bluuebunny/crossref_metadata_2025 huggingface dataset:
Cell‐type dependence of necroptosis pathways triggered by viral infection
Authors: Koehler Heather , Titus Derek , Lawson Crystal
Published: December 2023

Necroptosis, a potent host defense mechanism, limits viral replication and pathogenesis through three distinct initiation pathways. Toll‐like receptor 3 (TLR3) via TIR‐domain‐containing adapter‐inducing interferon‐β (TRIF), Z‐DNA‐binding protein 1 (ZBP1) and tumor necrosis factor (TNF)α mediate necroptosis, with ZBP1 and TNF playing pivotal roles in controlling viral infections, with the role of TLR3–TRIF being less clear. ZBP1‐mediated necroptosis is initiated when host ZBP1 senses viral Z‐form double stranded RNA and recruits receptor‐interacting serine/threonine‐protein kinase 3 (RIPK3), driving a mixed lineage kinase domain‐like pseudokinase (MLKL)‐dependent necroptosis pathway, whereas TNF‐mediated necroptosis is initiated by TNF signaling, which drives a RIPK1–RIPK3–MLKL pathway, resulting in necroptosis. Certain viruses (cytomegalovirus, herpes simplex virus and vaccinia) have evolved to produce proteins that compete with host defense systems, preventing programmed cell death pathways from being initiated. Two engineered viruses deficient of active forms of these proteins, murine cytomegalovirus M45mutRHIM and vaccinia virus E3∆Zα, trigger ZBP1‐dependent necroptosis in mouse embryonic fibroblasts. By contrast, when bone‐marrow‐derived macrophages are infected with the viruses, necroptosis is initiated predominantly through the TNF‐mediated pathway. However, when the TNF pathway is blocked by RIPK1 inhibitors or a TNF blockade, ZBP1‐mediated necroptosis becomes the prominent pathway in bone‐marrow‐derived macrophages. Overall, these data implicate a cell‐type preference for either TNF‐mediated or ZBP1‐mediated necroptosis pathways in host responses to viral infections. These preferences are important to consider when evaluating disease models that incorporate necroptosis because they may contribute to tissue‐specific reactions that could alter the balance of inflammation versus control of virus, impacting the organism as a whole.
Small‐molecule positive allosteric modulation of homomeric kainate receptors GluK1‐3: development of screening assays and insight into GluK3 structure
Authors: Bay Yasmin , Venskutonytė Raminta , Frantsen Stine M. , Thorsen Thor S. , Musgaard Maria , Frydenvang Karla , Francotte Pierre , Pirotte Bernard , Biggin Philip C. , Kristensen Anders S. , Boesen Thomas , Pickering Darryl S. , Gajhede Michael , Kastrup Jette S.
Published: December 2023

The kainate receptors GluK1‐3 (glutamate receptor ionotropic, kainate receptors 1–3) belong to the family of ionotropic glutamate receptors and are essential for fast excitatory neurotransmission in the brain, and are associated with neurological and psychiatric diseases. How these receptors can be modulated by small‐molecule agents is not well understood, especially for GluK3. We show that the positive allosteric modulator BPAM344 can be used to establish robust calcium‐sensitive fluorescence‐based assays to test agonists, antagonists, and positive allosteric modulators of GluK1‐3. The half‐maximal effective concentration (EC50) of BPAM344 for potentiating the response of 100 μm kainate was determined to be 26.3 μm for GluK1, 75.4 μm for GluK2, and 639 μm for GluK3. Domoate was found to be a potent agonist for GluK1 and GluK2, with an EC50 of 0.77 and 1.33 μm, respectively, upon co‐application of 150 μm BPAM344. At GluK3, domoate acts as a very weak agonist or antagonist with a half‐maximal inhibitory concentration (IC50) of 14.5 μm, in presence of 500 μm BPAM344 and 100 μm kainate for competition binding. Using H523A‐mutated GluK3, we determined the first dimeric structure of the ligand‐binding domain by X‐ray crystallography, allowing location of BPAM344, as well as zinc‐, sodium‐, and chloride‐ion binding sites at the dimer interface. Molecular dynamics simulations support the stability of the ion sites as well as the involvement of Asp761, Asp790, and Glu797 in the binding of zinc ions. Using electron microscopy, we show that, in presence of glutamate and BPAM344, full‐length GluK3 adopts a dimer‐of‐dimers arrangement.
A novel reddish‐orange emitting NaSrBiTeO6:Sm3+ phosphor with high moisture resistance and thermostability for horticultural light emitting diode applications
Authors: Tang Rong , Yang Yue , Yang Ya , Ouyang Xuan , Che Junzhi , Chen Shigao , Yao Xuemei , Deng Bin , Yu Ruijin
Published: December 2023

A sequence of reddish‐orange emitting NaSrBi(1−x)TeO6:xSm3+ phosphors were successfully composited by a favorable solid‐phase chemical process. The sintering temperature is optimized as 850°C. NaSrBiTeO6 (NSBTO):Sm3+ samples were assigned to the cubic double‐perovskite structure, exhibiting the space group of Fmm (225). Photoluminescence emission spectra display the main peaks at 562, 598, 645, and 705 nm at 404 nm excitation. Among them, the most intense emission peak is at 645 nm. The concentration‐quenching mechanism of Sm3+ follows the interaction between electric dipoles and quadrupoles. The asymmetric ratio (I(4G5/2‐6H9/2)/I(4G5/2‐6H5/2) of NSBTO:2 mol%Sm3+ phosphor is extracted as 4.96. The optimum NSBTO:2 mol%Sm3+ phosphor has excellent thermal stability with a remained emission intensity riches 93.61% (420 K). After testing with high‐pressure water steam at various periods, the phosphor remains chemically stable for moisture resistance. For the NSBTO:2 mol%Sm3+ phosphor, the satisfactory color purity and low correlated color temperature (CCT) are obtained with 99.9% and 1494 K, respectively. The electroluminescence spectra of the prepared red light emitting diode (LED) are in conformity with the phytochrome absorption spectra of chlorophyll a, chlorophyll b, PFR, and PR. The white light‐emitting diode (w‐LED) is also perfectly packaged, obtaining an efficient color rendering index (Ra) of 92.12 and CCT of 5553 K. These exceeding optical properties certified that NaSrBi(1−x)TeO6:xSm3+ demonstrates promising prospects and can be efficiently applied in horticultural LED applications.
Atomic arrangement analysis and crystal growth mechanism of HAp nanocrystal synthesized from amorphous calcium phosphate
Authors: Nakashima Kouichi , Misawa Sakino , Kobayashi Yoshio , Ishigaki Toru , Ishikawa Yoshihisa , Yin Shu , Kakihana Masato , Goto Tomoyo , Sekino Tohru
Published: December 2023

The crystal growth of hydroxyapatite (HAp) using a solvothermal method was investigated under various reaction conditions. The purpose of the present study is the synthesis of HAp nanocrystals with multiple facets and the atomic arrangement for various orientations of the crystal was clarified. HAp nanorods were obtained at 200°C after 24 h using a reaction medium of water. The atomic arrangement in HAp nanorods was examined for a characteristic facet in the direction of [21−13−] and [0110−] incidences and crystal orientation along the c‐axis of HAp nanorod was confirmed using electron microscopy. The present study clarifies the HAp crystal growth mechanism, where the adsorption of cations or anions onto the crystal facet determines the HAp crystal shape. The crystals of HAp grew along the c‐axis and became rod‐shaped suggesting that cations were adsorbed predominantly over anions.
Amino acid‐specific nitrogen stable isotope analysis reveals the trophic behavior of Icelandic fin whales in winter and suggests variable feeding strategies
Authors: Rita Diego , Borrell Asunción , Wodarg Dirk , Víkingsson Gísli , García‐Vernet Raquel , Aguilar Alex , Loick‐Wilde Natalie
Published: December 2023

Fin whales (Balaenoptera physalus) aggregate west of Iceland during summer, where they feed mostly on krill, but their winter distribution is poorly known. This study investigates the winter behavior of fin whales that summer off Iceland by analyzing amino acid‐specific nitrogen isotope ratios in the baleen plates. This technique is increasingly used in large marine vertebrates to discriminate between changes in trophic position and changes in isotopic baseline levels, while ruling out the effect of fasting on these patterns. Analyses were conducted on samples extracted from two baleen plate segments: one corresponding to winter, another to summer. The trophic position was higher and slightly more variable in winter (3.6 ± 0.7) than in summer (3.0 ± 0.4), which indicates that during winter fin whales tend to feed on fish and are more generalist in feeding habits. Interestingly, the δ15NPhevalues were not lower in winter than in summer (winter: 5.2‰ ± 2.4‰; summer: 4.0‰ ± 2.1‰). This was unexpected because baseline δ15N values (e.g., zooplankton) tend to be lower at low latitudes, where fin whales are believed to spend the winter. Fin whales may remain in relatively high latitudes during winter or visit low‐latitude areas with locally higher than average δ15N values, such as upwelling areas.
Mild asthma: Conundrums, complexities and the need to customize care
Authors: Jenkins Christine R.
Published: December 2023

Mild and moderate asthma cover a wide range of asthma presentations, phenotypes and symptom burden, and account for the majority of people with asthma worldwide. Mild asthma has been difficult to define because of its heterogeneity and wide spectrum of impact and outcomes, including being associated with severe exacerbations. Assessment of mild–moderate asthma is best made by combining asthma symptom control and exacerbation risk as the principle means by which to determine treatment needs. Incontrovertible evidence and guidelines support treatment initiation with anti‐inflammatory medication, completely avoiding reliever‐only treatment of mild asthma. Shared decision making with patients and a treatable traits approach will ensure that a holistic approach is taken to maximize patient outcomes. Most importantly, mild asthma should be regarded as a reversible, potentially curable condition, remaining in long‐term remission through minimizing triggers and optimizing care.
Retaining Teachers Through Building Confidence in Collaboration Skills: Promoting 21st-Century Teaching Skills in Teacher Education
Authors: Shidler Linda
Published: December 2023

In 2023, the United States faces a monumental teacher shortage. Murdock (2022) has quoted an estimated 36,504 full-time teaching positions as unfilled, with another 163,650 positions filled by underqualified individuals or by those teaching outside of their field of study. Although this figure falls short of the 53 percent of all schools reported by the National Education Association (NEA) as understaffed, it is a figure of consequence for educators and schools (Walker, 2022). The NEA has also reported that 55percent of those in teaching and in educational support positions have considered leaving the profession earlier than planned. In 2018, the Organization for Economic Co-operation and Development (OECD) conducted their Teaching and Learning International Survey (TALIS) and found similar results. In the survey, 14 percent of teachers aged 50 years or younger stated their desire to leave the profession within the next five years.
Energy Transfer and Dissipation in Combined-Stiffness Nonlinear Energy Sink Systems
Authors: Jun Wang , Zi-Jian Yang , Yun-Hao Zhang , Jian-Chao Zhang
Published: December 2023

Nonlinear energy sinks (NES) are highly efficient vibration energy absorption and dissipation devices, and play an important vibration-suppression role in many types of structures. In this study, the influence of parameters on the combined stiffness nonlinear energy sink system is revealed from the perspective of energy, in which combined-stiffness terms are composed of piecewise linear stiffness and cubic stiffness. First, the slow-varying derivative of the combined-stiffness nonlinear energy sink system is calculated based on the complexification-averaging and multiscale methods. Second, an approximate expression for the extreme points on the slow-invariant manifold (SIM) of the system is derived by polynomial approximation, and the energy dissipation equation of the combined-stiffness nonlinear energy sink system is derived. The impacts of the stiffness gap, piecewise linear stiffness coefficient, and cubic stiffness coefficient on the system are analyzed by studying the energy transfer efficiency equation. Additionally, the relationship between the damping ratio of the primary structure and the dissipation time is analyzed.
Faster Modular Composition
Authors: Neiger Vincent , Salvy Bruno , Schost Éric , Villard Gilles
Published: December 2023

A new Las Vegas algorithm is presented for the composition of two polynomials modulo a third one, over an arbitrary field. When the degrees of these polynomials are bounded by 
n
, the algorithm uses 
O
(
n
1.43
) field operations, breaking through the 3/2 barrier in the exponent for the first time. The previous fastest algebraic algorithms, due to Brent and Kung in 1978, require 
O
(
n
1.63
) field operations in general, and 
n

3/2+
o
(1)

field operations in the special case of power series over a field of large enough characteristic. If cubic-time matrix multiplication is used, the new algorithm runs in 
n

5/3+
o
(1)

operations, while previous ones run in 
O
(
n
2
) operations.

Our approach relies on the computation of a matrix of algebraic relations that is typically of small size. Randomization is used to reduce arbitrary input to this favorable situation.
Exploring Visual Relationships via Transformer-based Graphs for Enhanced Image Captioning
Authors: Li Jingyu , Mao Zhendong , Li Hao , Chen Weidong , Zhang Yongdong
Published: December 2023

Image captioning (IC), bringing vision to language, has drawn extensive attention. A crucial aspect of IC is the accurate depiction of visual relations among image objects. Visual relations encompass two primary facets: content relations and structural relations. Content relations, which comprise geometric positions content (i.e., distances and sizes) and semantic interactions content (i.e., actions and possessives), unveil the mutual correlations between objects. In contrast, structural relations pertain to the topological connectivity of object regions. Existing Transformer-based methods typically resort to geometric positions to enhance the visual relations, yet only using the shallow geometric content is unable to precisely cover actional content correlations and structural connection relations. In this article, we adopt a comprehensive perspective to examine the correlations between objects, incorporating both content relations (i.e., geometric and semantic relations) and structural relations, with the aim of generating plausible captions. To achieve this, first, we construct a geometric graph from bounding box features and a semantic graph from the scene graph parser to model the content relations. Innovatively, we construct a topology graph that amalgamates the sparsity characteristics of the geometric and semantic graphs, enabling the representation of image structural relations. Second, we propose a novel unified approach to enrich image relation representations by integrating semantic, geometric, and structural relations into self-attention. Finally, in the language decoding stage, we further leverage the semantic relation as prior knowledge to generate accurate words. Extensive experiments on MS-COCO dataset demonstrate the effectiveness of our model, with improvements of CIDEr from 128.6% to 136.6%. Codes have been released at
https://github.com/CrossmodalGroup/ER-SAN/tree/main/VG-Cap
.
Controlling Media Player with Hands: A Transformer Approach and a Quality of Experience Assessment
Authors: Floris Alessandro , Porcu Simone , Atzori Luigi
Published: December 2023

In this article, we propose a Hand Gesture Recognition (HGR) system based on a novel deep transformer (DT) neural network for media player control. The extracted hand skeleton features are processed by separate transformers for each finger in isolation to better identify the finger characteristics to drive the following classification. The achieved HGR accuracy (0.853) outperforms state-of-the-art HGR approaches when tested on the popular NVIDIA dataset. Moreover, we conducted a subjective assessment involving 30 people to evaluate the Quality of Experience (QoE) provided by the proposed DT-HGR for controlling a media player application compared with two traditional input devices, i.e., mouse and keyboard. The assessment participants were asked to evaluate objective (accuracy) and subjective (physical fatigue, usability, pragmatic quality, and hedonic quality) measurements. We found that (i) the accuracy of DT-HGR is very high (91.67%), only slightly lower than that of traditional alternative interaction modalities; and that (ii) the perceived quality for DT-HGR in terms of satisfaction, comfort, and interactivity is very high, with an average Mean Opinion Score (MOS) value as high as 4.4, whereas the alternative approaches did not reach 3.8, which encourages a more pervasive adoption of the natural gesture interaction.
WISP1 induces the expression of macrophage migration inhibitory factor in human lung fibroblasts through Src kinases and EGFR-activated signaling pathways
Authors: Christopoulou Maria-Elpida , Skandalis Spyros S. , Papakonstantinou Eleni , Stolz Daiana , Aletras Alexios J.
Published: December 2023

The present study demonstrates for the first time that Wnt1-inducible signaling protein 1 (WISP1) regulates migration inhibitory factor (MIF) expression and activity and identifies the main signaling pathways involved. The newly discovered WISP1-MIF axis may drive lung inflammation and could result in the design of novel targeted therapies in inflammatory lung diseases.
Cholesterol trafficking, lysosomal function, and atherosclerosis
Authors: Skeyni Alaa , Pradignac Alain , Matz Rachel L. , Terrand Jérôme , Boucher Philippe
Published: December 2023

Despite years of study and major research advances over the past 50 years, atherosclerotic diseases continue to rank as the leading global cause of death. Accumulation of cholesterol within the vascular wall remains the main problem and represents one of the early steps in the development of atherosclerotic lesions. There is a complex relationship between vesicular cholesterol transport and atherosclerosis, and abnormalities in cholesterol trafficking can contribute to the development and progression of the lesions. The dysregulation of vesicular cholesterol transport and lysosomal function fosters the buildup of cholesterol within various intracytoplasmic compartments, including lysosomes and lipid droplets. This, in turn, promotes the hallmark formation of foam cells, a defining feature of early atherosclerosis. Multiple cellular processes, encompassing endocytosis, exocytosis, intracellular trafficking, and autophagy, play crucial roles in influencing foam cell formation and atherosclerotic plaque stability. In this review, we highlight recent advances in the understanding of the intricate mechanisms of vesicular cholesterol transport and its relationship with atherosclerosis and discuss the importance of understanding these mechanisms in developing strategies to prevent or treat this prevalent cardiovascular disease.
Potentiation of force by extracellular potassium is not dependent on muscle length in mouse EDL muscle
Authors: Angelidis Angelos , Overgaard Kristian , Vandenboom Rene
Published: December 2023

Here, we investigated the length-dependence of twitch force potentiation by extracellular potassium in mouse extensor digitorum longus (EDL) in vitro, at 25°C. Potentiation magnitude did not display a statistically significant difference between the examined muscle lengths. These results describe, for the first time, the relationship of this form of potentiation with muscle length, thus furthering the understanding of how it is integrated in in vivo muscle function.
The slow-release adiponectin analog ALY688-SR modifies early-stage disease development in the D2.mdx mouse model of Duchenne muscular dystrophy
Authors: Bellissimo Catherine A. , Gandhi Shivam , Castellani Laura N. , Murugathasan Mayoorey , Delfinis Luca J. , Thuhan Arshdeep , Garibotti Madison C. , Seo Yeji , Rebalka Irena A. , Hsu Henry H. , Sweeney Gary , Hawke Thomas J. , Abdul-Sater Ali A. , Perry Christopher G. R.
Published: December 2023

There are limited therapies for the treatment of Duchenne muscular dystrophy. As fibrosis involves an accumulation of collagen that replaces muscle fibers, antifibrotics may help preserve muscle function. We report that the novel adiponectin receptor agonist ALY688-SR prevents fibrosis in the diaphragm of D2. mdx mice with short-term treatment early in disease progression. These responses were related to altered inflammation and mitochondrial functions and serve as a foundation for the development of this class of therapy.
Autologous mitochondrial transplantation in male mice as a strategy to prevent deleterious effects of peripheral ischemia-reperfusion
Authors: Boutonnet Lauréline , Mallard Joris , Charles Anne-Laure , Hucteau Elyse , Gény Bernard , Lejay Anne , Grandperrin Antoine
Published: December 2023

Ischemia-reperfusion injury leads to severe muscular damage. Even if prompt revascularization is the treatment of choice, muscular alterations can lead to severe sequalae as mitochondrial dysfunction. Accordingly, adjunctive strategies are needed to overcome the muscular damage. Mitochondrial transplantation has shown beneficial effects in cardiac ischemia-reperfusion, but its role in peripheral muscle is not well established. In this study, we found that mitochondrial transplantation partially restored muscular function when submitted to ischemia reperfusion.
Dynamic dysregulation of transcriptomic networks in brainstem autonomic nuclei during hypertension development in the female spontaneously hypertensive rat
Authors: Moss Alison , Kuttippurathu Lakshmi , Srivastava Ankita , Schwaber James S. , Vadigepalli Rajanikanth
Published: December 2023

Hypertension is a major disease and is the primary risk factor for cardiovascular complications and stroke. The gene expression changes in the central nervous system circuits driving hypertension are understudied. Here, we show that coordinated and region-specific gene expression changes occur in the brainstem autonomic circuits over time during the development of a high blood pressure phenotype in a rat model of human essential hypertension.
Temporal shifts to the gut microbiome associated with cognitive dysfunction following high-fat diet consumption in a juvenile model of traumatic brain injury
Authors: Smith Allie M. , Challagundla Lavanya , McGee Ian G. , Warfield Zyra J. , Santos Christiano Dos Santos E. , Garrett Michael R. , Grayson Bernadette E.
Published: December 2023

Traumatic brain injury (TBI) produces memory and learning difficulties. Diet profoundly influences the populations of gut microbiota. Following traumatic brain injury in a pediatric model consuming either a healthy or high-fat diet (HFD), significant shifts in bacterial populations occur, of which, some are associated with diet, whereas others are associated with neurocognitive performance. More work is needed to determine whether these microbes can therapeutically improve learning following trauma to the brain.
Peripheral Neuropathy Associated with Higher Mortality in Population with Chronic Kidney Disease: National Health and Nutrition Examination Surveys
Authors: Cai Xiao-Yu , Li Wei-Lan , Ge Shu-Wang , Xu Gang
Published: December 2023

Introduction: Peripheral neuropathy (PN), one of the commonest neurological complications of chronic kidney disease (CKD), was associated with physical limitation. Studies showed that a decrease in physical capability in patients with CKD is related with an increased risk of mortality. The objective of our research was to directly explore the relationship between PN and risk of mortality in patients with CKD. Method: 1,836 participants with CKD and 6,036 participants without CKD, which were classified by PN based on monofilament examination in National Health and Nutrition Examination Survey (NHANES), were collected from the 1999 to 2004 National Health and Nutrition Examination Surveys. Multivariable Cox proportional hazard models were conducted to assess the relationships of PN and deaths in patients with CKD and non-CKD. Results: During 14 years of a median follow-up from 1999 to 2015 and 2004 to 2015, 1,072 (58.4%) and 1,389 (23.0%) deaths were recorded in participants with CKD and without CKD, respectively. PN was related with increased all-cause mortality even after adjusting possible confounding factors in population with CKD (hazard ratio [HR] 1.34, 95% confidence interval [CI] 1.17–1.53) and without CKD (HR 1.27, 95% CI 1.12–1.43). And the adjusted HRs (95% CI) for cardiovascular mortality of the people with CKD and without CKD who suffered from PN were 1.42 (1.07, 1.90) and 1.23 (0.91, 1.67), respectively, versus those without PN. Conclusion: PN was related with a higher risk of all-cause and cardiovascular death in people with CKD, which clinically suggests that the adverse prognostic impact of PN in the CKD population deserves attention and is an important target for intervention.
Ultrastructural Details of Epiretinal Membrane Foveoschisis
Authors: Vogt Denise , Schumann Ricarda G. , Zaytseva Yulia , Wiecha Caroline , Wolf Armin , Priglinger Siegfried G. , Klaas Julian E.
Published: December 2023

Introduction: The aim of this study was to describe differences in the vitreomacular interface (VMI) in idiopathic epiretinal membrane (ERM) foveoschisis compared to macular pseudohole (MPH) and lamellar macular hole (LMH). Methods: We analysed surgically excised epiretinal material and internal limiting membrane (ILM) specimens obtained from 16 eyes of 16 patients with ERM foveoschisis (6 eyes), MPH (5 eyes), and LMH (5 eyes) during standard pars plana vitrectomy (PPV) with membrane peeling. The three entities were classified according to the newly introduced optical coherence tomography (OCT) terminology. Transmission electron microscopy (TEM) was used to describe the ultrastructural features. Results: We found fibrocellular epiretinal tissues in all samples analysed. However, the cell and collagen composition of the VMI differed between groups. Eyes with ERM foveoschisis were characterized by a higher number of cells, multilayered membranes, and thick strands of vitreous collagen embedding the major cell types of myofibroblasts compared to MPH. Eyes with MPH also showed a predominance of myofibroblasts, but these were located directly on the ILM with no collagen between the cells and the ILM. Eyes with LMH showed a thick, multilayered epiretinal proliferation consisting mainly of non-tractional glial cells, corresponding to hypodense epiretinal proliferation on OCT. Eyes with ERM foveoschisis and MPH were more likely to have incomplete PVD compared to LMH in terms of posterior hyaloid status. Discussion/Conclusion: Tractional ERMs in eyes with ERM foveoschisis and MPH differ in their ultrastructure. The main difference is in the amount and topographical distribution of vitreous collagen. However, the epiretinal cell types are predominantly myofibroblasts in both entities. This highlights the importance of distinguishing ERM foveoschisis from both MPH and LMH in terms of pathogenesis and surgical peeling procedures.
Response of Neuropeptides to Hunger Signals in Teleost
Authors: Yu Xiaozheng , Li Jiaqi , Guo Yixuan , Yu Yang , Cai Ran , Chen Benliang , Chen Minyao , Sun Caiyun , Li Wensheng
Published: December 2023

Introduction: The perception of hunger is a complex physiological process that requires precise coordination between the central and peripheral tissues. Methods: In this study, tilapia fasted for 24 h was chosen to establish a hunger model to study the mechanism of homeostasis recovery under the joint regulation of the central nervous system (CNS) and peripheral tissues. Results: The gastric and intestinal contents of tilapia were predominantly depleted after a fasting period of 9 h and 24 h, respectively. The serum glucose level significantly decreased at the 9-h and 24-h fasting, respectively, and the glucokinase-dependent glucosensing mechanism in the liver was identified as well as the significant activation of phospho-AMPK. However, fasting for 24 h did not activate glucosensing mechanisms and AMPK signaling pathways in the hypothalamus. On the other hand, significant reductions were observed in the mRNA levels of the lipid synthesis-related genes fas and accα, and the serum triglyceride levels as well. The mRNA levels of npy, agrp, pomc, and cart in the hypothalamus fluctuated during the fasting period without significant differences. With in situ hybridization npy signals upregulated in the ventral zone of posterior periventricular nucleus after 24-h fasting, pomc signals enhanced in the lateral tuberal nucleus. Based on the serum metabolomic analysis, the levels of branched-chain amino acids, butyrate, and short-chain acylcarnitine decreased, while those of medium- and long-chain acylcarnitine increased. Conclusion: Fasting for 24 h resulted in changes in npy and pomc signals within the hypothalamus and triggered the glucosensing mechanism in the liver of tilapia. This study is beneficial for elucidating the response of neuropeptides in the CNS to the changes of nutritional factors when hungry.
The Prognostic Impact of Renal Function Decline during Hospitalization for Heart Failure
Authors: Mayer, Jr. Otto , Bruthans Jan , Bílková Simona , Filipovský Jan
Published: December 2023

Introduction: We aimed to evaluate the prognostic impact of renal insufficiency and fluctuation of glomerular filtration observed during hospitalization for heart failure (HF). Methods: We followed 3,639 patients hospitalized for acute HF and assessed the mortality risk associated with moderate or severe renal insufficiency, either permanent or transient. Results: After adjustment, severe renal failure defined as estimated glomerular filtration (eGFR) <30 mL/min indicates ≈60% increase in 5-year mortality risk. Similar risk also had patients with only transient decline of eGFR to this range. In contrast, we did not observe any apparent mortality risk attributable to mild/moderate renal insufficiency (eGFR 30–59.9 mL/min), regardless of whether it was transient or permanent. Conclusion: Even transient severe renal failure during hospitalization indicates poor long-term prognosis of patients with manifested HF. In contrast, only moderate renal insufficiency observed during hospitalization has no additive long-term mortality impact.
Online Depiction of Urticaria Is Often Flawed and Does Not Reflect the Spectrum of Clinical Manifestation
Authors: Zhang Ditte Georgina , Sørensen Jennifer Astrup , Pedersen Nadja Højgaard , Ali Zarqa , Kocatürk Emek , Maurer Marcus , Thomsen Simon Francis
Published: December 2023

Introduction: The internet is a popular source of health information including images of disease manifestations. Online photographs of skin lesions may aid patients in identifying their disease, if these pictures are of good quality and of the disease they claim to show. If not, patients may be at risk of delayed diagnosis, misdiagnosis, and suboptimal treatment. For urticaria, the mismatch rate and quality of online pictures are unknown. The objective of this study was therefore to evaluate the content and quality of online images of urticaria. Methods: The search term “urticaria” was applied to Google Images and Shutterstock. The top 100 photographs from each search engine were retrieved on October 9th, 2022. Illustrations, drawings, and heavily edited photographs were excluded. Each image was evaluated for patient characteristics, characteristics of urticarial lesions, and image quality. Results: Across 194 unique images of urticaria (after removing duplicates), 35 (18.0%) did not depict urticarial lesions, and 38 (19.6%) were ambiguous. Less than two-thirds of images 121 (62.4%) showed bona fide urticarial lesions. Pictures of urticarial lesions under-represented children and did not reflect female preponderance of the disease. Images predominantly depicted urticaria lesions on Caucasian skin (59.8%) and were typical of spontaneous rather than inducible urticaria. Only 3 (1.5%) pictures showed angioedema, a common clinical sign in patients with urticaria. The overall quality of online urticaria pictures was mostly good or very good. Conclusion: Physicians and patients should be aware that one in five online pictures of urticaria does not show urticarial skin lesions, and children, females, non-Caucasian patients, inducible urticaria, and angioedema are under-represented. These findings should prompt efforts to improve the accuracy and representativeness of online urticaria pictures.
Morphologic Heterogeneity of Carcinoma with Signet Ring Cell Features at Different Primary Sites
Authors: Dahoud Wissam , Gullo Irene , Imam Rami , Tang Laura H.
Published: December 2023

Introduction: Signet ring cells (SRCs) may be observed in carcinomas from multiple primary sites. Elucidating unknown primaries from metastases with SRCs represents a diagnostic challenge. This study examined morphologic characteristics of adenocarcinomas with SRCs from stablished primary sites and described objective features, which can aid in identifying the site of origin. Methods: The series encompasses 257 cases of adenocarcinomas with SRCs from gastroesophageal junction (GEJ, n = 38), stomach (n = 48), pancreatobiliary system (n = 16), colorectum (n = 40), appendix (n = 32), breast (n = 41), and lung (n = 42). H&E sections were examined and scored using architectural and cytologic criteria. Morphometric analysis was performed using QuPath software. Results: Extracellular mucin was more abundant in GEJ, colorectal, and appendiceal carcinomas. Poorly cohesive morphology was the most frequent pattern in gastric and breast carcinomas. The cytoplasmic mucin/vacuole was predominantly clear and targetoid in breast carcinomas. Breast and gastric carcinomas showed the highest nuclear to cytoplasmic (N/C) ratio, whereas appendiceal carcinoma the lowest. Conclusion: Morphological evaluation (extracellular mucin, architectural patterns, and the nature of cytoplasmic mucin/vacuole) represents an important step to determine the cancer site of origin in adenocarcinomas with SRCs and guides further ancillary studies. Cytological morphometry may help further refine morphological criteria and facilitate the construction of digital-pathology algorithms.
DHX37 and the Implications in Disorders of Sex Development: An Update Review
Authors: de Oliveira Felipe Rodrigues , Guaragna Mara Sanches , Maciel-Guerra Andréa Trevas , Barros Beatriz Amstalden , de Mello Maricilda Palandi , Guerra-Junior Gil , Fabbri-Scallet Helena
Published: December 2023

Background: DHX37 is an autosomal gene responsible for encoding a helicase from the DExD/H-box family that plays an essential role in ribosome biogenesis. Variants in this gene were previously reported in two different phenotypes: neurodevelopmental disorders and disorders/differences of sex development (DSD). Particularly for the DSD group, variants were mainly reported associated with gonadal dysgenesis and testicular regression syndrome. Summary: Focusing specifically in the DSD group, we revised the 21 DHX37 variants described across a total of 55 cases published in the literature so far. We summarized the most important clinical and molecular features of all cases, trying to have a better comprehensiveness about this gene in the sexual development. Key Messages: The trick question regarding DHX37 is how a helicase involved in basic cell function could have a specific role in testis development. Little is known about the impact of DHX37 variants in DSD individuals. Nevertheless, current research strongly suggests that DHX37 is involved in the male sex development pathway, particularly in testis determination and maintenance. This is evidenced by the predominant assignment of affected individuals as males and the presence of Wolffian structures in most of the cases. Advancements in molecular techniques, such as the generation of induced pluripotent stem cells and the digenic inheritance for DHX37 cases, are also addressed in this paper. This represents the first comprehensive review of all DHX37 variants published in the literature to date.
Deep neural network for lateral control of self-driving cars in urban environment
Authors: El Farnane Abdelhafid , Youssefi My Abdelkader , Mouhsen Ahmed , El Ihyaoui Abdelilah
Published: December 2023

The exponential growth of the automotive industry clearly indicates that self-driving cars are the future of transportation. However, their biggest challenge lies in lateral control, particularly in urban bottlenecking environments, where disturbances and obstacles are abundant. In these situations, the ego vehicle has to follow its own trajectory while rapidly correcting deviation errors without colliding with other nearby vehicles. Various research efforts have focused on developing lateral control approaches, but these methods remain limited in terms of response speed and control accuracy. This paper presents a control strategy using a deep neural network (DNN) controller to effectively keep the car on the centerline of its trajectory and adapt to disturbances arising from deviations or trajectory curvature. The controller focuses on minimizing deviation errors. The Matlab/Simulink software is used for designing and training the DNN. Finally, simulation results confirm that the suggested controller has several advantages in terms of precision, with lateral deviation remaining below 0.65 meters, and rapidity, with a response time of 0.7 seconds, compared to traditional controllers in solving lateral control. 
Transfer learning for epilepsy detection using spectrogram images
Authors: Edderbali Fatima , Harmouchi Mohammed , Essoukaki Elmaati
Published: December 2023

Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG). Manual inspection of EEG brain signals is a slow and arduous process, which puts heavy load on neurologists and affects their performance. The aim of this study is to find the best result of classification using the transfer learning model that automatically identify the epileptic and the normal activity, to classify EEG signals by using images of spectrogram which represents the percentage of energy for each coefficient of the continuous wavelet. Dataset includes the EEG signals recorded at monitoring unit of epilepsy used in this study to presents an application of transfer learning by comparing three models Alexnet, visual geometry group (VGG19) and residual neural network ResNet using different combinations with seven different classifiers. This study tested the models and reached a different value of accuracy and other metrics used to judge their performances, and as a result the best combination has been achieved with ResNet combined with support vector machine (SVM) classifier that classified EEG signals with a high success rate using multiple performance metrics such as 97.22% accuracy and 2.78% the value of the error rate.
Sophisticated face mask dataset: a novel dataset for effective coronavirus disease surveillance
Authors: Ul Haque Sheikh Burhan , Wani Mohd Hanief
Published: December 2023

Efficient and accurate coronavirus disease (COVID-19) surveillance necessitates robust identification of individuals wearing face masks. This research introduces the sophisticated face mask dataset (SFMD), a comprehensive compilation of high-quality face mask images enriched with detailed annotations on mask types, fits, and usage patterns. Leveraging cutting-edge deep learning models—EfficientNet-B2, ResNet50, and MobileNet-V2—, we compare SFMD against two established benchmarks: the real-world masked face dataset (RMFD) and the masked face recognition dataset (MFRD). Across all models, SFMD consistently outperforms RMFD and MFRD in key metrics, including accuracy, precision, recall, and F1 score. Additionally, our study demonstrates the dataset's capability to cultivate robust models resilient to intricate scenarios like low-light conditions and facial occlusions due to accessories or facial hair.
Multi-channel microseismic signals classification with convolutional neural networks
Authors: Shu Hongmei , Dawod Ahmad Yahya , Tepsan Worawit , Mou Lei , Tang Zheng
Published: December 2023

Identifying and classifying microseismic signals is essential to warn of mines’ dangers. Deep learning has replaced traditional methods, but labor-intensive manual identification and varying deep learning outcomes pose challenges. This paper proposes a transfer learning-based convolutional neural network (CNN) method called microseismic signals-convolutional neural network (MS-CNN) to automatically recognize and classify microseismic events and blasts. The model was instructed on a limited sample of data to obtain an optimal weight model for microseismic waveform recognition and classification. A comparative analysis was performed with an existing CNN model and classical image classification models such as AlexNet, GoogLeNet, and ResNet50. The outcomes demonstrate that the MS-CNN model achieved the best recognition and classification effect (99.6% accuracy) in the shortest time (0.31 s to identify 277 images in the test set). Thus, the MS-CNN model can efficiently recognize and classify microseismic events and blasts in practical engineering applications, improving the recognition timeliness of microseismic signals and further enhancing the accuracy of event classification.
Deep learning based biometric authentication using electrocardiogram and iris
Authors: Kailas Ashwini , Keshava Murthy Geevagondanahalli Narayanappa
Published: December 2023

Authentication systems play an important role in wide range of applications. The traditional token certificate and password-based authentication systems are now replaced by biometric authentication systems. Generally, these authentication systems are based on the data obtained from face, iris, electrocardiogram (ECG), fingerprint and palm print. But these types of models are unimodal authentication, which suffer from accuracy and reliability issues. In this regard, multimodal biometric authentication systems have gained huge attention to develop the robust authentication systems. Moreover, the current development in deep learning schemes have proliferated to develop more robust architecture to overcome the issues of tradition machine learning based authentication systems. In this work, we have adopted ECG and iris data and trained the obtained features with the help of hybrid convolutional neural network- long short-term memory (CNN-LSTM) model. In ECG, R peak detection is considered as an important aspect for feature extraction and morphological features are extracted. Similarly, gabor-wavelet, gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM) and principal component analysis (PCA) based feature extraction methods are applied on iris data. The final feature vector is obtained from MIT-BIH and IIT Delhi Iris dataset which is trained and tested by using CNN-LSTM. The experimental analysis shows that the proposed approach achieves average accuracy, precision, and F1-core as 0.985, 0.962 and 0.975, respectively.
Embedded artificial intelligence system using deep learning and raspberrypi for the detection and classification of melanoma
Authors: Dahdouh Yousra , Anouar Abdelhakim Boudhir , Ahmed Mohamed Ben
Published: December 2023

Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
Optically processed Kannada script realization with Siamese neural network model
Authors: Parathra Sreedharanpillai Ambili , Abraham Biku , Kotapuzakal Varghese Arun
Published: December 2023

Optical character recognition (OCR) is a technology that allows computers to recognize and extract text from images or scanned documents. It is commonly used to convert printed or handwritten text into machine-readable format. This Study presents an OCR system on Kannada Characters based on siamese neural network (SNN). Here the SNN, a Deep neural network which comprises of two identical convolutional neural network (CNN) compare the script and ranks based on the dissimilarity. When lesser dissimilarity score is identified, prediction is done as character match. In this work the authors use 5 classes of Kannada characters which were initially preprocessed using grey scaling and convert it to pgm format. This is directly input into the Deep convolutional network which is learnt from matching and non-matching image between the CNN with contrastive loss function in Siamese architecture. The Proposed OCR system uses very less time and gives more accurate results as compared to the regular CNN. The model can become a powerful tool for identification, particularly in situations where there is a high degree of variation in writing styles or limited training data is available.
Efficient plant leaf detection through machine learning approach based on corn leaf image classification
Authors: Pujar Premakumari , Kumar Ashutosh , Kumar Vineet
Published: December 2023

Since maize is a staple diet for people, especially vegetarians and vegans, maize leaf disease has a significant influence here on the food industry including maize crop productivity. Therefore, it should be understood that maize quality must be optimal; yet, to do so, maize must be safeguarded from several illnesses. As a result, there is a great demand for such an automated system that can identify the condition early on and take the appropriate action. Early disease identification is crucial, but it also poses a major obstacle. As a result, in this research project, we adopt the fundamental k-nearest neighbor (KNN) model and concentrate on building and developing the improved k-nearest neighbor (EKNN) model. EKNN aids in identifying several classes of disease. To gather discriminative, boundary, pattern, and structurally linked information, additional high-quality fine and coarse features are generated. This information is then used in the classification process. The classification algorithm offers high-quality gradient-based features. Additionally, the proposed model is assessed using the Plant-Village dataset, and a comparison with many standard classification models using various metrics is also done.