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The state of knowledge with regard to static and cyclic liquefaction has progressed tremendously since the mid-1960s. In fact, it is generally accepted, based on the steady-state concepts of Casagrande, Castro, and Poulos that the end or steady-state condition of a liquefied loose sand is the same whether due to monotonic, cyclic, or dynamic undrained loading. Even so, a straightforward and fundamental explanation of the initiation and end-state liquefaction behavior is still needed. Toward that end, an effective stress approach that allows the assessment of the whole undrained stress-strain curve (including the peak and residual strength values) and the undrained effective stress path of a monotonic test from drained triaxial tests is presented. Results from three series of tests are shown to demonstrate the validity of the method. This method makes it possible for a geotechnical firm with the capability of performing the traditional drained test with volume change measurements to participate in static liquefaction and residual strength assessment. More importantly, it provides the conditions and logic for the development of complete versus limited liquefaction (and even dilative) response.

Neutron time-of-flight spectroscopy has been employed to study the crystal-field interaction in the pyrochlore titanate Ho[sub 2]Ti[sub 2]O[sub 7]. The crystal-field parameters and corresponding energy-level scheme have been determined from a profile fit to the observed neutron spectra. The ground state is a well separated E[sub g] doublet with a strong Ising-like anisotropy, which can give rise to frustration in the pyrochlore lattice. Using the crystal-field parameters determined for the Ho compound as an estimate of the crystal-field potential in other pyrochlore magnets, we also find the Ising type behavior for Dy. In contrast, the almost planar anisotropy found for Er and Yb prevents frustration, because of the continuous range of possible spin orientations in this case. © 2000 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Traffic speed deflection devices (TSDDs) are actively used around the world in network level pavement management applications because of their relative merits over traditional deflection devices (e.g., FWD, Benkelman beam, etc.) in evaluating the pavement structural condition. Past researchers utilized several deflection indices based on FWD surface deflections to estimate critical responses such as load induced fatigue strains at the bottom of the asphalt concrete (AC) layer. However, the differences in loading configuration and viscoelastic material behavior under moving nature of the load of TSDDs may limit the use of the same indices from the FWD deflections to predict those critical responses. The two TSDDs: traffic speed deflectometer (TSD) and rolling weight deflectometer (RWD) were evaluated as a part of a FHWA study at the MnROAD facility in September 2013. Load-induced pavement response data were collected during field tests along with TSDD surface deflection measurements. 3D-Move program was chosen to undertake the analytical modeling since it can model moving load and accommodate the rate-dependent material properties (viscoelastic). A number of indices that are best related to fatigue strain were identified from 48 individual indices. The study verified the capability of TSDD surface deflection measurements to predict the critical fatigue strains at the network level. However, the prediction accuracy can be improved if response lag between the maximum load and the deflection is accounted in the TSD deflection computation algorithm. Evaluation of the RWD device with two surface deflection measurements reveals that both RWD sensors need to be positioned in front of the rear axle to have a better related index to fatigue strains. [ABSTRACT FROM AUTHOR]

Structural adequacy, which is an important aspect of pavement management activities, is typically evaluated using surface pavement deflections measured by the falling weight deflectometer (FWD). Technology that can collect continuous deflection data at traffic speeds can potentially supplant single-point location deflection measurements provided by the FWD for network-level pavement management system (PMS) applications. Previous studies have evaluated the traffic speed deflection devices (TSDD) performance by comparing their results with the FWD measurements. Although appropriate correlations and indices to compensate for the differences in load have been made, a direct comparison under the same loading conditions has not been reported in the literature. The purpose of this paper was to present the results of a field study to evaluate TSDDs by comparing their pavement deflection parameters with those from embedded sensors under actual loads. Two TSDDs were used in this study: the Greenwood traffic speed deflectometer (TSD), and the Applied Research Associates, Inc. (ARA) rolling wheel deflectometer (RWD). The lessons learned from the study are emphasized for further consideration toward developing a calibration methodology for value-added future analyses. [ABSTRACT FROM AUTHOR]

Authors Branko Ladanyi, Ron C.K. Wong and Marolo C. Alfaro have made very constructive comments and contributions. It should be pointed out that Ladanyi's effective stress path curve plots clearly show that the peak undrained resistance sigmaD = sigma1 - sigma3 occurs at a low value of mobilized friction. From the numbers shown, the horizontal axis is sigma'3. This leads to certain conclusions necessitating the consideration of consolidation rebound data. Ladanyi must clarify this point.

This paper presents the results of a testing program designed to determine the dynamic axial stiffness of some typical underground pipe joints, both restrained ,and unrestrained. Pipelines have been shown to be vulnerable to seismic motions and ground movement, causing joint damage and system failure. Many of these failures have occurred at unrestrained bell-and-spigot type pipe joints due to pull-out and intrusion of the spigot end into the bell end. The results of this research program are joint stiffness values and force capacities that can be used in the analysis and evaluation of failure potential of underground piping systems. [ABSTRACT FROM AUTHOR]

MRI examination of 41 patients with clinical definite multiple sclerosis showed white matter lesions of high proton T2 signal consistent with demyelination in 76% and CSF abnormalities present in 76%. Of patients with CSF abnormalities, 26% had normal MRI scans; conversely 26% of patients with MRI abnormalities had negative CSF studies. Thus a significant number of multiple sclerosis patients had negative results on either MRI or CSF examination, while only 5% had normal results on both tests. [ABSTRACT FROM AUTHOR]

Abstract Background While most multiple sequence alignment programs expect that all or most of their input is known to be homologous, and penalise insertions and deletions, this is not a reasonable assumption for non-coding DNA, which is much less strongly conserved than protein-coding genes. Arguing that the goal of sequence alignment should be the detection of homology and not similarity, we incorporate an evolutionary model into a previously published multiple sequence alignment program for non-coding DNA, Sigma, as a sensitive likelihood-based way to assess the significance of alignments. Version 1 of Sigma was successful in eliminating spurious alignments but exhibited relatively poor sensitivity on synthetic data. Sigma 1 used a p-value (the probability under the "null hypothesis" of non-homology) to assess the significance of alignments, and, optionally, a background model that captured short-range genomic correlations. Sigma version 2, described here, retains these features, but calculates the p-value using a sophisticated evolutionary model that we describe here, and also allows for a transition matrix for different substitution rates from and to different nucleotides. Our evolutionary model takes separate account of mutation and fixation, and can be extended to allow for locally differing functional constraints on sequence. Results We demonstrate that, on real and synthetic data, Sigma-2 significantly outperforms other programs in specificity to genuine homology (that is, it minimises alignment of spuriously similar regions that do not have a common ancestry) while it is now as sensitive as the best current programs. Conclusions Comparing these results with an extrapolation of the best results from other available programs, we suggest that conservation rates in intergenic DNA are often significantly over-estimated. It is increasingly important to align non-coding DNA correctly, in regulatory genomics and in the context of whole-genome alignment, and Sigma-2 is an important step in that direction.

Abstract Background Existing tools for multiple-sequence alignment focus on aligning protein sequence or protein-coding DNA sequence, and are often based on extensions to Needleman-Wunsch-like pairwise alignment methods. We introduce a new tool, Sigma, with a new algorithm and scoring scheme designed specifically for non-coding DNA sequence. This problem acquires importance with the increasing number of published sequences of closely-related species. In particular, studies of gene regulation seek to take advantage of comparative genomics, and recent algorithms for finding regulatory sites in phylogenetically-related intergenic sequence require alignment as a preprocessing step. Much can also be learned about evolution from intergenic DNA, which tends to evolve faster than coding DNA. Sigma uses a strategy of seeking the best possible gapless local alignments (a strategy earlier used by DiAlign), at each step making the best possible alignment consistent with existing alignments, and scores the significance of the alignment based on the lengths of the aligned fragments and a background model which may be supplied or estimated from an auxiliary file of intergenic DNA. Results Comparative tests of sigma with five earlier algorithms on synthetic data generated to mimic real data show excellent performance, with Sigma balancing high "sensitivity" (more bases aligned) with effective filtering of "incorrect" alignments. With real data, while "correctness" can't be directly quantified for the alignment, running the PhyloGibbs motif finder on pre-aligned sequence suggests that Sigma's alignments are superior. Conclusion By taking into account the peculiarities of non-coding DNA, Sigma fills a gap in the toolbox of bioinformatics.

Objectives: Monitoring of fetal growth and estimation of birth weight is of clinical importance. During pregnancy, ultrasound fetal biometry values including femur length, head circumference, abdominal circumference, biparietal diameter are measured and used to place fetuses on "growth charts". There is no simple growth-model-based, predictive formula in use for fetal biometry. Estimation of fetal weight at birth currently depends on ultrasound data taken a short time before birth., Study Design: Our cohort ("Seethapathy cohort") consists of ultrasound biometry measurements and other data for 774 pregnant women in Chennai, India, 2015-2017. We use the Gompertz model, a standard model for constrained growth, with just three intuitive parameters, to model the growth of fetal biometry, and a machine learning (ML) model trained on these parameters to predict birth weight (BW)., Results: The Gompertz model convincingly fits the growth of fetal biometry values. Two Gompertz parameters- t 0 (inflection time) and c (rate of decrease of growth rate)-seem universal to all fetuses, while the third, A , is an overall scale specific to each fetus, capturing individual variation. On the Seethapathy cohort we can infer A for each fetus from ultrasound data available by the 24 or 35 weeks. Our ML model predicts birth weight with < 8 % error, outperforming published methods that have access to late-term ultrasound data. The same model gives an 8.4 % error in BW prediction on an independent validation cohort of 365 women., Conclusions: The Gompertz model fits fetal biometry growth and enables birth weight estimation without need of late-term ultrasounds. Aside from its clinical predictive value, we suggest its use for future growth standards, with almost all variation described by a single scale parameter A ., Competing Interests: The authors (Chandrani Kumari, Gautam Menon, Leelavati Narlikar, Uma Ram, Rahul Siddharthan) declare that they have no financial and personal relationships with other people or organizations that could inappropriately influence (bias) their work., (© 2024 The Authors.)

We provide new algorithms for two tasks relating to heterogeneous tabular datasets: clustering, and synthetic data generation. Tabular datasets typically consist of heterogeneous data types (numerical, ordinal, categorical) in columns, but may also have hidden cluster structure in their rows: for example, they may be drawn from heterogeneous (geographical, socioeconomic, methodological) sources, such that the outcome variable they describe (such as the presence of a disease) may depend not only on the other variables but on the cluster context. Moreover, sharing of biomedical data is often hindered by patient confidentiality laws, and there is current interest in algorithms to generate synthetic tabular data from real data, for example via deep learning. We demonstrate a novel EM-based clustering algorithm, MMM ("Madras Mixture Model"), that outperforms standard algorithms in determining clusters in synthetic heterogeneous data, and recovers structure in real data. Based on this, we demonstrate a synthetic tabular data generation algorithm, MMMsynth, that pre-clusters the input data, and generates cluster-wise synthetic data assuming cluster-specific data distributions for the input columns. We benchmark this algorithm by testing the performance of standard ML algorithms when they are trained on synthetic data and tested on real published datasets. Our synthetic data generation algorithm outperforms other literature tabular-data generators, and approaches the performance of training purely with real data., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kumari, Siddharthan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Candida auris poses threats to the global medical community due to its multidrug resistance, ability to cause nosocomial outbreaks and resistance to common sterilization agents. Different variants that emerged at different geographical zones were classified as clades. Clade-typing becomes necessary to track its spread, possible emergence of new clades, and to predict the properties that exhibit a clade bias. We previously reported a colony-Polymerase Chain Reaction-based, clade-identification method employing whole genome alignments and identification of clade-specific sequences of four major geographical clades. Here, we expand the panel by identifying clade 5 which was later isolated in Iran, using specific primers designed through in silico analyses., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology.)

Transcription factor binding sites (TFBS), like other DNA sequence, evolve via mutation and selection relating to their function. Models of nucleotide evolution describe DNA evolution via single-nucleotide mutation. A stationary vector of such a model is the long-term distribution of nucleotides, unchanging under the model. Neutrally evolving sites may have uniform stationary vectors, but one expects that sites within a TFBS instead have stationary vectors reflective of the fitness of various nucleotides at those positions. We introduce 'position-specific stationary vectors' (PSSVs), the collection of stationary vectors at each site in a TFBS locus, analogous to the position weight matrix (PWM) commonly used to describe TFBS. We infer PSSVs for human TFs using two evolutionary models (Felsenstein 1981 and Hasegawa-Kishino-Yano 1985). We find that PSSVs reflect the nucleotide distribution from PWMs, but with reduced specificity. We infer ancestral nucleotide distributions at individual positions and calculate 'conditional PSSVs' conditioned on specific choices of majority ancestral nucleotide. We find that certain ancestral nucleotides exert a strong evolutionary pressure on neighbouring sequence while others have a negligible effect. Finally, we present a fast likelihood calculation for the F81 model on moderate-sized trees that makes this approach feasible for large-scale studies along these lines., Competing Interests: We declare we have no competing interests., (© 2024 The Authors.)

Early onset of type 2 diabetes and cardiovascular disease are common complications for women diagnosed with gestational diabetes. Prediabetes refers to a condition in which blood glucose levels are higher than normal, but not yet high enough to be diagnosed as type 2 diabetes. Currently, there is no accurate way of knowing which women with gestational diabetes are likely to develop postpartum prediabetes. This study aims to predict the risk of postpartum prediabetes in women diagnosed with gestational diabetes. Our sparse logistic regression approach selects only two variables - antenatal fasting glucose at OGTT and HbA1c soon after the diagnosis of GDM - as relevant, but gives an area under the receiver operating characteristic curve of 0.72, outperforming all other methods. We envision this to be a practical solution, which coupled with a targeted follow-up of high-risk women, could yield better cardiometabolic outcomes in women with a history of GDM., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)