bluuebunny/crossref_metadata_2025_split huggingface dataset:
Volatile working memory representations crystallize with practice
Authors: Bellafard Arash , Namvar Ghazal , Kao Jonathan C. , Vaziri Alipasha , Golshani Peyman 
Published: May 2024

AbstractWorking memory, the process through which information is transiently maintained and manipulated over a brief period, is essential for most cognitive functions1–4. However, the mechanisms underlying the generation and evolution of working-memory neuronal representations at the population level over long timescales remain unclear. Here, to identify these mechanisms, we trained head-fixed mice to perform an olfactory delayed-association task in which the mice made decisions depending on the sequential identity of two odours separated by a 5 s delay. Optogenetic inhibition of secondary motor neurons during the late-delay and choice epochs strongly impaired the task performance of the mice. Mesoscopic calcium imaging of large neuronal populations of the secondary motor cortex (M2), retrosplenial cortex (RSA) and primary motor cortex (M1) showed that many late-delay-epoch-selective neurons emerged in M2 as the mice learned the task. Working-memory late-delay decoding accuracy substantially improved in the M2, but not in the M1 or RSA, as the mice became experts. During the early expert phase, working-memory representations during the late-delay epoch drifted across days, while the stimulus and choice representations stabilized. In contrast to single-plane layer 2/3 (L2/3) imaging, simultaneous volumetric calcium imaging of up to 73,307 M2 neurons, which included superficial L5 neurons, also revealed stabilization of late-delay working-memory representations with continued practice. Thus, delay- and choice-related activities that are essential for working-memory performance drift during learning and stabilize only after several days of expert performance.
Engineered CD47 protects T cells for enhanced antitumour immunity
Authors: Yamada-Hunter Sean A. , Theruvath Johanna , McIntosh Brianna J. , Freitas Katherine A. , Lin Frank , Radosevich Molly T. , Leruste Amaury , Dhingra Shaurya , Martinez-Velez Naiara , Xu Peng , Huang Jing , Delaidelli Alberto , Desai Moksha H. , Good Zinaida , Polak Roel , May Audre , Labanieh Louai , Bjelajac Jeremy , Murty Tara , Ehlinger Zach , Mount Christopher W. , Chen Yiyun , Heitzeneder Sabine , Marjon Kristopher D. , Banuelos Allison , Khan Omair , Wasserman Savannah L. , Spiegel Jay Y. , Fernandez-Pol Sebastian , Kuo Calvin J. , Sorensen Poul H. , Monje Michelle , Majzner Robbie G. , Weissman Irving L. , Sahaf Bita , Sotillo Elena , Cochran Jennifer R. , Mackall Crystal L. 
Published: May 2024

AbstractAdoptively transferred T cells and agents designed to block the CD47–SIRPα axis are promising cancer therapeutics that activate distinct arms of the immune system1,2. Here we administered anti-CD47 antibodies in combination with adoptively transferred T cells with the goal of enhancing antitumour efficacy but observed abrogated therapeutic benefit due to rapid macrophage-mediated clearance of T cells expressing chimeric antigen receptors (CARs) or engineered T cell receptors. Anti-CD47-antibody-mediated CAR T cell clearance was potent and rapid enough to serve as an effective safety switch. To overcome this challenge, we engineered the CD47 variant CD47(Q31P) (47E), which engages SIRPα and provides a ‘don’t eat me’ signal that is not blocked by anti-CD47 antibodies. TCR or CAR T cells expressing 47E are resistant to clearance by macrophages after treatment with anti-CD47 antibodies, and mediate substantial, sustained macrophage recruitment to the tumour microenvironment. Although many of the recruited macrophages manifested an M2-like profile3, the combined therapy synergistically enhanced antitumour efficacy. Our study identifies macrophages as major regulators of T cell persistence and illustrates the fundamental challenge of combining T-cell-directed therapeutics with those designed to activate macrophages. It delivers a therapeutic approach that is capable of simultaneously harnessing the antitumour effects of T cells and macrophages, offering enhanced potency against solid tumours.
Synthesis and evaluation of pyridine-3-carboxamide analogs as effective agents against bacterial wilt in tomatoes
Authors: Mohammed Yasser Hussein Issa , Shamkh Israa M. , Shntaif Ahmed Hassen , Sufyan Muhammad , Rehman Md Tabish , AlAjmi Mohamed F. , Shahwan Moayad , Alghamdi Saad , Abd El-Lateef Amal Ezzat , Khidir Elshiekh B. , Abouzied Amr S. , Khalifa Nasrin E. , A. Khojali Weam M. , Huwaimel Bader , Al Farraj Dunia A. , Almutairi Saeedah Musaed 
Published: May 2024

AbstractThis study focused on developing novel pyridine-3-carboxamide analogs to treat bacterial wilt in tomatoes caused by Ralstonia solanacearum. The analogs were synthesized through a multistep process and their structures confirmed using spectroscopy. Molecular docking studies identified the most potent analog from the series. A specific analog, compound 4a, was found to significantly enhance disease resistance in tomato plants infected with R. solanacearum. The structure–activity relationship analysis showed the positions and types of substituents on the aromatic rings of compounds 4a–i strongly influenced their biological activity. Compound 4a, with a chloro group at the para position on ring C and hydroxyl group at the ortho position on ring A, was exceptionally effective against R. solanacearum. When used to treat seeds, the analogs displayed remarkable efficacy, especially compound 4a which had specific activity against bacterial wilt pathogens. Compound 4a also promoted vegetative and reproductive growth of tomato plants, increasing seed germination and seedling vigor. In plants mechanically infected with bacteria, compound 4a substantially reduced the percentage of infection, pathogen quantity in young tissue, and disease progression. The analogs were highly potent due to their amide linkage. Molecular docking identified the best compounds with strong binding affinities. Overall, the strategic design and synthesis of these pyridine-3-carboxamide analogs offers an effective approach to targeting and controlling R. solanacearum and bacterial wilt in tomatoes.
A dual fusion recognition model for sleep posture based on air mattress pressure detection
Authors: Li Zebo , Zhou Yipeng , Zhou Guoping 
Published: May 2024

AbstractIn order to solve the difficult portability problem of traditional non-invasive sleeping posture recognition algorithms arising from the production cost and computational cost, this paper proposes a sleeping posture recognition model focusing on human body structural feature extraction and integration of feature space and algorithms based on a specific air-spring mattress structure, called SPR-DE (SPR-DE is the Sleep Posture Recognition-Data Ensemble acronym form). The model combines SMR (SMR stands for Principle of Spearman Maximal Relevance) with horizontal and vertical division based on the barometric pressure signals in the human body’s backbone region to reconstruct the raw pressure data into strongly correlated non-image features of the sleep postures in different parts and directions and construct the feature set. Finally, the recognit-ion of the two sleep postures is accomplished using the AdaBoost-SVM integrated classifier. SPR-DE is compared with the base and integrated classifiers to verify its performance. The experimental results show that the amount of significant features helps the algorithm to classify different sleeping patterns more accurately, and the f1 score of the SPR-DE model determined by the comparison experiments is 0.998, and the accuracy can reach 99.9%. Compared with other models, the accuracy is improved by 2.9% ~ 7.7%, and the f1-score is improved by 0.029 ~ 0.076. Therefore, it is concluded that the SMR feature extraction strategy in the SPR-DE model and the AdaBoost-SVM can achieve high accuracy and strong robustness in the task of sleep posture recognition in a small area, low-density air-pressure mattress, taking into account the comfort of the mattress structural design and the sleep posture recognition, integrated with the mattress adaptive adjustment system.
Multi-channel polarization manipulation based on graphene for encryption communication
Authors: Farzin Parsa , Hajiahmadi Mohammad Javad , Soleimani Mohammad 
Published: May 2024

AbstractWave-based cryptography, at the vanguard of advancing technologies in advanced information science, is essential for establishing a diverse array of secure cryptographic platforms. The realization of these platforms hinges on the intelligent application of multiplexing techniques, seamlessly combined with appropriate metasurface technology. Nevertheless, existing multi-channel encryption technologies based on metasurfaces face challenges related to information leakage during partial channel decoding processes. In this paper, we present a reprogrammable metasurface for polarization modulation. This metasurface not only allows for the arbitrary customization of linearly polarized reflected waves but also enables real-time amplitude modulation. Here, relying on polarization amplitude control, a fully secure communication protocol is developed precisely in the terahertz (THz) spectrum to achieve real-time information encryption based on polarization modulation metasurfaces where access to information is highly restricted. The proposed metasurface employs the double random phase encryption (DRPE) algorithm for information encryption. It transmits the encrypted data through different polarization channels using two graphene nanoribbons, exclusively controlled by external biasing conditions. Various encryption scenarios have been outlined to fortify information protection against potential eavesdroppers. The simulated results show that this unique technology for hiding images by manipulating the polarization of the reflected wave provides new opportunities for various applications, including encryption, THz communications, THz secure data storage, and imaging.
Chemical and biophysical characterization of novel potassium channel blocker 3-fluoro-5-methylpyridin-4-amine
Authors: Sun Yang , Rodríguez-Rangel Sofia , Zhang Lauren L. , Sánchez-Rodríguez Jorge E. , Brugarolas Pedro 
Published: May 2024

Abstract4-aminopyridine (4AP) is a potassium (K+) channel blocker used clinically to improve walking in people with multiple sclerosis (MS). 4AP binds to exposed K+ channels in demyelinated axons, reducing the leakage of intracellular K+ and enhancing impulse conduction. Multiple derivatives of 4AP capable of blocking K+ channels have been reported including three radiolabeled with positron emitting isotopes for imaging demyelinated lesions using positron emission tomography (PET). However, there remains a demand for novel molecules with suitable physicochemical properties and binding affinity that can potentially be radiolabeled and used as PET radiotracers. In this study, we introduce 3-fluoro-5-methylpyridin-4-amine (5Me3F4AP) as a novel trisubstituted K+ channel blocker with potential application in PET. 5Me3F4AP has comparable potency to 4AP and the PET tracer 3-fluoro-4-aminopyridine (3F4AP). Compared to 3F4AP, 5Me3F4AP exhibits comparable basicity (pKa = 7.46 ± 0.01 vs. 7.37 ± 0.07, P-value = 0.08), greater lipophilicity (logD = 0.664 ± 0.005 vs. 0.414 ± 0.002, P-value < 0.0001) and higher permeability to an artificial brain membrane (Pe = 88.1 ± 18.3 vs. 31.1 ± 2.9 nm/s, P-value = 0.03). 5Me3F4AP is also more stable towards oxidation in vitro by the cytochrome P450 enzyme CYP2E1 (IC50 = 36.2 ± 2.5 vs. 15.4 ± 5.1, P-value = 0.0003); the enzyme responsible for the metabolism of 4AP and 3F4AP. Taken together, 5Me3F4AP has promising properties as a candidate for PET imaging warranting additional investigation.
A constrained machine learning surrogate model to predict the distribution of water-in-oil emulsions in electrostatic fields
Authors: Kooti Ghazal , Dabir Bahram , Butscher Christoph , Taherdangkoo Reza 
Published: May 2024

AbstractAccurately describing the evolution of water droplet size distribution in crude oil is fundamental for evaluating the water separation efficiency in dehydration systems. Enhancing the separation of an aqueous phase dispersed in a dielectric oil phase, which has a significantly lower dielectric constant than the dispersed phase, can be achieved by increasing the water droplet size through the application of an electrostatic field in the pipeline. Mathematical models, while being accurate, are computationally expensive. Herein, we introduced a constrained machine learning (ML) surrogate model developed based on a population balance model. This model serves as a practical alternative, facilitating fast and accurate predictions. The constrained ML model, utilizing an extreme gradient boosting (XGBoost) algorithm tuned with a genetic algorithm (GA), incorporates the key parameters of the electrostatic dehydration process, including droplet diameter, voltage, crude oil properties, temperature, and residence time as input variables, with the output being the number of water droplets per unit volume. Furthermore, we modified the objective function of the XGBoost algorithm by incorporating two penalty terms to ensure the model’s predictions adhere to physical principles. The constrained model demonstrated accuracy on the test set, with a mean squared error of 0.005 and a coefficient of determination of 0.998. The efficiency of the model was validated through comparison with the experimental data and the results of the population balance mathematical model. The analysis shows that the initial droplet diameter and voltage have the highest influence on the model, which aligns with the observed behaviour in the real-world process.
Research on key technology of cooled infrared bionic compound eye camera based on small lens array
Authors: Wang Xiaoyu , Li Linhan , Chi Yinghao , Liu Jie , Yue Juan , Gao Sili , Yuan Xiupeng , Yu Yang 
Published: May 2024

AbstractTraditional 2D imaging technologies are limited by the need for a large field of view and their sensitivity to small target motion. Inspired by the characteristics of insect compound eye structure, we propose an infrared bionic compound eye camera based on a small lens array. The camera is composed of 61 small lens arrays mounted on a curved spherical shell and a relay optical system. The imaging device is a high-performance cooled mid-wave infrared detector. This is an innovative design for an infrared biomimetic compound eye camera system that provides a wide field of view and all-day detection capability. Aimed to meet the specified requirements. The optical system achieves a 100% cold-membrane match between the infrared optical system and the cooled detector, and the relay optical system optimizes the large-field aberration by introducing a higher-order aspheric surface and modifying the geometric surface of the lenses. Our entire system enables an observation field angle of $$108^\circ \times 108^\circ$$

108
∘

×

108
∘

. The experiments showed that the image quality of the system is high, each ommatidium was effective within the imaging range of the compound eye camera, resulting in an improved signal-to-noise ratio in various scenes.
Transcriptomic signature, bioactivity and safety of a non-hepatotoxic analgesic generating AM404 in the midbrain PAG region
Authors: Bazan Hernan A. , Bhattacharjee Surjyadipta , Reid Madigan M. , Jun Bokkyoo , Polk Connor , Strain Madeleine , St Pierre Linsey A. , Desai Neehar , Daly Patrick W. , Cucinello-Ragland Jessica A. , Edwards Scott , Recio Javier , Alvarez-Builla Julio , Cai James J. , Bazan Nicolas G. 
Published: May 2024

AbstractSafe and effective pain management is a critical healthcare and societal need. The potential for acute liver injury from paracetamol (ApAP) overdose; nephrotoxicity and gastrointestinal damage from chronic non-steroidal anti-inflammatory drug (NSAID) use; and opioids’ addiction are unresolved challenges. We developed SRP-001, a non-opioid and non-hepatotoxic small molecule that, unlike ApAP, does not produce the hepatotoxic metabolite N-acetyl-p-benzoquinone-imine (NAPQI) and preserves hepatic tight junction integrity at high doses. CD-1 mice exposed to SRP-001 showed no mortality, unlike a 70% mortality observed with increasing equimolar doses of ApAP within 72 h. SRP-001 and ApAP have comparable antinociceptive effects, including the complete Freund’s adjuvant-induced inflammatory von Frey model. Both induce analgesia via N-arachidonoylphenolamine (AM404) formation in the midbrain periaqueductal grey (PAG) nociception region, with SRP-001 generating higher amounts of AM404 than ApAP. Single-cell transcriptomics of PAG uncovered that SRP-001 and ApAP also share modulation of pain-related gene expression and cell signaling pathways/networks, including endocannabinoid signaling, genes pertaining to mechanical nociception, and fatty acid amide hydrolase (FAAH). Both regulate the expression of key genes encoding FAAH, 2-arachidonoylglycerol (2-AG), cannabinoid receptor 1 (CNR1), CNR2, transient receptor potential vanilloid type 4 (TRPV4), and voltage-gated Ca2+ channel. Phase 1 trial (NCT05484414) (02/08/2022) demonstrates SRP-001’s safety, tolerability, and favorable pharmacokinetics, including a half-life from 4.9 to 9.8 h. Given its non-hepatotoxicity and clinically validated analgesic mechanisms, SRP-001 offers a promising alternative to ApAP, NSAIDs, and opioids for safer pain treatment.
Using optical coherence tomography to assess luster of pearls: technique suitability and insights
Authors: Zhou Yang , Zhou Lifeng , Yan Jun , Yan Xuejun , Chen Zhengwei 
Published: May 2024

AbstractLuster is one of the vital indexes in pearl grading. To find a fast, nondestructive, and low-cost grading method, optical coherence tomography (OCT) is introduced to predict the luster grade through the texture features. After background removal, flattening, and segmentation, the speckle pattern of the region of interest is described by seven kinds of feature textures, including center-symmetric auto-correlation (CSAC), fractal dimension (FD), Gabor, gray level co-occurrence matrix (GLCM), histogram of oriented gradients (HOG), laws texture energy (LAWS), and local binary patterns (LBP). To find the relations between speckle-derived texture features and luster grades, four Four groups of pearl samples were used in the experiment to detect texture differences based on support vector machines (SVMs) and random forest classifier (RFC)) for investigating the relations between speckle-derived texture features and luster grades. The precision, recall, F1-score, and accuracy are more significant than 0.9 in several simulations, even after dimension reduction. This demonstrates that the texture feature from OCT images can be applied to class the pearl luster based on speckle changes.
Severe decline in large farmland trees in India over the past decade
Authors: Brandt Martin , Gominski Dimitri , Reiner Florian , Kariryaa Ankit , Guthula Venkanna Babu , Ciais Philippe , Tong Xiaoye , Zhang Wenmin , Govindarajulu Dhanapal , Ortiz-Gonzalo Daniel , Fensholt Rasmus 
Published: May 2024

AbstractAgroforestry practices that include the integration of multifunctional trees within agricultural lands can generate multiple socioecological benefits, in addition to being a natural climate solution due to the associated carbon sequestration potential. Such agroforestry trees represent a vital part of India’s landscapes. However, despite their importance, a current lack of robust monitoring mechanisms has contributed to an insufficient grasp of their distribution in relation to management practices, as well as their vulnerability to climate change and diseases. Here we map 0.6 billion farmland trees, excluding block plantations, in India and track them over the past decade. We show that around 11 ± 2% of the large trees (about 96 m2 crown size) mapped in 2010/2011 had disappeared by 2018. Moreover, during the period 2018–2022, more than 5 million large farmland trees (about 67 m2 crown size) have vanished, due partly to altered cultivation practices, where trees within fields are perceived as detrimental to crop yields. These observations are particularly unsettling given the current emphasis on agroforestry as a pivotal natural climate solution, playing a crucial role in both climate change adaptation and mitigation strategies, in addition to being important for supporting agricultural livelihoods and improving biodiversity.
From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation
Authors: Hoffmann Cassandra , Cho Ellie , Zalesky Andrew , Di Biase Maria A. 
Published: May 2024

AbstractDigital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation.
Examining Adolescent Language Performance in Discourse Production Across Four Elicitation Tasks
Authors: Wallis Adele K. , Westerveld Marleen F. 
Published: May 2024

Purpose:
Comprehensive spoken language assessment should include the evaluation of language use in naturalistic contexts. Discourse elicitation and analysis provides the opportunity for such an evaluation to occur. In this article, our overall aim was to describe adolescents' language performance on four elicitation tasks and determine if there are task-related differences across the elicitation tasks.

Method:

Forty-four typically developing adolescents with ages ranging from 12;2 to 17;11 (years;months;
M =
15;2; 21 boys and 23 girls) participated in the study. They completed four spoken discourse tasks: (a) story generation using a wordless picture book, (b) fable retell, (c) six personal narratives in response to emotion-based prompts, and (d) monologic response to two stories that contained a moral dilemma. Responses were transcribed and analyzed for four language performance measures tapping into language productivity, syntactic complexity, lexical diversity, and verbal facility.

Results:
Despite individual variability in performance, mean scores were close to median scores for most measures, suggesting a symmetrical distribution. As expected, all four language performance measures were significantly different across the four elicitation tasks. The personal narrative task elicited the longest samples, with the highest verbal fluency. In contrast, both lexical diversity and syntactic complexity were the strongest in response to the fable retell and the moral dilemma tasks.

Conclusions:
This investigation provides speech-language pathologists with an overview of how task-related factors may impact adolescent language performance. These findings may be used to support their clinical decision-making processes in choosing a suitable discourse task when conducting a comprehensive spoken language assessment. Three hypothetical case examples are used to illustrate the decision-making process.

Supplemental Material:

https://doi.org/10.23641/asha.25761768