Genetic Techniques Research Articles

A multidisciplinary approach unveils the distribution of the Alpine long-eared bat Plecotus macrobullaris (Vespertilionidae) in Italy

Abstract Genetic techniques have allowed to identify a number of cryptic species of bats and to better define their distribution ranges and conservation status. Long-eared bats Plecotus spp. occur throughout Eurasia and Africa, with a high number of morphologically similar species, often occurring in sympatry. Therefore, disentangling the distribution of one species from another may be challenging. Italy represents a diversity hotspot for long-eared bats in Europe, hosting six species belonging to the genus Plecotus. In this study we report on both published and unpublished investigations on the Alpine long-eared bat Plecotus macrobullaris by integrating field and molecular methods, as well as opportunistic verified records from citizen scientists (i.e., individuals showing the diagnostic triangular chin pad in photos), to redefine the distribution of this species in Italy. We retrieved a total of 149 published records and 16 new confirmed records, namely 7 from iNaturalist, 6 from Facebook and 3 from molecular analyses. In Italy, the species occurs throughout the Alpine and Pre-Alpine chain, as well as in close proximity to the sea, and in Northeastern plains. Based on new records, we proved that it occurs also in other areas, including the Northern Apennine ridge. Our findings call for the application of an integrated approach in investigating cryptic species, that provides valuable data to support conservation assessments and the establishment of proper protection measures for poorly known species.

Development and biological characterization of an infectious cDNA clone of NADC34-like PRRSV

IntroductionPorcine Reproductive and Respiratory Syndrome virus (PRRSV) causes high abortion rates in gestating sows and stillbirths, as well as high piglet mortality, seriously jeopardizing the pig industry in China and worldwide.MethodsIn this study, an infectious clone containing the full-length genome of NADC34-like PRRSV was constructed for the first time using reverse genetic techniques. The gene was amplified segmentally onto a plasmid, transfected into BHK-21 cells, and the transfected supernatant was harvested and transfected into PAM cells, which showed classical cytopathic effects (CPE).ResultsThe virus rJS-KS/2021 was successfully rescued which could be demonstrated by Western Blot and indirect immunofluorescence assays. Its growth curve was similar to the original strain. Replace the 5’UTR and 3’UTR of rJS-KS/2021 with 5’UTR and 3’UTR of HP-PRRSV (strain SH1) also failed to propagate on MARC-145.DiscussionIn this study, an infectious clone of NADC34-like was constructed by reverse genetics, replacing the UTR and changing the cellular tropism of the virus. These findings provide a solid foundation for studying the recombination of different PRRSVs and the adaption of PRRSVs on MARC-145 in the future.

Biological Underpinnings of Criminal Behavior: A Comprehensive Review

Background: Traditional criminological theories often emphasize social and environmental factors in criminal behavior. However, early biological research, which was initially reductionist and focused on physical traits, has evolved significantly with advancements in genetics and neuroimaging techniques. This evolution has led to a more nuanced understanding of the role of biological factors in criminal behavior. Objective: This review aims to critically examine how genetic, neurobiological, and psychophysiological factors contribute to criminal behavior, and to explore the interplay between these biological predispositions and environmental influences. Methods: We systematically analyzed empirical studies that investigate the impact of abnormalities in brain structures, neurotransmitter systems, and genetic predispositions on antisocial behaviors, integrating these findings with socio-environmental conditions. Results: Findings from recent studies indicate significant contributions of abnormalities in brain areas such as the prefrontal cortex and amygdala, and dysregulation in neurotransmitter systems to antisocial behaviors. These biological factors, when combined with environmental influences, enhance our understanding of criminal tendencies. Conclusion: The integration of biological perspectives into criminological theories marks a significant shift towards a more holistic approach in the study of criminal behavior. This review advocates for the development of targeted intervention strategies and ethical policy formulations, emphasizing the potential of biological research to improve the efficacy and humanity of the criminal justice system. Ongoing interdisciplinary research and collaboration are essential to continue advancing our understanding and management of criminal behavior.

[Advances of pathological research and classification in malformations of cortical development associated with refractory epilepsy].

With rapid development of genetic testing techniques, neuroimaging and neuroelectrophysiological technologies, our understanding of malformations of cortical development continues to be deepened and updated. In particular, mutations in genes related to the mammalian target of rapamycin (mTOR) signaling pathway have been successively discovered in focal cortical dysplasia (FCD). At the same time, the classification consensus on FCD issued by the International League Against Epilepsy (ILAE) in 2011 has encountered problems and challenges in diagnostic practice. Therefore, in 2022, ILAE proposed an updated version of the FCD classification based on the progress in molecular genetics over the past decade. The main addition to the classification system is "white matter lesions, " and it is also suggested to integrate histopathological, neuroimaging, and molecular testing results for multi-level integrated diagnosis to achieve reliable, clinically relevant, and therapeutic targeted final diagnosis.

The Role of Genetic Testing in Adult CKD

The Role of Genetic Testing in Adult CKD

Application of Developmental Regulators for Enhancing Plant Regeneration and Genetic Transformation.

Establishing plant regeneration systems and efficient genetic transformation techniques plays a crucial role in plant functional genomics research and the development of new crop varieties. The inefficient methods of transformation and regeneration of recalcitrant species and the genetic dependence of the transformation process remain major obstacles. With the advancement of plant meristematic tissues and somatic embryogenesis research, several key regulatory genes, collectively known as developmental regulators, have been identified. In the field of plant genetic transformation, the application of developmental regulators has recently garnered significant interest. These regulators play important roles in plant growth and development, and when applied in plant genetic transformation, they can effectively enhance the induction and regeneration capabilities of plant meristematic tissues, thus providing important opportunities for improving genetic transformation efficiency. This review focuses on the introduction of several commonly used developmental regulators. By gaining an in-depth understanding of and applying these developmental regulators, it is possible to further enhance the efficiency and success rate of plant genetic transformation, providing strong support for plant breeding and genetic engineering research.

Intraspecific genetic diversity and coexistence in phytoplankton populations

AbstractThe past two decades have seen a drastic increase in the availability and use of genetic techniques to study phytoplankton communities. As a result, it is now well documented that phytoplankton populations are genetically diverse, despite predominantly asexual reproduction and minute morphological variation. Genetic variation can lead to variation also in phenotype, and some traits vary more among genotypes than between species. Trait‐based approaches tackle this by focusing on traits rather than on species. However, trait‐based models often have difficulty predicting and explaining the huge trait‐diversity among coexisting individuals competing for the same few resources. Thus, we ask the question: How do hundreds, if not thousands, of genotypes coexist in a highly competitive environment? In this review, we gather information on genetic and phenotypic variations in coexisting genotypes and elaborate on three mechanisms by which broad intraspecific genetic diversity may be possible: neutral mutations, environmental fluctuations, and trade‐offs among traits. These have all been applied on an interspecies level, and we discuss their use also among coexisting genotypes. We find that genetic diversity to be almost exclusively studied in blooming species and that clonal diversity frequently measure above 0.95 (i.e., 95% of individuals sampled are genetically different). Genetic diversity seems stable throughout blooms, suggesting that competitive exclusion is low or that new genetic material is frequently being introduced into populations. Further, we find high intraspecific trait‐variation in several key traits among coexisting strains but also that trait‐variation is often neglected in studies on phytoplankton, making coexistence difficult to predict.

Prenatal diagnosis of a trisomy 7 mosaic case: CMA, CNV-seq, karyotyping, interphase FISH, and MS-MLPA, which technique to choose?

ObjectiveThis study aims to perform a prenatal genetic diagnosis of a high-risk fetus with trisomy 7 identified by noninvasive prenatal testing (NIPT) and to evaluate the efficacy of different genetic testing techniques for prenatal diagnosis of trisomy mosaicism.MethodsFor prenatal diagnosis of a pregnant woman with a high risk of trisomy 7 suggested by NIPT, karyotyping and chromosomal microarray analysis (CMA) were performed on an amniotic fluid sample. Low-depth whole-genome copy number variation sequencing (CNV-seq) and fluorescence in situ hybridization (FISH) were used to clarify the results further. In addition, methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) was performed to analyze the possibility of uniparental disomy(UPD).ResultsAmniotic fluid karyotype analysis revealed a 46, XX result. Approximately 20% mosaic trisomy 7 was detected according to the CMA result. About 16% and 4% of mosaicism was detected by CNV-seq and FISH, respectively. MS-MLPA showed no methylation abnormalities. The fetal ultrasound did not show any detectable abnormalities except for mild intrauterine growth retardation seen at 39 weeks of gestation. After receiving genetic counseling, the expectant mother decided to continue the pregnancy, and follow-up within three months of delivery was normal.ConclusionIn high-risk NIPT diagnosis, a combination of cytogenetic and molecular genetic techniques proves fruitful in detecting low-level mosaicism. Furthermore, the exclusion of UPD on chromosome 7 remains crucial when NIPT indicates a positive prenatal diagnosis of trisomy 7.

Genomic Advances in Biofortification of Iron and Zinc in Wheat (Triticum aestivum L.)

Majority of population that depend on wheat are found to be deficient in iron and zinc. About 20% of calories worldwide are derived from wheat. Milling of wheat reduces the bioavaibility of Fe and Zn in wheat grains. Micronutrient deficiency is the main cause of malnutrition worldwide and hidden hunger in underdeveloped countries. To overcome this problem it becomes necessary to develop varieties that are rich in iron and zinc content. Biofortification gets around these issues by enhancing the micronutrient content of wheat. The genetic approaches of biofortification particularly the non-conventional methods have proved to be the most efficient ones. These method includes QTL mapping approach, GWAS and transgenics. Although conventional methods for a long time has been used for developing varieties of wheat rich in Fe and Zn but conventional breeding for certain traits may be inconvenient and time-consuming and are prone to errors due to their reliance on traditional phenotyping or visual assessments. Several research have been undertaken to map QTLs of Fe and Zn concentration in wheat grains and have been used in elite genetic backgrounds to improve micronutrient content. Genomic studies revealed through GWAS exhibit high QTL resolution and allele coverage, which can be used for improvement of Fe and Zn content in wheat. Genetic engineering techniques or transgenics approaches allows genetic alteration of crops to improve the nutritional quality of wheat. Genomic approach of biofortification of wheat has proved to be the most efficient methods for increasing iron and zinc content in the crop.

Membraneless organelles assembled by AuNPs-enzyme integration in non-photosynthetic bacteria: Achieving high specificity and selectivity for solar hydrogen production

The emergence of semiartificial photosynthesis holds great promise for improving light energy capture and conversion efficiency. However, achieving high product specificity and selectivity in semiartificial photosynthetic systems remains a challenge. In this study, we employed genetic engineering techniques to construct a membraneless organelle in E. coli, serving as the photoreaction carrier. The self-assembly of photosensitizers and hydrogenase within the carrier has successfully established a semiartificial photosynthetic model characterized by high product specificity and selectivity. In this model system, electrons were directly transferred to hydrogenase upon AuNPs light excitation in membraneless organelle, eliminating the need for additional electron mediators and bypassing the use of intracellular NADH/NAD+ as a reducing agent to avoid byproduct formation. The semiartificial photosynthetic system demonstrated an 11782-fold and 40-fold increase in hydrogen production compared to bare AuNPs and the empty vector E. coli hybrid system, respectively. This membraneless organelle holds the potential to robustly support the specific synthesis of high-energy-density, value-added compounds through solar energy conversion.

Machine learning approach for the effectual production of a novel esterase and its application in bioremediation of dairy effluent

The present work intends to maximize the production of an industrially important novel esterase from Trichoderma harzianum using both machine learning and statistical approaches and its application in the bioremediation of dairy effluent. Significant medium compounds, such as almond oil, casein, NaH2PO4, and trace elements, were screened from a pool of carbon, nitrogen, oil and surfactant sources along with other micronutrients using a first order statistical design. The optimum value of screened parameters was found using response surface methodology that resulted in the maximum esterase production of 2985.31U/L. Artificial neural network trained with the experimental response of RSM experiments coupled with genetic algorithm optimization technique has resulted in the maximum esterase production of 3256.73U/L, which is 150-folds higher as compared to the unoptimized condition and also found to be the higher than the outcome of statistical optimization. Dairy effluent was used for the production of esterase as well as for its bioremediation. At 50% load, the maximum reduction in chemical oxygen demand and the production of esterase were observed to be 46.84% and 34.12U/L, respectively. The COD reduction of diary effluent was modelled using Michaelis-Menten kinetics and the kinetic parameters, half saturation constant (Km) and maximum reaction rate (rmax) value were evaluated as 286.88mg/L and 26.04mg/L/d, respectively.

Current Diagnostic Approaches in the Genetic Diagnosis of Disorders of Sex Development.

Disorders of sex development (DSD) are a clinically and genetically highly heterogeneous group of congenital disorders. The most accurate and rapid diagnosis may be possible with a complementary multidisciplinary diagnostic approach, including comprehensive clinical, hormonal, and genetic investigations. Rapid and accurate diagnosis of DSD requires urgency in terms of gender selection and management of the case. Despite the genetic tests performed in current daily practice, the genetic cause is still not elucidated in a significant proportion of cases. Karyotype analysis can be used as a standard for sex chromosome identification. In addition, quantitative fluorescent polymerase chain reaction (QF-PCR) or fluorescence in situ hybridization (FISH) analysis can be used for faster and more cost-effective detection of the sex chromosome and SRY gene. Multiplex ligation-dependent probe amplification (MLPA), single-gene sequence analysis, next-generation sequence analysis (NGSA), targeted NGSA, whole-exome sequencing (WES), and whole-genome sequencing (WGS) analyses can be performed according to preliminary diagnoses. Microarray analysis (array comparative genomic hybridization (aCGH) and single nucleotide polymorphism array (SNPa)) should be performed in cases with syndromic findings and if no pathology is detected with other tests. In DSD cases, the use of optical genome mapping and techniques, which will probably be in daily practice in near future, may be considered. In conclusion, the clinical and genetic diagnosis of DSD is difficult, and molecular genetic diagnosis is often not available. This has psychosocial and health implications for patients and their families. New genetic techniques, especially those targeting the whole genome, may provide a better understanding of DSD through the identification of little-known genetic causes. This review focuses on conventional genetic and next-generation genetic techniques used in the genetic diagnosis of DSD, as well as possible genetic diagnostic techniques and approaches that may be used in routine practice in near future.

Advancements in genetic techniques and functional genomics for enhancing crop traits and agricultural sustainability.

Genetic variability is essential for the development of new crop varieties with economically beneficial traits. The traits can be inherited from wild relatives or induced through mutagenesis. Novel genetic elements can then be identified and new gene functions can be predicted. In this study, forward and reverse genetics approaches were described, in addition to their applications in modern crop improvement programs and functional genomics. By using heritable phenotypes and linked genetic markers, forward genetics searches for genes by using traditional genetic mapping and allele frequency estimation. Despite recent advances in sequencing technology, omics and computation, genetic redundancy remains a major challenge in forward genetics. By analyzing close-related genes, we will be able to dissect their functional redundancy and predict possible traits and gene activity patterns. In addition to these predictions, sophisticated reverse gene editing tools can be used to verify them, including TILLING, targeted insertional mutagenesis, gene silencing, gene targeting and genome editing. By using gene knock-down, knock-up and knock-out strategies, these tools are able to detect genetic changes in cells. In addition, epigenome analysis and editing enable the development of novel traits in existing crop cultivars without affecting their genetic makeup by increasing epiallelic variants. Our understanding of gene functions and molecular dynamics of various biological phenomena has been revised by all of these findings. The study also identifies novel genetic targets in crop species to improve yields and stress tolerances through conventional and non-conventional methods. In this article, genetic techniques and functional genomics are specifically discussed and assessed for their potential in crop improvement.

A simple method to identify sex at pre‐pupal stages of Spodoptera frugiperda (Lepidoptera: Noctuidae)

AbstractThe sex of Lepidoptera species can typically be identified at the pupal or adult stage by their morphological characteristics. However, for most species, no obvious sex‐specific traits can be used at the larval or embryonic stage. The growing interest in studying sex determination and differentiation, along with the potential application of research findings in genetic regulation techniques, is promoting the advancement of new sexing methods at early developmental stages. In this study, the sex of individual eggs and larvae was successfully identified by means of W chromosome molecular markers, which were identified through analysing the previously published W chromosome sequence data of Spodoptera frugiperda (Lepidoptera: Noctuidae). Additionally, we present methods to extract DNA and RNA from individual eggs and larval hemolymph. These techniques provide a simple and dependable method to identify sex at pre‐pupal stages. This approach could potentially be extended to other Lepidoptera species of which the W chromosome information is available.

Toward the Exploitation of Sustainable Green Factory: Biotechnology Use of Nannochloropsis spp.

Securing food, energy, and raw materials for a growing population is one of the most significant challenges of our century. Algae play a central role as an alternative to plants. Wastewater and flue gas can secure nutrients and CO2 for carbon fixation. Unfortunately, algae domestication is necessary to enhance biomass production and reduce cultivation costs. Nannochloropsis spp. have increased in popularity among microalgae due to their ability to accumulate high amounts of lipids, including PUFAs. Recently, the interest in the use of Nannochloropsis spp. as a green bio-factory for producing high-value products increased proportionally to the advances of synthetic biology and genetic tools in these species. In this review, we summarized the state of the art of current nuclear genetic manipulation techniques and a few examples of their application. The industrial use of Nannochloropsis spp. has not been feasible yet, but genetic tools can finally lead to exploiting this full-of-potential microalga.

Enhancing barley resilience: advanced genetic techniques to improve drought tolerance for sustainable cultivation under current climatic fluctuations

Enhancing barley resilience: advanced genetic techniques to improve drought tolerance for sustainable cultivation under current climatic fluctuations

Optimization of short-term hydropower scheduling with dynamic reservoir capacity based on improved genetic algorithm and parallel computing

The optimal scheduling of hydropower plants is important for the efficient operation and management of hydropower energy systems. In the short-term optimization scheduling of river-type reservoirs, the dynamic reservoir capacity is a key factor. Currently, the short-term power generation scheduling model considering dynamic reservoir capacity was solved by the enumeration method, providing a more accurate simulation of the water flow process compared to the model considering static reservoir capacity. However, the solving process had the disadvantage of a large computational burden and excessive time consumption, which restricted application in the actual scheduling of reservoirs. Therefore, to solve the time-consuming problem of repeated calculation of hydrodynamics model in optimization scheduling, an improved genetic algorithm including adaptive and elitist-selection strategies combined with the parallel computing technique was proposed. The optimal scheduling plan generated by this model were compared with the optimization results of the enumeration method and the genetic algorithm scheduling model. The results showed that the optimal power generation selected by the three models were extremely similar and the optimization model could find the optimal value for the scheduling period. In terms of computing speed, the improved genetic algorithm parallel technique took approximately 0.8 h, around 246 times faster than the enumeration method and 20 times faster than the model without using parallel computing. It demonstrates that the present algorithm is practically usable, significantly enhancing the optimization efficiency of scheduling solutions. Furthermore, it satisfies the engineering application efficiency requirements and can provide technical support for the subsequent power generation scheduling of the Three Gorges Reservoir.

Pathological changes of biochemical, hematological and coagulation analyses in patients with COVID-19 disease

The identification of patients with poor prognosis and early detection of COVID-19 disease complications are made possible by pathological analyses of routine hematological, coagulation, and biochemical tests. Interpreting analyses needs to be done within the framework of each patient’s unique clinical picture. It’s also critical to keep an eye on changes at the individual parameter level. From May 20th, 2021, to March 30th, 2024, a comprehensive search of literature was carried out using international databases, such as PubMed, Embase, Web of Science, Scopus, and the Cochrane Library, in compliance with the PRISMA guidelines. The research question was formulated using the PICO strategy. The following terms were used: biochemical parameters in COVID-19, hematological parameters in COVID-19, blood coagulation parameters in COVID-19, indicators of inflammation, and indicators of tissue damage in SARS-CoV-2. Routine hematological, coagulation, and biochemical tests are primarily used to monitor the progression of the disease and the effectiveness of treatment rather than being utilized for the established diagnosis of COVID-19 due to their low specificity. Molecular genetics and immunological techniques should be used to determine the COVID-19 disease diagnosis.

Reconfiguration of distribution network-based wind energy resource allocation considering time-varying load using hybrid optimization method

This study proposed a method for optimizing a radial distribution network by integrating wind turbine allocation, considering fluctuating load demands, through the use of a hybrid Grey Wolf Optimizer-Genetic Algorithm (HGWOGA). This approach aims to decrease the network’s energy loss costs. By incorporating genetic algorithm techniques, the method enhances the Grey Wolf Optimizer’s efficiency, speeding up convergence and avoiding local optima. The strategy determines the network’s open lines and the placement and capacity of wind turbines, adhering to radiality and operational constraints. It categorizes load levels into residential, commercial, and industrial, providing a comprehensive analysis of energy losses and their cost implications under various scenarios, including constant and dynamic loads. The study suggests that managing time-varying demand offers a more accurate depiction of network challenges, enabling effective reconfiguration throughout different demand phases. Moreover, HGWOGA demonstrates its ability to find the global optimum efficiently, even with reduced population sizes—a feat not achievable with the Grey Wolf Optimizer alone. Comparative analyses reveal HGWOGA’s effectiveness in curbing network energy loss costs better than previous methodologies. By simultaneously applying network reconfiguration and wind turbine allocation, as opposed to merely reconfiguring the network, this approach notably reduces power loss, diminishes the cost of losses, and enhances the voltage profile. This synergistic strategy leverages the dynamic allocation of wind turbines within the network, optimizing energy flow and distribution efficiency, thereby offering a substantial improvement over conventional network reconfiguration methods.

Optimization of wear parameters for ECAP-processed ZK30 alloy using response surface and machine learning approaches: a comparative study

The present research applies different statistical analysis and machine learning (ML) approaches to predict and optimize the processing parameters on the wear behavior of ZK30 alloy processed through equal channel angular pressing (ECAP) technique. Firstly, The ECAPed ZK30 billets have been examined at as-annealed (AA), 1-pass, and 4-passes of route Bc (4Bc). Then, the wear output responses in terms of volume loss (VL) and coefficient of friction (COF) have been experimentally investigated by varying load pressure (P) and speed (V) using design of experiments (DOE). In the second step, statistical analysis of variance (ANOVA), 3D response surface plots, and ML have been employed to predict the output responses. Subsequently, genetic algorithm (GA), hybrid DOE–GA, and multi-objective genetic algorithm techniques have been used to optimize the input variables. The experimental results of ECAP process reveal a significant reduction in the average grain size by 92.7% as it processed through 4Bc compared to AA counterpart. Furthermore, 4Bc exhibited a significant improvement in the VL by 99.8% compared to AA counterpart. Both regression and ML prediction models establish a significant correlation between the projected and the actual data, indicating that the experimental and predicted values agreed exceptionally well. The minimal VL at different ECAP passes was obtained at the highest condition of the wear test. Also, the minimal COF for all ECAP passes was obtained at maximum wear load. However, the optimal speed in the wear process decreased with the number of billets passes for minimum COF. The validation of predicted ML models and VL regression under different wear conditions have an accuracy range of 70–99.7%, respectively.