A parameter-free deep embedded clustering method for single-cell RNA-seq data.

Clustering analysis is widely used in single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) data to discover cell heterogeneity and cell states. While many clustering methods have been developed for scRNA-seq analysis, most of these methods require to provide the number of clusters. However, it is not easy to know the exact number of cell types in advance, and experienced determination is not always reliable. Here, we have developed ADClust, an automatic deep embedding clustering method for scRNA-seq data, which can accurately cluster cells without requiring a predefined number of clusters. Specifically, ADClust first obtains low-dimensional representation through pre-trained autoencoder and uses the representations to cluster cells into initial micro-clusters. The clusters are then compared in between by a statistical test, and similar micro-clusters are merged into larger clusters. According to the clustering, cell representations are updated so that each cell will be pulled toward centers of its assigned cluster and similar clusters, while cells are separated to keep distances between clusters. This is accomplished through jointly optimizing the carefully designed clustering and autoencoder loss functions. This merging process continues until convergence. ADClust was tested on 11 real scRNA-seq datasets and was shown to outperform existing methods in terms of both clustering performance and the accuracy on the number of the determined clusters. More importantly, our model provides high speed and scalability for large datasets.

Quick colorimetric determination of choline in milk and serum based on the use of MoS2 nanosheets as a highly active enzyme mimetic.

The authors have synthesized molybdenum disulfide nanosheets (MoS2 nanosheets) by using a bottom-up hydrothermal method. The nanosheets display strong catalytic (enzyme mimetic) activity in catalyzing the oxidation of peroxidase substrate of 3,3',5,5'-tetramethylbenzidine (TMB) in presence of H2O2 to produce a blue product. The peroxidase mimicking properties of MoS2 nanosheets depend on temperature, H2O2 concentration and pH value. A choline assay was worked out where choline was oxidized by choline oxidase in presence of oxygen to produce H2O2 which is colorimetrically detected, best at 652 nm. The method works in the 1 to 180 µM choline concentration range with a 0.4 µM detection limit. Color changes may also be detected visually. The assay is simple, highly sensitive, selective and rapid. It was applied in the determination of choline in (spiked) milk and serum. Graphical abstract Basic principle of intrinsic peroxidase-like activity of MoS2 nanosheets, applied to design a rapid and selective colorimetric assay for choline detection based on the tetramethylbenzidine (TMB) color reaction.

What is a "unimodal" cell population? Using statistical tests as criteria for unimodality in automated gating and quality control.

Many automated gating algorithms for flow cytometry data are based on the concept of unimodal cell populations. However, in this article, we show that criteria previously used to make decisions on unimodality cannot adequately distinguish unimodal from bimodal densities. We show that dip and bandwidth tests for unimodality, taken from the statistics literature, can do this with consistent and low error rates. These tests also have the possibility to adjust the significance level to handle the trade-off between failing to detect a second mode and seeing a second mode when there is none. The differences between the dip and bandwidth tests are elucidated using real data from the FlowCAP I challenge, also guidelines for flow cytometry data preprocessing are given. © 2017 International Society for Advancement of Cytometry.

The Bruce effect revisited: is pregnancy termination in female rodents an adaptation to ensure breeding success after male turnover in low densities?

Pregnancy termination after encountering a strange male, the Bruce effect, is regarded as a counterstrategy of female mammals towards anticipated infanticide. While confirmed in caged rodent pairs, no verification for the Bruce effect existed from experimental field populations of small rodents. We suggest that the effect may be adaptive for breeding rodent females only under specific conditions related to populations with cyclically fluctuating densities. We investigated the occurrence of delay in birth date after experimental turnover of the breeding male under different population composition in bank voles (Myodes glareolus) in large outdoor enclosures: one-male-multiple-females (n = 6 populations/18 females), multiple-males-multiple-females (n = 15/45), and single-male-single-female (MF treatment, n = 74/74). Most delays were observed in the MF treatment after turnover. Parallel we showed in a laboratory experiment (n = 205 females) that overwintered and primiparous females, the most abundant cohort during population lows in the increase phase of cyclic rodent populations, were more likely to delay births after turnover of the male than year-born and multiparous females. Taken together, our results suggest that the Bruce effect may be an adaptive breeding strategy for rodent females in cyclic populations specifically at low densities in the increase phase, when isolated, overwintered animals associate in MF pairs. During population lows infanticide risk and inbreeding risk may then be higher than during population highs, while also the fitness value of a litter in an increasing population is higher. Therefore, the Bruce effect may be adaptive for females during annual population lows in the increase phases, even at the costs of delaying reproduction.

A newly developed kit for the measurement of urinary liver-type fatty acid-binding protein as a biomarker for acute kidney injury in patients with critical care.

In recent years, it has been reported that the urinary level of Liver-type fatty acid-binding protein (L-FABP) serves as a useful biomarker for diagnosing acute kidney injury (AKI) or sepsis complicated by AKI. However, because the urinary level of L-FABP is currently measured by enzyme-linked immunosorbent assay (ELISA), several days may elapse before the results of the measurement become available. We have newly developed a simplified kit, the Dip-test, for measuring the urinary level of L-FABP. The Dip-test was measured at 80 measurement points (22 points in noninfectious disease, 13 points in SIRS, 20 points in infectious disease, and 25 points in sepsis) in 20 patients. The urinary L-FABP levels as determined by ELISA in relation to the results of the Dip-test were as follows: 10.10 ± 12.85 ng/ml in patients with a negative Dip-test ([-] group), 41.93 ± 50.51 ng/ml in patients with a ± test ([±] group), 70.36 ± 73.70 ng/ml in patients with a positive test ([+] group), 1048.96 ± 2117.68 ng/ml in patients with a 2 + test ([2+] group), and 23,571.55 ± 21,737.45 ng/ml in patients with a 3 + test ([3+] group). The following tendency was noted: the stronger the positive Dip-test reaction, the higher the urinary L-FABP level. Multigroup comparison revealed a significant differences in the urinary L-FABP levels between the Dip-test (-) group and each of the other groups. In this study, the usefulness of the Dip-test, our newly developed simplified kit for measuring the urinary L-FABP level, is suggested.

ATR-FTIR Spectroscopy with Chemometrics for Analysis of Saliva Samples Obtained in a Lung-Cancer-Screening Programme: Application of Swabs as a Paradigm for High Throughput in a Clinical Setting.

There is an increasing need for inexpensive and rapid screening tests in point-of-care clinical oncology settings. Herein, we develop a swab "dip" test in saliva obtained from consenting patients participating in a lung-cancer-screening programme being undertaken in North West England. In a pilot study, a total of 211 saliva samples (n = 170 benign, 41 designated cancer-positive) were randomly taken during the course of this prospective lung-cancer-screening programme. The samples (sterile Copan blue rayon swabs dipped in saliva) were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. An exploratory analysis using principal component analysis (PCA,) with or without linear discriminant analysis (LDA), was then undertaken. Three pairwise comparisons were undertaken including: (1) benign vs. cancer following swab analysis; (2) benign vs. cancer following swab analysis with the subtraction of dry swab spectra; and (3) benign vs. cancer following swab analysis with the subtraction of wet swab spectra. Consistent and remarkably similar patterns of clustering for the benign control vs. cancer categories, irrespective of whether the swab plus saliva sample was analysed or whether there was a subtraction of wet or dry swab spectra, was observed. In each case, MANOVA demonstrated that this segregation of categories is highly significant. A k-NN (using three nearest neighbours) machine-learning algorithm also showed that the specificity (90%) and sensitivity (75%) are consistent for each pairwise comparison. In detailed analyses, the swab as a substrate did not alter the level of spectral discrimination between benign control vs. cancer saliva samples. These results demonstrate a novel swab "dip" test using saliva as a biofluid that is highly applicable to be rolled out into a larger lung-cancer-screening programme.

Point-of-Care Disease Screening in Primary Care Using Saliva: A Biospectroscopy Approach for Lung Cancer and Prostate Cancer.

Saliva is a largely unexplored liquid biopsy that can be readily obtained noninvasively. Not dissimilar to blood plasma or serum, it contains a vast array of bioconstituents that may be associated with the absence or presence of a disease condition. Given its ease of access, the use of saliva is potentially ideal in a point-of-care screening or diagnostic test. Herein, we developed a swab "dip" test in saliva obtained from consenting patients participating in a lung cancer-screening programme being undertaken in north-west England. A total of 998 saliva samples (31 designated as lung-cancer positive and 17 as prostate-cancer positive) were taken in the order in which they entered the clinic (i.e., there was no selection of participants) during the course of this prospective screening programme. Samples (sterile Copan blue rayon swabs dipped in saliva) were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. In addition to unsupervised classification on resultant infrared (IR) spectra using principal component analysis (PCA), a range of feature selection/extraction algorithms were tested. Following preprocessing, the data were split between training (70% of samples, 22 lung-cancer positive versus 664 other) and test (30% of samples, 9 lung-cancer positive versus 284 other) sets. The training set was used for model construction and the test set was used for validation. The best model was the PCA-quadratic discriminant analysis (QDA) algorithm. This PCA-QDA model was built using 8 PCs (90.4% of explained variance) and resulted in 93% accuracy for training and 91% for testing, with clinical sensitivity at 100% and specificity at 91%. Additionally, for prostate cancer patients amongst the male cohort (n = 585), following preprocessing, the data were split between training (70% of samples, 12 prostate-cancer positive versus 399 other) and test (30% of samples, 5 prostate-cancer positive versus 171 other) sets. A PCA-QDA model, again the best model, was built using 5 PCs (84.2% of explained variance) and resulted in 97% accuracy for training and 93% for testing, with clinical sensitivity at 100% and specificity at 92%. These results point to a powerful new approach towards the capability to screen large cohorts of individuals in primary care settings for underlying malignant disease.

Baseline comparative analysis and review of election forensics: Application to Ghana's 2012 and 2020 presidential elections.

Many allegations have been levelled against the electoral process of many countries across the world by most opposition leaders, especially when they lose a presidential election e.g. Ghana in 2012 and 2020. Therefore, the need to apply election forensic techniques to the certified election results data of valid votes count to statistically verify if some suspected or possible anomalies and irregularities exist in the voting pattern. This paper seeks to provide a comprehensive review of election forensics techniques and make a comparative analysis of Benford's Second-order test of conformity (using the first two digits) and Hartigans' dip test of unimodality to examine the existence of possible anomalies and irregularities in the 2012 and 2020 presidential elections held in Ghana. The findings of the two tests suggest that the electoral process produced possible anomalous data in the 2012 presidential election results (with an overall 16.67% suspected anomalies), whilst possible non-anomalous data was produced in the 2020 presidential election results (with an overall 0% suspected anomaly) of valid votes count. Therefore, the study recommends that for better statistical data analysis on election anomaly detection, Benford's test of conformity and Hartigans' dip test of unimodality should serve as baseline tests (initial screening tools), highlighting areas that may require further investigation or more rigorous analysis and progressively dig deeper into the application of finite mixture fraud models and machine learning techniques. In spite of the promising results Benford's Law, dip test, machine learning algorithms, and network analysis have produced in detecting irregularities in election data, real-world applications remain challenging, particularly when dealing with complex and evolving forms of fraud. Therefore, there is the need for continuous research and innovation to improve the accuracy and effectiveness of these methods and promote transparency and accountability in democratic societies.

Athletes with a clinical rating of good and poor lumbopelvic stability have different kinematic variables during single leg squat and dip test.

OBJECTIVES: To examine the kinematics of athletes with good and poor lumbopelvic stability (LPS) based on clinical rating criteria of single leg squat (SLS) and dip test (DT) The aim was to establish if good and poor LPS categorization is supported by differences in kinematic variables. METHODS: Sixty-two recreational athletes had their LPS categorized using clinical rating criteria for SLS and DT as good, poor or neither good nor poor. Kinematic measures were examined in those with good (N = 8) or poor (N = 14) LPS and results compared to the rating criteria. RESULTS: Multiple clinical rating criteria for good and poor LPS groups were distinguished by kinematic measures. Smoothness of motion for both SLS and DT distinguished good and poor LPS. Minimal (good) or discernible movement (poor) out of the starting plane was confirmed with kinematic measures. For SLS these movements were: weight-bearing hip adduction, non-weightbearing hip abduction, pelvic rotation, and trunk sideflexion, and for DT: weightbearing hip adduction, non-weightbearing hip abduction and pelvic obliquity. Additionally, hip dissociation (SLS) distinguished good and poor LPS. CONCLUSION: Athletes with good and poor LPS have different kinematic measures in single leg squat and dip test. Multiple clinical rating criteria of LPS that distinguish good and poor stability were confirmed by kinematic measures.

'Statistical Irreproducibility' Does Not Improve with Larger Sample Size: How to Quantify and Address Disease Data Multimodality in Human and Animal Research.

Poor study reproducibility is a concern in translational research. As a solution, it is recommended to increase sample size (N), i.e., add more subjects to experiments. The goal of this study was to examine/visualize data multimodality (data with >1 data peak/mode) as cause of study irreproducibility. To emulate the repetition of studies and random sampling of study subjects, we first used various simulation methods of random number generation based on preclinical published disease outcome data from human gut microbiota-transplantation rodent studies (e.g., intestinal inflammation and univariate/continuous). We first used unimodal distributions (one-mode, Gaussian, and binomial) to generate random numbers. We showed that increasing N does not reproducibly identify statistical differences when group comparisons are repeatedly simulated. We then used multimodal distributions (>1-modes and Markov chain Monte Carlo methods of random sampling) to simulate similar multimodal datasets A and B (t-test-p = 0.95; N = 100,000), and confirmed that increasing N does not improve the 'reproducibility of statistical results or direction of the effects'. Data visualization with violin plots of categorical random data simulations with five-integer categories/five-groups illustrated how multimodality leads to irreproducibility. Re-analysis of data from a human clinical trial that used maltodextrin as dietary placebo illustrated multimodal responses between human groups, and after placebo consumption. In conclusion, increasing N does not necessarily ensure reproducible statistical findings across repeated simulations due to randomness and multimodality. Herein, we clarify how to quantify, visualize and address disease data multimodality in research. Data visualization could facilitate study designs focused on disease subtypes/modes to help understand person-person differences and personalized medicine.

Comparing core stability and traditional trunk exercise on chronic low back pain patients using three functional lumbopelvic stability tests.

It is a matter of controversy whether core stability exercise is preferred to other types of exercise for chronic low back pain. Lumbopelvic stability is an important element in low back pain. No study was found using lumbopelvic stability tests in comparing core stability and other exercises. The single leg squat, dip test, and runner pose test appear to be suitable as tests for lumbopelvic stability. The aim of this study was to compare "core stability" and "traditional trunk exercise" using these tests and also the Oswestry disability questionnaire and pain intensity. Twenty-nine non-specific chronic low back pain subjects were alternately allocated in one of the two exercise groups. For both groups, a 16-sessions exercise program was provided. Before and after training: (1) video was recorded while subjects performed the tests; (2) Oswestry disability questionnaire was completed; and (3) pain intensity was measured by visual analogue scale. The test videos were scored by three physiotherapists. Statistical analysis revealed a significant improvement in stability test scores (p = 0.020 and p = 0.041) and reduction in disability (p < 0.001) and pain (p < 0.001) within each group. No significant difference was seen between two groups in the three outcomes p = 0.41, p = 0.14, and p = 0.72. Insignificant differences between the two groups may indicate either non-specificity of CSE to increase lumbopelvic stability or equal effectiveness of TTE and CSE on improving LPS. The non-significant differences may also be attributable to the lack of sensitivity of our tests to assess stability change in two groups after training given the relatively small sample size.

The use of haplotype-specific transcripts improves sample annotation consistency.

BACKGROUND: Exact sample annotation in expression microarray datasets is essential for any type of pharmacogenomics research. RESULTS: Candidate markers were explored through the application of Hartigans' dip test statistics to a publically available human whole genome microarray dataset. The marker performance was tested on 188 serial samples from 53 donors and of variable tissue origin from five public microarray datasets. A qualified transcript marker panel consisting of three probe sets for human leukocyte antigens HLA-DQA1 (2 probe sets) and HLA-DRB4 identified sample donor identifier inconsistencies in six of the 188 test samples. About 3% of the test samples require root-cause analysis due to unresolvable inaccuracies. CONCLUSIONS: The transcript marker panel consisting of HLA-DQA1 and HLA-DRB4 represents a robust, tissue-independent composite marker to assist control donor annotation concordance at the transcript level. Allele-selectivity of HLA genes renders them good candidates for "fingerprinting" with donor specific expression pattern.