bluuebunny/crossref_metadata_2025 huggingface dataset:
Intracellular Macromolecular Crowding within Individual Stress Fibers Analyzed by Fluorescence Correlation Spectroscopy
Authors: Buenaventura Aria , Saito Takumi , Kanao Taiga , Matsunaga Daiki , Matsui Tsubasa S. , Deguchi Shinji
Published: June 2024

Introduction

The diffusion of cell components such as proteins is crucial to the function of all living cells. The abundance of macromolecules in cells is likely to cause a state of macromolecular crowding, but its effects on the extent of diffusion remain poorly understood.

Methods

Here we investigate the diffusion rate in three distinct locations in mesenchymal cell types, namely the open cytoplasm, the stress fibers in the open cytoplasm, and those below the nucleus using three kinds of biologically inert green fluorescent proteins (GFPs), namely a monomer, dimer, and trimer GFP. Fluorescence correlation spectroscopy (FCS) was used to determine the diffusion coefficients.

Results

We show that diffusion tends to be lowered on average in stress fibers and is significantly lower in those located below the nucleus. Our data suggest that the diffusive properties of GFPs, and potentially other molecules as well, are hindered by macromolecular crowding. However, although the size dependence on protein diffusion was also studied for monomer, dimer, and trimer GFPs, there was no significant difference in the diffusion rates among the GFPs of these sizes. These results could be attributed to the lack of significant change in protein size among the selected GFP multimers.

Conclusion

The data presented here would provide a basis for better understanding of the complex protein diffusion in the nonuniform cytoplasm, shedding light on cellular responses to mechanical stress, their local mechanical properties, and reduced turnover in senescent cells.
Geoconservation History of a Basalt Quarry – The Case of Mt. Wilkołak, Land of Extinct Volcanoes Geopark, SW Poland
Authors: Migoń Piotr , Pijet-Migoń Edyta
Published: June 2024

Mt. Wilkołak is one among many hills built of mid-Cenozoic basalts in SW Poland, within the Land of Extinct Volcanoes UNESCO Global Geopark. Appreciated as a recreational ground since the first half of the 19th century, it began to be quarried at the beginning of the 20th century. The quarry located on the western slope was soon abandoned, leaving impressive exposures of variously oriented basaltic columns due to thermal jointing and volcanic breccia. Briefly re-used after World War II, the quarry was definitely closed in 1956 and a small geological reserve was established in 1959. In the meantime, basalt exploitation developed at a much larger scale within the eastern slope, leading to the major conflict between conservation and industry, with frequent voices to delist the reserve and continue quarrying towards complete exhaustion of resources and removal of the entire hilltop. In addition, as a result of poor management of the reserve and uncontrolled vegetation growth its values significantly diminished. In the 21st century various actions initiated by the local community, supported by scientists, concurrent with incoming termination of quarrying, opened the debate about the future of Mt. Wilkołak and its quarries. Stone extraction ended in 2019, after a catastrophic collapse of one quarry wall, and rehabilitation works were executed in 2021–2023, aimed at the conversion of the locality into a place focused on geo-education and recreation. Opened in May 2023, it has various interpretation facilities and offers unprecedented insights into the internal structure of a volcanic plug. However, challenges related to site management, enhancement of educational content, and the future of the nature reserve remain.
Navigating tensions in climate change-related planned relocation
Authors: Gini Giovanna , Piggott-McKellar Annah , Wiegel Hanne , Neu Friedrich Nikolaus , Link Ann-Christine , Fry Claudia , Tabe Tammy , Adegun Olumuyiwa , Wade Cheikh Tidiane , Bower Erica Rose , Koeltzow Sarah , Harrington-Abrams Rachel , Jacobs Carolien , van der Geest Kees , Zivdar Narjes , Alaniz Ryan , Cherop Carolyne , Durand-Delacre David , Pill Melanie , Shekhar Himanshu , Yates Olivia , Khan Md Abdul Awal , Nansam-Aggrey Frank Kwesi , Grant Lauren , Nizar Danang Aditya , Owusu-Daaku Kwame Nitri , Preato Alberto , Stefancu Oana , Yee Merewalesi
Published: June 2024

The planned relocation of communities away from areas of climate-related risk has emerged as a critical strategy to adapt to the impacts of climate change. Empirical examples from around the world show, however, that such relocations often lead to poor outcomes for affected communities. To address this challenge, and contribute to developing guidelines for just and sustainable relocation processes, this paper calls attention to three fundamental tensions in planned relocation processes: (1) conceptualizations of risk and habitability; (2) community consultation and ownership; and (3) siloed policy frameworks and funding mechanisms. Drawing on the collective experience of 29 researchers, policymakers and practitioners from around the world working on planned relocations in the context of a changing climate, we provide strategies for collectively and collaboratively acknowledging and navigating these tensions among actors at all levels, to foster more equitable and sustainable relocation processes and outcomes.
Advancing the In-Class Dialogic Quality: Developing an Artificial Intelligence-Supported Framework for Classroom Dialogue Analysis
Authors: Li Xian , Han Guangxin , Fang Bei , He Juhou
Published: June 2024

The development of artificial intelligence (AI) significantly improves the effectiveness of classroom dialogue systems, but their integration into the learning environment remains challenging. To address this gap, this research presents a framework for automatic intelligent dialogue analysis, intending to promote high-quality classroom dialogue and facilitate teaching and learning. The proposed framework includes two main components: a dialogue-oriented interactive classroom and an artificial intelligence-powered analysis system. We present a synthesis of essential principles that ought to be adhered to in the dialogue-oriented interactive classroom, as viewed through the lens of three key domains: the environment, the community and the teaching–learning. The AI system will analyse the dialogues generated from the interactive classroom. The utilization of feedback obtained from the AI system assists educators who adjust their pedagogical strategies, consequently improving the quality of classroom dialogues. Elevated-quality dialogues will reciprocally boost the performance of the AI system, engendering a sustainable improvement for the entire framework. Moreover, we also propose “Guide of AI”, a union of classroom participants and experts, which serves as the bridge between the classroom and technology to guide the operation of AI system. For the validation of the framework, we conduct an empirical study that mainly investigates the effectiveness of processed essential principles and AI systems. We select 6 pre-service teachers who are randomly divided into three groups. Three groups have different levels of involvement in AI system and each teacher gives three lessons. We record and analyse all teaching dialogue records and also use questionnaires to obtain teachers’ attitudes. The results show that timely feedback from AI system can promote the improvement of dialogue quality, which demonstrates the effectiveness of AI dialogue analysis system. In addition, the proposed essential principles also show a constructive impact.
Boundary Traces of Holomorphic Functions on the Unit Ball in $$\mathbb {C}^n$$
Authors: Gryc William E.
Published: June 2024

It is a classical theorem that if a function on the unit circle is integrable, then it is the nontangential limit of a holomorphic function on the open disc (subject to a certain growth condition) if and only if its Fourier coefficients for nonnegative integers are zero. In this article we generalize this result to higher complex dimensions by proving that for an integrable function on the unit sphere, it is a “boundary trace” of a holomorphic function on the open unit ball if and only if two particular families of integral equations are satisfied. To do this, we use the theory of Hardy spaces as well as the invariant Poisson and Cauchy integrals.
Further regularity results in $$P-$$extremal setting
Authors: Perera Menuja
Published: June 2024

In our paper, Pluripotential theory and convex bodies: a Siciak-Zaharjuta theorem (Computational Methods and Function Theory (2020), 20(3-4), 571-590) by Bayraktar et al., we proved a regularity result of $$P-$$

P
-

extremal function. In this paper, we prove some other stronger regularity results analogue to those in the standard setting. Specifically we focus on proving the following proposition: Proposition: Let $$K={\bar{D}}\subset {\mathbb {C}}^d$$

K
=

D

¯

⊂

C

d

be the closure of a bounded domain D with $$\partial D$$

∂
D

of class $$C^{1,1}$$

C

1
,
1

. Then, for any $$Q\in {\mathcal {C}}_\alpha (K)$$

Q
∈

C
α

(
K
)

, where $${\mathcal {C}}_\alpha (K)$$

C
α

(
K
)

is the Hölder class $$\alpha$$
α
on K, we have $$V_{P,K,Q}\in {\mathcal {C}}_\alpha ({\mathbb {C}}^d) \text { for a convex body } P\subset (\mathbb {R^+})^d.$$

V

P
,
K
,
Q

∈

C
α

(

C

d

)

for a convex body

P
⊂

(

R
+

)

d

.
GABoT: A Lightweight Real-Time Adaptable Approach for Intelligent Fault Diagnosis of Rotating Machinery
Authors: Bagci Das Duygu , Das Oguzhan
Published: June 2024

Purpose

As the concept of Industry 4.0 is introduced, the significance of Smart Fault Diagnosis in the industry is increased. Therefore, it is essential to develop accurate, robust, and lightweight intelligent fault diagnosis approach that can be executed in real-time even with embedded systems. Additionally, it is preferable to use a single method for multi-purposes such as the fault detection, identification, and severity assessment. This study proposed a new approach called GaBoT for fault diagnosis of rotating machinery to satisfy those requirements.

Method

The proposed approach adopted the concept of the ensemble of ensembles by boosting random forest. The statistical features of discrete wavelet transform were considered since they are easy and fast to obtain. Model optimization was conducted by employing genetic algorithm to alleviate the computational load without decreasing the model performance. The proposed approach has been validated by unseen data from an experimental dataset including shaft, rotor, and bearing faults.

Results

The experimental results indicate that the proposed approach can effectively find the fault type with 99.85% accuracy. Besides, it successfully determines the fault severity by accuracy values between 96.45 and 99.72%. GABoT can also determine the imbalance severity in the presence of three bearing faults.

Conclusion

Employing GA eliminated most of the redundant features and reduced the model execution time consumption. The results yielded that GABoT is a highly accurate model, and can be utilized in real-time fault diagnosis of rotating machinery.
A survey on applications of reinforcement learning in spatial resource allocation
Authors: Zhang Di , Wang Moyang , Mango Joseph , Li Xiang , Xu Xianrui
Published: June 2024

The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Despite the progress, reinforcement learning still faces hurdles when it comes to spatial resource allocation. There remains a gap in its ability to fully grasp the diversity and intricacy of real-world resources. The environmental models used in reinforcement learning may not always capture the spatial dynamics accurately. Moreover, in situations laden with strict and numerous constraints, reinforcement learning can sometimes fall short in offering feasible strategies. Consequently, this paper is dedicated to summarizing and reviewing current theoretical approaches and practical research that utilize reinforcement learning to address issues pertaining to spatial resource allocation. In addition, the paper accentuates several unresolved challenges that urgently necessitate future focus and exploration within this realm and proposes viable approaches for these challenges. This research furnishes valuable insights that may assist scholars in gaining a more nuanced understanding of the problems, opportunities, and potential directions concerning the application of reinforcement learning in spatial resource allocation.
FERONIA homologs in stress responses of horticultural plants: current knowledge and missing links
Authors: Huang Xinhua , Liu Yuhan , Jia Yanhong , Ji Lizhu , Luo Xiaomin , Tian Shiping , Chen Tong
Published: June 2024

Owing to its versatile roles in almost all aspects of plants, FERONIA (FER), a receptor-like kinase of the Catharanthus roseus receptor-like kinase 1-like (CrRLK1L) subfamily, has received extensive research interests during the past decades. Accumulating evidence has been emerged that FER homologs in horticultural crops also play crucial roles in reproductive biology and responses to environmental stimuli (abiotic and biotic stress factors). Here, we provide a review for the latest advances in the studies on FER homologs in modulating stress responses in horticultural crops, and further analyze the underlying mechanisms maintained by FER. Moreover, we also envisage the missing links in current work and provide a perspective for future studies on this star protein.
Acetylation modification in the regulation of macroautophagy
Authors: Huang Li , Guo Hongwei
Published: June 2024

Macroautophagy, commonly referred to as autophagy, is an evolutionarily conserved cellular process that plays a crucial role in maintaining cellular homeostasis. It orchestrates the delivery of dysfunctional or surplus cellular materials to the vacuole or lysosome for degradation and recycling, particularly during adverse conditions. Over the past few decades, research has unveiled intricate regulatory mechanisms governing autophagy through various post-translational modifications (PTMs). Among these PTMs, acetylation modification has emerged as a focal point in yeast and animal studies. It plays a pivotal role in autophagy by directly targeting core components within the central machinery of autophagy, including autophagy initiation, nucleation, phagophore expansion, and autophagosome maturation. Additionally, acetylation modulates autophagy at the transcriptional level by modifying histones and transcription factors. Despite its well-established significance in yeast and mammals, the role of acetylation in plant autophagy remains largely unexplored, and the precise regulatory mechanisms remain enigmatic. In this comprehensive review, we summarize the current understanding of the function and underlying mechanisms of acetylation in regulating autophagy across yeast, mammals, and plants. We particularly highlight recent advances in deciphering the impact of acetylation on plant autophagy. These insights not only provide valuable guidance but also inspire further scientific inquiries into the intricate role of acetylation in plant autophagy.
Unraveling the Atmospheric Energy Input and Ionization Due To EMIC‐Driven Electron Precipitation From ELFIN Observations
Authors: Capannolo L. , Marshall R. , Li W. , Berland G. , Duderstadt K. , Sivadas N. , Turner D. L. , Angelopoulos V.
Published: June 2024

Energetic electron precipitation (EEP) from the radiation belts into Earth's atmosphere leads to several profound effects (e.g., enhancement of ionospheric conductivity, possible acceleration of ozone destruction processes). An accurate quantification of the energy input and ionization due to EEP is still lacking due to instrument limitations of low‐Earth‐orbit satellites capable of detecting EEP. The deployment of the Electron Losses and Fields InvestigatioN (ELFIN) CubeSats marks a new era of observations of EEP with an improved pitch‐angle (0°–180°) and energy (50 keV–6 MeV) resolution. Here, we focus on the EEP recorded by ELFIN coincident with electromagnetic ion cyclotron (EMIC) waves, which play a major role in radiation belt electron losses. The EMIC‐driven EEP (∼200 keV–∼2 MeV) exhibits a pitch‐angle distribution (PAD) that flattens with increasing energy, indicating more efficient high‐energy precipitation. Leveraging the combination of unique electron measurements from ELFIN and a comprehensive ionization model known as Boulder Electron Radiation to Ionization (BERI), we quantify the energy input of EMIC‐driven precipitation (on average, ∼3.3 × 10−2 erg/cm2/s), identify its location (any longitude, 50°–70° latitude), and provide the expected range of ion‐electron production rate (on average, 100–200 pairs/cm3/s), peaking in the mesosphere—a region often overlooked. Our findings are crucial for improving our understanding of the magnetosphere‐ionosphere‐atmosphere system as they accurately specify the contribution of EMIC‐driven EEP, which serves as a crucial input to state‐of‐the‐art atmospheric models (e.g., WACCM) to quantify the accurate impact of EMIC waves on both the atmospheric chemistry and dynamics.
Malaria Transmission in Africa Shifts with the Climate—and Hydrology
Authors: Wilke Carolyn
Published: June 2024

Rainfall data alone can’t predict where malaria may pop up. Factoring in hydrological processes helps researchers paint a more nuanced picture of transmission.
Two-photon emission from a superlattice-based superconducting light-emitting structure
Authors: Bouscher Shlomi , Panna Dmitry , Jacovi Ronen , Jabeen Fauzia , Schneider Christian , Höfling Sven , Hayat Alex
Published: June 2024

Superconductor-semiconductor hybrid devices can bridge the gap between solid-state-based and photonics-based quantum systems, enabling new hybrid computing schemes, offering increased scalability and robustness. One example for a hybrid device is the superconducting light-emitting diode (SLED). SLEDs have been theoretically shown to emit polarization-entangled photon pairs by utilizing radiative recombination of Cooper pairs. However, the two-photon nature of the emission has not been shown experimentally before. We demonstrate two-photon emission in a GaAs/AlGaAs SLED. Measured electroluminescence spectra reveal unique two-photon superconducting features below the critical temperature (Tc), while temperature-dependent photon-pair correlation experiments (g(2)(τ,T)) demonstrate temperature-dependent time coincidences below Tc between photons emitted from the SLED. Our results pave the way for compact and efficient superconducting quantum light sources and open new directions in light-matter interaction studies.
Simple and robust 3D MINFLUX excitation with a variable phase plate
Authors: Deguchi Takahiro , Ries Jonas
Published: June 2024

MINFLUX has achieved extraordinary resolution in superresolution imaging and single fluorophore tracking. It is based on localizing single fluorophores by rapid probing with a patterned beam that features a local intensity minimum. Current implementations, however, are complex and expensive and are limited in speed and robustness. Here, we show that a combination of an electro-optical modulator with a segmented birefringent element such as a spatial light modulator produces a variable phase plate for which the phase can be scanned on the MHz timescale. Bisected or top-hat phase patterns generate high-contrast compact excitation point-spread functions for MINFLUX localization in the x, y, and z-direction, respectively, which can be scanned across a fluorophore within a microsecond, switched within 60 microseconds and alternated among different excitation wavelengths. We discuss how to compensate for non-optimal performance of the components and present a robust 3D and multi-color MINFLUX excitation module, which we envision as an integral component of a high-performance and cost-effective open-source MINFLUX.
Neurocognition and NMDAR co-agonists pathways in individuals with treatment resistant first-episode psychosis: a 3-year follow-up longitudinal study
Authors: Camporesi Sara , Xin Lijing , Golay Philippe , Eap Chin Bin , Cleusix Martine , Cuenod Michel , Fournier Margot , Hashimoto Kenji , Jenni Raoul , Ramain Julie , Restellini Romeo , Solida Alessandra , Conus Philippe , Do Kim Q. , Khadimallah Ines
Published: June 2024

This study aims to determine whether 1) individuals with treatment-resistant schizophrenia display early cognitive impairment compared to treatment-responders and healthy controls and 2) N-methyl-D-aspartate-receptor hypofunction is an underlying mechanism of cognitive deficits in treatment-resistance. In this case‒control 3-year-follow-up longitudinal study, n = 697 patients with first-episode psychosis, aged 18 to 35, were screened for Treatment Response and Resistance in Psychosis criteria through an algorithm that assigns patients to responder, limited-response or treatment-resistant category (respectively resistant to 0, 1 or 2 antipsychotics). Assessments at baseline: MATRICS Consensus Cognitive Battery; N-methyl-D-aspartate-receptor co-agonists biomarkers in brain by MRS (prefrontal glutamate levels) and plasma (D-serine and glutamate pathways key markers). Patients were compared to age- and sex-matched healthy controls (n = 114). Results: patient mean age 23, 27% female. Treatment-resistant (n = 51) showed lower scores than responders (n = 183) in processing speed, attention/vigilance, working memory, verbal learning and visual learning. Limited responders (n = 59) displayed an intermediary phenotype. Treatment-resistant and limited responders were merged in one group for the subsequent D-serine and glutamate pathway analyses. This group showed D-serine pathway dysregulation, with lower levels of the enzymes serine racemase and serine-hydroxymethyltransferase 1, and higher levels of the glutamate-cysteine transporter 3 than in responders. Better cognition was associated with higher D-serine and lower glutamate-cysteine transporter 3 levels only in responders; this association was disrupted in the treatment resistant group. Treatment resistant patients and limited responders displayed early cognitive and persistent functioning impairment. The dysregulation of NMDAR co-agonist pathways provides underlying molecular mechanisms for cognitive deficits in treatment-resistant first-episode psychosis. If replicated, our findings would open ways to mechanistic biomarkers guiding response-based patient stratification and targeting cognitive improvement in clinical trials.
The Caspase-Activated DNase drives inflammation and contributes to defense against viral infection
Authors: Moeed Abdul , Thilmany Nico , Beck Frederic , Puthussery Bhagya K. , Ortmann Noemi , Haimovici Aladin , Badr M. Tarek , Haghighi Elham Bavafaye , Boerries Melanie , Öllinger Rupert , Rad Roland , Kirschnek Susanne , Gentle Ian E. , Donakonda Sainitin , Petric Philipp P. , Hummel Jonas F. , Pfaffendorf Elisabeth , Zanetta Paola , Schell Christoph , Schwemmle Martin , Weber Arnim , Häcker Georg
Published: June 2024

Mitochondria react to infection with sub-lethal signals in the apoptosis pathway. Mitochondrial signals can be inflammatory but mechanisms are only partially understood. We show that activation of the caspase-activated DNase (CAD) mediates mitochondrial pro-inflammatory functions and substantially contributes to host defense against viral infection. In cells lacking CAD, the pro-inflammatory activity of sub-lethal signals was reduced. Experimental activation of CAD caused transient DNA-damage and a pronounced DNA damage response, involving major kinase signaling pathways, NF-κB and cGAS/STING, driving the production of interferon, cytokines/chemokines and attracting neutrophils. The transcriptional response to CAD-activation was reminiscent of the reaction to microbial infection. CAD-deficient cells had a diminished response to viral infection. Influenza virus infected CAD-deficient mice displayed reduced inflammation in lung tissue, higher viral titers and increased weight loss. Thus, CAD links the mitochondrial apoptosis system and cell death caspases to host defense. CAD-driven DNA damage is a physiological element of the inflammatory response to infection.
Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design
Authors: Yamamoto Akiyasu , Tokuta Shinnosuke , Ishii Akimitsu , Yamanaka Akinori , Shimada Yusuke , Ainslie Mark D.
Published: June 2024

Iron-based high-temperature (high-T
c) superconductors have good potential to serve as materials in next-generation superstrength quasipermanent magnets owing to their distinctive topological and superconducting properties. However, their unconventional high-T
c superconductivity paradoxically associates with anisotropic pairing and short coherence lengths, causing challenges by inhibiting supercurrent transport at grain boundaries in polycrystalline materials. In this study, we employ machine learning to manipulate intricate polycrystalline microstructures through a process design that integrates researcher- and data-driven approaches via tailored software. Our approach results in a bulk Ba0.6K0.4Fe2As2 permanent magnet with a magnetic field that is 2.7 times stronger than that previously reported. Additionally, we demonstrate magnetic field stability exceeding 0.1 ppm/h for a practical 1.5 T permanent magnet, which is a vital aspect of medical magnetic resonance imaging. Nanostructural analysis reveals contrasting outcomes from data- and researcher-driven processes, showing that high-density defects and bipolarized grain boundary spacing distributions are primary contributors to the magnet’s exceptional strength and stability.
Developmental origin of oligodendrocytes determines their function in the adult brain
Authors: Foerster Sarah , Floriddia Elisa M. , van Bruggen David , Kukanja Petra , Hervé Bastien , Cheng Shangli , Kim Eosu , Phillips Benjamin U. , Heath Christopher J. , Tripathi Richa B. , Call Cody , Bartels Theresa , Ridley Katherine , Neumann Björn , López-Cruz Laura , Crawford Abbe H. , Lynch Cian J. , Serrano Manuel , Saksida Lisa , Rowitch David H. , Möbius Wiebke , Nave Klaus-Armin , Rasband Matthew N. , Bergles Dwight E. , Kessaris Nicoletta , Richardson William D. , Bussey Timothy J. , Zhao Chao , Castelo-Branco Gonçalo , Franklin Robin J. M.
Published: June 2024

In the mouse embryonic forebrain, developmentally distinct oligodendrocyte progenitor cell populations and their progeny, oligodendrocytes, emerge from three distinct regions in a spatiotemporal gradient from ventral to dorsal. However, the functional importance of this oligodendrocyte developmental heterogeneity is unknown. Using a genetic strategy to ablate dorsally derived oligodendrocyte lineage cells (OLCs), we show here that the areas in which dorsally derived OLCs normally reside in the adult central nervous system become populated and myelinated by OLCs of ventral origin. These ectopic oligodendrocytes (eOLs) have a distinctive gene expression profile as well as subtle myelination abnormalities. The failure of eOLs to fully assume the role of the original dorsally derived cells results in locomotor and cognitive deficits in the adult animal. This study reveals the importance of developmental heterogeneity within the oligodendrocyte lineage and its importance for homeostatic brain function.
Distinct origin and region-dependent contribution of stromal fibroblasts to fibrosis following traumatic injury in mice
Authors: Holl Daniel , Hau Wing Fung , Julien Anais , Banitalebi Shervin , Kalkitsas Jannis , Savant Soniya , Llorens-Bobadilla Enric , Herault Yann , Pavlovic Guillaume , Amiry-Moghaddam Mahmood , Dias David Oliveira , Göritz Christian
Published: June 2024

Fibrotic scar tissue formation occurs in humans and mice. The fibrotic scar impairs tissue regeneration and functional recovery. However, the origin of scar-forming fibroblasts is unclear. Here, we show that stromal fibroblasts forming the fibrotic scar derive from two populations of perivascular cells after spinal cord injury (SCI) in adult mice of both sexes. We anatomically and transcriptionally identify the two cell populations as pericytes and perivascular fibroblasts. Fibroblasts and pericytes are enriched in the white and gray matter regions of the spinal cord, respectively. Both cell populations are recruited in response to SCI and inflammation. However, their contribution to fibrotic scar tissue depends on the location of the lesion. Upon injury, pericytes and perivascular fibroblasts become activated and transcriptionally converge on the generation of stromal myofibroblasts. Our results show that pericytes and perivascular fibroblasts contribute to the fibrotic scar in a region-dependent manner.
A materials terminology knowledge graph automatically constructed from text corpus
Authors: Zhang Yuwei , Chen Fangyi , Liu Zeyi , Ju Yunzhuo , Cui Dongliang , Zhu Jinyi , Jiang Xue , Guo Xi , He Jie , Zhang Lei , Zhang Xiaotong , Su Yanjing
Published: June 2024

A scalable, reusable, and broad-coverage unified material knowledge representation shows its importance and will bring great benefits to data sharing among materials communities. A knowledge graph (KG) for materials terminology, which is a formal collection of term entities and relationships, is conceptually important to achieve this goal. In this work, we propose a KG for materials terminology, named Materials Genome Engineering Database Knowledge Graph (MGED-KG), which is automatically constructed from text corpus via natural language processing. MGED-KG is the most comprehensive KG for materials terminology in both Chinese and English languages, consisting of 8,660 terms and their explanations. It encompasses 11 principal categories, such as Metals, Composites, Nanomaterials, each with two or three levels of subcategories, resulting in a total of 235 distinct category labels. For further application, a knowledge web system based on MGED-KG is developed and shows its great power in improving data sharing efficiency from the aspects of query expansion, term, and data recommendation.
Electroanalytical analysis of phenol oxidation using bacteria immobilized by a polycaprolactone coating on the copper electrode surface
Authors: Moutcine Abdelaziz , Laghlimi Charaf , Ziat Younes , Isaad Jalal , El Bahraoui Soumia , Chtaini Abdelilah
Published: June 2024

The copper electrode modified by bacteria immobilised by a polycaprolactone film was successfully developed by electropolymerisation for the purpose of determining the presence of phenol. Electrochemical techniques such as square-wave voltammetry (SWV), cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were used to characterize the electrochemical properties of the Cu-polymer/bacteria electrode. The results show that the intensity of the phenol oxidation peak increases with concentration, allowing us to obtain good analytical results with DL of 2.156 × 10–7 M and QL which is 7.2 × 10–7 M , confirming that the biosensor has excellent electroanalytical activity for phenol oxidation, with good stability and a wide linear range. Our electrode is based on a easily available and inexpensive material, as well as on its simple preparation, which has demonstrated high performance for phenol.
Impacts of thyme and/or garlic oils on growth, immunity, antioxidant and net farm income in Damascus goats
Authors: Imbabi Tharwat , Hassan Tamer M. M. , Osman Ali , El Aziz Ayman H. Abd , Tantawi Abuelkassem A. , Nasr Mohammed A. F.
Published: June 2024

This study aimed to evaluate the impact of thyme and/or garlic oil administration on growth performance, immunity, antioxidant, biochemical parameters, and net farm income of Damascus goats. Forty weaned Damascus goats were allocated into four groups. The first group was the control without oral administration, while the 2nd (Th), 3rd (Gr), and 4th (ThGr) groups were orally administrated by (2 ml/goat/day) of thyme oil, garlic oil and their mixture (1:1), respectively during the whole experiment period. The final body weight of goats orally administered oil mixture was the heaviest group, it was 10, 4.5 and 3.5% than the control, Th. and Gr. groups, respectively with better feed conversion ratio and high net farm income. Goats of ThGr. group revealed the best immunity, antioxidant and general health condition than the control group with 50% reduction of MDA. Liver (AST, 33% and ALT, 38%) and kidney (creatinine, 88%) functions improved by oils mixtures orally administration compared with the control group. LDL, triglyceride and cholesterol were reduced by 47, 33 and 21% compared with the control group, respectively. Thus, mixture oil administration (thyme and garlic at the ratio of 1:1, 2 ml/goat/day) improved growth (10%), antioxidant status (MDA 50%), liver (AST, 33% and ALT, 38%), kidney function (creatinine, 88%), the FCR (17.4%) and net farm income (21%), of Damascus goats.
Enhanced efficiency of bifacial perovskite solar cells using computational study
Authors: Hossain Mohammad Istiaque , Chelvanathan Puvaneswaran , Khandakar Amith , Thomas Kevin , Rahman Ahasanur , Mansour Said
Published: June 2024

The most rapidly expanding type of solar cells are the Perovskite Solar Cells (PSCs), because of its high device performance, ease of synthesis, high open-circuit voltage, and affordability. Despite these advantages, the development of perovskite-based solar cells continues to be impeded by the issues with perovskite stability and the utilization of the hazardous heavy element lead (Pb). The study emphasizes on the bifacial structure that maintains the conventional absorber layer and electron transport layer (ETL) in the optimized PSC structure. This study employs SCAPS software for device simulation to comprehensively analyze how various parameters affect the performance of solar cells. Additionally, doping concentration variation in both ETL and HTL are explored. The simulation reveals that changing device structure from monofacial to bifacial significantly influences PSC performance, demonstrating that optimizing individual layers effectively enhances overall solar cell performance. The optimized structure achieves impressive PSC performance metrics through parametric analysis, such as voltage (VOC) of 1.18 V, fill factor (FF) of 82.24%, current density (JSC) of 27.12 mA/cm2, power conversion efficiency (PCE) of 27.90% for an incident solar spectrum from the ETL side, and power conversion efficiency (PCE) of 19.86% for an incident solar spectrum from the HTL side, the calculated bifaciality factor (BF) for this structure is 71.18%.
BCG priming followed by a novel interleukin combination activates Natural Killer cells to selectively proliferate and become anti-tumour long-lived effectors
Authors: Felgueres María-José , Esteso Gloria , García-Jiménez Álvaro F. , Dopazo Ana , Aguiló Nacho , Mestre-Durán Carmen , Martínez-Piñeiro Luis , Pérez-Martínez Antonio , Reyburn Hugh T. , Valés-Gómez Mar
Published: June 2024

The short-lived nature and heterogeneity of Natural Killer (NK) cells limit the development of NK cell-based therapies, despite their proven safety and efficacy against cancer. Here, we describe the biological basis, detailed phenotype and function of long-lived anti-tumour human NK cells (CD56highCD16+), obtained without cell sorting or feeder cells, after priming of peripheral blood cells with Bacillus Calmette-Guérin (BCG). Further, we demonstrate that survival doses of a cytokine combination, excluding IL18, administered just weekly to BCG-primed NK cells avoids innate lymphocyte exhaustion and leads to specific long-term proliferation of innate cells that exert potent cytotoxic function against a broad range of solid tumours, mainly through NKG2D. Strikingly, a NKG2C+CD57-FcεRIγ+ NK cell population expands after BCG and cytokine stimulation, independently of HCMV serology. This strategy was exploited to rescue anti-tumour NK cells even from the suppressor environment of cancer patients’ bone marrow, demonstrating that BCG confers durable anti-tumour features to NK cells.
A novel smart hybrid multimorph piezoelectric spherical shell cloak for broadband near-perfect underwater acoustic camouflage applications
Authors: Hasheminejad Seyyed M. , Kasaeisani Ali
Published: June 2024

Non-visual auditory camouflage plays a major role in the art of underwater deception. In this work, a hybrid active/semi-active omnidirectional cloaking shell structure composed of alternate complementary piezoelectric and smart viscoelastic (PZT/SVE) actuator layers is proposed that can effectively conceal a three dimensional underwater macroscopic object from broadband incident sound waves. The smart hybrid structure incorporates a finite sequence of fully active parallel-connected multimorph PZT constraining layers inter-stacked with semi-active SVE core layers both of which are collaboratively operative in the framework of a Particle Swarm Optimized (PSO) multiple-input multiple-output active damping control (MIMO-ADC) scheme. The elasto-acoustic modeling of the problem is conducted by coupling the spatial state space methodology based on the classical three-dimensional exact piezoelasticity theory with the wave equations for the inner and outer acoustic domains. The acoustic cloaking performance of proposed configuration is evaluated for four distinct classes of highly functional SVE interlayer materials with tunable (field-dependent) rheological properties, namely, magnetorheological elastomer (MRE), shape memory polymer (SMP), electrorheological fluid (ERF), and magnetorheological shear thickening polishing fluid (MRSTPF). Extensive numerical results reveal significant broadband reductions of the far-field backscattering amplitude in the ($$\left|{f}_{\infty }\left(\theta =\pi ,{k}_{\text{ex}}{R}_{\text{ex }}\right)\right|)$$

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as well as the percentage error of external cloaked field $$(\%\text{Err})$$

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by incorporating a sufficient number of smart multimorph PZT/SVE material layers. Furthermore, it is concluded that comparable low frequency acoustic cloaking effects is possible without expenditure of any external energy just by employing the entirely inactive MRSTPF-based cloak as an alternative to the semiactive or fully active multimorph PZT/SVE cloaks. The outcome of proposed study can advantageously serve as the first step towards practical development and experimental implementation of future high performance smart acoustic cloaking devices with expanded broadband near-perfect omnidirectional invisibility for three dimensional objects of diverse geometries.
Unveiling bacterial communication with a MATLAB GUI implementing the diffusion-based quorum sensing model
Authors: Singh Urvashi , Saifi Zeeshan , Tirumalai Prem Saran , Krishnananda Soami Daya
Published: June 2024

Bacteria employ quorum sensing as a remarkable mechanism for coordinating behaviors and communicating within their communities. In this study, we introduce a MATLAB Graphical User Interface (GUI) that offers a versatile platform for exploring the dynamics of quorum sensing. Our computational framework allows for the assessment of quorum sensing, the investigation of parameter dependencies, and the prediction of minimum biofilm thickness required for its initiation. A pivotal observation from our simulations underscores the pivotal role of the diffusion coefficient in quorum sensing, surpassing the influence of bacterial cell dimensions. Varying the diffusion coefficient reveals significant fluctuations in autoinducer concentration, highlighting its centrality in shaping bacterial communication. Additionally, our GUI facilitates the prediction of the minimum biofilm thickness necessary to trigger quorum sensing, a parameter contingent on the diffusion coefficient. This feature provides valuable insights into spatial constraints governing quorum sensing initiation. The interplay between production rates and cell concentrations emerges as another critical facet of our study. We observe that higher production rates or cell concentrations expedite quorum sensing, underscoring the intricate relationship between cell communication and population dynamics in bacterial communities. While our simulations align with mathematical models reported in the literature, we acknowledge the complexity of living organisms, emphasizing the value of our GUI for standardizing results and facilitating early assessments of quorum sensing. This computational approach offers a window into the environmental conditions conducive to quorum sensing initiation, encompassing parameters such as the diffusion coefficient, cell concentration, and biofilm thickness. In conclusion, our MATLAB GUI serves as a versatile tool for understanding the diverse aspects of quorum sensing especially for non-biologists. The insights gained from this computational framework advance our understanding of bacterial communication, providing researchers with the means to explore diverse ecological contexts where quorum sensing plays a pivotal role.
Developing a three stage coordinated approach to enhance efficiency and reliability of virtual power plants
Authors: Amissah Jeremiah , Abdel-Rahim Omar , Mansour Diaa-Eldin A. , Bajaj Mohit , Zaitsev Ievgen , Abdelkader Sobhy
Published: June 2024

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy.
Auditory gamma-band entrainment enhances default mode network connectivity in dementia patients
Authors: Lahijanian Mojtaba , Aghajan Hamid , Vahabi Zahra
Published: June 2024

Dementia, and in particular Alzheimer’s disease (AD), can be characterized by disrupted functional connectivity in the brain caused by beta-amyloid deposition in neural links. Non-pharmaceutical treatments for dementia have recently explored interventions involving the stimulation of neuronal populations in the gamma band. These interventions aim to restore brain network functionality by synchronizing rhythmic energy through various stimulation modalities. Entrainment, a newly proposed non-invasive sensory stimulation method, has shown promise in improving cognitive functions in dementia patients. This study investigates the effectiveness of entrainment in terms of promoting neural synchrony and spatial connectivity across the cortex. EEG signals were recorded during a 40 Hz auditory entrainment session conducted with a group of elderly participants with dementia. Phase locking value (PLV) between different intraregional and interregional sites was examined as an attribute of network synchronization, and connectivity of local and distant links were compared during the stimulation and rest trials. Our findings demonstrate enhanced neural synchrony between the frontal and parietal regions, which are key components of the brain’s default mode network (DMN). The DMN operation is known to be impacted by dementia’s progression, leading to reduced functional connectivity across the parieto-frontal pathways. Notably, entrainment alone significantly improves synchrony between these DMN components, suggesting its potential for restoring functional connectivity.
A fully automated classification of third molar development stages using deep learning
Authors: Milani Omid Halimi , Atici Salih Furkan , Allareddy Veerasathpurush , Ramachandran Vinitha , Ansari Rashid , Cetin Ahmet Enis , Elnagar Mohammed H.
Published: June 2024

Accurate classification of tooth development stages from orthopantomograms (OPG) is crucial for dental diagnosis, treatment planning, age assessment, and forensic applications. This study aims to develop an automated method for classifying third molar development stages using OPGs. Initially, our data consisted of 3422 OPG images, each classified and curated by expert evaluators. The dataset includes images from both Q3 (lower jaw left side) and Q4 (lower right side) regions extracted from panoramic images, resulting in a total of 6624 images for analysis. Following data collection, the methodology employs region of interest extraction, pre-filtering, and extensive data augmentation techniques to enhance classification accuracy. The deep neural network model, including architectures such as EfficientNet, EfficientNetV2, MobileNet Large, MobileNet Small, ResNet18, and ShuffleNet, is optimized for this task. Our findings indicate that EfficientNet achieved the highest classification accuracy at 83.7%. Other architectures achieved accuracies ranging from 71.57 to 82.03%. The variation in performance across architectures highlights the influence of model complexity and task-specific features on classification accuracy. This research introduces a novel machine learning model designed to accurately estimate the development stages of lower wisdom teeth in OPG images, contributing to the fields of dental diagnostics and treatment planning.