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Nevin Manimala Statistics

High-Performance CMOS Inverter Array with Monolithic 3D Architecture Based on CVD-Grown n-MoS2 and p-MoTe2

Small. 2023 Feb 7:e2207927. doi: 10.1002/smll.202207927. Online ahead of print.

ABSTRACT

In this work, monolithic three-dimensional complementary metal oxide semiconductor (CMOS) inverter array has been fabricated, based on large-scale n-MoS2 and p-MoTe2 grown by the chemical vapor deposition method. In the CMOS device, the n- and p-channel field-effect transistors (FETs) stack vertically and share the same gate electrode. High k HfO2 is used as the gate dielectric. An Al2 O3 seed layer is used to protect the MoS2 from heavily n-doping in the later-on atomic layer deposition process. P-MoTe2 FET is intentionally designed as the upper layer. Because p-doping of MoTe2 results from oxygen and water in the air, this design can guarantee a higher hole density of MoTe2 . An HfO2 capping layer is employed to further balance the transfer curves of n- and p-channel FETs and improve the performance of the inverter. The typical gain and power consumption of the CMOS devices are about 4.2 and 0.11 nW, respectively, at VDD of 1 V. The statistical results show that the CMOS array is with high device yield (60%) and an average voltage gain value of about 3.6 at VDD of 1 V. This work demonstrates the advantage of two-dimensional semi-conductive transition metal dichalcogenides in fabricating high-density integrated circuits.

PMID:36748299 | DOI:10.1002/smll.202207927

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Nevin Manimala Statistics

Stratification and recovery time jointly shape ant functional reassembly in a Neotropical forest

J Anim Ecol. 2023 Feb 7. doi: 10.1111/1365-2656.13896. Online ahead of print.

ABSTRACT

1. Microhabitat differentiation of species communities such as vertical stratification in tropical forests contributes to species coexistence and thus biodiversity. However, little is known about how the extent of stratification changes during forest recovery and influences community reassembly. Environmental filtering determines community reassembly in time (succession) and in space (stratification), hence functional and phylogenetic composition of species communities are highly dynamic. It is poorly understood if and how these two concurrent filters – forest recovery and stratification – interact. 2. In a tropical forest chronosequence in Ecuador spanning 34 years of natural recovery, we investigated the recovery trajectory of ant communities in three overlapping strata (ground, leaf litter, lower tree trunk) by quantifying 13 traits, as well as the functional and phylogenetic diversity of the ants. We expected that functional and phylogenetic diversity would increase with recovery time and that each ant community within each stratum would show a distinct functional reassembly. We predicted that traits related to ant diet would show divergent trajectories reflecting an increase in niche differentiation with recovery time. On the other hand, traits related to the abiotic environment were predicted to show convergent trajectories due to a more similar microclimate across strata with increasing recovery age. 3. Most of the functional traits and the phylogenetic diversity of the ants were clearly stratified, confirming previous findings. However, neither functional nor phylogenetic diversity increased with recovery time. Community-weighted trait means had complex relationships to recovery time and the majority were shaped by a statistical interaction between recovery time and stratum, confirming our expectations. However, most trait trajectories converged among strata with increasing recovery time regardless of whether they were related to ant diet or environmental conditions. 4. We confirm the hypothesized interaction among environmental filters during the functional reassembly in tropical forests. Communities in individual strata respond differently to recovery, and possible filter mechanisms likely arise from both abiotic (e.g., microclimate) and biotic (e.g., diet) conditions. Since vertical stratification is prevalent across animal and plant taxa, our results highlight the importance of stratum-specific analysis in dynamic ecosystems and may generalize beyond ants.

PMID:36748273 | DOI:10.1111/1365-2656.13896

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Nevin Manimala Statistics

Prevalence of Apical Periodontitis in Patients with Autoimmune Diseases: A Case Control Study

Int Endod J. 2023 Feb 6. doi: 10.1111/iej.13902. Online ahead of print.

ABSTRACT

AIM: The purpose of this case-control study was to compare the prevalence of Apical Periodontitis (AP) in patients affected by Autoimmune Disorders (AD) [Inflammatory Bowel Disease (IBD), Rheumatoid Arthritis (RA), and Psoriasis (Ps)] with the prevalence of AP in subjects without AD. The prevalences of AP in patients taking biologic medications, conventional medications and no medication were also compared.

METHODOLOGY: 89 patients (2,145 teeth) with AD were investigated and the control group included 89 patients (2,329 teeth) with no systemic diseases. Full dental panoramic tomograms were used to determine the periapical status of the teeth. Additional variables investigated included patient’s socio-demographic characteristics, medications taken by AD patients, the Decayed, Missing, and Filled Teeth (DMFT) index. The chi-square test and logistic regression analysis were used to evaluate the correlation between AD and AP. P-values lower than 0.05 were considered to be statistically significant.

RESULTS: The prevalence of AP was 89.9% in AD patients and 74.2% in control subjects (odds ratio [OR]=3.75, p=0.015). The DMFT score was found to be significantly higher in the AD group (p=0.004). Patients with RA had the highest risk of being affected by AP, whereas those with IBD had the lowest risk. Multiple binary logistic regression analysis indicated that the teeth of AD patients who were not taking any medication or were being treated with biologic Disease Modifying Anti-Rheumatic Drugs (bDMARDs) had a higher risk of being affected by AP than did the teeth of the control subjects (OR=1.42 and OR=2.03 respectively; p=0.010). The teeth of patients taking conventional DMARDs (cDMARDs) were less affected by AP compared with those of patients taking bDMARDs.

CONCLUSIONS: Patients with AD, whether treated or not with biologic medications, showed a higher prevalence of AP than did those in the control group. The DMFT index score, which was higher in AD patients compared with controls was identified as a significant predictor of AP prevalence.

PMID:36747086 | DOI:10.1111/iej.13902

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Nevin Manimala Statistics

Three-dimensional evaluation of the maxillary sinus in patients with different skeletal classes and cranio-maxillary relationships assessed with cone beam computed tomography

Sci Rep. 2023 Feb 6;13(1):2098. doi: 10.1038/s41598-023-29391-5.

ABSTRACT

The objective was to evaluate the relationship between the dimensions of the maxillary sinuses (MSs) and various cephalometric parameters. MS volume (MSV), MS surface (MSS), linear maximum depth (LMD), linear maximum width (LMW), and linear maximum height (LMH) were calculated on CBCT scans of 99 adults. Two sets of two-way (ANOVA) assessed the influence respectively of ANB and SNA angles and of the gender on MS dimensions. Pearson’s correlation was calculated between MS dimensions and different cephalometric variables. Reliability and accuracy of the proposed method was tested with intra-operator and inter-operator intraclass correlation coefficient (ICC). Two-way ANOVA showed no statistically significant difference in MSV, MSS and LMH between ANB groups, whilst males were associated with bigger sinuses. LMW showed statistically significant difference in both ANB and gender groups. LMD showed no statistically significant difference. The second Two-way ANOVA showed significantly larger MSV, MSS and LMD in patients with increased or reduced SNA angle but not between genders. LMW and LMH also showed a significant difference between genders. All linear measurements showed a significant interaction of the two factors. The intra-observer and inter-observer ICC scored high for all the tested measurements. MSV and MSS showed a positive correlation with S-N, PNS-A, S-Go, N-Me, N-Ans and the distance between Mx points. LMW had a negative correlation with Ba-S-N angle and N-Me, LMH with Ba-S-N angle, S-Go and Mx r-Mx l and LMD with N-Me and N-ANS. LMW had a positive correlation with Mx r-Mx l, LMH with S-N, S-N^Ans-Pns, N-Me, N-Ans and LMD with S-N, Ba-S-N, PNS-A, S-Go and distance between Mx points. In conclusion, MSV and MSS did not differ between the three skeletal classes, males showed significantly larger MS than in females. Concerning the influence of the cranio-maxillary relationship (SNA) and gender on MS dimension, subjects with a retrusion (SNA < 80°) or protusion (SNA > 84°) of the maxillary alveolar bone had larger MSV, MSS, LMW, LMH and LMD than subjects with a normal cranio-maxillary relationship (SNA 82 ± 2°). A statistically significant high positive correlation was observed between S-N, Pns-A, S-Go, Mx-R/Mx-r and MS dimension. Further studies that evaluate similar outcomes in different races may be able to enrich our knowledge on this topic.

PMID:36747077 | DOI:10.1038/s41598-023-29391-5

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Nevin Manimala Statistics

Author Correction: Survival impact of treatment for chronic obstructive pulmonary disease in patients with advanced non-small-cell lung cancer

Sci Rep. 2023 Feb 6;13(1):2117. doi: 10.1038/s41598-023-29102-0.

NO ABSTRACT

PMID:36747076 | DOI:10.1038/s41598-023-29102-0

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Nevin Manimala Statistics

Machine learning classifiers predict key genomic and evolutionary traits across the kingdoms of life

Sci Rep. 2023 Feb 6;13(1):2088. doi: 10.1038/s41598-023-28965-7.

ABSTRACT

In this study, we investigate how an organism’s codon usage bias can serve as a predictor and classifier of various genomic and evolutionary traits across the domains of life. We perform secondary analysis of existing genetic datasets to build several AI/machine learning models. When trained on codon usage patterns of nearly 13,000 organisms, our models accurately predict the organelle of origin and taxonomic identity of nucleotide samples. We extend our analysis to identify the most influential codons for phylogenetic prediction with a custom feature ranking ensemble. Our results suggest that the genetic code can be utilized to train accurate classifiers of taxonomic and phylogenetic features. We then apply this classification framework to open reading frame (ORF) detection. Our statistical model assesses all possible ORFs in a nucleotide sample and rejects or deems them plausible based on the codon usage distribution. Our dataset and analyses are made publicly available on GitHub and the UCI ML Repository to facilitate open-source reproducibility and community engagement.

PMID:36747072 | DOI:10.1038/s41598-023-28965-7

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Nevin Manimala Statistics

Mind evolutionary algorithm optimization in the prediction of satellite clock bias using the back propagation neural network

Sci Rep. 2023 Feb 6;13(1):2095. doi: 10.1038/s41598-023-28855-y.

ABSTRACT

Satellite clock bias is the key factor affecting the accuracy of the single point positioning of a global navigation satellite system. The traditional model back propagation (BP) neural network is prone to local optimum problems. This paper presents a prediction model and algorithm for the clock bias of the BP neural network based on the optimization of the mind evolutionary algorithm (MEA), which is used to optimize the initial weights and thresholds of the BP neural network. The accuracy of the comparison between clock bias data is verified with and without one-time difference processing. Compared with grey model (GM (1,1)) and BP neural network, this paper discusses the advantages and general applicability of this method from different constellation satellites, different atomic clock type satellites, and the amount of modeling data. The accuracy of the grey model (GM(1,1)), BP, and MEA-BP models for satellite clock bias prediction is analyzed and the root mean square error, range difference error, and the mean of the clock bias data compared. The results demonstrate that the prediction accuracy of the three satellites significantly increased after one-time difference processing and that they have good stability. The prediction accuracy of four sessions of 2 h, 3 h, 6 h, and 12 h obtained using the MEA-BP model was better than 0.74, 0.80, 1.12, and 0.87 ns, respectively. The MEA-BP model has a specific degree of improvement in the prediction accuracy of the different sessions. Additionally, the prediction accuracy of different models has a specific relationship with the length of the original modeling sequence, of which BP model is the most affected, and MEABP is relatively less affected by the length of the modeling sequence, indicating that the MEA-BP model has strong anti-interference ability.

PMID:36747070 | DOI:10.1038/s41598-023-28855-y

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Nevin Manimala Statistics

Exact solutions of [Formula: see text]-dependent Schrödinger equation with quantum pseudo-harmonic oscillator and its applications for the thermodynamic properties in normal and superstatistics

Sci Rep. 2023 Feb 6;13(1):2108. doi: 10.1038/s41598-023-28973-7.

ABSTRACT

The effects of the curvature parameters on the energy eigenvalues and thermodynamic properties of quantum pseudoharmonic oscillator are investigated within the framework of nonrelativistic quantum mechanics. By employing Nikiforov-Uvarov method, the energy spectra are obtained and used to study the ordinary statistics and q-deformed superstatistics as a function of temperature in the presence and absence of the curvature parameters. It is shown that the q-deformed supertatistics properties of the quantum pseudoharmonic oscillator reduce to the ordinary statistical properties in the absence of the deformation parameter. Finally, our results are illustrated graphically to show the behaviour of the energy spectra and thermodynamic properties for the three curvature parameters:[Formula: see text].

PMID:36747069 | DOI:10.1038/s41598-023-28973-7

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Nevin Manimala Statistics

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection

Sci Rep. 2023 Feb 6;13(1):2105. doi: 10.1038/s41598-023-28785-9.

ABSTRACT

Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million “in-stock” molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.

PMID:36747041 | DOI:10.1038/s41598-023-28785-9

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Nevin Manimala Statistics

Spatiotemporal dynamics and environmental determinants of scrub typhus in Anhui Province, China, 2010-2020

Sci Rep. 2023 Feb 6;13(1):2131. doi: 10.1038/s41598-023-29373-7.

ABSTRACT

This study aims to describe the epidemiological characteristics of scrub typhus, detect the spatio-temporal patterns of scrub typhus at county level, and explore the associations between the environmental variables and scrub typhus cases in Anhui Province. Time-series analysis, spatial autocorrelation analysis, and space-time scan statistics were used to explore the characteristics and spatiotemporal patterns of the scrub typhus in Anhui Province. Negative binomial regression analysis was used to explore the association between scrub typhus and environmental variables. A total of 16,568 clinically diagnosed and laboratory-confirmed cases were reported from 104 counties of 16 prefecture-level cities. The number of female cases was higher than male cases, with a proportion of 1.32:1. And the proportion of cases over 65 years old was the highest, accounting for 33.8% of the total cases. Two primary and five secondary high-risk clusters were detected in the northwestern, northeastern, and central-eastern parts of Anhui Province. The number of cases in primary and secondary high-risk clusters accounted for 60.27% and 3.00%, respectively. Scrub typhus incidence in Anhui Province was positively correlated with the population density, normalized difference vegetation index, and several meteorological variables. The mean monthly sunshine duration with 3 lags (SSD_lag3), mean monthly ground surface temperature with 1 lag (GST_lag1), and mean monthly relative humidity with 3 lags (RHU_lag3) had the most significant association with increased cases of scrub typhus. Our findings indicate that public health interventions need to be focused on the elderly farmers in north of the Huai River in Anhui Province.

PMID:36747027 | DOI:10.1038/s41598-023-29373-7