A Population Genomic Assessment of Three Decades of Evolution in a Natural Drosophila Population

Population genetics seeks to illuminate the forces shaping genetic variation, often based on a single snapshot of genomic variation. However, utilizing multiple sampling times to study changes in allele frequencies can help clarify the relative roles of neutral and non-neutral forces on short time scales. This study compares whole-genome sequence variation of recently collected natural population samples of Drosophila melanogaster against a collection made approximately 35 years prior from the same locality - encompassing roughly 500 generations of evolution. The allele frequency changes between these time points would suggest a relatively small local effective population size on the order of 10,000, significantly smaller than the global effective population size of the species. Some loci display stronger allele frequency changes than would be expected anywhere in the genome under neutrality - most notably the tandem paralogs Cyp6a17 and Cyp6a23, which are impacted by structural variation associated with resistance to pyrethroid insecticides. We find a genome-wide excess of outliers for high genetic differentiation between old and new samples, but a larger number of adaptation targets may have affected SNP-level differentiation versus window differentiation. We also find evidence for strengthening latitudinal allele frequency clines: northern-associated alleles have increased in frequency by an average of nearly 2.5% at SNPs previously identified as clinal outliers, but no such pattern is observed at random SNPs. This project underscores the scientific potential of using multiple sampling time points to investigate how evolution operates in natural populations, by quantifying how genetic variation has changed over ecologically relevant timescales.

Diversity and Distribution of Forest Ants (Hymenoptera: Formicidae) in Nepal: Implications for Sustainable Forest Management

The information available on the diversity of ant species and their distribution and interaction with forest health in Nepal remains limited. As part of a nationwide project on forest health, we conducted inventories to assess the diversity and distribution of forest ants and their role in forest management in Nepal. Ants were collected from 187 plots of 10 m × 10 m size along the north–south belt transects in eastern, central, and western Nepal. We used vegetation beating, sweeping, and hand collection methods in selected forest types. In each transect, we designed six plots in each major forest type (Sal, Schima–Castanopsis, and broadleaf mixed forests) and three plots each in deodar, Alnus, riverine, and Cryptomeria forests. We recorded 70 ant species from 36 genera and six subfamilies. This includes five genera and nine species new for the country, as well as eight tramp species, four of which are major ecological, agricultural, and/or household pests. Our study indicates that forest ant species richness is high in western Nepal and the Siwaliks, and it decreases as elevation increases. The high diversity of ant species in the forests of Nepal needs to be assessed with further exploration using multiple sampling methods covering all seasons and forest types. Ants can be useful indicators for ecosystem management and human impacts on forests. Reports of invasive ants in Nepalese forests indicate the relevance of urgent interventions through sustainable forest management initiatives to prevent future incursions.

Interwell Saturation Prediction by Artificial Intelligence Analysis of Well Logs

Well log analysis, through deploying advanced artificial intelligence (AI) algorithms, is key for wellbore geological studies. By analyzing different well characteristics with modern AI tools it becomes possible to estimate interwell saturation with improved accuracy, outlining primary fluid channels and saturation propagations in the reservoirs interwell region. The development of modern deep learning and artificial intelligence methods allows analysts to predict interwell saturation as a function of observed data in the near wellbore logged geological layers. This work addresses the use of deep neural network architectures as well as tensor regression models for predicting interwell saturation from other well characteristics, such as resistivity and porosity, as well as local near-well saturation. Several algorithms are compared in terms of both accuracy and computational efficiency. Sensitivity analysis for model parameters is carried out, which is based on the wells’ geometry, radius, and multiple sampling techniques. Additionally, the impact of local saturation prior knowledge on the model accuracy is analyzed. A reservoir box model encompassing volumetric interwell porosity, resistivity and saturation data was utilized for the validating and testing of the AI algorithms. A prototype is developed with Python 3.6 programming language.

Thai Student Teacher Learning Innovation: A Second-Order Confirmatory Factor Analysis

The study’s objective was to analyze the corroborating elements of a Thai student teacher’s (TST) learning innovation as perceived through the expertise of their teachers. Therefore, from 12 Thai teaching universities (Rajabhats) located across four Thai regions, multiple sampling techniques were used to select a sample of 151 teaching professionals. The research instrument was a questionnaire using a 5-level scale to assess the opinions on the four latent and 12 observed variables. The results showed that of the three latent variables analyzed that contributed to TST learning innovation (IN), learning innovation use (US) were perceived as most important, followed by the learning innovation development process (DE) and then the TST’s creative collaboration (CO) abilities, respectively. Finally, all the observed variables were determined to be compatible with TST innovation learning at a ‘high’ level.

Wiring multiple microenvironment proteomes uncovers the biology in head and neck cancer

The poor prognosis of head and neck cancer (HNC) is associated with the presence of metastasis within the lymph nodes (LNs). Herein, the proteome of 140 multisite samples from a 59-HNC patient cohort, including primary and matched LN-negative or -positive tissues, saliva, and blood cells, reveals insights into the biology and potential metastasis biomarkers that may assist in clinical decision making. Protein profiles are strictly associated with immune modulation across datasets, and this provides the basis for investigating immune markers associated with metastasis. The proteome of LN metastatic cells recapitulates the proteome of the primary tumor sites. Conversely, the LN microenvironment proteome highlights the candidate prognostic markers. By integrating prioritized peptide, protein, and transcript levels with machine learning models, we identified a nodal metastasis signature in the blood and saliva. In summary, we present the deepest proteome characterization wiring multiple sampling sites in HNC, thus providing a promising basis for understanding tumoral biology and identifying metastasis-associated signatures.

MODELING THE POVERTY LEVEL IN THE RUSSIAN FEDERATION BASED ON A NEURAL NETWORK APPROACH

The article raises the question of modeling the level of poverty as one of the most important socio-economic indicators. A review of publications by domestic and foreign scientists-economists proves the relevance of the topic chosen for the study. Today, the time series apparatus acts as one of the popular tools for studying the dynamics of the poverty level and the factors that directly influence it, but classical statistical forecasting methods impose rather strict assumptions on the construction of models. The article discusses the possibility of using automated neural networks of the STATISTICA package for analyzing and forecasting a time series composed of annual data reflecting the dynamics of the poverty level in the Russian Federation over the past 20 years. The study took into account the strengths and weaknesses of the use of the neural network apparatus for predicting socio-economic processes. The construction of economic and mathematical models was carried out by building automated neural networks, custom neural networks and the method of multiple sampling. When choosing the most preferable model, a multidimensional criterion was used. The comparison of the real poverty level with the values obtained using the models is made, the quality assessment of the developed models is calculated, the poverty level forecast for 2021-2022 is constructed.

Offshore Caspian Sea: Appraisal Well Monitoring Using Inflow Tracer Technology

A gas condensate field development in the offshore Caspian Sea experienced monitoring challenges and costly operations. In regular field-wide surveillance it is a challenging task to evaluate the numerous well monitoring options on the market, such as production logging, permanent downhole gauges, and distributed temperature sensing along the wellbore. These solutions require wellbore interventions and introduce operational risk during well logging or completion installation risk when fiber is installed. Permanently installed inflow tracer technology is an alternative monitoring solution which avoids the above-mentioned risks but still obtain valuable inflow information concerning well performance over several years. An appraisal well in the field was selected to pilot inflow tracing technology for assessment of reserves and productivity, for the first time in the Caspian Sea. Multiple sampling campaigns to capture the data was incorporated into a well testing programme to complement the pressure transient data collection and interpretation. The inflow tracer interpretations were successful in providing additional insight towards clean-up efficiency and flow distribution between zones. The latter was verified later by production logging, strengthening confidence with inflow tracer technology. The application of the permanent inflow tracers has proven to be a viable alternative to other well monitoring solutions without any risk and will become an effective long-term monitoring solution for planned production wells in the field development.

Fine-scale morphological, genomic, reproductive, and symbiont differences delimit the Caribbean octocorals Plexaura homomalla and P. kükenthali

Octocorals are conspicuous members of coral reefs and deep-sea ecosystems. Yet, species boundaries and taxonomic relationships within this group remain poorly understood, hindering our understanding of this essential component of the marine fauna. We used a multifaceted approach to revisit the systematics of the Caribbean octocorals Plexaura homomalla and Plexaura kükenthali, two taxa that have a long history of taxonomic revisions. We integrated morphological and reproductive analyses with high-throughput sequencing technology to clarify the relationship between these common gorgonians. Although size and shape of the sclerites are significantly different, there is overlap in the distributions making identification based on sclerites alone difficult. Differences in reproductive timing and mode of larval development were detected, suggesting possible mechanisms of pre-zygotic isolation. Furthermore, there are substantial genetic differences and clear separation of the two species in nuclear introns and single-nucleotide polymorphisms obtained from de novo assembled transcriptomes. Despite these differences, analyses with SNPs suggest that hybridization is still possible between the two groups. The two nascent species also differed in their symbiont communities (genus Breviolum) across multiple sampling sites in the Caribbean. Despite a complicated history of taxonomic revisions, our results support the differentiation of P. homomalla and P. kükenthali, emphasizing that integrative approaches are essential for Anthozoan systematics.

Towards precision ecology: Relationships of multiple sampling methods quantifying abundance for comparisons among studies

ABSTRACTBecause different sampling techniques will provide different abundance values, it is currently difficult to compare results among many studies to form holistic understandings of how abundance influences ant ecology. Using three sampling methods in the same location we found pitfall traps best confirmed A. gracilipes presence recording the fewest zero values (9.1%), card counts were the least reliable (67.1%), and tuna lures were intermediate (30.1%). The abundance of A. gracilipes from card counts ranged from 0 to 20, in pitfall traps from 0 to 325, and the full range of tuna lure abundance scores (0-7) were sampled. We then determined the relationships between these three standard ant sampling techniques for the abundance of yellow crazy ant Anoplolepis gracilipes. Irrespective of the data transformation method, the strongest relationship was between pitfall traps and tuna lures, and the least strong was between pitfall traps and card counts. We then demonstrate the utility of this knowledge by analysing A. gracilipes abundance reported within published literature to show where the populations in those studies sit on an abundance spectrum. We also comment on insights into the relative utility of the three methods we used to determine A. gracilipes abundance among populations of varying abundance. Pitfall traps was the most reliable method to determine if the species was present at the sample level. Tuna lures were predominantly reliable for quantifying the presence of workers, but were limited by the number of workers that can gather around a spoonful of tuna. Card counts were the quickest method, but were seemingly only useful when A. gracilipes abundance is not low. Finally we discuss how environmental and biological variation needs to be accounted for in future studies to better standardise sampling protocols to help progress ecology as a precision science.

Effectiveness and policies analysis of pool testing method for COVID-19

PurposeThis research aims to figure out whether the pool testing method of SARS-CoV-2 for COVID-19 is effective and the optimal sample size is in one bunch. Additionally, since the infection rate was unknown at the beginning, this research aims to propose a multiple sampling approach that enables the pool testing method to be utilized successfully.Design/methodology/approachThe authors verify that the pool testing method of SARS-CoV-2 for COVID-19 is effective under the situation of the shortage of nucleic acid detection kits based on probabilistic modeling. In this method, the testing is performed on several samples of the cases together as a bunch. If the test result of the bunch is negative, then it is shown that none of the cases in the bunch has been infected with the novel coronavirus. On the contrary, if the test result of the bunch is positive, then the samples are tested one by one to confirm which cases are infected.FindingsIf the infection rate is extremely low, while the same number of detection kits is used, the expected number of cases that can be tested by the pool testing method is far more than that by the one-by-one testing method. The pool testing method is effective only when the infection rate is less than 0.3078. The higher the infection rate, the smaller the optimal sample size in one bunch. If N samples are tested by the pool testing method, while the sample size in one bunch is G, the number of detection kits required is in the interval (N/G, N).Originality/valueThis research proves that the pool testing method is not only suitable for the situation of the shortage of detection kits but also the situation of the overall or sampling detection for a large population. More importantly, it calculates the optimal sample size in one bunch corresponding to different infection rates. Additionally, a multiple sampling approach is proposed. In this approach, the whole testing process is divided into several rounds in which the sample sizes in one bunch are different. The actual infection rate is estimated gradually precisely by sampling inspection in each round.