Mechanism of discoloration of Antarctic krill oil upon storage: A study based on model systems.

The reddish-orange color of Antarctic krill oil fades during storage, and the mechanism remains unclear. Model systems containing different combinations of astaxanthin (ASTA), phosphatidylethanolamine (PE), and tocopherol were subjected to accelerated storage. Among all groups containing ASTA, only the ones with added PE showed significant fading. Meanwhile, the specific UV-visible absorption (A470 and A495) showed a similar trend. Peroxide value and thiobarbituric acid reactive substances increased during storage, while ASTA and PE contents decreased. Correlation analysis suggested that oxidized PE promoted fading by accelerating the transformation of ASTA. PE content exceeded the critical micelle concentration (1µg/g) indicating the formation of reverse micelles. Molecular docking analysis indicated that PE also interacted with ASTA in an anchor-like manner. Therefore, it is speculated that amphiphilic ASTA is more readily distributed at the oil-water interface of reverse micelles and captured by oxidized PE, which facilitates oxidation transfer, leading to ASTA oxidation and color fading.

Nonlinear Regularization Decoding Method for Speech Recognition.

Existing end-to-end speech recognition methods typically employ hybrid decoders based on CTC and Transformer. However, the issue of error accumulation in these hybrid decoders hinders further improvements in accuracy. Additionally, most existing models are built upon Transformer architecture, which tends to be complex and unfriendly to small datasets. Hence, we propose a Nonlinear Regularization Decoding Method for Speech Recognition. Firstly, we introduce the nonlinear Transformer decoder, breaking away from traditional left-to-right or right-to-left decoding orders and enabling associations between any characters, mitigating the limitations of Transformer architectures on small datasets. Secondly, we propose a novel regularization attention module to optimize the attention score matrix, reducing the impact of early errors on later outputs. Finally, we introduce the tiny model to address the challenge of overly large model parameters. The experimental results indicate that our model demonstrates good performance. Compared to the baseline, our model achieves recognition improvements of 0.12%, 0.54%, 0.51%, and 1.2% on the Aishell1, Primewords, Free ST Chinese Corpus, and Common Voice 16.1 datasets of Uyghur, respectively.

Individual large-scale functional network mapping for major depressive disorder with electroconvulsive therapy.

Personalized functional connectivity mapping has been demonstrated to be promising in identifying underlying neurophysiological basis for brain disorders and treatment effects. Electroconvulsive therapy (ECT) has been proved to be an effective treatment for major depressive disorder (MDD) while its active mechanisms remain unclear. Here, 46 MDD patients before and after ECT as well as 46 demographically matched healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. A spatially regularized form of non-negative matrix factorization (NMF) was used to accurately identify functional networks (FNs) in individuals to map individual-level static and dynamic functional network connectivity (FNC) to reveal the underlying neurophysiological basis of therepetical effects of ECT for MDD. Moreover, these static and dynamic FNCs were used as features to predict the clinical treatment outcomes for MDD patients. We found that ECT could modulate both static and dynamic large-scale FNCs at individual level in MDD patients, and dynamic FNCs were closely associated with depression and anxiety symptoms. Importantly, we found that individual FNCs, particularly the individual dynamic FNCs could better predict the treatment outcomes of ECT suggesting that dynamic functional connectivity analysis may be better to link brain functional characteristics with clinical symptoms and treatment outcomes. Taken together, our findings provide new evidence for the active mechanisms and biomarkers for ECT to improve diagnostic accuracy and to guide individual treatment selection for MDD patients.

A faster killing effect of plastid-mediated RNA interference on a leaf beetle through induced dysbiosis of the gut bacteria.

The expression of double-stranded RNAs (dsRNAs) from the plastid genome has been proven to be an effective method for controlling herbivorous pests by targeting essential insect genes. However, there are limitations to the efficiency of plastid-mediated RNA interference (PM-RNAi) due to the initial damage caused by the insects and their slow response to RNA interference. In this study, we developed transplastomic poplar plants that express dsRNAs targeting the ß-Actin (dsACT) and Srp54k (dsSRP54K) genes of Plagiodera versicolora. Feeding experiments showed that transplastomic poplar plants can cause significantly higher mortality in P. versicolora larvae compared with nuclear transgenic or wild-type poplar plants. The efficient killing effect of PM-RNAi on P. versicolora larvae was found to be dependent on the presence of gut bacteria. Importantly, foliar application of a gut bacterial strain, Pseudomonas putida, will induce dysbiosis in the gut bacteria of P. versicolora larvae, leading to a significant acceleration in the speed of killing by PM-RNAi. Overall, our findings suggest that interfering with gut bacteria could be a promising strategy to enhance the effectiveness of PM-RNAi for insect pest control, offering a novel and effective approach for crop protection based on RNAi technology.

CD276 enhances sunitinib resistance in clear cell renal cell carcinoma by promoting DNA damage repair and activation of FAK-MAPK signaling pathway.

OBJECTIVE: This study aimed to explore the effect of CD276 expression on the sunitinib sensitivity of clear cell renal cell carcinoma (ccRCC) cell and animal models and the potential mechanisms involved. METHODS: CD276 expression levels of ccRCC and normal samples were analyzed via online databases and real-time quantitative PCR (RT-qPCR). CD276 was knocked down in ccRCC cell models (sunitinib-resistant 786-O/R cells and sunitinib-sensitive 786-O cells) using shRNA transfection, and the cells were exposed to a sunitinib (2 µM) environment. Cells proliferation was then analyzed using MTT assay and colony formation experiment. Alkaline comet assay, immunofluorescent staining, and western blot experiments were conducted to assess the DNA damage repair ability of the cells. Western blot was also used to observe the activation of FAK-MAPK pathway within the cells. Finally, a nude mouse xenograft model was established and the nude mice were orally administered sunitinib (40 mg/kg/d) to evaluate the in vivo effects of CD276 knockdown on the therapeutic efficacy of sunitinib against ccRCC. RESULTS: CD276 was significantly upregulated in both ccRCC clinical tissue samples and cell models. In vitro experiments showed that knocking down CD276 reduced the survival rate, IC50 value, and colony-forming ability of ccRCC cells. Knocking down CD276 increased the comet tail moment (TM) values and γH2AX foci number, and reduced BRCA1 and RAD51 protein levels. Knocking down CD276 also decreased the levels of p-FAK, p-MEK, and p-ERK proteins. CONCLUSION: Knocking down CD276 effectively improved the sensitivity of ccRCC cell and animal models to sunitinib treatment.

Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity.

BACKGROUND: Early prediction of pathological complete response (pCR) is important for deciding appropriate treatment strategies for patients. In this study, we aimed to quantify the dynamic characteristics of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) and investigate its value to improve pCR prediction as well as its association with tumor heterogeneity in breast cancer patients. METHODS: The DCE-MRI, clinicopathologic record, and full transcriptomic data of 785 breast cancer patients receiving neoadjuvant chemotherapy were retrospectively included from a public dataset. Dynamic features of DCE-MRI were computed from extracted phase-varying radiomic feature series using 22 CAnonical Time-sereis CHaracteristics. Dynamic model and radiomic model were developed by logistic regression using dynamic features and traditional radiomic features respectively. Various combined models with clinical factors were also developed to find the optimal combination and the significance of each components was evaluated. All the models were evaluated in independent test set in terms of area under receiver operating characteristic curve (AUC). To explore the potential underlying biological mechanisms, radiogenomic analysis was implemented on patient subgroups stratified by dynamic model to identify differentially expressed genes (DEGs) and enriched pathways. RESULTS: A 10-feature dynamic model and a 4-feature radiomic model were developed (AUC = 0.688, 95%CI: 0.635-0.741 and AUC = 0.650, 95%CI: 0.595-0.705) and tested (AUC = 0.686, 95%CI: 0.594-0.778 and AUC = 0.626, 95%CI: 0.529-0.722), with the dynamic model showing slightly higher AUC (train p = 0.181, test p = 0.222). The combined model of clinical, radiomic, and dynamic achieved the highest AUC in pCR prediction (train: 0.769, 95%CI: 0.722-0.816 and test: 0.762, 95%CI: 0.679-0.845). Compared with clinical-radiomic combined model (train AUC = 0.716, 95%CI: 0.665-0.767 and test AUC = 0.695, 95%CI: 0.656-0.714), adding the dynamic component brought significant improvement in model performance (train p < 0.001 and test p = 0.005). Radiogenomic analysis identified 297 DEGs, including CXCL9, CCL18, and HLA-DPB1 which are known to be associated with breast cancer prognosis or angiogenesis. Gene set enrichment analysis further revealed enrichment of gene ontology terms and pathways related to immune system. CONCLUSION: Dynamic characteristics of DCE-MRI were quantified and used to develop dynamic model for improving pCR prediction in breast cancer patients. The dynamic model was associated with tumor heterogeniety in prognostic-related gene expression and immune-related pathways.

Global responses of wetland methane emissions to extreme temperature and precipitation.

As global warming continues, events of extreme heat or heavy precipitation will become more frequent, while events of extreme cold will become less so. How wetlands around the globe will react to these extreme events is unclear yet critical, because they are among the greatest natural sources of methane(CH4). Here we use seven indices of extreme climate and the rate of methane emission from global wetlands(WME) during 2000-2019 simulated by 12 published models as input data. Our analyses suggest that extreme cold (particularly extreme low temperatures) inhibits WME, whereas extreme heat (particularly extreme high temperatures) accelerates WME. Our results also suggest that daily precipitation >10 mm accelerates WME, while much higher daily precipitation levels can slow WME. The correlation of extreme high temperature and precipitation with rate of WME became stronger during the study period, while the correlation between extreme low temperature and WME rate became weaker.

Subchondral bone expansion in advanced knee osteoarthritis: Relation with radiographic severity and role in surgical decision-making.

Background: Joint space width (JSW) is a traditional imaging marker for knee osteoarthritis (OA) severity, but it lacks sensitivity in advanced cases. We propose tibial subchondral bone area (TSBA), a new CT imaging marker to explore its relationship with OA radiographic severity, and to test its performance for classifying surgical decisions between unicompartmental knee arthroplasty (UKA) and total knee arthroplasty (TKA) compared to JSW. Methods: We collected clinical, radiograph, and CT data from 182 patients who underwent primary knee arthroplasty (73 UKA, 109 TKA). The radiographic severity was scored using Kellgren-Lawrence (KL) grading system. TSBA and JSW were extracted from 3D CT-reconstruction model. We used independent t-test to investigate the relationship between TSBA and KL grade, and binary logistic regression to identify factors associated with TKA risk. The accuracy of TSBA, JSW and established classification model in differentiating between UKA and TKA was assessed using AUC. Results: All parameters exhibited inter- and intra-class coefficients greater than 0.966. Patients with KL grade 4 had significantly larger TSBA than those with KL grade 3. TSBA (0.708 of AUC) was superior to minimal/average JSW (0.547/0.554 of AUC) associated with the risk of receiving TKA. Medial TSBA, together with gender and Knee Society Knee Score, emerged as independent classification factors in multivariate analysis. The overall AUC of composite model for surgical decision-making was 0.822. Conclusion: Tibial subchondral bone area is an independent imaging marker for radiographic severity, and is superior to JSW for surgical decision-making between UKA and TKA in advanced OA patients.

Two new species of the mealybug genus Paracoccus from Jiangxi, South China (Hemiptera, Coccomorpha, Pseudococcidae).

Two new mealybug species, Paracoccusgillianwatsonae Zhang, sp. nov. and P.wui Zhang, sp. nov., collected from Jiangxi, South China, are described and illustrated based on the morphology of adult females. Paracoccusgillianwatsonae is similar to P.burnerae (Brain, 1915), but it differs in having fewer pairs of cerarii, and in lacking both ventral oral collar tubular ducts on the margins of the head and translucent pores on the hind femur. Paracoccuswui resembles P.keralae Williams, 2004 and P.neocarens (Lit, 1992), but it differs in lacking ventral oral collar tubular ducts on the margins of the head and in having multilocular disc-pores usually in double rows at the posterior edges of abdominal segments V and VI. A key to the Paracoccus species found in China is provided.

Exploring the roles and potential therapeutic strategies of inflammation and metabolism in the pathogenesis of vitiligo: a mendelian randomization and bioinformatics-based investigation.

Introduction: Vitiligo, a common autoimmune acquired pigmentary skin disorder, poses challenges due to its unclear pathogenesis. Evidence suggests inflammation and metabolism's pivotal roles in its onset and progression. This study aims to elucidate the causal relationships between vitiligo and inflammatory proteins, immune cells, and metabolites, exploring bidirectional associations and potential drug targets. Methods: Mendelian Randomization (MR) analysis encompassed 4,907 plasma proteins, 91 inflammatory proteins, 731 immune cell features, and 1400 metabolites. Bioinformatics analysis included Protein-Protein Interaction (PPI) network construction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Subnetwork discovery and hub protein identification utilized the Molecular Complex Detection (MCODE) plugin. Colocalization analysis and drug target exploration, including molecular docking validation, were performed. Results: MR analysis identified 49 proteins, 39 immune cell features, and 59 metabolites causally related to vitiligo. Bioinformatics analysis revealed significant involvement in PPI, GO enrichment, and KEGG pathways. Subnetwork analysis identified six central proteins, with Interferon Regulatory Factor 3 (IRF3) exhibiting strong colocalization evidence. Molecular docking validated Piceatannol's binding to IRF3, indicating a stable interaction. Conclusion: This study comprehensively elucidates inflammation, immune response, and metabolism's intricate involvement in vitiligo pathogenesis. Identified proteins and pathways offer potential therapeutic targets, with IRF3 emerging as a promising candidate. These findings deepen our understanding of vitiligo's etiology, informing future research and drug development endeavors.

Molecular mechanisms underlying structural plasticity of electroconvulsive therapy in major depressive disorder.

Although previous studies reported structural changes associated with electroconvulsive therapy (ECT) in major depressive disorder (MDD), the underlying molecular basis of ECT remains largely unknown. Here, we combined two independent structural MRI datasets of MDD patients receiving ECT and transcriptomic gene expression data from Allen Human Brain Atlas to reveal the molecular basis of ECT for MDD. We performed partial least square regression to explore whether/how gray matter volume (GMV) alterations were associated with gene expression level. Functional enrichment analysis was conducted using Metascape to explore ontological pathways of the associated genes. Finally, these genes were further assigned to seven cell types to determine which cell types contribute most to the structural changes in MDD patients after ECT. We found significantly increased GMV in bilateral hippocampus in MDD patients after ECT. Transcriptome-neuroimaging association analyses showed that expression levels of 726 genes were positively correlated with the increased GMV in MDD after ECT. These genes were mainly involved in synaptic signaling, calcium ion binding and cell-cell signaling, and mostly belonged to excitatory and inhibitory neurons. Moreover, we found that the MDD risk genes of CNR1, HTR1A, MAOA, PDE1A, and SST as well as ECT related genes of BDNF, DRD2, APOE, P2RX7, and TBC1D14 showed significantly positive associations with increased GMV. Overall, our findings provide biological and molecular mechanisms underlying structural plasticity induced by ECT in MDD and the identified genes may facilitate future therapy for MDD.

Band-like transport in solution-processed perylene diimide dianion films with high Hall mobility.

It is crucial to prepare high-mobility organic polycrystalline film through solution processing. However, the delocalized carrier transport of polycrystalline films in organic semiconductors has rarely been investigated through Hall-effect measurement. This study presents a strategy for building strong intermolecular interactions to fabricate solution-crystallized p-type perylene diimide (PDI) dianion films with a closer intermolecular π-π stacking distance of 3.25 Å. The highly delocalized carriers enable a competitive Hall mobility of 3 cm2 V-1 s-1, comparable to that of the reported high-mobility organic single crystals. The PDI dianion films exhibit a high electrical conductivity of 17 S cm-1 and typical band-like transport, as evidenced by the negative temperature linear coefficient of mobility proportional to T-3/2. This work demonstrates that, as the intermolecular π-π interactions become strong enough, they will display high mobility and conductivity, providing a new approach to developing high-mobility organic semiconductor materials.

Mucosal-associated invariant T cells promote ductular reaction through amphiregulin in biliary atresia.

BACKGROUND: Biliary atresia (BA) is a neonatal fibro-inflammatory cholangiopathy with ductular reaction as a key pathogenic feature predicting poor survival. Mucosal-associated invariant T (MAIT) cells are enriched in human liver and display multiple roles in liver diseases. We aimed to investigate the function of MAIT cells in BA. METHODS: First, we analyzed correlations between liver MAIT cell and clinical parameters (survival, alanine transaminase, bilirubin, histological inflammation and fibrosis) in two public cohorts of patients with BA (US and China). Kaplan-Meier survival analysis and spearman correlation analysis were employed for survival data and other clinical parameters, respectively. Next, we obtained liver samples or peripheral blood from BA and control patients for bulk RNA sequencing, flow cytometry analysis, immunostaning and functional experiments of MAIT cells. Finally, we established two in vitro co-culture systems, one is the rhesus rotavirus (RRV) infected co-culture system to model immune dysfunction of human BA which was validated by single cell RNA sequencing and the other is a multicellular system composed of biliary organoids, LX-2 and MAIT cells to evaluate the role of MAIT cells on ductular reaction. FINDINGS: Liver MAIT cells in BA were positively associated with low survival and ductular reaction. Moreover, liver MAIT cells were activated, exhibited a wound healing signature and highly expressed growth factor Amphiregulin (AREG) in a T cell receptor (TCR)-dependent manner. Antagonism of AREG abrogated the proliferative effect of BA MAIT cells on both cholangiocytes and biliary organoids. A RRV infected co-culture system, recapitulated immune dysfunction of human BA, disclosed that RRV-primed MAIT cells promoted cholangiocyte proliferation via AREG, and further induced inflammation and fibrosis in the multicellular system. INTERPRETATION: MAIT cells exhibit a wound healing signature depending on TCR signaling and promote ductular reaction via AREG, which is associated with advanced fibrosis and predictive of low survival in BA. FUNDING: This work was funded by National Natural Science Foundation of China grant (82001589 and 92168108), National Key R&D Program of China (2023YFA1801600) and by Basic and Applied Basic Research Foundation of Guangdong (2020A1515110921).

Pro-cancer role of CD276 as a novel biomarker for clear cell renal cell carcinoma.

OBJECTIVE: Renal cell carcinoma (RCC) is a common malignant tumor with a high incidence in males and the elderly, and clear cell RCC (ccRCC) is the most common RCC subtype. ccRCC is highly metastatic with a poor prognosis. Therefore, it is crucial to obtain a detailed understanding of the molecular mechanism of ccRCC and to identify suitable biomarkers to realize early diagnosis and improve prognosis. METHODS: We analyzed data from the Cancer Genome Atlas, investigated the overall differential expression of CD276 in ccRCC, and evaluated the influence of CD276 on patient survival and prognosis. We also performed gene set enrichment analysis (GSEA) and pathway enrichment analysis and investigated cell infiltration and drug responsiveness to further assess the regulatory effect of CD276 on ccRCC. Furthermore, we verified CD276 expression in RCC cell lines and control cell lines. RESULTS: The CD276 expression level in ccRCC samples was higher than that in corresponding samples adjacent to the tumors. Moreover, high CD276 expression levels were positively correlated with poor prognosis in patients with RCC. GSEA revealed that CD276 was significantly involved in immune-related pathways, and the level of CD276 expression was confirmed as associated with immune cell infiltration to some extent. Notably, some drugs were predicted to act on CD276, and this was confirmed by molecular docking. Furthermore, high levels of CD276 expression in RCC cell lines were verified. CONCLUSION: CD276 expression was significantly increased in ccRCC tissues and cells and positively correlated with patient prognosis. CD276 is a potential prognostic biomarker of ccRCC. Overall, this study provides a potential therapeutic strategy for ccRCC.

Control of two insect pests by expression of a mismatch corrected double-stranded RNA in plants.

RNA interference (RNAi) has emerged as an efficient technology for pest control by silencing the essential genes of targeted insects. Owing to its nucleotide sequence-guided working mechanism, RNAi has a high degree of species-specificity without impacts on non-target organisms. However, as plants are inevitably under threat by two or more insect pests in nature, the species-specific mode of RNAi-based technology restricts its wide application for pest control. In this study, we artificially designed an intermediate dsRNA (iACT) targeting two ß-Actin (ACT) genes of sap-sucking pests Bemisia tabaci and Myzus persicae by mutual correction of their mismatches. When expressing hairpin iACT (hpiACT) from tobacco nuclear genome, transgenic plants are well protected from both B. tabaci and M. persicae, either individually or simultaneously, as evidenced by reduced fecundity and suppressed ACT gene expression, whereas expression of hpRNA targeting BtACT or MpACT in transgenic tobacco plants could only confer specific resistance to either B. tabaci or M. persicae, respectively. In sum, our data provide a novel proof-of-concept that two different insect species could be simultaneously controlled by artificial synthesis of dsRNA with sequence optimization, which expands the range of transgenic RNAi methods for crop protection.

Cytokinin oxidase gene CKX5 is modulated in the immunity of Arabidopsis to Botrytis cinerea.

In our previous work, cytokinin (CK) signaling and biosynthesis were found to be modulated during Arabidopsis defense against infection by the necrotrophic pathogen Botrytis cinerea. Notably, the expression level of CYTOKININ OXIDASE/DEHYDROGENASE 5 (CKX5) was significantly induced in B. cinerea-infected leaves and later in distant B. cinerea-untreated leaves of the same plant. To confirm and determine how CKX5 is involved in the response to B. cinerea infection, transcript levels of CKX family genes were analyzed in B. cinerea-inoculated leaves, and only CKX5 was remarkably induced by B. cinerea infection. Furthermore, CKX5-overexpressing Arabidopsis plants were more resistant to B. cinerea than wild-type plants. Transcription factors (TFs) binding to the CKX5 promoter were then screened by yeast one-hybrid assays. Quantitative Real-Time Reverse Transcription PCR (qRT-PCR) analysis further showed that genes encoding TFs, including WRKY40, WRKY33, ERF6, AHL15, AHL17, ANAC003, TCP13 and ANAC019, were also strongly induced in infected leaves, similar to CKX5. Analysis of ERF6-overexpressing plants and ERF6-and AHL15-knockout mutants indicated that ERF6 and AHL15 are involved in plant immunity to B. cinerea. Furthermore, CKX5 upregulation by B. cinerea infection was affected when ERF6 or AHL15 levels were altered. Our work suggests that CKX5 levels are controlled by the plant defense system to defend against attack by the pathogen B. cinerea.

Radiomics analysis of patellofemoral joint improves knee replacement risk prediction: Data from the Multicenter Osteoarthritis Study (MOST).

Objective: Knee replacement (KR) is the last-resort treatment for knee osteoarthritis. Although radiographic evidence of tibiofemoral joint has been widely adopted for prognostication, patellofemoral joint has gained little attention and may hold additional value for further improvements. We aimed to quantitatively analyse patellofemoral joint through radiomics analysis of lateral view radiographs for improved KR risk prediction. Design: From the Multicenter Osteoarthritis Study dataset, we retrospectively retrieved the initial-visit lateral left knee radiographs of 2943 patients aged 50 to 79. They were split into training and test cohorts at a 2:1 ratio. A comprehensive set of radiomic features were extracted within the best-performing subregion of patellofemoral joint and combined into a radiomics score (RadScore). A KR risk score, derived from Kellgren-Lawrence grade (KLG) of tibiofemoral joint and RadScore of patellofemoral joint, was developed by multivariate Cox regression and assessed using time-dependent area under receiver operating characteristic curve (AUC). Results: While patellofemoral osteoarthritis (PFOA) was insignificant during multivariate analysis, RadScore was identified as an independent risk factor (multivariate Cox p-value < 0.001) for KR. The subgroup analysis revealed that RadScore was particularly effective in predicting rapid progressor (KR occurrence before 30 months) among early- (KLG < 2) and mid-stage (KLG â€‹= â€‹2) patients. Combining two joints radiographic information, the AUC reached 0.89/0.87 for predicting 60-month KR occurrence. Conclusions: The RadScore of the patellofemoral joint on lateral radiographs emerges as an independent prognostic factor for improving KR prognosis prediction. The KR risk score could be instrumental in managing progressive knee osteoarthritis interventions.

Spatial multi-attention conditional neural processes.

Spatial prediction tasks are challenging when observed samples are sparse and prediction samples are abundant. Gaussian processes (GPs) are commonly used in spatial prediction tasks and have the advantage of measuring the uncertainty of the interpolation result. However, as the sample size increases, GPs suffer from significant overhead. Standard neural networks (NNs) provide a powerful and scalable solution for modeling spatial data, but they often overfit small sample data. Based on conditional neural processes (CNPs), which combine the advantages of GPs and NNs, we propose a new framework called Spatial Multi-Attention Conditional Neural Processes (SMACNPs) for spatial small sample prediction tasks. SMACNPs are a modular model that can predict targets by employing different attention mechanisms to extract relevant information from different forms of sample data. The task representation is inferred by measuring the spatial correlation contained in different sample points and the relationship contained in attribute variables, respectively. The distribution of the target variable is predicted by GPs parameterized by NNs. SMACNPs allow us to obtain accurate predictions of the target value while quantifying the prediction uncertainty. Experiments on spatial prediction tasks on simulated and real-world datasets demonstrate that this framework flexibly incorporates spatial context and correlation into the model, achieving state-of-the-art results in spatial small sample prediction tasks in terms of both predictive performance and reliability. For example, on the California housing dataset, our method reduces MAE by 8% and MSE by 7% compared to the second-best method. In addition, a spatiotemporal prediction task to forecast traffic speed further confirms the effectiveness and generality of our method.