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Ubiquitous post-consumer plastic waste is often physically mixed combining recalcitrant petroleum-based plastics with bioplastics, forming (petro-bio)plastic streams. Finding appropriate end-of-life (EoL) strategies for mixed (petro-bio)plastic waste is highly pertinent in achieving environmental protection, sustainability for plastic value chain industries including recyclers and government policy makers worldwide. The presence of bioplastic mixed in with polyethylene terephthalate (PET) or other petroleum-based plastic streams poses a substantial drawback to mechanical recycling and strongly impedes the development of sustainable EoL routes. Here, we present a model system for the sustainable management of mixed (petro-bio)plastic waste, demonstrating a biotechnological route through synergy-promoted enzymatic degradation of PET–representing petrochemical polyester plastic–mixed with thermoplastic starch (TPS)–as a model bioplastic. Leaf-branch compost cutinase (LCCICCG) and commercial amylase (AMY) deliver effective depolymerization of this mixed (petro-bio)plastic material, with subsequent bio-upcycling of the mixed waste stream into bacterial nanocellulose (BNC) by Komagataeibacter medellinensis. Compared to LCCICCG and AMY, the LCCICCG/AMY combined treatment synergistically produced a 2.6- and 4.4-fold increase in enzymatic decomposition at 70 °C in four days, respectively, yielding sugars and terephthalic acid (TPA) as the main depolymerization building blocks. Bio-upcycling of post-enzymatic degradation hydrolysates resulted in a high BNC yield of 3 g L−1 after 10 days. This work paves the way for sustainable management routes for challenging mixed recalcitrant plastic and bioplastic waste and prepares opportunities for its participation in the circular production of sustainable eco-polymers.

Doa10 (MARCHF6 in metazoans) is a large polytopic membrane-embedded E3 ubiquitin ligase in the endoplasmic reticulum (ER) that plays an important role in quality control of cytosolic and ER proteins. Although Doa10 is highly conserved across eukaryotes, it is not understood how Doa10 recognizes its substrates. Here, we define the substrate recognition mechanism of Doa10 by structural and functional analyses on Saccharomyces cerevisiae Doa10 and its model substrates. Cryo-EM analysis shows that Doa10 has unusual architecture with a large lipid-filled central cavity, and its conserved middle domain forms an additional water-filled lateral tunnel open to the cytosol. Our biochemical data and molecular dynamics simulations suggest that the entrance of the substrates degron peptide into the lateral tunnel is required for efficient polyubiquitination. The N- and C-terminal membrane domains of Doa10 seem to form fence-like features to restrict polyubiquitination to those proteins that can access the central cavity and lateral tunnel. Our study reveals how extended hydrophobic sequences at the termini of substrate proteins are recognized by Doa10 as a signal for quality control.

Intelligent task-oriented dialogue systems (ToDs) are expected to continuously acquire new knowledge, also known as Continual Learning (CL), which is crucial to fit ever-changing user needs. However, catastrophic forgetting dramatically degrades the model performance in face of a long streamed curriculum. In this paper, we aim to overcome the forgetting problem in ToDs and propose a method (HESIT) with hyper-gradient-based exemplar strategy, which samples influential exemplars for periodic retraining. Instead of unilaterally observing data or models, HESIT adopts a profound exemplar selection strategy that considers the general performance of the trained model when selecting exemplars for each task domain. Specifically, HESIT analyzes the training data influence by tracing their hyper-gradient in the optimization process. Furthermore, HESIT avoids estimating Hessian to make it compatible for ToDs with a large pre-trained model. Experimental results show that HESIT effectively alleviates catastrophic forgetting by exemplar selection, and achieves state-of-the-art performance on the largest CL benchmark of ToDs in terms of all metrics., Comment: ACL 2024

This study introduces a groundbreaking optical coherence tomography (OCT) imaging system dedicated for high-throughput screening applications using ex vivo tissue culture. Leveraging OCT's non-invasive, high-resolution capabilities, the system is equipped with a custom-designed motorized platform and tissue detection ability for automated, successive imaging across samples. Transformer-based deep learning segmentation algorithms further ensure robust, consistent, and efficient readouts meeting the standards for screening assays. Validated using retinal explant cultures from a mouse model of retinal degeneration, the system provides robust, rapid, reliable, unbiased, and comprehensive readouts of tissue response to treatments. This fully automated OCT-based system marks a significant advancement in tissue screening, promising to transform drug discovery, as well as other relevant research fields.

The YidC family of proteins are membrane insertases that catalyze the translocation of the periplasmic domain of membrane proteins via a hydrophilic groove located within the inner leaflet of the membrane. All homologs have a strictly conserved, positively charged residue in the center of this groove. In Bacillus subtilis, the positively charged residue has been proposed to be essential for interacting with negatively charged residues of the substrate, supporting a hypothesis that YidC catalyzes insertion via an early-step electrostatic attraction mechanism. Here, we provide data suggesting that the positively charged residue is important not for its charge but for increasing the hydrophilicity of the groove. We found that the positively charged residue is dispensable for Escherichia coli YidC function when an adjacent residue at position 517 was hydrophilic or aromatic, but was essential when the adjacent residue was apolar. Additionally, solvent accessibility studies support the idea that the conserved positively charged residue functions to keep the top and middle of the groove sufficiently hydrated. Moreover, we demonstrate that both the E. coli and Streptococcus mutans YidC homologs are functional when the strictly conserved arginine is replaced with a negatively charged residue, provided proper stabilization from neighboring residues. These combined results show that the positively charged residue functions to maintain a hydrophilic microenvironment in the groove necessary for the insertase activity, rather than to form electrostatic interactions with the substrates.

The YidC family of proteins are membrane insertases that catalyze the translocation of the periplasmic domain of membrane proteins via a hydrophilic groove located within the inner leaflet of the membrane. All homologs have a strictly conserved, positively charged residue in the center of this groove. In Bacillus subtilis, the positively charged residue has been proposed to be essential for interacting with negatively charged residues of the substrate, supporting a hypothesis that YidC catalyzes insertion via an early-step electrostatic attraction mechanism. Here, we provide data suggesting that the positively charged residue is important not for its charge but for increasing the hydrophilicity of the groove. We found that the positively charged residue is dispensable for Escherichia coli YidC function when an adjacent residue at position 517 was hydrophilic or aromatic, but was essential when the adjacent residue was apolar. Additionally, solvent accessibility studies support the idea that the conserved positively charged residue functions to keep the top and middle of the groove sufficiently hydrated. Moreover, we demonstrate that both the E. coli and Streptococcus mutans YidC homologs are functional when the strictly conserved arginine is replaced with a negatively charged residue, provided proper stabilization from neighboring residues. These combined results show that the positively charged residue functions to maintain a hydrophilic microenvironment in the groove necessary for the insertase activity, rather than to form electrostatic interactions with the substrates.

Lei Ji,1 Tingting Jiang,1 Xin Zhao,2 Damin Cai,1 Kouzhen Hua,1 Peng Du,1 Yuanyuan Chen,3 Jianping Xie4 1School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, Hangzhou, Zhejiang, People’s Republic of China; 2Department of International Registration, Ustar Biotechnologies (Hangzhou) Ltd, Hangzhou, Zhejiang, People’s Republic of China; 3Tuberculosis Diagnosis and Treatment Center, Hangzhou Red Cross Hospital, Hangzhou, Zhejiang, People’s Republic of China; 4Institute of Modern Biopharmaceuticals, State Key Laboratory Breeding Base of Eco-Environment and Bio-Resource of the Three Gorges Area, Key Laboratory of Ministry of Education Eco-Environment of the Three Gorges Reservoir Region, Ministry of Education, Chongqing Municipal Key Laboratory of Karst Environment, School of Life Sciences, Southwest University, Chongqing, People’s Republic of ChinaCorrespondence: Lei Ji, School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, No. 481 Binwen Road, Hangzhou, Zhejiang, 310053, People’s Republic of China, Tel +86 571 8769 2681, Fax +86 571 8769 2673, Email jilei@hmc.edu.cnPurpose: Fatty acid metabolism plays an important role in the survival and pathogenesis of Mycobacterium tuberculosis. During dormancy, lipids are considered to be the main source of energy. A previous study found that Rv0494 is a starvation-inducible, lipid-responsive transcriptional regulator. However, the role of Rv0494 in bacterial persister survival has not been studied.Methods: We constructed a Rv0494 deletion mutant strain of Mycobacterium tuberculosis H37Rv and evaluated the susceptibility of the mutant strain to antibiotics using a persistence test.Results: We found that mutations in Rv0494 lead to survival defects of persisters, which reflected in increased sensitivity to isoniazid.Conclusion: We conclude that Rv0494 is important for persister survival and may serve as a good target for developing new antibiotics that kill persister ba

In recent years, various functional fabrics capable of responding to multistimuli have been widely recognized as promising wearable devices. However, the obtained composite functional fabrics have only been applied in a few scenarios, rendering the achievement of multifunctional wearable application scenarios a difficult goal. Therefore, there is an urgent need to expand the diversity of wearable applications for functional fabrics. Herein, we design hydrogel composite fabrics capable of responding to multiple stimuli, including vibration, temperature, strain, and pressure, to enable wearable multiapplication scenarios. The hydrogel composite fabrics, based on nylon fabrics (NFs), are fabricated with polyacrylamide (PAM)-poly(vinyl alcohol) (PVA)-sodium alginate (SA)-reduced graphene oxide (rGO)/NFs (PAM-PVA-SA-rGO/NFs). The PAM-PVA-SA-rGO/NFs exhibit a higher elastic stiffness coefficient (2.79 N cm-1) than the blank NFs (1.76 N cm-1), good temperature sensitivity in the range of 30-80 °C, and excellent detecting ability for urine presence with a threshold of unit area of 2.55 × 10-3 mL cm-2. The PAM-PVA-SA-rGO/NFs can not only respond to multiple stimuli but also be integrated into clothing for wearable multiapplication scenarios, such as detecting human speaking and breathing, intelligent sleeves, and diaper alarms. Additionally, the mechanisms of the above phenomena are revealed. These results indicate that the PAM-PVA-SA-rGO/NFs will provide inspiration for the development of intelligence systems, feedback devices, soft robotics, wearable devices, etc., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Electronic Components, Technology and Materials

Hydrogel materials have biocompatibility, flexibility, transparency, self-healing ability, adhesion with various substrates, anti-freeze ability, and high-temperature resistance. However, the existing hydrogel devices cannot continue to operate in the case of damage, and they cannot work during the repair period, which brings great challenges and threats to life safety. Herein, we have designed a bio-inspired combinable low-power device by imitating the generation of nerve signals whose components can be disassembled and can continue to operate during the period of reconstruction. And the mechanism and determinants of the above phenomena are revealed. The results indicate that this device can establish some information interaction relationships with the body or its surroundings to reflect and identify certain changes, implying that it will possess promising potential in feedback systems, power transformers, intelligence systems, soft robotics, wearable devices, implanted electronics with flexible characteristics matching biological tissues, etc., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Electronic Components, Technology and Materials

Biotechnological treatment of plastic waste has gathered substantial attention as an efficient and generally greener approach for polyethylene terephthalate (PET) depolymerization and upcycling in comparison to mechanical and chemical processes. Nevertheless, a suitable combination of mechanical and microbial degradation may be the key to bringing forward PET upcycling. In this study, a new strain with an excellent bis(2 hydroxyethyl)terephthalate (BHET) degradation potential (1000 mg/mL in 120 h at 30 °C) and wide temperature (20-47 °C) and pH (5-10) tolerance was isolated from a pristine soil sample. It was identified as Bacillus subtilis BPM12 via phenotypical and genome analysis. A number of enzymes with potential polymer degrading activities were identified, including carboxylesterase BPM12CE that was efficiently expressed both, homologously in B. subtilis BPM12 and heterologously in B. subtilis 168 strain. Overexpression of this enzyme enabled B. subtilis 168 to degrade BHET, while the activity of BPM12 increased up to 1.8-fold, confirming its BHET-ase activity. Interaction of B. subtilis BPM12 with virgin PET films and films that were re-extruded up to 5 times mimicking mechanical recycling, revealed the ability of the strain to attach and form biofilm on each surface. Mechanical recycling resulted in PET materials that are more susceptible to chemical hydrolysis, however only slight differences were detected in biological degradation when BPM12 whole-cells or cell-free enzyme preparations were used. Mixed mechano/bio-degradation with whole-cells and crude enzyme mixes from this strain can serve to further increase the percentage of PET- based plastics that can enter circularity.

Sulfide minerals hold significant importance in both fundamental science and industrial advancement. However, certain natural sulfide minerals, such as NaFe3S5·2H2O (NFS), pose great challenges for exploitation and synthesis due to their high susceptibility to oxidation. To date, no successful precedent exists for synthesizing NFS. Here, a novel approach to synthesizing low-cost and pollution-free NFS with high stability using the high-pressure hydrothermal method based solely on knowledge of its chemical formula is presented. Moreover, an innovative strategy inspired by the cicada's molting process to develop unstable natural materials is proposed. The mechanical, thermal, optical, electrochemical, and magnetic properties of the NFS are thoroughly investigated. The storage of lithium, sodium, and potassium ions is primarily concentrated in the gap between (0 0 1) crystal planes. Additionally, as a catalyst for hydrogen evolution reaction (HER) at 10 mA cm−2, micron-sized NFS exhibits an excellent overpotential of 6.5 mV at 90 °C, surpassing those of reported HER catalysts of similar size. This research bridges the gap in the sulfide mineral family, overcomes limitations of the high-pressure hydrothermal method, and paves the way for future synthesis of natural minerals, lunar minerals, and Martian minerals., Electronic Components, Technology and Materials

The Wide Field Survey Telescope (WFST) is a dedicated photometric surveying facility being built jointly by the University of Science and Technology of China and the Purple Mountain Observatory. It is equipped with a 2.5-meter diameter primary mirror, an active optics system, and a mosaic CCD camera with 0.73 gigapixels on the primary focal plane for high-quality image capture over an FOV of 6.5-square-degree. It is anticipated that WFST will be set up at the Lenghu site in the summer of 2023 and begin to observe the northern sky in four optical bands (u, g, r, and i) with a range of cadences, from hourly/daily in the Deep High-Cadence Survey (DHS) program to semiweekly in the Wide-Field Survey (WFS) program, three months later. During a photometric night, a nominal 30 s exposure in the WFS program will reach a depth of 22.27, 23.32, 22.84, and 22.31 (AB magnitudes) in these four bands, respectively, allowing for the detection of a tremendous amount of transients in the low-z universe and a systematic investigation of the variability of Galactic and extragalactic objects. In the DHS program, intranight 90 s exposures as deep as 23 (u) and 24 mag (g), in combination with target of opportunity follow-ups, will provide a unique opportunity to explore energetic transients in demand for high sensitivities, including the electromagnetic counterparts of gravitational wave events, supernovae within a few hours of their explosions, tidal disruption events and fast, luminous optical transients even beyond a redshift of unity. In addition, the final 6-year co-added images, anticipated to reach g=25.8 mag in WFS or 1.5 mags deeper in DHS, will be of fundamental importance to general Galactic and extragalactic science. The highly uniform legacy surveys of WFST will serve as an indispensable complement to those of LSST that monitor the southern sky., Comment: 48 pages

Artificial intelligence (AI) is a promising technology to transform the healthcare industry. Due to the highly sensitive nature of patient data, federated learning (FL) is often leveraged to build models for smart healthcare applications. Existing deployed FL frameworks cannot address the key issues of varying data quality and heterogeneous data distributions across multiple institutions in this sector. In this paper, we report our experience developing and deploying the Contribution-Aware Federated Learning (CAreFL) framework for smart healthcare. It provides an efficient and accurate approach to fairly evaluate FL participants' contributions to model performance without exposing their private data, and improves the FL model training protocol by allowing the best performing intermediate sub-models to be distributed to participants for FL training. Since its deployment by Yidu Cloud Technology Inc. in March 2021, CAreFL has served eight well-established medical institutions in China to build healthcare decision support models. It can perform contribution evaluations close to three times faster than the best existing approach and has improved the average accuracy of the resulting models by more than 2% compared to the previous system (which is significant in industrial settings). To the best of our knowledge, it is the first CAreFL successfully deployed in the healthcare industry.

Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications. Since complex industrial systems often involve multiple industrial plants (possibly belonging to different companies or subsidiaries) with sensitive data collected and stored in a distributed manner, collaborative fault diagnostic model training often needs to leverage federated learning (FL). As the scale of the industrial fault diagnostic models are often large and communication channels in such systems are often not exclusively used for FL model training, existing deployed FL model training frameworks cannot train such models efficiently across multiple institutions. In this paper, we report our experience developing and deploying the Federated Opportunistic Block Dropout (FEDOBD) approach for industrial fault diagnostic model training. By decomposing large-scale models into semantic blocks and enabling FL participants to opportunistically upload selected important blocks in a quantized manner, it significantly reduces the communication overhead while maintaining model performance. Since its deployment in ENN Group in February 2022, FEDOBD has served two coal chemical plants across two cities in China to build industrial fault prediction models. It helped the company reduce the training communication overhead by over 70% compared to its previous AI Engine, while maintaining model performance at over 85% test F1 score. To our knowledge, it is the first successfully deployed dropout-based FL approach.

Generative AI tools such as ChatGPT have recently gained significant attention in higher education. This study aims to understand how universities establish policies regarding the use of AI tools and explore the factors that influence their decisions. Our study examines ChatGPT policies implemented at universities around the world, including their existence, content, and issuance dates. Specifically, we analyzed the top 500 universities according to the 2022 QS World University Rankings. Our findings indicate that there is significant variation in university policies. Less than one-third of the universities included in the study had implemented ChatGPT policies. Of the universities with ChatGPT policies, approximately 67 percent embraced ChatGPT in teaching and learning, more than twice the number of universities that banned it. The majority of the universities that ban the use of ChatGPT in assessments allow individual instructors to deviate from this restrictive policy. Our empirical analysis identifies several factors that are significantly and positively correlated with a university's likelihood of having a ChatGPT policy, including the university's academic reputation score, being in an English-speaking country, and the general public attitudes toward ChatGPT. In addition, we found that a university's likelihood of having a ban policy is positively associated with faculty student ratio, citations, and the English-speaking country dummy, while negatively associated with the number of peer universities within the same country that have banned ChatGPT. We discuss the challenges faced by universities based our empirical findings., Comment: 33 pages with 2 figures

While our world is filled with its own natural sounds that we can't resist enjoying, it is also chock-full of other sounds that can be irritating, this is noise. Noise not only influences the working efficiency but also the human's health. The problem of reducing noise is one of great importance and great difficulty. The problem has been addressed in many ways over the years. The current methods for noise reducing mostly rely on the materials and transmission medium, which are only effective to some extent for the high frequency noise. However, the effective reduction noise method especially for low frequency noise is very limited. Here we come up with a noise reduction system consist of a sensor to detect the noise in the environment. Then the noise will be sent to an electronic control system to process the noise, which will generate a reverse phase frequency signal to counteract the disturbance. Finally, the processed smaller noise will be broadcasted by the speaker. Through this smart noise reduction system, even the noise with low-frequency can be eliminated. The system is also integrated with sleep tracking and music player applications. It can also remember and store settings for the same environment, sense temperature, and smart control of home furniture, fire alarm, etc. This smart system can transfer data easily by Wi-Fi or Bluetooth and controlled by its APP. In this project, we will present a model of the above technology which can be used in various environments to prevent noise pollution and provide a solution to the people who have difficulties finding a peaceful and quiet environment for sleep, work or study.

Insufficient data is a long-standing challenge for Brain-Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG data from multiple sites is still challenging due to the heterogeneity of devices. The significance of this challenge cannot be overstated, given the critical role of data diversity in fostering model robustness. However, existing works rarely discuss this issue, predominantly centering their attention on model training within a single dataset, often in the context of inter-subject or inter-session settings. In this work, we propose a hierarchical personalized Federated Learning EEG decoding (FLEEG) framework to surmount this challenge. This innovative framework heralds a new learning paradigm for BCI, enabling datasets with disparate data formats to collaborate in the model training process. Each client is assigned a specific dataset and trains a hierarchical personalized model to manage diverse data formats and facilitate information exchange. Meanwhile, the server coordinates the training procedure to harness knowledge gleaned from all datasets, thus elevating overall performance. The framework has been evaluated in Motor Imagery (MI) classification with nine EEG datasets collected by different devices but implementing the same MI task. Results demonstrate that the proposed frame can boost classification performance up to 16.7% by enabling knowledge sharing between multiple datasets, especially for smaller datasets. Visualization results also indicate that the proposed framework can empower the local models to put a stable focus on task-related areas, yielding better performance. To the best of our knowledge, this is the first end-to-end solution to address this important challenge.

Xiuhua Jiang,1,* Shiqi Chai,1,* Yan Huang,2,* Zuxiong Huang,3,* Wenting Tan,4,* Yanhang Gao,5,* Xiaobo Lu,6,* Zhongji Meng,7,* Huayou Zhou,8 Wenbing Kong,8 Xiaoting Tang,1 Yujun Tang,1 Tingting Qi,1 Chengjin Liao,2 Qiaorong Gan,3 Xiaomei Xiang,4 Yanan Zhang,5 Shuai Wang,6 Yuanyuan Chen,7 Jinjun Chen1,9 1Hepatology Unit, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 2Department of Infectious Diseases, Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 3Department of Hepatology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, People’s Republic of China; 4Department of Infectious Diseases, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, People’s Republic of China; 5Department of Hepatology, the First Hospital of Jilin University, Changchun, People’s Republic of China; 6Infectious Disease Center, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China; 7Department of Infectious Diseases, Hubei Clinical Research Center for Precise Diagnosis and Treatment of Liver Cancer, Taihe Hospital, Hubei University of Medicine, Shiyan, People’s Republic of China; 8Department of Blood Transfusion, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 9Hepatology Unit, Zengcheng Branch, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jinjun Chen, Hepatology Unit, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, No. 1838, Guangzhou Dadao Bei, Guangzhou, 510515, People’s Republic of China, Email chjj@smu.edu.cnBackground: Acute-on-chronic liver failure (ACLF) has high short-term mortali

Large-scale neural networks possess considerable expressive power. They are well-suited for complex learning tasks in industrial applications. However, large-scale models pose significant challenges for training under the current Federated Learning (FL) paradigm. Existing approaches for efficient FL training often leverage model parameter dropout. However, manipulating individual model parameters is not only inefficient in meaningfully reducing the communication overhead when training large-scale FL models, but may also be detrimental to the scaling efforts and model performance as shown by recent research. To address these issues, we propose the Federated Opportunistic Block Dropout (FedOBD) approach. The key novelty is that it decomposes large-scale models into semantic blocks so that FL participants can opportunistically upload quantized blocks, which are deemed to be significant towards training the model, to the FL server for aggregation. Extensive experiments evaluating FedOBD against four state-of-the-art approaches based on multiple real-world datasets show that it reduces the overall communication overhead by more than 88% compared to the best performing baseline approach, while achieving the highest test accuracy. To the best of our knowledge, FedOBD is the first approach to perform dropout on FL models at the block level rather than at the individual parameter level.

Artificial intelligence (AI) is a promising technology to transform the healthcare industry. Due to the highly sensitive nature of patient data, federated learning (FL) is often leveraged to build models for smart healthcare applications. Existing deployed FL frameworks cannot address the key issues of varying data quality and heterogeneous data distributions across multiple institutions in this sector. In this paper, we report our experience developing and deploying the Contribution-Aware Federated Learning (CAreFL) framework for smart healthcare. It provides fair and explainable FL participant contribution evaluation in an efficient and privacy preserving manner, and optimizes the FL model aggregation approach based on the evaluation results. Since its deployment in Yidu Cloud Technology Inc. in March 2021, CAreFL has served 8 well-established medical institutions in China to build healthcare decision support models. It can perform contribution evaluations 2.84 times faster than the best existing approach, and has improved the average accuracy of the resulting models by 2.62% compared to the previous system (which is significant in industrial settings). To our knowledge, it is the first contribution-aware federated learning successfully deployed in the healthcare industry.

This article investigates the surface electrical properties of polyether-ether-ketone (PEEK) and cross-linked polyethylene (XLPE) before and after the fluorination modification. The samples are tested for surface conductivity, surface morphology, and surface antiaging characteristics. The samples are also tested for flashover voltage with ramping voltage under 100, 20, and 10 kPa. The results showed that the surfaces of PEEK and XLPE become slightly smoother with the increase in the fluorination time and the conductivity increased with the increase in the fluorination time. After fluorination, the surface of PEEK shows significant hydrophilicity, while the surface hydrophobicity of XLPE is slightly reduced, and there is a trend of recovery with the extension of the fluorination time. After fluorination, the surface antiaging performance is significantly improved. Compared with the untreated samples, the fluorinated PEEK has a higher flashover voltage at 100 kPa, and the flashover voltage is slightly increased at 10 and 20 kPa, while the flashover voltage of XLPE after fluorination shows a downward trend, especially under low pressures. This study provides a reference for fluorinated polymers as promising candidates for the insulation systems for all-electric aircraft.

AU In contrast: Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly to the adult mammalian central nervous system (CNS), : the neurons in the peripheral nervous system (PNS) can regenerate their axons. However, the underlying mechanism dictating the regeneration program after PNS injuries remains poorly understood. Combining chemical inhibitor screening with gain- and loss-of-function analyses, we identified p90 ribosomal S6 kinase 1 (RSK1) as a crucial regulator of axon regeneration in dorsal root ganglion (DRG) neurons after sciatic nerve injury (SNI). Mechanistically, RSK1 was found to preferentially regulate the synthesis of regeneration-related proteins using ribosomal profiling. Interestingly, RSK1 expression was up-regulated in injured DRG neurons, but not retinal ganglion cells (RGCs). Additionally, RSK1 overexpression enhanced phosphatase and tensin homolog (PTEN) deletion-induced axon regeneration in RGCs in the adult CNS. Our findings reveal a critical mechanism in inducing protein synthesis that promotes axon regeneration and further suggest RSK1 as a possible therapeutic target for neuronal injury repair. Copyright: © 2022 Mao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Previously, α-Fe2O3 nanocrystals are recognized as anode materials owing to their high capacity and multiple properties. Now, this work provides high-voltage α-Fe2O3 nanoceramics cathodes fabricated by the solvothermal and calcination processes for sodium-ion batteries (SIBs). Then, their structure and electrical conductivity were investigated by the first-principles calculations. Also, the SIB with the α-Fe2O3 nanoceramics cathode exhibits a high initial charge-specific capacity of 692.5 mA h g-1 from 2.0 to 4.5 V at a current density of 25 mA g-1. After 800 cycles, the discharge capacity is still 201.8 mA h g-1, well exceeding the one associated with the present-state high-voltage SIB. Furthermore, the effect of the porous structure of the α-Fe2O3 nanoceramics on sodium ion transport and cyclability is investigated. This reveals that α-Fe2O3 nanoceramics will be a remarkably promising low-cost and pollution-free high-voltage cathode candidate for high-voltage SIBs., Electronic Components, Technology and Materials

Using viscoelastic mass/spring model simulations, we explore tidal evolution and migration of compact binary asteroid systems. We find that after the secondary is captured into a spin-synchronous state, non-principal axis rotation in the secondary can be long-lived. The secondary's long axis can remain approximately aligned along the vector connecting secondary to primary while the secondary rocks back and forth about its long axis. Inward orbital semi-major axis migration can also resonantly excite non-principal axis rotation. By estimating solar radiation forces on triangular surface meshes, we show that the magnitude of the BYORP effect induced torque is sensitive to the secondary's spin state. Non-principal axis rotation within the 1:1 spin-orbit resonance can reduce the BYORP torque or cause frequent reversals in its direction., Comment: to be published in Icarus

NaTi2(PO4)3 (NTP), a well-known anode material, could be used as a solid wide-band gap electrolyte. Herein, a novel solid-state sodium-ion battery (SSIB) with the thickness of electrolyte up to the millimeter level is proposed. The results of the difference in charge density investigated by the first-principles calculations imply that using the NTP nanocrystals as electrolytes to transport sodium ions is feasible. Moreover, the SSIB exhibits a high initial discharge capacity of 3250 mAh g-1 at the current density of 50 mA g-1. As compared with other previously reported SSIBs, our results are better than those reported and suggest that the NTP nanocrystals have potential application in SSIBs as solid electrolytes., Electronic Components, Technology and Materials

Yuhan Chen,1,* Yuanyuan Chen,1,* Xueze Jiang,1 Mengkun Shi,2 Zhenwei Yang,1 Zhiyong Chen,1 Xuesheng Hua,1 Jie Chen,1 Yuepeng Wang1 1Department of Cardiology, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200092, People’s Republic of China; 2Department of Cardio-Thoracic Surgery, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuepeng Wang; Jie ChenDepartment of Cardiology, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200092, People’s Republic of ChinaTel +86 18217267289; +86 15280380968Email wangyuepeng@xinhuamed.com.cn; jieyuepeng@msn.comBackground: Activation of vascular adventitial fibroblasts (VAFs) upon vascular injury contributes greatly to the medial vascular smooth muscle cells (VSMCs) proliferation, migration and the subsequent neointima formation. A number of factors including fibroblast growth factors (FGFs) have been shown to control VSMC growth, proliferation and phenotypic switching, suggesting that they may function as paracrine signals for VAFs to modulate VSMCs functions. However, little is known about the signaling molecule(s) and its mechanism of action. This study is set to identify which and how FGF family members are involved in VAFs mediated vascular remodeling.Methods: We used qPCR, Western blot and Immunohistochemistry to observe the spatiotemporal expression of FGF10 and FGFR2 in injured vascular tissue. The proliferation and migration assays of VSMCs were performed in a co-culture system. The activation of signaling pathway was detected by Western blot, immunohistochemistry and immunofluorescence. Hematoxylin-eosin and immunofluorescence were used to assess the effects of exogenous FGF10 and siFGF10 on the neointima formation.Results: The expression of FGF10 and FGFR2 were increased from day 3 through day 14 post injury. FGF10 wa

Natural habitat plays an important role in maintaining biodiversity. Both cropland expansion and urban expansion have an influence on natural habitat. However, it is not clear which one impacts more seriously on both the quantity and quality of natural habitat. This study compared the impacts of cropland expansion on the quantity and quality of natural habitat in China between 2000 and 2015 with the impacts of urban expansion. Map algebra in ArcGIS 10.6 was used to calculate the changes in the quantity of natural habitat, while the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Habitat Quality model was used to assess the changes in its quality. The results indicated that cropland expansion led to a loss of 35,811 km2 of natural habitat, which was twelve-times as much as that from urban expansion. Furthermore, the area of the heaviest habitat degradation due to cropland expansion was 9,530 km2, which was eight-times as much as that due to urban expansion. Noticeably, the greatest impacts of cropland expansion on natural habitat mostly occurred in areas where the ecological environment is already vulnerable (namely, the resistance and resilience of ecosystems in response to external interference are weak), whereas the impacts of urban expansion were much less in these areas. This study highlights that the impacts of cropland expansion on both the natural habitat loss and degradation far exceeded the impacts of urban expansion. It is necessary to improve cropland protection policies to guarantee food security while ensuring little or no harm to natural habitat.

Using viscoelastic mass/spring model simulations, we explore tidal evolution and migration of compact binary asteroid systems. We find that after the secondary is captured into a spin-synchronous state, non-principal axis rotation in the secondary can be long-lived. The secondary's long axis can remain approximately aligned along the vector connecting secondary to primary while the secondary rocks back and forth about its long axis. Inward orbital semi-major axis migration can also resonantly excite non-principal axis rotation. By estimating solar radiation forces on triangular surface meshes, we show that the magnitude of the BYORP effect induced torque is sensitive to the secondary's spin state. Non-principal axis rotation within the 1:1 spin-orbit resonance can reduce the BYORP torque or cause frequent reversals in its direction., Comment: to be published in Icarus

Visual object detection is an important artificial intelligence (AI) technique for safety monitoring applications. Current approaches for building visual object detection models require large and well-labeled dataset stored by a centralized entity. This not only poses privacy concerns under the General Data Protection Regulation (GDPR), but also incurs large transmission and storage overhead. Federated learning (FL) is a promising machine learning paradigm to address these challenges. In this paper, we report on FedVision-a machine learning engineering platform to support the development of federated learning powered computer vision applications-to bridge this important gap. The platform has been deployed through collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Through actual usage, it has demonstrated significant efficiency improvement and cost reduction while fulfilling privacy-preservation requirements (e.g., reducing communication overhead for one company by 50 fold and saving close to 40,000RMB of network cost per annum). To the best of our knowledge, this is the first practical application of FL in computer vision based tasks.

NaTi2(PO4)3 (NTP), a well-known anode material, could be used as a solid wide-band gap electrolyte. Herein, a novel solid-state sodium-ion battery (SSIB) with the thickness of electrolyte up to the millimeter level is proposed. The results of the difference in charge density investigated by the first-principles calculations imply that using the NTP nanocrystals as electrolytes to transport sodium ions is feasible. Moreover, the SSIB exhibits a high initial discharge capacity of 3250 mAh g-1 at the current density of 50 mA g-1. As compared with other previously reported SSIBs, our results are better than those reported and suggest that the NTP nanocrystals have potential application in SSIBs as solid electrolytes., Electronic Components, Technology and Materials

Federated Learning (FL) bridges the gap between collaborative machine learning and preserving data privacy. To sustain the long-term operation of an FL ecosystem, it is important to attract high quality data owners with appropriate incentive schemes. As an important building block of such incentive schemes, it is essential to fairly evaluate participants' contribution to the performance of the final FL model without exposing their private data. Shapley Value (SV)-based techniques have been widely adopted to provide fair evaluation of FL participant contributions. However, existing approaches incur significant computation costs, making them difficult to apply in practice. In this paper, we propose the Guided Truncation Gradient Shapley (GTG-Shapley) approach to address this challenge. It reconstructs FL models from gradient updates for SV calculation instead of repeatedly training with different combinations of FL participants. In addition, we design a guided Monte Carlo sampling approach combined with within-round and between-round truncation to further reduce the number of model reconstructions and evaluations required, through extensive experiments under diverse realistic data distribution settings. The results demonstrate that GTG-Shapley can closely approximate actual Shapley values, while significantly increasing computational efficiency compared to the state of the art, especially under non-i.i.d. settings.

Radiotherapy is a treatment where radiation is used to eliminate cancer cells. The delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to avoid damage to healthy organs. For nasopharyngeal cancer, more than 20 OARs are needed to be precisely segmented in advance. The challenge of this task lies in complex anatomical structure, low-contrast organ contours, and the extremely imbalanced size between large and small organs. Common segmentation methods that treat them equally would generally lead to inaccurate small-organ labeling. We propose a novel two-stage deep neural network, FocusNetv2, to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ localization and segmentation sub-networks while maintaining the accuracy of large organ segmentation. In addition to our original FocusNet, we employ a novel adversarial shape constraint on small organs to ensure the consistency between estimated small-organ shapes and organ shape prior knowledge. Our proposed framework is extensively tested on both self-collected dataset of 1,164 CT scans and the MICCAI Head and Neck Auto Segmentation Challenge 2015 dataset, which shows superior performance compared with state-of-the-art head and neck OAR segmentation methods., Comment: Accepted by Medical Image Analysis

The behaviors of deep neural networks (DNNs) are notoriously resistant to human interpretations. In this paper, we propose Hypergradient Data Relevance Analysis, or HYDRA, which interprets the predictions made by DNNs as effects of their training data. Existing approaches generally estimate data contributions around the final model parameters and ignore how the training data shape the optimization trajectory. By unrolling the hypergradient of test loss w.r.t. the weights of training data, HYDRA assesses the contribution of training data toward test data points throughout the training trajectory. In order to accelerate computation, we remove the Hessian from the calculation and prove that, under moderate conditions, the approximation error is bounded. Corroborating this theoretical claim, empirical results indicate the error is indeed small. In addition, we quantitatively demonstrate that HYDRA outperforms influence functions in accurately estimating data contribution and detecting noisy data labels. The source code is available at https://github.com/cyyever/aaai_hydra_8686.

Jianmiao Wang,1 Yuping Zhang,1 Yuanyuan Chen,2 Mei Li,1 Hongyan Yang,2 Jinhua Chen,2 Qiaomin Tang,2 Jingfen Jin1,3 1Nursing Department, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People’s Republic of China; 2Neurology Department, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People’s Republic of China; 3Changxing Branch Hospital, The Second Affiliated Hospital of Zhejiang University School of Medicine, Huzhou, Zhejiang Province, People’s Republic of ChinaCorrespondence: Jingfen JinThe Second Affiliated Hospital of Zhejiang University School of Medicine, No. 88 Jiefang Road, Shangcheng District, Hangzhou, Zhejiang Province, 310009, People’s Republic of ChinaFax +86-0571-87783778Email zrjzkhl@zju.edu.cnPurpose: To determine the effectiveness of rehabilitation nursing program interventions in patients with acute ischemic stroke.Patients and Methods: An assessment-blinded randomized controlled trial was conducted at a tertiary referral hospital in China. Eligible patients were stratified according to their weighted corticospinal tract lesion load and then randomly assigned to an experimental group (n = 121) or a control group (n = 103). The experimental group received rehabilitation nursing from well-trained, qualified nurses (30 minutes per session, two sessions per day for seven consecutive days). The control group received therapist-led rehabilitation with the same timing and frequency. Comparative analysis of the primary outcomes was performed to determine non-inferiority with a predetermined non-inferiority margin. The primary outcomes were the Motor Assessment Scale, Fugl-Meyer Assessment, and the Action Research Arm Test assessed at baseline and after seven days of treatment. The secondary outcomes were the modified Barthel Index, the National Institutes of Health Stroke Scale, and the modified Rankin Scale

Prolonged storage will alter the biophysical properties of red blood cells (RBCs), and it decreases the quality of stored blood for blood transfusion. It has been known that less deformable aged RBCs can be separated by margination, but the recognition of the storage time from the separation efficiency of the stiff RBCs is still a challenge. In this study, we realized enhanced separation of aged RBCs from normal RBCs by controlling the channel cross section and demonstrated that the storage time can be deduced from the percentage of the separated RBCs in the stored RBCs. This separation technology helps to reveal the regulation of time on the RBC aging mechanism and offer a new method to separate stiffened cells with high efficiency.

Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to privacy concerns and the high cost of transmitting video data, it is highly challenging to build object detection models on centrally stored large training datasets following the current approach. Federated learning (FL) is a promising approach to resolve this challenge. Nevertheless, there currently lacks an easy to use tool to enable computer vision application developers who are not experts in federated learning to conveniently leverage this technology and apply it in their systems. In this paper, we report FedVision - a machine learning engineering platform to support the development of federated learning powered computer vision applications. The platform has been deployed through a collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Over four months of usage, it has achieved significant efficiency improvement and cost reduction while removing the need to transmit sensitive data for three major corporate customers. To the best of our knowledge, this is the first real application of FL in computer vision-based tasks.

All-solid-state sodium-ion batteries (SIBs) possess the advantages of rich resources, low price, and high security, which are one of the best alternatives for large-scale energy storage systems in the future. Also, the chalcogenide solid electrolytes (CSEs) of SIBs have the characteristics of excellent room-temperature ionic conductivity (10−3-10−2 S cm−1), low activation energy (<0.6 eV), easy cold-pressing consolidation, etc. Hence, CSEs have become a very active area of all-solid-state SIB research in recent years. In this review, the modification methods and implementation technologies of CSEs are summarized, and the structure and electrochemical performance of the CSEs are discussed. Furthermore, the auxiliary function of first-principle calculations for modification is introduced. Ultimately, we describe the challenges regarding CSEs and propose some strategic suggestions., Electronic Components, Technology and Materials

Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to privacy concerns and the high cost of transmitting video data, it is highly challenging to build object detection models on centrally stored large training datasets following the current approach. Federated learning (FL) is a promising approach to resolve this challenge. Nevertheless, there currently lacks an easy to use tool to enable computer vision application developers who are not experts in federated learning to conveniently leverage this technology and apply it in their systems. In this paper, we report FedVision - a machine learning engineering platform to support the development of federated learning powered computer vision applications. The platform has been deployed through a collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Over four months of usage, it has achieved significant efficiency improvement and cost reduction while removing the need to transmit sensitive data for three major corporate customers. To the best of our knowledge, this is the first real application of FL in computer vision-based tasks.

All-solid-state sodium-ion batteries (SIBs) possess the advantages of rich resources, low price, and high security, which are one of the best alternatives for large-scale energy storage systems in the future. Also, the chalcogenide solid electrolytes (CSEs) of SIBs have the characteristics of excellent room-temperature ionic conductivity (10−3-10−2 S cm−1), low activation energy (<0.6 eV), easy cold-pressing consolidation, etc. Hence, CSEs have become a very active area of all-solid-state SIB research in recent years. In this review, the modification methods and implementation technologies of CSEs are summarized, and the structure and electrochemical performance of the CSEs are discussed. Furthermore, the auxiliary function of first-principle calculations for modification is introduced. Ultimately, we describe the challenges regarding CSEs and propose some strategic suggestions., Electronic Components, Technology and Materials

Federated learning is a distributed machine learning framework which enables different parties to collaboratively train a model while protecting data privacy and security. Due to model complexity, network unreliability and connection in-stability, communication cost has became a major bottleneck for applying federated learning to real-world applications. Current existing strategies are either need to manual setting for hyperparameters, or break up the original process into multiple steps, which make it hard to realize end-to-end implementation. In this paper, we propose a novel compression strategy called Residual Pooling Network (RPN). Our experiments show that RPN not only reduce data transmission effectively, but also achieve almost the same performance as compared to standard federated learning. Our new approach performs as an end-to-end procedure, which should be readily applied to all CNN-based model training scenarios for improvement of communication efficiency, and hence make it easy to deploy in real-world application without much human intervention., Comment: Accepted by the 24th European Conference on Artificial Intelligence (ECAI 2020)

Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to privacy concerns and the high cost of transmitting video data, it is highly challenging to build object detection models on centrally stored large training datasets following the current approach. Federated learning (FL) is a promising approach to resolve this challenge. Nevertheless, there currently lacks an easy to use tool to enable computer vision application developers who are not experts in federated learning to conveniently leverage this technology and apply it in their systems. In this paper, we report FedVision - a machine learning engineering platform to support the development of federated learning powered computer vision applications. The platform has been deployed through a collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Over four months of usage, it has achieved significant efficiency improvement and cost reduction while removing the need to transmit sensitive data for three major corporate customers. To the best of our knowledge, this is the first real application of FL in computer vision-based tasks.

Margination of stiffened red blood cells has been implicated in many vascular diseases. Here, we report the margination of stiffened RBCs in vivo, and reveal the crucial role of the vessel geometry in the margination by calculations when the blood is seen as viscoelastic fluid. The vessel-geometry-regulated margination is then confirmed by in vitro experiments in microfluidic devices, and it establishes new insights to cell sorting technology and artificial blood vessel fabrication.

Margination of stiffened red blood cells has been implicated in many vascular diseases. Here, we report the margination of stiffened RBCs in vivo, and reveal the crucial role of the vessel geometry in the margination by calculations when the blood is seen as viscoelastic fluid. The vessel-geometry-regulated margination is then confirmed by in vitro experiments in microfluidic devices, and it establishes new insights to cell sorting technology and artificial blood vessel fabrication.

The YidC/Oxa1/Alb3 family of membrane proteins function to insert proteins into membranes in bacteria, mitochondria, and chloroplasts. Recent X-ray structures of YidC from Bacillus halodurans and Escherichia coli revealed a hydrophilic groove that is accessible from the lipid bilayer and the cytoplasm. Here, we explore the water accessibility within the conserved core region of the E. coli YidC using in vivo cysteine alkylation scanning and molecular dynamics (MD) simulations of YidC in POPE/POPG membranes. As expected from the structure, YidC possesses an aqueous membrane cavity localized to the membrane inner leaflet. Both the scanning data and the MD simulations show that the lipid-exposed transmembrane helices 3, 4, and 5 are short, leading to membrane thinning around YidC. Close examination of the MD data reveals previously unrecognized structural features that are likely important for protein stability and function.

[EN] This research paper is about a general strategic analysis of the chain of toy stores Imaginarium, the Spanish pioneer of educational toys. During past years, their business turnover has been annually decreasing face every year. The objective of this research is to suggest a strategic turnaround proposal that could be applied to Imaginarium to offset and overcome their problem. These proposals will be focus on a strategic point of view, including an international expansion to a new country with high market demand, as well as other business suggestions which could potentially lead the company to overcome their sales problem., [ES] En este trabajo de investigación se presenta un análisis estratégico general de la cadena de tiendas de juguetes Imaginarium, la empresa española pionera de juguetes educativos. En los últimos años se ha visto afectada con la disminución de sus ventas cada año. El objetivo de este trabajo es realizar una propuesta de un cambio estratégico que se le podría aplicar a Imaginarium para superar o compensar el problema. Dichas propuestas se centrarán en un punto de vista estratégico, como la expansión internacional a un nuevo país con una alta demanda del mercado, así como otras sugerencias de marketing que potencialmente podrían llevar a la empresa a superar su problema de ventas.

[EN] This research paper is about a general strategic analysis of the chain of toy stores Imaginarium, the Spanish pioneer of educational toys. During past years, their business turnover has been annually decreasing face every year. The objective of this research is to suggest a strategic turnaround proposal that could be applied to Imaginarium to offset and overcome their problem. These proposals will be focus on a strategic point of view, including an international expansion to a new country with high market demand, as well as other business suggestions which could potentially lead the company to overcome their sales problem., [ES] En este trabajo de investigación se presenta un análisis estratégico general de la cadena de tiendas de juguetes Imaginarium, la empresa española pionera de juguetes educativos. En los últimos años se ha visto afectada con la disminución de sus ventas cada año. El objetivo de este trabajo es realizar una propuesta de un cambio estratégico que se le podría aplicar a Imaginarium para superar o compensar el problema. Dichas propuestas se centrarán en un punto de vista estratégico, como la expansión internacional a un nuevo país con una alta demanda del mercado, así como otras sugerencias de marketing que potencialmente podrían llevar a la empresa a superar su problema de ventas.

[EN] This research paper is about a general strategic analysis of the chain of toy stores Imaginarium, the Spanish pioneer of educational toys. During past years, their business turnover has been annually decreasing face every year. The objective of this research is to suggest a strategic turnaround proposal that could be applied to Imaginarium to offset and overcome their problem. These proposals will be focus on a strategic point of view, including an international expansion to a new country with high market demand, as well as other business suggestions which could potentially lead the company to overcome their sales problem., [ES] En este trabajo de investigación se presenta un análisis estratégico general de la cadena de tiendas de juguetes Imaginarium, la empresa española pionera de juguetes educativos. En los últimos años se ha visto afectada con la disminución de sus ventas cada año. El objetivo de este trabajo es realizar una propuesta de un cambio estratégico que se le podría aplicar a Imaginarium para superar o compensar el problema. Dichas propuestas se centrarán en un punto de vista estratégico, como la expansión internacional a un nuevo país con una alta demanda del mercado, así como otras sugerencias de marketing que potencialmente podrían llevar a la empresa a superar su problema de ventas.

The retinoid cycle is a metabolic process in the vertebrate retina that continuously regenerates 11-cis-retinal (11-cisRAL) from the all-trans-retinal (atRAL) isomer. atRAL accumulation can cause photoreceptor degeneration and irreversible visual dysfunction associated with incurable blinding retinal diseases, such as Stargardt disease, retinitis pigmentosa (RP), and atrophic age-related macular degeneration (AMD). The underlying cellular mechanisms leading to retinal degeneration remain uncertain, although previous studies have shown that atRAL promotes calcium influx associated with cell apoptosis. To identify compounds that mitigate the effects of atRAL toxicity, here we developed an unbiased and robust image-based assay that can detect changes in intracellular calcium levels in U2OS cells. Using our assay in a high-throughput screen of 2,400 compounds, we noted that selective estrogen receptor modulators (SERMs) potently stabilize intracellular calcium and thereby counteract atRAL-induced toxicity. In a light-induced retinal degeneration mouse model (Abca4 -/- Rdh8 -/-), raloxifene (a benzothiophene-type scaffold SERM) prevented the onset of photoreceptor apoptosis and thus protected the retina from degeneration. The minor structural differences between raloxifene and one of its derivatives (Y 134) had a major impact on calcium homeostasis after atRAL exposure in vitro, and we verified this differential impact in vivo In summary, the SERM raloxifene has structural and functional neuroprotective effects in the retina. We propose that the highly sensitive image-based assay developed here could be applied for the discovery of additional drug candidates preventing photoreceptor degeneration.

We study the bilinear Weyl product acting on quasi-Banach modulation spaces. We find sufficient conditions for continuity of the Weyl product and we derive necessary conditions. The results extend known results for Banach modulation spaces.

In this paper, we propose an end-to-end deep neural network for solving the problem of imbalanced large and small organ segmentation in head and neck (HaN) CT images. To conduct radiotherapy planning for nasopharyngeal cancer, more than 10 organs-at-risk (normal organs) need to be precisely segmented in advance. However, the size ratio between large and small organs in the head could reach hundreds. Directly using such imbalanced organ annotations to train deep neural networks generally leads to inaccurate small-organ label maps. We propose a novel end-to-end deep neural network to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ sub-networks while maintaining the accuracy of large organ segmentation. A strong main network with densely connected atrous spatial pyramid pooling and squeeze-and-excitation modules is used for segmenting large organs, where large organs' label maps are directly output. For small organs, their probabilistic locations instead of label maps are estimated by the main network. High-resolution and multi-scale feature volumes for each small organ are ROI-pooled according to their locations and are fed into small-organ networks for accurate segmenting small organs. Our proposed network is extensively tested on both collected real data and the \emph{MICCAI Head and Neck Auto Segmentation Challenge 2015} dataset, and shows superior performance compared with state-of-the-art segmentation methods., Comment: MICCAI 2019