Adaptive Structure Research Articles

A case study of the development of a valid and pragmatic implementation science measure: the Barriers and Facilitators in Implementation of Task-Sharing Mental Health interventions (BeFITS-MH) measure.

Few implementation science (IS) measures have been evaluated for validity, reliability and utility - the latter referring to whether a measure captures meaningful aspects of implementation contexts. We present a real-world case study of rigorous measure development in IS that assesses Barriers and Facilitators in Implementation of Task-Sharing in Mental Health services (BeFITS-MH), with the objective of offering lessons-learned and a framework to enhance measurement utility. We summarize conceptual and empirical work that informed the development of the BeFITS-MH measure, including a description of the Delphi process, detailed translation and local adaptation procedures, and concurrent pilot testing. As validity and reliability are key aspects of measure development, we also report on our process of assessing the measure's construct validity and utility for the implementation outcomes of acceptability, appropriateness, and feasibility. Continuous stakeholder involvement and concurrent pilot testing resulted in several adaptations of the BeFITS-MH measure's structure, scaling, and format to enhance contextual relevance and utility. Adaptations of broad terms such as "program," "provider type," and "type of service" were necessary due to the heterogeneous nature of interventions, type of task-sharing providers employed, and clients served across the three global sites. Item selection benefited from the iterative process, enabling identification of relevance of key aspects of identified barriers and facilitators, and what aspects were common across sites. Program implementers' conceptions of utility regarding the measure's acceptability, appropriateness, and feasibility clustered across several common categories. This case study provides a rigorous, multi-step process for developing a pragmatic IS measure. The process and lessons learned will aid in the teaching, practice and research of IS measurement development. The importance of including experiences and knowledge from different types of stakeholders in different global settings was reinforced and resulted in a more globally useful measure while allowing for locally-relevant adaptation. To increase the relevance of the measure it is important to target actionable domains that predict markers of utility (e.g., successful uptake) per program implementers' preferences. With this case study, we provide a detailed roadmap for others seeking to develop and validate IS measures that maximize local utility and impact.

Cultural adaptation of an internet-based self-help app for grieving Syrian refugees in Switzerland.

Loss and grief pose significant challenges for victims of armed conflicts, such as Syrian refugees. Internet-based interventions (IBIs) present a promising solution to address this treatment gap and provide adequate support. However, research on grief, grief support, and IBIs remain largely limited to Western cultural contexts, and culturally adapted IBIs for grief are needed. Following the Reporting Cultural Adaptation in Psychological Trials (RECAPT) framework, this study aimed to develop and further adapt a culturally sensitive IBI for bereaved Syrian refugees in Switzerland. The study employed qualitative methods. Initially, formative research was conducted to create a first version of the intervention, including semi-structured interviews with 10 experts to identify necessary cultural adaptations. The preliminary version of the intervention was then presented to six potential users and three experts to gather feedback on additional cultural adaptations through two iterative feedback rounds. The first round involved semi-structured interviews using a "paper version" of the intervention, followed by a second round with a walk-through think-aloud protocol with a beta version. Data were analyzed using framework analysis. The input from various key informants at different stages of development provided valuable feedback on surface and deep structure adaptation, which may enhance treatment adherence, acceptance, and motivation. These findings provide important insights and recommendations for the cultural adaptation of interventions and may help address the treatment gap for bereaved Syrian refugees.

Forecasting techniques based on the use of time series models for forecasting expenditures on social protection and social security

In the context of a military conflict, it is growing the number of people who are in difficult life circumstances and need state assistance. Therefore, the issue of forecasting spending on social protection and social security is becoming increasingly relevant. The aim of the paper is to consider modern methods of analyzing and forecasting time series and propose their application to determine the need for spending on social protection and social security. The article provides a brief overview of regression models, their advantages and features of use for analyzing and predicting nonlinear and linear processes described by time series of statistical indicators used in the population social protection system. The paper offers a combination of predictions that can be made based on locally linear and nonlinear models, such as threshold autoregression (TAR) and exponential autoregression (ExAR), which have known quality characteristics. This approach primarily concerns time series that contain data on structural changes in the analyzed processes. Using combined predictions, it is possible to estimate the contribution of each component for both different and selected time points using variable weights. To build forecasts, depending on the scenario of distribution of social protection and social security expenditures, it is proposed to use Bayes networks. Their advantages are flexibility, high quality of results, the possibility of structural and parametric optimization and adaptation to new data and conditions, support for quality assessment by appropriate sets of statistical criteria, which makes it possible to analyze thoroughly intermediate and final results. It helps to increase the adequacy of models and the high quality of the final result of their application.

Local adaptive insulation in amorphous powder cores with low core loss and high DC bias via ultrasonic rheomolding

Amorphous powder cores are promising components for next-generation power electronics. However, they present inherent challenges of internal air gaps and stresses during cold compaction, which significantly deteriorate soft magnetic properties. Here, we report the formation of a local adaptive insulation structure of biconcave lens in amorphous powder cores by ultrasonic rheomolding. Consequently, compared with conventional cold-compacted powder cores, the ultrasonic rheomolded powder cores offer significant simultaneous improvements in the permeability from 31.3–32.4 to 41.8–43.3 and the direct-current bias performance from 69.4–69.7% to 87.4–87.8% (7960 A/m), thereby overcoming the trade-off between permeability and direct-current bias performance. In particular, their core losses are as low as 13.73–15.45 kW/m3, approximately one twentieth of that of the cold-compacted powder cores (282.84–304.03 kW/m3) at a magnetic field of 100 mT and 100 kHz. The biconcave-lens insulation structure can effectively buffer the impact of high mechanical stress on the magnetization of magnetic powder particles, allowing for the ultrasonic rheomolded powder cores to maintain better magnetization efficiency and consequently resulting in excellent soft magnetic properties under the cooperative effect of very low internal stresses and low porosity. The ultrasonic rheomolded powder cores can be used as alternative core components in next generation miniaturized power electronics.

Adaptive Weighted Particle Swarm Optimization for Controlling Multiple Switched Reluctance Motors with Enhanced Deviatoric Coupling Control

Switched reluctance motors (SRMs) are widely used in industrial applications due to their advantages. Multi-motor synchronous control systems are crucial in modern industry, as their control strategies significantly impact synchronization performance. Traditional deviation coupling control structures face limitations during the startup phase, leading to excessive tracking errors and exacerbated by uneven load distribution, resulting in desynchronized motor acceleration and increased speed synchronization errors. This study proposes a modified deviation coupling control method based on an adaptive weighted particle swarm optimization (PSO) algorithm to enhance multi-motor synchronization performance. Traditional deviation coupling control applies equal reference torque inputs to each motor’s current loop, failing to address uneven load distribution and causing inconsistent accelerations. To resolve this, a gain equation based on speed deviation is introduced, incorporating self-tracking error and gain coefficients for dynamic synchronization error compensation. The gain coefficients are optimized using the adaptive weighted PSO algorithm to improve system adaptability. A simulation model of a synchronization control system for three SRMs was developed in the Matlab/Simulink R2023b environment. This model compares the synchronization performance of traditional deviation coupling, Fuzzy-PID improved structure, and adaptive PSO improved structure during motor startup, sudden speed increases, and load disturbances. The validated deviation coupling control structure achieved the initial set speed in approximately 0.236 s, demonstrating faster convergence and a 6.35% reduction in settling time. In both the motor startup and sudden speed increase phases, the two optimized methods outperformed the traditional structure in dynamic performance and synchronization accuracy, with the adaptive PSO structure improving synchronization accuracy by 54% and 37.17% over the Fuzzy-PID structure, respectively. Therefore, the PSO-optimized control system demonstrates faster convergence, improved stability, and enhanced synchronization performance.

Human Impacts on Great Lakes Walleye Sander vitreus Structure, Diversity and Local Adaptation.

Artificial propagation and wild release may influence the genetic integrity of wild populations. This practice has been prevalent in fisheries for centuries and is often termed 'stocking'. In the Laurentian Great Lakes (Great Lakes here-on), walleye populations faced declines from the 1950s to the 1970s, prompting extensive stocking efforts for restoration. By the mid-2010s, walleye populations showed signs of recovery, but the genetic legacy of stocking on population structure at the genomic level remains unclear. Using a dataset of 45,600 genome-aligned SNP loci genotyped in 1075 walleye individuals, we investigated the genetic impacts of over 50 years of stocking across the Great Lakes. Population structure was associated with both natural geographic barriers and stocking from non-native sources. Admixture between Lake Erie walleye and walleye from the re-populated Tittabawassee River indicate that stocking may have re-distributed putatively adaptive alleles around the Great Lakes. Genome scans identified FST outliers and evidence of selective sweeps, indicating local adaptation of spawning populations is likely. Notably, one genomic region showed strong differentiation between Muskegon River and walleye from the Tittabawassee River, which was re-populated by Muskegon strain walleye, suggesting admixture and selection both impact the observed genetic diversity. Overall, our study underscores how artificial propagation and translocations can significantly alter the evolutionary trajectory of populations. The findings highlight the complex interplay between stocking practices and population genetic diversity, emphasising the need for careful management strategies to preserve the genetic integrity of wild populations amidst conservation efforts.

The impact of exercise intensity and duration for swim training-induced adaptations in cardiac structure and function in women with mild hypertension.

This study aimed to investigate the impact of swim training intensity and duration on cardiac structure and function in mildly hypertensive women. Sixty-two mildly hypertensive women were randomized to 15 weeks of either (1) high-intensity swimming (HIS, n = 21), (2) moderate-intensity swimming (MOD, n = 21) or (3) control (CON, n = 20). Training sessions occurred three times per week. Cardiac measurements were conducted using echocardiography pre- and post-intervention. Both the HIS and MOD groups demonstrated significant within-group increases in left ventricular mass: 7.3% [1.2; 13.2] (p = 0.02) for HIS and 6.2% [0.5; 11.8] (p = 0.03) for MOD. The MOD group also demonstrated a significant increase in left ventricular internal dimension at end-diastole by 2.4% [0.2; 4.6] (p = 0.03).Post-hoc analysis of diastolic function markers revealed reduced mitral valve A velocity in both HIS (-14% [-25; -3], p = 0.02) and MOD (-13% [-23; -3], p = 0.01), leading to increased mitral valve E/A ratios of 27% [10; 47] (p = 0.003) and 22% [5; 40] (p = 0.01), respectively. Additionally, only MOD demonstrated increased left atrial diameter of 4.9% [0.7; 9.1] (p =0.02). A significant time×group effect (p = 0.02) existed for global longitudinal strain, which increased by 1.6% [0.2; 3.0] (p = 0.03) in MOD only. In conclusion, swim training for 15 weeks increased left ventricular mass and improved markers of diastolic function in mildly hypertensive women. These independent of exercise intensity and duration in mildly hypertensive women.

Multistream Adaptive Attention-Enhanced Graph Convolutional Networks for Youth Fencing Footwork Training.

The popularity of fencing and intense sports competition has burdened adolescents with excessive training, harming their immature bodies. Traditional training methods fail to provide timely movement corrections and personalized plans, leading to ineffective exercises. This paper aims to use artificial intelligence technology to reduce ineffective exercises and alleviate the training burden. We propose an action recognition algorithm based on the characteristics of adolescent athletes. This algorithm uses multimodal input data to comprehensively extract action information. Each modality is processed by the same network structure, utilizing attention mechanisms and adaptive graph structures. A multibranch feature fusion method is used to determine the final action category. We gathered the fencing footwork data set 2.0. Our model achieved 93.3% accuracy, with the highest precision at 95.8% and the highest F1-Score at 94.5% across all categories. It effectively recognized actions of adolescents with different heights and speeds, outperforming traditional methods. Our artificial intelligence-based training solution improves training efficiency and reduces the training burden on adolescents.

Image-Based Quantitative Analysis of Epidermal Morphology in Wild Potato Leaves

The epidermal leaf patterns of plants exhibit remarkable diversity in cell shapes, sizes, and arrangements, driven by environmental interactions that lead to significant adaptive changes even among closely related species. The Solanaceae family, known for its high diversity of adaptive epidermal structures, has traditionally been studied using qualitative phenotypic descriptions. To advance this, we developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology. Applied to nine wild potato species, this workflow quantified key morphological parameters, identifying descriptors for trichomes, stomata, and pavement cells, and revealing interdependencies among these traits. Principal component analysis (PCA) highlighted two main axes, accounting for 45% and 21% of variance, corresponding to features such as guard cell shape, trichome length, stomatal density, and trichome density. These axes aligned well with the historical and geographical origins of the species, separating southern from Central American species, and forming distinct clusters for monophyletic groups. This workflow thus establishes a quantitative foundation for investigating leaf epidermal cell morphology within phylogenetic and geographic contexts.

Reinforcement Learning for Fuzzy Structured Adaptive Optimal Control of Discrete-Time Nonlinear Complex Networks

Reinforcement Learning for Fuzzy Structured Adaptive Optimal Control of Discrete-Time Nonlinear Complex Networks

Directing Organometallic Ring-Chain Equilibrium by Electrostatic Interactions.

Dynamic chemistry, which falls into the realm of both supramolecular and covalent chemistry, enables intriguing properties, such as structural diversity, self-healing, and adaptability. Due to robustness of covalent bonds compared to noncovalent ones, dynamic covalent chemistry has been exploited to synthesize complex molecular nanostructures at solid/liquid interfaces under ambient conditions, generally responsive to internal factors that directly regulate intermolecular covalent bonds. However, directing dynamics of covalent nanostructures, e.g., the typical ring-chain equilibria, on surface by extrinsic interactions remains elusive and challenging. Herein, we have controllably directed the ring-chain equilibrium of covalent organometallic structures by regulating intermolecular electrostatic interactions, thus achieving on-surface dynamic covalent chemistry under ultrahigh vacuum conditions. Our findings unravel the dynamic mechanism of covalent polymers governed by weak intermolecular interactions at the submolecular level, which not only bridges the gap between supramolecular and covalent chemistry but also offers great opportunities for the fabrication of adaptive polymeric nanostructures that respond to different conditions.

A new concept of structural smart fabric activated by shape memory alloys

Abstract This work presents the development, prototyping and validation of a new concept of structure called Structural Smart Fabric (SSF). An SSF is a chainmail fabric composed by interconnected elements, called cells, which can be stiffened by reactive elements, such as those made by Shape Memory Alloy (SMA). Exploiting the shape memory functionality, SSFs are capable of sensing and reacting to external stimuli. Based on this concept, in this study we propose an SSF that integrates SMA wires into a 3D-printed chainmail structure. The shape memory effect of these wires is used as an actuating mechanism that, at high temperature, tightens the adjacent cells together and provides increased structural stiffness. In this study, finite element simulations were initially conducted to enhance the comprehension of the SSF’s mechanical behaviour. The influence of the initial cell spacing on the SSF stiffness and the wire stress profiles was evaluated during bending loading, as well as the evolution of contact pressure profiles between adjacent cells. This numerical approach enabled to tune the design of the SSF prototypes which were successively manufactured using Selective Laser Sintering (SLS) additive manufacturing technology with PA12. After the integration with the SMA wires, the SSF prototypes were tested under a 3-point bending configuration at different temperatures. The results revealed a remarkable increase in structural stiffness at elevated temperatures compared to ambient conditions. This study set the basis for a deeper understanding of SSF's unique capabilities and potential applications in fields where adaptive and responsive structures are required.

Evaluating Ecosystem Services Incorporation in River Policy: A Multidimensional Framework Applied to China's River Chief System

ABSTRACTThis study develops a comprehensive evaluation framework to assess the incorporation of river ecosystem services (ESs) into policy content, focusing on China's River Chief System (RCS) policy. The framework introduces a quantitative tool that bridges the gap between policy formulation evaluation and policy effect evaluation by integrating a hierarchical indicator system, reference values, and a method for quantifying ES incorporation. Key findings from 32 province‐level RCS policy documents revealed selective incorporation of certain ES indicators, significant mismatches between policy focus and the typical structure of river ESs, and notable regional disparities. Provisioning services (i.e., water supply) and regulating services (i.e., pollution mitigation) are prioritized, while critical regulating services (e.g., flood mitigation and habitat maintenance) and cultural services (e.g., education and science) are underrepresented. Stakeholder and relationship indicators are limited, primarily focusing on managers and spatial relationships, while the value of river ESs is largely ignored. Policy optimization should emphasize expanding the coverage of ESs, incorporating broader stakeholder involvement, and integrating financial incentives. Drawing on international river policies, policymakers could create a more flexible and adaptive river governance structure. While the study offers a quantitative framework for policy evaluation, it acknowledges limitations, including reliance on textual data and potential biases in the reference values. Future research should include stakeholder perspectives and dynamically adjust reference values as new data become available.

Blood biomarkers to detect functional impairment in adult patients with repaired Tetralogy of Fallot

Abstract Background The relationship between plasma brain natriuretic peptide (NT-proBNP) and soluble suppression of tumorigenicity-2 (sST2) with structural adaptions and exercise capacity remains incompletely described in patients with repaired Tetralogy of Fallot (rTOF). Methods Peripheral venous blood samples were drawn for 99 patients with repaired TOF, 59 patients with severe pulmonary regurgitation (PR) and 40 patients with no or mild PR. NT-proBNP was measured using enzyme-linked immunosorbent assays (Roche Diagnostics, Indianapolis, IN). Soluble ST2 levels were assessed on Aspect-plus ST2 quantitative rapid test. Results The mean value of NT-proBNP was 160±137 pg/ml, and sST2 was 29±13, ng/ml in the entire population. Mean NT-proBNP was significantly higher in patients with severe PR (169±150 vs145±118, pg/ml, p<0.001), while similar sST2 levels were observed in both groups (29±14 vs30±12, ng/ml, p>0.05). NT-proBNP and sST2 levels were higher in patients with transannular patch when compared to other RVOT intervention. Both biomarkers were significantly associated with exercise capacity, but NT-proBNP (r=-.60, p<0.001) was stronger. The optimal cut-off of 90pg/ml for NT-proBNP had a sensitivity of 74% and specificity of 63% for detection of impaired exercise capacity. Conclusions Serum levels of sST2 and NT-proBNP are elevated in patients with repaired TOF, with higher values observed in those with severe PR, but also in patients undergoing transannular patch. Incorporating both markers in these patients increased the ability to detect impairment in exercise capacity.

4.A. Skills building seminar: Waves of community health engagement and governance: innovating through a design-as-policy approach

Abstract In this workshop we will engage our participants in a practice oriented exercise to experiment with a collaborative design-as-policy approach for a community health action plan. The exercise will be based on the lessons learned in two large evaluation projects: 1) an 8-year evaluation of the Dutch All about Health (AaH) platform of social health initiatives on the local, regional and national level; and 2) a comparative multiple case study, including Portugal, building a framework for sustainable citizen engagement, published in the WHO Handbook ‘Voice, Agency, Empowerment: The Handbook on Social Participation for UHC’. Both projects show striking similarities in findings, and developed guidance for devising collaborative action through (a) the use of community platforms, (b) concrete engagement mechanisms and (c) mediating civil society organizations. These will be brought together in a Guidance Tool that participants will work with in the exercise. The aim of the tool is to support initiatives to bring together both top-down participatory efforts and bottom-up grassroots movements in order to anchor community and minority voices into policy decision-making for a more level playing field for policy influence. The aim of the workshop is to let participants experiment with the Guidance Tool and develop collaborative design-as-policy skills. The outline of the skills building exercise: 1. Group introductions using Mentimeter and invitation to pick a particular complex health issue from participants’ own work or community setting (5 mins). 2. Three pitches on the projects, the Portugal case and the Guidance Tool (10 mins). 3. Exercise instructions (5 mins). 4. Exercise (35 mins) a. Upon room entrance participants receive a colored badge based on their professional background: public health professional/practitioner, community resident, researcher or policymaker; b. Form small sub groups of 6-8 participants with different backgrounds (2 mins); c. Introduce with key words on paper their respective complex community health issues and sub group jointly selects one issue for which most information and experience is present (6 mins); d. First round in duos within sub group: participants work through the guidance steps to an initial idea for the collaborative action approach consisting of a community platform, engagement mechanisms, and mediating civil society organizations, drawn out on paper (10 mins); e. Second round in duos within the sub group: duos review another duos initial design idea and conduct an intuitive SWOT analysis of feasibility, acceptability, and practicability (8 mins); f. Third round in sub group: compare the three proposals in SWOT perspective, select the most salient one, and improve with strong elements from other proposals (8 mins); g. Summarize in key words the final proposal and enter into Mentimeter. 5. Plenary wrap up using Mentimeter: select the winning proposal, and evaluate the exercise (5 mins). Key messages • Collaborative action is enhanced considerably through (a) the use of community platforms, (b) concrete engagement mechanisms and (c) mediating civil society organizations. • The Guidance Tool offers concrete steps for designing a reflexive and adaptive structure and process for community collaborative action and self-governance. Speakers/Panelists Marleen Bekker Wageningen University & Research, Wageningen, Netherlands Dheepa Rajan WHO, Brussels, Belgium Goncalo Figueiredo Augusto National Health Council, Lisbon, Portugal

Social Infrastructure and Organizational Resilience - A Theoretical Model

Abstract Background Considering resilience as a necessity for health care organisations (HCOs), many researchers argue that fluidity is a must and that organisational structures (formal and informal) hinder the adaptability and resilience of health care organisations. Contrary to this statement, my thesis posits that fundamental adaptive processes leading to new equilibria depend on a stable core that persists amidst change, enabling and catalysing these transformations. Methods The phenomenon of resilience within healthcare organisations is examined through the lens of sociological systems theory. Rooted in Talcott Parsons’ assertion that social systems, including healthcare organisations, must perform four basic functions (Adaptation, Goal Achievement, Integration, Latency: AGIL) to survive and function, our research uses the deductive method to derive the necessary structural social components required to cope with external demands. Results The application of the methods yields four complex basic structures rooted in the social structure of healthcare organisations: adaptive structures (flexible structures & resource generating structures), goal achievement structures (governance enabling collective goal setting, decision making and persistence), integrative structures (social capital and social networks) and sustainability structures (socialisation structure, knowledge base, organisational culture). Conclusions As the core of stability, this ‘four-component social infrastructure’ is essential for facilitating higher-order forms of resilience, such as new equilibria. In its absence, healthcare organisations would flounder during change, posing risks to the resilience of these HCOs. Thus, structure is indeed inherent in the chaos of resilience activities. In other words, there is order in the chaos of resilience, and it is essential. Key messages • Fundamental adaptive processes leading to new equilibria depend on a stable core that persists amidst change, enabling and catalysing these transformations. • There is order and structure in the chaos of resilience.

Ensemble-Enhanced Semi-Supervised Learning with Optimized Graph Construction for High-Dimensional Data.

Graph-based methods have demonstrated exceptional performance in semi-supervised classification. However, existing graph-based methods typically construct either a predefined graph in the original space or an adaptive graph within the output space, which often limits their ability to fully utilize prior information and capture the optimal intrinsic data distribution, particularly in high-dimensional data with abundant redundant and noisy features. This paper introduces a novel approach: Semi-Supervised Classification with Optimized Graph Construction (SSC-OGC). SSC-OGC leverages both predefined and adaptive graphs to explore intrinsic data distribution and effectively employ prior information. Additionally, a graph constraint regularization term (GCR) and a collaborative constraint regularization term (CCR) are incorporated to further enhance the quality of the adaptive graph structure and the learned subspace, respectively. To eliminate the negative effect of constructing a predefined graph in the original data space, we further propose a Hybrid Subspace Ensemble-enhanced framework based on the proposed Optimized Graph Construction method (HSE-OGC). Specifically, we construct multiple hybrid subspaces, which consist of meticulously chosen features from the original data to achieve high-quality and diverse space representations. Then, HSE-OGC constructs multiple predefined graphs within hybrid subspaces and trains multiple SSC-OGC classifiers to complement each other, significantly improving the overall performance. Experimental results conducted on various high-dimensional datasets demonstrate that HSE-OGC exhibits outstanding performance.

Recent Advances in the Synthesis of Acyclic Nucleosides and Their Therapeutic Applications.

DNA is commonly known as the "molecule of life" because it holds the genetic instructions for all living organisms on Earth. The utilization of modified nucleosides holds the potential to transform the management of a wide range of human illnesses. Modified nucleosides and their role directly led to the 2023 Nobel prize. Acyclic nucleosides, due to their distinctive physiochemical and biological characteristics, rank among the most adaptable modified nucleosides in the field of medicinal chemistry. Acyclic nucleosides are more resistant to chemical and biological deterioration, and their adaptable acyclic structure makes it possible for them to interact with various enzymes. A phosphonate group, which is linked via an aliphatic functionality to a purine or a pyrimidine base, distinguishes acyclic nucleoside phosphonates (ANPs) from other nucleotide analogs. Acyclic nucleosides and their derivatives have demonstrated various biological activities such as anti-viral, anti-bacterial, anti-cancer, anti-microbial, etc. Ganciclovir, Famciclovir, and Penciclovir are the acyclic nucleoside-based drugs approved by FDA for the treatment of various diseases. Thus, acyclic nucleosides are extremely useful for generating a variety of unique bioactive chemicals. Their biological activities as well as selectivity is significantly influenced by the stereochemistry of the acyclic nucleosides because chiral acyclic nucleosides have drawn a lot of interest due to their intriguing biological functions and potential as medicines. For example, tenofovir's (R) enantiomer is roughly 50 times more potent against HIV than its (S) counterpart. We can confidently state, "The most promising developments are yet to come in the realm of acyclic nucleosides!" Herein, we have covered the most current developments in the field of chemical synthesis and therapeutic applications of acyclic nucleosides based upon our continued interest and activity in this field since mid-1990's.

A Grafting Hydrogen‐bonded Organic Framework for Benchmark Selectivity of C2H2/CO2 Separation under Ambient Conditions

Reticular chemistry and pore engineering have garnered significant advancements in metal‐organic frameworks and covalent organic frameworks, leveraging robust metal‐coordination and covalent bonds. However, these achievements remain elusive in hydrogen‐bonded organic frameworks, hindered by their inherent weakness in hydrogen bonding. Herein, we strategically manipulate the porosity of hydrogen‐bonded frameworks through a grafting approach, culminating in the synthesis of two isomorphic HOFs, HOF‐FJU‐99 and HOF‐FJU‐100, with distinct pore environments. Remarkably, HOF‐FJU‐100, with its microporous architecture, not only showcases exceptional stability but also achieves unparalleled separation efficiency and ultrahigh selectivity for C2H2/CO2 mixtures (50/50, v/v) under ambient conditions. Its IAST selectivity value of 201 stands as a benchmark, towering over all previously reported HOFs. The pore of HOF‐FJU‐100 boasts an electrostatic potential highly favourable for C2H2 adsorption, as evidenced by single crystal X‐ray diffraction analysis revealing multiple hydrogen bonding interactions between C2H2 molecules and the framework. In situ gas‐carrier powder X‐ray diffraction analysis underscores the adaptability of pore structure, dynamically adjusting its orientation in response to C2H2, thereby enabling a highly efficient and specific separation of C2H2/CO2 mixtures through specific adsorptive interactions.

A Grafting Hydrogen-bonded Organic Framework for Benchmark Selectivity of C2H2/CO2 Separation under Ambient Conditions.

Reticular chemistry and pore engineering have garnered significant advancements in metal-organic frameworks and covalent organic frameworks, leveraging robust metal-coordination and covalent bonds. However, these achievements remain elusive in hydrogen-bonded organic frameworks, hindered by their inherent weakness in hydrogen bonding. Herein, we strategically manipulate the porosity of hydrogen-bonded frameworks through a grafting approach, culminating in the synthesis of two isomorphic HOFs, HOF-FJU-99 and HOF-FJU-100, with distinct pore environments. Remarkably, HOF-FJU-100, with its microporous architecture, not only showcases exceptional stability but also achieves unparalleled separation efficiency and ultrahigh selectivity for C2H2/CO2 mixtures (50/50, v/v) under ambient conditions. Its IAST selectivity value of 201 stands as a benchmark, towering over all previously reported HOFs. The pore of HOF-FJU-100 boasts an electrostatic potential highly favourable for C2H2 adsorption, as evidenced by single crystal X-ray diffraction analysis revealing multiple hydrogen bonding interactions between C2H2 molecules and the framework. In situ gas-carrier powder X-ray diffraction analysis underscores the adaptability of pore structure, dynamically adjusting its orientation in response to C2H2, thereby enabling a highly efficient and specific separation of C2H2/CO2 mixtures through specific adsorptive interactions.