bluuebunny/crossref_metadata_2025_split huggingface dataset:
Orthogonal luminescence lifetime encoding by intermetallic energy transfer in heterometallic rare-earth MOFs
Authors: Deneff Jacob I. , Rohwer Lauren E. S. , Butler Kimberly S. , Kaehr Bryan , Vogel Dayton J. , Luk Ting S. , Reyes Raphael A. , Cruz-Cabrera Alvaro A. , Martin James E. , Sava Gallis Dorina F. 
Published: February 2023

AbstractLifetime-encoded materials are particularly attractive as optical tags, however examples are rare and hindered in practical application by complex interrogation methods. Here, we demonstrate a design strategy towards multiplexed, lifetime-encoded tags via engineering intermetallic energy transfer in a family of heterometallic rare-earth metal-organic frameworks (MOFs). The MOFs are derived from a combination of a high-energy donor (Eu), a low-energy acceptor (Yb) and an optically inactive ion (Gd) with the 1,2,4,5 tetrakis(4-carboxyphenyl) benzene (TCPB) organic linker. Precise manipulation of the luminescence decay dynamics over a wide microsecond regime is achieved via control over metal distribution in these systems. Demonstration of this platform’s relevance as a tag is attained via a dynamic double encoding method that uses the braille alphabet, and by incorporation into photocurable inks patterned on glass and interrogated via digital high-speed imaging. This study reveals true orthogonality in encoding using independently variable lifetime and composition, and highlights the utility of this design strategy, combining facile synthesis and interrogation with complex optical properties.
Temperature, species identity and morphological traits predict carbonate excretion and mineralogy in tropical reef fishes
Authors: Ghilardi Mattia , Salter Michael A. , Parravicini Valeriano , Ferse Sebastian C. A. , Rixen Tim , Wild Christian , Birkicht Matthias , Perry Chris T. , Berry Alex , Wilson Rod W. , Mouillot David , Bejarano Sonia 
Published: February 2023

AbstractAnthropogenic pressures are restructuring coral reefs globally. Sound predictions of the expected changes in key reef functions require adequate knowledge of their drivers. Here we investigate the determinants of a poorly-studied yet relevant biogeochemical function sustained by marine bony fishes: the excretion of intestinal carbonates. Compiling carbonate excretion rates and mineralogical composition from 382 individual coral reef fishes (85 species and 35 families), we identify the environmental factors and fish traits that predict them. We find that body mass and relative intestinal length (RIL) are the strongest predictors of carbonate excretion. Larger fishes and those with longer intestines excrete disproportionately less carbonate per unit mass than smaller fishes and those with shorter intestines. The mineralogical composition of excreted carbonates is highly conserved within families, but also controlled by RIL and temperature. These results fundamentally advance our understanding of the role of fishes in inorganic carbon cycling and how this contribution will change as community composition shifts under increasing anthropogenic pressures.
Ongoing movement controls sensory integration in the dorsolateral striatum
Authors: de la Torre-Martinez Roberto , Ketzef Maya , Silberberg Gilad 
Published: February 2023

AbstractThe dorsolateral striatum (DLS) receives excitatory inputs from both sensory and motor cortical regions. In the neocortex, sensory responses are affected by motor activity, however, it is not known whether such sensorimotor interactions occur in the striatum and how they are shaped by dopamine. To determine the impact of motor activity on striatal sensory processing, we performed in vivo whole-cell recordings in the DLS of awake mice during the presentation of tactile stimuli. Striatal medium spiny neurons (MSNs) were activated by both whisker stimulation and spontaneous whisking, however, their responses to whisker deflection during ongoing whisking were attenuated. Dopamine depletion reduced the representation of whisking in direct-pathway MSNs, but not in those of the indirect-pathway. Furthermore, dopamine depletion impaired the discrimination between ipsilateral and contralateral sensory stimulation in both direct and indirect pathway MSNs. Our results show that whisking affects sensory responses in DLS and that striatal representation of both processes is dopamine- and cell type-dependent.
Evidence of high-temperature exciton condensation in a two-dimensional semimetal
Authors: Gao Qiang , Chan Yang-hao , Wang Yuzhe , Zhang Haotian , Jinxu Pu , Cui Shengtao , Yang Yichen , Liu Zhengtai , Shen Dawei , Sun Zhe , Jiang Juan , Chiang Tai C. , Chen Peng 
Published: February 2023

AbstractElectrons and holes can spontaneously form excitons and condense in a semimetal or semiconductor, as predicted decades ago. This type of Bose condensation can happen at much higher temperatures in comparison with dilute atomic gases. Two-dimensional (2D) materials with reduced Coulomb screening around the Fermi level are promising for realizing such a system. Here we report a change in the band structure accompanied by a phase transition at about 180 K in single-layer ZrTe2 based on angle-resolved photoemission spectroscopy (ARPES) measurements. Below the transition temperature, gap opening and development of an ultra-flat band top around the zone center are observed. This gap and the phase transition are rapidly suppressed with extra carrier densities introduced by adding more layers or dopants on the surface. The results suggest the formation of an excitonic insulating ground state in single-layer ZrTe2, and the findings are rationalized by first-principles calculations and a self-consistent mean-field theory. Our study provides evidence for exciton condensation in a 2D semimetal and demonstrates strong dimensionality effects on the formation of intrinsic bound electron–hole pairs in solids.
The spatio-temporal distribution of alkaline phosphatase activity and phoD gene abundance and diversity in sediment of Sancha Lake
Authors: Li Yong , Yu Xintao , Liu Huan , Gong Zhilian 
Published: February 2023

AbstractThe bacterial phoD gene encoding alkaline phosphatase (ALP) plays an important role in the release of soluble reactive phosphorus (SRP) from organic phosphorus in ecosystems. However, phoD gene diversity and abundance in ecosystems is poorly understood. In the present study, we sampled the surface sediments and the overlying water of Sancha Lake at 9 different sampling sites, a typical eutrophic sub-deep freshwater lake in China, in April 15 (spring) and November 3 (autumn), 2017. High-throughput sequencing and qPCR were performed to analyze the diversity and abundance of the bacterial phoD gene in the sediments. We further discussed the relationships between the diversity and abundance of the phoD gene and environmental factors and ALP activity. A total of 881,717 valid sequences were obtained from 18 samples and were classified into 41 genera, 31 families, 23 orders, 12 classes, and 9 phyla and grouped into 477 OTUs. The dominant phyla were Proteobacteria and Actinobacteria. The phylogenetic tree based on the sequences of the phoD gene was plotted and composed of three branches. The genetic sequences were aligned predominantly with genera Pseudomonas, Streptomyces, Cupriavidus, and Paludisphaer. The phoD-harboring bacterial community structure showed a significant difference in spring and autumn, but no apparent spatial heterogeneity. The phoD gene abundances at different sampling points were significantly higher in autumn than in spring. In autumn and spring, the phoD gene abundance was significantly higher in the tail of lake and where cage culture used to be intensive. pH value, dissolved oxygen (DO), total organic carbon (TOC), ALP, and phosphorus were important environmental factors affecting the diversity of the phoD gene and the phoD-harboring bacterial community structure. Changes in phoD-harboring bacterial community structure, phoD gene abundance, and ALP activity were negatively correlated with SRP in overlying water. Our study indicated phoD-harboring bacteria in the sediments of Sancha Lake with the characteristics of high diversity and significant spatial and temporal heterogeneity in abundance and community structure, which played a important role in the release of SRP.
Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism
Authors: Aurangzeb Sana , Aleem Muhammad 
Published: February 2023

AbstractWith the rise in popularity and usage of Android operating systems, malicious applications are targeted by applying innovative ways and techniques. Today, malware becomes intelligent that uses several ways of obfuscation techniques to hide its functionality and evade anti-malware engines. For mainstream smartphone users, Android malware poses a severe security danger. An obfuscation approach, however, can produce malware versions that can evade current detection strategies and dramatically lower the detection accuracy. Attempting to identify Android malware obfuscation variations, this paper proposes an approach to address the challenges and issues related to the classification and detection of malicious obfuscated variants. The employed detection and classification scheme uses both static and dynamic analysis using an ensemble voting mechanism. Moreover, this study demonstrates that a small subset of features performs consistently well when they are derived from the basic malware (non-obfuscated), however, after applying a novel feature-based obfuscation approach, the study shows a drastic change indicating the relative importance of these features in obfuscating benign and malware applications. For this purpose, we present a fast, scalable, and accurate mechanism for obfuscated Android malware detection based on the Deep learning algorithm using real and emulator-based platforms. The experiments show that the proposed model detects malware effectively and accurately along with the identification of features that are usually obfuscated by malware attackers.
Retinal and peripapillary vascular deformations in prematurely born children aged 4–12 years with a history of retinopathy of prematurity
Authors: Lee Ga-In , Park Kyung-Ah , Oh Sei Yeul , Kim Sang Jin 
Published: February 2023

AbstractIn this study, foveal, parafoveal, peripapillary anatomical, and microvascular anomalies in prematurely born children aged 4–12 years with a history of retinopathy of prematurity (ROP) were evaluated. Seventy-eight eyes of 78 prematurely born children ([tROP]: ROP with laser treatment, [srROP]: spontaneously regressed ROP) and 43 eyes of 43 healthy children were included. Foveal and peripapillary morphological parameters (including ganglion cell and inner plexiform layer (GCIPL) thickness, peripapillary retinal nerve fiber layer (pRNFL) thickness) and vasculature parameters (including foveal avascular zone area, vessel density from superficial retinal capillary plexus (SRCP), deep retinal capillary plexus (DRCP), and radial peripapillary capillary (RPC) segments) were analyzed. Foveal vessel densities in SRCP and DRCP were increased and parafoveal vessel densities in SRCP and RPC segments vessel density were decreased in both ROP groups compared with those of control eyes. The best-corrected visual acuity was negatively correlated with pRNFL thickness in the tROP group. Refractive error was negatively correlated with vessel density of RPC segments in the srROP group. In children born preterm with a history of ROP, it was found that foveal, parafoveal, and peripapillary structural and vascular anomalies and redistribution were accompanied. These retinal vascular and anatomical structure anomalies showed close relationships with visual functions.
Gene knockdown in HaCaT cells by small interfering RNAs entrapped in grapefruit-derived extracellular vesicles using a microfluidic device
Authors: Itakura Shoko , Shohji Ayaka , Amagai Sayaka , Kitamura Masashi , Takayama Kozo , Sugibayashi Kenji , Todo Hiroaki 
Published: February 2023

AbstractSmall interfering RNAs (siRNAs) knockdown the expression of target genes by causing mRNA degradation and are a promising therapeutic modality. In clinical practice, lipid nanoparticles (LNPs) are used to deliver RNAs, such as siRNA and mRNA, into cells. However, these artificial nanoparticles are toxic and immunogenic. Thus, we focused on extracellular vesicles (EVs), natural drug delivery systems, for the delivery of nucleic acids. EVs deliver RNAs and proteins to specific tissues to regulate various physiological phenomena in vivo.Here, we propose a novel method for the preparation siRNAs encapsulated in EVs using a microfluidic device (MD). MDs can be used to generate nanoparticles, such as LNPs, by controlling flow rate to the device, but the loading of siRNAs into EVs using MDs has not been reported previously. In this study, we demonstrated a method for loading siRNAs into grapefruit-derived EVs (GEVs), which have gained attention in recent years for being plant-derived EVs developed using an MD. GEVs were collected from grapefruit juice using the one-step sucrose cushion method, and then GEVs-siRNA-GEVs were prepared using an MD device. The morphology of GEVs and siRNA-GEVs was observed using a cryogenic transmission electron microscope. Cellular uptake and intracellular trafficking of GEVs or siRNA-GEVs to human keratinocytes were evaluated by microscopy using HaCaT cells. The prepared siRNA-GEVs encapsulated 11% of siRNAs. Moreover, intracellular delivery of siRNA and gene suppression effects in HaCaT cells were achieved using these siRNA-GEVs. Our findings suggested that MDs can be used to prepare siRNA-EV formulations.
Taxonomic composition, community structure and molecular novelty of microeukaryotes in a temperate oligomesotrophic lake as revealed by metabarcoding
Authors: Mitsi Konstantina , Richter Daniel J. , Arroyo Alicia S. , López-Escardó David , Antó Meritxell , Oterino Antonio Guillén , Ruiz-Trillo Iñaki 
Published: February 2023

AbstractMicrobial eukaryotes are diverse and ecologically important organisms, yet sampling constraints have hindered the understanding of their distribution and diversity in freshwater ecosystems. Metabarcoding has provided a powerful complement to traditional limnological studies, revealing an unprecedented diversity of protists in freshwater environments. Here, we aim to expand our knowledge of the ecology and diversity of protists in lacustrine ecosystems by targeting the V4 hypervariable region of the 18S rRNA gene in water column, sediment and biofilm samples collected from Sanabria Lake (Spain) and surrounding freshwater ecosystems. Sanabria is a temperate lake, which are relatively understudied by metabarcoding in comparison to alpine and polar lakes. The phylogenetic diversity of microbial eukaryotes detected in Sanabria spans all currently recognized eukaryotic supergroups, with Stramenopiles being the most abundant and diverse supergroup in all sampling sites. Parasitic microeukaryotes account for 21% of the total protist ASVs identified in our study and were dominated by Chytridiomycota, both in terms of richness and abundance, in all sampling sites. Sediments, biofilms and water column samples harbour distinct microbial communities. Phylogenetic placement of poorly assigned and abundant ASVs indicates molecular novelty inside Rhodophyta, Bigyra, early-branching Nucletmycea and Apusomonadida. In addition, we report the first freshwater incidence of the previously exclusively marine genera Abeoforma and Sphaeroforma. Our results contribute to a deeper understanding of microeukaryotic communities in freshwater ecosystems, and provide the first molecular reference for future biomonitoring surveys in Sanabria Lake.
CDF‐net: A convolutional neural network fusing frequency domain and spatial domain features
Authors: Yang Aitao , Li Min , Wu Zhaoqing , He Yujie , Qiu Xiaohua , Song Yu , Du Weidong , Gou Yao 
Published: February 2023

AbstractConvolutional neural network (CNN), as a classic deep learning algorithm, has been applied to various computer vision tasks. However, most classic CNN models focus on the extraction and utilisation of spatial domain features, while ignoring the potential ability of frequency domain feature extraction. In this study, the mechanism in the backbone design is explored. Firstly, the traditional DCT formula is converted into a convolution form through mathematical derivation. On the basis of a a new type of convolution, namely the DCT Convolution is designed. It is more applicable to deep learning network architectures. Secondly, based on the DCT Convolution, a new cross‐domain fusion network named CDF‐Net is designed. The frequency domain and spatial domain features of the input sample are extracted and fused by the network. CDF‐Net is a general network framework which can be applied to most existing prevalent networks. Finally, various experiments are conducted. On image classification task, for Imagenet2012 dataset, the method proposed was applied to ResNet50, and the accuracy of Top1 was increased by 3.684%. On object detection task, for COCO2017 dataset, the method proposed in this study was applied to ResNet50 and ResNeXt50, mAP were improved by 0.5% and 1.2% respectively.
Analysis assaulting pattern for the security problem monitoring in 5G‐enabled sensor network systems with big data environment using artificial intelligence/machine learning
Authors: Rajawat Anand Singh , Goyal S. B. , Bedi Pradeep , Kautish Sandeep , Shrivastava Divya Prakash 
Published: February 2023

AbstractThe 5G‐enabled sensor network systems make it possible to connect cyber and real ‘things’ in many ways. Even so, the flow of data between 5G‐enabled sensor devices brings big data environment problems, such as huge amounts of data, duplicated data, a lack of scalability etc. Some of these problems are as follows. It is hard to keep an eye on 5G‐enabled sensor systems in a ‘big data’ environment. Cyberattacks that could put the safety of the sensor network systems at risk are hard to find, which make the situation even more complicated. The security challenges of 5G‐enabled Sensor Network Systems are studied and analyzed due to some constraints associated with the sensor nodes. The proposed advanced algorithm for securing the 5G‐enabled sensor systems is a Multidimensional big data environment using artificial intelligence/machine learning (AI/ML). Using a structure that depends on both geographical and temporal data, an improved clear point selection operation may get important information from multidimensional time series data that is spread across a wide range of sensor nodes. Therefore, the actions of the 5G‐enabled sensor network can be shown accurately and a complete model of its underlying data structure is built to analysis attacking, pattern on 5G‐enabled Sensor Network Systems using the AI/ML Algorithm.
Fluoroscopic Categorization of Cementless Acetabular Component Positioning
Authors: Sadowitz Peter M. , Karlin William M. , Lirtzman Ross A. , Kowaleski Michael P. 
Published: February 2023

AbstractObjectives The purpose of this study was to evaluate a fluoroscopic method of angle of lateral opening (ALO) categorization based on identification of the visible portion of a pre-existing, circular recess within the metal shell of the BioMedtrix BFX acetabular component, which projects as an ellipse at clinically relevant ALO values. Our hypothesis was that there will be an association between the actual ALO and the categorization of ALO based on identification of the visible portion of the elliptical recess on a lateral fluoroscopic image at clinically relevant values.Study Design A custom plexiglass jig was fitted with a two-axis inclinometer and a 24 mm BFX acetabular component attached to its tabletop. Fluoroscopic reference images were obtained with the cup positioned at an ALO of 35, 45 and 55 degrees with a fixed 10 degrees of retroversion. Thirty study fluoroscopic images (10 images at each ALO) were obtained based on randomization at an ALO of 35, 45 and 55 degrees (±0.5 degrees) with 10 degrees of retroversion. The order of the study images was randomized, and a single, blinded observer categorized the 30 study images as representing an ALO of 35, 45 or 55 degrees by comparison to the reference images.Results Analysis showed perfect (30/30) agreement with a weighted kappa coefficient of 1 (95% confidence interval: −0.717 to 1).Clinical Relevance The results demonstrate that accurate categorization of ALO can be achieved using this fluoroscopic method. This method may prove to be a simple but effective method of estimating intraoperative ALO.
Evaluation of Feature Selection Methods for Preserving Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine
Authors: Lemmon Joshua , Guo Lin Lawrence , Posada Jose , Pfohl Stephen R. , Fries Jason , Fleming Scott Lanyon , Aftandilian Catherine , Shah Nigam , Sung Lillian 
Published: February 2023

AbstractBackground Temporal dataset shift can cause degradation in model performance as discrepancies between training and deployment data grow over time. The primary objective was to determine whether parsimonious models produced by specific feature selection methods are more robust to temporal dataset shift as measured by out-of-distribution (OOD) performance, while maintaining in-distribution (ID) performance.Methods Our dataset consisted of intensive care unit patients from MIMIC-IV categorized by year groups (2008–2010, 2011–2013, 2014–2016, and 2017–2019). We trained baseline models using L2-regularized logistic regression on 2008–2010 to predict in-hospital mortality, long length of stay (LOS), sepsis, and invasive ventilation in all year groups. We evaluated three feature selection methods: L1-regularized logistic regression (L1), Remove and Retrain (ROAR), and causal feature selection. We assessed whether a feature selection method could maintain ID performance (2008–2010) and improve OOD performance (2017–2019). We also assessed whether parsimonious models retrained on OOD data performed as well as oracle models trained on all features in the OOD year group.Results The baseline model showed significantly worse OOD performance with the long LOS and sepsis tasks when compared with the ID performance. L1 and ROAR retained 3.7 to 12.6% of all features, whereas causal feature selection generally retained fewer features. Models produced by L1 and ROAR exhibited similar ID and OOD performance as the baseline models. The retraining of these models on 2017–2019 data using features selected from training on 2008–2010 data generally reached parity with oracle models trained directly on 2017–2019 data using all available features. Causal feature selection led to heterogeneous results with the superset maintaining ID performance while improving OOD calibration only on the long LOS task.Conclusions While model retraining can mitigate the impact of temporal dataset shift on parsimonious models produced by L1 and ROAR, new methods are required to proactively improve temporal robustness.