Synthesis Tools Research Articles

Umbrella Reviews: A methodological guide.

This paper aims to explore the significance of umbrella reviews as a methodological advancement in evidence synthesis, emphasizing their role in aggregating data from multiple systematic reviews and their impact on evidence-based practice and policy-making. The paper examines the evolution of systematic reviews and meta-analyses, highlighting the emergence of umbrella reviews as a comprehensive synthesis method. It details the key steps involved in conducting an umbrella review, including literature searches, study selection, quality appraisal, and data synthesis. Various types of umbrella reviews are discussed, along with the integration of advanced methodologies and interdisciplinary approaches that enhance their effectiveness. Umbrella reviews have become an essential tool in evidence synthesis, offering a higher-level aggregation of systematic reviews to inform decision-making in clinical and policy contexts. Their evolving methodologies, combined with interdisciplinary approaches, make them crucial in shaping the future of evidence-based practice globally.

MAC-DNN: An Optimized Hardware Implementation of MAC Unit in Neuron Engine for Deep Neural Network Applications

Object detection and analysis using deep neural networks (DNNs) have become significant challenges due to their computational and power requirements. Hence, such computation is possible on general-purpose platforms like central processing units (CPUs), graphic processing units (GPUs), application-specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs). However, the development of high computational platforms is a critical challenge for efficient edge computing tasks like object detection, where power and bandwidth are low but faster and energy-efficient solutions are required. System-on-chip (SoC) designs are an optimistic solution for addressing these challenges. This study presents the power and delay-optimized Multiply-Accumulate (MAC) unit architecture for DNN and compares the parameters of 4-bit, 8-bit, 12-bit, and 16-bit MAC units. Vivado software has been employed to construct the MAC unit. It can carry out addition, accumulation, and multiplication operations. The design is analyzed and simulated using the Vivado High-Level Synthesis (HLS) tool, which is subsequently deployed on the Zybo Evaluation and Development Kit. The proposed approach outperforms the existing state-of-the-art models in terms of processing time and power for different precisions.

Enantioselective Heck/Tsuji−Trost reaction of flexible vinylic halides with 1,3-dienes

The enantioselective domino Heck/cross-coupling has emerged as a powerful tool in modern chemical synthesis for decades. Despite significant progress in relative rigid skeleton substrates, the implementation of asymmetric Heck/cross-coupling cascades of highly flexible haloalkene substrates remains a challenging and and long-standing goal. Here we report an efficient asymmetric domino Heck/Tsuji−Trost reaction of highly flexible vinylic halides with 1,3-dienes enabled by palladium catalysis. Specifically, the Heck insertion as stereodetermining step to form ƞ3 allyl palladium complex and in situ trapping with nucleophiles enable efficient Heck/etherification in a formal (4 + 2) cycloaddition manner. Engineering the Sadphos bearing androgynous non-C2-symmetric chiral sulfinamide phosphine ligands are vital component in achieving excellent catalytic reactivity and enantioselectivity. This strategy offers a general, modular and divergent platform for rapidly upgrading feedstock flexible vinylic halides and dienes to various value-added molecules and is expected to inspire the development of other challenging enantioselective domino Heck/cross-couplings.

A case study in statistical software development for advanced evidence synthesis: the combined value of analysts and research software engineers

BackgroundSince 2015, the Complex Reviews Synthesis Unit (CRSU) has developed a suite of web-based applications (apps) that conduct complex evidence synthesis meta-analyses through point-and-click interfaces. This has been achieved in the R programming language by combining existing R packages that conduct meta-analysis with the shiny web-application package. The CRSU apps have evolved from two short-term student projects into a suite of eight apps that are used for more than 3,000 h per month.AimHere, we present our experience of developing production grade web-apps from the point-of-view of individuals trained primarily as statisticians rather than software developers in the hopes of encouraging and inspiring other groups to develop valuable open-source statistical software whilst also learning from our experiences.Key challengesWe discuss how we have addressed challenges to research software development such as responding to feedback from our real-world users to improve the CRSU apps, the implementation of software engineering principles into our app development process and gaining recognition for non-traditional research work within the academic environment.Future developmentsThe CRSU continues to seek funding opportunities both to maintain and further develop our shiny apps. We aim to increase our user base by implementing new features within the apps and building links with other groups developing complementary evidence synthesis tools.

C–H Functionalization of Quinoxalines

AbstractAs a powerful tool in modern organic synthesis, C–H functionalization offers an elegant shortcut to molecular complexity, enabling direct bond transformations without any pre-functionalization. In this connection, the C–H functionalization of quinoxalines offers a direct and efficient approach to modify the quinoxaline scaffold, a key structural motif in biologically active compounds and materials. This personal account describes the methodologies developed by our group for the C–H activation and functionalization of quinoxaline compounds.1 Introduction2 Difluoroalkylamidation3 ortho C–H Alkylation4 ortho C–H Amination5 Conclusion

Acid-Enhanced Photoiniferter Polymerization under Visible Light.

Photoiniferter (PI) is a promising polymerization methodology, often used to overcome restrictions posed by thermal reversible addition-fragmentation chain-transfer (RAFT) polymerization. However, in the overwhelming majority of reports, high energy UV irradiation is required to effectively trigger photolysis of RAFT agents and facilitate the polymerization, significantly limiting its potential, scope, and applicability. Although visible light PI has emerged as a highly attractive alternative, most current approaches are limited to the synthesis of lower molecular weight polymers (i.e. 10,000 g/mol), and typically suffer from prolonged reaction times, extended induction periods, and higher dispersities when high activity CTAs (photoiniferters), such as trithiocarbonates, are employed. Herein, an acid-enhanced PI polymerization is introduced that efficiently operates under visible light irradiation. The presence of small amounts of biocompatible citric acid fully eliminates the lengthy induction period (21 hours) by enhancing photolysis, rapidly consuming the CTA, and accelerating the reaction rate, yielding polymers with narrow molar mass distributions (Ð ~1.1), near-quantitative conversions (>97 %), and high end-group fidelity in just two hours. A particularly noteworthy aspect of this work is the possibility to target very high degrees of polymerization (i.e. DP=3,000) within short timescales (i.e. less than five hours) without compromising the control over the dispersity (Ð ~1.1). The versatility of the technique is further demonstrated through the synthesis of well-defined diblock copolymers and its compatibility to various polymer classes (i.e. acrylamides, acrylates, methacrylates), thus establishing visible-light PI as a robust tool for polymer synthesis.

Technology-Dependent Synthesis and Optimization of Circuits for Small S-boxes

Boolean formula minimization is a notoriously hard problem. Circuit minimization, typically studied in the context of a much broader subject known as synthesis and optimization of circuits, introduces another layer of complexity since ultimately those technology-independent representations (e.g., Boolean formulas and truth tables) has to be transformed into a netlist of cells of the target technology library. To manage those complexities, the industrial community typically separates the synthesis process into two steps: technology-independent optimization and technology mapping. In each step, this approach only tries to find the local optimal solution and relies heavily on heuristics rather than a systematic search. However, for small S-boxes, a more systematic exploration of the design space is possible. Aiming at the global optimum, we propose a method which can synthesize a truth table for a small S-box directly into a netlist of the cells of a given technology library. Compared with existing technology-dependent synthesis tools like LIGHTER and PEIGEN, our method produces improved results for many S-boxes with respect to circuit area. In particular, by applying our method to the GF(2^4)-inverter involved in the tower field implementation of the AES S-box, we obtain the currently known lightest implementation of the AES S-box. The search framework can be tweaked to take circuit delay into account. As a result, we find implementations for certain S-boxes with both latency and area improved.

Mapping the Gaps: A Scoping Review of Virtual Care Solutions for Caregivers of Children with Chronic Illnesses.

Background/Objectives: Caregivers of children with chronic illnesses, including chronic pain, experience high levels of distress, which impacts their own mental and physical health as well as child outcomes. Virtual care solutions offer opportunities to provide accessible support, yet most overlook caregivers' needs. We conducted a scoping review to create an interactive Evidence and Gap Map (EGM) of virtual care solutions across a stepped care continuum (i.e., from self-directed to specialized care) for caregivers of youth with chronic illnesses. Methods: The review methodology was co-designed with four caregivers. Data sources were the peer-reviewed scientific literature and a call for innovations. Records were independently coded and assessed for quality. Results: Overall, 73 studies were included. Most virtual care solutions targeted caregivers of children with cancer, neurological disorders, and complex chronic illnesses. Over half were noted at lower levels of stepped care (i.e., self-guided apps and websites), with psychological strategies being predominant (84%). However, very few addressed caregivers' physical health (15%) or provided family counseling (19%) or practical support (1%). Significant gaps were noted in interventions for managing caregiver chronic pain, despite its high prevalence and impact on child outcomes. Conclusions: Evidence and Gap Maps are innovative visual tools for knowledge synthesis, facilitating rapid, evidence-informed decision-making for patients, families, health professionals, and policymakers. This EGM highlighted high-quality virtual care solutions ready for immediate scaling and identified critical evidence gaps requiring prioritization. To address the complexities of pediatric chronic illnesses, including chronic pain, virtual care initiatives must prioritize family-centered, accessible, and equitable approaches. Engaging caregivers as partners is critical to ensure interventions align with their needs and priorities.

Mechanistic Investigations of Cobalt-Catalyzed, Aminoquinoline-Directed C(sp2)-H Bond Functionalization.

Monoanionic, bidentate-auxiliary-directed, cobalt-catalyzed C-H bond functionalization has become a very useful tool in organic synthesis. A comprehensive investigation into isolated organometallic intermediates and their reactivity within the catalytic cycle is lacking. We report here mechanistic studies of cobalt-catalyzed, aminoquinoline-directed C(sp2)-H bond functionalization. A number of organometallic Co(III) intermediates have been isolated and structurally characterized, including, for the first time in the aminoquinoline system, complexes arising from migratory insertion into cobalt-carbon bonds. The catalytic and stoichiometric reactions of cobalt(III) aryls with alkenes, alkynes, carbon monoxide, cyclic secondary amines, and aminoquinoline benzamides have been explored. The oxidation state of cobalt intermediates in the product-forming step depends on the nature of the coupling component. Specifically, annulation with alkynes and carbonylation with CO likely proceed via a Co(I)/Co(III) catalytic cycle. Carbon-hydrogen bond functionalization with alkenes and amines, as well as benzamide homocoupling, likely proceed via a (formally) Co(IV) species and involve oxidatively induced reductive elimination.

TinyHLS: a novel open source high level synthesis tool targeting hardware accelerators for artificial neural network inference.

In recent years, wearable devices such as smartwatches and smart patches have revolutionized biosignal acquisition and analysis, particularly for monitoring electrocardiography (ECG). However, the limited power supply of these devices often precludes real-time data analysis on the patch itself. This paper introduces a novel Python package, tinyHLS (High Level Synthesis), designed
to address these challenges by converting Python-based AI models into platform-independent hardware description language (HDL) code accelerators. Specifically designed for convolutional neural networks (CNNs), tinyHLS seamlessly integrates into the AI developer's workflow in Python TensorFlow Keras. Our methodology leverages a template-based hardware compiler that ensures flexibility, efficiency, and ease of use. In this work, tinyHLS is first-published featuring templates for several layers of neural networks, such as dense, convolution, max and global average pooling. In the first version, rectified linear unit (ReLU) is supported as activation. It targets one-dimensional data, with a particular focus on time series data. The generated accelerators are validated in detecting atrial fibrillation (AF) on electrocardiogram (ECG) data, demonstrating significant improvements in processing speed (62-fold) and energy efficiency (4.5-fold). Quality of code and synthesizability are ensured by validating the outputs with commercial ASIC design tools. Importantly, tinyHLS is open-source and does not rely on commercial tools, making it a versatile solution for both academic and commercial applications. The paper also discusses the integration with an open-source RISCV and potential for future enhancements of tinyHLS, including its application in edge servers and cloud computing. The source code is available on GitHub: https://github.com/Fraunhofer-IMS/tinyHLS.

Nucleophilic addition of bulk chemicals with imines using N-functionalized hydroxylamine reagents as precursors

C–C and C–X bond forming reactions are essential tools in organic synthesis, constantly revolutionizing human life. Among the key methods for constructing new chemical bonds are nucleophilic addition reactions involving imines. However, the inherent challenges in synthesizing and storing imines have stimulated interest in designing stable precursors, which generates imines in situ during the reaction. This approach offers a promising alternative to traditional strategies and holds significant potential for future applications. Here we report a direct and general nucleophilic addition of imines with cost-effective feedstocks and easily accessible nucleophiles, specifically utilizing N‑functionalized hydroxylamine reagents as bench-stable precursors. This methodology streamlines the synthesis of various products, such as amino acid derivatives, through a wide range of reaction types, including C–C, C–N, C–O, and C–S bond constructions. Mechanistic studies and DFT calculations provide insights into a plausible reaction mechanism that supports the in-situ imine formation.

Reacon: a template- and cluster-based framework for reaction condition prediction.

Computer-assisted synthesis planning has emerged as a valuable tool for organic synthesis. Prediction of reaction conditions is crucial for applying the planned synthesis routes. However, achieving diverse suggestions while ensuring the reasonableness of predictions remains an underexplored challenge. In this study, we introduce an innovative method for forecasting reaction conditions using a combination of graph neural networks, reaction templates, and clustering algorithm. Our method, trained on the refined USPTO dataset, excels with a top-3 accuracy of 63.48% in recalling the recorded conditions. Moreover, when focusing solely on recalling reactions within the same cluster, the top-3 accuracy increases to 85.65%. Finally, by applying the method to recently published molecule synthesis routes and achieving an 85.00% top-3 accuracy at the cluster level, we demonstrate our approach's capability to deliver reliable and diverse condition predictions.

Acid‐Enhanced Photoiniferter Polymerization under Visible Light

Photoiniferter (PI) is a promising polymerization methodology, often used to overcome restrictions posed by thermal reversible addition‐fragmentation chain‐transfer (RAFT) polymerization. However, in the overwhelming majority of reports, high energy UV irradiation is required to effectively trigger photolysis of RAFT agents and facilitate the polymerization, significantly limiting its potential, scope, and applicability. Although visible light PI has emerged as a highly attractive alternative, most current approaches are limited to the synthesis of lower molecular weight polymers, and typically suffer from prolonged reaction times, extended induction periods, and higher dispersities. Herein, an acid‐enhanced PI RAFT polymerization is introduced that efficiently operates under visible light irradiation. The presence of small amounts of biocompatible citric acid fully eliminates the lengthy induction period by enhancing photolysis, rapidly consumes the CTA, and accelerates the reaction rate, yielding polymers with narrow molar mass distributions, near‐quantitative conversions, and high end‐group fidelity. Ultra‐high molecular weight polymers can also be synthesized within short timescales without compromising the control over the dispersity (Ð ~1.1). The versatility of the technique is further demonstrated through the synthesis of well‐defined diblock copolymers and its compatibility to various polymer classes (i.e. acrylamides, acrylates, methacrylates), thus establishing visible‐light PI as a robust tool for polymer synthesis.

Fast Design Space Exploration for Always-On Neural Networks

An analytical model can quickly predict performance and energy efficiency based on information about the neural network model and neural accelerator architecture, making it ideal for rapid pre-synthesis design space exploration. This paper proposes a new analytical model specifically targeted for convolutional neural networks used in always-on applications. To validate the proposed model, the performance and energy efficiency estimated by the model were compared with actual hardware and post-synthesis gate-level simulations of hardware synthesized with a state-of-the-art electronic design automation (EDA) synthesis tool. Comparisons with hardware created for the Eyeriss neural accelerator showed average execution time and energy consumption error rates of 3.33% and 13.54%, respectively. Comparisons with hardware synthesis results showed an error of 3.18% to 9.44% for two example neural accelerator configurations used to execute MobileNet, EfficientNet, and DarkNet neural network models. Finally, the utility of the proposed model was demonstrated by using it to evaluate the effects of different channel sizes, pruning rates, and batch sizes in several neural network designs for always-on vision, text, and audio processing.

Solvent Dictated Organic Transformations.

Solvent plays an important role in many chemical reactions. The C-H activation has been one of the most powerful tools in organic synthesis. These reactions are often assisted by solvents which not only provide a medium for the chemical reactions but also facilitate reaching to the product stage. The solvent helps the reaction profile both chemically and energetically to reach the targeted product. Organic transformations via C-H activation from the solvent assistance perspective has been discussed in this review. Various solvents such as tetrahydrofuran (THF), MeCN, dichloromethane (DCM), dimethoxyethane (DME), 1,2-dichloroethane (1,2-DCE), dimethylformamide (DMF), dimethylsulfoxide (DMSO), isopropyl nitrile (iPrCN), 1,4-dioxane, AcOH, trifluoroacetic acid (TFA), Ac2O, PhCF3, chloroform (CHCl3), H2O, N-methylpyrrolidone (NMP), acetone, methyl tert-butyl ether (MTBE), toluene, p-xylene, alcohols, MeOH, 1,1,1-trifluoroethanol (TFE), 1,1,1,3,3,3-hexafluoroisopropanol (HFIP), tert-amyl alcohol and their roles are discussed. The exclusive role of the solvent in various transformations has been deliberated by highlighting the substrate scope, along with the proposed mechanisms. For easy classification, the review has been divided into three parts: (i) solvent-switched divergent C-H activation; (ii) C-H bond activation with solvent as the coupling reagent, and (iii) C-H activation with solvent caging and solvent-assisted electron donor acceptor (EDA) complex formation and autocatalysis.

AI-Driven WSN for Precise Aquatic Pollution Detection Using an Intelligent Monitoring Approach

The proliferation of digital devices, sensors, and interconnected systems has led to an explosion of data. Simple sensors like pH, conductivity, and Turbidity sensors can be used for the classification of gasoline and diesel in water. These sensors are easy to set up and deploy, so they can be installed in vast numbers and can get real-time data from the site. FPGAs are very fast in processing complex data and have low latency time compared to other traditional microcontrollers. But FPGA accepts coding in Hardware Description Languages like Verilog or VHDL, which can be very complex to code complex models that are trained in other high-level languages like Python and C++. Simple classification models in machine learning are implemented using High-Level Synthesis tools, which accept codes written in languages like C, C++, or SystemC, and translate them into hardware-implementable RTL (Register- Transfer Level) code. The data from two major components of oil, gasoline, and petrol are used to train various classification models with widely used libraries in Python. The trained parameters are extracted from the trained model. The parameters are then assembled and then coded in C++ as currently most of the tools support C++. Some modifications need to be made to the original code to make it compatible with the synthesis tool.

Visible‐Light Driven Photocatalytic Strategies for the Synthesis of Pyrrolines

Pyrroline is a five‐membered nitrogen‐containing heterocyclic compound found in various biologically active compounds, pharmaceuticals, and agrochemicals. Several synthetic methods have been reported for the preparation of these heterocycles, along with their notable biological activities. Visible light and photoredox catalysis have become powerful and enduring tools in organic synthesis. This review highlights the impact of visible light‐driven chemistry on the synthesis and modification of heterocyclic compounds containing pyrroline ring for their medicinal importance.

Maleimide-Dependent Rh(III)-Catalyzed Site-Selective Mono and Dual C-H Functionalization of 2-Arylbenzo[d]thiazole and Oxazole Derivatives.

The site-selective functionalization of aromatic compounds via C-H activation has emerged as a popular tool in organic synthesis. In this study, we report a regioselective coupling of maleimide to 2-arylbenzo[d]thiazoles in the presence of a rhodium(III) catalyst. Depending upon the nature of the substituent (R2-group) present in the maleimide substrate, either mono- or bis-1,4-addition products were observed in this methodology. In the case of R2 = aryl, cyclohexyl, and tert-butyl, mono coupling was observed, whereas substituents, such as methyl, ethyl, benzyl, and methyl thiophene, provided bis coupling as the major products. Similar selectivity was also observed in the case of 2-arylbenzo[d]oxazoles.

Visible Light‐Mediated Radical Decarboxylative Strategies for the Synthesis of Heterocyclic Compounds

AbstractHeterocycles are an important structural motif found in many bioactive compounds, natural products and pharmaceuticals. Heterocycles have been synthesised at large employing conventional methods which are usually associated with limitations such as the need for an external oxidant or reductant, harsh reaction conditions, lower selectivity and its unsustainable nature. To circumvent these drawbacks, visible light photocatalysis has emerged as a milder, energetically efficient and environmentally benign tool in organic synthesis. Visible light‐mediated radical decarboxylation of carboxylic acid and its derivatives has been widely explored to affect a wide range of chemical transformation reactions. Redox‐active NHPI derivatives are easily prepared from carboxylic acids which are inexpensive, non‐toxic and stable finding wide applications across various fields. Further, carboxylic acids are one of the most plentiful and renewable feedstock of our planet. Hence, this review brings forth the current developments in the area of visible light‐mediated decarboxylative strategies employing carboxylic acid and its derivatives for the synthesis of heterocyclic compounds.

Reporting of nutritional screening, status, and intake in trials of nutritional and physical rehabilitation following critical illness: a systematic review.

Surviving critical illness leads to prolonged physical and functional recovery with both nutritional and physical rehabilitation interventions for prevention and treatment being investigated. Nutritional status and adequacy may influence outcome, but no consensus on which nutritional-related variables should be measured and reported in clinical trials exists. This study aimed to undertake a systematic review investigating the reporting of nutritional screening, nutritional status, and nutritional intake/delivery in randomized controlled trials (RCTs) evaluating nutritional and/or physical rehabilitation on physical and functional recovery during and following critical illness. Five electronic databases (MEDLINE, Web of Science, EMBASE, CINAHL, and Cochrane) were searched (last update 9 August, 2023). Search terms included both free text and standardized indexed terms. Studies included were RCTs assessing nutritional and/or physical interventions either during or following intensive care unit (ICU) admission in adults (18 y or older) with critical illness, and who required invasive mechanical ventilation for any duration during ICU admission. Study quality was assessed using the Cochrane Collaboration Risk of Bias tool for RCTs and descriptive data synthesis was performed and presented as counts (%). n t RESULTS: In total, 123 RCTs (30 nutritional, 87 physical function, and 6 combined) were included. Further, ≥1 nutritional variable was measured and/or reported in 99 (80%) of the studies including BMI (n = 69), body weight (n = 57), nutritional status (n = 11), nutritional risk (n = 10), energy delivery (n = 41), protein delivery (n = 35), handgrip strength (n = 40), and other nutritional-related muscle variables (n = 41). Only 3 studies were considered to have low risk of bias in all categories. Few RCTs of physical rehabilitation measure and report nutritional or related variables. Future studies should measure and report specific nutritional factors that could impact physical and functional recovery to support interpretation where studies do not show benefit. This protocol was preregistered at PROSPERO as CRD42022315122.