128 research outputs found

    Population structure of the European anchovy, Engraulis encrasicolus (Linnaeus, 1758) in Lake Manzala, Egypt

    Get PDF
    The present study is to identify the population and stock characteristics of Engraulis encrasicolus in the Mediterranean lagoon “Lake Manzala” of Egypt. A total of 1536 specimens were collected seasonally by a local trammel net (El-Balla), from 2019 to 2021. The length ranged from 4.2 to 12.1 cm, where the dominance was of medium sizes. Two age groups were observed with a short longevity (tmax = 3.16 y). Parameters of Von Bertalanffy, L?, and K, were estimated as 12.52 cm and 0.95 y-1, respectively. The growth performance index (Ø) was estimated as 2.17, expressing liner growth and environmental suitability. The calculated length at first maturity (Lm) = 8.1 cm, compared to 6.9 cm of length at first capture (Lc), expressing high fishing effort. Mortality indices include: total mortality (Z) = 3.71 y-1, and natural mortality (M) = 1.46 y-1. According to biological reference points, Fopt = 0.73 y-1 and Flimit = 0.97 y-1, the fishing mortality (F = 2.25 y-1) indicated overfishing of the anchovy stock in Lake Manzala. The current exploitation rate, E = 0.61 expressed the occurrence of overexploitation. Based on the results, reducing fishing efforts is vital to maintaining stock stability

    Predicting market performance using machine and deep learning techniques

    Get PDF
    Today, forecasting the stock market has been one of the most challenging issues for the “artificial intelligence” AI research community. Stock market investment methods are sophisticated and rely on analyzing massive volumes of data. In recent years, machine-learning techniques have come under increasing scrutiny to assess and improve market predictions over traditional approaches. The observation in time is due to their dependence. Their predictions are crucial tasks in data mining and have attracted great interest and considerable effort over the past decades. Tackling this challenge remains difficult due to the inherent characteristics of time series data, including its high dimensionality, large volume of data, and constant updates. Exploration of Machine Learning and Deep Learning methods undertaken to enhance the effectiveness of conventional approaches. In this document, we aim precisely to forecast the performance of the stock market at the close of the day by applying various machine-learning algorithms on the two data sets “CoinMarketCap, CryptoCurrency” and thus analyze the predictions of the architectures

    Machine learning algorithms for forecasting and categorizing euro-to-dollar exchange rates

    Get PDF
    Forecasting changes in foreign exchange rates is a well-explored and widely recognized area within finance. Numerous research endeavors have delved into the utilization of methods in machine learning to analyze and predict movements in the foreign exchange market. This work employed several machine-learning techniques such as Adaboost, logistic regression, gradient boosting, random forest classifier, bagging, Gaussian naĂŻve Bayes, extreme gradient boosting classifier, decision tree classifier, and our approach (we have combined three models: logistic regression, random forest classifier, and Gaussian naive Bayes). Our objective is to predict the most advantageous times for purchasing and selling the euro about the dollar. We integrated a range of technical indicators into the training dataset to enhance the precision of our techniques and strategy. The outcomes of our experiment demonstrate that our approach outperforms alternative methods, achieving superior prediction performance. Our methodology yielded an accuracy of 0.948. This study will empower investors to make informed decisions about their future EUR/USD transactions, helping them identify the most advantageous times to buy and sell within the market

    INDEXATION DES OBJETS 3D BASEE SUR UNE ANALOGIE PARTIELLE DES SEGMENTS

    Get PDF
    L’indexation 3D est un domaine qui s’impose dans un certain nombre important d'applications liĂ©es aux bases de donnĂ©es d’objets 3D. Plusieurs descripteurs ont Ă©tĂ© dĂ©finis dont la plupart utilisent la signature gĂ©omĂ©trique globale des objets 3D et peu d'entre eux sont basĂ©s sur une correspondance partielle des segments de ces objets. Dans cet article, nous proposons de raffiner les rĂ©sultats d’une indexation globale par la prise en compte des signatures des segments composant un objet 3D. L’approche proposĂ©e amĂ©liore, significativement, les rĂ©sultats de l’indexation globale et permet de dĂ©tecter les modĂšles similaires ayant des poses diffĂ©rentes

    Segmentation des objets 3D par Ligne de Partage des Eaux sans biais

    Get PDF
    Dans ce travail nous prĂ©sentons une mĂ©thode de segmentation des objets de maillage 3D basĂ©e sur le concept de ligne de partage des eaux (LPE) et l’utilisation des Files d’Attentes hiĂ©rarchiques (FAH) sans biais. Contrairement aux techniques traditionnelles qui utilisent les vertex du modĂšle 3D, notre mĂ©thode considĂšre la structure des faces de ce modĂšle comme un relief oĂč chaque face est positionnĂ©e Ă  une certaine hauteur, ce relief est plongĂ© progressivement dans l’eau, des inondations sont simulĂ©es Ă  partir des minima. Nous proposons une nouvelle fonction de hauteur, basĂ©e sur l’index de forme qui caractĂ©rise la courbure d’une face. L’utilisation de cet index, nous a permis de rĂ©duire l'effet de la sur-segmentation observĂ©e pour les mĂ©thodes LPE basĂ©es sur les vertex. Pour obtenir une segmentation finale de l’objet 3D nous appliquons un processus de fusion basĂ© sur la profondeur des rĂ©gions obtenues par la segmentation LPE. Les rĂ©sultats obtenus montrent l'efficacitĂ© de la mĂ©thode proposĂ©e. En effet, notre approche offre de bons rĂ©sultats, elle permet de dĂ©tecter les diffĂ©rentes parties d’un modĂšle 3D

    A short-term assessment of nascent HIV-1 transmission clusters among newly diagnosed individuals using envelope sequence-based phylogenetic analyses

    Get PDF
    The identification of transmission clusters (TCs) of HIV-1 using phylogenetic analyses can provide insights into viral transmission network and help improve prevention strategies. We compared the use of partial HIV-1 envelope fragment of 1,070 bp with its loop 3 (108 bp) to determine its utility in inferring HIV-1 transmission clustering. Serum samples of recently (n = 106) and chronically (n = 156) HIV-1-infected patients with status confirmed were sequenced. HIV-1 envelope nucleotide-based phylogenetic analyses were used to infer HIV-1 TCs. Those were constructed using ClusterPickerGUI_1.2.3 considering a pairwise genetic distance of £10% threshold. Logistic regression analyses were used to examine the relationship between the demographic factors that were likely associated with HIV-1 clustering. Ninety-eight distinct consensus envelope sequences were subjected to phylogenetic analyses. Using a partial envelope fragment sequence, 42 sequences were grouped into 15 distinct small TCs while the V3 loop reproduces 10 clusters. The agreement between the partial envelope and the V3 loop fragments was significantly moderate with a Cohen’s kappa (j) coefficient of 0.59, p < .00001. The mean age (<38.8 years) and HIV-1 B subtype are two factors identified that were significantly associated with HIV-1 transmission clustering in the cohort, odds ratio (OR) = 0.25, 95% confidence interval (CI, 0.04–0.66), p = .002 and OR: 0.17, 95% CI (0.10–0.61), p = .011, respectively. The present study confirms that a partial fragment of the HIV-1 envelope sequence is a better predictor of transmission clustering. However, the loop 3 segment may be useful in screening purposes and may be more amenable to integration in surveillance programs

    HIV-1 envelope sequence-based diversity measures for identifying recent infections

    Get PDF
    Identifying recent HIV-1 infections is crucial for monitoring HIV-1 incidence and optimizing public health prevention efforts. To identify recent HIV-1 infections, we evaluated and compared the performance of 4 sequence-based diversity measures including percent diversity, percent complexity, Shannon entropy and number of haplotypes targeting 13 genetic segments within the env gene of HIV-1. A total of 597 diagnostic samples obtained in 2013 and 2015 from recently and chronically HIV-1 infected individuals were selected. From the selected samples, 249 (134 from recent versus 115 from chronic infections) env coding regions, including V1-C5 of gp120 and the gp41 ectodomain of HIV-1, were successfully amplified and sequenced by next generation sequencing (NGS) using the Illumina MiSeq platform. The ability of the four sequence-based diversity measures to correctly identify recent HIV infections was evaluated using the frequency distribution curves, median and interquartile range and area under the curve (AUC) of the receiver operating characteristic (ROC). Comparing the median and interquartile range and evaluating the frequency distribution curves associated with the 4 sequence-based diversity measures, we observed that the percent diversity, number of haplotypes and Shannon entropy demonstrated significant potential to discriminate recent from chronic infections (p<0.0001). Using the AUC of ROC analysis, only the Shannon entropy measure within three HIV-1 env segments could accurately identify recent infections at a satisfactory level. The env segments were gp120 C2_1 (AUC = 0.806), gp120 C2_3 (AUC = 0.805) and gp120 V3 (AUC = 0.812). Our results clearly indicate that the Shannon entropy measure represents a useful tool for predicting HIV-1 infection recency

    HIV-1 envelope glycoprotein amino acids signatures associated with clade B transmitted/founder and recent viruses

    Full text link
    Background: HIV-1 transmitted/founder viruses (TF) are selected during the acute phase of infection from a multitude of virions present during transmission. They possess the capacity to establish infection and viral dissemination in a new host. Deciphering the discrete genetic determinant of infectivity in their envelope may provide clues for vaccine design. Methods: One hundred twenty-six clade B HIV-1 consensus envelope sequences from untreated acute and early infected individuals were compared to 105 sequences obtained from chronically infected individuals using next generation sequencing and molecular analyses. Results: We identified an envelope amino acid signature associated with TF viruses. They are more likely to have an isoleucine (I) in position 841 instead of an arginine (R). This mutation of R to I (R841I) in the gp41 cytoplasmic tail (gp41CT), specifically in lentivirus lytic peptides segment 1 (LLP-1), is significantly enriched compared to chronic viruses (OR = 0.2, 95% CI (0.09, 0.44), p = 0.00001). Conversely, a mutation of lysine (K) to isoleucine (I) located in position six (K6I) of the envelope signal peptide was selected by chronic viruses and compared to TF (OR = 3.26, 95% CI (1.76–6.02), p = 0.0001). Conclusions: The highly conserved gp41 CT_ LLP-1 domain plays a major role in virus replication in mediating intracellular traffic and Env incorporation into virions in interacting with encoded matrix protein. The presence of an isoleucine in gp41 in the TF viruses’ envelope may sustain its role in the successful establishment of infection during the acute stage

    Exploring the link between pyrethroids exposure and dopaminergic degeneration through morphometric, immunofluorescence, and in-silico approaches: the therapeutic role of chitosan-encapsulated curcumin nanoparticles

    Get PDF
    Introduction: The synthetic pyrethroid derivative fenpropathrin (FNE), a commonly used insecticide, has been associated with various toxic effects in mammals, particularly neurotoxicity. The study addressed the hallmarks of the pathophysiology of Parkinson's disease upon oral exposure to fenpropathrin (FNE), mainly the alteration of dopaminergic markers, oxidative stress, and molecular docking in rat models. In addition, the protective effect of curcumin-encapsulated chitosan nanoparticles (CRM-Chs-NPs) was also assessed. Methods: In a 60-day trial, 40 male Sprague Dawley rats were divided into 4 groups: Control, CRM-Chs-NPs (curcumin-encapsulated chitosan nanoparticles), FNE (15 mg/kg bw), and FNE + CRM-Chs-NPs. Results: FNE exposure induced reactive oxygen species generation, ATP production disruption, activation of inflammatory and apoptotic pathways, mitochondrial function and dynamics impairment, neurotransmitter level perturbation, and mitophagy promotion in rat brains. Molecular docking analysis revealed that FNE interacts with key binding sites of dopamine synthesis and transport proteins. On the other hand, CRM-Chs-NPs mitigated FNE's toxic effects by enhancing mitochondrial dynamics, antioxidant activity, and ATP production and promoting anti-inflammatory and antiapoptotic responses. Conclusion: In summary, FNE appears to induce dopaminergic degeneration through various mechanisms, and CRM-Chs-NPs emerged as a potential therapeutic intervention for protecting the nervous tissue microenvironment
    • 

    corecore