bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biologically plausible information propagation in a complementary metal-oxide semiconductor integrate-and-fire artificial neuron circuit with memristive synapses
Authors: Benatti Lorenzo , Zanotti Tommaso , Gandolfi Daniela , Mapelli Jonathan , Puglisi Francesco Maria 
Published: April 2023

Abstract
Neuromorphic circuits based on spikes are currently envisioned as a viable option to achieve brain-like computation capabilities in specific electronic implementations while limiting power dissipation given their ability to mimic energy-efficient bioinspired mechanisms. While several network architectures have been developed to embed in hardware the bioinspired learning rules found in the biological brain, such as spike timing-dependent plasticity, it is still unclear if hardware spiking neural network architectures can handle and transfer information akin to biological networks. In this work, we investigate the analogies between an artificial neuron combining memristor synapses and rate-based learning rule with biological neuron response in terms of information propagation from a theoretical perspective. Bioinspired experiments have been reproduced by linking the biological probability of release with the artificial synapse conductance. Mutual information and surprise have been chosen as metrics to evidence how, for different values of synaptic weights, an artificial neuron allows to develop a reliable and biological resembling neural network in terms of information propagation and analysis.
In-situ monitoring of plasmon-induced nanoscale photocatalytic activity from Au-decorated TiO2 microflowers
Authors: Sahoo Subhashree , Kumar Sahu Binaya , Shukla Shivam , Srivastava Sanjeev K , Sahoo Pratap K 
Published: April 2023

Abstract
Noble-metal-decorated semiconductor photocatalysts have attracted noticeable attention due to their enhanced photocatalytic activity. Herein, we have synthesized the pure rutile phase of TiO2 nanorods, with microflower morphology, using a hydrothermal method and decorated them with Au to observe plasmon-induced enhanced photocatalytic efficiency. The optical bandgap engineering through Au-decorated TiO2 introduces midgap states that help with charge compensation during photodegradation studies. The surface plasmonic resonance peak of Au is observed together with the defect peak of TiO2, extending the absorption of the solar spectrum from the UV to the visible region. The quenching in photoluminescence intensity with increased Au thickness indicates the formation of a Schottky junction at the interface of Au and TiO2 that helps to reduce photogenerated charge carrier recombination. The softening of the E
g
Raman mode and photothermal effects originate from the nonradiative decay of localized surface plasmons through electron–phonon and phonon–phonon relaxation. The photocatalytic degradation of Rhodamine 6G is monitored by exposing the sample to UV and visible light sources under Raman spectroscopy. The Au decoration plays a crucial role in promoting charge separation, Schottky junction creation, photothermal effects, and UV to visible light absorption to enhance photocatalytic activity, which can be explained on the basis of the charge transfer mechanism. Our in-situ photodegradation study at the interface of noble metal and semiconducting materials will pave the way toward improving the understanding of plasmon-enhanced photocatalytic applications.
Region-specified inverse design of absorption and scattering in nanoparticles by using machine learning
Authors: Vallone Alex , Estakhri Nooshin M , Mohammadi Estakhri Nasim 
Published: April 2023

Abstract
Machine learning provides a promising platform for both forward modeling and the inverse design of photonic structures. Relying on a data-driven approach, machine learning is especially appealing for situations when it is not feasible to derive an analytical solution for a complex problem. There has been a great amount of recent interest in constructing machine learning models suitable for different electromagnetic problems. In this work, we adapt a region-specified design approach for the inverse design of multilayered nanoparticles. Given the high computational cost of dataset generation for electromagnetic problems, we specifically investigate the case of a small training dataset, enhanced via random region specification in an inverse convolutional neural network. The trained model is used to design nanoparticles with high absorption levels and different ratios of absorption over scattering. The central design wavelength is shifted across 350–700 nm without re-training. We discuss the implications of wavelength, particle size, and the training dataset size on the performance of the model. Our approach may find interesting applications in the design of multilayer nanoparticles for biological, chemical, and optical applications as well as the design of low-scattering absorbers and antennas.
Modeling selective narrowband light absorption in coaxial InAs-GaAs0.1Sb0.9 nanowires with partial shell segment coverage
Authors: Abrand Alireza , Anttu Nicklas , Mohseni Parsian K 
Published: April 2023

Abstract
Vertical III-V nanowire (NW) arrays are promising candidates for infrared (IR) photodetection applications. Generally, NWs with large diameters are required for efficient absorption in the IR range. However, increasing the NW diameter results in a loss of spectral selectivity and an enhancement in the photodetector dark current. Here, we propose a nanophotonic engineering approach to achieving spectrally-selective light absorption while minimizing the volume of the absorbing medium. Based on simulations performed using rigorous coupled-wave analysis (RCWA) techniques, we demonstrate dramatic tunability of the short-wavelength infrared (SWIR) light absorption properties of InAs NWs with base segments embedded in a reflective backside Au layer and with partial GaAs0.1Sb0.9 shell segment coverage. Use of a backside reflector results in the generation of a delocalized evanescent field around the NW core segment that can be selectively captured by the partially encapsulating GaAs0.1Sb0.9 shell layer. By adjusting the core and shell dimensions, unity absorption can be selectively achieved in the 2 to 3 μm wavelength range. Due to the transparency of the GaAs0.1Sb0.9 shell segments, wavelength-selective absorption occurs only along the InAs core segments where they are partially encapsulated. The design presented in this work paves the path toward spectrally-selective and polarization-dependent NW array-based photodetectors, in which carrier collection efficiencies can be enhanced by positioning active junctions at the predefined locations of the partial shell segments.
Offshore wind data assessment near the Iberian Peninsula over the last 25 years
Authors: Plecha Sandra M , Teles-Machado Ana , Tomé Ricardo , Mateus Pedro 
Published: April 2023

Abstract
Numerous processes affecting coastal ocean dynamics and water properties occur at the air-sea interface as a result of wind blowing on the ocean surface. In Earth system research, it is crucial to appropriately characterize the ocean surface wind (OSW) field because of its significance in many academic and economic activities. This study aimed to evaluate the accuracy of the most recent OSW datasets based on numerical modeling and remote sensing products in estimating in situ observations along the Atlantic coast of the Iberian Peninsula. The results are three-fold: (1) when high temporal resolutions are not necessary, remote sensing products are an excellent choice because they provide reliable OSW estimates; (2) for analyses that require high temporal resolution, numerical weather models are the best choice because they can statistically reproduce the main trend; (3) fifth generation of European ReAnalysis (ERA5) showed that, despite having a lower spatial resolution than the dynamically downscaled weather research and forecasting simulation, it captures the spatial and temporal dynamics and variability of coastal winds and may be used as forcing of the atmosphere-ocean interface modeling without compromising its accuracy.
Increasing risk of compound wind and precipitation extremes due to tropical cyclones in India
Authors: Rajeev Akshay , Mishra Vimal 
Published: April 2023

Abstract
Tropical cyclones (TCs) cause compound extremes of rainfall and wind gust. However, their occurrence and impacts on India still need to be better understood. Using ERA5 reanalysis and cyclone eAtlas, we examine the compound extremes of precipitation and wind gust driven by TCs that made landfall over India during 1981–2021. Based on the joint return period of compound extremes, the five worst TCs occurred in May 1990, May 1999, May 2010 (Laila), October 2014 (Hudhud), and May 2020 (Amphan). A majority of TCs during 1981–2021 originated from the Bay of Bengal (BoB) and only a few from the Arabian Sea (AS). While the frequency of all the TCs has either declined or remained stable in the North Indian Ocean (NIO, BoB, AS) during 1981–2021, the frequency of TCs with compound extremes has increased by about three-fold during the most recent decade (2011–2021). Compound extremes driven by TCs affect large regions along the coast and risk infrastructure and human lives. The frequency of TCs with large area of impact (greater than 200 000 km2) compound wind and precipitation extreme extent exhibits a three-fold rise during 1981–2021, indicating an increase in the hazard associated with the compound extremes driven by TCs in India.
Redefining the Rule of Law: An Eighteenth-Century Case Study
Authors: Burset Christian R 
Published: April 2023

Abstract
Scholars who write about the relationship between law and empire tend to adopt one of two frameworks. The first describes imperial rule and the rule of law as fundamentally incompatible. The second praises empires—especially the British Empire—for exporting the rule of law to lands that lacked it. These competing approaches have very different political valences, but they agree in suggesting that colonial rule made no substantial contribution to today’s rule of law tradition. At best, the metropole exported a premade rule of law abroad; at worst, colonialism corrupted preexisting commitments to legality. But in neither case did imperial rule alter rule of law ideals.
This Article argues, in contrast, that colonialism helped to reshape how anglophones defined the rule of law. It begins by reconstructing two conceptions of legality in the eighteenth-century British Empire. At the start of that century, most Britons subscribed to what this Article calls the traditional conception of the rule of law. This conception aimed at a thick set of political, social, and economic ends, which proponents sought to advance through specific English institutions, such as juries. A second, thinner conception of legality—what this Article calls the modern rule of law—emerged in the second half of the eighteenth century. Like many rule of law theories today, the modern conception focused on abstract ideals, such as legal certainty, rather than particular institutions. Proponents of the modern rule of law aimed to provide a cosmopolitan standard that would transcend national and cultural boundaries.
Although these two conceptions were often compatible, their differences became apparent in the 1770s, as politicians debated whether to extend English law to conquered colonies. Britain’s ultimate decision to embrace colonial legal pluralism encouraged commentators to embrace the newer, thinner conception, which was easier to reconcile with the growing diversity of the empire’s many legal systems. This debate over the colonial rule of law continues to shape our efforts to theorize the rule of law. Understanding the rule of law’s history also offers new insights into the potential utility of different versions of that concept today. Finally, this Article shows how invoking the rule of law in everyday political debates can ultimately redefine the concept itself.
Repair of Congenitally Defective Type of Earlobe Clefts With Residual Lobular Tissue: A Convenient and Effective Surgical Technique
Authors: Cai Siyuan , Sheng Yang , Lin Liheng , Cui Wei , Jiang Zhiyuan , Zhang Liyuan , Li Qingqing , Cai Zhen , Chen Zaihong 
Published: April 2023

Abstract

Background
For patients with a congenitally defective type of earlobe cleft, repair techniques similar to those utilized for earlobe lacerations are not suitable due to the presence of certain tissue defects. However, traditional earlobe reconstruction techniques imply the need to form complex flaps with adjacent normal tissues, which may lead to many complications.

Objectives
In this study, we developed a technique to repair the earlobe with residual lobular tissue based on the characteristics of congenitally defective earlobe clefts.

Methods
We designed a triangular random flap out of the remnant lobular tissue, rotated and embedded into the edge of the remnant earlobe to repair the earlobe.

Results
In the past 3 years, this technique was applied to 15 patients with congenitally defective earlobe clefts. With this technique, the new earlobe achieved the desired aesthetic results, such as smooth edges and fullness of form, without complicated and unnecessary incisions. It also solved the aesthetic problem of residual lobular tissue on the face.

Conclusions
Based on the characteristics of the congenitally defective type of earlobe cleft, we innovatively proposed the utilization of residual lobular tissues for earlobe repair, and the feasibility and multiple advantages of this technique have been demonstrated in practice.

Level of Evidence: 4
Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review
Authors: Sherkatghanad Zeinab , Abdar Moloud , Charlier Jeremy , Makarenkov Vladimir 
Published: April 2023

AbstractCRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9) is a popular and effective two-component technology used for targeted genetic manipulation. It is currently the most versatile and accurate method of gene and genome editing, which benefits from a large variety of practical applications. For example, in biomedicine, it has been used in research related to cancer, virus infections, pathogen detection, and genetic diseases. Current CRISPR/Cas9 research is based on data-driven models for on- and off-target prediction as a cleavage may occur at non-target sequence locations. Nowadays, conventional machine learning and deep learning methods are applied on a regular basis to accurately predict on-target knockout efficacy and off-target profile of given single-guide RNAs (sgRNAs). In this paper, we present an overview and a comparative analysis of traditional machine learning and deep learning models used in CRISPR/Cas9. We highlight the key research challenges and directions associated with target activity prediction. We discuss recent advances in the sgRNA–DNA sequence encoding used in state-of-the-art on- and off-target prediction models. Furthermore, we present the most popular deep learning neural network architectures used in CRISPR/Cas9 prediction models. Finally, we summarize the existing challenges and discuss possible future investigations in the field of on- and off-target prediction. Our paper provides valuable support for academic and industrial researchers interested in the application of machine learning methods in the field of CRISPR/Cas9 genome editing.
TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level
Authors: Liu Yan , Wei Guo , Li Chen , Shen Long-Chen , Gasser Robin B , Song Jiangning , Chen Dijun , Yu Dong-Jun 
Published: April 2023

Abstract
Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.
Graph convolutional network-based feature selection for high-dimensional and low-sample size data
Authors: Chen Can , Weiss Scott T , Liu Yang-Yu 
Published: April 2023

AbstractMotivationFeature selection is a powerful dimension reduction technique which selects a subset of relevant features for model construction. Numerous feature selection methods have been proposed, but most of them fail under the high-dimensional and low-sample size (HDLSS) setting due to the challenge of overfitting.ResultsWe present a deep learning-based method—GRAph Convolutional nEtwork feature Selector (GRACES)—to select important features for HDLSS data. GRACES exploits latent relations between samples with various overfitting-reducing techniques to iteratively find a set of optimal features which gives rise to the greatest decreases in the optimization loss. We demonstrate that GRACES significantly outperforms other feature selection methods on both synthetic and real-world datasets.Availability and implementationThe source code is publicly available at https://github.com/canc1993/graces.
In search of the (Black) international: The Black Scholar and the challenge to communication and media studies
Authors: Towns Armond R 
Published: April 2023

Abstract
In the 1970s, the journal The Black Scholar (TBS) published two special issues largely under-read in communication studies, titled “Black Media I” and “Black Media II.” The lack of engagement in these special issues by communication scholars represents not merely disciplinary differences but it also signifies different trajectories that both fields take on Marxism. Communication studies would take Europe as its starting point, while Black studies would not dismiss Europe, but would take the Third World as its point of departure. In the process, media and communication analysis in TBS holds more critical potential than what currently exists in communication studies approaches to both Blackness and Marxism. Using a sociology of knowledge approach, I argue that the place to start a wholistic, Marxist, Black studies analysis of communication begins not in communication studies, but in Black studies, which TBS’s special issues are representative of.
Early Intestinal Ultrasound Predicts Long-Term Endoscopic Response to Biologics in Ulcerative Colitis
Authors: Allocca Mariangela , Dell’Avalle Cecilia , Furfaro Federica , Zilli Alessandra , D’Amico Ferdinando , Peyrin-Biroulet Laurent , Fiorino Gionata , Danese Silvio 
Published: April 2023

Abstract

Background and Aims
The Milan ultrasound criteria [MUC] is a validated score to assess endoscopic activity in ulcerative colitis [UC]. MUC &gt; 6.2 detects Mayo endoscopic score [MES] &gt; 1. In this study we evaluated the predictive value of MUC for biologic treatment response, using colonoscopy [CS] as a reference standard.

Methods
Consecutive UC patients starting biologic therapy were included, and underwent CS, IUS, clinical assessment and faecal calprotectin [FC] measurement at baseline and within 1 year. In addition, IUS, clinical and FC assessments were performed at week 12. The primary objective was to evaluate whether ultrasound improvement [MUC ≤ 6.2] at week 12 predicted endoscopic improvement at reassessment [MES ≤ 1]. Endoscopic remission was defined as MES = 0.

Results
Forty-nine patients were included [59% under infliximab, 29% under vedolizumab, 8% under adalimumab, 4% under ustekinumab]. MUC ≤ 6.2 at week 12 was the only independent predictor for MES ≤ 1 and MES = 0 at reassessment (odds ratio [OR] 5.80, p = 0.010; OR 10.41, p = 0.041; respectively). MUC ≤ 6.2 at week 12 showed a negative predictive value of 96% for detecting MES = 0. A ≥2 reduction of the MUC predicted MES = 0 (area under the curve [AUC] 0.816). MUC ≤ 4.3 was the most accurate cut-off value for MES = 0 [AUC 0.876]. Guyatt’s responsiveness ratio for the MUC was 1.73 [&gt;0.8].

Conclusion
MUC ≤ 6.2 at week 12 predicts long-term endoscopic response. MUC is accurate in monitoring treatment response and may be used in both clinical trials and routine practice.