Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Point Cloud Segmentation With Noisy Annotations

Point cloud segmentation is a fundamental task in 3D. Despite recent progress on point cloud segmentation with the power of deep networks, current learning methods based on the clean label assumptions may fail with noisy labels. Yet, class labels are often mislabeled at both instance-level and boundary-level in real-world datasets. In this work, we take the lead in solving the instance-level label noise by proposing a Point Noise-Adaptive Learning (PNAL) framework. Compared to noise-robust methods on image tasks, our framework is noise-rate blind, to cope with the spatially variant noise rate specific to point clouds. Specifically, we propose a point-wise confidence selection to obtain reliable labels from the historical predictions of each point. A cluster-wise label correction is proposed with a voting strategy to generate the best possible label by considering the neighbor correlations. To handle boundary-level label noise, we also propose a variant “PNAL-boundary ” with a progressive boundary label cleaning strategy. Extensive experiments demonstrate its effectiveness on both synthetic and real-world noisy datasets. Even with <inline-formula><tex-math notation="LaTeX">$60\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>60</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liao-ieq1-3225323.gif"/></alternatives></inline-formula> symmetric noise and high-level boundary noise, our framework significantly outperforms its baselines, and is comparable to the upper bound trained on completely clean data. Moreover, we cleaned the popular real-world dataset ScanNetV2 for rigorous experiment. Our code and data is available at <uri>https://github.com/pleaseconnectwifi/PNAL</uri>.
Systematic identification of biochemical networks in cancer cells by functional pathway inference analysis

Abstract Motivation Pathway inference methods are important for annotating the genome, for providing insights into the mechanisms of biochemical processes and allow the discovery of signalling members and potential new drug targets. Here, we tested the hypothesis that genes with similar impact on cell viability across multiple cell lines belong to a common pathway, thus providing a conceptual basis for a pathway inference method based on correlated anti-proliferative gene properties. Methods To test this concept, we used recently available large-scale RNAi screens to develop a method, termed functional pathway inference analysis (FPIA), to systemically identify correlated gene dependencies. Results To assess FPIA, we initially focused on PI3K/AKT/MTOR signalling, a prototypic oncogenic pathway for which we have a good sense of ground truth. Dependencies for AKT1, MTOR and PDPK1 were among the most correlated with those for PIK3CA (encoding PI3Kα), as returned by FPIA, whereas negative regulators of PI3K/AKT/MTOR signalling, such as PTEN were anti-correlated. Following FPIA, MTOR, PIK3CA and PIK3CB produced significantly greater correlations for genes in the PI3K-Akt pathway versus other pathways. Application of FPIA to two additional pathways (p53 and MAPK) returned expected associations (e.g. MDM2 and TP53BP1 for p53 and MAPK1 and BRAF for MEK1). Over-representation analysis of FPIA-returned genes enriched the respective pathway, and FPIA restricted to specific tumour lineages uncovered cell type-specific networks. Overall, our study demonstrates the ability of FPIA to identify members of pro-survival biochemical pathways in cancer cells. Availability and implementation FPIA is implemented in a new R package named ‘cordial’ freely available from https://github.com/CutillasLab/cordial. Supplementary information Supplementary data are available at Bioinformatics online.
A cross-sectional study of the psychological impact of the COVID-19 pandemic on camped refugees in Ghana

Vulnerable populations such as camped refugees are often exposed to spread of infectious diseases because of their living conditions, limited resources available to them and exclusion from social services. This study examined the psychological state of camped refugees in Ghana during the COVID-19 pandemic and how their background characteristics predict the severity of the pandemic’s psychological impact. It covered 763 refugees aged 15 years and above resident in two (Krisan = 316 and Ampain = 447) camps. Nine COVID-19 Anxiety Scale indicators were used to examine the psychological state of camped refugees. A composite indicator was derived to examine the overall psychological impact. Logistic regression was used to examine the factors that were associated with severe psychological impact. The multivariate analysis revealed that sex of the respondent, marital status and age of head of household were the only socio-demographic factors associated with having a severe psychological impact of the pandemic. There was very strong evidence that respondents who had moderate (OR = 1.74, 95% CI = 1.12, 2.7) and high (OR = 1.66, 95% CI = 1.05, 2.63) knowledge of the disease had increased odds of severe psychological impact. Also, those with moderate (OR = 2.97, 95% CI = 1.78, 4.97) and high (OR = 12.98, 95% CI = 7.86, 21.42) adherence had increased odds of severe psychological impact. None of the pre-existing health conditions and challenges were not significantly associated with severe psychological impact. The limited number of significant socio-demographic covariates suggests that severe psychological impact of the pandemic was a problem in the general population, and thus interventions should target the general population of camped refugees. Also, health education should not only focus on enhancing knowledge and promoting preventive measures but also on managing psychological distress.
A tale of four surveys:What have we learned about the variable sky?

Four tales concerning a set of photometric imaging surveys are spun. The reader is lead through a brief description of each survey and major results are presented. The four surveys are summarized in a few simple “rules”: 1) The fraction of point sources that are variable with respect to those that are found to be constant, increases as a power law as the photometric precision of the survey improves, and 2) This fact can be simply formulated as a power law function granting the user a predictive power. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Quantification of Accurate Composition and Total Abundance of Homologous Proteins by Conserved-Plus-Surrogate Peptide Approach: Quantification of UDP Glucuronosyltransferases in Human Tissues

Characterization of accurate compositions and total abundance of homologous drug-metabolizing enzymes, such as UDP glucuronosyltransferases (UGTs), is important for predicting the fractional contribution of individual isoforms involved in the metabolism of a drug for applications in physiologically based pharmacokinetic (PBPK) modeling. Conventional targeted proteomics utilizes surrogate peptides, which often results in high technical and interlaboratory variability due to peptide-specific digestion leading to data inconsistencies. To address this problem, we developed a novel conserved-plus-surrogate peptide (CPSP) approach for determining the accurate compositions and total or cumulative abundance of homologous UGTs in commercially available pooled human liver microsomes (HLM), human intestinal microsomes (HIM), human kidney microsomes (HKM), and human liver S9 (HLS9) fraction. The relative percent composition of UGT1A and UGT2B isoforms in the human liver was 35:5:36:11:13 for UGT1A1:1A3:1A4:1A6:1A9 and 20:32:22:21:5 for UGT2B4:2B7:2B10:2B15:2B17. The human kidney and intestine also showed unique compositions of UGT1As and UGT2Bs. The reproducibility of the approach was validated by assessing correlations of UGT compositions between HLM and HLS9 (R2> 0.91). The analysis of the conserved peptides also provided the abundance for individual UGT isoforms included in this investigation as well as the total abundance (pmol/mg protein) of UGT1As and UGT2Bs across tissues, i.e., 268 and 342 (HLM), 21 and 92 (HIM), and 138 and 99 (HKM), respectively. The CPSP approach could be used for applications in the in-vitro-to-in-vivo extrapolation of drug metabolism and PBPK modeling. SIGNIFICANCE STATEMENT We quantified the absolute compositions and total abundance of UDP glucuronosyltransferases (UGTs) in pooled human liver, intestine, and kidney microsomes using a novel conserved-plus-surrogate peptide (CPSP) approach. The CPSP approach addresses the surrogate peptide–specific variability in the determination of the absolute composition of UGTs. The data presented in this manuscript are applicable for the estimation of the fraction metabolized by individual UGTs towards better in vitro-to-in vivo extrapolation of UGT-mediated drug metabolism.
Genomes of Vibrio cholerae O1 Serotype Ogawa Associated with Current Cholera Activity in Pakistan

Here, this report presents two genomes of Vibrio cholerae O1 serotype Ogawa, recovered from cholera cases in Australia linked to travel to Pakistan in 2022. Their multidrug-resistant genotype represents the current activity of cholera within the seventh pandemic. One of the genome sequences was assembled using both short- and long-read sequences. ABSTRACT Here, this report presents two genomes of Vibrio cholerae O1 serotype Ogawa, recovered from cholera cases in Australia linked to travel to Pakistan in 2022. Their multidrug-resistant genotype represents the current activity of cholera within the seventh pandemic. One of the genome sequences was assembled using both short- and long-read sequences.
Roe linearization for the van der Waals gas

An extension of the Roe linearization method to nonideal gases is described and applied to the particular case of the van der Waals gas. A supplementary relation connecting the thermodynamic variables is introduced to decouple the evaluation of the intermediate velocity and total specific enthalpy from the determination of the intermediate density, needed in the Jacobian matrix of the linearization due to the general thermodynamic character of the gas. The density value is obtained by solving the supplementary equation, which involves the Roe average of velocity and enthalpy, and that in the case of the polytropic van der Waals gas is a third-order algebraic equation. Numerical results are shown including classical and nonclassical behaviour in one-dimensional shock tube problems.
Analysis and optimization of dynamically loaded porous metal sliding bearings under conditions of elastohydrodynamic lubrication

Purpose – This paper aims to develop a simulating model of a journal porous metal bearing under elastohydrodynamic conditions and combined (radial, friction and thermal) load distribution and to carry out structural optimization.Design/methodology/approach – The structure analysis is carried out for each kind of load separately and for the combined load distribution of the bearing, where a dynamically loaded porous metal bearing is simulated. This simulating model is developed by finite elements method using the structure analysis module of the CATIA V5 software. Further, a parameter optimization of a porous metal bearing is presented considering the elastic deformations of the bearing shell.Findings – It is revealed that the bearing, even at points with maximum displacements, could not reach the mounting clearance value during its operational life. Relatively small bearing dimensions produce very high values of eigenfrequency response (over 150 kHz) and common dynamic loads met in all sorts of sliding be.
Reducing Uncertainties of SNO-Estimated Intersatellite AMSU-A Brightness Temperature Biases for Surface-Sensitive Channels

Abstract In this study, a technique has been developed to improve collocation of two passive-microwave satellite instrument datasets at a simultaneous nadir overpass (SNO). The technique has been designed for the purpose of reducing uncertainties related to SNO-inferred intersatellite brightness temperature (Tb) biases, and it involves replacing the current “nearest-neighbor pixel matching” collocation technique with quality-controlled bilinear interpolation. Since the largest Tb bias estimation uncertainties of the SNO method are associated with highly variable earth scenes and window channels of microwave radiometers that have relatively large (∼50 km) separation between measurements, the authors have used Advanced Microwave Sounding Unit A (AMSU-A) data to develop the technique. It is found that using the new data collocation technique reduces SNO ensemble mean Tb bias confidence intervals in the SNO method, as applied to surface-sensitive channels of AMSU-A, by nearly 70% on average. This improvement .
Effects of nonlocality on second-harmonic generation in bulk semiconductors

J. L. Cabellos,1 Bernardo S. Mendoza,1,* M. A. Escobar,1 F. Nastos,2 and J. E. Sipe2 1Department of Photonics, Centro de Investigaciones en Optica, Leon, Guanajuato, Mexico 2Department of Physics and Institute for Optical Sciences, University of Toronto, 60 St. George Street, Toronto, Ontario, Canada M5S 1A7 Received 21 July 2009; revised manuscript received 5 October 2009; published 28 October 2009
Degeneracy of semi‐analytic solution for axially symmetric lens equations

We derive a simple semi-analytical approximation for lens equations with an arbitrary radially symmetric mass density ρ(r), when r/ξ0≪ 1 and ξ0 is the scalelength of the density profile. At the strong lensing regime, which is mostly constrained by the inner part of the mass density profile, we assume ρ∝rα. A dark matter (DM) haloes (GNFW model) are parametrized through a shape parameter α, a concentration parameter c1 and the total mass M. We apply our semi-analytical model to show how the solutions of the axially symmetric lens equations are degenerated in respect to the parameters α and c1. In the case of an asymmetric dual image lens system, similar effective degeneracy is produced when the geometry of the lens is relaxed. Because it is impossible to determine the exact location of the source image, a family of solutions is acquired when the mass of the lens object and location of the observed images are fixed. Our results indicate that the amount of degeneration is only weakly affected by the asymmetry in the lensing geometry set-up, e.g. the observational effective degeneracy is very close to the true physical degeneracy of the Einstein ring solutions. Basically with high-enough values for the concentration parameter, the degeneracy spawns the whole range for the shape parameter α=[−2.0, −1.0].
LSTM-Modeling of Emotion Recognition Using Peripheral Physiological Signals in Naturalistic Conversations

The automated recognition of human emotions plays an important role in developing machines with emotional intelligence. Major research efforts are dedicated to the development of emotion recognition methods. However, most of the affective computing models are based on images, audio, videos and brain signals. Literature lacks works that focus on utilizing only peripheral signals for emotion recognition (ER), which can be ideally implemented in daily life settings. Therefore, this paper present a framework for ER on the arousal and valence space, based on using multi-modal peripheral signals. The data used in this work were collected during a debate between two people using wearable devices. The emotions of the participants were rated by multiple raters and converted into classes in correspondence to the arousal and valence space. The use of a dynamic threshold for ratings conversion was investigated. An ER model is proposed that uses a Long Short-Term Memory (LSTM)-based architecture for classification. The model uses heart rate (HR), temperature (T), and electrodermal activity (EDA) signals as its inputs with emotional cues. Additionally, a post-processing prediction mechanism is introduced to enhance the recognition performance. The model is implemented to study the use of individual and different combinations of the peripheral signals, as well as utilizing annotations from different ratings. Additionally, it is employed for classification of valence and arousal in an independent and combined fashion, under subject dependent and independent scenarios. The experimental results have justified the efficient performance of the proposed framework, achieving classification accuracy $>$96% and $>$93% for the independent and combined classification scenarios, accordingly. The comparison of the achieved performance against the baseline methods shows the superiority of the proposed framework and the ability to recognize arousal-valance levels with high accuracy from peripheral signals, in real-life scenarios.
The Role of Convective Model Choice in Calculating the Climate Impact of Doubling CO2

Abstract The role of the parameterization of vertical convection in calculating the climate impact of doubling CO2 is assessed using both one-dimensional radiative-convective vertical models and in the latitude-dependent Hadley-baroclinic model of Lindzen and Farrell (1980). Both the conventional 6.5 K km−1 and the moist-adiabat adjustments are compared with a physically-based, cumulus-type parameterization. The model with parameterized cumulus convection has much less sensitivity than the 6.5 K km−1 adjustment model at low latitudes, a result that can be to some extent imitated by the moist-adiabat adjustment model. However, when averaged over the globe, the use of the cumulus-type parameterization in a climate model reduces sensitivity only ∼34% relative to models using 6.5 K km−1 convective adjustment. Interestingly, the use of the cumulus-type parameterization appears to eliminate the possibility of a runaway greenhouse.
Optimization of the Extraction Process and Antioxidant Activity of Polysaccharide Extracted from Centipeda minima

The purpose of this study is to optimize the extraction process and study antioxidant activity of Polysaccharide extracted from Centipeda minima. The Box‐Behnken design‐response surface methodology was adopted to optimize the extraction process of polysaccharides from Centipeda minima. We purified the crude polysaccharides from Centipeda minima, as well as determined the purity, monosaccharide composition, and molecular weight of the purified fraction. Fourier transform infrared spectrometer (FT‐IR) and scanning electron microscopy (SEM) were used to analyze the structural features of the polysaccharides. Further, we investigated the antioxidant activities of different fractions of polysaccharides. Consequently, the results showed that the optimum extraction conditions for polysaccharides were: a liquid‐solid ratio of 26 mL/g, extraction temperature of 85.5 °C, and extraction time of 2.4 h. Moreover, the yield of polysaccharides measured under these conditions was close to the predicted value. After purification, we obtained four components of Centipeda minima polysaccharides (CMP). The purity, monosaccharide composition, molecular weight, and structural characteristics of CMP were different, but with similar infrared absorption spectra. CMP exhibited a typical infrared absorption characteristic of a polysaccharide. Besides, CMP displayed good antioxidant activity, with potential to scavenge DPPH radical, hydroxyl radical, and superoxide radical. Therefore, this study provides a reference for future research on the structure and biological activity of CMP, and lays a theoretical foundation for food processing and medicinal development of CMP.
Orbits in asymmetric toroidal magnetic fields

Orbits in an asymmetric toroidal magnetic field are studied for the case in which the local variation of the field strength due to ripple is rapid compared with that due to toroidicity. In this case, to lowest order the poloidal variables are constant and particles move primarily in the toroidal direction. Invariants and averaged equations of motion for the locally passing and locally trapped particles are derived based on this approximation. The equations imply that transitions between the locally trapped and locally passing states occur. The probabilities for these transitions are calculated.
A case study of the features and holistic athlete impacts of a UK sports-friendly school: Student-athlete, coach and teacher perspectives

In order to understand the features of sport schools and their impacts on the holistic development of student-athletes, it is important to take into account the voice of multiple stakeholders central to the programmes (student-athletes, coaches, teachers). Through a case-study approach, using five focus groups, with 19 student-athletes, and six semi-structured interviews with three coaches and three dual coach and teachers, this study explored the perceived impacts of one sport-friendly school (pseudonym–“Salkeld High”) on holistic athlete development and the features that drove these impacts. Using a critical realist approach to thematic analysis, findings indicated a multitude of immediate, intermediate and long-term positive and negative impacts associated with academic/vocational (e.g., academic security vs. second/third choice university), athletic/physical (e.g., performance development vs. injuries), psychosocial (e.g., social skills vs. social scarifies) and psychological (e.g., sport confidence vs. performance pressure) development of “Salkeld High” student-athletes. Overall, “Salkeld High” was viewed as an integrated school environment for sport, academics, and boarding, where academic (e.g., extra-tutoring), athletic (e.g., high volume/frequency of training), and psychosocial/psychological (e.g., pastoral services) features are all in one location. The student-athletes tended to get a well-rounded, balanced holistic experience. However, the intensified and challenging nature of involvement did present some negative impacts that stakeholders should be aware of when designing, implementing, and evaluating sport-friendly school programmes. Furthermore, although “Salkeld High” was seen as an integrated environment within the school, it could do better at collaborating with wider sporting structures.
In silico analysis of the antidepressant fluoxetine and similar drugs as inhibitors of the human protein acid sphingomyelinase: a related SARS-CoV-2 inhibition pathway

Abstract Acid Sphingomyelinase (ASM) is a human phosphodiesterase that catalyzes the metabolism of sphingomyelin (SM) to ceramide and phosphocholine. ASM is involved in the plasma membrane cell repair and is associated with the lysosomal inner lipid membrane by nonbonding interactions. The disruption of those interaction would result in ASM release into the lysosomal lumen and consequent degradation of its structure. Furthermore, SARS-CoV-2 infection has been linked with ASM activation and with a ceramide domain formation in the outer leaflet of the plasma membrane that is thought to be crucial for the viral particles recognition by the host cells. In this study, we have explored in silico the behavior of fluoxetine and related drugs as potential inhibitors of ASM. Theoretically, these drugs would be able to overpass lysosomal membrane and reach the interactions that sustain ASM structure, breaking them and inhibiting the ASM. The analyses of docking data indicated that fluoxetine allocated mainly in the N-terminal saposin domain via nonbonding interactions, mostly of hydrophobic nature. Similar results were obtained for venlafaxine, citalopram, atomoxetine, nisoxetine and fluoxetine’s main metabolite norfluoxetine. In conclusion, it was observed that the saposin allocation may be a good indicative of the drugs inhibition mechanism, once this domain is responsible for the binding of ASM to lysosomal membrane and some of those drugs have previously been reported to inhibit the phosphodiesterase by releasing its structure in the lysosomal lumen. Our MD data also provides some insight about natural ligand C18 sphingomyelin conformations on saposin. Communicated by Ramaswamy H. Sarma