Human Papilloma Virus (HPV) Oral Prevalence in Scotland (HOPSCOTCH):a feasibility study in dental settings

The purpose of this study was to test the feasibility of undertaking a full population investigation into the prevalence, incidence, and persistence of oral Human Papilloma Virus (HPV) in Scotland via dental settings. Male and female patients aged 16-69 years were recruited by Research Nurses in 3 primary care and dental outreach teaching centres and 2 General Dental Practices (GDPs), and by Dental Care Teams in 2 further GDPs. Participants completed a questionnaire (via an online tablet computer or paper) with socioeconomic, lifestyle, and sexual history items; and were followed up at 6-months for further questionnaire through appointment or post/online. Saline oral gargle/rinse samples, collected at baseline and follow-up, were subject to molecular HPV genotyping centrally. 1213 dental patients were approached and 402 individuals consented (participation rate 33.1%). 390 completed the baseline questionnaire and 380 provided a baseline oral specimen. Follow-up rate was 61.6% at 6 months. While recruitment was no different in Research Nurse vs Dental Care Team models the Nurse model ensured more rapid recruitment. There were relatively few missing responses in the questionnaire and high levels of disclosure of risk behaviours (99% answered some of the sexual history questions). Data linkage of participant data to routine health records including HPV vaccination data was successful with 99.1% matching. Oral rinse/gargle sample collection and subsequent HPV testing was feasible. Preliminary analyses found over 95% of samples to be valid for molecular HPV detection prevalence of oral HPV infection of 5.5% (95%CI 3.7, 8.3). It is feasible to recruit and follow-up dental patients largely representative / reflective of the wider population, suggesting it would be possible to undertake a study to investigate the prevalence, incidence, and determinants of oral HPV infection in dental settings

Performance of Telenomus remus (Hymenoptera: Scelionidae), an egg parasitoid of Spodoptera frugiperda (Lepidoptera: Noctuidae), under different temperature regimes

Maize fall armyworm, Spodoptera frugiperda (J.E. Smith) (Noctuidae: Lepidoptera), is a highly destructive and invasive pest of maize, causing havoc in major maize growing states of the country since 2018. Integrated Pest Management (IPM) strategies are being advocated to farmers for the containment of the pest. Among the various IPM components, biological control using the egg parasitoid, Telenomus remus (Nixon) (Scelionidae: Hymenoptera) could be considered a promising strategy, as the pest can be managed at a much earlier stage. This study evaluated the influence of five temperature regimes (20, 25, 30, 35 and 40  °C) on the developmental and reproductive performance of T. remus. Results indicated that 25-30  °C was optimal, with the highest parasitism (80.70 ± 5.29 eggs/female/24 hrs) and adult emergence (99.51 ± 0.20 %) at 25  °C. Developmental time decreased with increase in temperature, ranging from 8.60 days (35 °C) to 21.95 days (20  °C). Peak fecundity (122.7 ± 3.56 eggs/female) and intrinsic rate of increase (rm = 0.479) occurred at 30  °C. No reproduction occurred at 40  °C. These findings underscore the critical role of temperature in optimizing T. remus performance, aiding its effective integration into biological control programs against S. frugiperda

Genomic Approaches to Enhance Stress Tolerance for Productivity Improvements in Pearl Millet

Pearl millet [Pennisetum glaucum (L.) R. Br.], the sixth most important cereal crop (after rice, wheat, maize, barley, and sorghum), is grown as a grain and stover crop by the small holder farmers in the harshest cropping environments of the arid and semiarid tropical regions of sub-Saharan Africa and South Asia. Millet is grown on ~31 million hectares globally with India in South Asia; Nigeria, Niger, Burkina Faso, and Mali in western and central Africa; and Sudan, Uganda, and Tanzania in Eastern Africa as the major producers. Pearl millet provides food and nutritional security to more than 500 million of the world’s poorest and most nutritionally insecure people. Global pearl millet production has increased over the past 15 years, primarily due to availability of improved genetics and adoption of hybrids in India and expanding area under pearl millet production in West Africa. Pearl millet production is challenged by various biotic and abiotic stresses resulting in a significant reduction in yields. The genomics research in pearl millet lagged behind because of multiple reasons in the past. However, in the recent past, several efforts were initiated in genomic research resulting into a generation of large amounts of genomic resources and information including recently published sequence of the reference genome and re-sequencing of almost 1000 lines representing the global diversity. This chapter reviews the advances made in generating the genetic and genomics resources in pearl millet and their interventions in improving the stress tolerance to improve the productivity of this very important climate-smart nutri-cereal

Machine Learning for Cloud-Based Privilege Escalation Attack Detection and Mitigation with CATBOOST

The exponential growth in attack frequency and complexity in the past few years has made cybersecurity a major concern with the advent of smart devices. Cloud computing has changed the way businesses operate, but users may find it more challenging to use dispersed services, such as security systems, due to their centralization. Organizations and cloud service suppliers exchange massive amounts of data, which poses a significant risk of accidental or intentional disclosure of sensitive information. Because of their increased access and potential to do substantial harm, an antagonistic insider poses a serious threat to the company. Only approved individuals within the organization have access to sensitive data and assets. This research details a machine learning-based strategy for classifying insider threats and finding out-of-the-ordinary events that can indicate privilege escalation security issues. The system uses a systematic approach to detect these irregularities. Machine learning and prediction accuracy are both enhanced by ensemble learning, which considers several models simultaneously. Using anomaly and weakness detection, some studies have attempted to identify security issues or hazards associated with privilege delegation in network systems. However, the assaults cannot be definitely identified from this research. Ensembles for machine learning (ML) are suggested and assessed in this research. The objective of this endeavor is to classify insider assaults using machine learning approaches. The dataset it uses has been modified from many files beneath the CERT dataset. The dataset is subjected to four machine learning techniques: Light GBM, XG Boost, Ada Boost, and three Random Forest (RF) methods. In terms of overall performance, light was superior. In contrast, RF and AdaBoost are two algorithms that may be better at preventing assaults from inside, such as attacks using behavioral biometrics. Consequently, it is possible that various internal threats may be better classified by combining various machine learning algorithms. With a 97% dependability rate, the Light GBM method outperforms the other suggested techniques; RF, AdaBoost, and XG Boost all have 88% accuracy rates

Highly luminescent and thermally stable lanthanide coordination polymers designed from 4-(dipyridin-2-yl)aminobenzoate: efficient energy transfer from Tb3+ to Eu3+ in a mixed lanthanide coordination compound

Herein, a new aromatic carboxylate ligand, namely, 4-(dipyridin-2-yl)aminobenzoic acid (HL), has been designed and employed for the construction of a series of lanthanide complexes (Eu3+ = 1, Tb3+ = 2, and Gd3+ = 3). Complexes of 1 and 2 were structurally authenticated by single-crystal X-ray diffraction and were found to exist as infinite 1D coordination polymers with the general formulas {Eu(L)(3)(H2O)(2)]}(n) (1) and {Tb(L)(3)(H2O)]center dot(H2O)}(n) (2). Both compounds crystallize in monoclinic space group C2/c. The photophysical properties demonstrated that the developed 4-(dipyridin-2-yl)aminobenzoate ligand is well suited for the sensitization of Tb3+ emission (Phi(overall) = 64%) thanks to the favorable position of the triplet state ((3)pi pi*) of the ligand the energy difference between the triplet state of the ligand and the excited state of Tb3+ (Delta E) = (3)pi pi* - D-5(4) = 3197 cm(-1)], as investigated in the Gd3+ complex. On the other hand, the corresponding Eu3+ complex shows weak luminescence efficiency (Phi(overall) = 7%) due to poor matching of the triplet state of the ligand with that of the emissive excited states of the metal ion (Delta E = (3)pi pi* - D-5(0) = 6447 cm(-1)). Furthermore, in the present work, a mixed lanthanide system featuring Eu3+ and Tb3+ ions with the general formula {Eu0.5Tb0.5(L)(3)(H2O)(2)]}(n) (4) was also synthesized, and the luminescent properties were evaluated and compared with those of the analogous single-lanthanide-ion systems (1 and 2). The lifetime measurements for 4 strongly support the premise that efficient energy transfer occurs between Tb3+ and Eu3+ in a mixed lanthanide system (eta = 86%)