bluuebunny/crossref_metadata_2025_split huggingface dataset:
Insights into substitution strategy towards thermodynamic and property regulation of chemically recyclable polymers
Authors: Tu Yi-Min , Gong Fu-Long , Wu Yan-Chen , Cai Zhongzheng , Zhu Jian-Bo 
Published: June 2023

AbstractThe development of chemically recyclable polymers serves as an attractive approach to address the global plastic pollution crisis. Monomer design principle is the key to achieving chemical recycling to monomer. Herein, we provide a systematic investigation to evaluate a range of substitution effects and structure−property relationships in the ɛ-caprolactone (CL) system. Thermodynamic and recyclability studies reveal that the substituent size and position could regulate their ceiling temperatures (Tc). Impressively,M4equipped with atert-butyl group displays aTcof 241 °C. A series of spirocyclic acetal-functionalized CLs prepared by a facile two-step reaction undergo efficient ring-opening polymerization and subsequent depolymerization. The resulting polymers demonstrate various thermal properties and a transformation of the mechanical performance from brittleness to ductility. Notably, the toughness and ductility of P(M13) is comparable to the commodity plastic isotactic polypropylene. This comprehensive study is aimed to provide a guideline to the future monomer design towards chemically recyclable polymers.
Critical roles of metal–ligand complexes in the controlled synthesis of various metal nanoclusters
Authors: Kim Ji Soo , Chang Hogeun , Kang Sungsu , Cha Seungwoo , Cho Hanguk , Kwak Seung Jae , Park Namjun , Kim Younhwa , Kang Dohun , Song Chyan Kyung , Kwag Jimin , Hahn Ji-Sook , Lee Won Bo , Hyeon Taeghwan , Park Jungwon 
Published: June 2023

AbstractMetal nanoclusters (NCs), an important class of nanoparticles (NPs), are extremely small in size and possess quasi-molecular properties. Due to accurate stoichiometry of constituent atoms and ligands, NCs have strong structure-property relationship. The synthesis of NCs is seemingly similar to that of NPs as both are formed by colloidal phase transitions. However, they are considerably different because of metal-ligand complexes in NC synthesis. Reactive ligands can convert metal salts to complexes, actual precursors to metal NCs. During the complex formation, various metal species occur, having different reactivity and fraction depending on synthetic conditions. It can alter their degree of participation in NC synthesis and the homogeneity of final products. Herein, we investigate the effects of complex formation on the entire NC synthesis. By controlling the fraction of various Au species showing different reactivity, we find that the extent of complex formation alters reduction kinetics and the uniformity of Au NCs. We demonstrate that this concept can be universally applied to synthesize Ag, Pt, Pd, and Rh NCs.
Circadian regulation of developmental synaptogenesis via the hypocretinergic system
Authors: Du Xu-Fei , Li Fu-Ning , Peng Xiao-Lan , Xu Bing , Zhang Yu , Li Guang , Liu Taole , Li Ying , Wang Han , Yan Jun , Du Jiu-Lin 
Published: June 2023

AbstractThe circadian clock orchestrates a wide variety of physiological and behavioral processes, enabling animals to adapt to daily environmental changes, particularly the day-night cycle. However, the circadian clock’s role in the developmental processes remains unclear. Here, we employ the in vivo long-term time-lapse imaging of retinotectal synapses in the optic tectum of larval zebrafish and reveal that synaptogenesis, a fundamental developmental process for neural circuit formation, exhibits circadian rhythm. This rhythmicity arises primarily from the synapse formation rather than elimination and requires the hypocretinergic neural system. Disruption of this synaptogenic rhythm, by impairing either the circadian clock or the hypocretinergic system, affects the arrangement of the retinotectal synapses on axon arbors and the refinement of the postsynaptic tectal neuron’s receptive field. Thus, our findings demonstrate that the developmental synaptogenesis is under hypocretin-dependent circadian regulation, suggesting an important role of the circadian clock in neural development.
Pressure-controlled magnetism in 2D molecular layers
Authors: Huang Yulong , Pathak Arjun K. , Tsai Jeng-Yuan , Rumsey Clayton , Ivill Mathew , Kramer Noah , Hu Yong , Trebbin Martin , Yan Qimin , Ren Shenqiang 
Published: June 2023

AbstractLong-range magnetic ordering of two-dimensional crystals can be sensitive to interlayer coupling, enabling the effective control of interlayer magnetism towards voltage switching, spin filtering and transistor applications. With the discovery of two-dimensional atomically thin magnets, a good platform provides us to manipulate interlayer magnetism for the control of magnetic orders. However, a less-known family of two-dimensional magnets possesses a bottom-up assembled molecular lattice and metal-to-ligand intermolecular contacts, which lead to a combination of large magnetic anisotropy and spin-delocalization. Here, we report the pressure-controlled interlayer magnetic coupling of molecular layered compounds via chromium-pyrazine coordination. Room-temperature long-range magnetic ordering exhibits pressure tuning with a coercivity coefficient up to 4 kOe/GPa, while pressure-controlled interlayer magnetism also presents a strong dependence on alkali metal stoichiometry and composition. Two-dimensional molecular interlayers provide a pathway towards pressure-controlled peculiar magnetism through charge redistribution and structural transformation.
Clinical outcomes of de novo metastatic HER2-positive inflammatory breast cancer
Authors: Garrido-Castro Ana C. , Regan Meredith M. , Niman Samuel M. , Nakhlis Faina , Remolano Claire , Rosenbluth Jennifer M. , Block Caroline , Warren Laura E. , Bellon Jennifer R. , Yeh Eren , Harrison Beth T. , Troll Elizabeth , Lin Nancy U. , Tolaney Sara M. , Overmoyer Beth , Lynce Filipa 
Published: June 2023

AbstractInflammatory breast cancer (IBC) is a rare, aggressive form of breast cancer that presents as de novo metastatic disease in 20–30% of cases, with one-third of cases demonstrating HER2-positivity. There has been limited investigation into locoregional therapy utilization following HER2-directed systemic therapy for these patients, and their locoregional progression or recurrence (LRPR) and survival outcomes. Patients with de novo HER2-positive metastatic IBC (mIBC) were identified from an IRB-approved IBC registry at Dana-Farber Cancer Institute. Clinical, pathology, and treatment data were abstracted. Rates of LRPR, progression-free survival (PFS), overall survival (OS), and pathologic complete response (pCR) were determined. Seventy-eight patients diagnosed between 1998 and 2019 were identified. First-line systemic therapy comprised chemotherapy for most patients (97.4%) and HER2-directed therapy for all patients (trastuzumab [47.4%]; trastuzumab+pertuzumab [51.3%]; or trastuzumab emtansine [1.3%]). At a median follow-up of 2.7 years, the median PFS was 1.0 year, and the median OS was 4.6 years. The 1- and 2-year cumulative incidence of LRPR was 20.7% and 29.0%, respectively. Mastectomy was performed after systemic therapy in 41/78 patients (52.6%); 10 had a pCR (24.4%) and all were alive at last follow-up (1.3–8.9 years after surgery). Among 56 patients who were alive and LRPR-free at one year, 10 developed LRPR (surgery group = 1; no-surgery group = 9). In conclusion, patients with de novo HER2-positive mIBC who undergo surgery have favorable outcomes. More than half of patients received systemic and local therapy with good locoregional control and prolonged survival, suggesting a potential role for local therapy.
Recombinant lipidated FLIPr effectively enhances mucosal and systemic immune responses for various vaccine types
Authors: Hsieh Ming-Shu , Chen Mei-Yu , Hsu Chia-Wei , Tsai Yu-Wen , Chiu Fang-Feng , Hsu Cheng-Lung , Lin Chang-Ling , Wu Chiao-Chieh , Tu Ling-Ling , Chiang Chen-Yi , Liu Shih-Jen , Liao Ching-Len , Chen Hsin-Wei 
Published: June 2023

AbstractFormyl peptide receptor-like 1 inhibitor protein (FLIPr) is an immune evasion protein produced by Staphylococcus aureus, and FLIPr is a potential vaccine candidate for reducing Staphylococcus aureus virulence and biofilm formation. We produced recombinant lipidated FLIPr (rLF) to increase the immunogenicity of FLIPr and showed that rLF alone elicited potent anti-FLIPr antibody responses to overcome the FLIPr-mediated inhibition of phagocytosis. In addition, rLF has potent immunostimulatory properties. We demonstrated that rLF is an effective adjuvant. When an antigen is formulated with rLF, it can induce long-lasting antigen-specific immune responses and enhance mucosal and systemic antibody responses as well as broad-spectrum T-cell responses in mice. These findings support further exploration of rLF in the clinic as an adjuvant for various vaccine types with extra benefits to abolish FLIPr-mediated immunosuppressive effects.
A battery dataset for electric vertical takeoff and landing aircraft
Authors: Bills Alexander , Sripad Shashank , Fredericks Leif , Guttenberg Matthew , Charles Devin , Frank Evan , Viswanathan Venkatasubramanian 
Published: June 2023

AbstractElectric vertical takeoff and landing aircraft have a unique duty cycle characterized by high discharge currents at the beginning and end of the mission (corresponding to takeoff and landing of the aircraft) and a moderate power requirement between them with no rest periods during the mission. Here, we generated a dataset of battery duty profiles for an electric vertical takeoff and landing aircraft using a cell typical for that application. The dataset features 22 cells, comprising a total of 21,392 charge and discharge cycles. 3 of the cells use the baseline cycle while each of the other cells vary either charge current, discharge power, discharge duration, ambient cooling conditions, or end of charge voltage. While it was designed to mimic the expected duty cycle of an electric aircraft, this dataset is relevant for training machine learning models on battery life, fitting physical or empirical models for battery performance and/or degradation, and countless other applications.
K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels
Authors: Kang Soowon , Choi Woohyeok , Park Cheul Young , Cha Narae , Kim Auk , Khandoker Ahsan Habib , Hadjileontiadis Leontios , Kim Heepyung , Jeong Yong , Lee Uichin 
Published: June 2023

AbstractWith the popularization of low-cost mobile and wearable sensors, several studies have used them to track and analyze mental well-being, productivity, and behavioral patterns. However, there is still a lack of open datasets collected in real-world contexts with affective and cognitive state labels such as emotion, stress, and attention; the lack of such datasets limits research advances in affective computing and human-computer interaction. This study presents K-EmoPhone, a real-world multimodal dataset collected from 77 students over seven days. This dataset contains (1) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices, (2) context and interaction data collected from individuals’ smartphones, and (3) 5,582 self-reported affect states, including emotions, stress, attention, and task disturbance, acquired by the experience sampling method. We anticipate the dataset will contribute to advancements in affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.
Curated and harmonized gut microbiome 16S rRNA amplicon data from dietary fiber intervention studies in humans
Authors: Rodriguez Cynthia I. , Keshavarzian Ali , Hamaker Bruce R. , Liu Feitong , Lunken Genelle R. , Rasmussen Heather , Zhou Hongwei , Tap Julien , Swanson Kelly S. , Ukhanova Maria , Leclerc Marion , Gotteland Martin , Navarrete Paola , Kovatcheva-Datchary Petia , Dahl Wendy J. , Martiny Jennifer B. H. 
Published: June 2023

AbstractNext generation amplicon sequencing has created a plethora of data from human microbiomes. The accessibility to this scientific data and its corresponding metadata is important for its reuse, to allow for new discoveries, verification of published results, and serving as path for reproducibility. Dietary fiber consumption has been associated with a variety of health benefits that are thought to be mediated by gut microbiota. To enable direct comparisons of the response of the gut microbiome to fiber, we obtained 16S rRNA sequencing data and its corresponding metadata from 11 fiber intervention studies for a total of 2,368 samples. We provide curated and pre-processed genetic data and common metadata for comparison across the different studies.
PANGAEA - Data Publisher for Earth & Environmental Science
Authors: Felden Janine , Möller Lars , Schindler Uwe , Huber Robert , Schumacher Stefanie , Koppe Roland , Diepenbroek Michael , Glöckner Frank Oliver 
Published: June 2023

AbstractThe information system PANGAEA provides targeted support for research data management as well as long-term data archiving and publication. PANGAEA is operated as an open access library for archiving, publishing, and distributing georeferenced data from earth and environmental sciences. It focuses on observational and experimental data. Citability, comprehensive metadata descriptions, interoperability of data and metadata, a high degree of structural and semantic harmonization of the data inventory as well as the commitment of the hosting institutions ensures the long-term usability of archived data. PANGAEA is a pioneer of FAIR and open data infrastructures to enable data intensive science and an integral component of national and international science and technology activities. This paper provides an overview of the recent organisational, structural, and technological advancements in developing and operating the information system.
Interpretable deep learning for diagnosis of fungal and acanthamoeba keratitis using in vivo confocal microscopy images
Authors: Essalat Mahmoud , Abolhosseini Mohammad , Le Thanh Huy , Moshtaghion Seyed Mohamadmehdi , Kanavi Mozhgan Rezaei 
Published: June 2023

AbstractInfectious keratitis refers to a group of corneal disorders in which corneal tissues suffer inflammation and damage caused by pathogenic infections. Among these disorders, fungal keratitis (FK) and acanthamoeba keratitis (AK) are particularly severe and can cause permanent blindness if not diagnosed early and accurately. In Vivo Confocal Microscopy (IVCM) allows for imaging of different corneal layers and provides an important tool for an early and accurate diagnosis. In this paper, we introduce the IVCM-Keratitis dataset, which comprises of a total of 4001 sample images of AK and FK, as well as non-specific keratitis (NSK) and healthy corneas classes. We use this dataset to develop multiple deep-learning models based on Convolutional Neural Networks (CNNs) to provide automated assistance in enhancing the diagnostic accuracy of confocal microscopy in infectious keratitis. Densenet161 had the best performance among these models, with an accuracy, precision, recall, and F1 score of 93.55%, 92.52%, 94.77%, and 96.93%, respectively. Our study highlights the potential of deep learning models to provide automated diagnostic assistance for infectious keratitis via confocal microscopy images, particularly in the early detection of AK and FK. The proposed model can provide valuable support to both experienced and inexperienced eye-care practitioners in confocal microscopy image analysis, by suggesting the most likely diagnosis. We further demonstrate that these models can highlight the areas of infection in the IVCM images and explain the reasons behind their diagnosis by utilizing saliency maps, a technique used in eXplainable Artificial Intelligence (XAI) to interpret these models.
Acute rotavirus infection is associated with the induction of circulating memory CD4+ T cell subsets
Authors: Malamba-Banda Chikondi , Mhango Chimwemwe , Benedicto-Matambo Prisca , Mandolo Jonathan J. , Chinyama End , Kumwenda Orpha , Barnes Kayla G. , Cunliffe Nigel A. , Iturriza-Gomara Miren , Jambo Kondwani C. , Jere Khuzwayo C. 
Published: June 2023

AbstractStrong CD4+ T cell-mediated immune protection following rotavirus infection has been observed in animal models, but its relevance in humans remains unclear. Here, we characterized acute and convalescent CD4+ T cell responses in children who were hospitalized with rotavirus-positive and rotavirus-negative diarrhoea in Blantyre, Malawi. Children presenting with laboratory-confirmed rotavirus infection had higher proportions of effector and central memory T helper 2 cells during acute infection i.e., at disease presentation compared to convalescence, 28 days post-infection defined by a follow-up 28 days after acute infection. However, circulating cytokine-producing (IFN-γ and/or TNF-α) rotavirus-specific VP6-specific CD4+ T cells were rarely detectable in children with rotavirus infection at both acute and convalescent stages. Moreover, following whole blood mitogenic stimulation, the responding CD4+ T cells were predominantly non-cytokine producers of IFN-γ and/or TNF-α. Our findings demonstrate limited induction of anti-viral IFN-γ and/or TNF-α-producing CD4+ T cells in rotavirus-vaccinated Malawian children following the development of laboratory-confirmed rotavirus infection.
Reliability prediction and evaluation of communication base stations in earthquake prone areas
Authors: Li Xueming , Wei Yao , Ming Zheng , Cong Hao , Zheng Xuanyu , Chang Qihai 
Published: June 2023

AbstractOne of the primary tasks for effective disaster relief after a catastrophic earthquake is robust communication. In this paper, we propose a simple logistic method based on two-parameter sets of geology and building structure for the failure prediction of the base stations in post-earthquake. Using the post-earthquake base station data in Sichuan, China, the prediction results are 96.7% and 90% for the two-parameter sets and all parameter sets, respectively, and 93.3% for the neural network method sets. The results show that the two-parameter method outweighs the whole parameter set logistic method and the neural network prediction and can effectively improve the accuracy of the prediction. The weight parameters of two-parameter set by the actual field data significantly show that the failure of base stations after earthquake is mainly due to the geological differences where the base stations are located. It can be envisioned that if the geological distribution between the earthquake source and the base station is parameterized, the multi-parameter sets logistic method can not only effectively solve the failure prediction after earthquakes and the evaluation of communication base stations under complex conditions, but also provide site selection evaluation for the construction of civil buildings and power grid towers in earthquake-prone areas.
Analysis of the impact of green urban areas in historic fortified cities using Landsat historical series and Normalized Difference Indices
Authors: Moreno M. , Ortiz P. , Ortiz R. 
Published: June 2023

AbstractUrban green areas minimize the negative effects of climatic change and improve the sustainability of historic cities. Despite this, green areas have traditionally been considered a threat to heritage buildings because they cause humidity changes, that accelerate degradation processes. Within this context, this study evaluates the trends in the inclusion of green areas in historic cities and the effects it causes on humidity and conservation of earthen fortifications. To achieve this goal, vegetative and humidity information has been obtained since 1985 from Landsat satellite images. The historical series of images has been statistically analysed in Google Earth Engine to obtain maps that show the means, 25th, and 75th percentiles of the variations registered in the last 35 years. The results allow visualizing spatial patterns and plotting the seasonal and monthly variations. In the decision-making process, the proposed method allows to monitor whether the presence of vegetation is an environmental degradation agent in the nearby earthen fortifications.The analysis of the historic fortified cities of Seville and Niebla (Spain) shows a gradual increase in green areas and an interest in locating them near the earthen fortifications. The impact on the fortifications is specific to each type of vegetation and can be positive or negative. In general, the low humidity registered indicates low danger, and the presence of green areas favours drying after heavy rains. This study suggests that increasing green spaces to historic cities does not necessarily endanger the preservation of earthen fortifications. Instead, managing both heritage sites and urban green areas together can encourage outdoor cultural activities, reduce the impacts of climate change, and enhance the sustainability of historic cities.
Self-organizing neural network for reproducing human postural mode alternation through deep reinforcement learning
Authors: Shen Keli , Li Guanda , Chemori Ahmed , Hayashibe Mitsuhiro 
Published: June 2023

AbstractA self-organized phenomenon in postural coordination is essential for understanding the auto-switching mechanism of in-phase and anti-phase postural coordination modes during standing and related supra-postural activities. Previously, a model-based approach was proposed to reproduce such self-organized phenomenon. However, if we set this problem including the process of how we establish the internal predictive model in our central nervous system, the learning process is critical to be considered for establishing a neural network for managing adaptive postural control. Particularly when body characteristics may change due to growth or aging or are initially unknown for infants, a learning capability can improve the hyper-adaptivity of human motor control for maintaining postural stability and saving energy in daily living. This study attempted to generate a self-organizing neural network that can adaptively coordinate the postural mode without assuming a prior body model regarding body dynamics and kinematics. Postural coordination modes are reproduced in head-target tracking tasks through a deep reinforcement learning algorithm. The transitions between the postural coordination types, i.e. in-phase and anti-phase coordination modes, could be reproduced by changing the task condition of the head tracking target, by changing the frequencies of the moving target. These modes are considered emergent phenomena existing in human head tracking tasks. Various evaluation indices, such as correlation, and relative phase of hip and ankle joint, are analyzed to verify the self-organizing neural network performance to produce the postural coordination transition between the in-phase and anti-phase modes. In addition, after learning, the neural network can also adapt to continuous task condition changes and even to unlearned body mass conditions keeping consistent in-phase and anti-phase mode alternation.