Mitogenome Characterization of Four Conus Species and Comparative Analysis

Cone snails, as a type of marine organism, have rich species diversity. Traditionally, classifications of cone snails were based mostly on radula, shell, and anatomical characters. Because of these phenotypic features' high population variability and propensity for local adaptation and convergence, identifying species can be difficult and occasionally inaccurate. In addition, mitochondrial genomes contain high phylogenetic information, so complete mitogenomes have been increasingly employed for inferring molecular phylogeny. To enrich the mitogenomic database of cone snails (Caenogastropoda: Conidae), mitogenomes of four Conus species, i.e., C. imperialis (15,505 bp), C. literatus (15,569 bp), C. virgo (15,594 bp), and C. marmoreus (15,579 bp), were characterized and compared. All 4 of these mitogenomes included 13 protein-coding genes, 2 ribosomal RNA genes, 22 tRNA genes, and non-coding regions. All the Protein Codon Genes (PCGs) of both newly sequenced mitogenomes used TAA or TAG as a terminal codon. Most PCGs used conventional start codon ATG, but an alternative initiation codon GTG was detected in a gene (NADH dehydrogenase subunit 4 (nad4)) of C. imperialis. In addition, the phylogenetic relationships were reconstructed among 20 Conus species on the basis of PCGs, COX1, and the complete mitogenome using both Bayesian Inference (BI) and Maximum Likelihood (ML). The phylogenetic results supported that C. litteratus, C. quercinus, and C. virgo were clustered together as a sister group (PP = 1, BS = 99), but they did not support the phylogenetic relation of C. imperialis and C. tribblei (PP = 0.79, BS = 50). In addition, our study established that PCGs and complete mitogenome are the two useful markers for phylogenetic inference of Conus species. These results enriched the data of the cone snail's mitochondrion in the South China Sea and provided a reliable basis for the interpretation of the phylogenetic relationship of the cone snail based on the mitochondrial genome.

Anterior segment dysgenesis: Insights into the genetics and pathogenesis

Childhood glaucoma is a treatable cause of blindness, provided it is recognized, diagnosed, and treated in time. WHO has estimated that it is responsible for Blind Years second only to cataracts. The fundamental pathophysiology of all childhood glaucoma is impaired outflow through the trabecular meshwork. Anterior segment Dysgeneses (ASD) are a group of non-acquired ocular anomalies associated with glaucoma, characterized by developmental abnormalities of the tissues of the anterior segment. The cause is multifactorial, and many genes are involved in the development of the anterior segment. Over the last decade, molecular and developmental genetic research has transformed our understanding of the molecular basis of ASD and the developmental mechanisms underlying these conditions. Identifying the genetic changes underlying ASD has gradually led to the recognition that some of these conditions may be parts of a disease spectrum. The characterization of genes responsible for glaucoma is the critical first step toward developing diagnostic and screening tests, which could identify individuals at risk for disease before irreversible optic nerve damage occurs. It is also crucial for genetic counseling and risk stratification of later pregnancies. It also aids pre-natal testing by various methods allowing for effective genetic counseling. This review will summarize the known genetic variants associated with phenotypes of ASD and the possible significance and utility of genetic testing in the clinic.

Ocular aberrations in eyes with Primary Congenital Glaucoma

PURPOSE

To highlight the magnitude of ocular higher order aberrations (HOA) and lower order aberrations (LOA), including component contributions from corneal and internal planes in Primary Congenital Glaucoma (PCG) patients.

METHODS

Consecutive treated PCG patients co-operative for ocular examination and aberrometry, were enrolled over two years for this cross-sectional, comparative, single centre, unmasked study. Best corrected visual acuity, refraction, IOP, wavefront aberrometry and topography (iTrace) were performed and results were compared with unaffected fellow eyes of unilateral glaucoma patients as well as age and sex matched controls with no ocular anomalies other than treatable refractive error.

RESULTS

Both eyes of 32 consecutive PCG patients (17 unilateral, 15 bilateral) and 39 controls were enrolled. The median LogMAR corrected distance visual acuity of PCG eyes was 0.68 (IQR: 0.2 - 1.8). Total ocular (Root mean square (RMS) 1.7µm vs 0.3µm, p = 0.014), corneal (RMS 1.1µm vs 0.3µm, p = 0.004) and internal (RMS 1.1µm vs 0.2µm, p = 0.013) aberrations, as well as HOAs and LOAs at each plane were significantly higher in PCG eyes than in controls. Component HOAs from corneal and internal planes were positively correlated with each other (p < 0.001; rs: 0.7). Total aberrations were greater in the affected eyes of PCG compared to the rest. The predominant subtype of HOAs in PCG were coma and trefoil. PCG with corneal opacity/Haab's striae had significantly higher astigmatism than the affected eyes with clear corneae at corneal plane (p = 0.02). The aberrations were not statistically associated with the corneal diameter or refractive error in PCG eyes.

CONCLUSIONS

Significantly greater aberrations (Total, HOAs and LOAs, at corneal as well as internal plane) were seen among eyes affected with PCG. Though the exact impact of these aberrations on the final visual outcome is difficult to determine, these could play a pertinent role in compromising visual function, thus impacting the management of visual rehabilitation in these patients.

Co-existing lacrimal drainage anomalies in eyes with congenital Glaucoma

AIM

To evaluate the occurrence of co-existing congenital nasolacrimal duct obstruction (CNLDO) and other lacrimal anomalies in eyes with congenital glaucoma (CG).

METHODS

Retrospective analysis of children aged ≤1-year with co-existing CG and CNLDO seen between 1998 and 2019, treatment interventions and outcomes.

RESULTS

During the study period, 1993 children had CG and 6203 children had CNLDO, among the children aged 1-year or less. Of these, 51 children (73 eyes) had co-existing CG and CNLDO. The prevalence of CNLDO in CG was 2.5% (51/1993) and the prevalence of CG among CNLDO was 0.8% (51/6203). Median age (Interquartile range, IQR) was 53 days (IQR: 8, 155), when they were diagnosed with CG. Among the children with CNLDO, 68 eyes (93.1%) had simple CNLDO, and 5 eyes (6.9%) had complex CNLDO. Associated lacrimal anomalies were present in 7/73 eyes, including congenital lacrimal fistula in two eyes, upper punctal agenesis and upper mid-canalicular obstruction in two eyes each, and upper single canalicular-wall hypoplasia in one eye. Lacrimal syringing and probing were successful in 69/72 eyes (95.8%), and failed in 3 eyes (4.2%). These 3 eyes had complex CNLDO and underwent Dacryocystorhinostomy (DCR) with Mitomycin-C and intubation. Sixty-two eyes underwent combined trabeculotomy and trabeculectomy, and two eyes underwent trabeculectomy. Median age at glaucoma surgery was 87 days (IQR: 54, 193). Median time delay for glaucoma surgery after probing was 18 days (IQR: 12, 35).

CONCLUSION

Among children aged ≤1-year presenting with congenital glaucoma coexisting lacrimal drainage anomalies was noted in 2.5% and simple CNLDO was the commonest. Early intervention is of paramount importance to prevent sight-threatening intraocular infection with CNLDO, and to minimize the delay of glaucoma surgery. The outcomes of lacrimal probing were excellent, however complex CNLDO required DCR.

Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis

Altered expression of protein coding gene (PCG) and long non-coding RNA (lncRNA) have been identified in SARS-CoV-2 infected cells and tissues from COVID-19 patients. The functional role and mechanism (s) of transcriptional regulation of deregulated genes in COVID-19 remain largely unknown. In the present communication, reanalyzing publicly available gene expression data, we observed that 66 lncRNA and 5491 PCG were deregulated in more than one experimental condition. Combining our earlier published results and using different publicly available resources, it was observed that 72 deregulated lncRNA interacted with 3228 genes/proteins. Many targets of deregulated lncRNA could also interact with SARS-CoV-2 coded proteins, modulated by IFN treatment and identified in CRISPR screening to modulate SARS-CoV-2 infection. The majority of the deregulated lncRNA and PCG were targets of at least one of the transcription factors (TFs), interferon responsive factors (IRFs), signal transducer, and activator of transcription (STATs), NFκB, MYC, and RELA/p65. Deregulated 1069 PCG was joint targets of lncRNA and TF. These joint targets are significantly enriched with pathways relevant for SARS-CoV-2 infection indicating that joint regulation of PCG could be one of the mechanisms for deregulation. Over all this manuscript showed possible involvement of lncRNA and mechanisms of deregulation of PCG in the pathogenesis of COVID-19.

Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process

Assessment of heart sounds which are generated by the beating heart and the resultant blood flow through it provides a valuable tool for cardiovascular disease (CVD) diagnostics. The cardiac auscultation using the classical stethoscope phonological cardiogram is known as the most famous exam method to detect heart anomalies. This exam requires a qualified cardiologist, who relies on the cardiac cycle vibration sound (heart muscle contractions and valves closure) to detect abnormalities in the heart during the pumping action. Phonocardiogram (PCG) signal represents the recording of sounds and murmurs resulting from the heart auscultation, typically with a stethoscope, as a part of medical diagnosis. For the sake of helping physicians in a clinical environment, a range of artificial intelligence methods was proposed to automatically analyze PCG signal to help in the preliminary diagnosis of different heart diseases. The aim of this research paper is providing an accurate CVD recognition model based on unsupervised and supervised machine learning methods relayed on convolutional neural network (CNN). The proposed approach is evaluated on heart sound signals from the well-known, publicly available PASCAL and PhysioNet datasets. Experimental results show that the heart cycle segmentation and segment selection processes have a direct impact on the validation accuracy, sensitivity (TPR), precision (PPV), and specificity (TNR). Based on PASCAL dataset, we obtained encouraging classification results with overall accuracy 0.87, overall precision 0.81, and overall sensitivity 0.83. Concerning Micro classification results, we obtained Micro accuracy 0.91, Micro sensitivity 0.83, Micro precision 0.84, and Micro specificity 0.92. Using PhysioNet dataset, we achieved very good results: 0.97 accuracy, 0.946 sensitivity, 0.944 precision, and 0.946 specificity.

Integrating multi-domain deep features of electrocardiogram and phonocardiogram for coronary artery disease detection

Electrocardiogram (ECG) and phonocardiogram (PCG) are both noninvasive and convenient tools that can capture abnormal heart states caused by coronary artery disease (CAD). However, it is very challenging to detect CAD relying on ECG or PCG alone due to low diagnostic sensitivity. Recently, several studies have attempted to combine ECG and PCG signals for diagnosing heart abnormalities, but only conventional manual features have been used. Considering the strong feature extraction capabilities of deep learning, this paper develops a multi-input convolutional neural network (CNN) framework that integrates time, frequency, and time-frequency domain deep features of ECG and PCG for CAD detection. Simultaneously recorded ECG and PCG signals from 195 subjects are used. The proposed framework consists of 1-D and 2-D CNN models and uses signals, spectrum images, and time-frequency images of ECG and PCG as inputs. The framework combining multi-domain deep features of two-modal signals is very effective in classifying non-CAD and CAD subjects, achieving an accuracy, sensitivity, and specificity of 96.51%, 99.37%, and 90.08%, respectively. The comparison with existing studies demonstrates that our method is very competitive in CAD detection. The proposed approach is very promising in assisting the real-world CAD diagnosis, especially under general medical conditions.

Direct Chill Casting and Extrusion of AA6111 Aluminum Alloy Formulated from Taint Tabor Scrap

AA6111 aluminum automotive body-sheet alloy has been formulated from 100% Taint Tabor scrap aluminum. Direct chill casting with and without high shear melt conditioning (HSMC) was used to produce the AA6111 alloy billets. Both homogenized and non-homogenized billets were extruded into sheets. The optical micrographs of the melt conditioned direct chill (MC-DC) samples showed refined equiaxed grains in comparison to direct chill (DC) cast and direct chill grain refined (DC-GR) samples. Optical metallography showed extensive peripheral coarse grain (PCG) for the DC, DC-GR and MC-DC planks extruded from the homogenized standard AA6111 billets while planks extruded from modified AA6111 billets (with recrystallization inhibitors) showed thin PCG band. The co-addition of recrystallization inhibitors Mn, Zr, and Cr with elimination of the billet homogenization step had a favorable impact on the microstructure of the AA6111 alloy following the extrusion process where a fibrous grain structure was retained across the whole section of the planks. The mechanical properties of as-cast planks extruded from non-homogenized billets were similar to those extruded from homogenized billets. Eliminating the homogenization heat treatment step prior to extrusion has important ramifications in terms of processing cost reduction.

Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network

We purpose a novel method that combines modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN) for classifying normal and abnormal heart sounds. A hidden Markov model is used to find the position of each cardiac cycle in the heart sound signal and determine the exact position of the four periods of S1, S2, systole, and diastole. Then the one-dimensional cardiac cycle signal was converted into a two-dimensional time-frequency picture using the MFSWT. Finally, two CNN models are trained using the aforementioned pictures. We combine two CNN models using sample entropy (SampEn) to determine which model is used to classify the heart sound signal. We evaluated our model on the heart sound public dataset provided by the PhysioNet Computing in Cardiology Challenge 2016. Experimental classification performance from a 10-fold cross-validation indicated that sensitivity (Se), specificity (Sp) and mean accuracy (MAcc) were 0.95, 0.93, and 0.94, respectively. The results showed the proposed method can classify normal and abnormal heart sounds with efficiency and high accuracy. Graphical abstract Block diagram of heart sound classification.

Trabeculotomy versus combined trabeculotomy-trabeculectomy for primary congenital glaucoma: study protocol of a randomised controlled trial

INTRODUCTION

Trabeculotomy and combined trabeculotomy-trabeculectomy (CTT) are major surgical options for primary congenital glaucoma (PCG). However, it is unclear which of these two surgical procedures should be recommended as the optimum first-line treatment for PCG. This trial aims to determine whether the outcomes of trabeculotomy are non-inferior to those of CTT in moderate PCG with a horizontal corneal diameter (HCD) of 12-14 mm.

METHODS AND ANALYSIS

This is a 3-year, non-inferiority, prospective, randomised controlled trial. We plan to recruite 248 participants (aged ≤3 years) with PCG with an HCD of 12-14 mm from the Department of Glaucoma, Zhongshan Ophthalmic Center, Guangzhou, China. One eye per participant will be randomly (1:1) assigned to receive trabeculotomy or CTT. The primary outcome is the 3-year postoperative success rate in lowering intraocular pressure (IOP), and the secondary clinical outcomes will include IOP reduction, visual acuity, HCD, central corneal thickness, axial length, cup-disc ratio, refractive error and postoperative complications. Data will be analysed by the intention-to-treat principle.

ETHICAL APPROVAL AND DISSEMINATION

The study protocol has been approved by the ethics committee of Zhongshan Ophthalmic Center (2014MEKY023) and the '5010 Plan' evaluation committee at Sun Yat-Sen University, Guangzhou, China. The results will be disseminated in international academic meetings and published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

Chinese Clinical Trial Registry, ChiCTR-IOR-14005588; Date registered: 20 November 2014.

Analysis of PCG signals using quality assessment and homomorphic filters for localization and classification of heart sounds

BACKGROUND AND OBJECTIVE

Accurate localization of heart beats in phonocardiogram (PCG) signal is very crucial for correct segmentation and classification of heart sounds into S1 and S2. This task becomes challenging due to inclusion of noise in acquisition process owing to number of different factors. In this paper we propose a system for heart sound localization and classification into S1 and S2. The proposed system introduces the concept of quality assessment before localization, feature extraction and classification of heart sounds.

METHODS

The signal quality is assessed by predefined criteria based upon number of peaks and zero crossing of PCG signal. Once quality assessment is performed, then heart beats within PCG signal are localized, which is done by envelope extraction using homomorphic envelogram and finding prominent peaks. In order to classify localized peaks into S1 and S2, temporal and time-frequency based statistical features have been used. Support Vector Machine using radial basis function kernel is used for classification of heart beats into S1 and S2 based upon extracted features. The performance of the proposed system is evaluated using Accuracy, Sensitivity, Specificity, F-measure and Total Error. The dataset provided by PASCAL classifying heart sound challenge is used for testing.

RESULTS

Performance of system is significantly improved by quality assessment. Results shows that proposed Localization algorithm achieves accuracy up to 97% and generates smallest total average error among top 3 challenge participants. The classification algorithm achieves accuracy up to 91%.

CONCLUSION

The system provides firm foundation for the detection of normal and abnormal heart sounds for cardiovascular disease detection.

Cardiorespiratory system monitoring using a developed acoustic sensor

This Letter proposes a wireless acoustic sensor for monitoring heartbeat and respiration rate based on phonocardiogram (PCG). The developed sensor comprises a processor, a transceiver which operates at industrial, scientific and medical band and the frequency of 2.54 GHz as well as two capacitor microphones which one for recording the heartbeat and another one for respiration rate. To evaluate the precision of the presented sensor in estimating heartbeat and respiration rate, the sensor is tested on the different volunteers and the obtained results are compared with a gold standard as a reference. The results reveal that root-mean-square error are determined <2.27 beats/min and 0.92 breaths/min for the heartbeat and respiration rate in turn. While the standard deviation of the error is obtained <1.26 and 0.63 for heartbeat and respiration rate, respectively. Also, the sensor estimate sounds of [Formula: see text] to [Formula: see text] obtained PCG signal with sensitivity and specificity 98.1% and 98.3% in turn that make 3% improvement than previous works. The results prove that the sensor can be appropriate candidate for recognising abnormal condition in the cardiorespiratory system.

Bimetallic Carbides-Based Nanocomposite as Superior Electrocatalyst for Oxygen Evolution Reaction

The development of highly efficient and low-cost oxygen evolution electrocatalysts is extremely imperative for the new energy technology. Transition metal carbides have been investigated as remarkable hydrogen evolution reaction (HER) electrocatalysts but undesired oxygen evolution reaction (OER) electrocatalysts and need further study. Here, a cobalt-molybdenum-based bimetallic carbide coated by N-doped porous carbon and anchored on N-doped reduced graphene oxide film (Co₆Mo₆C₂/NCRGO) is synthesized by directly carbonizing the Co-doped polyoxometalate/conductive polymer/graphene oxide (Co-PCG) precursors. The precise control of the Co/Mo molar ratio in the Co-PCG precursor is of critical importance to synthesize pure phase bimetallic carbide of Co₆Mo₆C₂. As the highly active and robust OER electrocatalyst, the Co₆Mo₆C₂/NCRGO composite exhibits excellent activity in alkaline solution, affording a low overpotential of 260 mV versus RHE at 10 mA cm^-2, a small Tafel slope of 50 mV dec^-1, as well as long-term stability. The superior OER performances are strongly associated with the active Co₆Mo₆C₂ particles, polypyrrole (PPy)-derived N-doped porous carbon, and the conductive RGO films. Remarkably, it is the first evidence that the bimetallic carbides were used as the OER catalysts with such high OER activity.

Self vs. other: neural correlates underlying agent identification based on unimodal auditory information as revealed by electrotomography (sLORETA)

Recent neuroscientific studies have identified activity changes in an extensive cerebral network consisting of medial prefrontal cortex, precuneus, temporo-parietal junction, and temporal pole during the perception and identification of self- and other-generated stimuli. Because this network is supposed to be engaged in tasks which require agent identification, it has been labeled the evaluation network (e-network). The present study used self- versus other-generated movement sounds (long jumps) and electroencephalography (EEG) in order to unravel the neural dynamics of agent identification for complex auditory information. Participants (N=14) performed an auditory self-other identification task with EEG. Data was then subjected to a subsequent standardized low-resolution brain electromagnetic tomography (sLORETA) analysis (source localization analysis). Differences between conditions were assessed using t-statistics (corrected for multiple testing) on the normalized and log-transformed current density values of the sLORETA images. Three-dimensional sLORETA source localization analysis revealed cortical activations in brain regions mostly associated with the e-network, especially in the medial prefrontal cortex (bilaterally in the alpha-1-band and right-lateralized in the gamma-band) and the temporo-parietal junction (right hemisphere in the alpha-1-band). Taken together, the findings are partly consistent with previous functional neuroimaging studies investigating unimodal visual or multimodal agent identification tasks (cf. e-network) and extent them to the auditory domain. Cortical activations in brain regions of the e-network seem to have functional relevance, especially the significantly higher cortical activation in the right medial prefrontal cortex.

Evaluation of early cerebral metabolic, perfusion and microstructural changes in HCV-positive patients: a pilot study

BACKGROUND & AIMS

The aim of the study was to evaluate early metabolic perfusion, and microstructural cerebral changes in patients with the hepatitis C virus (HCV) infection and normal appearing brain on plain MR using advanced MR techniques, as well as to assess correlations of MR measurements with the liver histology activity index (HAI).

METHODS

Fifteen HCV-positive patients and 18 control subjects underwent single voxel MR spectroscopy (MRS), perfusion weighted imaging (PWI), and diffusion tensor imaging (DTI), using a 1.5T MR unit. MRS metabolite ratios (NAA/Cr, Cho/Cr, mI/Cr) were calculated. PWI values of relative cerebral blood volume (rCBV) were assessed from 8 areas including several cortical locations, basal ganglia, and fronto-parietal white matter. DTI fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were obtained from 14 white matter tracts.

RESULTS

Compared to controls, HCV-positive patients showed significantly (p < 0.05) lower NAA/Cr ratios within frontal and parietal white matters, lower rCBV values within frontal and temporo-parietal cortices, decreased FA values, as well as increased ADC values in several white matter tracts. We also found elevated rCBV values in basal ganglia regions. The increase in mI/Cr and Cho/Cr ratio was correlated with a higher HAI score.

CONCLUSIONS

The results of advanced MR techniques indicate neurotoxicity of HCV reflected by neuronal impairment within white matter, cortical hypoperfusion, and disintegrity within several white matter tracts. Hyperperfusion in basal ganglia may be an indicator of brain inflammation in HCV patients. Our findings may suggest a biologic link between HCV-related liver disease and cerebral dysfunction.

Neural correlates of dysfunctional emotion regulation in major depressive disorder. A systematic review of neuroimaging studies

Abnormal emotion processing is a core feature of major depressive disorder (MDD). Since the emergence of functional neuroimaging techniques, many studies have been conducted in MDD subjects to elucidate the underlying abnormalities in the neural systems involved in emotion regulation. In this systematic review, we discuss this research in the context of the neural model of emotion regulation previously described by Phillips et al. (2008). This model differentiates between automatic and voluntary emotion regulation subprocesses. Automatic regulation subprocesses were shown to involve predominantly medial prefrontal cortical structures, in addition to the hippocampus and parahippocampus, while voluntary regulation processes additionally recruited lateral prefrontal cortical regions. In conclusion, although the available data is limited, findings suggest that MDD subjects demonstrate abnormally reduced activity in lateral prefrontal cortices during explicit voluntary control of emotional experience. During early, automatic stages of emotion regulation, on the other hand, MDD subjects appear to achieve successful emotion regulation by recruiting additional lateral prefrontal neural regions, that may be mediated by medial prefrontal, especially rostral/dorsal anterior cingulate gyrus (ACG) functioning. Dysfunctional automatic regulation may impair successful voluntary emotion regulation, and may present a target for novel therapeutic approaches in MDD.

Roles of long noncoding RNAs in brain development, functional diversification and neurodegenerative diseases

Long noncoding RNAs (lncRNAs) have been attracting immense research interest, while only a handful of lncRNAs have been characterized thoroughly. Their involvement in the fundamental cellular processes including regulate gene expression at epigenetics, transcription, and post-transcription highlighted a central role in cell homeostasis. However, lncRNAs studies are still at a relatively early stage, their definition, conservation, functions, and action mechanisms remain fairly complicated. Here, we give a systematic and comprehensive summary of the existing knowledge of lncRNAs in order to provide a better understanding of this new studying field. lncRNAs play important roles in brain development, neuron function and maintenance, and neurodegenerative diseases are becoming increasingly evident. In this review, we also highlighted recent studies related lncRNAs in central nervous system (CNS) development and neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD) and amyotrophic lateral sclerosis (ALS), and elucidated some specific lncRNAs which may be important for understanding the pathophysiology of neurodegenerative diseases, also have the potential as therapeutic targets.

The complete mitochondrial genome of Sasakia funebris (Leech) (Lepidoptera: Nymphalidae) and comparison with other Apaturinae insects

Sasakia funebris, a member of the lepidopteran family, Nymphalidae (superfamily Papilionoidea) is a rare species and is found only in some areas of South China. In this study, the 15,233 bp long complete mitochondrial genome of S. funebris was determined, and harbors the gene arrangement identical to all other sequenced lepidopteran insects. The nucleotide composition of the genome is highly A+T biased, accounting for 81.2%. All protein-coding genes (PCGs) start with typical ATN codons, except for COI which begins with the CGA codon. All tRNAs have a typical clover-leaf secondary structure, except for tRNASer(AGN), the dihydrouridine (DHU) arm of which forms a simple loop. The S. funebris A+T-rich region of 370 bp contains several features common to the Lepidoptera insects, including the motif ATAGA followed by a 19 bp poly-T stretch, and two tandem repeats consisting of 18 bp repeat units and 14 bp repeat units. The phylogenetic analyses of Apaturinae based on mitogenome sequences showed: (S. funebris+Sasakia charonda)+(Apatura metis+Apatura ilia). This result is consistent with the morphological classification.