bluuebunny/crossref_metadata_2025 huggingface dataset:
Incontinentia pigmenti underlies thymic dysplasia, autoantibodies to type I IFNs, and viral diseases
Authors: Rosain Jérémie , Le Voyer Tom , Liu Xian , Gervais Adrian , Polivka Laura , Cederholm Axel , Berteloot Laureline , Parent Audrey V. , Pescatore Alessandra , Spinosa Ezia , Minic Snezana , Kiszewski Ana Elisa , Tsumura Miyuki , Thibault Chloé , Esnaola Azcoiti Maria , Martinovic Jelena , Philippot Quentin , Khan Taushif , Marchal Astrid , Charmeteau-De Muylder Bénédicte , Bizien Lucy , Deswarte Caroline , Hadjem Lillia , Fauvarque Marie-Odile , Dorgham Karim , Eriksson Daniel , Falcone Emilia Liana , Puel Mathilde , Ünal Sinem , Geraldo Amyrath , Le Floc’h Corentin , Li Hailun , Rheault Sylvie , Muti Christine , Bobrie-Moyrand Claire , Welfringer-Morin Anne , Fuleihan Ramsay L. , Lévy Romain , Roelens Marie , Gao Liwei , Materna Marie , Pellegrini Silvia , Piemonti Lorenzo , Catherinot Emilie , Goffard Jean-Christophe , Fekkar Arnaud , Sacko-Sow Aissata , Soudée Camille , Boucherit Soraya , Neehus Anna-Lena , Has Cristina , Hübner Stefanie , Blanchard-Rohner Géraldine , Amador-Borrero Blanca , Utsumi Takanori , Taniguchi Maki , Tani Hiroo , Izawa Kazushi , Yasumi Takahiro , Kanai Sotaro , Migaud Mélanie , Aubart Mélodie , Lambert Nathalie , Gorochov Guy , Picard Capucine , Soudais Claire , L’Honneur Anne-Sophie , Rozenberg Flore , Milner Joshua D. , Zhang Shen-Ying , Vabres Pierre , Trpinac Dusan , Marr Nico , Boddaert Nathalie , Desguerre Isabelle , Pasparakis Manolis , Miller Corey N. , Poziomczyk Cláudia S. , Abel Laurent , Okada Satoshi , Jouanguy Emmanuelle , Cheynier Rémi , Zhang Qian , Cobat Aurélie , Béziat Vivien , Boisson Bertrand , Steffann Julie , Fusco Francesca , Ursini Matilde Valeria , Hadj-Rabia Smail , Bodemer Christine , Bustamante Jacinta , Luche Hervé , Puel Anne , Courtois Gilles , Bastard Paul , Landegren Nils , Anderson Mark S. , Casanova Jean-Laurent
Published: October 2024

Human inborn errors of thymic T cell tolerance underlie the production of autoantibodies (auto-Abs) neutralizing type I IFNs, which predispose to severe viral diseases. We analyze 131 female patients with X-linked dominant incontinentia pigmenti (IP), heterozygous for loss-of-function (LOF) NEMO variants, from 99 kindreds in 10 countries. Forty-seven of these patients (36%) have auto-Abs neutralizing IFN-α and/or IFN-ω, a proportion 23 times higher than that for age-matched female controls. This proportion remains stable from the age of 6 years onward. On imaging, female patients with IP have a small, abnormally structured thymus. Auto-Abs against type I IFNs confer a predisposition to life-threatening viral diseases. By contrast, patients with IP lacking auto-Abs against type I IFNs are at no particular risk of viral disease. These results suggest that IP accelerates thymic involution, thereby underlying the production of auto-Abs neutralizing type I IFNs in at least a third of female patients with IP, predisposing them to life-threatening viral diseases.
Imaging exciton interactions in two-dimensional materials and heterostructures with spectroscopic microscopy
Authors: L Purz Torben , W Martin Eric , T Hipsley Blake , T Cundiff Steven
Published: October 2024

Exciton interactions are at the heart of carrier dynamics and applications of two-dimensional materials derived from their strong light-matter interactions. Exciton-phonon interactions affect the dephasing time of excitons in few-layer transition metal dichalcogenides (TMDs) and excitons can couple to each other coherently and incoherently in TMD heterostructures. This coupling introduces a mechanism for quantum coherent control and fast and efficient solar cells and detectors. Exciton-magnon interactions bear the potential for control of spintronics application, while exciton-continuum interactions can lead to new exciton states in twisted bilayer graphene. Linear spectroscopic techniques, including photoluminescence and spontaneous Raman, are ubiquitous yet limited in their ability to study interactions. Nonlinear, ultrafast spectroscopic techniques such as multidimensional coherent spectroscopy and transient absorption spectroscopy are uniquely suited to study exciton interactions with other quasiparticles and give unprecedented access to the most fundamental physics in two-dimensional materials. Because many high-quality samples are still based on exfoliated monolayers, properties change drastically across a sample and in between samples. Therefore, combining spectroscopic techniques with microscopic imaging is an important advance in experimental studies of two-dimensional materials. Spectroscopic microscopy gives statistical information as much as it allows researchers to understand how exciton interactions are affected by changes in the surrounding potential, whether due to strain, doping, or atomic alignment. In this review, we will highlight the progression of experimental techniques alongside the exciton interactions of interest, as different exciton interactions require different tools to study them.
A tracer-free photographic method for the estimation of the velocity field in a liquid metal pool
Authors: Houssein Jad , Goïc Gaëtan Le , Courtois Mickaël , Pierre Thomas , Carin Muriel
Published: October 2024

Accurately measuring the velocity field (VF) of liquid metals is essential for optimizing and controlling high-temperature industrial processes, yet remains a challenging task despite the numerous existing velocimetry techniques. In this paper, a methodology for estimating the VF of a liquid metal pool is proposed. The experimental set-up used here to heat the metal sample up to the liquid state and collect data is presented. This set-up primarly intented to perform flash experimetns on liquid metals, allows contactless heating and data measurements by a high speed camera. The proposed methodology for estimating the VF consists then in implementing a Non-Rigid Registration between the different frames of the recorded temperature fields (TFs). This method is applied on TFs resulting from multiple experimental tests conducted on iron samples in the temperature range of

1

850
−
2

100

 K. The consistency of the results is put to test via multiple validation tests. The influence of the main parameters is also evaluated. The results show that the proposed methodology allows a fine experimental quantification of liquid metal VFs with an estimated error of

 
8
%

and confirm the feasibility of the proposed approach.
LRSDet: lightweight remote sensing target detection network with local-global information awareness and rapid sample assignment
Authors: Fan Fei , Zhang Ming , Liu Yang , Yu Dahua , Li Jianjun
Published: October 2024

With the rapid development of aerospace and unmanned aerial vehicles, using neural networks for object detection in optical remote sensing images (O-RSI) has encountered heightened challenges. The optical remote sensing images have the characteristics of complex geometric scenes, dense groups of objects, and significant multi-scale variations of objects; researchers need to use more complex models to achieve higher accuracy. However, this complexity also brings challenges to the application of lightweight scenes. Therefore, to cope with the trade-off challenge between model complexity and detection accuracy, we propose a lightweight network model LRSDet in this study. The model integrates local and global information processing mechanisms and introduces a fast positive sample assignment strategy to adapt to resource-constrained embedded and mobile platforms. By constructing a lightweight feature extraction network and a lightweight path aggregation network and incorporating the ESM-Attention module, the feature extraction capability of the model in complex remote sensing scenarios is significantly improved. In addition, the application of a dynamic soft threshold strategy further optimizes the positive sample selection process and improves the detection efficiency of the model. Experimental on the O-RSI datasets DIOR, NWPU VHR-10, and RSOD, while analyzing model real-time performance on aerial video and embedded devices, outperforming other state-of-the-art methods.
Enhancement of photoresponse for InGaAs infrared photodetectors using plasmonic WO

3
−
x

/CsyWO

3
−
x

nanocrystals
Authors: Merino Zach D , Jaics Gyorgy , Jordan Andrew W M , Shetty Arjun , Yin Penghui , Tam Man C , Wang Xinning , Wasilewski Zbig R , Radovanovic Pavle V , Baugh Jonathan
Published: October 2024

Fast and accurate detection of light in the near-infrared (NIR) spectral range plays a crucial role in modern society, from alleviating speed and capacity bottlenecks in optical communications to enhancing the control and safety of autonomous vehicles through NIR imaging systems. Several technological platforms are currently under investigation to improve NIR photodetection, aiming to surpass the performance of established III–V semiconductor p-i-n (PIN) junction technology. These platforms include in situ-grown inorganic nanocrystals (NCs) and nanowire arrays, as well as hybrid organic–inorganic materials such as graphene-perovskite heterostructures. However, challenges remain in NC and nanowire growth, large-area fabrication of high-quality 2D materials, and the fabrication of devices for practical applications. Here, we explore the potential for tailored semiconductor NCs to enhance the responsivity of planar metal–semiconductor–metal (MSM) photodetectors. MSM technology offers ease of fabrication and fast response times compared to PIN detectors. We observe enhancement of the optical-to-electric conversion efficiency by up to a factor of ∼2.5 through the application of plasmonically-active semiconductor nanorods and NCs. We present a protocol for synthesizing and rapidly testing the performance of non-stoichiometric tungsten oxide (WO

3
−
x

) nanorods and cesium-doped tungsten oxide (Cs
y
WO

3
−
x

) hexagonal nanoprisms prepared in colloidal suspensions and drop-cast onto photodetector surfaces. The results demonstrate the potential for a cost-effective and scalable method exploiting tailored NCs to improve the performance of NIR optoelectronic devices.
Nanoarchitectonics of tunable aminosalicylate sodium encapsulated gold nanoparticles enabling multi-faceted role as capping, reducing, stabilizing and colorimetric detection of metal ions
Authors: Das Nabojit , Kumar Akash , Rayavarapu Raja Gopal
Published: October 2024

Despite all the advancements in aqueous synthesis of gold nanoparticles, certain features like one-pot/one-step method with minimal reactants using greener solvents are still demanding. The challenge in the aqueous phase synthesis is to balance the nucleation and precise growth of nanoparticles avoiding aggregation. In this work, we report a unique versatile unexplored molecule aminosalicylate sodium (Na-4-ASA) which functions as a capping, reducing, stabilizing and more interestingly as an encapsulating agent for gold nanoparticles. This multi-faceted molecule showed excellent control in synthesizing monodisperse tunable encapsulated nanoparticles of sizes (60 nm, 53 nm and 12 nm) exhibiting absorbance bands at 560 nm, 540 nm and 520 nm respectively. X-ray diffraction and Fourier Transmission Infra-Red validated crystalline structure and binding of Na-4-ASA onto gold nanoparticles surface respectively. Furthermore, the AuNPs were investigated for their ability to detect metal ions through colorimetric change where purification via centrifugation turned out to be a key parameter in enabling the detection. Selectivity towards Al3+ was observed with the 12 nm sized nanoparticles at 0.5 ppm metal ion concentration. The AuNPs of sizes 60 nm and 53 nm detected Al3+/Cr3+/Fe3+ and Al3+/Fe3+ respectively indicating the impact of size in heavy metal ions detection. The greater the size of AuNPs, lower is the selectivity where detection of three metal ions were observed and vice versa i.e. smaller-sized AuNPs showed high selectivity by detecting single metal ion. Also, the time duration for detection increased with decreasing size of the AuNPs. Finally, LOD for the heavy metal ions Al3+, Cr3+, and Fe3+ were calculated as 67 ppb, 78 ppb, 76 ppb respectively.
A dynamic approach for MR T2-weighted pelvic imaging
Authors: Cheng Jing , Li Qingneng , Liu Naijia , Yang Jun , Fu Yu , Cui Zhuo-Xu , Wang Zhenkui , Li Guobin , Zhang Huimao , Liang Dong
Published: October 2024

Objective. T2-weighted 2D fast spin echo sequence serves as the standard sequence in clinical pelvic MR imaging protocols. However, motion artifacts and blurring caused by peristalsis present significant challenges. Patient preparation such as administering antiperistaltic agents is often required before examination to reduce artifacts, which discomfort the patients. This work introduce a novel dynamic approach for T2 weighted pelvic imaging to address peristalsis-induced motion issue without any patient preparation. Approach. A rapid dynamic data acquisition strategy with complementary sampling trajectory is designed to enable highly undersampled motion-resistant data sampling, and an unrolling method based on deep equilibrium model is leveraged to reconstruct images from the dynamic sampled k-space data. Moreover, the fix-point convergence of the equilibrium model ensures the stability of the reconstruction. The high acceleration factor in each temporal phase, which is much higher than that in traditional static imaging, has the potential to effectively freeze pelvic motion, thereby transforming the imaging problem from conventional motion prevention or removal to motion reconstruction. Main results. Experiments on both retrospective and prospective data have demonstrated the superior performance of the proposed dynamic approach in reducing motion artifacts and accurately depicting structural details compared to standard static imaging. Significance. The proposed dynamic approach effectively captures motion states through dynamic data acquisition and deep learning-based reconstruction, addressing motion-related challenges in pelvic imaging.
Enhancing nitrogen fixation efficiency in glow-like discharge by reducing cathode-fall voltage
Authors: Li Yiheng , Luo Yi , Man Chenxi , Pei Xuekai , Lu Xinpei , B Graves David
Published: October 2024

In plasma nitrogen fixation devices, discharge electrodes are crucial yet susceptible to oxidation and corrosion due to plasma’s high temperatures and oxygen content, which could alter discharge modes. This research evaluates the impact of different electrode materials, including iron, chromium, nickel, copper, and 304 stainless steel, on nitrogen fixation efficiency in glow-like discharges driven by high-voltage DC power. Notably, iron and 304 stainless steel cathodes undergo a mode transition at increased currents, evident from plasma color shifts and significant voltage reductions. Fourier transform infrared spectroscopy analyses reveal that such mode changes minimally affect nitrogen oxide production rates, leading to a notable decrease in energy consumption for nitrogen fixation by up to 40%. OES and SEM-EDS measurements suggest that iron oxide, with its higher secondary electron emission, replaces metal as the cathode material, facilitating mode transitions and maintaining discharge current at lower voltages. This voltage change is largely attributed to the cathode voltage drop, highlighting the minimal role of the cathode fall region in NO
x
synthesis. These findings underscore the potential for improving plasma nitrogen fixation energy efficiency by choosing suitable cathode materials to lower the cathode-fall voltage.
Intelligent prediction of rock bolt debonding employing the fractal theory and relevance vector machine (FT-RVM) with piezoceramic transducers
Authors: Liu Yang , Bai Yixuan , Hu Nanyan , Luo Binyu , Zhang Ge
Published: October 2024

A new intelligent prediction model incorporated fractal theory and relevance vector machine (FT-RVM) was proposed to detect the debonding status of the rock bolt by using the piezoceramic transducer-induced stress waves. In the FT-RVM model, the original signals under different debonding status are firstly decomposed by the wavelet packet decomposition, the box dimension of decomposed signal is extracted by FT. The fractal box dimension of decomposed signals and root mean square value of the original signal are used as the as the inputs to the FT-RVM model, and the different debonding status of the glass fiber reinforced polymer rock bolt is the output. After the training, the prediction model is used to estimate the debonding status of the rock bolt. In the FT-RVM model, the kernel function utilized is the Gaussian radial basis function, and its optimal value is obtained by using particle swarm optimization. The experimental results show that the average relative error of the FT-RVM prediction model is 3.04%, and the accuracy and reliability of the model are high, which demonstrates the intelligent identification of GFRP rock bolt debonding status. The proposed intelligent prediction model based on FT-RVM could be used to quickly evaluate rock bolt debonding status.
Investigating pump harmonics generation in a SNAIL-based traveling wave parametric amplifier
Authors: Levochkina A Yu , Ahmad H G , Mastrovito P , Chatterjee I , Serpico G , Di Palma L , Ferroiuolo R , Satariano R , Darvehi P , Ranadive A , Cappelli G , Le Gal G , Planat L , Montemurro D , Massarotti D , Tafuri F , Roch N , Pepe G P , Esposito M
Published: October 2024

Traveling wave parametric amplifiers (TWPAs) are extensively employed in experiments involving weak microwave signals for their highly desirable quantum-limited and broadband characteristics. However, TWPAs’ broadband nature comes with the disadvantage of admitting the activation of spurious nonlinear processes, such as harmonics generation, that can potentially degrade amplification performance. Here we experimentally investigate a Josephson TWPA device with superconducting nonlinear asymmetric inductive element-based unit cells focusing on the amplification behaviour along with the generation of second and third harmonics of the pump. By comparing experimental results with transient numerical simulations, we demonstrate the influence of Josephson junctions’ fabrication imperfections on the occurrence of harmonics and on the gain behaviour.
Steering internal and outgoing electron dynamics in bilayer graphene cavities by cavity design
Authors: Seemann Lukas , Knothe Angelika , Hentschel Martina
Published: October 2024

Ballistic, gate-defined devices in two-dimensional materials offer a platform for electron optics phenomena influenced by the material’s properties and gate control. We study the ray trajectory dynamics of all-electronic, gate-defined cavities in bilayer graphene to establish how distinct regimes of the internal and outgoing charge carrier dynamics can be tuned and optimized by the cavity shape, symmetry, and parameter choice, e.g. the band gap and the cavity orientation. In particular, we compare the dynamics of two cavity shapes, oŉigiri, and Limaçon cavities, which fall into different symmetry classes. We demonstrate that for stabilising regular, internal cavity modes, such as periodic and whispering gallery orbits, it is beneficial to match the cavity shape to the bilayer graphene Fermi line contour. Conversely, a cavity of a different symmetry than the material dispersion allows one to determine preferred emission directionalities in the emitted far-field.
Turbulence spreading induced

E

×

B

vortex flow generation in a magnetic island
Authors: Yoon E.S. , Hahm T.S. , Choi G.J. , Cho Y.W. , Ishizawa A. , Choi M.J. , Kwon J.M.
Published: October 2024

We derive a self-consistent relation between turbulence spreading flux and vortex flow acceleration for time-stationary turbulence inside a magnetic island (MI) by extending the Charney-Drazin momentum theorem. The result, which consists of flux-surface-averaged expressions with geometrical weight, indicates a dominant balance between the turbulence spreading flux through regions around X-points and the vortex flow away from the X-points on the same flux surface. This is in qualitative agreement with experimental observations from DIII-D, KSTAR and HL-2A.
Mechanical behavior and strengthening mechanism of loess stabilized with xanthan gum and guar gum biopolymers
Authors: Rong Xuewen , Deng Shuo , Liang Baozhen , Zhuang Jia , Yu Yongtang , Wu Zheng
Published: October 2024

The structural properties of loess are susceptible to change when subjected to external loads and complex environments, leading to various geological disasters. To investigate the mechanical behavior and strengthening mechanism of loess stabilized with biopolymers such as xanthan gum and guar gum, especially for soils with low bearing capacity and stability in engineering applications, we conducted research on the improvement of soil with xanthan gum and guar gum, tests including unconfined compressive strength, disintegration, direct shear, and microstructure tests were conducted. Among the four different dosages of biopolymers (0%, 0.5%, 1%, 2%) and four different curing ages (1 day, 3 days, 7 days, 14 days), the 2% content of biopolymer and 14 days had the greatest impact on the mechanical properties of loess, Both the compressive and shear strength, as well as the water stability of solidified loess, improve with higher content of xanthan gum and guar gum or prolonged curing time; however, the disintegration rate decreases. Microscopic analysis indicates that the biopolymers effectively fill the gaps between soil particles and attach to the particle surfaces, forming fibrous and reticular structures that improve the interparticle bonding and ultimately increase the strength and water stability of the loess. Xanthan gum and guar gum biopolymers can improve the mechanical properties and water stability of loess, enhance the erosion resistance and improve the water-holding capacity. These outcomes suggest that guar gum and xanthan gum biopolymers have the potential to serve as environmentally sustainable alternatives to conventional soil stabilizers.
Development of geopolymer from gold mine tailings utilizing Taguchi robust design
Authors: de Leon Lanz Nathaniel , Labastida Pauleen Bea , Jan Macalino Eren , Magdaluyo Jr Eduardo
Published: October 2024

This study focuses on the production of geopolymers incorporating gold mine tailings (GMTs) as the primary aluminosilicate source. Utilizing the Taguchi experimental design, the effects of varying the amounts of sodium silicate (Na2SiO3), rice husk ash (RHA), and copper (Cu) on the compressive strength and apparent porosity of the geopolymers were investigated. The results demonstrated that increasing Na2SiO3 to 10 wt% and RHA to 20 wt% led to a significant increase in compressive strength, peaking at 3.22 MPa, while elevated Cu levels correlated with decreased strength owing to increased apparent porosity. Analysis of variance highlighted all three parameters as significant signal factors that influenced the compressive strength. However, only the amounts of Cu and RHA showed a significant effect on the material’s apparent porosity, with a maximum of 7.3% increased porosity. Signal-to-noise ratio analysis identified Na2SiO3 and Cu as critical factors in minimizing variations of compressive strength. Microstructural analysis via x-ray diffraction confirmed geopolymerization, with the surface morphology displaying grain homogeneity as seen in scanning electron microscope. These findings underscore the potential of GMTs incorporation in geopolymers, emphasizing their sustainability in construction materials and non-load bearing applications, as well as their implications for environmental impact mitigation.
Fuzzy inventory model for breakable items, including damage costs and inspection errors in a green environment
Authors: C Sugapriya , A Fariya Azleena , D Nagarajan , J Kavikumar
Published: October 2024

Conventional Economic Order Quantity (EOQ) models operate on the implicit assumption that all received products are flawless. Current EOQ models for items of defectiveness implicitly suggest that suppliers do not conduct a comprehensive assessment, even though they loosen this presumption and are more applicable to scenarios in reality. This paper presents a significant EOQ model. This model accounts for the possibility of mistakes occurring while inspection of the product. Because things are piled high in stock, breaking of products takes place. These goods are stored and sold at once, with damage costs included. Furthermore, governments have made limiting carbon emissions their top priority. In an effort to improve the environment, the tax rates in the price of carbon emissions have been included. This model is distinct because it closes the research gap by taking into account breakable items under inspection errors with damage cost, and carbon emission cost. The ideal order quantity that optimizes the total profit is determined. It is important to note that this model is constructed in a fuzzy manner by using a triangular fuzzy number to represent the cost of accepting a broken object. Applications of this idea include shipping commodities. A numerical illustration of the ideal solution is given and the sensitivity studies based on analytical results are provided. The current study addresses how changes in a few parameters influence the ideal overall cost. Findings reveal that altering the products screening cost does not greatly affect the total profit. So retailers should increase the inspection cost so that they can give good products to their customers. Additionally, managerial implications are also provided.
Global warming level indicators of climate change and hotspots of exposure
Authors: Werning Michaela , Hooke Daniel , Krey Volker , Riahi Keywan , van Ruijven Bas , Byers Edward A
Published: October 2024

In the 21st century, a growing population will be exposed to various hazards caused by a warming climate. Here we present a new database of 12 climate change indicators with a total of 42 variants at different global warming levels (GWLs) (1.2 °C–3.5 °C), which is based on global climate and hydrological model data from the Coupled Model Intercomparison Project round 6 and the Inter-Sectoral Impact Model Intercomparison Project round 3b at 0.5° spatial resolution. It comprises of indicators relating to temperature and precipitation extremes, heatwave events, and hydrological variability. To facilitate the comparison of hazards from different indicators, including for an audience without a scientific background, we have developed a bivariate hazard score which is applied on the grid cell level and incorporates statistics on both the absolute hazard (e.g. low or high precipitation) and the relative change under global warming compared to the reference period (e.g. a large change from low to high precipitation). Additionally, we calculate exposed land area and population through the 21st century for a large set of countries and regions by combining this score with gridded projections of population from the Shared Socioeconomic Pathways (SSPs). The datasets are intended for use by the wider research community and analysts seeking digestible climate hazard and exposure data summarized by GWLs. To illustrate potential uses of the data, in a preliminary analysis we find that even at 1.5 °C large parts of the land area and population face substantial unavoidable risks from multiple indicators, with 86% of the world’s population exposed to at least three indicators with at least medium hazard using the population projections for SSP2 in the year 2050. This picture only worsens with increasing warming, as the land areas facing the highest number of impacts coincide with some of the most densely populated parts of the world.
Transitivity, Valency, and Voice
Authors: Creissels Denis
Published: October 2024

Transitivity, valency, and voice are at the very heart of the most basic aspects of clause structure in any language, but the details of the constraints that regulate the way verbs combine with noun phrases referring to the participants in the event they denote or to circumstances of the event vary greatly from language to language. The book sets up a consistent theoretical and terminological framework, novel in many respects, within which a typological approach to the phenomena commonly dealt with in terms of transitivity, valency, and voice can be developed. Throughout the book, the emphasis is on accounting for the cross-linguistic variation in this domain and capturing regularities in the variation. Much emphasis is also placed on the historical phenomena that may explain the emergence of constructions and markers and their evolution.
Participant roles and participant coding
Authors: Creissels Denis
Published: October 2024

The first section of this chapter is devoted to some clarifications about participant roles in the events denoted by verbs, with a particular attention to those which feature prominently in the discussion of the questions addressed in the book. The following sections examine in turn the three mechanisms that may ensure the existence of formal contrasts between noun phrases representing distinct participants in the events denoted by a given verb. Section 2.2 is devoted to the possible involvement of constituent order in the coding of participant roles. Section 2.3 is devoted to the typology of indexation. Section 2.4 is devoted to flagging, a notion that encompasses case marking and the use of adpositions.
Transitive coding and valency
Authors: Creissels Denis
Published: October 2024

The topic of this chapter is transitivity prominence, i.e., the extent to which languages make use of transitive coding to encode events that are not prototypically transitive. Section 7.1 examines this question for semantically bivalent verbs, for which transitive coding constitutes the default coding frame. Section 7.2 analyzes the variation in the coding frames of trivalent verbs, whose participant structure can generally be analyzed as including an agent, a transferee, and a goal. As a rule, trivalent verbs select transitive coding frames, but there is important cross-linguistic variation in the choice between double-P constructions, indirective alignment, and secundative alignment. Section 7.3 analyzes the possible uses of the transitive construction in clauses projected by semantically monovalent verbs.
Passivization and S-denucleativization
Authors: Creissels Denis
Published: October 2024

In this chapter, Sections 9.1 to 9.8 are devoted to passivization. Section 9.1 introduces passivization as a denucleativizing valency alternation. Section 9.2 discusses various issues in the analysis of passive constructions. Section 9.3 deals with the relationship between passive and resultative. Section 9.4 analyzes the relationship between passive and decausative and introduces the notion of quasipassive. Sections 9.5 and 9.6 examine the co-expression patterns in which passive markers may be involved. Section 9.7 discusses constructions that look like passive constructions but are not really passive constructions. Section 9.8 is devoted to the impersonal variant of passivization. Section 9.9 is devoted to S-denucleativization, a type of voice alternation that generally involves the same markers as passivization. Section 9.10 deals with the relationship between R-impersonals and passivization.
Genetic analysis of selection bias in a natural experiment: Investigating in-utero famine effects on elevated body mass index in the Dutch Hunger Winter Families Study
Authors: Zhou Jiayi , Indik Claire E , Kuipers Thomas B , Li Chihua , Nivard Michel G , Ryan Calen P , Tucker-Drob Elliot M , Taeubert M Jazmin , Wang Shuang , Wang Tian , Conley Dalton , Heijmans Bastiaan T , Lumey Lambert H , Belsky Daniel W
Published: October 2024

Natural-experiment designs that compare survivors of in-utero famine exposure to unaffected controls suggest that in-utero undernutrition predisposes to development of obesity. However, birth rates drop dramatically during famines. Selection bias could arise if factors that contribute to obesity also protect fertility and/or fetal survival under famine conditions. We investigated this hypothesis using genetic analysis of a famine-exposed birth cohort. We genotyped participants in the Dutch Hunger Winter Families Study (DHWFS, N=950; 45% male), of whom 51% were exposed to the 1944-1945 Dutch Famine during gestation and 49% were their unexposed same-sex siblings or “time controls” born before or after the famine in the same hospitals. We computed body-mass index (BMI) polygenic indices (PGIs) in DHWFS participants and compared BMI PGIs between famine-exposed and control groups. Participants with higher polygenic risk had higher BMIs (Pearson r=0.42, p<0.001). However, differences between BMI PGIs of famine-exposed participants and controls were small and not statistically different from zero across specifications (Cohen’s d=0.10, p>0.092). Our findings did not indicate selection bias, supporting the validity of the natural-experiment design within DHWFS. In summary, our study outlines a novel approach to explore the presence of selection bias in famine and other natural experiment studies.
Interactive associations of cannabis and alcohol outlet densities with assault injuries in California: A spatiotemporal analysis
Authors: Matthay Ellicott C , Charris Rafael , Ahern Jennifer , Apollonio Dorie E , Jent Victoria , Jacobs Laurie M , Jung Shelley , Schmidt Laura A , Gruenewald Paul
Published: October 2024

Recreational cannabis outlets may influence rates of interpersonal violence, but research has yielded inconsistent findings. Modification by alcohol outlet density may help explain inconsistencies. We estimated the impacts of recreational cannabis outlets on neighborhood-level assault injury rates in California and evaluated whether alcohol outlet density moderated these associations. We applied Bayesian spatiotemporal analyses to ZIP code-level statewide data on alcohol outlets, recreational cannabis outlets, and injuries and deaths due to firearm and nonfirearm assault, 2017-2019, accounting for confounders and spatial autocorrelation. Using the model posteriors, we estimated parameters corresponding to hypothetical shifts in outlet densities, overall and by age, sex, and race/ethnicity. If recreational cannabis outlets were never introduced, we estimated that nonfirearm assault injuries would have been 1.63 per 100,000 lower (95%CI: -3.08, 0.01) but we observed no association with firearm assault injuries (RD per 100,000: -0.07; 95%CI: -0.34, 0.21). These associations did not depend on alcohol outlet density, but a hypothetical 20% reduction in alcohol outlet densities was associated with fewer firearm (RD per 100,000: -1.89; 95%CI: -0.46, 0.09) and nonfirearm (RD per 100,000: -5.67; 95%CI: -7.44, -3.95) assault injuries. The introduction of recreational cannabis outlets may have contributed to a small increase in nonfirearm assault injuries.
Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis
Authors: Yang Fang , Li Chaoqun , Yang Wanting , He Yumei , Wu Liping , Jiang Kui , Sun Chao
Published: October 2024

We sought to develop and validate a machine learning (ML) model for predicting multidimensional frailty based on clinical and laboratory data. Moreover, an explainable ML model utilizing SHapley Additive exPlanations (SHAP) was constructed. This study enrolled 622 patients hospitalized due to decompensating episodes at a tertiary hospital. The cohort data were randomly divided into training and test sets. External validation was carried out using 131 patients from other tertiary hospitals. The frail phenotype was defined according to a self-reported questionnaire (Frailty Index). The area under the receiver operating characteristics curve was adopted to compare the performance of five ML models. The importance of the features and interpretation of the ML models were determined using the SHAP method. The proportions of cirrhotic patients with nonfrail and frail phenotypes in combined training and test sets were 87.8% and 12.2%, respectively, while they were 88.5% and 11.5% in the external validation dataset. Five ML algorithms were used, and the random forest (RF) model exhibited substantially predictive performance. Regarding the external validation, the RF algorithm outperformed other ML models. Moreover, the SHAP method demonstrated that neutrophil-to-lymphocyte ratio, age, lymphocyte-to-monocyte ratio, ascites, and albumin served as the most important predictors for frailty. At the patient level, the SHAP force plot and decision plot exhibited a clinically meaningful explanation of the RF algorithm. We constructed an ML model (RF) providing accurate prediction of frail phenotype in decompensated cirrhosis. The explainability and generalizability may foster clinicians to understand contributors to this physiologically vulnerable situation and tailor interventions.
tidysbml: R/Bioconductor package for SBML extraction into dataframes
Authors: Paparozzi Veronica , Nardini Christine
Published: October 2024

We present tidysbml, an R package able to perform compartments, species, and reactions data extraction from Systems Biology Markup Language (SBML) documents (up to Level 3) in tabular data structures (i.e. R dataframes) to easily access and handle the richness of the biological information. Thanks to its output format, the package facilitates data manipulation, enabling manageable construction, and therefore analysis, of custom networks, as well as data retrieval, by means of R packages such as igraph, RCy3, and biomaRt. Exemplar data (i.e. SBML files) are extracted from Reactome.

Availability and implementation

The tidysbml R package is distributed under CC BY 4.0 License and can be found publicly available in Bioconductor (https://bioconductor.org/packages/tidysbml) and on GitHub (https://github.com/veronicapaparozzi/tidysbml).
Pair-EGRET: enhancing the prediction of protein–protein interaction sites through graph attention networks and protein language models
Authors: Alam Ramisa , Mahbub Sazan , Bayzid Md Shamsuzzoha
Published: October 2024

Motivation

Proteins are responsible for most biological functions, many of which require the interaction of more than one protein molecule. However, accurately predicting protein–protein interaction (PPI) sites (the interfacial residues of a protein that interact with other protein molecules) remains a challenge. The growing demand and cost associated with the reliable identification of PPI sites using conventional experimental methods call for computational tools for automated prediction and understanding of PPIs.

Results

We present Pair-EGRET, an edge-aggregated graph attention network that leverages the features extracted from pretrained transformer-like models to accurately predict PPI sites. Pair-EGRET works on a k-nearest neighbor graph, representing the 3D structure of a protein, and utilizes the cross-attention mechanism for accurate identification of interfacial residues of a pair of proteins. Through an extensive evaluation study using a diverse array of experimental data, evaluation metrics, and case studies on representative protein sequences, we demonstrate that Pair-EGRET can achieve remarkable performance in predicting PPI sites. Moreover, Pair-EGRET can provide interpretable insights from the learned cross-attention matrix.

Availability and implementation

Pair-EGRET is freely available in open source form at the GitHub Repository https://github.com/1705004/Pair-EGRET.
Learning to explain is a good biomedical few-shot learner
Authors: Chen Peng , Wang Jian , Luo Ling , Lin Hongfei , Yang Zhihao
Published: October 2024

Motivation

Significant progress has been achieved in biomedical text mining using deep learning methods, which rely heavily on large amounts of high-quality data annotated by human experts. However, the reality is that obtaining high-quality annotated data is extremely challenging due to data scarcity (e.g. rare or new diseases), data privacy and security concerns, and the high cost of data annotation. Additionally, nearly all researches focus on predicting labels without providing corresponding explanations. Therefore, in this paper, we investigate a more realistic scenario, biomedical few-shot learning, and explore the impact of interpretability on biomedical few-shot learning.

Results

We present LetEx—Learning to explain—a novel multi-task generative approach that leverages reasoning explanations from large language models (LLMs) to enhance the inductive reasoning ability of few-shot learning. Our approach includes (1) collecting high-quality explanations by devising a suite of complete workflow based on LLMs through CoT prompting and self-training strategies, (2) converting various biomedical NLP tasks into a text-to-text generation task in a unified manner, where collected explanations serve as additional supervision between text-label pairs by multi-task training. Experiments are conducted on three few-shot settings across six biomedical benchmark datasets. The results show that learning to explain improves the performances of diverse biomedical NLP tasks in low-resource scenario, outperforming strong baseline models significantly by up to 6.41%. Notably, the proposed method makes the 220M LetEx perform superior reasoning explanation ability against LLMs.

Availability and implementation

Our source code and data are available at https://github.com/cpmss521/LetEx.
BEATRICE: Bayesian fine-mapping from summary data using deep variational inference
Authors: Ghosal Sayan , Schatz Michael C , Venkataraman Archana
Published: October 2024

Motivation

We introduce a novel framework BEATRICE to identify putative causal variants from GWAS statistics. Identifying causal variants is challenging due to their sparsity and high correlation in the nearby regions. To account for these challenges, we rely on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to sample from the space of causal configurations, which we use to compute the posterior inclusion probabilities and determine credible sets for each causal variant. We conduct a detailed simulation study to quantify the performance of our framework against two state-of-the-art baseline methods across different numbers of causal variants and noise paradigms, as defined by the relative genetic contributions of causal and noncausal variants.

Results

We demonstrate that BEATRICE achieves uniformly better coverage with comparable power and set sizes, and that the performance gain increases with the number of causal variants. We also show the efficacy BEATRICE in finding causal variants from the GWAS study of Alzheimer’s disease. In comparison to the baselines, only BEATRICE can successfully find the APOE ϵ2 allele, a commonly associated variant of Alzheimer’s.

Availability and implementation

BEATRICE is available for download at https://github.com/sayangsep/Beatrice-Finemapping.
ROMI: a randomized two-stage basket trial design to optimize doses for multiple indications
Authors: Wang Shuqi , Thall Peter F , Takeda Kentaro , Yuan Ying
Published: October 2024

Optimizing doses for multiple indications is challenging. The pooled approach of finding a single optimal biological dose (OBD) for all indications ignores that dose-response or dose-toxicity curves may differ between indications, resulting in varying OBDs. Conversely, indication-specific dose optimization often requires a large sample size. To address this challenge, we propose a Randomized two-stage basket trial design that Optimizes doses in Multiple Indications (ROMI). In stage 1, for each indication, response and toxicity are evaluated for a high dose, which may be a previously obtained maximum tolerated dose, with a rule that stops accrual to indications where the high dose is unsafe or ineffective. Indications not terminated proceed to stage 2, where patients are randomized between the high dose and a specified lower dose. A latent-cluster Bayesian hierarchical model is employed to borrow information between indications, while considering the potential heterogeneity of OBD across indications. Indication-specific utilities are used to quantify response-toxicity trade-offs. At the end of stage 2, for each indication with at least one acceptable dose, the dose with highest posterior mean utility is selected as optimal. Two versions of ROMI are presented, one using only stage 2 data for dose optimization and the other optimizing doses using data from both stages. Simulations show that both versions have desirable operating characteristics compared to designs that either ignore indications or optimize dose independently for each indication.
Echo of extinction: The Ivory-billed Woodpecker's tragic legacy and its impact on scientific integrity
Authors: Michalak Pawel
Published: October 2024

Among the current surge in extinction rates, exceeding between 100 and 1000 times the prehuman background rate, a multitude of species quietly fade away, largely escaping the notice of our collective awareness. The extinction of only a select few species has the power to captivate public attention, stir emotions, and ignite debates comparable to the case of the ivory-billed woodpecker (Campephilus principalis). Reports purporting a rediscovery of this long-extinct species periodically surface and find their way into peer-reviewed journals, attracting worldwide attention from the public media. This phenomenon persists despite a glaring lack of evidence supporting such an exceptional claim, in disconnect with principles of population biology or even common sense, thereby risking erosion of public trust in the foundations of nature conservancy and the integrity of scientific methodologies in general. The tragic fate of this iconic species therefore holds significance beyond ecology, providing deep insights into extinction and conservation while highlighting the need for refinement of scientific rigor and standards of evidence in the peer review process and beyond.
Investigating Concomitant RAG-2 and LRBA Mutations in SCID and Autoimmunity
Authors: Spivak Ilia , Frizinsky Shirly , Mandola Amarilla , Lev Atar , Simon Amos J , Barel Ortal , Vishnevskia-Dai Vicktoria , Somech Raz , Somekh Ido
Published: October 2024

Inborn errors of immunity (IEI) are a large heterogenous group of diseases characterized by immunodeficiency, immune dysregulation, allergy, auto-inflammation and predisposition for malignancies. Most are inherited in an autosomal recessive trait. We studied a patient with severe combined immunodeficiency (SCID) and immune dysregulation who harbored two distinct bi-allelic IEI-associated genetic mutations. Clinical, immunological and genetic data were collected. Genetic investigation included whole exome sequencing on DNA extracted from skin fibroblasts. Family segregation was performed by Sanger sequencing. Immunological evaluation included absolute and functional evaluation of lymphocytes and chimerism analysis post hematopoietic stem cell transplantation (HSCT). Treg subsets, LRBA and CTLA4 expression levels were measured by flow-cytometric analysis. A nineteen-year-old female patient from a consanguine background underwent unconditioned matched sibling related HSCT during infancy due to clinical presentation of SCID with an Omenn phenotype. At that time her underlying genetic defect was not defined. Years after HSCT, severe auto-immune phenomena were noted, including a systemic lupus erythematosus-like syndrome and ophthalmic manifestations. Genetic evaluation revealed bi-allelic homozygous mutations in RAG-2 (c.685C>T, p.Arg229Trp) and a previously undescribed mutation in LRBA (c.3325G>T, p.Asp1109Tyr). LRBA and CTLA4 expression levels were normal, suggesting that the LRBA variant identified in these kindred is unlikely to be pathogenic. Multiple genetic defects causing complex IEIs may be identified in the same individual in highly consanguineous populations. Functional immunological testing is essential for evaluation of novel genetic variants.