Chromosome-level Dinobdella ferox genome provided a molecular model for its specific parasitism.

BACKGROUND: Dinobdella ferox is the most frequently reported leech species parasitizing the mammalian nasal cavity. However, the molecular mechanism of this special parasitic behavior has remained largely unknown. METHODS: PacBio long-read sequencing, next-generation sequencing (NGS), and Hi-C sequencing were employed in this study to generate a novel genome of D. ferox, which was annotated with strong certainty using bioinformatics methods. The phylogenetic and genomic alterations of D. ferox were then studied extensively alongside the genomes of other closely related species. The obligatory parasitism mechanism of D. ferox was investigated using RNA-seq and proteomics data. RESULTS: PacBio long-read sequencing and NGS yielded an assembly of 228 Mb and contig N50 of 2.16 Mb. Along Hi-C sequencing, 96% of the sequences were anchored to nine linkage groups and a high-quality chromosome-level genome was generated. The completed genome included 19,242 protein-coding genes. For elucidating the molecular mechanism of nasal parasitism, transcriptome data were acquired from the digestive tract and front/rear ends of D. ferox. Examining secretory proteins in D. ferox saliva helped to identify intimate connections between these proteins and membrane proteins in nasal epithelial cells. These interacting proteins played important roles in extracellular matrix (ECM)-receptor interaction, tight junction, focal adhesion, and adherens junction. The interaction between D. ferox and mammalian nasal epithelial cells included three major steps of pattern recognition, mucin connection and breakdown, and repair of ECM. The remodeling of ECM between epithelial cells of the nasal mucosa and epithelial cells of D. ferox may produce a stable adhesion environment for parasitism. CONCLUSIONS: Our study represents the first-ever attempt to propose a molecular model for specific parasitism. This molecular model may serve as a practical reference for parasitism models of other species and a theoretical foundation for a molecular process of parasitism.

An application of stereo matching algorithm based on transfer learning on robots in multiple scenes.

Robot vision technology based on binocular vision holds tremendous potential for development in various fields, including 3D scene reconstruction, target detection, and autonomous driving. However, current binocular vision methods used in robotics engineering have limitations such as high costs, complex algorithms, and low reliability of the generated disparity map in different scenes. To overcome these challenges, a cross-domain stereo matching algorithm for binocular vision based on transfer learning was proposed in this paper, named Cross-Domain Adaptation and Transfer Learning Network (Ct-Net), which has shown valuable results in multiple robot scenes. First, this paper introduces a General Feature Extractor to extract rich general feature information for domain adaptive stereo matching tasks. Then, a feature adapter is used to adapt the general features to the stereo matching network. Furthermore, a Domain Adaptive Cost Optimization Module is designed to optimize the matching cost. A disparity score prediction module was also embedded to adaptively adjust the search range of disparity and optimize the cost distribution. The overall framework was trained using a phased strategy, and ablation experiments were conducted to verify the effectiveness of the training strategy. Compared with the prototype PSMNet, on KITTI 2015 benchmark, the 3PE-fg of Ct-Net in all regions and non-occluded regions decreased by 19.3 and 21.1% respectively, meanwhile, on the Middlebury dataset, the proposed algorithm improves the sample error rate at least 28.4%, which is the Staircase sample. The quantitative and qualitative results obtained from Middlebury, Apollo, and other datasets demonstrate that Ct-Net significantly improves the cross-domain performance of stereo matching. Stereo matching experiments in real-world scenes have shown that it can effectively address visual tasks in multiple scenes.

Automated measurement of leg length discrepancy from infancy to adolescence based on cascaded LLDNet and comprehensive assessment.

Background: Deep learning (DL) has been suggested for the automated measurement of leg length discrepancy (LLD) on radiographs, which could free up time for pediatric radiologists to focus on value-adding duties. The purpose of our study was to develop a unified solution using DL for both automated LLD measurements and comprehensive assessments in a large and comprehensive radiographic dataset covering children at all stages, from infancy to adolescence, and with a wide range of diagnoses. Methods: The bilateral femurs and tibias were segmented by a cascaded convolutional neural network (CNN), referred to as LLDNet. Each LLDNet was conducted through use of residual blocks to learn more abundant features, a residual convolutional block attention module (Res-CBAM) to integrate both spatial and channel attention mechanisms, and an attention gate structure to alleviate the semantic gap. The leg length was calculated by localizing anatomical landmarks and computing the distances between them. A comprehensive assessment based on 9 indices (5 similarity indices and 4 stability indices) and the paired Wilcoxon signed-rank test was undertaken to demonstrate the superiority of the cascaded LLDNet for segmenting pediatric legs through comparison with alternative DL models, including ResUNet, TransUNet, and the single LLDNet. Furthermore, the consistency between the ground truth and the DL-calculated measurements of leg length was also comprehensively evaluated, based on 5 indices and a Bland-Altman analysis. The sensitivity and specificity of LLD >5 mm were also calculated. Results: A total of 976 children were identified (0-19 years old; male/female 522/454; 520 children between 0 and 2 years, 456 children older than 2 years, 4 children excluded). Experiments demonstrated that the proposed cascaded LLDNet achieved the best pediatric leg segmentation in both similarity indices (0.5-1% increase; P<0.05) and stability indices (13-47% percentage decrease; P<0.05) compared with the alternative DL methods. A high consistency of LLD measurements between DL and the ground truth was also observed using Bland-Altman analysis [Pearson correlation coefficient (PCC) =0.94; mean bias =0.003 cm]. The sensitivity and specificity established for LLD >5 mm were 0.792 and 0.962, respectively, while those for LLD >10 mm were 0.938 and 0.992, respectively. Conclusions: The cascaded LLDNet was able to achieve promising pediatric leg segmentation and LLD measurement on radiography. A comprehensive assessment in terms of similarity, stability, and measurement consistency is essential in computer-aided LLD measurement of pediatric patients.

How segmentation methods affect hippocampal radiomic feature accuracy in Alzheimer's disease analysis?

OBJECTIVES: Hippocampal radiomic features (HRFs) can serve as biomarkers in Alzheimer's disease (AD). However, how different hippocampal segmentation methods affect HRFs in AD is still unknown. The aim of the study was to investigate how different segmentation methods affect HRF accuracy in AD analysis. METHODS: A total of 1650 subjects were identified from the Alzheimer's Disease Neuroimaging Initiative database (ADNI). The mini-mental state examination (MMSE) and Alzheimer's disease assessment scale (ADAS-cog13) were also adopted. After calculating the HRFs of intensity, shape, and textural features from each side of the hippocampus in structural magnetic resonance imaging (sMRI), the consistency of HRFs calculated from 7 different hippocampal segmentation methods was validated, and the performance of machine learning-based classification of AD vs. normal control (NC) adopting the different HRFs was also examined. Additional 571 subjects from the European DTI Study on Dementia database (EDSD) were to validate the consistency of results. RESULTS: Between different segmentations, HRFs showed a high measurement consistency (R > 0.7), a high significant consistency between NC, mild cognitive impairment (MCI), and AD (T-value plot, R > 0.8), and consistent significant correlations between HRFs and MMSE/ADAS-cog13 (p < 0.05). The best NC vs. AD classification was obtained when the hippocampus was sufficiently segmented by primitive majority voting (threshold = 0.2). High consistent results were reproduced from independent EDSD cohort. CONCLUSIONS: HRFs exhibited high consistency across different hippocampal segmentation methods, and the best performance in AD classification was obtained when HRFs were extracted by the naïve majority voting method with a more sufficient segmentation and relatively low hippocampus segmentation accuracy. KEY POINTS: • The hippocampal radiomic features exhibited high measurement/statistical/clinical consistency across different hippocampal segmentation methods. • The best performance in AD classification was obtained when hippocampal radiomics were extracted by the naïve majority voting method with a more sufficient segmentation and relatively low hippocampus segmentation accuracy.

A new species of medicinal leech in the genus Hirudo Linnaeus, 1758 (Hirudiniformes, Hirudinidae) from Tianjin City, China.

Medicinal leeches in the genus Hirudo have been utilized for therapeutic procedures for thousands of years. To date, six known species of Hirudo are widely distributed in different regions of the Eurasian continent. In this study, a new medicinal leech species Hirudotianjinensis Liu, sp. nov. is described based upon specimens collected from Tianjin City, China. The new species can be distinguished from its congeners by a combination of characters: blackish green dorsum with five continuous yellow longitudinal stripes; six sensillae on dorsal annulus a2 of segments VIII-XXV; greyish green ventrum with irregular bilateral dark brown spots; dorsum and abdomen separated by a pair of pale yellow stripes; front half atrium wrapped by white prostate; apparent albumen gland; epididymis massive in relation to ejaculatory bulb. The phylogenetic tree based upon COI implies a sister relationship to H.nipponia Whitman, 1886. A key to the known species is provided.

Insights into gut microbiota communities of Poecilobdella manillensis, a prevalent Asian medicinal leech.

AIMS: Medicinal leeches (Annelida: Hirudinea) are fresh water ectoparasitic species which have been applied as traditional therapy. However, gut microbiota could bring high risks of opportunistic infections after leeching and arouses great interests. Here, gut bacterial and fungal communities of an Asian prevalent leech Poecilobdella manillensis were characterized and analysed through culture-independent sequencing. METHODS AND RESULTS: With high coverage in 18 samples (>0.999), a more complicated community was apparent after comparing with previous leech studies. A total of 779/939 OTUs of bacteria and fungi were detected from leech guts. The bacterial community was dominated by the phylum Bacteroidetes and Synergistetes. Genera Mucinivorans and Fretibacterium accounted mostly at the genus level, and genus Aeromonas showed an extremely low abundance (2.02%) on average. The fungal community was dominated by the phylum Ascomycota and Basidiomycota. At the genus level, the dominant OTUs included Mortierella, Geminibasidium and Fusarium. The analysis of core taxa included those above dominant genera and some low-abundance genera (>1%). The functional annotation of the bacterial community showed a close correlation with metabolism (34.8 ± 0.6%). Some fungal species were predicted as opportunistic human pathogens including Fusarium and Chaetomiaceae. CONCLUSIONS: The present study provides fundamental rationales for further studies of such issues as bacteria-fungi-host interactions, host fitness, potential pathogens, and infecting risks after leeching. It shall facilitate in-depth explorations on the safe utilization of leech therapy. SIGNIFICANCE AND IMPACT OF STUDY: Present paper is the first-ever exploration on microbiota of a prevalent Asian medicinal leech based on culture-independent technical. And it is also the first report of gut fungi community of medicinal leech. The diversity and composition of bacteria in P. manillensis was far different from that of the European leech. The main components and core OTUs indicate a particular gut environment of medicinal leech. Unknown bacterial and fungal species were also recovered from leech gut.

The genome of medicinal leech (Whitmania pigra) and comparative genomic study for exploration of bioactive ingredients.

BACKGROUND: Leeches are classic annelids that have a huge diversity and are closely related to people, especially medicinal leeches. Medicinal leeches have been widely utilized in medicine based on the pharmacological activities of their bioactive ingredients. Comparative genomic study of these leeches enables us to understand the difference among medicinal leeches and other leeches and facilitates the discovery of bioactive ingredients. RESULTS: In this study, we reported the genome of Whitmania pigra and compared it with Hirudo medicinalis and Helobdella robusta. The assembled genome size of W. pigra is 177 Mbp, close to the estimated genome size. Approximately about 23% of the genome was repetitive. A total of 26,743 protein-coding genes were subsequently predicted. W. pigra have 12346 (46%) and 10295 (38%) orthologous genes with H. medicinalis and H. robusta, respectively. About 20 and 24% genes in W. pigra showed syntenic arrangement with H. medicinalis and H. robusta, respectively, revealed by gene synteny analysis. Furthermore, W. pigra, H. medicinalis and H. robusta expanded different gene families enriched in different biological processes. By inspecting genome distribution and gene structure of hirudin, we identified a new hirudin gene g17108 (hirudin_2) with different cysteine patterns. Finally, we systematically explored and compared the active substances in the genomes of three leech species. The results showed that W. pigra and H. medicinalis exceed H. robusta in both kinds and gene number of active molecules. CONCLUSIONS: This study reported the genome of W. pigra and compared it with other two leeches, which provides an important genome resource and new insight into the exploration and development of bioactive molecules of medicinal leeches.

Simultaneous image reconstruction and lesion segmentation in accelerated MRI using multitasking learning.

PURPOSE: Magnetic resonance imaging (MRI) serves as an important medical imaging modality for a variety of clinical applications. However, the problem of long imaging time limited its wide usage. In addition, prolonged scan time will cause discomfort to the patient, leading to severe image artifacts. On the other hand, manually lesion segmentation is time consuming. Algorithm-based automatic lesion segmentation is still challenging, especially for accelerated imaging with low quality. METHODS: In this paper, we proposed a multitask learning-based method to perform image reconstruction and lesion segmentation simultaneously, called "RecSeg". Our hypothesis is that both tasks can benefit from the usage of the proposed combined model. In the experiment, we validated the proposed multitask model on MR k-space data with different acceleration factors (2×, 4×, and 6×). Two connected U-nets were used for the tasks of liver and renal image reconstruction and segmentation. A total of 50 healthy subjects and 100 patients with hepatocellular carcinoma were included for training and testing. For the segmentation part, we use healthy subjects to verify organ segmentation, and hepatocellular carcinoma patients to verify lesion segmentation. The organs and lesions were manually contoured by an experienced radiologist. RESULTS: Experimental results show that the proposed RecSeg yielded the highest PSNR (RecSeg: 32.39 ± 1.64 vs. KSVD: 29.53 ± 2.74 and single U-net: 31.18 ± 1.68, respectively, p < 0.05) and highest structural similarity index measure (SSIM) (RecSeg: 0.93 ± 0.01 vs. KSVD: 0.88 ± 0.02 and single U-net: 0.90 ± 0.01, respectively, p < 0.05) under 6× acceleration. Moreover, in the task of lesion segmentation, it is proposed that RecSeg produced the highest Dice score (RecSeg: 0.86 ± 0.01 vs. KSVD: 0.82 ± 0.01 and single U-net: 0.84 ± 0.01, respectively, p < 0.05). CONCLUSIONS: This study focused on the simultaneous reconstruction of medical images and the segmentation of organs and lesions. It is observed that the multitask learning-based method can improve performances of both image reconstruction and lesion segmentation.

Characterization of the complete mitogenome of a land leech, Haemadipsa crenata Ngamprasertwong (Arhynchobdellida: Haemadipsidae).

Land leeches of genus Haemadipsa (Family Haemadipsidae) are widely distributed in South East Asia. Haemadipsa crenata Ngamprasertwong is a blood-feeding species firstly reported from Thailand. A complete mitochondrial genome of H. crenata was characterized in this study for further genetic exploration on land leech. The reads were assembled into a circular mitogenome of 14,725 bp in length. The AT content of H. crenata mitogenome is 76.79%. The annotated mitogenome contains 22 tRNAs, 2 rRNAs, and 13 protein-coding genes (PCGs), and the structure of PCG open reading frames was confirmed. Finally, the phylogenetic relationship of H. crenata and other leech species were reconstructed using mitogenomes.

Transfer learning enhanced generative adversarial networks for multi-channel MRI reconstruction.

Deep learning based generative adversarial networks (GAN) can effectively perform image reconstruction with under-sampled MR data. In general, a large number of training samples are required to improve the reconstruction performance of a certain model. However, in real clinical applications, it is difficult to obtain tens of thousands of raw patient data to train the model since saving k-space data is not in the routine clinical flow. Therefore, enhancing the generalizability of a network based on small samples is urgently needed. In this study, three novel applications were explored based on parallel imaging combined with the GAN model (PI-GAN) and transfer learning. The model was pre-trained with public Calgary brain images and then fine-tuned for use in (1) patients with tumors in our center; (2) different anatomies, including knee and liver; (3) different k-space sampling masks with acceleration factors (AFs) of 2 and 6. As for the brain tumor dataset, the transfer learning results could remove the artifacts found in PI-GAN and yield smoother brain edges. The transfer learning results for the knee and liver were superior to those of the PI-GAN model trained with its own dataset using a smaller number of training cases. However, the learning procedure converged more slowly in the knee datasets compared to the learning in the brain tumor datasets. The reconstruction performance was improved by transfer learning both in the models with AFs of 2 and 6. Of these two models, the one with AF = 2 showed better results. The results also showed that transfer learning with the pre-trained model could solve the problem of inconsistency between the training and test datasets and facilitate generalization to unseen data.

Parallel imaging with a combination of sensitivity encoding and generative adversarial networks.

BACKGROUND: Magnetic resonance imaging (MRI) has the limitation of low imaging speed. Acceleration methods using under-sampled k-space data have been widely exploited to improve data acquisition without reducing the image quality. Sensitivity encoding (SENSE) is the most commonly used method for multi-channel imaging. However, SENSE has the drawback of severe g-factor artifacts when the under-sampling factor is high. This paper applies generative adversarial networks (GAN) to remove g-factor artifacts from SENSE reconstructions. METHODS: Our method was evaluated on a public knee database containing 20 healthy participants. We compared our method with conventional GAN using zero-filled (ZF) images as input. Structural similarity (SSIM), peak signal to noise ratio (PSNR), and normalized mean square error (NMSE) were calculated for the assessment of image quality. A paired student's t-test was conducted to compare the image quality metrics between the different methods. Statistical significance was considered at P<0.01. RESULTS: The proposed method outperformed SENSE, variational network (VN), and ZF + GAN methods in terms of SSIM (SENSE + GAN: 0.81±0.06, SENSE: 0.40±0.07, VN: 0.79±0.06, ZF + GAN: 0.77±0.06), PSNR (SENSE + GAN: 31.90±1.66, SENSE: 22.70±1.99, VN: 31.35±2.01, ZF + GAN: 29.95±1.59), and NMSE (×10-7) (SENSE + GAN: 0.95±0.34, SENSE: 4.81±1.33, VN: 0.97±0.30, ZF + GAN: 1.60±0.84) with an under-sampling factor of up to 6-fold. CONCLUSIONS: This study demonstrated the feasibility of using GAN to improve the performance of SENSE reconstruction. The improvement of reconstruction is more obvious for higher under-sampling rates, which shows great potential for many clinical applications.

Task Allocation Model Based on Worker Friend Relationship for Mobile Crowdsourcing.

With the rapid development of mobile devices, mobile crowdsourcing has become an important research focus. According to the task allocation, scholars have proposed many methods. However, few works discuss combining social networks and mobile crowdsourcing. To maximize the utilities of mobile crowdsourcing system, this paper proposes a task allocation model considering the attributes of social networks for mobile crowdsourcing system. Starting from the homogeneity of human beings, the relationship between friends in social networks is applied to mobile crowdsourcing system. A task allocation algorithm based on the friend relationships is proposed. The GeoHash coding mechanism is adopted in the process of calculating the strength of worker relationship, which effectively protects the location privacy of workers. Utilizing synthetic dataset and the real-world Yelp dataset, the performance of the proposed task allocation model was evaluated. Through comparison experiments, the effectiveness and applicability of the proposed allocation mechanism were verified.

An Incentive Mechanism in Mobile Crowdsourcing Based on Multi-Attribute Reverse Auctions.

In order to avoid malicious competition and select high quality crowd workers to improve the utility of crowdsourcing system, this paper proposes an incentive mechanism based on the combination of reverse auction and multi-attribute auction in mobile crowdsourcing. The proposed online incentive mechanism includes two algorithms. One is the crowd worker selection algorithm based on multi-attribute reverse auction that adopts dynamic threshold to make an online decision for whether accept a crowd worker according to its attributes. Another is the payment determination algorithm which determines payment for a crowd worker based on its reputation and quality of sensing data, that is, a crowd worker can get payment equal to the bidding price before performing task only if his reputation reaches good reputation threshold, otherwise he will get payment based on his data sensing quality. We prove that our proposed online incentive mechanism has the properties of computational efficiency, individual rationality, budget-balance, truthfulness and honesty. Through simulations, the efficiency of our proposed online incentive mechanism is verified which can improve the efficiency, adaptability and trust degree of the mobile crowdsourcing system.

De novo assembly and comparative transcriptome characterization of Poecilobdella javanica provide insight into blood feeding of medicinal leeches.

Leeches (family Hirudinidae) are classic model invertebrates used in diverse clinical treatments, such as reconstructive microsurgery, hypertension, and gangrene treatment. The blood-feeding habit is essential for these therapies, yet the molecular mechanisms underlying the process are poorly understood. In the present study, the transcriptome of Poecilobdella javanica from five time points (days 0, 1, 10, 20, and 30 separately) of blood feeding was sequenced with short paired-end reads. After stringent quality control, ∼380 million high-quality reads were assembled using SOAPdenovo-Trans with optimal parameters into a non-redundant set of 48 784 transcripts (≥100 base pairs), representing about 38 Mb of unique transcriptome sequence. The average length of the transcripts was 570 bp with N50 lengths of 5751 to 7413 bp among different time points. We have assessed the effect of sequence quality and various assembly parameters on the final assembly output. Functional categorization revealed the conservation of genes involved in various biological processes, such as basal transcription factors and ribosome biogenesis in eukaryotes. In addition, we found that DNA/RNA related pathways were predominantly expressed in the starving state while fatty acid metabolism, the anticoagulant pathway, and amino acid biosynthesis were activated during blood feeding. The leech transcriptome provides a resource for gene discovery and development of functional molecular markers during clinical applications.

Comparative transcriptomic analysis reveals the mechanism of leech environmental adaptation.

The medicinal leeches have been widely utilized in medical procedures for thousands of years. Recently, there were more and more transcriptomes of leech published online including the medicinal leech (Hirudo medicinalis) and some other leeches. However, leech's genetic backgrounds are still largely unknown. In this report, transcriptomes of three phylogenetically close leeches (Poecilobdella javanica, Whitmania pigra, and Haemadipsa cavatuses) were established by RNA-seq technique for studying their genetic mechanisms of environmental adaption. Over 110 million high-quality reads were generated and assembled into unique transcriptome (reads = 200 bp). 27,138 out of de novo assembled transcripts (41.77%) were assigned to one or more GO terms. Additionally, the transcripts were detected in 217 predicted KEGG pathways. The enriched genes were involved in protein metabolism, GPCRs and pathogen-resistant pathways. The results showed that the great variations existed in gene expression of olfactory transduction pathway among three leech species. The comparisons of leech species hinted at the underlying mechanism of leeches adapting well in various environments. Our study will provide useful rationales for future studies of leeches and other annelid species.

Small-Activating RNA Can Change Nucleosome Positioning in Human Fibroblasts.

RNA activation (RNAa) is a mechanism of positive gene expression regulation mediated by small-activating RNAs (saRNAs), which target gene promoters and have been used as tools to manipulate gene expression. Studies have shown that RNAa is associated with epigenetic modifications at promoter regions; however, it is unclear whether these modifications are the cause or a consequence of RNAa. In this study, we examined changes in nucleosome repositioning and the involvement of RNA polymerase II (RNAPII) in this process. We screened saRNAs for OCT4 (POU5F1), SOX2, and NANOG, and identified several novel saRNAs. We found that nucleosome positioning was altered after saRNA treatment and that the formation of nucleosome-depleted regions (NDRs) contributed to RNAa at sites of RNAPII binding, such as the TATA box, CpG islands (CGIs), proximal enhancers, and proximal promoters. Moreover, RNAPII appeared to be bound specifically to NDRs. These results suggested that changes in nucleosome positions resulted from RNAa. We thus propose a hypothesis that targeting promoter regions using exogenous saRNAs can induce the formation of NDRs, exposing regulatory binding sites to recruit RNAPII, a key component of preinitiation complex, and leading to increased initiation of transcription.

A game theory-based trust measurement model for social networks.

BACKGROUND: In social networks, trust is a complex social network. Participants in online social networks want to share information and experiences with as many reliable users as possible. However, the modeling of trust is complicated and application dependent. Modeling trust needs to consider interaction history, recommendation, user behaviors and so on. Therefore, modeling trust is an important focus for online social networks. METHODS: We propose a game theory-based trust measurement model for social networks. The trust degree is calculated from three aspects, service reliability, feedback effectiveness, recommendation credibility, to get more accurate result. In addition, to alleviate the free-riding problem, we propose a game theory-based punishment mechanism for specific trust and global trust, respectively. RESULTS AND CONCLUSIONS: We prove that the proposed trust measurement model is effective. The free-riding problem can be resolved effectively through adding the proposed punishment mechanism.

Alpha interferon potently enhances the anti-human immunodeficiency virus type 1 activity of APOBEC3G in resting primary CD4 T cells.

The interferon (IFN) system, including various IFNs and IFN-inducible gene products, is well known for its potent innate immunity against wide-range viruses. Recently, a family of cytidine deaminases, functioning as another innate immunity against retroviral infection, has been identified. However, its regulation remains largely unknown. In this report, we demonstrate that through a regular IFN-alpha/beta signal transduction pathway, IFN-alpha can significantly enhance the expression of apolipoprotein B mRNA-editing enzyme-catalytic polypeptide-like 3G (APOBEC3G) in human primary resting but not activated CD4 T cells and the amounts of APOBEC3G associated with a low molecular mass. Interestingly, short-time treatments of newly infected resting CD4 T cells with IFN-alpha will significantly inactivate human immunodeficiency virus type 1 (HIV-1) at its early stage. This inhibition can be counteracted by APOBEC3G-specific short interfering RNA, indicating that IFN-alpha-induced APOBEC3G plays a key role in mediating this anti-HIV-1 process. Our data suggest that APOBEC3G is also a member of the IFN system, at least in resting CD4 T cells. Given that the IFN-alpha/APOBEC3G pathway has potent anti-HIV-1 capability in resting CD4 T cells, augmentation of this innate immunity barrier could prevent residual HIV-1 replication in its native reservoir in the post-highly active antiretroviral therapy era.