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

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

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

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 naïve 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

L’indexation 3D est un domaine qui s’impose dans un certain nombre important d'applications liées aux bases de données d’objets 3D. Plusieurs descripteurs ont été définis dont la plupart utilisent la signature géométrique globale des objets 3D et peu d'entre eux sont basés sur une correspondance partielle des segments de ces objets. Dans cet article, nous proposons de raffiner les résultats d’une indexation globale par la prise en compte des signatures des segments composant un objet 3D. L’approche proposée améliore, significativement, les résultats de l’indexation globale et permet de détecter les modèles similaires ayant des poses différentes

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

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

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

Characterization of the TRBP domain required for Dicer interaction and function in RNA interference

<p>Abstract</p> <p>Background</p> <p>Dicer, Ago2 and TRBP are the minimum components of the human RNA-induced silencing complex (RISC). While Dicer and Ago2 are RNases, TRBP is the double-stranded RNA binding protein (dsRBP) that loads small interfering RNA into the RISC. TRBP binds directly to Dicer through its C-terminal domain.</p> <p>Results</p> <p>We show that the TRBP binding site in Dicer is a 165 amino acid (aa) region located between the ATPase and the helicase domains. The binding site in TRBP is a 69 aa domain, called C4, located at the C-terminal end of TRBP. The TRBP1 and TRBP2 isoforms, but not TRBPs lacking the C4 site (TRBPsΔC4), co-immunoprecipitated with Dicer. The C4 domain is therefore necessary to bind Dicer, irrespective of the presence of RNA. Immunofluorescence shows that while full-length TRBPs colocalize with Dicer, TRBPsΔC4 do not. <it>tarbp2</it>^-/-cells, which do not express TRBP, do not support RNA interference (RNAi) mediated by short hairpin or micro RNAs against EGFP. Both TRBPs, but not TRBPsΔC4, were able to rescue RNAi function. In human cells with low RNAi activity, addition of TRBP1 or 2, but not TRBPsΔC4, rescued RNAi function.</p> <p>Conclusion</p> <p>The mapping of the interaction sites between TRBP and Dicer show unique domains that are required for their binding. Since TRBPsΔC4 do not interact or colocalize with Dicer, we suggest that TRBP and Dicer, both dsRBPs, do not interact through bound dsRNA. TRBPs, but not TRBPsΔC4, rescue RNAi activity in RNAi-compromised cells, indicating that the binding of Dicer to TRBP is critical for RNAi function.</p