bluuebunny/crossref_metadata_2025_split huggingface dataset:
A comparative study of deconvolution techniques for quantum-gas microscope images
Authors: La Rooij A , Ulm C , Haller E , Kuhr S 
Published: August 2023

Abstract
Quantum-gas microscopes are used to study ultracold atoms in optical lattices at the single-particle level. In these systems atoms are localised on lattice sites with separations close to or below the diffraction limit. To determine the lattice occupation with high fidelity, a deconvolution of the images is often required. We compare three different techniques, a local iterative deconvolution algorithm, Wiener deconvolution and the Lucy–Richardson algorithm, using simulated microscope images. We investigate how the reconstruction fidelity scales with varying signal-to-noise ratio, lattice filling fraction, varying fluorescence levels per atom, and imaging resolution. The results of this study identify the limits of singe-atom detection and provide quantitative fidelities which are applicable for different atomic species and quantum-gas microscope setups.
Detecting the driver nodes of temporal networks
Authors: Qin Tingting , Duan Gaopeng , Li Aming 
Published: August 2023

Abstract
Detecting the driver nodes of complex networks has garnered significant attention recently to control complex systems to desired behaviors, where nodes represent system components and edges encode their interactions. Driver nodes, which are directly controlled by external inputs, play a crucial role in controlling all network nodes. While many approaches have been proposed to identify driver nodes of static networks, we still lack an effective algorithm to control ubiquitous temporal networks, where network structures evolve over time. Here we propose an effective online time-accelerated heuristic algorithm (OTaHa) to detect driver nodes of temporal networks. Together with theoretical analysis and numerical simulations on synthetic and empirical temporal networks, we show that OTaHa offers multiple sets of driver nodes, and noticeably outperforms existing methods in terms of accuracy and execution time. We further report that most edges are redundant in controlling temporal networks although the complete instantaneous signal-carrying edges cannot be guaranteed. Moreover, removing edges with high edge betweenness (the number of all-pairs shortest paths passing through the edge) significantly impedes the overall controllability. Our work provides an effective algorithm and paves the way for subsequent explorations on achieving the ultimate control of temporal networks.
Learning to concentrate: multi-tracer forecasts on local primordial non-Gaussianity with machine-learned bias
Authors: Sullivan James M. , Prijon Tijan , Seljak Uroš 
Published: August 2023

Abstract
Local primordial non-Gaussianity (LPNG) is predicted by many non-minimal models of inflation, and creates a scale-dependent contribution to the power spectrum of large-scale structure (LSS) tracers, whose amplitude is characterized by bϕ
.
Knowledge of bϕ
for the observed tracer population is therefore crucial for learning about inflation from LSS.
Recently, it has been shown that the relationship between linear bias b1
and bϕ
for simulated halos exhibits significant secondary dependence on halo concentration.
We leverage this fact to forecast multi-tracer constraints on f
loc
NL.
We train a machine learning model on observable properties of simulated IllustrisTNG galaxies to predict bϕ
for samples constructed to approximate DESI emission line galaxies (ELGs) and luminous red galaxies (LRGs). We find σ(f
loc
NL) = 2.3, and σ(f
loc
NL = 3.7, respectively.
These forecasted errors are roughly factors of 3, and 35% improvements over the single-tracer case for each sample, respectively.
When considering both ELGs and LRGs in their overlap region, we forecast σ(f
loc
NL) = 1.5 is attainable with our learned model, more than a factor of 3 improvement over the
single-tracer case,
while the ideal split by bϕ
could reach σ(f
loc
NL) < 1.
We also perform multi-tracer forecasts for upcoming spectroscopic surveys targeting LPNG (MegaMapper, SPHEREx) and show that splitting tracer samples by bϕ
can lead to an order-of-magnitude reduction in projected σ(f
loc
NL for these surveys.
Towards detecting super-GeV dark matter via annihilation to neutrinos
Authors: Salvador Miranda L. , Basegmez du Pree S. , Ng K.C.Y. , Cheek A. , Arina C. 
Published: August 2023

Abstract
The next generation of neutrino telescopes will feature unprecedented sensitivities in the detection of neutrinos. Here we study the capabilities of a large-scale neutrino telescope, like the fully-operating KM3NeT experiment in the near future, for detecting dark matter annihilation signals from the Galactic Centre.
We consider both ORCA and ARCA detectors, covering dark matter masses from a few GeV to 100 TeV. We obtain the sensitivities with a maximum-likelihood analysis method and present them as upper limits in the thermally averaged annihilation cross-section into Standard Model fermions. Our projections show that the sensitivity of such a neutrino telescope can reach the thermal relic line for mχ
≳ 1 TeV and for mχ
≃ few GeV, for the NFW dark matter density profile. This demonstrates that ORCA- and ARCA-like detectors will be able to perform competitive dark matter searches in a wide range of masses. The implications of these striking projections are investigated in a few selected dark matter particle models, where we show that neutrino telescopes are able to probe new parameter space.
Different elements, same results: time-resolved temperature determination by oxygen and nitrogen elements
Authors: Arjmand S. , Anania M.P. , Biagioni A. , Ferrario M. , Galletti M. , Lollo V. , Pellegrini D. , Pompili R. , Zigler A. 
Published: August 2023

Abstract
The core purpose of this research is to use optical emission spectroscopy to determine the electron temperature (Te
) of a hydrogen plasma generated in a capillary discharge plasma, with a focus on its temporal variation. The plasma density (ne
) is first determined using the Stark broadening technique, which measures the broadening of spectral lines as a result of the electric field in the plasma. Subsequently, a passive spectroscopic technique is employed to estimate the electron plasma temperature by detecting the emitted light from the plasma. This spectral detection is performed using a visible range spectrometer. In this study, two elements, oxygen and nitrogen, are specifically selected based on the chemical composition of the capillary. The electron plasma temperature is estimated using the line ratio method, which involves comparing the intensities of two specific spectral lines emitted by the selected elements. By analyzing these line ratios, the electron plasma temperature can be inferred. The combination of the Stark broadening technique and line ratio method provides valuable insights into the plasma's physical characteristics, specifically its density and temperature.
Experimental studies on the effect of nano silica modified novel concrete CFST columns
Authors: Vasanthi P , Selvan S Senthil , Natrayan L , Thanappan Subash 
Published: August 2023

Abstract
Under axial compression, the present article analyses and compares the behaviour of Plain Concrete filled and Nano Silica modified concrete filled with light gauge steel rectangular columns. In 18 work samples, plain and nano silica-modified concrete was tested on light gauge steel sections. Investigations were conducted into several essential variables, including the geometry of the cross-section, the tubular thickness of light gauge steel, the depth-to-thickness ratio, and the type of concrete infill used. From the experimental results, effects of flat width-to-thickness ratio (w/t), axial load-end shortening, axial load-deflection, axial load-strain characteristics, and failure modes have been studied under the linear stage. To evaluate their confinement effects, this study compared plain and silica-modified nano concrete tube columns with light gauge steel hollow hollow hollow columns. The experimental results are compared using BS5400, EC4, and ACI codes to determine the ultimate sectional capacity. Test outcomes showed that tested nano-silica modified concrete filled steel tubular (CFST) columns exhibited higher strength and outstanding ductility than the plain CFST and hollow light gauge steel columns. The nano silica-modified concrete in-filled columns showed a good yield plateau under the non-linear stage compared with the other columns.
Transfer learning application of self-supervised learning in ARPES
Authors: Ekahana Sandy Adhitia , Winata Genta Indra , Soh Y , Tamai Anna , Milan Radovic , Aeppli Gabriel , Shi Ming 
Published: August 2023

Abstract
There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lasers, together with focusing beam optics and advanced electron spectrometers, are beginning to enable angle-resolved photoemission spectroscopy (ARPES) in scanning mode with a spatial resolution of near to and below microns, two- to three orders of magnitude smaller than what has been typical for ARPES hitherto. The results are vast data sets inhabiting a five-dimensional subspace of the ten-dimensional space spanned by two scanning dimensions of real space, three of reciprocal space, three of spin-space, time, and energy. In this work, we demonstrate that recent developments in representational learning (self-supervised learning) combined with k-means clustering can help automate the labeling and spatial mapping of dispersion cuts, thus saving precious time relative to manual analysis, albeit with low performance. Finally, we introduce a few-shot learning (k-nearest neighbor) in representational space where we selectively choose one (k = 1) image reference for each known label and subsequently label the rest of the data with respect to the nearest reference image. This last approach demonstrates the strength of self-supervised learning to automate image analysis in ARPES in particular and can be generalized to any scientific image analysis.
Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier
Authors: Grönroos Sonja , Pierini Maurizio , Chernyavskaya Nadezda 
Published: August 2023

Abstract
More than a thousand 8″ silicon sensors will be visually inspected to look for anomalies on their surface during the quality control preceding assembly into the High-Granularity Calorimeter for the CMS experiment at CERN. A deep learning-based algorithm that pre-selects potentially anomalous images of the sensor surface in real time was developed to automate the visual inspection. The anomaly detection is done by an ensemble of independent deep convolutional neural networks: an autoencoder and a classifier. The algorithm was deployed and has been continuously running in production, and data gathered were used to evaluate its performance. The pre-selection reduces the number of images requiring human inspection by 85%, with recall of 97%, and saves 15 person-hours per a batch of a hundred sensors. Data gathered in production can be used for continuous learning to improve the accuracy incrementally.
The emergence of a new wintertime Arctic energy balance regime
Authors: Miyawaki O , Shaw T A , Jansen M F 
Published: August 2023

Abstract
The modern Arctic climate during wintertime is characterized by sea-ice cover, a strong surface temperature inversion, and the absence of convection. Correspondingly, the energy balance in the Arctic atmosphere today is dominated by atmospheric radiative cooling and advective heating, so-called radiative advective equilibrium. Climate change in the Arctic involves sea-ice melt, vanishing of the surface inversion, and emergence of convective precipitation. Here we show climate change in the Arctic involves the emergence of a new energy balance regime characterized by radiative cooling, convective heating, and advective heating, so-called radiative convective advective equilibrium. A time-dependent decomposition of the atmospheric energy balance shows the regime transition is associated with enhanced radiative cooling followed by decreased advective heating. The radiative cooling response consists of a robust clear-sky greenhouse effect and a transient cloud contribution that varies across models. Mechanism-denial experiments in an aquaplanet with and without interactive sea ice highlight the important role of sea-ice melt in both the radiative cooling and advective heating responses. The results show that climate change in the Arctic involves temporally evolving mechanisms, suggesting that an emergent constraint based on historical data or trends may not constrain the long-term response.
The role of the performer in Hanslick’s On the Musically Beautiful
Authors: Sousa Tiago 
Published: August 2023

Abstract
In his On the Musically Beautiful, Hanslick vigorously argued that musical beauty depends neither on the representation nor on the evocation of feelings. However, despite his formalism, Hanslick regards the performance of a musical work as a moment of emotional release. Given the tension between the formalist nature of Hanslick’s general theory and this expressionist drift in his conception of the performer, in this article I pursue two objectives: 1) to argue that there is, in fact, an important internal inconsistency in Hanslick’s theory regarding the nature of performance, and 2) to deepen and render intelligible this perspective of performance. Although internally inconsistent, this perspective, in its search for a synergistic conciliation of the composer’s creativity with the performer’s expressivity, is worthy of analysis.
Evidence of Hematoma Prevention After Facelift
Authors: Stewart Christopher M , Bassiri-Tehrani Brian , Jones Hannah Elizabeth , Nahai Foad 
Published: August 2023

Abstract

Hematoma is a common complication after facelift procedures. Multiple factors have been shown to increase the risk of hematoma formation, such as male gender, anticoagulant medication use, perioperative hypertension, increased intrathoracic pressure, and operative technique. The purpose of this manuscript is to provide an overview of existing literature to provide surgeons with evidence-based recommendations on how to minimize hematoma risk during facelift surgery. A literature search for hematoma and facelift surgery was performed that identified 478 unique manuscripts. Abstracts were reviewed, excluding articles not describing facelift surgery, those written before 1970, studies with a sample size of fewer than 5 patients, non-English studies, and those that did not provide postoperative hematoma rates. Forty-five articles were included in this text, with their recommendations. Measures such as the prophylactic management of pain, nausea, and hypertension, the use of fibrin glue tissue sealants, the use of local anesthesia rather than general anesthesia, and strict blood pressure control of at least &lt;140 mmHg were found to significantly reduce hematoma formation. Quilting sutures has shown benefit in some high-risk patients. Measures such as drains, compression dressings, perioperative use of selective serotonin reuptake inhibitors, and perioperative steroids had no significant effect on hematoma formation. In addition to appropriate patient selection and careful intraoperative hemostasis, many adjunct measures have been shown to reduce postoperative hematoma formation in facelift procedures.

Level of Evidence: 3
Ultrahigh-throughput screening of Trichoderma reesei strains capable of carbon catabolite repression release and cellulase hyperproduction using a microfluidic droplet platform
Authors: Luu Xuan Chinh , Shida Yosuke , Suzuki Yoshiyuki , Kuwahara Daiki , Fujimoto Takeshi , Takahashi Yuka , Sato Naomi , Nakamura Akihiro , Ogasawara Wataru 
Published: August 2023

ABSTRACT
Trichoderma reesei is the most well-known cellulase producer in the biorefinery industry. Its cellulase biosynthesis is repressed by glucose via carbon catabolite repression (CCR), making CCR-releasing strains with cellulase hyperproduction desirable. Here, we employed a microfluidic droplet platform to culture and screen T. reesei mutants capable of CCR release and cellulase overproduction from extensive mutagenesis libraries. With 3 mutagenesis rounds, about 6.20 × 103 droplets were sorted from a population of 1.51 × 106 droplets in a period of 4.4 h; 76 recovery mutants were screened on flask fermentation, and 2 glucose uptake retarded mutants, MG-9-3 and MG-9-3-30, were eventually isolated. We also generated a hypercellulase producer, M-5, with CCR release via a single mutagenesis round. The hyphal morphology and molecular mechanisms in the mutants were analyzed. This versatile approach combined with a comprehensive understanding of CCR release mechanisms will provide innovative and effective strategies for low-cost cellulase production.
Cobind: quantitative analysis of the genomic overlaps
Authors: Ma Tao , Guo Lingyun , Yan Huihuang , Wang Liguo 
Published: August 2023

Abstract

Motivation
Analyzing the overlap between two sets of genomic intervals is a frequent task in the field of bioinformatics. Typically, this is accomplished by counting the number (or proportion) of overlapped regions, which applies an arbitrary threshold to determine if two genomic intervals are overlapped. By making binary calls but disregarding the magnitude of the overlap, such an approach often leads to biased, non-reproducible, and incomparable results.

Results
We developed the cobind package, which incorporates six statistical measures: the Jaccard coefficient, Sørensen–Dice coefficient, Szymkiewicz–Simpson coefficient, collocation coefficient, pointwise mutual information (PMI), and normalized PMI. These measures allow for a quantitative assessment of the collocation strength between two sets of genomic intervals. To demonstrate the effectiveness of these methods, we applied them to analyze CTCF’s binding sites identified from ChIP-seq, cancer-specific open-chromatin regions (OCRs) identified from ATAC-seq of 17 cancer types, and oligodendrocytes-specific OCRs identified from scATAC-seq. Our results indicated that these new approaches effectively re-discover CTCF’s cofactors, as well as cancer-specific and oligodendrocytes-specific master regulators implicated in disease and cell type development.

Availability and implementation
The cobind package is implemented in Python and freely available at https://cobind.readthedocs.io/en/latest/.
Ionmob: a Python package for prediction of peptide collisional cross-section values
Authors: Teschner David , Gomez-Zepeda David , Declercq Arthur , Łącki Mateusz K , Avci Seymen , Bob Konstantin , Distler Ute , Michna Thomas , Martens Lennart , Tenzer Stefan , Hildebrandt Andreas 
Published: August 2023

Abstract

Motivation
Including ion mobility separation (IMS) into mass spectrometry proteomics experiments is useful to improve coverage and throughput. Many IMS devices enable linking experimentally derived mobility of an ion to its collisional cross-section (CCS), a highly reproducible physicochemical property dependent on the ion’s mass, charge and conformation in the gas phase. Thus, known peptide ion mobilities can be used to tailor acquisition methods or to refine database search results. The large space of potential peptide sequences, driven also by posttranslational modifications of amino acids, motivates an in silico predictor for peptide CCS. Recent studies explored the general performance of varying machine-learning techniques, however, the workflow engineering part was of secondary importance. For the sake of applicability, such a tool should be generic, data driven, and offer the possibility to be easily adapted to individual workflows for experimental design and data processing.

Results
We created ionmob, a Python-based framework for data preparation, training, and prediction of collisional cross-section values of peptides. It is easily customizable and includes a set of pretrained, ready-to-use models and preprocessing routines for training and inference. Using a set of ≈21 000 unique phosphorylated peptides and ≈17 000 MHC ligand sequences and charge state pairs, we expand upon the space of peptides that can be integrated into CCS prediction. Lastly, we investigate the applicability of in silico predicted CCS to increase confidence in identified peptides by applying methods of re-scoring and demonstrate that predicted CCS values complement existing predictors for that task.

Availability and implementation
The Python package is available at github: https://github.com/theGreatHerrLebert/ionmob.
SHEPHARD: a modular and extensible software architecture for analyzing and annotating large protein datasets
Authors: Ginell Garrett M , Flynn Aidan J , Holehouse Alex S 
Published: August 2023

Abstract

Motivation
The emergence of high-throughput experiments and high-resolution computational predictions has led to an explosion in the quality and volume of protein sequence annotations at proteomic scales. Unfortunately, sanity checking, integrating, and analyzing complex sequence annotations remains logistically challenging and introduces a major barrier to entry for even superficial integrative bioinformatics.

Results
To address this technical burden, we have developed SHEPHARD, a Python framework that trivializes large-scale integrative protein bioinformatics. SHEPHARD combines an object-oriented hierarchical data structure with database-like features, enabling programmatic annotation, integration, and analysis of complex datatypes. Importantly SHEPHARD is easy to use and enables a Pythonic interrogation of largescale protein datasets with millions of unique annotations. We use SHEPHARD to examine three orthogonal proteome-wide questions relating protein sequence to molecular function, illustrating its ability to uncover novel biology.

Availability and implementation
We provided SHEPHARD as both a stand-alone software package (https://github.com/holehouse-lab/shephard), and as a Google Colab notebook with a collection of precomputed proteome-wide annotations (https://github.com/holehouse-lab/shephard-colab).
Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources
Authors: Karatzas Evangelos , Baltoumas Fotis A , Aplakidou Eleni , Kontou Panagiota I , Stathopoulos Panos , Stefanis Leonidas , Bagos Pantelis G , Pavlopoulos Georgios A 
Published: August 2023

Abstract

Summary: Functional enrichment is the process of identifying implicated functional terms from a given input list of genes or proteins. In this article, we present Flame (v2.0), a web tool which offers a combinatorial approach through merging and visualizing results from widely used functional enrichment applications while also allowing various flexible input options. In this version, Flame utilizes the aGOtool, g: Profiler, WebGestalt, and Enrichr pipelines and presents their outputs separately or in combination following a visual analytics approach. For intuitive representations and easier interpretation, it uses interactive plots such as parameterizable networks, heatmaps, barcharts, and scatter plots. Users can also: (i) handle multiple protein/gene lists and analyse union and intersection sets simultaneously through interactive UpSet plots, (ii) automatically extract genes and proteins from free text through text-mining and Named Entity Recognition (NER) techniques, (iii) upload single nucleotide polymorphisms (SNPs) and extract their relative genes, or (iv) analyse multiple lists of differentially expressed proteins/genes after selecting them interactively from a parameterizable volcano plot. Compared to the previous version of 197 supported organisms, Flame (v2.0) currently allows enrichment for 14 436 organisms.

Availability and implementation
Web Application: http://flame.pavlopouloslab.info. Code: https://github.com/PavlopoulosLab/Flame. Docker: https://hub.docker.com/r/pavlopouloslab/flame.