Effects of Lactobacillus plantarum on Broiler Health: Integrated Microbial and Metabolomics Analysis.

Given China's prohibition on the utilization of antibiotics as feed additives in 2020, we aim to investigate nutrition additives that are both efficient and safe. Lactobacillus, a well-recognized beneficial probiotic, has explicitly been investigated for its effects on health status of the host and overall impact on food industry. To evaluate effects of Lactobacillus plantarum (LW) supplementation on broiler chicken, we conducted comprehensive multi-omics analysis, growth performance evaluation, RT-qPCR analysis, and immunofluorescence. The findings revealed that LW supplementation resulted in a substantial progress in growth performance (approximately 205 g increase in final body weight in comparison to the control group (p < 0.01)). Additionally, LW exhibited promising potential for enhancing antioxidant properties of serum and promoting gut integrity and growth as evidenced by improved antioxidant indices (p < 0.01), intestinal villus morphology (p < 0.01), and enhanced gut barrier function (p < 0.01). Meanwhile, the multi-omics analysis, including 16S rRNA sequencing and liquid chromatography-tandem mass spectrometry, revealed an enrichment of beneficial microbes in the gut of broilers that were supplemented with LW, while simultaneously depleting harmful microorganisms. Moreover, a noteworthy modification was observed in gut metabolic profiling subsequent to the execution of the probiotic strategy. Specifically, variations were noticed in the levels of metabolites and metabolic pathways such as parathyroid hormone synthesis, inflammatory mediator regulation of TRP channels, oxidative phosphorylation, and mineral absorption. Taken together, our findings validate that LW administration produces valuable effects on the health and growth performance of broilers owing to its capability to boost the gut microbiota homeostasis and intestinal metabolism. Present findings signify the potential of LW as a dietary additive to promote growth and development in broiler chickens.

Regulating the Scaling Relations in Ammonia Synthesis through a Light-driven Bendable Seesaw Effect on Tailored Iron Catalyst.

Advancing the energy-intensive Haber-Bosch process faces significant challenges due to the intrinsic constraints of scaling relations in heterogeneous catalysis. Herein, we reported an approach of bending the "seesaw effect" to regulate the scaling relations over a tailored α-Fe metallic material (α-Fe-110s), realizing highly efficient light-driven thermal catalytic ammonia synthesis rate of 1260 µmol gcatalyst-1 h-1 without additional heating. Specifically, the thermal catalytic activity of α-Fe-110s was significantly enhanced by the novel stepped {110} surface, exhibiting a 3.8-fold increase compared to the commercial fused-iron catalyst with promoters at 350 °C. The photo-induced hot electron transfer further accelerates the dinitrogen dissociation and hydrogenation simultaneously, effectively overcoming the limitation of scaling relation over identical sites. Consequently, the ammonia production rate of α-Fe-110s was further enhanced by 30 times at the same temperature with irradiation. This work designs an efficient and sustainable system for ammonia synthesis and provides a novel approach for regulating the scaling relations in heterogeneous catalysis.

Versatile Curve Design by Level Set with Quadratic Convergence.

Many 3D mesh processing tasks revolve around generating and manipulating curves on surface meshes. While it is intuitive to explicitly model these curves using mesh edges or parametric curves in the ambient space, these methods often suffer from numerical instability or inaccuracy due to the projection operation. Another natural strategy is to adapt spline based tools, these methods are quite fast but are hard to be extended to more versatile constraints and need heavy manual interactions. In this paper, we present an efficient and versatile approach to curve design based on an implicit representation known as the level set. While previous works have explored the use of the level set to generate curves with minimal length, they typically have limitations in accommodating additional conditions for rich and robust control. To address these challenges, we formulate curve editing with constraints like smoothness, interpolation, tangent control, etc., via a level set based variational problem by constraining the values or derivatives of the level set function. However, the widely used gradient flow strategy converges very slowly for this complicated variational problem compared to the classical geodesic one. Thus, we propose to solve it via Newton's method enhanced by local Hessian correction and a trust-region strategy. As a result, our method not only enables versatile control, but also excels in terms of performance due to nearly quadratic convergence and almost linear complexity in each iteration via narrow band acceleration. In practice, these advantages effectively benefit various applications, such as interactive curve manipulation, boundary smoothing for surface segmentation and path planning with obstacles as demonstrated.

Transient dataset of household appliances with Intensive switching events.

With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China.

Cobalt-Ion Superhygroscopic Hydrogels Serve as Chip Heat Sinks Achieving a 5 °C Temperature Reduction via Evaporative Cooling.

In the rapidly advancing semiconductor sector, thermal management of chips remains a pivotal concern. Inherent heat generation during their operation can lead to a range of issues such as potential thermal runaway, diminished lifespan, and current leakage. To mitigate these challenges, the study introduces a superhygroscopic hydrogel embedded with metal ions. Capitalizing on intrinsic coordination chemistry, the metallic ions in the hydrogel form robust coordination structures with non-metallic nitrogen and oxygen through empty electron orbitals and lone electron pairs. This unique structure serves as an active site for water adsorption, beginning with a primary layer of chemisorbed water molecules and subsequently facilitating multi-layer physisorption via Van der Waals forces. Remarkably, the cobalt-integrated hydrogel demonstrates the capability to harvest over 1 and 5 g g-1 atmospheric water at 60% RH and 95% RH, respectively. Furthermore, the hydrogel efficiently releases the entirety of its absorbed water at a modest 40°C, enabling its recyclability. Owing to its significant water absorption capacity and minimal dehydration temperature, the hydrogel can reduce chip temperatures by 5°C during the dehydration process, offering a sustainable solution to thermal management in electronics.

An Electrode-Crosstalk-Suppressing Smart Polymer Electrolyte for High Safety Lithium-Ion Batteries.

Electrode crosstalk between anode and cathode at elevated temperatures is identified as a real culprit triggering the thermal runaway of lithium-ion batteries. Herein, to address this challenge, a novel smart polymer electrolyte is prepared through in situ polymerization of methyl methacrylate and acrylic anhydride monomers within a succinonitrile-based dual-anion deep eutectic solvent. Owing to the abundant active unsaturated double bonds on the as-obtained polymer matrix end, this smart polymer electrolyte can spontaneously form a dense crosslinked polymer network under elevated temperatures, effectively slowing down the crosstalk diffusion kinetics of lithium ions and active gases. Impressively, LiCoO2/graphite pouch cells employing this smart polymer electrolyte demonstrate no thermal runaway even at the temperature up to 250 °C via accelerating rate calorimeter testing. Meanwhile, because of its abundance of functional motifs, this smart polymer electrolyte can facilitate the formation of stable and thermally robust electrode/electrolyte interface on both electrodes, ensuring the long cycle life and high safety of LIBs. In specific, this smart polymer electrolyte endows 1.1 Ah LiCoO2/graphite pouch cell with a capacity retention of 96% after 398 cycles at 0.2 C.

Amino-functionalized cellulose composite for efficient simultaneous adsorption of tetracycline and copper ions: Performance, mechanism and DFT study.

A novel cellulose composite (denoted as PEI@MMA-1) with porous interconnected structure was prepared by adsorbing methyl cellulose (MC) onto microcrystalline cellulose (MCC) and cross-linking polyethyleneimine (PEI) with MCC by the action of epichlorohydrin, which had the excellent adsorption property, wettability and elasticity. The performances of PEI@MMA-1 composite for removing tetracycline (TC), Cu2+ and coexistent pollutant (TC and Cu2+ mixture) were systematically explored. For single TC or Cu2+ contaminant, the maximum adsorption capacities were 75.53 and 562.23 mg/g at 30 °C, respectively, while in the dual contaminant system, they would form complexes and Cu2+ could play a "bridge" role to remarkably promote the adsorption of TC with the maximum adsorption capacities of 281.66 and 253.58 mg/g for TC and Cu2+. In addition, the adsorption kinetics, isotherms and adsorption mechanisms of single-pollutant and dual-pollutant systems have been thoroughly investigated. Theoretical calculations indicated that the amide group of TC molecule with the assistance of Cu2+ interacted with the hydroxyl group of PEI@MMA-1 composite to enhance the TC adsorption capacity. Cycle regeneration and fixed bed column experiments revealed that the PEI@MMA-1 possessed the excellent stability and utility. Current PEI@MMA-1 cellulose composite exhibited a promising application for remediation of heavy metals and antibiotics coexistence wastewater.

Activity of the Ubiquitin-activating Enzyme Inhibitor TAK-243 in Adrenocortical Carcinoma Cell Lines, Patient-derived Organoids, and Murine Xenografts.

Current treatment options for metastatic adrenocortical carcinoma (ACC) have limited efficacy, despite the common use of mitotane and cytotoxic agents. This study aimed to identify novel therapeutic options for ACC. An extensive drug screen was conducted to identify compounds with potential activity against ACC cell lines. We further investigated the mechanism of action of the identified compound, TAK-243, its synergistic effects with current ACC therapeutics, and its efficacy in ACC models including patient-derived organoids and mouse xenografts. TAK-243, a clinical ubiquitin-activating enzyme (UAE) inhibitor, showed potent activity in ACC cell lines. TAK-243 inhibited protein ubiquitination in ACC cells, leading to the accumulation of free ubiquitin, activation of the unfolded protein response, and induction of apoptosis. TAK-243 was found to be effluxed out of cells by MDR1, a drug efflux pump, and did not require Schlafen 11 (SLFN11) expression for its activity. Combination of TAK-243 with current ACC therapies (e.g., mitotane, etoposide, cisplatin) produced synergistic or additive effects. In addition, TAK-243 was highly synergistic with BCL2 inhibitors (Navitoclax and Venetoclax) in preclinical ACC models including patient-derived organoids. The tumor suppressive effects of TAK-243 and its synergistic effects with Venetoclax were further confirmed in a mouse xenograft model. These findings provide preclinical evidence to support the initiation of a clinical trial of TAK-243 in patients with advanced-stage ACC. TAK-243 is a promising potential treatment option for ACC, either as monotherapy or in combination with existing therapies or BCL2 inhibitors. SIGNIFICANCE: ACC is a rare endocrine cancer with poor prognosis and limited therapeutic options. We report that TAK-243 is active alone and in combination with currently used therapies and with BCL2 and mTOR inhibitors in ACC preclinical models. Our results suggest implementation of TAK-243 in clinical trials for patients with advanced and metastatic ACC.

Spacecraft Homography Pose Estimation with Single-Stage Deep Convolutional Neural Network.

Spacecraft pose estimation using computer vision has garnered increasing attention in research areas such as automation system theory, control theory, sensors and instruments, robot technology, and automation software. Confronted with the extreme environment of space, existing spacecraft pose estimation methods are predominantly multi-stage networks with complex operations. In this study, we propose an approach for spacecraft homography pose estimation with a single-stage deep convolutional neural network for the first time. We formulated a homomorphic geometric constraint equation for spacecraft with planar features. Additionally, we employed a single-stage 2D keypoint regression network to obtain homography 2D keypoint coordinates for spacecraft. After decomposition to obtain the rough spacecraft pose based on the homography matrix constructed according to the geometric constraint equation, a loss function based on pixel errors was employed to refine the spacecraft pose. We conducted extensive experiments using widely used spacecraft pose estimation datasets and compared our method with state-of-the-art techniques in the field to demonstrate its effectiveness.

The Crystallization and Melting Behavior of Neodymium-Based Butadiene Rubber Blends.

This study investigates the influence of poly(butadiene-isoprene) copolymer rubber (BIR) and TDAE oil on the crystallization and melting behavior of neodymium-based butadiene rubber (Nd-BR). The study demonstrates that the melting points of Nd-BR and its blends decrease with lower crystallization temperatures. Below the critical crystallization temperature (Tc,c), the melting behavior shows dual peaks in distinct temperature ranges, which are attributed to different spherulitic sizes. The addition of BIR or TDAE oil lowers the Tc,c, with TDAE oil exerting a more substantial effect. These diluents mainly influence the nucleation temperature and crystallinity level of Nd-BR while having a minimal effect on the crystallization mechanism. A master curve, which overlaps for various samples, is developed by correlating the peak melting temperature (Tm,peak) with the Tc. This curve facilitates a quantitative assessment of the effects of BIR and TDAE oil on Nd-BR, highlighting the greater influence of TDAE oil on the crystalline structure compared with BIR at equivalent mass fractions. By applying the Lorentz equation and multi-peak fitting, a relationship between the melting points and crystallization temperatures is established, enabling the calculation of the equilibrium melting points (Tm0) for different samples. The findings show a reduction in the Tm0 due to the diluents; specifically, the Tm0 is approximately 0 °C for pure Nd-BR, and it decreases to -4.579 °C and -6.579 °C for samples with 50 PHR TDAE oil and 60 wt.% BIR, respectively.

Study on the Crystallization Behavior of Neodymium Rare-Earth Butadiene Rubber Blends and Its Effect on Dynamic Mechanical Properties.

Utilizing neodymium-based butadiene rubber as a baseline, this study examines the effect of eco-friendly aromatic TDAE oil, fillers, and crosslinking reactions on neodymium-based rare-earth butadiene rubber (Nd-BR) crystallization behavior. The findings suggest that TDAE oil hinders crystallization, resulting in decreased crystallization temperatures and heightened activation energies (Ea). The crystallization activation energies for 20 parts per hundreds of rubber (PHR) and 37.5 PHR oil stand at -116.8 kJ/mol and -48.1 kJ/mol, respectively, surpassing the -264.3 kJ/mol of the unadulterated rubber. Fillers act as nucleating agents, hastening crystallization, which in turn elevates crystallization temperatures and diminishes Ea. In samples containing 20 PHR and 37.5 PHR oil, the incorporation of carbon black and silica brought the Ea down to -224.9 kJ/mol and -239.1 kJ/mol, respectively. Crosslinking considerably restricts molecular motion and crystallization potential. In the examined conditions, butadiene rubber containing 37.5 PHR oil displayed no crystallization following crosslinking, albeit crystallization was discernible with filler inclusion. Simultaneously, the crystallinity level sharply declined, manifesting cold crystallization behavior. The crosslinking process elevates Ea, while the equilibrium melting point (Tm0) noticeably diminishes. For instance, the Tm0 of pure Nd-BR is approximately -0.135 °C. When blended with carbon black and silica, the Tm0 values are -3.13 °C and -5.23 °C, respectively. After vulcanization, these values decrease to -21.6 °C and -10.16 °C. Evaluating the isothermal crystallization kinetics of diverse materials via the Avrami equation revealed that both the oil and crosslinking process can bring about a decrease in n values, with the Avrami index n for various samples oscillating between 1.5 and 2.5. Assessing the dynamic mechanical attributes of different specimens reveals that Nd-BR crystallization notably curtails its glass transition, marked by a modulus shift in the transition domain and a decrement in loss factor. The modulus in the rubbery state also witnesses a substantial augmentation.

Covalently Cross-Linked Chemistry of a Three-Dimensional Network Binder at Limited Dosage Enables Practical Si/C Composite Electrode Applications.

Currently, Si (or SiOx, 1 < x < 2) and graphite composite (Si/C) electrodes (e.g., Si/C450 and Si/C600 with specific capacities of 450 and 600 mAh g-1 at 0.1 C, respectively) have become the most promising alternative to traditional graphite anodes toward high-energy lithium-ion battery (LIB) applications by virtue of their higher specific capacity compared to graphite ones and improved cycle performance compared to Si (or SiOx) ones. However, such composite electrodes remain challenging to practical for implementation owing to electrode structure disintegration and interfacial instability caused by a large volume change of inner Si-based particles. Herein, we develop a covalent-bond cross-linking network binder for Si/C450 and Si/C600 electrodes via reversible addition-fragmentation chain transfer (RAFT) polymerization. The as-developed binder with a 3 mol % cross-linker of other monomers [termed P(SH-BA3%)] achieves improved mechanical and adhesive properties and decreased Si/C anode volume expansion, compared to the linear binder counterpart. Impressively, the P(SH-BA3%) binder at only 3 wt % dosage enables 83.56% capacity retention after 600 cycles at 0.5 C in Si/C450 anode based half-cells and retains 86.42% capacity retention at 0.3 C after 200 cycles and 80.95% capacity retention at 0.5 C after 300 cycles in LiNi0.8Co0.1Mn0.1O2 cathode (15 mg cm-2) based homemade soft package full cells. This work provides insight into binder cross-linking chemistry under limited dosage and enlightens cross-linking binder design toward practical Si/C electrode applications.

A method for small-sized wheat seedlings detection: from annotation mode to model construction.

The number of seedlings is an important indicator that reflects the size of the wheat population during the seedling stage. Researchers increasingly use deep learning to detect and count wheat seedlings from unmanned aerial vehicle (UAV) images. However, due to the small size and diverse postures of wheat seedlings, it can be challenging to estimate their numbers accurately during the seedling stage. In most related works in wheat seedling detection, they label the whole plant, often resulting in a higher proportion of soil background within the annotated bounding boxes. This imbalance between wheat seedlings and soil background in the annotated bounding boxes decreases the detection performance. This study proposes a wheat seedling detection method based on a local annotation instead of a global annotation. Moreover, the detection model is also improved by replacing convolutional and pooling layers with the Space-to-depth Conv module and adding a micro-scale detection layer in the YOLOv5 head network to better extract small-scale features in these small annotation boxes. The optimization of the detection model can reduce the number of error detections caused by leaf occlusion between wheat seedlings and the small size of wheat seedlings. The results show that the proposed method achieves a detection accuracy of 90.1%, outperforming other state-of-the-art detection methods. The proposed method provides a reference for future wheat seedling detection and yield prediction.

Interphase Regulation by Multifunctional Additive Empowering High Energy Lithium-Ion Batteries with Enhanced Cycle Life and Thermal Safety.

High energy density lithium-ion batteries (LIBs) adopting high-nickel layered oxide cathodes and silicon-based composite anodes always suffer from unsatisfied cycle life and poor safety performance, especially at elevated temperatures. Electrode /electrolyte interphase regulation by functional additives is one of the most economic and efficacious strategies to overcome this shortcoming. Herein, cyano-groups (-CN) are introduced into lithium fluorinated phosphate to synthesize a novel multifunctional additive of lithium tetrafluoro (1,2-dihydroxyethane-1,1,2,2-tetracarbonitrile) phosphate (LiTFTCP), which endows high nickel LiNi0.8 Co0.1 Mn0.1 O2 /SiOx -graphite composite full cell with an ultrahigh cycle life and superior safety characteristics, by adding only 0.5 wt % LiTFTCP into a LiPF6 -carbonate baseline electrolyte. It is revealed that LiTFTCP additive effectively suppresses the HF generation and facilitates the formation of a robust and heat-resistant cyano-enriched CEI layer as well as a stable LiF-enriched SEI layer. The favorable SEI/CEI layers greatly lessen the electrode degradation, electrolyte consumption, thermal-induced gassing and total heat-releasing. This work illuminates the importance of additive molecular engineering and interphase regulation in simultaneously promoting the cycling and thermal safety of LIBs with high-nickel NCMxyz cathode and silicon-based composite anode.

Transcriptome analysis of the adenoma-carcinoma sequences identifies novel biomarkers associated with development of canine colorectal cancer.

The concept of adenoma-to-cancer transformation in human colorectal cancer (CRC) is widely accepted. However, the relationship between transcriptome features and adenoma to carcinoma transformation in canines is not clear. We collected transcriptome data from 8 normal colon tissues, 4 adenoma tissues, and 15 cancer tissues. Differential analysis was unable to determine the dynamic changes of genes but revealed that PFKFB3 may play a key role in this process. Enrichment analysis explained metabolic dysregulation, immunosuppression, and typical cancer pathways in canine colorectal tumors. MFuzz generated specific dynamic expression patterns of five differentially expressed genes (DEGs). Weighted correlation network analysis showed that DEGs in cluster 3 were associated with malignant tissues, revealing the key role of inflammatory and immune pathways in canine CRC, and the S100A protein family was also found to be involved in the malignant transformation of canine colorectal tumors. By comparing strategies between humans and dogs, we found five novel markers that may be drivers of CRC. Among them, GTBP4 showed excellent diagnostic and prognostic ability. This study was the first systematic exploration of transformation in canine CRC, complemented the molecular characteristics of the development and progression of canine CRC, and provided new potential biomarkers and comparative oncologic evidence for biomarker studies in human colorectal cancer.

Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet.

Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.

Inhibition of lysosome-tethered Ragulator-Rag-3D complex restricts the replication of Enterovirus 71 and Coxsackie A16.

Enterovirus 71 (EV71) and Coxsackie A16 (CVA16) are two major causative agents of hand, foot, and mouth disease (HFMD) in young children. However, the mechanisms regulating the replication and pathogenesis of EV71/CVA16 remain incompletely understood. We performed a genome-wide CRISPR-Cas9 knockout screen and identified Ragulator as a mediator of EV71-induced apoptosis and pyroptosis. The Ragulator-Rag complex is required for EV71 and CVA16 replication. Upon infection, the Ragulator-Rag complex recruits viral 3D protein to the lysosomal surface through the interaction between 3D and RagB. Disruption of the lysosome-tethered Ragulator-Rag-3D complex significantly impairs the replication of EV71/CVA16. We discovered a novel EV71 inhibitor, ZHSI-1, which interacts with 3D and significantly reduces the lysosomal tethering of 3D. ZHSI-1 treatment significantly represses replication of EV71/CVA16 as well as virus-induced pyroptosis associated with viral pathogenesis. Importantly, ZHSI-1 treatment effectively protects against EV71 infection in neonatal and young mice. Thus, our study indicates that targeting lysosome-tethered Ragulator-Rag-3D may be an effective therapeutic strategy for HFMD.

A marine coating: Self-healing, stable release of Cu2+, anti-biofouling.

We developed a marine coating consisting of Cu-MOF-74, multi-walled carbon nanotube containing carboxyl groups (MWCNT-COOH) and self-healing polymers, which simultaneously possesses self-healing and anti-biofouling properties. Cu-MOF-74 can stably release Cu2+ by virtue of the coordination dissociative mechanism. Studies have proved that MWCNT can inhibit the growth of bacteria, so adding the MWCNT can help to reduce the amount of the copper ions and ensure the antibacterial effect of the coating. In addition, the cross-linked network and abundant -COOH provided by the polymers and MWCNT-COOH further prevent the loss of copper ions. Moreover, the coating we prepared has good performance of self-healing at room temperature or slightly heated because the polymers possess abundant non-covalent hydrogen bonds. Finally, the coating not only has superior antibacterial property, but also effectively prevents the adhesion of macrofouling organism. Therefore, the coating has a longer service life and its environmental friendliness has also been improved.

Preclinical Evaluation of the FGFR-Family Inhibitor Futibatinib for Pediatric Rhabdomyosarcoma.

Rhabdomyosarcoma (RMS) is the most common pediatric soft tissue sarcoma. Despite decades of clinical trials, the overall survival rate for patients with relapsed and metastatic disease remains below 30%, underscoring the need for novel treatments. FGFR4, a receptor tyrosine kinase that is overexpressed in RMS and mutationally activated in 10% of cases, is a promising target for treatment. Here, we show that futibatinib, an irreversible pan-FGFR inhibitor, inhibits the growth of RMS cell lines in vitro by inhibiting phosphorylation of FGFR4 and its downstream targets. Moreover, we provide evidence that the combination of futibatinib with currently used chemotherapies such as irinotecan and vincristine has a synergistic effect against RMS in vitro. However, in RMS xenograft models, futibatinib monotherapy and combination treatment have limited efficacy in delaying tumor growth and prolonging survival. Moreover, limited efficacy is only observed in a PAX3-FOXO1 fusion-negative (FN) RMS cell line with mutationally activated FGFR4, whereas little or no efficacy is observed in PAX3-FOXO1 fusion-positive (FP) RMS cell lines with FGFR4 overexpression. Alternative treatment modalities such as combining futibatinib with other kinase inhibitors or targeting FGFR4 with CAR T cells or antibody-drug conjugate may be more effective than the approaches tested in this study.

Phase-wise evaluation and optimization of non-pharmaceutical interventions to contain the COVID-19 pandemic in the U.S.

Given that the effectiveness of COVID-19 vaccines and other therapies is greatly limited by the continuously emerging variants, non-pharmaceutical interventions have been adopted as primary control strategies in the global fight against the COVID-19 pandemic. However, implementing strict interventions over extended periods of time is inevitably hurting the economy. Many countries are faced with the dilemma of how to take appropriate policy actions for socio-economic recovery while curbing the further spread of COVID-19. With an aim to solve this multi-objective decision-making problem, we investigate the underlying temporal dynamics and associations between policies, mobility patterns, and virus transmission through vector autoregressive models and the Toda-Yamamoto Granger causality test. Our findings reveal the presence of temporal lagged effects and Granger causality relationships among various transmission and human mobility variables. We further assess the effectiveness of existing COVID-19 control measures and explore potential optimal strategies that strike a balance between public health and socio-economic recovery for individual states in the U.S. by employing the Pareto optimality and genetic algorithms. The results highlight the joint power of the state of emergency declaration, wearing face masks, and the closure of bars, and emphasize the necessity of pursuing tailor-made strategies for different states and phases of epidemiological transmission. Our framework enables policymakers to create more refined designs of COVID-19 strategies and can be extended to other countries regarding best practices in pandemic response.