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Time series data recorded from physiological systems often innately exhibit inherent physiological complexity variation on a long-range temporal scale. Multiscale analysis is considered vital for characterising the features of physiological signals. In this research, we propose a novel multiscale analysis method called multiscale increment entropy (MIE), which integrates incremental entropy (IncrEn) and multiscale analysis. MIE inherits the nature of IncrEn, which considers the fluctuation directions and amplitude of a time series. Experiments on both synthetic and real-world signals indicate that MIE performs better than popular approaches as a complexity index. On each temporal scale, MIE corroborates the complexity-loss theory of ageing and disease well. Furthermore, it reliably discriminates either between the EEG time series and heartbeat intervals of healthy subjects and patients or between the oxygen saturation variability of young and elderly, while commonly used algorithms do not perform well in the above cases. MIE requires less computational time compared to several popular approaches. It also has lower variations and is always defined across scales, even for short time series. These merits make it suitable for analysing unknown physiological time series.

Kadsura coccinea (Lem.) A. C. Smith is an evergreen liana widely cultivated in China for its economic importance in traditional medicine. Many phytochemical studies on the stems and roots of K. coccinea have shown a variety of biological activities, such as anti-hepatitis, anti-HIV, and anti-tumor (Yang et al. 2020). In July 2021, symptoms of leaf spot were observed in a plantation of K. coccinea in Longan (23°03´N, 107°54´E), Guangxi province, China. The incidence of this disease was 36%, and severity varies from approximately 20 to 40% of leaf surface coverage. Symptoms began as small brown spots that expanded into irregular to nearly flower-shaped lesions. To isolate the pathogen, leaves with spots were collected, sterilized with 75% ethanol for 15 s followed by 2% sodium hypochlorite for 120 s, rinsed three times in sterilized distilled water, cut into 5 × 5 mm pieces, and placed onto potato dextrose agar (PDA) plates. The plates were kept in an incubator at 26°C in the dark for at least 2 days. A total of 27 fungal colonies of similar morphology out of 30 pieces of infected tissues were isolated. Four representative isolates (HBB1 to HBB4) were selected to study for further characterization. Fungal colonies were initially grayish-white and then turned greenish-gray on PDA. The black pycnidium and immature conidia appeared over PDA plates after 18 days. The immature conidia were colorless and transparent, elliptical, and had a single-cell structure. After 5 days, the immature conidia gradually become black and develop into mature conidia. The mature conidia were dark brown and two-celled with longitudinal striations, 20.41–29.93 × 12.42–17.19 μm (average 26.07×14.51 μm; n = 100). For DNA-based identification, the internal transcribed spacer (ITS) region, translation elongation factor 1 alpha (EF1-α), and β-tubulin (TUB) genes of the isolates were amplified and sequenced using the primers ITS1/ITS4 (White et al. 1990), EF1-728F/EF1-986R (Carbone and Kohn 1999), and Bt2a/Bt2b (Glass and Donaldson 1995), respectively. Sequences were submitted to GenBank (Accession nos. MW045412 to MW045415 for ITS, MW065559 to MW065562 for EF1-α, and MW065555 to MW065558 for TUB). A phylogenetic analysis was conducted using the Maximum Likelihood method on concatenated sequences of the three genes, which showed that the four Chinese isolates from K. coccinea were clustered with reference isolates of Lasiodiplodia theobromae including the ex-neotype CBS 164.96. Pathogenicity tests were performed on young, fully expanded leaves of 2-year seedlings. A 10 μL conidial suspension (1×106 conidia/mL) was inoculated on each wound on the left-half leaf and a 10 μL sterile water was inoculated on each wound on the right-half leaf (control). Each treatment was repeated three times. Inoculated leaves were wrapped in plastic bags for 5 days and plants were maintained in a growth chamber at 27°C, 85% relative humidity. Brown leaf spots appeared 5 to 6 days after inoculation, whereas the control leaves treated with sterile water showed no symptoms. All re-isolations from spots produced colonies with the same morphological characters as L. theobromae, completing Koch’s postulates. To our knowledge, this is the first report of L. theobromae causing leaf spot on K. coccinea in China and worldwide. Severe leaf disease caused by L. theobromae threatens K. coccinea production. The disease threatens K. coccinea growth, and effective control measures should be identified to reduce losses.

Background Cuproptosis is a new form of programmed cell death which induced by copper. The signature of cuproptosis related lncRNAs and mRNAs (CRLM) has not been reported before in clear cell renal cell carcinoma. Methods The RNA-seq, clinical data, and single nucleotide variants (SNV) data of clear cell renal cell carcinoma (ccRCC) were obtained from the Cancer Genome Atlas (TCGA). Cuproptosis related long non-coding RNAs (lncRNAs) were identified via Pearson’s test. Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were used to further screen prognosis related lncRNAs and mRNAs for signature construction. Cases were then randomly divided into training and testing set groups at ratio 1:1 to validate the cuproptosis signature. Kaplan–Meier survival analysis, receiver operating characteristic (ROC) curve analysis, nomogram, principal components analysis (PCA), gene set enrichment analyses (GSEA), mutation landscape, and therapies response were subsequently conducted with R or perl algorithms. A Japanese ccRCC cohort (n = 98) was used to validate this signature. Results A total of 245 lncRNAs were identified as cuproptosis related. Three of them (AL731577.2, LINC00460, AL133215.2) and cuproptosis related mRNA DBT were finally incorporated into the signature construction. High-risk group patients suffered from lower survival rate. The area under curve (AUC) of the novel signature for 1-, 3-, 5-year survival rates was 0.731, 0.718, 0.745, respectively. The concordance index (C-index) was 0.692. The Japanese cohort showed a good consistence with the results derived from the TCGA dataset. Tumor mutation burden (TMB) was also found to be a risking factor for ccRCC patients. Immune related Gene ontology terms were significantly enriched in high-risk patients. Besides, low-risk patients may be more sensitive to immune check inhibitors (ICIs), Sunitinib, and Pazopanib. High-risk patients may be more sensitive to Sorafenib. Conclusion The cuproptosis related signature is a promising and potential prognostic tool in predicting the survival of patients with ccRCC. It could contribute to precise and individualized ccRCC treatment.

Illicium difengpi B. N. Chang et al., a shrub with aromatic odor in the Illicium genus, is extensively used as a medicinal plant in China. In June of 2020, a leaf spot on I. difengpi with incidence of about sixty percent was observed in a field located in Guilin (25°4'40"N; 110°18'21"E), Guangxi Province, China. Initial leaf symptoms were round spots with gray centers, surrounded by yellow halos. The spots gradually spread and merged. Six samples of symptomatic leaves were collected from six diseased plants, and they were surface disinfested before isolation. Potato dextrose agar (PDA) was used to culture pathogens. Successively, pure cultures were obtained by transferring hyphal tips to new PDA plates. A total of 10 isolates were obtained from the affected leaves. Two single-spore isolates (GX-1 and GX-2) were obtained and confirmed to be identical based on morphological characteristics. The representative isolate GX-2 was selected for further study on morphological and molecular characteristics. The colony of isolate GX-2 was about 4 cm in diameter on a PDA plate in 5 days, dark green with a granular surface, and irregular white edge. Conidia were hyaline, unicellular, oval, narrow at the end with a single apical appendage, and 8.2 to 13.8 × 3.7 to 7.2 µm (n = 50). Spermatia were hyaline, bacilliform with swollen ends, 3.8 to 8.9 × 1.3 to 1.9 µm (n = 50). Morphological characteristics of isolate GX-2 were consistent with the description of Phyllosticta capitalensis (Wikee et al. 2013). The internal transcribed spacer (ITS) region, translation elongation factor 1-α (tef1-α), glyceraldehyde-3-phosphate dehydrogenase (GPDH) and actin (ACT) were amplified using primers ITS1/ITS4, EF-728F/EF-986R, Gpd1-LM/Gpd2-LM and ACT-512F/ACT-783R, respectively (Wikee et al. 2013). Sequences were deposited in GenBank with accession numbers OL505439 for ITS, OL539429 for ACT, OL539430 for tef1-α and OL539431 for GPDH. BLAST analysis in GenBank showed that these sequences were 99 to 100% similar to the corresponding ITS (MT649668), ACT (MN958710), tef1-α (MN958711) and GPDH (KU716077) sequences of P. capitalensis. Also, the phylogenetic tree based on genes of ITS, tef1-α, GPDH and ACT by the maximum likelihood method showed that isolate GX-2 clustered together with P. capitalensis. The pathogenicity tests were carried out on a healthy 3 year-old plant in the greenhouse with 80% relative humidity at 25 °C. Four sterilized leaves were wounded with a needle and inoculated with 20 μL spore suspension (1 × 106 spores/ml). Another four sterilized leaves were inoculated with 20 μL sterile water as a control. All plants were incubated in a chamber with 98% relative humidity at 25 ± 1°C. After 12 days, disease symptoms similar to the field were observed on leaves, whereas control plants remained healthy. P. capitalensis was successfully reisolated only from the inoculated leaves and identified based on morphological characters. P. capitalensis caused leaf spots on various host plants around the world (Wikee et al. 2013), including on tea plants in China (Cheng et al. 2019) and oil palm in Malaysia (Nasehi et al. 2020), but it has not been reported on I. difengpi. Thus, this is the first report of P. capitalensis causing leaf spot on I. difengpi. This study will provide an important reference for the control of the disease. The epidemiology of this disease should be investigated in further research.

Background Paris polyphylla is a herb widely used in traditional Chinese medicine to treat various diseases. Stem rot diseases seriously affected the yield of P. polyphylla in subtropical areas of China. Therefore, cost-effective, chemical-free, eco-friendly strategies to control stem rot on P. polyphylla are valuable and urgently needed. Results In this paper, we reported the biocontrol efficiency of Paenibacillus peoriae HJ-2 and its complete genome sequence. Strain HJ-2 could serve as a potential biocontrol agent against stem rot on P. polyphylla in the greenhouse and field. The genome of HJ-2 consists of a single 6,001,192 bp chromosome with an average GC content of 45% and 5,237 predicted protein coding genes, 39 rRNAs and 108 tRNAs. The phylogenetic tree indicated that HJ-2 is most closely related to P. peoriae IBSD35. Functional analysis of genome revealed numerous genes/gene clusters involved in plant colonization, biofilm formation, plant growth promotion, antibiotic and resistance inducers synthesis. Moreover, metabolic pathways that potentially contribute to biocontrol mechanisms were identified. Conclusions This study revealed that P. peoriae HJ-2 could serve as a potential BCA against stem rot on P. polyphylla. Based on genome analysis, the genome of HJ-2 contains more than 70 genes and 12 putative gene clusters related to secondary metabolites, which have previously been described as being involved in chemotaxis motility, biofilm formation, growth promotion, antifungal activity and resistance inducers biosynthesis. Compared with other strains, variation in the genes/gene clusters may lead to different antimicrobial spectra and biocontrol efficacies.

The powerful capabilities of deep convolutional neural networks make them useful in various fields. However, most edge devices are difficult to afford the huge amount of parameters and high computational cost. Therefore, it is highly imperative to compress these huge models to make them lightweight to enable real-time inference on edge devices. Channel pruning is a mainstream method of network compression. Generally, the Lasso constraint is imposed on the scaling factor in the batch normalization layer to make them tend to zero for selecting unimportant channels and then prune them. However, Lasso is a non-smooth function that is not derivable at zero, we experimentally find that when the value of the loss function is small, it is difficult to decline continuously. Aiming at the above problems, this paper proposes a pruning strategy based on the derivable function Smooth-Lasso, using Smooth-Lasso as a regularization constraint to perform sparse training and then prune the network. Experiments on benchmark datasets and convolutional networks show that our method can not only make the loss function converge quickly, but also save more storage space and computational cost than the baseline method while maintaining the same level of accuracy as the original network.

Kadsura coccinea (Lem.) A. C. Smith, an evergreen liana, is widely cultivated in China for its economic importance in traditional medicine. Many phytochemical studies on the stems and roots of K. c...

Kadsura coccinea (Lem.) A. C. Smith, an evergreen liana, is widely cultivated in China for its economic importance in traditional medicine. Many phytochemical studies on the stems and roots of K. coccinea have shown numerous biological activities, such as anti-tumor, anti-HIV, and anti-oxidant (Yang et al. 2020). In June 2019, an anthracnose on K. coccinea was observed in a plantation in Longan (23°03´N, 107°54´E), Guangxi province. Disease incidence was up to 30% in a plantation. Its symptoms began as small brown spots that expanded into nearly circular spots (Fig. 1A). To isolate pathogen, diseased leaves were collected. The leaves were sterilized with 75% ethanol for 15 s followed by 2% sodium hypochlorite for 90 s, then rinsed three times in sterilized distilled water, cut into 5 × 5 mm pieces, and placed into potato dextrose agar (PDA) plates. The plates were incubated in an incubator at 25°C in dark for 2-3 days. Fungal colonies with similar morphology of 27 isolates were isolated from the 30 infected tissues. Six representative isolates (YB1 to YB6) were selected to further study their characterization. Fungal colonies were grayish-white, orange-yellow conidial masses could be observed in colonies (Fig. 1C). The mature conidia were colorless and transparent, elliptical, and single-celled, 13.0-21.0 × 4.0-8.0 μm (average 16.92 × 5.92 µm; n =100) (Fig. 1B). The DNA sequences of ribosomal internal transcribed spacer region (ITS), glyceraldehyde-3-phosphate (GAPDH), calmodulin (CAL), actin (ACT), chitin synthase (CHS-1) and β-tubulin (TUB2) were amplified by PCR using the primer pairs ITS1/ITS4, GDF/GDR, CL1C/CL2C, ACT-512F/ACT-783R, CHS-79F/CHS-354R, and T1/Bt2b (Wang et al. 2020), respectively. Sequences were submitted to GenBank (Accession nos. MZ040489 to MZ040494 for ITS, MZ069043 to MZ069048 for GAPDH, MZ069049 to MZ069054 for CAL, MZ069055 to MZ069060 for ACT, MZ069061 to MZ069066 for CHS-1, and MZ069067 to MZ069072 for TUB2). These sequences were 98%-100% identical to that of reference isolates JX010278, JX010019, JX009709, GQ856775, GQ856730, and JX010410 of Colletotrichum siamense CBS 125378 ex-type recorded in GenBank. Phylogenetic analysis of combined ITS, GAPDH, CAL, ACT, CHS-1, TUB2 genes with 16 sequences obtained from GenBank using maximum likelihood method showed that the six isolates clustered with two reference isolates of Colletotrichum siamense as a distinct clade (Fig. 2). Based on morphological characteristics and phylogenetic analysis, six isolates were identified as C. siamense. Pathogenicity tests were performed on young, fully expanded leaves of 1-year seedlings. Every leaf was punctured at 6 points on the right half and 6 points on the left half using a sterile needle. A 10 μl conidial suspension (1×106 conidia/ml) was inoculated on each wound on the left-half leaf and a 10 μl sterile water was inoculated on each wound on the right-half leaf (control). Each treatment was repeated three times. Inoculated leaves were wrapped in plastic bags for 2 days and after removing the bags, plants were maintained in a growth chamber at 28°C, 80% relative humidity, and a 12-h photoperiod. Anthracnose spots formed 2 to 3 days after inoculation, whereas the control leaves remained symptomless. Morphological characters matched the descriptions of C. siamense. The pathogen was previously reported to cause anthracnose on Aloe vera (Azad et al. 2020), postharvest anthracnose in mango (Liu et al. 2017), pod rot in cacao (Serrato-Diaz et al. 2020). To our knowledge, this is the first report of anthracnose on K. coccinea caused by C. siamense in China.

In human-robots interfaces studies have shown that stress caused by these factors results in variations in speaker's pronunciation, making highly reliable speech recognition systems of interface difficult to achieve. In this paper, we focus on the glottal source of stress speech under workload condition. The glottal flow resulted from vibration of the vocal folds is mainly discussed and features derived from glottal source are investigated to separate stressed speech from neutral speech. Experimental results show the proposed features representing glottal source could be able to lead to improvements in classification for the stressed speech.

Physical stress from workload for speaker exerts limitations on his speech production in physiological system causing speech variability, and thereby reduces speech system performance. The speech under stress presents a marked difference from the speech under neural condition. The distribution for the stress samples in the feature space shows the discontinuity because of the existence of different stress levels and different physiological characteristics for each speaker under the stress condition. In this paper, we use a Gaussian Mixture Models (GMM) framework with the physical features derived from the speech production model for neutral/stress speech classification. Cluster analysis is performed within stress class, and several cluster areas can be found for the classification, which is following Gaussian distribution. Experimental results show that GMM outperforms other classifiers for differentiating neutral speech from stress.