bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cell Rover—a miniaturized magnetostrictive antenna for wireless operation inside living cells
Authors: Joy Baju , Cai Yubin , Bono David C. , Sarkar Deblina 
Published: September 2022

AbstractAn intracellular antenna can open up new horizons for fundamental and applied biology. Here, we introduce the Cell Rover, a magnetostrictive antenna which can operate wirelessly inside a living cell and is compatible with 3D biological systems. It is sub-mm in size, acoustically actuated by an AC magnetic field and resonantly operated at low MHz frequencies, which is ideal for living systems. We developed an injection scheme involving non-uniform magnetic fields for intracellular injection of the Cell Rovers and demonstrated their operation in fully opaque, stage VI Xenopus oocytes, for which real-time imaging with conventional technologies is challenging. We also show that they provide a pathway for multiplexing applications to individually address multiple cells or to tune to more than one antenna within the same cell for versatile functionalities. This technology forms the foundation stone that can enable the integration of future capabilities such as smart sensing, modulation as well as energy harvesting to power in-cell nanoelectronic computing and can potentially bring the prowess of information technology inside a living cell. This could lead to unprecedented opportunities for fundamental understanding of biology as well as diagnostics and therapeutics.
Protein condensation diseases: therapeutic opportunities
Authors: Vendruscolo Michele , Fuxreiter Monika 
Published: September 2022

AbstractCondensed states of proteins, including liquid-like membraneless organelles and solid-like aggregates, contribute in fundamental ways to the organisation and function of the cell. Perturbations of these states can lead to a variety of diseases through mechanisms that we are now beginning to understand. We define protein condensation diseases as conditions caused by the disruption of the normal behaviour of the condensed states of proteins. We analyze the problem of the identification of targets for pharmacological interventions for these diseases and explore opportunities for the regulation of the formation and organisation of aberrant condensed states of proteins.
Wireless multi-lateral optofluidic microsystems for real-time programmable optogenetics and photopharmacology
Authors: Wu Yixin , Wu Mingzheng , Vázquez-Guardado Abraham , Kim Joohee , Zhang Xin , Avila Raudel , Kim Jin-Tae , Deng Yujun , Yu Yongjoon , Melzer Sarah , Bai Yun , Yoon Hyoseo , Meng Lingzi , Zhang Yi , Guo Hexia , Hong Liu , Kanatzidis Evangelos E. , Haney Chad R. , Waters Emily A. , Banks Anthony R. , Hu Ziying , Lie Ferrona , Chamorro Leonardo P. , Sabatini Bernardo L. , Huang Yonggang , Kozorovitskiy Yevgenia , Rogers John A. 
Published: September 2022

AbstractIn vivo optogenetics and photopharmacology are two techniques for controlling neuronal activity that have immense potential in neuroscience research. Their applications in tether-free groups of animals have been limited in part due to tools availability. Here, we present a wireless, battery-free, programable multilateral optofluidic platform with user-selected modalities for optogenetics, pharmacology and photopharmacology. This system features mechanically compliant microfluidic and electronic interconnects, capabilities for dynamic control over the rates of drug delivery and real-time programmability, simultaneously for up to 256 separate devices in a single cage environment. Our behavioral experiments demonstrate control of motor behaviors in grouped mice through in vivo optogenetics with co-located gene delivery and controlled photolysis of caged glutamate. These optofluidic systems may expand the scope of wireless techniques to study neural processing in animal models.
Pauling-type adsorption of O2 induced electrocatalytic singlet oxygen production on N–CuO for organic pollutants degradation
Authors: Xie Liangbo , Wang Pengfei , Li Yi , Zhang Dongpeng , Shang Denghui , Zheng Wenwen , Xia Yuguo , Zhan Sihui , Hu Wenping 
Published: September 2022

AbstractDue to environmentally friendly operation and on-site productivity, electrocatalytic singlet oxygen (1O2) production via O2 gas is of immense interest in environment purification. However, the side-on configuration of O2 on the catalysts surface will lead to the formation of H2O, which seriously limits the selectivity and activity of 1O2 production. Herein, we show a robust N-doped CuO (N–CuO) with Pauling-type (end-on) adsorption of O2 at the N–Cu–O3 sites for the selective generation of 1O2 under direct-current electric field. We propose that Pauling-type configuration of O2 not only lowers the overall activation energy barrier, but also alters the reaction pathway to form 1O2 instead of H2O, which is the key feature determining selectivity for the dissociation of Cu–O bonds rather than the O–O bonds. The proposed N dopant strategy is applicable to a series of transition metal oxides, providing a universal electrocatalysts design scheme for existing high-performance electrocatalytic 1O2 production.
High-temperature stability in air of Ti3C2Tx MXene-based composite with extracted bentonite
Authors: Liu Na , Li Qiaoqiao , Wan Hujie , Chang Libo , Wang Hao , Fang Jianhua , Ding Tianpeng , Wen Qiye , Zhou Liujiang , Xiao Xu 
Published: September 2022

AbstractAlthough Ti3C2Tx MXene is a promising material for many applications such as catalysis, energy storage, electromagnetic interference shielding due to its metallic conductivity and high processability, it’s poor resistance to oxidation at high temperatures makes its application under harsh environments challenging. Here, we report an air-stable Ti3C2Tx based composite with extracted bentonite (EB) nanosheets. In this case, oxygen molecules are shown to be preferentially adsorbed on EB. The saturated adsorption of oxygen on EB further inhibits more oxygen molecules to be adsorbed on the surface of Ti3C2Tx due to the weakened p-d orbital hybridization between adsorbed O2 and Ti3C2Tx, which is induced by the Ti3C2Tx/EB interface coupling. As a result, the composite is capable of tolerating high annealing temperatures (above 400 °C for several hours) both in air or humid environment, indicating highly improved antioxidation properties in harsh condition. The above finding is shown to be independent on the termination ratio of Ti3C2Tx obtained through different synthesis routes. Utilized as terahertz shielding materials, the composite retains its shielding ability after high-temperature treatment even up to 600 °C, while pristine Ti3C2Tx is completely oxidized with no terahertz shielding ability. Joule heating and thermal cycling performance are also demonstrated.
Excitonic Bloch–Siegert shift in CsPbI3 perovskite quantum dots
Authors: Li Yuxuan , Han Yaoyao , Liang Wenfei , Zhang Boyu , Li Yulu , Liu Yuan , Yang Yupeng , Wu Kaifeng , Zhu Jingyi 
Published: September 2022

AbstractCoherent interaction between matter and light field induces both optical Stark effect and Bloch–Siegert shift. Observing the latter has been historically challenging, because it is weak and is often accompanied by a much stronger Stark shift. Herein, by controlling the light helicity, we can largely restrict these two effects to different spin-transitions in CsPbI3 perovskite quantum dots, achieving room-temperature Bloch–Siegert shift as strong as 4 meV with near-infrared pulses. The ratio between the Bloch–Siegert and optical Stark shifts is however systematically higher than the prediction by the non-interacting, quasi-particle model. With a model that explicitly accounts for excitonic effects, we quantitatively reproduce the experimental observations. This model depicts a unified physical picture of the optical Stark effect, biexcitonic optical Stark effect and Bloch–Siegert shift in low-dimensional materials displaying strong many-body interactions, forming the basis for the implementation of these effects to information processing, optical modulation and Floquet engineering.
Using 2D qNMR analysis to distinguish between frozen and frozen/thawed chicken meat and evaluate freshness
Authors: Kim Hyun Cheol , Baek Ki Ho , Lee Yee Eun , Kang Taemin , Kim Hyun Jun , Lee Dongheon , Jo Cheorun 
Published: September 2022

AbstractWe identified key metabolites reflecting microbial spoilage and differentiated unfrozen meat from frozen/thawed (FT) using 2D qNMR analysis. Unfrozen and FT chicken breasts were prepared, individually aerobically packaged, and stored for 16 days at 2 °C. Only volatile basic nitrogen (VBN) was significantly changed after 6 log CFU/g of total aerobic bacteria (p < 0.05). Extended storage resulted in an increase in organic acids, free amino acids, biogenic amines, and hypoxanthine and a decrease in N,N-dimethylglycine, inosine 5′-monophosphate, and proline. Acetic acid demonstrated the highest correlation with VBN (r = 0.97). Unfrozen and FT breast meat can be differentiated by uniform concentration of carnosine, β-alanine, and histidine levels, consistent changes in nucleotides by storage time, and changes in microbial metabolism patterns that are reflected by some free amino acids. Thus, NMR-based metabolomics can be used to evaluate chicken breast meat freshness and distinguish between unfrozen and FT meat.
A humanized minipig model for the toxicological testing of therapeutic recombinant antibodies
Authors: Flisikowska Tatiana , Egli Jerome , Flisikowski Krzysztof , Stumbaum Marlene , Küng Erich , Ebeling Martin , Schmucki Roland , Georges Guy , Singer Thomas , Kurome Mayuko , Kessler Barbara , Zakhartchenko Valeri , Wolf Eckhard , Weber Felix , Schnieke Angelika , Iglesias Antonio 
Published: September 2022

AbstractThe safety of most human recombinant proteins can be evaluated in transgenic mice tolerant to specific human proteins. However, owing to insufficient genetic diversity and to fundamental differences in immune mechanisms, small-animal models of human diseases are often unsuitable for immunogenicity testing and for predicting adverse outcomes in human patients. Most human therapeutic antibodies trigger xenogeneic responses in wild-type animals and thus rapid clearance of the drugs, which makes in vivo toxicological testing of human antibodies challenging. Here we report the generation of Göttingen minipigs carrying a mini-repertoire of human genes for the immunoglobulin heavy chains γ1 and γ4 and the immunoglobulin light chain κ. In line with observations in human patients, the genetically modified minipigs tolerated the clinically non-immunogenic IgG1κ-isotype monoclonal antibodies daratumumab and bevacizumab, and elicited antibodies against the checkpoint inhibitor atezolizumab and the engineered interleukin cergutuzumab amunaleukin. The humanized minipigs can facilitate the safety and efficacy testing of therapeutic antibodies.
Genetic footprints of assortative mating in the Japanese population
Authors: Yamamoto Kenichi , Sonehara Kyuto , Namba Shinichi , Konuma Takahiro , Masuko Hironori , Miyawaki Satoru ,   The BioBank Japan Project, Kamatani Yoichiro , Hizawa Nobuyuki , Ozono Keiichi , Yengo Loic , Okada Yukinori 
Published: September 2022

AbstractAssortative mating (AM) is a pattern characterized by phenotypic similarities between mating partners. Detecting the evidence of AM has been challenging due to the lack of large-scale datasets that include phenotypic data on both partners, especially in populations of non-European ancestries. Gametic phase disequilibrium between trait-associated alleles is a signature of parental AM on a polygenic trait, which can be detected even without partner data. Here, using polygenic scores for 81 traits in the Japanese population using BioBank Japan Project genome-wide association studies data (n = 172,270), we found evidence of AM on the liability to type 2 diabetes and coronary artery disease, as well as on dietary habits. In cross-population comparison using United Kingdom Biobank data (n = 337,139) we found shared but heterogeneous impacts of AM between populations.
A fluorescent sensor for real-time measurement of extracellular oxytocin dynamics in the brain
Authors: Ino Daisuke , Tanaka Yudai , Hibino Hiroshi , Nishiyama Masaaki 
Published: September 2022

AbstractOxytocin (OT), a hypothalamic neuropeptide that acts as a neuromodulator in the brain, orchestrates a variety of animal behaviors. However, the relationship between brain OT dynamics and complex animal behaviors remains largely elusive, partly because of the lack of a suitable technique for its real-time recording in vivo. Here, we describe MTRIAOT, a G-protein-coupled receptor-based green fluorescent OT sensor that has a large dynamic range, suitable affinity, ligand specificity for OT orthologs, minimal effects on downstream signaling and long-term fluorescence stability. By combining viral gene delivery and fiber photometry-mediated fluorescence measurements, we demonstrate the utility of MTRIAOT for real-time detection of brain OT dynamics in living mice. MTRIAOT-mediated measurements indicate variability of OT dynamics depending on the behavioral context and physical condition of an animal. MTRIAOT will likely enable the analysis of OT dynamics in a variety of physiological and pathological processes.
Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images
Authors: Tampu Iulian Emil , Eklund Anders , Haj-Hosseini Neda 
Published: September 2022

AbstractIn the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolution of OCT systems, consecutive images are often very similar in both visible structures and noise. Thus, an inappropriate data split can result in overlap between the training and testing sets, with a large portion of the literature overlooking this aspect. In this study, the effect of improper dataset splitting on model evaluation is demonstrated for three classification tasks using three OCT open-access datasets extensively used, Kermany’s and Srinivasan’s ophthalmology datasets, and AIIMS breast tissue dataset. Results show that the classification performance is inflated by 0.07 up to 0.43 in terms of Matthews Correlation Coefficient (accuracy: 5% to 30%) for models tested on datasets with improper splitting, highlighting the considerable effect of dataset handling on model evaluation. This study intends to raise awareness on the importance of dataset splitting given the increased research interest in implementing deep learning on OCT data.
Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning
Authors: Lee Seung Mi , Nam Yonghyun , Choi Eun Saem , Jung Young Mi , Sriram Vivek , Leiby Jacob S. , Koo Ja Nam , Oh Ig Hwan , Kim Byoung Jae , Kim Sun Min , Kim Sang Youn , Kim Gyoung Min , Joo Sae Kyung , Shin Sue , Norwitz Errol R. , Park Chan-Wook , Jun Jong Kwan , Kim Won , Kim Dokyoon , Park Joong Shin 
Published: September 2022

AbstractClinical guidelines recommend several risk factors to identify women in early pregnancy at high risk of developing pregnancy-associated hypertension. However, these variables result in low predictive accuracy. Here, we developed a prediction model for pregnancy-associated hypertension using graph-based semi-supervised learning. This is a secondary analysis of a prospective study of healthy pregnant women. To develop the prediction model, we compared the prediction performances across five machine learning methods (semi-supervised learning with both labeled and unlabeled data, semi-supervised learning with labeled data only, logistic regression, support vector machine, and random forest) using three different variable sets: [a] variables from clinical guidelines, [b] selected important variables from the feature selection, and [c] all routine variables. Additionally, the proposed prediction model was compared with placental growth factor, a predictive biomarker for pregnancy-associated hypertension. The study population consisted of 1404 women, including 1347 women with complete follow-up (labeled data) and 57 women with incomplete follow-up (unlabeled data). Among the 1347 with complete follow-up, 2.4% (33/1347) developed pregnancy-associated HTN. Graph-based semi-supervised learning using top 11 variables achieved the best average prediction performance (mean area under the curve (AUC) of 0.89 in training set and 0.81 in test set), with higher sensitivity (72.7% vs 45.5% in test set) and similar specificity (80.0% vs 80.5% in test set) compared to risk factors from clinical guidelines. In addition, our proposed model with graph-based SSL had a higher performance than that of placental growth factor for total study population (AUC, 0.71 vs. 0.80, p < 0.001). In conclusion, we could accurately predict the development pregnancy-associated hypertension in early pregnancy through the use of routine clinical variables with the help of graph-based SSL.
Effects of adrenergic-stimulated lipolysis and cytokine production on in vitro mouse adipose tissue–islet interactions
Authors: Oquendo Morgana Barroso , Lorza-Gil Estela , Juarez-Lopez David , Wagner Robert , Birkenfeld Andreas L. , Ullrich Susanne , Gerst Felicia 
Published: September 2022

AbstractInflammatory cytokines and non-esterified fatty acids (NEFAs) are obesity-linked factors that disturb insulin secretion. The aim of this study was to investigate whether pancreatic adipose tissue (pWAT) is able to generate a NEFA/cytokine overload within the pancreatic environment and as consequence to impact on insulin secretion. Pancreatic fat is a minor fat depot, therefore we used high-fat diet (HFD) feeding to induce pancreatic steatosis in mice. Relative Adipoq and Lep mRNA levels were higher in pWAT of HFD compared to chow diet mice. Regardless of HFD, Adipoq and Lep mRNA levels of pWAT were at least 10-times lower than those of epididymal fat (eWAT). Lipolysis stimulating receptors Adrb3 and Npr1 were expressed in pWAT and eWAT, and HFD reduced their expression in eWAT only. In accordance, HFD impaired lipolysis in eWAT but not in pWAT. Despite expression of Npr mRNA, lipolysis was stimulated solely by the adrenergic agonists, isoproterenol and adrenaline. Short term co-incubation of islets with CD/HFD pWAT did not alter insulin secretion. In the presence of CD/HFD eWAT, glucose stimulated insulin secretion only upon isoproterenol-induced lipolysis, i.e. in the presence of elevated NEFA. Isoproterenol augmented Il1b and Il6 mRNA levels both in pWAT and eWAT. These results suggest that an increased sympathetic activity enhances NEFA and cytokine load of the adipose microenvironment, including that of pancreatic fat, and by doing so it may alter beta-cell function.
Interaction with a reactive partner improves learning in contrast to passive guidance
Authors: Ivanova Ekaterina , Eden Jonathan , Carboni Gerolamo , Krüger Jörg , Burdet Etienne 
Published: September 2022

AbstractMany tasks such as physical rehabilitation, vehicle co-piloting or surgical training, rely on physical assistance from a partner. While this assistance may be provided by a robotic interface, how to implement the necessary haptic support to help improve performance without impeding learning is unclear. In this paper, we study the influence of haptic interaction on the performance and learning of a shared tracking task. We compare in a tracking task the interaction with a human partner, the trajectory guidance traditionally used in training robots, and a robot partner yielding human-like interaction. While trajectory guidance resulted in the best performance during training, it dramatically reduced error variability and hindered learning. In contrast, the reactive human and robot partners did not impede the adaptation and allowed the subjects to learn without modifying their movement patterns. Moreover, interaction with a human partner was the only condition that demonstrated an improvement in retention and transfer learning compared to a subject training alone. These results reveal distinctly different learning behaviour in training with a human compared to trajectory guidance, and similar learning between the robotic partner and human partner. Therefore, for movement assistance and learning, algorithms that react to the user’s motion and change their behaviour accordingly are better suited.
A comparison of impact of comorbidities and demographics on 60-day mortality in ICU patients with COVID-19, sepsis and acute respiratory distress syndrome
Authors: Ahlström Björn , Frithiof Robert , Larsson Ing-Marie , Strandberg Gunnar , Lipcsey Miklos , Hultström Michael 
Published: September 2022

AbstractSevere Coronavirus disease 2019 (COVID-19) is associated with several pre-existing comorbidities and demographic factors. Similar factors are linked to critical sepsis and acute respiratory distress syndrome (ARDS). We hypothesized that age and comorbidities are more generically linked to critical illness mortality than a specific disease state. We used national databases to identify ICU patients and to retrieve comorbidities. The relative importance of risk factors for 60-day mortality was evaluated using the interaction with disease group (Sepsis, ARDS or COVID-19) in logistic regression models. We included 32,501 adult ICU patients. In the model on 60-day mortality in sepsis and COVID-19 there were significant interactions with disease group for age, sex and asthma. In the model on 60-day mortality in ARDS and COVID-19 significant interactions with cohort were found for acute disease severity, age and chronic renal failure. In conclusion, age and sex play particular roles in COVID-19 mortality during intensive care but the burden of comorbidity was similar between sepsis and COVID-19 and ARDS and COVID-19.
DefectTrack: a deep learning-based multi-object tracking algorithm for quantitative defect analysis of in-situ TEM videos in real-time
Authors: Sainju Rajat , Chen Wei-Ying , Schaefer Samuel , Yang Qian , Ding Caiwen , Li Meimei , Zhu Yuanyuan 
Published: September 2022

AbstractIn-situ irradiation transmission electron microscopy (TEM) offers unique insights into the millisecond-timescale post-cascade process, such as the lifetime and thermal stability of defect clusters, vital to the mechanistic understanding of irradiation damage in nuclear materials. Converting in-situ irradiation TEM video data into meaningful information on defect cluster dynamic properties (e.g., lifetime) has become the major technical bottleneck. Here, we present a solution called theDefectTrack, the first dedicated deep learning-based one-shot multi-object tracking (MOT) model capable of tracking cascade-induced defect clusters in in-situ TEM videos in real-time.DefectTrackhas achieved a Multi-Object Tracking Accuracy (MOTA) of 66.43% and a Mostly Tracked (MT) of 67.81% on the test set, which are comparable to state-of-the-art MOT algorithms. We discuss the MOT framework, model selection, training, and evaluation strategies for in-situ TEM applications. Further, we compare theDefectTrackwith four human experts in quantifying defect cluster lifetime distributions using statistical tests and discuss the relationship between the material science domain metrics and MOT metrics. Our statistical evaluations on the defect lifetime distribution suggest that theDefectTrackoutperforms human experts in accuracy and speed.