Targeting IL-33 reprograms the tumor microenvironment and potentiates antitumor response to anti-PD-L1 immunotherapy.

BACKGROUND: The main challenge against patients with cancer to derive benefits from immune checkpoint inhibitors targeting PD-1/PD-L1 appears to be the immunosuppressive tumor microenvironment (TME), in which IL-33/ST2 signal fulfills critical functions. However, whether IL-33 limits the therapeutic efficacy of anti-PD-L1 remains uncertain. METHODS: Molecular mechanisms of IL-33/ST2 signal on anti-PD-L1 treatment lewis lung carcinoma tumor model were assessed by RNA-seq, ELISA, WB and immunofluorescence (IF). A sST2-Fc fusion protein was constructed for targeting IL-33 and combined with anti-PD-L1 antibody for immunotherapy in colon and lung tumor models. On this basis, bifunctional fusion proteins were generated for PD-L1-targeted blocking of IL-33 in tumors. The underlying mechanisms of dual targeting of IL-33 and PD-L1 revealed by RNA-seq, scRNA-seq, FACS, IF and WB. RESULTS: After anti-PD-L1 administration, tumor-infiltrating ST2+ regulatory T cells (Tregs) were elevated. Blocking IL-33/ST2 signal with sST2-Fc fusion protein potentiated antitumor efficacy of PD-L1 antibody by enhancing T cell responses in tumor models. Bifunctional fusion protein anti-PD-L1-sST2 exhibited enhanced antitumor efficacy compared with combination therapy, not only inhibited tumor progression and extended the survival, but also provided long-term protective antitumor immunity. Mechanistically, the superior antitumor activity of targeting IL-33 and PD-L1 originated from reducing immunosuppressive factors, such as Tregs and exhausted CD8+ T cells while increasing tumor-infiltrating cytotoxic T lymphocyte cells. CONCLUSIONS: In this study, we demonstrated that IL-33/ST2 was involved in the immunosuppression mechanism of PD-L1 antibody therapy, and blockade by sST2-Fc or anti-PD-L1-sST2 could remodel the inflammatory TME and induce potent antitumor effect, highlighting the potential therapeutic strategies for the tumor treatment by simultaneously targeting IL-33 and PD-L1.

Semantic memory-based dynamic neural network using memristive ternary CIM and CAM for 2D and 3D vision.

The brain is dynamic, associative, and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing. We propose a hardware-software co-design, a semantic memory-based dynamic neural network using a memristor. The network associates incoming data with the past experience stored as semantic vectors. The network and the semantic memory are physically implemented on noise-robust ternary memristor-based computing-in-memory (CIM) and content-addressable memory (CAM) circuits, respectively. We validate our co-designs, using a 40-nm memristor macro, on ResNet and PointNet++ for classifying images and three-dimensional points from the MNIST and ModelNet datasets, which achieves not only accuracy on par with software but also a 48.1 and 15.9% reduction in computational budget. Moreover, it delivers a 77.6 and 93.3% reduction in energy consumption.

Physical Reservoir Computing Using van der Waals Ferroelectrics for Acoustic Keyword Spotting.

Acoustic keyword spotting (KWS) plays a pivotal role in the voice-activated systems of artificial intelligence (AI), allowing for hands-free interactions between humans and smart devices through information retrieval of the voice commands. The cloud computing technology integrated with the artificial neural networks has been employed to execute the KWS tasks, which however suffers from propagation delay and the risk of privacy breach. Here, we report a single-node reservoir computing (RC) system based on the CuInP2S6 (CIPS)/graphene heterostructure planar device for implementing the KWS task with low computation cost. Through deliberately tuning the Schottky barrier height at the ferroelectric CIPS interfaces for the thermionic injection and transport of the electrons, the typical nonlinear current response and fading memory characteristics are achieved in the device. Additionally, the device exhibits diverse synaptic plasticity with an excellent separation capability of the temporal information. We construct a RC system through employing the ferroelectric device as the physical node to spot the acoustic keywords, i.e., the natural numbers from 1 to 9 based on simulation, in which the system demonstrates outstanding performance with high accuracy rate (>94.6%) and recall rate (>92.0%). Our work promises physical RC in single-node configuration as a prospective computing platform to process the acoustic keywords, promoting its applications in the artificial auditory system at the edge.

Dynamic m6A mRNA methylation reveals the involvement of AcALKBH10 in ripening-related quality regulation in kiwifruit.

Kiwifruit ripening is a complex and highly coordinated process that occurs in conjunction with the formation of fruit edible quality. The significance of epigenetic changes, particularly the impact of N6-methyladenosine (m6A) RNA modification on fruit ripening and quality formation, has been largely overlooked. We monitored m6A levels and gene expression changes in kiwifruit at four different stages using LC-MS/MS, MeRIP, RNA-seq, and validated the function of AcALKBH10 through heterologous transgenic expression in tomato. Notable m6A modifications occurred predominantly at the stop codons and the 3' UTRs and exhibited a gradual reduction in m6A levels during the fruit ripening process. Moreover, these m6A modifications in the aforementioned sites demonstrated a discernible inverse relationship with the levels of mRNA abundance throughout the ripening process, suggesting a repression effect of m6A modification in the modulation of kiwifruit ripening. We further demonstrated that AcALKBH10 rather than AcECT9 predominantly regulates m6A levels in ripening-related genes, thereby exerting the regulatory control over the ripening process and the accumulation of soluble sugars and organic acids, ultimately influencing fruit ripening and quality formation. In conclusion, our findings illuminate the epi-regulatory mechanism involving m6A in kiwifruit ripening, offering a fresh perspective for cultivating high-quality kiwifruit with enhanced nutritional attributes.

Identification of Differentially Expressed Genes and microRNAs in the Gray and White Feather Follicles of Shitou Geese.

The Shitou goose, a highly recognized indigenous breed with gray plumage originating from Chaozhou Raoping in Guangdong Province, China, is renowned for being the largest goose species in the country. Notably, during the pure breeding process of Shitou geese, approximately 2% of the offspring in each generation unexpectedly exhibited white plumage. To better understand the mechanisms underlying white plumage color formation in Shitou geese, we conducted a comparative transcriptome analysis between white and gray feather follicles, aiming to identify key genes and microRNAs that potentially regulate white plumage coloration in this unique goose breed. Our results revealed a number of pigmentation genes, encompassing TYR, TYRP1, EDNRB2, MLANA, SOX10, SLC45A2, GPR143, TRPM1, OCA2, ASIP, KIT, and SLC24A5, which were significantly down-regulated in the white feather follicles of Shitou geese. Among these genes, EDNRB2 and KIT emerged as the most promising candidate genes for white plumage coloration in Shitou geese. Additionally, our analysis also uncovered 46 differentially expressed miRNAs. Of these, miR-144-y may play crucial roles in the regulation of feather pigmentation. Furthermore, the expression of novel-m0086-5p, miR-489-y, miR-223-x, miR-7565-z, and miR-3535-z exhibits a significant negative correlation with the expression of pigmentation genes including TYRP1, EDNRB2, MLANA, SOX10, TRPM1, and KIT, suggesting these miRNAs may indirectly regulate the expression of these genes, thereby influencing feather color. Our findings provide valuable insights into the genetic mechanisms underlying white plumage coloration in Shitou geese and contribute to the broader understanding of avian genetics and coloration research.

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance.

Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

Ontological representation, modeling, and analysis of parasite vaccines.

BACKGROUND: Pathogenic parasites are responsible for multiple diseases, such as malaria and Chagas disease, in humans and livestock. Traditionally, pathogenic parasites have been largely an evasive topic for vaccine design, with most successful vaccines only emerging recently. To aid vaccine design, the VIOLIN vaccine knowledgebase has collected vaccines from all sources to serve as a comprehensive vaccine knowledgebase. VIOLIN utilizes the Vaccine Ontology (VO) to standardize the modeling of vaccine data. VO did not model complex life cycles as seen in parasites. With the inclusion of successful parasite vaccines, an update in parasite vaccine modeling was needed. RESULTS: VIOLIN was expanded to include 258 parasite vaccines against 23 protozoan species, and 607 new parasite vaccine-related terms were added to VO since 2022. The updated VO design for parasite vaccines accounts for parasite life stages and for transmission-blocking vaccines. A total of 356 terms from the Ontology of Parasite Lifecycle (OPL) were imported to VO to help represent the effect of different parasite life stages. A new VO class term, 'transmission-blocking vaccine,' was added to represent vaccines able to block infectious transmission, and one new VO object property, 'blocks transmission of pathogen via vaccine,' was added to link vaccine and pathogen in which the vaccine blocks the transmission of the pathogen. Additionally, our Gene Set Enrichment Analysis (GSEA) of 140 parasite antigens used in the parasitic vaccines identified enriched features. For example, significant patterns, such as signal, plasma membrane, and entry into host, were found in the antigens of the vaccines against two parasite species: Plasmodium falciparum and Toxoplasma gondii. The analysis found 18 out of the 140 parasite antigens involved with the malaria disease process. Moreover, a majority (15 out of 54) of P. falciparum parasite antigens are localized in the cell membrane. T. gondii antigens, in contrast, have a majority (19/24) of their proteins related to signaling pathways. The antigen-enriched patterns align with the life cycle stage patterns identified in our ontological parasite vaccine modeling. CONCLUSIONS: The updated VO modeling and GSEA analysis capture the influence of the complex parasite life cycles and their associated antigens on vaccine development.

Polysaccharide of Atractylodes macrocephala Koidz alleviates LPS-induced proliferation, differentiation inhibition and excessive apoptosis in chicken embryonic myogenic cells.

BACKGROUND: Lipopolysaccharide (LPS) can induce systemic inflammation and affect the growth and development of poultry. As a kind of traditional Chinese medicine, polysaccharide of Atractylodes macrocephala Koidz (PAMK) can effectively improve the growth performance of animals and improve the immunity of animal bodies. OBJECTIVES: The purpose of this study was to investigate the effects of PAMK on LPS-induced inflammatory response, proliferation, differentiation and apoptosis of chicken embryonic myogenic cells. METHODS: We used chicken embryonic myogenic cells as a model by detecting EdU/MYHC immunofluorescence, the expression of inflammation, proliferation, differentiation-related genes and proteins and the number of apoptotic cells in the condition of adding LPS, PAMK, belnacasan (an inhibitor of Caspase1) or their combinations. RESULTS: The results showed that LPS stimulation increased the expression of inflammatory factors, inhibited proliferation and differentiation, and excessive apoptosis in chicken embryonic myogenic cells, and PAMK alleviated these adverse effects induced by LPS. After the addition of belnacasan (inhibitor of Caspase1), apoptosis in myogenic cells was inhibited, and therefore, the number of apoptotic cells and the expression of pro-apoptotic genes Caspase1 and Caspase3 were increased. In addition, belnacasan inhibited the increased expression of inflammatory factors, inhibited proliferation, differentiation and excessive apoptosis in chicken embryonic myogenic cells induced by LPS. CONCLUSIONS: This study provides a theoretical basis for further exploring the mechanism of action of PAMK and exogenous LPS on chicken embryonic myogenic cells and lays the foundation for the development and application of green feed additives in animal husbandry industry.

A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing.

Neuromorphic computing aims to emulate the computing processes of the brain by replicating the functions of biological neural networks using electronic counterparts. One promising approach is dendritic computing, which takes inspiration from the multi-dendritic branch structure of neurons to enhance the processing capability of artificial neural networks. While there has been a recent surge of interest in implementing dendritic computing using emerging devices, achieving artificial dendrites with throughputs and energy efficiency comparable to those of the human brain has proven challenging. In this study, we report on the development of a compact and low-power neurotransistor based on a vertical dual-gate electrolyte-gated transistor (EGT) with short-term memory characteristics, a 30 nm channel length, a record-low read power of ~3.16 fW and a biology-comparable read energy of ~30 fJ. Leveraging this neurotransistor, we demonstrate dendrite integration as well as digital and analog dendritic computing for coincidence detection. We also showcase the potential of neurotransistors in realizing advanced brain-like functions by developing a hardware neural network and demonstrating bio-inspired sound localization. Our results suggest that the neurotransistor-based approach may pave the way for next-generation neuromorphic computing with energy efficiency on par with those of the brain.

Transcriptomic Analysis on Pectoral Muscle of European Meat Pigeons and Shiqi Pigeons during Embryonic Development.

In avian muscle development, embryonic muscle development determines the number of myofibers after birth. Therefore, in this study, we investigated the phenotypic differences and the molecular mechanism of pectoral muscle development of the European meat pigeon Mimas strain (later called European meat pigeon) and Shiqi pigeon on embryonic day 6 (E6), day 10 (E10), day 14 (E14) and day 1 after birth (P1). The results showed that the myofiber density of the Shiqi pigeon was significantly higher than that of the European meat pigeon on E6, and myofibers with a diameter in the range of 50~100 µm of the Shiqi pigeon on P1 were significantly higher than those of European meat pigeon. A total of 204 differential expressed genes (DEGs) were obtained from RNA-seq analysis in comparison between pigeon breeds at the same stage. DEGs related to muscle development were found to significantly enrich the cellular amino acid catabolism, carboxylic acid catabolism, extracellular matrix receptor interaction, REDOX enzyme activity, calcium signaling pathway, ECM receptor interaction, PPAR signaling pathway and other pathways. Using Cytoscape software to create mutual mapping, we identified 33 candidate genes. RT-qPCR was performed to verify the 8 DEGs selected-DES, MYOD, MYF6, PTGS1, MYF5, MYH1, MSTN and PPARG-and the results were consistent with RNA-seq. This study provides basic data for revealing the distinct embryonic development mechanism of pectoral muscle between European meat pigeons and Shiqi pigeons.

Ferroelectric-defined reconfigurable homojunctions for in-memory sensing and computing.

Recently, the increasing demand for data-centric applications is driving the elimination of image sensing, memory and computing unit interface, thus promising for latency- and energy-strict applications. Although dedicated electronic hardware has inspired the development of in-memory computing and in-sensor computing, folding the entire signal chain into one device remains challenging. Here an in-memory sensing and computing architecture is demonstrated using ferroelectric-defined reconfigurable two-dimensional photodiode arrays. High-level cognitive computing is realized based on the multiplications of light power and photoresponsivity through the photocurrent generation process and Kirchhoff's law. The weight is stored and programmed locally by the ferroelectric domains, enabling 51 (>5 bit) distinguishable weight states with linear, symmetric and reversible manipulation characteristics. Image recognition can be performed without any external memory and computing units. The three-in-one paradigm, integrating high-level computing, weight memorization and high-performance sensing, paves the way for a computing architecture with low energy consumption, low latency and reduced hardware overhead.

Distinguishing artificial spin ice states using magnetoresistance effect for neuromorphic computing.

Artificial spin ice (ASI) consisting patterned array of nano-magnets with frustrated dipolar interactions offers an excellent platform to study frustrated physics using direct imaging methods. Moreover, ASI often hosts a large number of nearly degenerated and non-volatile spin states that can be used for multi-bit data storage and neuromorphic computing. The realization of the device potential of ASI, however, critically relies on the capability of transport characterization of ASI, which has not been demonstrated so far. Using a tri-axial ASI system as the model system, we demonstrate that transport measurements can be used to distinguish the different spin states of the ASI system. Specifically, by fabricating a tri-layer structure consisting a permalloy base layer, a Cu spacer layer and the tri-axial ASI layer, we clearly resolve different spin states in the tri-axial ASI system using lateral transport measurements. We have further demonstrated that the tri-axial ASI system has all necessary required properties for reservoir computing, including rich spin configurations to store input signals, nonlinear response to input signals, and fading memory effect. The successful transport characterization of ASI opens up the prospect for novel device applications of ASI in multi-bit data storage and neuromorphic computing.

DNA Demethylation of Myogenic Genes May Contribute to Embryonic Leg Muscle Development Differences between Wuzong and Shitou Geese.

Skeletal muscle development from embryonic stages to hatching is critical for poultry muscle growth, during which DNA methylation plays a vital role. However, it is not yet clear how DNA methylation affects early embryonic muscle development between goose breeds of different body size. In this study, whole genome bisulfite sequencing (WGBS) was conducted on leg muscle tissue from Wuzong (WZE) and Shitou (STE) geese on embryonic day 15 (E15), E23, and post-hatch day 1. It was found that at E23, the embryonic leg muscle development of STE was more intense than that of WZE. A negative correlation was found between gene expression and DNA methylation around transcription start sites (TSSs), while a positive correlation was observed in the gene body near TTSs. It was also possible that earlier demethylation of myogenic genes around TSSs contributes to their earlier expression in WZE. Using pyrosequencing to analyze DNA methylation patterns of promoter regions, we also found that earlier demethylation of the MyoD1 promoter in WZE contributed to its earlier expression. This study reveals that DNA demethylation of myogenic genes may contribute to embryonic leg muscle development differences between Wuzong and Shitou geese.

Short-term synaptic plasticity in emerging devices for neuromorphic computing.

Neuromorphic computing is a promising computing paradigm toward building next-generation artificial intelligence machines, in which diverse types of synaptic plasticity play an active role in information processing. Compared to long-term plasticity (LTP) forming the foundation of learning and memory, short-term plasticity (STP) is essential for critical computational functions. So far, the practical applications of LTP have been widely investigated, whereas the implementation of STP in hardware is still elusive. Here, we review the development of STP by bridging the physics in emerging devices and biological behaviors. We explore the computational functions of various STP in biology and review their recent progress. Finally, we discuss the main challenges of introducing STP into synaptic devices and offer the potential approaches to utilize STP to enrich systems' capabilities. This review is expected to provide prospective ideas for implementing STP in emerging devices and may promote the construction of high-level neuromorphic machines.

Comprehensive Transcriptomic and Metabolomic Analysis Revealed the Functional Differences in Pigeon Lactation between Male and Female during the Reproductive Cycle.

Lactation is a unique reproductive behavior in pigeons, with the crop serving as the organ responsible for secreting pigeon milk. Both male and female pigeons can produce crop milk and rear their offspring through a division of labor. Since the time of the secretion of pigeon crop milk is different in the process of feeding the young, whether the metabolism and formation of pigeon milk use the same mechanism is a very interesting scientific question. However, the metabolic dynamics and underlying genetic mechanisms involved in the formation of pigeon crop milk remain unclear, particularly during the incubation-feeding reproductive cycle. In this study, we integrated lactation-associated metabolism and transcriptome data from the crop tissues of both male and female pigeons during the brooding and feeding stages. We mapped the changes in metabolites related to milk formation in the crop tissues during these stages. Through metabolome profiling, we identified 1413 metabolites among 18 crop tissues. During the breeding cycles, the concentrations of estrone, L-ergothioneine, and L-histidine exhibited the most dynamic changes in females. In contrast, estrone, L-anserine, 1-methylhistidine, homovanillate, oxidized glutathione, and reducing glutathione showed the most dynamic changes in males. Gender-specific differences were observed in the metabolome, with several metabolites significantly differing between males and females, many of which were correlated with cytokine binding, immunity, and cytochrome P450 activity. Using this dataset, we constructed complex regulatory networks, enabling us to identify important metabolites and key genes involved in regulating the formation of pigeon milk in male and female pigeons, respectively. Additionally, we investigated gender-associated differences in the crop metabolites of pigeons. Our study revealed differences in the modulation of pigeon crop milk metabolism between males and females and shed light on the potential functions of male and female pigeon milk in the growth, development, and immunity of young pigeons, an area that has not been previously explored. In conclusion, our results provide new insights into the metabolic regulation of pigeon crop milk formation during the brooding and breeding stages. Furthermore, our findings lay the foundation for the accurate development of artificial pigeon milk.

In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array.

Detection and recognition of latent fingerprints play crucial roles in identification and security. However, the separation of sensor, memory, and processor in conventional ex-situ fingerprint recognition system seriously deteriorates the latency of decision-making and inevitably increases the overall computing power. In this work, a photoelectronic reservoir computing (RC) system, consisting of DUV photo-synapses and nonvolatile memristor array, is developed to detect and recognize the latent fingerprint with in-sensor and parallel in-memory computing. Through the Ga-rich design, we achieve amorphous GaOx (a-GaOx) photo-synapses with an enhanced persistent photoconductivity (PPC) effect. The PPC effect, which induces nonlinearly tunable conductivity, renders the a-GaOx photo-synapses an ideal deep ultraviolet (DUV) photoelectronic reservoir, thus mapping the complex input vector into a dimensionality-reduced output vector. Connecting the reservoirs and a memristor array, we further construct an in-sensor RC system for latent fingerprint identification. The system maintains over 90% recognition accuracy for latent fingerprint within 15% stochastic noise level via the proposed dual-feature strategy. This work provides a subversive prototype system of DUV in-sensor RC for highly efficient recognition of latent fingerprints.

Lipopolysaccharide Promotes the Proliferation and Differentiation of Goose Embryonic Myoblasts by Promoting Cytokine Expression and Appropriate Apoptosis Processes.

Lipopolysaccharide (LPS) can trigger a series of immune reactions, leading to the occurrence of disease and a decrease in the growth performance of geese. However, the mechanisms of LPS in geese muscle development have not been reported. This study aimed to investigate the effects and mechanisms of LPS on proliferation and differentiation of goose embryonic myoblasts. Embelin and belnacasan combined with LPS were used to explore these effects. Our results demonstrated that LPS significantly induced inflammatory cytokine production in both proliferation and differentiation stages. LPS and embelin treatment significantly improved the proliferation ability (p < 0.05), while LPS reduced the differentiation ability of goose embryonic myoblasts. By adding embelin, the differentiation ability of myoblasts was enhanced, while by adding belnacasan, LPS treatment led to a lower differentiation ability. Combined with the correlation of the expression levels of myogenic, cell cycle, and inflammatory-related genes and proteins, it is speculated that one of the reason for the decrease of differentiation ability of goose embryo myoblasts induced by LPS is the increase of the expression levels of pro-inflammatory factors. Moreover, LPS, embelin and belnacasan, and LPS treatments could significantly increase the apoptosis rate of goose embryonic myoblasts. Taken together, these findings suggest that LPS promotes the proliferation and differentiation of goose embryonic myoblasts by promoting cytokine expression and appropriate apoptosis processes. These findings lay a foundation for the study of the mechanisms of LPS in goose muscle development.

Compact artificial neuron based on anti-ferroelectric transistor.

Neuromorphic machines are intriguing for building energy-efficient intelligent systems, where spiking neurons are pivotal components. Recently, memristive neurons with promising bio-plausibility have been developed, but with limited reliability, bulky capacitors or additional reset circuits. Here, we propose an anti-ferroelectric field-effect transistor neuron based on the inherent polarization and depolarization of Hf0.2Zr0.8O2 anti-ferroelectric film to meet these challenges. The intrinsic accumulated polarization/spontaneous depolarization of Hf0.2Zr0.8O2 films implements the integration/leaky behavior of neurons, avoiding external capacitors and reset circuits. Moreover, the anti-ferroelectric neuron exhibits low energy consumption (37 fJ/spike), high endurance (>1012), high uniformity and high stability. We further construct a two-layer fully ferroelectric spiking neural networks that combines anti-ferroelectric neurons and ferroelectric synapses, achieving 96.8% recognition accuracy on the Modified National Institute of Standards and Technology dataset. This work opens the way to emulate neurons with anti-ferroelectric materials and provides a promising approach to building high-efficient neuromorphic hardware.