Linking variability of tree water use and growth with species resilience to environmental changes

Tree growth is an indicator of tree vitality and its temporal variability is linked to species resilience to environmental changes. Second-order statistics that quantify the cross-scale temporal variability of ecophysiological time series (statistical memory) could provide novel insights into species resilience. Species with high statistical memory in their tree growth may be more affected by disturbances, resulting in lower overall resilience and higher vulnerability to environmental changes. Here, we assessed the statistical memory, as quantified with the decay in standard deviation with increasing time scale, in tree water use and growth of co-occurring European larch Larix decidua and Norway spruce Picea abies along an elevational gradient in the Swiss Alps using measurements of stem radius changes, sap flow and tree-ring widths. Local-scale interspecific differences between the two conifers were further explored at the European scale using data from the International Tree-Ring Data Bank. Across the analysed elevational gradient, tree water use showed steeper variability decay with increasing time scale than tree growth, with no significant interspecific differences, highlighting stronger statistical memory in tree growth processes. Moreover, Norway spruce displayed slower decay in growth variability with increasing time scale (higher statistical memory) than European larch; a pattern that was also consistent at the European scale. The higher statistical memory in tree growth of Norway spruce in comparison to European larch is indicative of lower resilience of the former in comparison to the latter, and could potentially explain the occurrence of European larch at higher elevations at the Alpine treeline. Single metrics of resilience cannot often summarize the multifaceted aspects of ecosystem functioning, thus, second-order statistics that quantify the strength of statistical memory in ecophysiological time series could complement existing resilience indicators, facilitating the assessment of how environmental changes impact forest growth trajectories and ecosystem services.     

Effect of treatment with silver(I) complex of metronidazole on ocular rosacea: Design and formulation of new silver drug with potent antimicrobial activity

BackgroundMetronidazole with well-soluble silver nitrate forms a complex with potent activity (synergism) against bacterial strains and fungi with simultaneous lower side effect than in the case of the two agents administered separately.This study aimed to establish effectiveness in the treatment of ocular rosacea with new formulation of drops and ointment with silver(I) complex of metronidazole.MethodsThree patients suffering from serious ophthalmic complications of acne rosacea were treated with drops and ointments applied 3 times a day for 3 months. The uncorrected visual acuity testing (UCVA), pachymetry as well as subjective and objective tear film assessment were evaluated.ResultsIn all patients, we have improved UCVA as well as objective and subjective evaluation of the tear film parameters. No significant differences in corneal thickness were observed.ConclusionWell soluble silver(I) complex of metronidazole might be an alternative method in ophthalmic complications of acne rosacea treatment.     

Small-molecule active pharmaceutical ingredients of approved cancer therapeutics inhibit human aspartate/asparagine-β-hydroxylase

Human aspartate/asparagine-β-hydroxylase (AspH) is a 2-oxoglutarate (2OG) dependent oxygenase that catalyses the hydroxylation of Asp/Asn-residues of epidermal growth factor-like domains (EGFDs). AspH is reported to be upregulated on the cell surface of invasive cancer cells in a manner distinguishing healthy from cancer cells. We report studies on the effect of small-molecule active pharmaceutical ingredients (APIs) of human cancer therapeutics on the catalytic activity of AspH using a high-throughput mass spectrometry (MS)-based inhibition assay. Human B-cell lymphoma-2 (Bcl-2)-protein inhibitors, including the (R)-enantiomer of the natural product gossypol, were observed to efficiently inhibit AspH, as does the antitumor antibiotic bleomycin A₂. The results may help in the design of AspH inhibitors with the potential of increased selectivity compared to the previously identified Fe(II)-chelating or 2OG-competitive inhibitors. With regard to the clinical use of bleomycin A₂ and of the Bcl-2 inhibitor venetoclax, the results suggest that possible side-effects mediated through the inhibition of AspH and other 2OG oxygenases should be considered.     

Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.     

Flexible Analytic Wavelet Transform Based Features for Physical Action Identification Using sEMG Signals

ObjectivesElectromyography (EMG) is recording of the electrical activity produced by skeletal muscles. The classification of the EMG signals for different physical actions can be useful in restoring some or all of the lost motor functionalities in these individuals. Accuracy in classifying the EMG signal indicates efficient control of prosthesis.Material and methodsThe flexible analytic wavelet transform (FAWT) is used for classification of surface electromyography (sEMG) signals for identification of physical actions. FAWT is an efficient method for decomposition of sEMG signal into eight sub-bands, features namely neg-entropy, mean absolute value (MAV), variance (VAR), modified mean absolute value type 1 (MAV1), waveform length (WL), simple square integral (SSI), Tsallis entropy, integrated EMG (IEMG) are extracted from the sub-bands. Extracted features are fed into an extreme learning machine (ELM) classifier with sigmoid activation function.ResultsComprehensive experiments are conducted on the input sEMG signals and the accuracy, sensitivity and specificity scores are used for performance measurement. Experiments showed that among all sub-bands, the seventh sub-band provided the best performance where the recorded accuracy, sensitivity and specificity values were 99.36%, 99.36% and 99.93%, respectively. The comparison results showed best efficiency of proposed method as compared to other methods on the same dataset.ConclusionThis paper investigates the usage of the FAWT and ELM on sEMG signal classification. The results show that the proposed method is quite efficient in classification of the sEMG signals. It is also observed that the seventh sub-band of the FAWT provides the best discrimination property. In the future works, recent wavelet transform methods will be used for improving the classification performance.     

Italian weedy rice—A case of de‐domestication?

Weedy rice is a representative of the extensive group of feral weeds that derive from crops, but has returned to the lifestyle of a wild species. These weeds develop either from a hybridization of crops with wild relatives (exoferality), or by mutation of crops to weedy forms (endoferality). Due to the close relation of weed and crop, the methods for weed‐targeted containment are limited to date. A deeper understanding of the development of such weeds might help to design more efficient and sustainable approaches for weed management. Weedy rice poses a serious threat to rice yields worldwide. It is widely accepted that weedy rice has originated independently in different regions all over the world. However, details of its evolution have remained elusive. In the current study, we investigated the history of weedy rice in northern Italy, the most important rice‐growing area in Europe. Our approach was to analyze genes related to weedy traits (SD1, sh4, Rc) in weedy rice accessions compared to cultivars, and to integrate these results with phenotypic and physiological data, as well as historical information about rice farming in Italy. We arrive at a working model for the timeline of evolution of weedy rice in Italy indicating that both exoferality and endoferality acted as forces driving the development of the diverse weedy rice populations found in the region today. Models of weed evolution can help to predict the direction which weed development might take and to develop new, sustainable methods to control feral weeds.     

Lamin B2 promotes the progression of triple negative breast cancer via mediating cell proliferation and apoptosis

Triple negative breast cancer (TNBC) is a more common type of breast cancer with high distant metastasis and poor prognosis. The potential role of lamins in cancer progression has been widely revealed. However, the function of lamin B2 (LMNB2) in TNBC progression is still unclear. The present study aimed to investigate the role of LMNB2 in TNBC. The cancer genome atlas (TCGA) database analysis and immunohistochemistry (IHC) were performed to examine LMNB2 expression levels. LMNB2 short hairpin RNA plasmid or lentivirus was used to deplete the expression of LMNB2 in human TNBC cell lines including MDA-MB-468 and MDA-MB-231. Alterations in cell proliferation and apoptosis in vitro and the nude mouse tumorigenicity assay in vivo were subsequently analyzed. The human TNBC tissues shown high expression of LMNB2 according to the bioinformation analysis and IHC assays. LMNB2 expression was correlated with the clinical pathological features of TNBC patients, including pTNM stage and lymph node metastasis. Through in vitro and in vivo assays, we confirmed LMNB2 depletion suppressed the proliferation and induced the apoptosis of TNBC cells, and inhibited tumor growth of TNBC cells in mice, with the decrease in Ki67 expression or the increase in caspase-3 expression. In conclusion, LMNB2 may promote TNBC progression and could serve as a potential therapeutic target for TNBC treatment.     

Prospects and future perspectives of selenium nanoparticles: An insight of growth promoter,antioxidant and anti-bacterial potentials in productivity of poultry

Nanoparticles have been attracted attention in poultry research due to their low toxicity, higher bio-availability, high surface area with sustained drug release. Dietary supplementation with selenium nanoparticles (Se-NPs) plays a regulatory role in maintaining growth performance, feed conversion ratio (FCR), antioxidant defense as well as microbial control. Se-NPs have emerging importance in modulating intestinal health through the maintenance of beneficial microbes (microflora) as well as the production of short-chain fatty acids (SCFA). Se-NPs regulate intrinsic redox status by scavenging free radicals. The antioxidant potentiality of Se-NPs is influenced by the activation of the seleno-enzymes such as thioredoxin reductase and glutathione peroxidase family (GPx) involved in scavenging of Reactive Oxygen Species (ROS). The emerging significance of Se-NPs on antimicrobial activity has been exploited due to their bio-accumulative effects and biocompatibility potentiality in the cellular systems against poultry pathogens. The present review highlights on growth performance, antioxidant defense, and anti-bacterial potentiality of Se-NPs in poultry and also provide insight into its significance in the poultry industry.