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Gastric cancer (GC) poses a significant health challenge worldwide, necessitating the identification of predictive biomarkers to improve prognosis. Dysregulated lipid metabolism is a well-recognized hallmark of tumorigenesis, prompting investigation into apolipoproteins (APOs). In this study, we focused on apolipoprotein D (APOD) following comprehensive analyses of APOs in pan-cancer. Utilizing data from the TCGA-STAD and GSE62254 cohorts, we elucidated associations between APOD expression and multiple facets of GC, including prognosis, tumor microenvironment (TME), cancer biomarkers, mutations, and immunotherapy response, and identified potential anti-GC drugs. Single-cell analyses and immunohistochemical staining confirmed APOD expression in fibroblasts within the GC microenvironment. Additionally, we independently validated the prognostic significance of APOD in the ZN-GC cohort. Our comprehensive analyses revealed that high APOD expression in GC patients was notably associated with unfavorable clinical outcomes, reduced microsatellite instability and tumor mutation burden, alterations in the TME, and diminished response to immunotherapy. These findings provide valuable insights into the potential prognostic and therapeutic implications of APOD in GC.

Abstract Background Systemic lupus erythematosus (SLE) is an autoimmune disease of unknown cause. N6-methyladenosine (m6A) is the most common mRNA modification and participates in various immune processes such as interferon production and immune cell regulation. However, the role of m6A in dysregulated immune response of SLE remains unknown. Methods PBMCs from SLE patients were collected to compare the m6A modification profile by methylated RNA immunoprecipitation sequencing (MeRIP-seq). Interferon regulatory factor 4 (IRF4) was identified by combination with MeRIP-seq and RNA-Seq. IRF4 and methyltransferase 3 (METTL3) were detected using qRT-PCR and WB. Clinical significance of IRF4 in SLE patients was explored subsequently. IRF4 expression in B cell subsets of female MRL/lpr mice was detected by flow cytometry. Adeno-associated viruses (AAV) including AAV9-METTL3-OE and/or AAV9-IRF4-sh were treated with female MRL/lpr mice. Autoantibody levels and kidney injury were tested by ELISA, pathological staining, and immunofluorescence. m6A level of IRF4 was detected by MeRIP-qPCR. The downstream effectors of IRF4 contributing to renal pathology were explored by RNA-seq and verified by qRT-PCR. Results m6A methylation features were obviously aberrant in SLE patients, and IRF4 was the upregulated gene modified by m6A. METTL3 and IRF4 expressions were elevated in SLE patients and kidney of MRL/lpr mice. Clinical analysis indicated that SLE patients with high IRF4 level were more prone to kidney damage. IRF4 expression was especially increased in plasma cells of MRL/lpr mice. METTL3 induced renal IRF4 expression, plasma creatinine, ANA and urine ALB levels, IgG and C3 deposition, and renal damage and plasma cell infiltration were aggravated in MRL/lpr mice. However, IRF4 depletion could partially reduce METTL3-induced kidney damage. Meanwhile, m6A level of IRF4 elevated with METTL3 overexpression. Also, the expression of Cxcl1, Bcl3, and Fos mRNA were significantly reduced after knockdown of IRF4, which were mainly involved in TNF signaling pathway. Conclusions Our study confirmed that upregulated METTL3 promoting IRF4 expression in an m6A-dependent manner, thus causing plasma cell infiltration-mediated kidney damage of SLE. This provides new evidence for the role of m6A in SLE kidney injury.

Abstract Under a warmer climate, the enhancement of dry-wet abrupt alternation (DWAA) risk poses a great challenge for sustainable development. Here, we introduce a novel framework for DWAA detection based on our proposed soil moisture concentration index. By the end of this century, over humid southern China, the shift of soil moisture time series from anomalously wet to anomalously dry pattern, or the other way around, will be more abrupt. In addition, the proposed framework driven by Coupled Model Intercomparison Project Phase 6 simulations projects more widespread DWAA-affected areas over southwestern China, coastal regions of southeastern China, and the lower reaches of the Yangtze River, especially under a high emission scenario. The framework proposed in this study provides an efficient system for DWAA detection and prediction, and the findings of this study provide a reference for upgrading hydraulic infrastructure and mitigating future DWAA events.

Abstract Background BAK1 (Brassinosteroid insensitive 1-associated receptor kinase 1) plays an important role in disease resistance in plants. However, the function of BAK1 family in cucumber and the decisive genes for disease-resistance remain elusive. Results Here, we identified 27 CsBAK1s in cucumber, and classified them into five subgroups based on phylogenetic analysis and gene structure. CsBAK1s in the same subgroup shared the similar motifs, but different gene structures. Cis-elements analysis revealed that CsBAK1s might respond to various stress and growth regulation. Three segmentally duplicated pairwise genes were identified in cucumber. In addition, Ka/Ks analysis indicated that CsBAK1s were under positive selection during evolution. Tissue expression profile showed that most CsBAK1s in Subgroup II and IV showed constitutive expression, members in other subgroups showed tissue-specific expression. To further explore whether CsBAK1s were involved in the resistance to pathogens, the expression patterns of CsBAK1s to five pathogens (gummy stem blight, powdery mildew, downy mildew, grey mildew, and fusarium wilt) reveled that different CsBAK1s had specific roles in different pathogen infections. The expression of CsBAK1-14 was induced/repressed significantly by five pathogens, CsBAK1-14 might play an important role in disease resistance in cucumber. Conclusions 27 BAK1 genes were identified in cucumber from a full perspective, which have important functions in pathogen infection. Our study provided a theoretical basis to further clarify the function of BAK1s to disease resistance in cucumber.

Abstract Purpose This study evaluates the efficacy of intrauterine hCG perfusion for RIF, as defined by ESHRE 2023 guidelines, highlighting hCG as a cost-effective alternative to other immunotherapies, especially suitable for less developed regions. It aims to clarify treatment guidance amidst previous inconsistencies. Methods This meta-analysis, registered with PROSPERO (CRD42024443241) and adhering to PRISMA guidelines, assessed the efficacy and safety of intrauterine hCG perfusion in enhancing implantation and pregnancy outcomes in RIF. Comprehensive literature searches were conducted through December 2023 in major databases including PubMed, Web of Science, Embase, the Cochrane Library, and key Chinese databases, without language restrictions. Inclusion and exclusion criteria were strictly aligned with the 2023 ESHRE recommendations, with exclusions for studies lacking robust control, clear outcomes, or adequate data integrity. The risk of bias was evaluated using the Newcastle-Ottawa Scale, ROBINS-I, and RoB2 tools. Data analysis was performed in R using the ‘meta’ package, employing both fixed and random effect models to account for study variability. Subgroup analyses by dosage, volume, hCG concentration, timing of administration, and type of embryo transfer were conducted to deepen insights, enhancing the reliability and depth of the meta-analysis in elucidating the role of hCG perfusion in RIF treatments. Results Data from 13 studies, comprising six retrospective and six prospective studies from single centers, along with one multi-center RCT, totaling 2,157 participants, were synthesized to evaluate the effectiveness of intrauterine hCG perfusion in enhancing implantation and pregnancy outcomes in patients with RIF. Significant improvements were observed in clinical pregnancy and embryo implantation rates across various dosages, timing of administration, and embryo developmental stages, without impacting miscarriage rates. Notably, the most significant efficacy within subgroups occurred with a 500 IU dosage and perfusion parameters of ≤ 500µL volume and ≥ 2 IU/µL concentration. Additionally, a limited number of studies showed no significant increases in ectopic pregnancy or multiple pregnancy rates, and a modest improvement in live birth rates, although the small number of these studies precludes definitive conclusions. Conclusions The analysis suggests that intrauterine hCG perfusion probably enhances embryo implantation, clinical pregnancy, and live birth rates slightly in RIF patients. Benefits are indicated with a dosage of 500 IU and a maximum volume of 500µL at concentrations of at least 2 IU/µL. However, substantial heterogeneity from varying study types and the limited number of studies necessitate cautious interpretation. These findings underscore the need for more rigorously designed RCTs to definitively assess the efficacy and safety.

Abstract Background With the increasing incidence of obesity and the childbearing-age delay among women, a debate over obesity’s impacts on pregnancy and neonatal outcomes becomes hot. The potential negative effects of obesity and aging on fertility lead to an idea, whether an obese female pursuing IVF treatment can benefit from an ideal BMI achieved over a long-time weight loss process at the cost of aging? We aimed to assess the association between body mass index (BMI) and clinical or neonatal outcomes in patients undergoing in vitro fertilization (IVF) treatment, for answering whether it is necessary to lose weight first for obese patients, particularly those at advanced age. Methods A retrospective cohort study was performed using multicentered data from China. The women were stratified into 5 groups in terms of pre-gravid BMI (kg/m2) with the WHO obesity standard (group 1: BMI

Farmland abandonment represents the extreme utilization of marginal land and results from the interplay between humans and the environment. In the Yangtze River Delta region of China, a significant economic hub, understanding the landscape patterns and driving mechanisms of farmland abandonment and recultivation land is crucial for regional sustainable development. This study aims to map the extent of farmland abandonment in 2020 and recultivation land in 2021 across the Yangtze River Delta, analyze the landscape patterns of farmland abandonment and recultivation land on multiple scales, and explore their driving mechanisms. Utilizing multi-source data and the Land Use Trajectory Change Method on the Google Earth Engine platform, we classified land use and identified farmland abandonment, fallow land, active farmland, and recultivation land. Spatial statistical analysis was conducted to investigate the distribution and patterns of farmland abandonment and recultivation land at Yangtze River Delta, provincial, and municipal scales. Additionally, a stepwise multiple linear regression model was employed to dissect the main driving forces of farmland abandonment and recultivation land. Results revealed that farmland abandonment in the Yangtze River Delta region exceeded an abandonment rate of 10%, with a reclamation rate in the following year of less than 30%. The spatial distribution of farmland abandonment and recultivation land varied significantly across different scales. On the Yangtze River Delta scale, farmland abandonment was positively influenced by altitude, distance to rivers, and temperature, while recultivation land was positively correlated with night light intensity. At the provincial and municipal scales, the driving forces exhibited scaling effects, with different factors becoming significant. Our findings contribute to a deeper understanding of the complexity and diversity of farmland abandonment in the Yangtze River Delta region. The results provide a scientific basis for formulating land use policies, promoting sustainable agricultural development, and ensuring ecological balance. By uncovering the land use status and ecosystem balance influenced by human-environment interactions, this study offers insights into achieving a harmonious coexistence between economy, society, and ecology in the Yangtze River Delta region.

ABSTRACTMachine learning has become an important approach for land use change modeling. However, conventional machine learning algorithms are limited in their ability to capture causal relationships in land use change, which are important knowledge for planners and decision makers. In this study, we showcase the usefulness of causal machine learning to understand the heterogeneous causal effect of changing land use on building height through a case study in Shenzhen, China. Also, by leveraging the power of causal machine learning, we identify the key conditions under which greater building height change would occur after land use interventions. The results suggest that building height would increase by 3.68 floors and 1.61 floors on average if industrial land is converted to residential and commercial, respectively, and 2.35 floors if commercial land is changed to residential land. The heterogeneity of causal effect is also captured for different land use change scenarios. The factor analysis based on the decision tree algorithm reveals the key conditions on which greater building height increase would occur by changing land use. Overall, this study can contribute to literature by providing an effective approach to counterfactual land use change modeling with enhanced explainability.

ABSTRACTWith the rapid expansion of urbanization, it is imperative to monitor built-up areas changes to promote the sustainable development of cities, aligning with the goals of Sustainable Development Goal 11(SDG 11). Remote sensing big data is valuable for automatically mapping these changes. Bi-temporal very high-resolution (Bi-VHR) optical images have been widely utilized for fine-grained change detection (CD). However, the significant spatiotemporal inconsistency due to imaging conditions and seasonal variations poses challenges for VHR optical CD. Unlike optical images, synthetic aperture radar (SAR) images are unaffected by atmospheric interference and provide robust spatiotemporal features as a supplement. Previous CD algorithms with SAR overlooked the exploration of long-term features of time series. In this study, we propose a novel CD framework combining long-term SAR with Bi-VHR images. It incorporates a spatial-frequency learning module to enhance SAR temporal features and a multisource feature fusion module to adaptively fuse the heterogeneous features. The experiments are conducted on OS-BCD dataset, which is the first dataset specifically designed for this task. The results demonstrate that our proposal outperforms advanced CD methods with F1 score, IoU and OA of 64.99%, 48.13%, and 95.11%, validating the efficacy of our proposal in accurately detecting changes in built-up areas.

Abstract The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a fully autonomous robotic ultrasound system, which is able to scan thyroid regions without human assistance and identify malignant nod- ules. In this system, human skeleton point recognition, reinforcement learning, and force feedback are used to deal with the difficulties in locating thyroid targets. The orientation of the ultrasound probe is adjusted dynamically via Bayesian optimization. Experimental results on human participants demonstrated that this system can perform high-quality ultrasound scans, close to manual scans obtained by clinicians. Additionally, it has the potential to detect thyroid nodules and provide data on nodule characteristics for American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) calculation.

Abstract JC polyomavirus (JCPyV) is a human polyomavirus that can establish lifelong persistent infection in the majority of adults. It is typically asymptomatic in immunocompetent individuals. However, there is a risk of developing progressive multifocal leukoencephalopathy (PML) in immunocompromised or immunosuppressed patients. Though JCPyV commonly resides in the kidney-urinary tract, its involvement in urinary system diseases is extremely rare. Here, we reported a case of a 60-year-old male patient with coronavirus disease 2019 (COVID-19) infection who developed hemorrhagic cystitis after receiving treatment with nirmatrelvir 300 mg/ritonavir 100 mg quaque die (QD). Subsequent metagenomic next-generation sequencing (mNGS) confirmed the infection to be caused by JCPyV type 2. Then, human immunoglobulin (PH4) for intravenous injection at a dose of 25 g QD was administered to the patient. Three days later, the hematuria resolved. This case illustrates that in the setting of compromised host immune function, JCPyV is not limited to causing central nervous system diseases but can also exhibit pathogenicity in the urinary system. Moreover, mNGS technology facilitates rapid diagnosis of infectious etiology by clinical practitioners, contributing to precise treatment for patients.

Abstract Objective IR emerges as a feature in the pathophysiology of PCOS, precipitating ovulatory anomalies and endometrial dysfunctions that contribute to the infertility challenges characteristic of this condition. Despite its clinical significance, a consensus on the precise mechanisms by which IR exacerbates PCOS is still lacking. This study aims to harness bioinformatics tools to unearth key IR-associated genes in PCOS patients, providing a platform for future therapeutic research and potential intervention strategies. Methods We retrieved 4 datasets detailing PCOS from the GEO, and sourced IRGs from the MSigDB. We applied WGCNA to identify gene modules linked to insulin resistance, utilizing IR scores as a phenotypic marker. Gene refinement was executed through the LASSO, SVM, and Boruta feature selection algorithms. qPCR was carried out on selected samples to confirm findings. We predicted both miRNA and lncRNA targets using the ENCORI database, which facilitated the construction of a ceRNA network. Lastly, a drug-target network was derived from the CTD. Results Thirteen genes related to insulin resistance in PCOS were identified via WGCNA analysis. LASSO, SVM, and Boruta algorithms further isolated CAPN2 as a notably upregulated gene, corroborated by biological verification. The ceRNA network involving lncRNA XIST and hsa-miR-433-3p indicated a possible regulatory link with CAPN2, supported by ENCORI database. Drug prediction analysis uncovered seven pharmacological agents, most being significant regulators of the endocrine system, as potential candidates for addressing insulin resistance in PCOS. Conclusions This study highlights the pivotal role of CAPN2 in insulin resistance within the context of PCOS, emphasizing its importance as both a critical biomarker and a potential therapeutic target. By identifying CAPN2, our research contributes to the expanding evidence surrounding the CAPN family, particularly CAPN10, in insulin resistance studies beyond PCOS. This work enriches our understanding of the mechanisms underlying insulin resistance, offering insights that bridge gaps in the current scientific landscape.

Forest fragmentation is a significant issue in China; however, a comprehensive understanding and assessment of the temporal trends and spatial modes of forest fragmentation remain lacking. For the first time, this study employs an object-based morphological image processing method to conduct multiscale spatiotemporal identification and analysis of China’s forest landscape from 1985 to 2023. Observations reveal an overall improvement in the integrity of China’s forest landscape from 1985 to 2023, with 79 % of counties showing a positive trend in forest landscape integrity and 5 % experiencing forest fragmentation. The forest core area in China has almost tripled from 1985 to 2023, expanding from 0.57 to 1.53 million km2. Nevertheless, 32.04 % of forests in China remain within 100 m of a non-forest edge, significantly higher than the global average (∼20 %). From 1985 to 2023, out of the 0.33 million km2 of newly planted forests, 29.5 % became forest cores, while the remaining 70.5 % constituted forest edges. This underscores the need for government planning to focus not only on expanding forest areas but also on enhancing the integrity of forest landscapes, thereby creating more ecologically valuable forest ecosystems. Analysis indicates that 25 % of forest fragmentation in China is associated with agriculture (16 %) and urbanization (9 %), while the remaining portion is linked to local topography, climate, and other natural boundaries for forest distribution. Although natural boundaries play a predominant role in forest fragmentation, there is a growing trend of agricultural activities and urbanization encroaching upon the edges. It is recommended that government policies prioritize strategic planning for forest landscape integrity, considering both quantitative expansion and qualitative optimization, to ensure the sustainable development of China’s forest ecosystems.

BackgroundThe plant root system is critical for the absorption of water and nutrients, and have a direct influence on growth and yield. In cucumber, a globally consumed crop, the molecular mechanism of root development remains unclear, and this has implications for developing stress tolerant varieties. This study sought to determine the genetic patterns and related genes of cucumber root weight. A core cucumber germplasms population was used to do the GWAS analysis in three environments.ResultsHere, we investigated four root-weight related traits including root fresh weight (RFW), root dry weight (RDW), ratio of root dry weight to root fresh weight (RDFW) and the comprehensive evaluation index, D-value of root weight (DRW) deduced based on the above three traits for the core germplasm of the cucumber global repository. According to the D-value, we identified 21 and 16 accessions with light and heavy-root, respectively. We also found that the East Asian ecotype accessions had significantly heavier root than other three ecotypes. The genome-wide association study (GWAS) for these four traits reveals that 4 of 10 significant loci (gDRW3.1, gDRW3.2, gDRW4.1 and gDRW5.1) were repeatedly detected for at least two traits. Further haplotype and expression analysis for protein-coding genes positioned within these 4 loci between light and heavy-root accessions predicted five candidate genes (i.e., Csa3G132020 and Csa3G132520 both encoding F-box protein PP2-B1 for gDRW3.1, Csa3G629240 encoding a B-cell receptor-associated protein for gDRW3.2, Csa4G499330 encodes a GTP binding protein for gDRW4.1, and Csa5G286040 encodes a proteinase inhibitor for gDRW5.1).ConclusionsWe conducted a systematic analysis of the root genetic basis and characteristics of cucumber core germplasms population. We detected four novel loci, which regulate the root weight in cucumber. Our study provides valuable candidate genes and haplotypes for the improvement of root system in cucumber breeding.

The biomass raw material magnolol with a biphenyl structure was used to synthesize bio-based epoxy resin, and flexible polyetheramine curing agents(D230, D400, D2000 and T403) were used to cure the bio-based epoxy anti-erosion coating. The effects of polyetheramine molecular weight and functionality on the mechanical properties, corrosion resistance and anti-erosion protection performance of magnolol epoxy resin were explored, and the anti-erosion mechanism of magnolol epoxy coatings was proposed. Results showed that the magnolol epoxy coating cured with D230 maintained a high impedance value(1.25×1011 Ω·cm2) after 15 d of immersion in 3.5%(mass fraction) NaCl solution. The coating cured with T403 possessed a high tensile strength(26.59 MPa), low friction coefficient(0.373) and wear rate [0.018 3 mm3/(N·m)], while the magnolol epoxy coating cured with D2000 exhibited lower mass loss(244.74 mg) and volume loss(157 mm3) in the erosion test. Under the coupled erosion of solid/liquid/gas three-phase flow, the longer flexible segment of D2000 endowed the magnolol epoxy coating with better elasticity and flexibility, and therefore reduced the impact of the erosion medium, thus showing excellent anti-erosion performance.

Abstract The northern terrestrial biomes are being remarkably altered by climate change. Higher springtime temperature induces the earlier greening of vegetation, which may further influence ecosystem functions during the subsequent season. However, the response of summer net ecosystem productivity to spring vegetation greenness and phenology changes has not yet been quantified. To understand the impact of such phenological changes on terrestrial carbon sink of the following season, here we integrate remotely-sensed vegetation data and model simulations of carbon flux with an explainable machine learning approach. We find that the lagged effects of widespread earlier spring greening are increasing the summer ecosystem carbon sink across the northern vegetated areas (30° to 90°N) from 1982 to 2015. In particular, response disparities exist in non-agricultural biomes, and the vegetation with moderate tree coverage is more sensitive to earlier spring greening. Furthermore, modest tree restoration can strengthen the beneficial effects of earlier spring greening. This study improves our understanding of interseasonal vegetation-climate-carbon coupling that drives the key ecological feedback within climate change projections.

Abstract Background Southwest China is one of the largest karst regions in the world. Karst environment is relatively fragile and vulnerable to human activities. Due to the discharge of sewage and domestic garbage, the karst system may be polluted by pathogenic bacteria. The detection of bacterial distribution and identification of phage capable of infecting them is an important approach for environmental assessment and resource acquisition. Methods Bacteria and phages were isolated from karst water in southwest China using the plate scribing and double plate method, respectively. Isolated phage was defined by transmission electron microscopy, one-step growth curve and optimal multiplicity of infection (MOI). Genomic sequencing, phylogenetic analysis, comparative genomic and proteomic analysis were performed. Results A Klebsiella quasipneumoniae phage was isolated from 32 isolates and named KL01. KL01 is morphologically identified as Caudoviricetes with an optimal MOI of 0.1, an incubation period of 10 min, and a lysis period of 60 min. The genome length of KL01 is about 45 kb, the GC content is 42.5%, and it contains 59 open reading frames. The highest average nucleotide similarity between KL01 and a known Klebsiella phage 6939 was 83.04%. Conclusions KL01 is a novel phage, belonging to the Autophagoviridae, which has strong lytic ability. This study indicates that there were not only some potential potentially pathogenic bacteria in the karst environment, but also phage resources for exploration and application.

For improving the anti-corrosion performance and durability of waterborne epoxy coatings in high-salt， high-humidity marine environments， polyaniline/boron nitride nanoparticles (PBN) were prepared by the in-situ polymerization of polyaniline (PANI) on the surface of hydroxylated hexagonal boron nitride (BN).In addition， PBN were used as anti-corrosion filler to enhance the anti-corrosion performance of waterborne epoxy coatings.The structure and micro-morphology of nano-particles were characterized by Fourier transform infrared spectroscopy(FT-IR) and scanning electron microscopy (SEM).Moreover， electrochemical tests and neutral salt spray tests (NSS) were used to explore the effects of BN， PANI modifications and PBN addition amount on the anti-corrosion performance of waterborne epoxy coatings.Results showed that PANI grew in situ on the surface of BN nanosheets to form a coral-like structure， and the bonding strength of waterborne epoxy coating reached 6.05 MPa after adding 0.50%(mass fraction，the same below) PBN.The corrosion current density (1.99×10-9 A/cm2) of the composite coating immersed in 3.5%NaCl solution after 120 h was reduced by about 3 orders of magnitude compared to that of pure epoxy coating (1.01×10-6 A/cm2).This was attributed to the fact that the addition of 0.50%PBN could evenly disperse in the epoxy resin， fill small gaps in the coating and enhance the barrier effect against corrosive medium.Meanwhile， PANI had a corrosion inhibition effect， which could synergistically improve the anti-corrosion performance of pure epoxy coatings.

Photonic neural networks are emerging as promising computing platforms for artificial intelligence (AI). Particularly, integrated photonic unitary neural networks (IPUNNs) are capable of mitigating gradient vanishing/explosion problems when deeper neural networks are constructed. Furthermore, their optical implementations are also much simpler compared to non-unitary counterparts. Meanwhile, real-valued datasets still dominate AI research and the encoding strategy is critical for IPUNNs' performances. However, there are few studies to compare different encoding strategies of IPUNNs to represent these real-valued datasets and their impacts on IPUNNs' performances. Here, in the scope of encoding strategies for real-valued features, we first compare different schemes, such as phase, amplitude and hybrid encoding using numerical simulations, with benchmarks of decision boundary and image recognition tasks. These encoding strategies of IPUNNs are also compared to non-unitary real-valued neural networks (RVNNs) with trainable biases for the same benchmarks. The results suggest that phase encoding outperforms amplitude and hybrid encoding, and exhibits comparable performances to non-unitary RVNNs. To verify the numerical results, a 10×10 IPUNN chip is designed and fabricated. The phase encoding is chosen to be implemented because of its superior performances in numerical studies. We reconfigure the IPUNN chip to perform decision boundary and image recognition tasks by on-chip in-situ training. The experimental results match the simulations well. Our work provides insights for implementing reconfigurable IPUNNs in AI computing.

Cryogenic data transfer has numerous applications at 4.2 K for extracting data from supercomputers and quantum computers to address interface bottlenecks. The external modulation of data transmission across temperature zones is achieved through the use of electro-optic modulators. The performance of a thin-film lithium niobate (TFLN) Mach-Zehnder modulator was examined at cryogenic temperatures. Low-frequency measurements indicate that the Vπ (voltage needed for π phase shift) of the modulator increases as the temperature decreases from 298 K to 4.2 K. A comparison was conducted between the readout performance of the electrical wire link and the optical fiber link, spanning from 4.2 K to 298 K. Additionally, high-speed data transmission was successfully demonstrated in a 4.2 K environment without the need for cryogenic electric amplifiers, achieving signals of up to 30 Gbps. The proposed solution holds potential benefits for both classical and quantum computers in alleviating interface bottlenecks.

An on-chip, high-extinction-ratio transverse electric (TE) pass polarizer utilizing a silicon oxynitride (SiON) slab has been proposed and experimentally verified. The power confinement ratio of the mode field is manipulated by using a SiON slab, where most of the power of the transverse magnetic (TM) mode is transferred to the upper SiON slab and then attenuated through radiation, while the TE mode passes through with relatively low propagation loss. Experimental results show that our proposed device can achieve an extinction ratio that varies from 20.5 to 32.7 dB in the wavelength range of 790 to 870 nm, with an insertion loss of 0.6 to 1.7 dB. Potentially, this design has lower material refractive index contrast, larger minimum etching size, smaller lengths, and less stray light crosstalk, which is beneficial for systems applications such as gyroscopes.