bluuebunny/crossref_metadata_2025_split huggingface dataset:
First regional reference database of northern Adriatic diatom transcriptomes
Authors: Knjaz Mia , Baricevic Ana , Tankovic Mirta Smodlaka , Kuzat Natasa , Vlasicek Ivan , Grizancic Lana , Podolsak Ivan , Pfannkuchen Martin , Kogovsek Tjasa , Pfannkuchen Daniela Maric 
Published: July 2024

AbstractMarine microbial communities form the basis for the functioning of marine ecosystems and the conservation of biodiversity. With the application of metagenomics and metatranscriptomics in marine environmental studies, significant progress has been made in analysing the functioning of microbial communities as a whole. These molecular techniques are highly dependent on reliable, well-characterised, comprehensive and taxonomically diverse sequenced reference transcriptomes of microbial organisms. Here we present a set of 12 individual transcriptome assemblies derived from 6 representative diatom species from the northern Adriatic Sea grown under 2 environmentally relevant growth conditions (phosphate replete vs. phosphate deprived). After filtering the reads and assembly, an average number of 64,932 transcripts per assembly was obtained, of which an average of 8856 were assigned to functionally known proteins. Of all assigned transcripts, an average of 6483 proteins were taxonomically assigned to diatoms (Bacillariophyta). On average, a higher number of assigned proteins was detected in the transcriptome assemblies of diatoms grown under replete media condition. On average, 50% of the mapped proteins were shared between the two growth conditions. All recorded proteins in the dataset were classified into 24 COG categories, with approximately 25% belonging to the unknown function and the remaining 75% belonging to all other categories. The resulting diatom reference database for the northern Adriatic, focussing on the response to nutrient limitation as characteristic for the region and predicted for the future world oceans, provides a valuable resource for analysing environmental metatranscriptome and metagenome data. Each northern Adriatic transcriptome can also be used by itself as a reference database for the (meta)transcriptomes and gene expression studies of the associated species that will be generated in the future.
Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS
Authors: Zhang Guanjin , Roslan Siti Nur Aliaa Binti , Shafri Helmi Zulhaidi Mohd , Zhao Yanxi , Wang Ci , Quan Ling 
Published: July 2024

AbstractTo obtain seasonable and precise crop yield information with fine resolution is very important for ensuring the food security. However, the quantity and quality of available images and the selection of prediction variables often limit the performance of yield prediction. In our study, the synthesized images of Landsat and MODIS were used to provide remote sensing (RS) variables, which can fill the missing values of Landsat images well and cover the study area completely. The deep learning (DL) was used to combine different vegetation index (VI) with climate data to build wheat yield prediction model in Hebei Province (HB). The results showed that kernel NDVI (kNDVI) and near-infrared reflectance (NIRv) slightly outperform normalized difference vegetation index (NDVI) in yield prediction. And the regression algorithm had a more prominent effect on yield prediction, while the yield prediction model using Long Short-Term Memory (LSTM) outperformed the yield prediction model using Light Gradient Boosting Machine (LGBM). The model combining LSTM algorithm and NIRv had the best prediction effect and relatively stable performance in single year. The optimal model was then used to generate 30 m resolution wheat yield maps in the past 20 years, with higher overall accuracy. In addition, we can define the optimum prediction time at April, which can consider simultaneously the performance and lead time. In general, we expect that this prediction model can provide important information to understand and ensure food security.
Cardiac adverse events associated with lacosamide: a disproportionality analysis of the FAERS database
Authors: Yang Chengcheng , Zhao Wanqi , Chen Huihui , Yao Yinhui , Zhang Jingmin 
Published: July 2024

AbstractLacosamide was the first approved third-generation antiepileptic drug. However, real-world data regarding its adverse cardiac reactions in large samples still need to be completed. We evaluated the cardiac safety profile of lacosamide using the Food and Drug Administration Adverse Event Reporting System (FAERS). We performed disproportionality analysis computing reporting odds ratio (ROR) as a quantitative metric to assess the signal of lacosamide-related cardiac adverse events (AEs) from 2013 Q1 to 2022 Q4. The signal was considered significant when the lower limit of the 95% confidence interval (CI) of the ROR exceeded 1, and ≥ 5 AEs were reported. Serious and nonserious cases were compared by statistical analysis, and signals were further prioritized using a rating scale. A total of 812 cardiac AEs associated with lacosamide were identified, and 92 signals were detected, of which 17 AEs were significantly associated signals. The median time-to-onset (TTO) for moderate priority signals was 10 days, whereas for weak priority signals, it was 54 days. Notably, all cardiac AEs exhibited an early failing pattern, indicating the risk gradually decreasing. Based on the comprehensive analysis of the FAERS database and prioritization of cardiac AE signals, our research enhances the awareness among healthcare professionals regarding cardiac AEs associated with lacosamide.
Contrasting morphometric responses to increasing urbanisation in congeneric sparrow species
Authors: Naidoo Sage K. , Chamberlain Dan , Reynolds Chevonne 
Published: July 2024

AbstractIncreased urbanisation influences the morphometric traits of various species, often resulting in urban individuals being smaller than their non-urban counterparts. Urbanisation can affect fundamental eco-evolutionary patterns and impact species’ ability to adapt to and occupy rapidly changing environments through morphological changes. We investigated the morphometric responses of two passerine species, the non-native house sparrow (Passer domesticus) and its native congener, the Cape sparrow (Passer melanurus), along gradients of spatial and temporal urbanisation in South Africa over a 52-year period. The house sparrow was significantly heavier, larger and in better condition with increasing urban infrastructure and lower urban vegetation cover, while the Cape sparrow showed opposing trends along these gradients. Temporally, the house sparrow’s body mass increased consistently over the 52-year study period, suggesting changes in morphology were concomitant with increasing urbanisation over time. This study demonstrates distinct differences in the morphological responses of the non-native house sparrow and the native Cape sparrow to increasing urban development. These morphological responses may also underpin community-level changes caused by urbanisation, enhancing the capabilities of non-native species to thrive over their native counterparts in these environments.
Cryogenic electron microscopy reveals morphologically distinct subtypes of extracellular vesicles among porcine ejaculate fractions
Authors: Parra Ana , Barranco Isabel , Martínez-Díaz Pablo , González Esperanza , Albóniga Oihane E. , Cabrera Diana , Falcón-Pérez Juan M. , Roca Jordi 
Published: July 2024

AbstractSeminal plasma (SP) is rich in extracellular vesicles (EVs), which are still poorly studied, especially in livestock species. To better understand their functional role in both spermatozoa and endometrial epithelial cells, proper characterization of EVs is an essential step. The objective was to phenotypically characterize porcine seminal EVs (sEVs) using cryogenic electron microscopy (cryo-EM), which allows visualization of EVs in their native state. Porcine ejaculates are released in fractions, each containing SP from different source. This allows characterization sEVs released from various male reproductive tissues. Two experiments were performed, the first with SP from the entire ejaculate (n:6) and the second with SP from three ejaculate fractions (n:15): the first 10 mL of the sperm-rich ejaculate fraction (SRF-P1) with SP mainly from the epididymis, the remainder of the SRF (SRF-P2) with SP mainly from the prostate, and the post-SRF with SP mainly from the seminal vesicles. The sEVs were isolated by size exclusion chromatography and 1840 cryo-EM sEV images were acquired using a Jeol-JEM-2200FS/CR-EM. The size, electron density, complexity, and peripheral corona layer were measured in each sEV using the ImageJ software. The first experiment showed that sEVs were structurally and morphologically heterogeneous, although most (83.1%) were small (less than 200 nm), rounded, and poorly electrodense, and some have a peripheral coronal layer. There were also larger sEVs (16.9%) that were irregularly shaped, more electrodense, and few with a peripheral coronal layer. The second experiment showed that small sEVs were more common in SRF-P1 and SRF-P2, indicating that they originated mainly from the epididymis and prostate. Large sEVs were more abundant in post-SRF, indicating that they originated mainly from seminal vesicles. Porcine sEVs are structurally and morphologically heterogeneous. This would be explained by the diversity of reproductive organs of origin.
Detecting localized damage in cantilevered structures under nonstationary ambient excitations via Gabor spectral mode transmissibility functions
Authors: Zhang HongJie , Sun Qigang , Li DanYu , Li Chen , He Chunhui , Liu Gang 
Published: July 2024

AbstractA method based on Gabor spectral mode transmissibility functions (GSMTFs) is proposed to detect local damage in a cantilevered structure under nonstationary ambient excitations. Gabor transformation and singular value decomposition are used to reduce the influences of other vibration modes on Gabor spectral mode transmissibility functions and process nonstationary structural responses, respectively. A new state characteristic based on the fundamental structure frequency is formulated on the basis of the GSMTFs, eventually leading to the development of a new damage indicator. The probability density functions of the damage indicator for healthy and damaged states can be estimated from the measured data, and the receiver operating characteristic (ROC) curve derived from these probability distributions and the corresponding area under the ROC curve (AUC) are used to determine the damage location. A six-degree-of-freedom system and a typical transmission tower are numerically studied, and the results show that the proposed method can estimate the structural damage location under nonstationary random loads. The proposed method is further validated with a planar frame in the laboratory, which exhibits multiple damage elements via random force hammer excitations. The results show that the AUC values computed for certain parts of the structure containing the damaged elements are greater than those for other parts of the structure, indicating the effectiveness of the proposed method. Moreover, the proposed method is compared with the dot product difference (DPD) index, and the results from the laboratory planar frame demonstrate that the proposed method can better identify damage.
Relaxation effects in transition metal dichalcogenide bilayer heterostructures
Authors: Li Wei , Brumme Thomas , Heine Thomas 
Published: July 2024

AbstractWhile moiré structures in twisted bilayer transition metal dichalcogenides (TMDCs) have been studied for over a decade, the importance of lattice relaxation effects was pointed out only in 2021 by DiAngelo and MacDonald1, who reported the emergence of a Dirac cone upon relaxation. TMDCs of group 6 transition metals MX2 (M = Mo, W, X = S, Se) share layered structures with pronounced interlayer interactions, exhibiting a direct band gap when exfoliated to a two-dimensional (2D) monolayer. As their heterolayers are incommensurable, moiré structures are present in the bilayers even if stacked without a twist angle. This study addresses the challenge of accurately modeling and understanding the structural relaxation in twisted TMDC heterobilayers. We show that the typical experimental situation of finite-size flakes stacked upon larger flakes can reliably be modeled by fully periodic commensurate models. Our findings reveal significant lattice reconstruction in TMDC heterobilayers, which strongly depend on the twist angle. We can categorize the results in two principal cases: at or near the untwisted configurations of 0° and 60°, domains with matching lattice constants form and the two constituting layers exhibit significant in-phase corrugation—their out-of-plane displacements are oriented towards the same direction in all local stackings—while at large twist angles—deviating from the 0° and 60°—the two layers show an out-of-phase corrugation. In particular, we reveal that the lattice reconstruction results from the competition between the strain energy cost and the van der Waals energy gain. Additionally, our systematical study highlights structural disparities between heterostructures composed of different or identical chalcogen atoms. Our research not only confirms the reliability of using periodic commensurate models to predict heterostructure behavior but also enriches the understanding of TMDC bilayer heterostructures.
Multicenter proteome-wide Mendelian randomization study identifies causal plasma proteins in melanoma and non-melanoma skin cancers
Authors: Li Yajia , Li Qiangxiang , Cao Ziqin , Wu Jianhuang 
Published: July 2024

AbstractThis study addresses the diagnostic and therapeutic challenges in malignant melanoma (MM) and non-melanoma skin cancers (NMSC). We aim to identify circulating proteins causally linked to MM and NMSC traits using a multicenter Mendelian randomization (MR) framework. We utilized large-scale cis-MR to estimate the impact of numerous plasma proteins on MM, NMSC, squamous cell carcinoma (SCC), and basal cell carcinoma (BCC). To ensure robustness, additional analyses like MR Steiger and Bayesian colocalization are conducted, followed by replication through meta-analytical methods. The associations between identified proteins and outcomes are also validated at the tissue level using Transcriptome-Wide Association Study methods. Furthermore, a protein-protein interaction analysis is conducted to explore the relationship between identified proteins and existing cancer medication targets. The MR analysis has identified associations of 13 plasma proteins with BCC, 2 with SCC, and 1 with MM. Specifically, ASIP and KRT5 are associated with BCC, with ASIP also potentially targeting MM. CTSS and TNFSF8 are identified as promising druggability candidates for BCC. This multidimensional approach nominates ASIP, KRT5, CTSS, and TNFSF8 as potential diagnostic and therapeutic targets for skin cancers.
Message passing variational autoregressive network for solving intractable Ising models
Authors: Ma Qunlong , Ma Zhi , Xu Jinlong , Zhang Hairui , Gao Ming 
Published: July 2024

AbstractDeep neural networks have been used to solve Ising models, including autoregressive neural networks, convolutional neural networks, recurrent neural networks, and graph neural networks. Learning probability distributions of energy configuration or finding ground states of disordered, fully connected Ising models is essential for statistical mechanics and NP-hard problems. Despite tremendous efforts, neural network architectures with abilities to high-accurately solve these intractable problems on larger systems remain a challenge. Here we propose a variational autoregressive architecture with a message passing mechanism, which effectively utilizes the interactions between spin variables. The architecture trained under an annealing framework outperforms existing neural network-based methods in solving several prototypical Ising spin Hamiltonians, especially for larger systems at low temperatures. The advantages also come from the great mitigation of mode collapse during training process. Considering these difficult problems to be solved, our method extends computational limits of unsupervised neural networks to solve combinatorial optimization problems.
Moisture availability and groundwater recharge paced by orbital forcing over the past 750,000 years in the southwestern USA
Authors: Steidle Simon D. , Wendt Kathleen A. , Dublyansky Yuri , Edwards R. Lawrence , Li Xianglei , McClure Gracelyn , Moseley Gina E. , Spötl Christoph 
Published: July 2024

AbstractQuaternary climate changes are driven in part by variations in the distribution and strength of insolation due to orbital parameters. Continental climate variability is well documented for the most recent glacial-interglacial cycles, yet few records extend further back in time. Such records are critically needed to comprehensively assess the entire spectrum of natural climate variability against the backdrop of anthropogenic warming. Here, we apply uranium isotope geochronology to calcite deposits to date groundwater-table changes in Devils Hole cave, Nevada. The deposits record multi-meter groundwater-table fluctuations over the last 750,000 years, reflecting the long-term evolution of hydroclimate in this presently arid region. During periods between glacial or interglacial extremes, the water table responded sensitively to variations in 65°N summer insolation, likely caused by the increasing extent of North American ice sheets during cold period, which steered moisture-laden trajectories towards the southwestern USA. These orbitally-driven hydroclimatic changes are superimposed on a tectonically-driven long-term decline in the regional groundwater table observed prior to 438,000 ± 14,000 years ago.
Guest editorial: Deep learning‐based point cloud processing, compression and analysis
Authors: Zhang Yun , Hamzaoui Raouf , Wang Xu , Hou Junhui , Valenzise Giuseppe 
Published: July 2024

AbstractPoint cloud data is a large collection of high dimensional 3D points with 3D coordinates and attributes, which has been one of the mainstream representations for emerging 3D applications, such as virtual reality, autonomous vehicles, and robotics. Due to the large‐scale unstructured high‐dimensional nature of point clouds, point cloud processing, transmitting and analysing has been challenging issues in multimedia signal processing and communication. Deep learning is a powerful tool to learn statistical knowledge from massive data. Advances in artificial intelligence, especially deep learning models are offering new opportunities for point cloud processing, compression and analysis. This special issue aims at promoting cutting‐edge research on deep learning‐based point cloud processing, including object detection, segmentation, registration, compression, and visual quality assessment.
A novel enlarging fractional bandwidth technique for acoustic‐wave‐lumped‐element resonator‐based bandpass filters
Authors: Tang Xianli , Jia Yonghao 
Published: July 2024

AbstractA novel enlarging fractional bandwidth (FBW) technique for acoustic‐wave‐lumped‐element resonator (AWLR)‐based bandpass filters is presented. The new technique is based on a series of matching inductors that replace the parallel or series acoustic wave (AW) resonators of AW filters to beak the bandwidth constraint of the ladder‐type structure. In addition, the transmission response analysis of the proposed AWLR‐based filter is provided. A prototype of the AWLR‐based filter is fabricated and tested to validate the proposed FBW widening technique. The measured insertion loss, FBW, and out‐of‐band rejection are 1.5 dB, 1.63kt2 (kt2 is the electromechanical coupling coefficient of the AWR), and 38 dB, respectively.
A 20‐Gb/s 4‐tap time‐domain DFE with pulse width modulation for a DQ‐DQS matched parallel receiver
Authors: Na Daehoon , Choi Woo‐Seok , Lee Seon‐Kyoo 
Published: July 2024

AbstractA 4‐tap time‐domain decision feedback equalizer (TD‐DFE) is presented to implement a multi‐tap DFE in a matched DQ (data)‐DQS (strobe) tree architecture. Traditionally, matched architecture holds an advantage in terms of power noise immunity, but it suffers from low‐speed performance due to the unavailability of decision feedback equalizer (DFE) applications. By adopting the proposed TD‐DFE, both high‐speed operation and power noise immunity can be achieved within the matched architecture. An 8‐DQ parallel receiver with the proposed 4‐tap TD‐DFE, designed in 28 nm CMOS, achieves a data rate of 20 Gb/s with 0.6 UI eye‐opening even with 215 mV power fluctuations.
Optimal harmonic resonance monitoring in electrical network considering area of harmonic pollution and system uncertainty
Authors: Shakeri Sina , Koochi Mohammad Hossein Rezaeian , Esmaeili Saeid 
Published: July 2024

AbstractThis paper proposes an optimization approach for allocating power quality monitors (PQMs) aiming to monitor all harmonic resonance conditions while taking power system uncertainties into account. The placement approach utilizes the frequency scan response for calculating impedances over a range of frequencies and consequently determining harmonic resonance conditions. Thereby, it is capable of building binary matrices, which include harmonic resonance conditions. Also, by utilizing the union operator at binary matrices, power system uncertainties such as photovoltaic generation and load level can be considered in the allocation method. The placement approach is expressed as a linear problem that determines the best locations of PQMs and their optimal number so that they monitor all harmonic resonance conditions. Besides, by considering the area of harmonic pollution of non‐linear loads in the proposed method, owners of electrical networks can find a solution with fewer PQMs to monitor harmonic resonance orders inside a particular area of the network. The performance of the presented approach is demonstrated using the 15‐node distribution network and a real electrical network, as well as a real large electrical network in Iran. Results show that the proposed method suggests fewer PQMs to monitor harmonic resonance conditions compared to previous methods.
A study on a target detection model for autonomous driving tasks
Authors: Chen Hao , Min Byung‐Won , Zhang Haifei 
Published: July 2024

AbstractTarget detection in autonomous driving tasks presents a complex and critical challenge due to the diversity of targets and the intricacy of the environment. To address this issue, this paper proposes an enhanced YOLOv8 model. Firstly, the original large target detection head is removed and replaced with a detection head tailored for small targets and high‐level semantic details. Secondly, an adaptive feature fusion method is proposed, where input feature maps are processed using dilated convolutions with different dilation rates, followed by adaptive feature fusion to generate adaptive weights. Finally, an improved attention mechanism is incorporated to enhance the model's focus on target regions. Additionally, the impact of Group Shuffle Convolution (GSConv) on the model's detection speed is investigated. Validated on two public datasets, the model achieves a mean Average Precision (mAP) of 53.7% and 53.5%. Although introducing GSConv results in a slight decrease in mAP, it significantly improves frames per second. These findings underscore the effectiveness of the proposed model in autonomous driving tasks.