system is controlled by a data logger supporting various telemetry options and external SDI-12 environmental sensors and the frontend electronics with its pulse shape analysis effectively separates neutron signals from background and electronic noise. Our results show high energy efficiency, with the latest generation close to 50 mW, allowing solar-powered operation even in challenging environments. The performance of the instruments has been validated within long-term field deployments in different settings, showing that boron-10-based systems provide a scalable, cost-effective and reliable alternative for the next generation of CRNS monitoring networks.

Abstract: Efficient recovery of critical raw materials such as lithium from metallurgical slags requires optimized liberation of target phases during comminution. To determine effective mechanical process parameters for target phase recovery, an in-depth understanding of the characteristics of slag particles is crucial. For this purpose, modern tomography techniques, such as computed tomography (CT), can provide high-resolution 3D images of micrometer-sized slag particles. However, analysis of such CT images poses challenges, such as insufficient grayscale contrast between mineral phases and partial- volume effects. This paper presents a scalable workflow for accurate phase- and particle-wise 3D characterization of particle systems by correlating 3D CT images with 2D mineral maps. For this purpose, high-resolution scanning electron microscopy (SEM) slices are registered in 3D CT images and used as ground truth to train 3D convolutional neural networks (CNNs) for the segmentation of individual particles and mineral phases. This approach addresses the principal challenges of obtaining CT-based mineralogical characterizations, allowing for the particle-wise 3D characterization of complex slag systems with minimum manual labeling effort. The trained CNNs are then applied to CT images of particle systems with different particle sizes (from 63 μm to 100 μm and from 100 μm to 250 μm) of a lithium-bearing slag with LiAlO2 as the target phase. Although virtual cross-sections of the predicted 3D segmentations show excellent agreement with mineral liberation obtained from 2D validation SEM-EDS data, the derived 3D mineral liberation statistics differ significantly from 2D estimates. In particular, our results show that the 2D analyses significantly overestimate mineral liberation compared to the 3D characterization. By addressing this stereological bias, the correlative 3D characterization workflow provides essential insights required to tailor pyrometallurgical and mechanical processing parameters to improve the recovery of raw materials.

Abstract: The Junggar Basin (NW China) is a polycyclic intracontinental basin formed within the Central Asian Orogenic Belt and characterized by multi-stage tectonic reactivation and composite petroleum systems. This study integrates tectonic evolution, source rock geochemistry, and basin modeling to clarify the spatial–temporal controls on hydro-carbon generation and accumulation. The basin evolved from Late Paleozoic rifting to Carboniferous–Permian collision, followed by Mesozoic thermal subsidence and Ce-nozoic inversion related to the uplift of the Tianshan. Major source rocks include Car-boniferous marine shales (total organic carbon 1.5–5%), Permian lacustrine deposits (up to 10–12% total organic carbon; hydrogen index up to 700 mg HC/g TOC), and Ju-rassic coal-bearing strata. Thermal maturity ranges from 0.6% to >2.0% vitrinite re-flectance, indicating multi-phase oil and gas generation and secondary cracking in deeply buried depocenters. Hydrocarbon accumulation differs across structural zones. Central depressions are dominated by deep gas generation and composite traps, whereas northwestern segments reflect lateral migration from Permian source kitch-ens. Cenozoic inversion significantly reactivated faults and controlled vertical migra-tion pathways. The results highlight that hydrocarbon distribution in the Junggar Ba-sin is governed by the synchronization of tectonic evolution and generation phases, providing predictive insights for exploration in polycyclic inversion basins.

Abstract: Endophytic bacteria are beneficial microbes that live within plant tissues and promote growth through nitrogen fixation, phosphate solubilization, and phytohormone production. Two endophytic isolates from bell pepper (Capsicum annuum L.) root were identified based on their morphology, and biochemistry properties using 16S rRNA gene sequencing. Winter barley (Hordeum vulgare L.) seeds were inoculated with two PGP (plant growth-promoting) bacterial strains (C-14 and C-27), previously characterized for IAA production and nitrogen fixation, and evaluated in a pot experiment with four treatments: A1(C-14), A2 (C-27), A3 (their consortium), and A4 (non-inoculated control). The results indicated that root and stem lengths increased in plants inoculated with bacteria compared to the uninoculated control. Among treatments, A2 produced the greatest root and shoot lengths (17.23 and 26.2 cm), while A3 showed the lowest (15.8 and 21.5 cm). SPAD values also increased by 6%, 10%, and 3.2% in A1, A2, and A3, respectively. This study clearly demonstrated that the endophytic isolates (C-14 and C-27) obtained from bell pepper roots significantly enhanced the growth and development of barley due to their abilities of IAA production and nitrogen fixation, thereby offering a promising alternate to chemical fertilizers.

Abstract: Edge computing requires safe, efficient, and generalizable online decision-making, yet existing methods suffer from reactive constraint handling, fragmented scheduling frameworks, and poor generalization. We propose PACE, a unified framework shifting from reactive remediation to proactive anticipation. PACE integrates a Proactive Constrained Policy Optimizer with preemptive penalty and constraint-aware intrinsic rewards, a Nested Index Scheduler with closed-form policies for preemptive and non-preemptive AoI minimization, and a Generalizable Multi-Objective Offloading Network with histogram encoding and masking for single-policy generalization. Experiments on safe locomotion, MEC scheduling, and multi-objective offloading show PACE achieves highest returns with strict constraint satisfaction, reduces AoI by up to 61.84%, and attains near-optimal Pareto performance within 0.3% of the upper bound using a single policy.

Abstract: Generative AI systems trained on synthetic data exhibit progressive degradation known as model collapse. This paper provides a theoretical explanation of this phenomenon using Shannon’s Data Processing Inequality (DPI), modeling iterative synthetic-data training as a Markov chain of lossy transformations. We show that mutual information with respect to the original data distribution must decrease monotonically, yielding quantitative predictions for exponential decay rates and identifying architectural constraints as the dominant source of information loss.Building on this analysis, we introduce the AI conceptual theorem, a generalized stability limit for computable systems. The theorem states that any purely computational system that generates outputs iteratively under finite precision, bounded capacity, and without external low-entropy input must experience cumulative information degradation after a finite number of steps. DPI-based collapse emerges as a special case of this broader principle. We emphasize that the AI Theorem is introduced as a conceptual stability principle rather than a formal mathematical theorem.Together, DPI and the AI Theorem provide a unified information-theoretic framework for understanding degradation in synthetic training, long-horizon inference, and other iterative computational processes. The resulting predictions are quantitatively falsifiable and offer guidance for designing more stable and information-preserving AI systems.

Abstract: Background: Chronic myeloid leukemia (CML) is a paradigm of targeted therapy, driven by the BCR::ABL1 fusion kinase. Over the past four decades, therapeutic strategies have evolved from early molecular targeting approaches and interferon-α to tyrosine kinase inhibitors (TKIs), dramatically improving survival and transforming CML into a largely controllable disease. Methods: We performed a narrative review of the literature, focusing on key milestones in CML therapy, including antisense strategies, interferon-based treatment, first-, second-, and third-generation TKIs, and the development of allosteric inhibitors. Current management strategies, treatment-free remission (TFR), and emerging therapies were also analyzed. Results: The introduction of imatinib established proof of principle for oncogene-targeted therapy, leading to sustained survival improvements. Second- and third-generation TKIs further enhanced response depth and addressed resistance, including the T315I mutation. The development of the allosteric inhibitor asciminib introduced a novel mechanism of action and expanded therapeutic options for pretreated patients. Deep molecular responses have enabled TFR in approximately 40–60% of selected patients, redefining treatment goals toward functional cure. Emerging agents, including next-generation ATP-competitive and allosteric inhibitors, are showing promising activity in resistant disease and may further improve outcomes. Conclusions: CML represents a unique model of translational oncology, demonstrating how mechanistic insight can drive therapeutic innovation. Future strategies will focus on increasing TFR rates, overcoming resistance, targeting leukemic stem cells, and improving global access to therapy and monitoring, with the ultimate aim of achieving functional cure in the majority of patients.

Abstract: Hydrogen is emerging as a clean, alternative energy source that can be used in a wide range of applications. Worldwide, the transport sector is heavily dependent on fossil fuels, and to transition this sector to hydrogen technology, it will require extensive deployment of a hydrogen refuelling station network across countries to support hydrogen-powered vehicles. This study examines the consequences of hydrogen leaks from a liquid hy-drogen storage tank and dispenser. A validated DNV PHAST software tool was used to evaluate the hydrogen leak dynamics under varied leak apertures (5mm and 25mm), to determine dispersion patterns, jet-fire thermal radiation intensity, and explosion over-pressure distance relationship under different wind speed intensities. The results indicate that wind speed, system operating pressure, and leak aperture size have a significant impact on the dispersion concentration, jet fire thermal radiation, and explosion over-pressure effect distance; a 25mm leakage elevates the radiation intensity and explosion overpressure, producing a harmful effect distance greater than 5mm leakage cases. Furthermore, a 25mm leak from a dispenser produces an explosion overpressure with greater harm effect distance than a liquid hydrogen leak from a storage tank. This study reveals fundamental hydrogen incident dynamics and provides valuable insights to be considered in the hydrogen refuelling stations to prevent hydrogen releases.

Abstract: The purpose of this paper is to assess the environmental impacts associated with the production and commercialization of concentrated soy protein manufactured by PORTA Hnos. S.A. in Córdoba, Argentina. The study was conducted to support the verification of an Environmental Product Declaration (EPD) under the International EPD® System, following ISO 14025, ISO 14040 and ISO 14067 standards. A cradle-to-gate with options approach was applied, encompassing upstream agricultural production, industrial processing of soybean meal, core manufacturing of soy protein, and downstream packaging disposal. Life Cycle Assessment (LCA) was performed using primary data from the 2022/2023 soybean crop season and industrial operations in 2023. Seven impact categories were evaluated, including global warming potential, ozone depletion, acidification, eutrophication (freshwater, marine, terrestrial), photochemical ozone formation, abiotic resource depletion, and water scarcity. The declared unit is one kilogram of packaged, concentrated soy protein. Results indicate that the industrial phase, particularly natural gas consumption, is the main contributor to most impact categories. Agricultural production significantly affects eutrophication and water scarcity. The findings provide actionable insights for improving environmental performance and guiding responsible sourcing and process optimization.

Abstract: A geometric-algebraic interpretation of electromagnetic gauge-invariance in nonrelativistic quantum electrodynamics is given. Using the Algebra of Physical Space, the Pauli spinor is defined as the rotor that carries a fixed reference axis to a massive fermion's spin vector. The spin vector is invariant under internal rotations about that reference axis; promoting those internal rotation angles to spacetime-dependent fields yields precisely the usual Spin(2) ≈ U(1) gauge transformations. From this construction the Schrödinger-Pauli equation (SPE) follows naturally as a differential equation of a rotor field, and a manifestly gauge-covariant SPE is constructed where the gauge group originates from the reference axis' rotational symmetry. So electromagnetic gauge-invariance is interpreted geometrically as the freedom to choose the starting orientation (about the reference axis) rather than as an abstract phase of a complex scalar field. This approach reproduces standard results while giving a concrete geometry that clarifies conceptual foundations. A discussion of these results concludes the paper.

Abstract: Employment recommendation systems are increasingly used to support graduate job matching. However, limited research has examined how graduating computer science students perceive and respond to a proposed employment recommendation approach that combines static information matching with dynamic interactive functions. Drawing on the Technology Acceptance Model (TAM) and Information System (IS) Success Model, this study conducted a questionnaire-based survey of 386 graduating students and included an exploratory assessment of the questionnaire’s internal consistency and construct structure. The findings show that only 38.3% of respondents reported willingness to use existing employment recommendation systems for job hunting, citing critical limitations including delayed matching to individual qualifications (71.0%), information lag (55.4%), and jobs not matching majors (54.1%). In contrast, respondents reported more favorable attitudes toward the proposed static-dynamic job recommendation approach: 67.6% expressed willingness to use it and 59.6% expressed willingness to recommend it to others. Subgroup analyses reveal that students from emerging computing fields (e.g., AI, Data Science) and those in active job-seeking status demonstrated significantly higher perceived usefulness (PU) and behavioral intention (BI) (p < 0.05). These results underscore a significant "trust gap" in current platforms and suggest that future systems must transition from passive matching to dynamic, user-centric engagement. This research provides a practical blueprint for developing more responsive digital career services that address the evolving complexities of the computer science labor market.

Abstract: Background/Objectives: Auditory neuropathy spectrum disorder (ANSD) is a well-established hearing disorder in the pediatric population and is estimated to account for at least 10% of children with sensorineural hearing loss. Compared to auditory function, vestibular function in children with ANSD has not been well described in the past. The purpose of this study is to examine vestibular testing results in children with ANSD and to better characterize vestibular dysfunction in these children. Methods: A retrospective review of vestibular laboratory testing results was conducted in pediatric patients diagnosed with ANSD. Vestibular evaluation included vestibular evoked myogenic potential (VEMP), rotary chair test, video head impulse test (vHIT), and videonystagmography (VNG). Results: A total of 30 pediatric patients with ANSD were identified, including 18 boys and 12 girls, with a mean age of 4.6 years. Bilateral ANSD was found in 24 cases, while 6 cases were unilateral. Etiologies of ANSD included a history of hyperbilirubinemia in infancy, cochlear nerve dysplasia, and genetic-related conditions, etc. Vestibular dysfunction was found in 12 cases, as indicated by at least one abnormal outcome in VEMP, vHIT, or rotary chair testing. Nineteen children were cochlear implant candidates and eventually underwent unilateral or bilateral implantation. Conclusions: Vestibular dysfunction is significant in pediatric patients with ANSD, and vestibular outcomes appear to be associated with underlying etiologies. Formal vestibular evaluation is necessary to identify such vestibular losses, and these findings will be helpful to guide clinical management and rehabilitation strategies for these children.

Abstract: Generative AI, retrieval-augmented architectures, and multi-source automated analytical tools are now being deployed in increasingly exacting risk-analytic environments. Yet faster processing has not yielded commensurate reductions in false alarms, missed alarms, hallucinated outputs, or failures of responsibility attribution. Against that background, this study develops a biomimetic framework that integrates collective sensing with immune-inspired verification for complex risk-information analysis. Using an openly documented two-layer data architecture that combines authentic public-source samples with rule-generated derivative synthetic samples, the study links biological-to-engineering mechanism translation, multi-objective optimisation, NIST-aligned evaluation, and a governance compatibility index within one auditable design chain. The present evidence indicates that risk level retains a stable positive association with threat scores, while fabricated relations, despite their smaller aggregate volume, are more likely to accumulate in high-risk intervals. These patterns suggest that structural perturbations are more consequential than mere high-frequency noise for judgment distortion. More importantly, the study establishes the empirical and methodological conditions required for formal comparison across recognition quality, system resilience, and governance compatibility. Taken together, the paper offers a testable biomimetic mechanism model and a reproducible evaluative blueprint for auditable optimisation in complex risk-information analysis.

Abstract: The 2026 education reforms in Sri Lanka require a paradigm change towards competency-based formative assessment (FA) as opposed to summative assessment, which is examination-based. But policy documents are not built into a solid pedagogical structure that can support this transition and would be at risk of implementing it superficially. This review conceptualizes recent empirical developments (2024-2026) in Self-Determination Theory (SDT) and research in the field of formative assessment to fill this gap. Three major contributions are presented by us. First, by combining a dual-process SDT model, we posit that the motivational power of FA is not only based on the support of autonomy, competence, and relatedness, as well as the active prevention of controlling, chaotic, or rejecting teaching behaviors, a difference that has far-reaching implications for intervention design. Second, we generalize findings of recent intervention studies that SDT-congruent FA practices are strongly associated with better learner attitudes and achievement, but with mediators of teacher assessment literacy. Third, we situate our findings in the specific implementation context of Sri Lanka, consisting of large classes, resource inequality, and an established exam culture, to suggest a context-sensitive, tiered implementation plan and a research agenda in the future. We are able to conclude that to make the 2026 reforms deliver on its transformative potential, FA needs to be applied not as a peripheral method but as an overhaul of pedagogy, which is based on the principles of SDT and grounded in ongoing and practice-based professional development grounded in teacher assessment literacy.

Abstract: The presence of toxic, corrosive, and environmentally harmful sulfur compounds within natural gas streams necessitates their removal to ensure compliance with fuel quality standards and regulations. Previous studies into MMOs (mixed metal oxides) as adsorbent or catalysts for sulfur compound removal have generally focused upon hydrogen sulfide (H₂S); however, few studies have assessed the removal of organic sulfur compounds like mercaptans. The purpose of this research is to investigate the effects of various preparation routes on the performance of supported metal-oxide catalysts that remove mercaptans from natural gases; specifically, filtration-based and evaporative based catalyst synthesis methods were investigated. A set of different catalysts; Mn, Cu, Zn, Ni and a composite (Mn-Cu-Zn-Ni) were prepared using filtration or evaporation solvent removal in this research and characterized by BET, FTIR, XRD, SEM, EDS and XPS, and their adsorption performance was assessed through fixed-bed breakthrough experiments under representative operating conditions (25°C, 200 psi, 36 mL/min). The results demonstrate that catalysts prepared via evaporation consistently exhibit greater sulfur compounds adsorption performance compared to catalysts prepared through filtration method, primarily due to enhanced retention of active metal species and improved surface accessibility. As confirmed from the characterization, all these improvements result from the fact that the evaporation method enhances the interaction between the metals and oxygen (FTIR); increases the amount of oxides formed as well as improves their distribution (XRD); provides access to more available metal surfaces (XPS/EDS); and creates pore structures and morphologies that are more open and accessible (SEM/BET). Among the catalysts studied, the Mn and Cu catalysts prepared by evaporation achieved the highest breakthrough times 1410 minutes and 1350 minutes, respectively, exceeding the performance of a commercial benchmark catalyst with breakthrough time of 1200 minutes under identical conditions. These findings demonstrate that the evaporation method enables more effective utilization of metal-oxygen active sites and significantly enhances sulfur adsorption capacity. Overall, this work establishes evaporation as a superior and scalable preparation strategy for metal oxide catalysts and provides important structure performance insights for the design of cost-effective catalyst for industrial natural gas desulfurization, particularly for the removal of organic sulfur compounds from natural gas.

Abstract: Land degradation, characterized by declining soil fertility and erosion, is a major constraint to maize productivity in Malawi, where more than half of the arable land is degraded. Although knowledge of soil fertility is critical for efficient input allocation, most smallholder farmers rely on subjective assessments of soil quality, potentially leading to imprecise decisions. This study examines how farmers’ perceptions of soil fertility and erosion influence input allocation and maize productivity among smallholder farmers in Malawi. Using plot level data from the Malawi Integrated Household Survey, we apply a Conditional Mixed Process estimator and Stochastic Frontier Analysis to assess input use behaviour and technical efficiency. Results indicate that farmers allocate more labour and inorganic fertilizer to plots perceived as fertile, and adoption of improved maize varieties is lower on plots perceived as poor. In contrast, organic manure is more frequently applied on degraded plots. Mean technical efficiency is estimated at 0.62, indicating substantial inefficiency relative to the production frontier. Technical efficiency declines monotonically with worsening soil conditions, falling from 0.76 on good plots to 0.52 on poor plots and 0.47 on highly eroded plots. These findings highlight sustainability risks and underline the need for improved soil diagnostics and targeted extension services.

Abstract: Plant steroid hormones, namely brassinosteroids (BRs), govern growth and resilience to environmental stress, yet little is known about how BR-signaling kinases (BSKs) operate in non-model horticultural species. Here, we carried out a whole-genome interrogation of the BSK family in Brassica rapa subsp. chinensis and examined its potential involvement in high-temperature stress responses. The search yielded 20 BcBSK members, each featuring a conserved kinase domain at the N-terminus and TPR repeats at the C-terminus. Phylogenetic reconstruction assigned them to separate subgroups, while collinearity assessment detected 16 duplicated gene pairs evolving under strong selection constraints. Upstream regulatory sequences harbored numerous cis-motifs linked to hormonal signals and stress perception. Interactome modeling pinpointed BcBSK2, BcBSK5, BcBSK14, and BcBSK18 as hub components. RNA-seq analysis under elevated temperature (38℃) uncovered distinct expression behaviors between cultivars: in the susceptible line “Aijiaohuang”, BcBSK1 and BcBSK2 transcripts increased sharply, whereas the resistant line “SHI” exhibited little fluctuation. Quantitative PCR results aligned with the RNA-seq findings. Exogenous application of 0.5 mg·L⁻¹ BR improved the activities of catalase, peroxidase, and superoxide dismutase, boosted proline levels, lowered malondialdehyde content, and preserved chlorophyll and carotenoid concentrations under heat exposure. Taken together, these data imply that BcBSK family members contribute to BR-facilitated heat adaptation by orchestrating changes at both transcript and metabolite levels, thus laying a groundwork for genetic enhancement of thermotolerance in this vegetable species.

Abstract: The scientific foundations of sport‑training methodology are commonly attributed to physiological principles; however, the extent to which these principles directly inform practical training models remains unclear. This narrative review examines the historical development of training theory—from early adaptology and integrative physiology to contemporary molecular discoveries in muscle biology—and evaluates their relevance to strength development. Strength expression is shown to be highly variable, influenced by neural, mechanical, technical, and psychological factors, challenging the traditional reliance on fixed percentages of maximal strength for training prescription. Additional complexities arise from individual response variability, performance plateaus, and the interference between molecular pathways activated by strength and endurance training. Emerging artificial intelligence systems offer new opportunities for individualized training optimization, injury prediction, and motor‑learning analysis, while advances in brain decoding technologies highlight the potential role of willpower and cognitive processes in strength expression. Overall, current training methodologies remain partly speculative, and substantial research is required to more clearly connect physiological mechanisms with practical training applications.

Abstract: Sentiment analysis of Thai social media texts remains challenging due to the lack of explicit word boundaries, informal language, and high linguistic variability, further exacerbated by class imbalance, short texts, and ambiguous categories. Recent transformer-based models, particularly WangchanBERTa, have shown strong performance in Thai NLP; however, their ability to extract fine-grained, sentiment-specific local features remains limited when applied to noisy, short social media texts. This study proposes a lightweight hybrid framework that integrates WangchanBERTa with multiple neural architectures, including convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and bidirectional gated recurrent units (BiGRU). A comparative evaluation is conducted to assess their effectiveness for four-class Thai sentiment classification. In addition, the impact of CNN kernel size configurations on sentiment feature extraction is systematically investigated, an aspect that has received limited attention in prior Thai NLP research. Experiments conducted on the WISESIGHT benchmark demonstrate that the WangchanBERTa–CNN model with medium kernel sizes [2, 3, 4] achieves the best performance. It achieves a macro-average F1-score of 65.80%, outperforming the current state-of-the-art on the WISESIGHT benchmark by 4.94%. In addition, it achieves a macro-average F1-score of 94.11% on the class-balanced dataset. These results are achieved while maintaining a relatively low parameter count. These findings confirm that combining contextualized global embeddings with local n-gram feature extraction provides an effective and efficient solution for sentiment classification of short, noisy, and linguistically diverse Thai social media texts.

Abstract: Lactic acid bacteria are important components of the early gut microbiota in piglets and may contribute to gastrointestinal stability and control of enteric pathogens, particularly under increasing restrictions on antibiotic use in livestock production. This study aimed to perform in vitro phenotypic screening and characterization of lactic acid bacteria isolated from feces of suckling piglets aged 7–28 days. A total of 42 fecal samples were collected and cultured on selective media, yielding 318 colonies, of which 135 Gram-positive, rod-shaped, catalase-negative isolates were retained for further evaluation. These isolates were assessed for tolerance to acidic conditions (pH 2.0–3.1), bile salts (0.3–0.5%), cell surface hydrophobicity, hemolytic activity, and antibacterial activ-ity against Escherichia coli and Staphylococcus aureus. Among them, only two isolates (PMvet212 and PMvet318) showed detectable tolerance to acidic and bile conditions and exhibited moderate antibacterial activity, with inhibition zones of approximately 10-12 mm. Identification using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry indicated that PMvet212 was closely related to Lactobacillus brevis, whereas PMvet318 was identified at the genus level as Lactobacillus sp. Both isolates displayed α-hemolytic activity, indicating the need for further safety evaluation. Overall, the findings provide preliminary evidence of selected functional properties of piglet-derived lactic acid bacteria; however, additional molecular characterization and in vivo studies are required before any practical application can be considered.