Nevin Manimala

Environ Monit Assess. 2022 Oct 28;195(1):44. doi: 10.1007/s10661-022-10561-3.

ABSTRACT

Farming has a plethora of difficult responsibilities, and plant monitoring is one of them. There is also an urgent need to increase the number of alternative techniques for detecting plant diseases, which is now lacking. The agriculture and agricultural support sectors in India provide employment for the great majority of the country’s people. In India, the agricultural production of the country is directly connected to the country’s economic growth rate. In order to sustain healthy plant development, a variety of processes must be followed, including consideration of environmental factors and water supply management for the optimal production of crops. It is inefficient and uncertain in its outcomes to use the traditional method of watering a lawn. The devastation of more than 18% of the world’s agricultural produce is caused by disease attacks on an annual basis. Because it is difficult to execute these activities manually, identifying plant diseases is essential to decreasing losses in the agricultural product business. In addition to diagnosing a wide range of plant ailments, our method also includes the identification of infections as a prophylactic step. Below is a detailed description of a farm-based module that includes numerous cloud data centers and data conversion devices for accurately monitoring and managing farm information and environmental elements. This procedure involves imaging the plant’s visually obvious signs in order to identify disease. It is recommended that the therapy be used in conjunction with an application to minimize any harm. Increased productivity as a result of the suggested approach would help both the agricultural and irrigation sectors. The plant area module is fitted with a mobile camera that captures images of all of the plants in the area, and all of the plants’ information is saved in a database, which is accessible from any computer with Internet access. It is planned to record information on the plant’s name, the type of illness that has been afflicted, and an image of the plant. In a wide range of applications, bots are used to collect images of various plants as well as to prevent disease transmission. To ensure that all information given is retained on the Internet, data is collected and stored in cloud storage as it becomes essential to regulate the condition. According to our findings from our research on wide images of healthy and ill fruit and plant leaves, real-time diagnosis of plant leaf diseases may be done with 98.78% accuracy in a laboratory environment. We utilized 40,000 photographs and then analyzed 10,000 photos to construct a DCDM deep learning model, which was then used to train additional models on the data set. Using a cloud-based image diagnostic and classification service, consumers may receive information about their condition in less than a second on average, with the process requiring only 0.349 s on average.

PMID:36302915 | DOI:10.1007/s10661-022-10561-3

Nat Commun. 2022 Oct 27;13(1):6400. doi: 10.1038/s41467-022-33666-2.

ABSTRACT

Shared cars will likely have larger annual vehicle driving distances than individually owned cars. This may accelerate passenger car retirement. Here we develop a semi-empirical lifetime-driving intensity model using statistics on Swedish vehicle retirement. This semi-empirical model is integrated with a carbon footprint model, which considers future decarbonization pathways. In this work, we show that the carbon footprint depends on the cumulative driving distance, which depends on both driving intensity and calendar aging. Higher driving intensities generally result in lower carbon footprints due to increased cumulative driving distance over the vehicle’s lifetime. Shared cars could decrease the carbon footprint by about 41% in 2050, if one shared vehicle replaces ten individually owned vehicles. However, potential empty travel by autonomous shared vehicles-the additional distance traveled to pick up passengers-may cause carbon footprints to increase. Hence, vehicle durability and empty travel should be considered when designing low-carbon car sharing systems.

PMID:36302850 | DOI:10.1038/s41467-022-33666-2

Sci Rep. 2022 Oct 27;12(1):18100. doi: 10.1038/s41598-022-22973-9.

ABSTRACT

The incidence of pancreatic ductal adenocarcinoma (PDAC) is different among males and females. This disparity cannot be fully explained by the difference in terms of exposure to known risk factors; therefore, the lower incidence in women could be attributed to sex-specific hormones. A two-phase association study was conducted in 12,387 female subjects (5436 PDAC cases and 6951 controls) to assess the effect on risk of developing PDAC of single nucleotide polymorphisms (SNPs) in 208 genes involved in oestrogen and pregnenolone biosynthesis and oestrogen-mediated signalling. In the discovery phase 14 polymorphisms showed a statistically significant association (P < 0.05). In the replication none of the findings were validated. In addition, a gene-based analysis was performed on the 208 selected genes. Four genes (NR5A2, MED1, NCOA2 and RUNX1) were associated with PDAC risk, but only NR5A2 showed an association (P = 4.08 × 10^-5) below the Bonferroni-corrected threshold of statistical significance. In conclusion, despite differences in incidence between males and females, our study did not identify an effect of common polymorphisms in the oestrogen and pregnenolone pathways in relation to PDAC susceptibility. However, we validated the previously reported association between NR5A2 gene variants and PDAC risk.

PMID:36302831 | DOI:10.1038/s41598-022-22973-9

Sci Rep. 2022 Oct 27;12(1):18094. doi: 10.1038/s41598-022-22819-4.

ABSTRACT

Despite the availability of efficacious direct-acting antiviral (DAA) therapy, the number of people infected with hepatitis C virus (HCV) continues to rise, and HCV remains a leading cause of liver-related morbidity, liver transplantation, and mortality. We developed and validated machine learning (ML) algorithms to predict DAA treatment failure. Using the HCV-TARGET registry of adults who initiated all-oral DAA treatment, we developed elastic net (EN), random forest (RF), gradient boosting machine (GBM), and feedforward neural network (FNN) ML algorithms. Model performances were compared with multivariable logistic regression (MLR) by assessing C statistics and other prediction evaluation metrics. Among 6525 HCV-infected adults, 308 patients (4.7%) experienced DAA treatment failure. ML models performed similarly in predicting DAA treatment failure (C statistic [95% CI]: EN, 0.74 [0.69-0.79]; RF, 0.74 [0.69-0.80]; GBM, 0.72 [0.67-0.78]; FNN, 0.75 [0.70-0.80]), and all 4 outperformed MLR (C statistic [95% CI]: 0.51 [0.46-0.57]), and EN used the fewest predictors (n = 27). With Youden index, the EN had 58.4% sensitivity and 77.8% specificity, and nine patients were needed to evaluate to identify 1 DAA treatment failure. Over 60% treatment failure were classified in top three risk decile subgroups. EN-identified predictors included male sex, treatment < 8 weeks, treatment discontinuation due to adverse events, albumin level < 3.5 g/dL, total bilirubin level > 1.2 g/dL, advanced liver disease, and use of tobacco, alcohol, or vitamins. Addressing modifiable factors of DAA treatment failure may reduce the burden of retreatment. Machine learning algorithms have the potential to inform public health policies regarding curative treatment of HCV.

PMID:36302828 | DOI:10.1038/s41598-022-22819-4

Sci Rep. 2022 Oct 27;12(1):18061. doi: 10.1038/s41598-022-22155-7.

ABSTRACT

Stray non-breeding cats (stray) represent the largest heterogeneous cat population subject to natural selection, while populations of the Siamese (SIAM) and Oriental Shorthair (OSH) breeds developed through intensive artificial selection for aesthetic traits. Runs of homozygosity (ROH) and demographic measures are useful tools to discover chromosomal regions of recent selection and to characterize genetic diversity in domestic cat populations. To achieve this, we genotyped 150 stray and 26 household non-breeding cats (household) on the Illumina feline 63 K SNP BeadChip and compared them to SIAM and OSH. The 50% decay value of squared correlation coefficients (r²) in stray (0.23), household (0.25), OSH (0.24) and SIAM (0.25) corresponded to a mean marker distance of 1.12 Kb, 4.55 Kb, 62.50 Kb and 175.07 Kb, respectively. The effective population size (N_e) decreased in the current generation to 55 in stray, 11 in household, 9 in OSH and 7 in SIAM. In the recent generation, the increase in inbreeding per generation (ΔF) reached its maximum values of 0.0090, 0.0443, 0.0561 and 0.0710 in stray, household, OSH and SIAM, respectively. The genomic inbreeding coefficient (F_ROH) based on ROH was calculated for three length categories. The F_ROH was between 0.014 (F_ROH60) and 0.020 (F_ROH5) for stray, between 0.018 (F_ROH60) and 0.024 (F_ROH5) for household, between 0.048 (F_ROH60) and 0.069 (F_ROH5) for OSH and between 0.053 (F_ROH60) and 0.073 (F_ROH5) for SIAM. We identified nine unique selective regions for stray through genome-wide analyses for regions with reduced heterozygosity based on F_ST statistics. Genes in these regions have previously been associated with reproduction (BUB1B), motor/neurological behavior (GPHN, GABRB3), cold-induced thermogenesis (DIO2, TSHR), immune system development (TSHR), viral carcinogenesis (GTF2A1), host immune response against bacteria, viruses, chemoattractant and cancer cells (PLCB2, BAHD1, TIGAR), and lifespan and aging (BUB1B, FGF23). In addition, we identified twelve unique selective regions for OSH containing candidate genes for a wide range of coat colors and patterns (ADAMTS20, KITLG, TYR, TYRO3-a MITF regulator, GPNMB, FGF7, RAB38) as well as congenital heart defects (PDE4D, PKP2) and gastrointestinal disorders (NLGN1, ALDH1B1). Genes in stray that represent unique selective events indicate, at least in part, natural selection for environmental adaptation and resistance to infectious disease, and should be the subject of future research. Stray cats represent an important genetic resource and have the potential to become a research model for disease resistance and longevity, which is why we recommend preserving semen before neutering.

PMID:36302822 | DOI:10.1038/s41598-022-22155-7

Sci Rep. 2022 Oct 27;12(1):18042. doi: 10.1038/s41598-022-22130-2.

ABSTRACT

Modern money transfer services are convenient, attracting fraudulent actors to run scams in which victims are deceived into transferring funds to fraudulent accounts. Machine learning models are broadly applied due to the poor fraud detection performance of traditional rule-based approaches. Learning directly from raw transaction data is impractical due to its high-dimensional nature; most studies construct features instead by extracting patterns from raw transaction data. Past literature categorizes these features into recency, frequency, monetary, and anomaly detection features. We use various machine learning algorithms to examine the performance of features in these four categories with real transaction data; we compare them with the performance of our feature generation guideline based on the statistical perspectives and characteristics of (non)-fraudulent accounts. The results show that except for the monetary category, other feature categories used in the literature perform poorly regardless of which machine learning algorithm is used; anomaly detection features perform the worst. We find that even statistical features generated based on financial knowledge yield limited performance on a real transaction dataset. Our atypical detection characteristic of normal accounts improves the ability to distinguish them from fraudulent accounts and hence improves the overall detection results, outperforming other existent methods.

PMID:36302818 | DOI:10.1038/s41598-022-22130-2

Sci Rep. 2022 Oct 27;12(1):18050. doi: 10.1038/s41598-022-22121-3.

ABSTRACT

Amblyopia is a common visual disorder that causes significant vision problems globally. Most non-ocular risk factors for amblyopia are closely related to the intrauterine environment, and are strongly influenced by parent-origin effects. Parent-origin perinatal factors may have a direct causal inference on amblyopia development; therefore, we investigated the causal association between perinatal factors and amblyopia risk using a one-sample Mendelian Randomization (MR) with data from the UK Biobank Cohort Data (UKBB). Four distinct MR methods were employed to analyze the association between three perinatal factors (birth weight [BW], maternal smoking, and breastfeeding) and amblyopia risk, based on the summary statistics of genome-wide association studies in the European population. The inverse variance weighting method showed an inverse causal association between BW and amblyopia risk (odds ratio, 0.48 [95% CI, 0.29-0.80]; p = 0.004). Maternal smoking and breastfeeding were not causally associated with amblyopia risk. Our findings provided a possible evidence of a significant genetic causal association between low BW and increased amblyopia risk. This evidence may highlight the potential of BW as a predictive factor for visual maldevelopment and the need for careful management of amblyopia risk in patients with low BW.

PMID:36302817 | DOI:10.1038/s41598-022-22121-3

Sci Rep. 2022 Oct 27;12(1):18083. doi: 10.1038/s41598-022-20230-7.

ABSTRACT

Despite some improvement in lowering HIV incidence, HIV-related challenges, such as intimate partner violence (IPV), remain unacceptably high among women in South Africa. For decades, researchers and activists have pointed to the complex and intertwined reality of the substance abuse, violence and AIDS (SAVA) syndemic that endangers women. However, more recent systematic review/meta-analysis evidence points to inconclusive association between IPV and alcohol use. Furthermore, much of the evidence is often non-population-based that focuses on the co-occurrence rather than synergistic SAVA interaction. In this study, using the latest data from the South Africa Demographic and Health Survey (SA-DHS), we identified geographic synergistic clustering of IPV associated with HIV and substance abuse in South Africa as a measure of population-level interactions among these factors. The SA-DHS is a nationally representative sample that includes wide-ranging data on health, social challenges and household geo-locations of 5,874 women who participated in the domestic violence module. First, geographical IPV, harmful alcohol use (as the substance abuse measure available in SA-DHS) and HIV clusters were identified using the Kulldorff spatial scan statistic in SaTScan. Second, synergistic interactions related to recent IPV (i.e. recent physical, sexual, emotional violence during the last 12 months) with harmful alcohol use and HIV challenge were measured using RERI [Relative excess risk due to interaction], AP [attributable proportion] and S [Synergy index]. In our results, we spatially identified geographical physical IPV syndemic interactions in parts of the Eastern Cape/Free State Provinces (RERI = 4.42 [95% CI: 2.34-6.51], AP = 0.56 [95% CI: 0.44-0.68], S = 2.77 [95% CI: 2.01-3.84], but not in other forms of IPV. Although IPV, based on decade old concept of SAVA syndemic, was less common/widespread than expected from the national scale population-based data, we identified population-level physical violence syndemic occurring in South Africa. Our study highlights the need to prioritize public health response targeting vulnerable populations residing in these high-risk areas of syndemic mechanisms linking these synergistic epidemics that women face in South Africa.

PMID:36302814 | DOI:10.1038/s41598-022-20230-7

Sci Rep. 2022 Oct 27;12(1):18066. doi: 10.1038/s41598-022-22505-5.

ABSTRACT

Autophagy plays a critical role in tumor pathogenesis. However, autophagy-related signature in Hepatocellular carcinoma (HCC) has not been revealed yet. We quantified the levels of various cancer hallmarks and identified ATG101 as the major risk factor for overall survival in HCC. A robust ATG101-related gene signature (ATS) for prognosis was constructed using a combination of bioinformatic and statistical approaches. Additionally, genetic and immunological properties were measured between ATS-high and ATS-low groups. The ATS signature was associated with shortened overall survival in HCC patients independently of clinicopathological characteristics. ATS status defines an inflamed yet exhausted tumor microenvironment, in which the activities of the exhausted CD8+ or CD4+ T cells were strongly associated with ATS. The ATS signature predicts the drug resistance to the immunotherapy, thus a combination of targeted therapy and immunotherapy might be suitable for ATS-high patients. This work shed light on the function of ATG101-related genes in HCC and revealed that the ATS signature may be a useful prognostic biomarker for differentiating molecular and immunological features and predicting probable response to the therapy.

PMID:36302799 | DOI:10.1038/s41598-022-22505-5

Sci Rep. 2022 Oct 27;12(1):18096. doi: 10.1038/s41598-022-21805-0.

ABSTRACT

In order to understand the characteristics of bio-convection and moving microorganisms in flows of magnetized Walters-B nano-liquid, we developed a model employing Riga plate with stretchy sheet. The Buongiorno phenomenon is likewise employed to describe nano-liquid motion in the Walters-B fluid. Expending correspondence transformations, the partial differential equation (PDE) control system has been transformed into an ordinary differential equation (ODE) control system. The COMSOL program is used to generate mathematical answers for non-linear equations by employing the Galerkin finite element strategy (G-FEM). Utilizing logical and graphical metrics, temperature, velocity, and microbe analysis are all studied. Various estimates of well-known physical features are taken into account while calculating nanoparticle concentrations. It is demonstrated that this model’s computations directly relate the temperature field to the current Biot number and parameter of the Walters-B fluid. The temperature field is increased to increase the approximations of the current Biot number and parameter of the Walters-B fluid.

PMID:36302798 | DOI:10.1038/s41598-022-21805-0