Palladium-Catalyzed Chemoselective Phosphorylation of Poly(pseudo)halides: A Route for Organophosphorus Synthesis.

We present an advancement in synthesizing organophosphorus compounds via chemoselective phosphorylation achieved by a palladium and SelectPhos ligand system (Pd/L1). This catalysis system exhibits remarkable chemoselectivity, even in poly(pseudo)halide substrates and overcoming toxicity and substrate scope limitations. The catalytic system is robust, which is demonstrated across diverse substrates such as chloroaryl and bromoaryl triflates. Furthermore, we present a one-pot sequential strategy combining phosphorylation with Suzuki-Miyaura coupling, providing a versatile platform for the efficient synthesis of complex organophosphorus compounds, challenging conventional reactivity paradigms.

Thermal stability and decomposition kinetics of mixed-cation halide perovskites.

Organic-inorganic halide perovskites (OIHPs) have emerged as one of the most efficient photovoltaic materials due to their superior properties. However, improving their stability remains a key challenge. Herein, we investigate the thermal decomposition properties of OIHP FAxMA1-xPbI3 with mixed cations of formamidinium (FA) and methylammonium (MA). Using thermogravimetric analysis together with Fourier transform infrared spectroscopy, we identify and monitor the gaseous decomposition products as a function of temperature and cation composition. Thermal decomposition products of both MA and FA cations were observed at all stages of the thermal decomposition process, contrary to previous expectations. The yield, release sequence and kinetics of the organic gaseous products were found to depend strongly on the ratio between FA and MA cations. Furthermore, cesium ion doping was investigated as a potential strategy to improve the thermal stability of mixed cation perovskites. These results provide new insights into the effect of cation mixing on perovskite stability, suggesting that optimizing the cation ratios and decomposition pathways can guide approaches to boost the stability and performance of mixed cation perovskites.

An indole-amide-based phosphine ligand enabling a general palladium-catalyzed sterically hindered Suzuki-Miyaura cross-coupling reaction.

A novel family of indole-amide-based phosphine ligands was designed and synthesized. The Pd/InAm-phos (L1) catalytic system exhibited excellent efficiency in the Suzuki-Miyaura cross-coupling of sterically hindered (hetero)aryl chlorides to synthesize tri-ortho-substituted biaryls. Excellent product yields were obtained in a short reaction time (e.g., 10 min), and a Pd catalyst loading down to 50 ppm was also achieved. The oxidative addition adduct of Pd-L1 with 2-chlorotoluene was also well-characterized by single-crystal X-ray crystallography and showed a κ2-P,O-coordination of L1 with palladium.

Exploration of Aryl Phosphates in Palladium-Catalyzed Mono-α-arylation of Aryl and Heteroaryl Ketones.

This paper presents the first general examples of selective palladium-catalyzed mono-α-arylation of aryl and heteroaryl ketones with aryl phosphates. The catalyst system, consisting of [Pd(2-butenyl)Cl]2 and MorDalPhos, exhibited high catalytic reactivity toward this reaction. A wide range of aryl phosphates were efficiently coupled with aryl and heteroaryl ketones with good selectivity. Excellent-to-good product yields were afforded. The gram-scale reaction was conducted smoothly. Reductive elimination or transmetalation might be a rate-determining step in this reaction.

Palladium-Catalyzed C(sp2)-N Bond Cross-Coupling with Triaryl Phosphates.

The first general palladium-catalyzed amination of aryl phosphates is described. The combination of MorDalPhos with [Pd(π-cinnamyl)Cl]2 enables the amination of electron-rich, electron-neutral, and electron-poor aryl phosphates with a board range of aromatic, aliphatic, and heterocyclic amines. Common functional groups such as ether, keto, ester, and nitrile show an excellent compatibility in this reaction condition. The solvent-free amination reactions are also successful in both solid coupling partners. The gram-scale cross-coupling is achieved by this catalytic system.

Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study.

BACKGROUND: Electronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children. METHODS AND FINDINGS: Real-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ≤ -6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records (≥3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered. CONCLUSIONS: To our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia.

Annulation of ß-naphthols and 4-hydroxycoumarins with vinylsulfonium salts: synthesis of dihydrofuran derivatives.

A new synthetic approach to dihydrofuran derivatives via the annulation reaction of ß-naphthols and 4-hydroxycoumarins with vinylsulfonium salts has been developed. A variety of dihydrofuran derivatives were prepared in moderate to good yields under mild conditions. The products could be readily transformed to the corresponding furans via the dehydrogenation with DDQ.

Construction and implications of structural equation modeling network for pediatric cataract: a data mining research of rare diseases.

BACKGROUND: The majority of rare diseases are complex diseases caused by a combination of multiple morbigenous factors. However, uncovering the complex etiology and pathogenesis of rare diseases is difficult due to limited clinical resources and conventional statistical methods. This study aims to investigate the interrelationship and the effectiveness of potential factors of pediatric cataract, for the exploration of data mining strategy in the scenarios of rare diseases. METHODS: We established a pilot rare disease specialized care center to systematically record all information and the entire treatment process of pediatric cataract patients. These clinical records contain the medical history, multiple structural indices, and comprehensive functional metrics. A two-layer structural equation model network was applied, and eight potential factors were filtered and included in the final modeling. RESULTS: Four risk factors (area, density, location, and abnormal pregnancy experience) and four beneficial factors (axis length, uncorrected visual acuity, intraocular pressure, and age at diagnosis) were identified. Quantifiable results suggested that abnormal pregnancy history may be the principle risk factor among medical history for pediatric cataracts. Moreover, axis length, density, uncorrected visual acuity and age at diagnosis served as the dominant factors and should be emphasized in regular clinical practice. CONCLUSIONS: This study proposes a generalized evidence-based pattern for rare and complex disease data mining, provides new insights and clinical implications on pediatric cataract, and promotes rare-disease research and prevention to benefit patients.

Intramolecular cyclization of diarylmethanols and α,ß-unsaturated amides promoted by KOt-Bu/DMF: a metal-free approach towards 3,4-disubstituted quinolinones.

A facile synthesis of quinolinones through intramolecular addition of diarylmethanols to α,ß-unsaturated amides promoted by KOt-Bu/DMF is reported. A series of 3,4-disubstituted quinolinones were obtained in moderate to good yields. A reaction pathway via the ketyl radical is proposed.

Intramolecular addition of diarylmethanols to imines promoted by KOt-Bu/DMF: a new synthetic approach to indole derivatives.

KOt-Bu/DMF promoted intramolecular addition of diarylmethanols to imines was developed. A series of 2,3-disubstituted indoles was obtained in good yields. A reaction mechanism of radical cyclization and subsequent dehydration is proposed.

Score-based Counterfactual Generation for Interpretable Medical Image Classification and Lesion Localization.

Deep neural networks (DNNs) have immense potential for precise clinical decision-making in the field of biomedical imaging. However, accessing high-quality data is crucial for ensuring the high-performance of DNNs. Obtaining medical imaging data is often challenging in terms of both quantity and quality. To address these issues, we propose a score-based counterfactual generation (SCG) framework to create counterfactual images from latent space, to compensate for scarcity and imbalance of data. In addition, some uncertainties in external physical factors may introduce unnatural features and further affect the estimation of the true data distribution. Therefore, we integrated a learnable FuzzyBlock into the classifier of the proposed framework to manage these uncertainties. The proposed SCG framework can be applied to both classification and lesion localization tasks. The experimental results revealed a remarkable performance boost in classification tasks, achieving an average performance enhancement of 3-5% compared to previous state-of-the-art (SOTA) methods in interpretable lesion localization.

A Statistical Physics Perspective: Understanding the Causality Behind Convolutional Neural Network Adversarial Vulnerability.

The adversarial vulnerability of convolutional neural networks (CNNs) refers to the performance degradation of CNNs under adversarial attacks, leading to incorrect decisions. However, the causes of adversarial vulnerability in CNNs remain unknown. To address this issue, we propose a unique cross-scale analytical approach from a statistical physics perspective. It reveals that the huge amount of nonlinear effects inherent in CNNs is the fundamental cause for the formation and evolution of system vulnerability. Vulnerability is spontaneously formed on the macroscopic level after the symmetry of the system is broken through the nonlinear interaction between microscopic state order parameters. We develop a cascade failure algorithm, visualizing how micro perturbations on neurons' activation can cascade and influence macro decision paths. Our empirical results demonstrate the interplay between microlevel activation maps and macrolevel decision-making and provide a statistical physics perspective to understand the causality behind CNN vulnerability. Our work will help subsequent research to improve the adversarial robustness of CNNs.

Adaptive prescribed settling time periodic event-triggered control for uncertain robotic manipulators with state constraints.

In this paper, an adaptive prescribed settling time periodic event-triggered control (APST-PETC) is investigated for uncertain robotic manipulators with state constraints. In order to economize network bandwidth occupancy and reduce computational burden, a periodic event-triggered control (PETC) strategy is proposed to reduce the update frequency of the control signal and avoid unnecessary continuous monitoring. Besides, considering that the maneuverable space of the actual robotic manipulators is often limited, the barrier Lyapunov function (BLF) is applied to deal with the influence of the constraint characteristics on the robotic manipulators. Further, based on the one-to-one nonlinear mapping function of the system tracking error, an adaptive prescribed settling time control (APSTC) is designed to ensure that the system tracking error reaches the predetermined precision residual set within the prescribed settling time. Finally, theoretical analysis and comparative experiments are given to verify its feasibility.

Adaptive 2-bits-triggered neural control for uncertain nonlinear multi-agent systems with full state constraints.

This paper investigates an adaptive 2-bits-triggered neural control for a class of uncertain nonlinear multi-agent systems (MASs) with full state constraints. Considering the limitations of practical physical devices and operating conditions, MASs may suffer performance degradation or even crash while the system states are not restricted. With this in mind, combined with barrier Lyapunov function (BLF), an adaptive neural consensus control is developed to guarantee that the state constraints of all followers are not violated. Further, the conversion relationship between the state constraints of MASs and the synchronization error constraints is clarified more precisely, which could improve the synchronization performance of MASs. In addition, considering both trigger threshold setting and control signal transmission bits issues, a 2-bit trigger strategy is proposed to maximize the utilization of MASs bandwidth resources. Theoretical analysis shows that all signals are uniformly ultimately bounded. And the simulation results demonstrate its effectiveness.

Palladium-catalyzed chemoselective direct α-arylation of carbonyl compounds with chloroaryl triflates at the C-Cl site.

This study described palladium-catalyzed chemoselective direct α-arylation of carbonyl compounds with chloroaryl triflates in the Ar-Cl bond. The Pd/SelectPhos system showed excellent chemoselectivity toward the Ar-Cl bond in the presence of the Ar-OTf bond with a broad substrate scope and excellent product yields. The electronic and steric hindrance offered by the -PR2 group of the ligand with the C2-alkyl group was found to be the key factor affecting the reactivity and chemoselectivity of the α-arylation reaction. The chemodivergent approach was also successfully employed in the synthesis of flurbiprofen and its derivatives (e.g., -OMe and -F).

Event-triggered prescribed settling time consensus control of uncertain nonlinear multiagent systems with given transient performance.

Multiagent systems (MASs) are usually used in unmanned aerial vehicle formations, multi-manipulator coordinated, traffic vehicle control and other fields, which have attracted a lot of attention from scholars. In this research, with the help of the designed performance function, the nonlinear transformation of synchronization error is realized. And the synchronization error of MASs with given transient performance could converge to the predefined interval. According to the designed transformation function, a prescribed setting time consensus control is investigated with the advantages of Radial Basis Function Neural Networks (RBFNNs) in dealing with unknown functions. It guarantees that the MASs under consideration are uniformly bounded convergent. Furthermore, event-triggered mechanism is applied to relieve pressure of MASs' communication resources. Simulation results demonstrate its effectiveness.

Robust optimization control for turbidity process with large inertia and time-delay in waterworks.

To address the difficulty of optimizing turbidity process with large inertia and long time delay, a twice-optimal control and construction pruning (TOCCP) strategy is proposed, which can be used to improve the fast stability and strong robustness of industrial process. The new approach owns the advantages of twice optimal control (TOC) and construction pruning (CP) method. The TOC constructs an infinite-dimensional state observer of the system, which can eliminate the influence of time delay on the system. The CP is adopted to improve the mid-band damping of the open-loop frequency characteristics to improve the robustness of the system. Moreover, an exhaustive algorithm is designed to find the optimal time scale, the most important parameter for adjusting the system response speed. In addition, the influences of different parameter changes on the robustness of the system are analyzed, and a practical parameter tuning formula is provided. Simulation-based comparisons of TOCCP and the other two algorithms were made. The results show that the system optimized by TOCCP has strong robustness, especially in disturbances rejection and set-point tracking. Moreover, TOCCP is practical and easily implemented to set parameters, providing guidelines for industrial application of large inertia and time-delay systems.

Enterovirus 71 non-structural protein 3A hijacks vacuolar protein sorting 25 to boost exosome biogenesis to facilitate viral replication.

Human enterovirus 71 (EV71) is one of the major agents of the hand, foot, and mouth disease (HFMD), and occasionally causes severe neurological complications. There is clinical evidence that EV71 infection increases the exosomes in the serum of severe HFMD patients, suggesting a role of exosomes in EV71 pathogenesis. However, the relationship between exosomes and EV71 replication remains elusive. In this study, we initially found that EV71 infection elevated exosome biogenesis in the cultured cells. Among EV71 non-structural proteins, we identified EV71 3A, but not 3B, constitutively promoted exosome secretion. In detail, EV71 3A protein interacted with vacuolar protein sorting 25 (VPS25), while knock-down of VPS25 reduced EV71 3A protein- and EV71-induced exosome production. Further studies revealed VPS25 located on exosomes and its expression correlated to the exosome production. During EV71 infection, knock-down of VPS25 decreased exosome biogenesis to attenuate viral replication. Consistently, GW4869, an exosome inhibitor, exerted an obviously antiviral activity against EV71 replication companied with the decrease of exosome secretion or formation. These findings suggest the binding of EV71 3A and VPS25 benefited exosome biogenesis, thereby boosting viral replication. This study uncovers a novel mechanism underlying EV71-mediated exosomes in the regulation of viral replication, which provides potential anti-viral strategies against the EV71 infection and transmission in HFMD.

Fuzzy Adaptive Two-Bit-Triggered Control for a Class of Uncertain Nonlinear Systems With Actuator Failures and Dead-Zone Constraint.

This article investigates a fuzzy adaptive two-bit-triggered control for uncertain nonlinear systems with actuator failures and dead-zone constraint. Actuator failures and dead-zone constraint exist frequently in practical systems, which will affect the system performance greatly. Based on the improved fuzzy-logic systems (FLSs), a fuzzy adaptive compensation control is established to address these issues. The approximation error is introduced to the control design as a time-varying function. In addition, for the limited transmission resources of the practical system, a two-bit-triggered control mechanism is proposed to further save system transmission resources. It is proved that the proposed method can guarantee the system tracking performance and all the signals are bounded. Its effectiveness is verified by the simulation examples.

Pd-Catalyzed Cross-Coupling of Highly Sterically Congested Enol Carbamates with Grignard Reagents via C-O Bond Activation.

The palladium-catalyzed cross-coupling reaction of enol carbamates to construct highly sterically congested alkenyl compounds is presented for the first time. This protocol demonstrates the potential of using thermally stable and highly atom-economic enol electrophiles as building blocks in bulky alkene synthesis. This reaction accommodates a broad substrate scope with excellent Z/E isomer ratios, which also provides a new synthetic pathway for accessing Tamoxifen.