On the origin and evolution of RNA editing in metazoans

Extensive adenosine-to-inosine (A-to-I) editing of nuclear-transcribed mRNAs is the hallmark of metazoan transcriptional regulation. Here, by profiling the RNA editomes of 22 species that cover major groups of Holozoa, we provide substantial evidence supporting A-to-I mRNA editing as a regulatory innovation originating in the last common ancestor of extant metazoans. This ancient biochemistry process is preserved in most extant metazoan phyla and primarily targets endogenous double-stranded RNA (dsRNA) formed by evolutionarily young repeats. We also find intermolecular pairing of sense-antisense transcripts as an important mechanism for forming dsRNA substrates for A-to-I editing in some but not all lineages. Likewise, recoding editing is rarely shared across lineages but preferentially targets genes involved in neural and cytoskeleton systems in bilaterians. We conclude that metazoan A-to-I editing might first emerge as a safeguard mechanism against repeat-derived dsRNA and was later co-opted into diverse biological processes due to its mutagenic nature

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Marginal Inference in Continuous Markov Random Fields Using Mixtures

Exact marginal inference in continuous graphical models is computationally challenging outside of a few special cases. Existing work on approximate inference has focused on approximately computing the messages as part of the loopy belief propagation algorithm either via sampling methods or moment matching relaxations. In this work, we present an alternative family of approximations that, instead of approximating the messages, approximates the beliefs in the continuous Bethe free energy using mixture distributions. We show that these types of approximations can be combined with numerical quadrature to yield algorithms with both theoretical guarantees on the quality of the approximation and significantly better practical performance in a variety of applications that are challenging for current state-of-the-art methods

Selecting an Appropriate Animal Model of Depression

Depression has become one of the most severe psychiatric disorders and endangers the health of living beings all over the world. In order to explore the molecular mechanism that underlies depression, different kinds of animal models of depression are used in laboratory experiments. However, a credible and reasonable animal model that is capable of imitating the pathologic mechanism of depression in mankind has yet to be found, resulting in a barrier to further investigation of depression. Nevertheless, it is possible to explain the pathologic mechanism of depression to a great extent by a rational modeling method and behavioral testing. This review aims to provide a reference for researchers by comparing the advantages and disadvantages of some common animal depression models

Higher serum β2-microglobulin is a predictive biomarker for cognitive impairment in spinal cord injury

Objective Recent studies have suggested that high levels of β2-microglobulin are linked to cognitive deterioration; however, it is unclear how this connects to spinal cord injury (SCI). This study sought to determine whether there was any association between cognitive decline and serum β2-microglobulin levels in patients with SCI. Methods A total of 96 patients with SCI and 56 healthy volunteers were enrolled as study participants. At the time of enrollment, specific baseline data including age, gender, triglycerides (TG), low-density lipoprotein (LDL), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), smoking, and alcohol use were recorded. Each participant was assessed by a qualified physician using the Montreal cognitive assessment (MoCA) scale. Serum β2-microglobulin levels were measured using an enzyme-linked immunosorbent assay (ELISA) reagent for β2-microglobulin. Results A total of 152 participants were enrolled, with 56 in the control group and 96 in the SCI group. There were no significant baseline data differences between the two groups (p > 0.05). The control group had a MoCA score of 27.4 ± 1.1 and the SCI group had a score of 24.3 ± 1.5, with the difference being significant (p < 0.05). The serum ELISA results revealed that the levels of β2-microglobulin in the SCI group were considerably higher (p < 0.05) than those in the control group (2.08 ± 0.17 g/mL compared to 1.57 ± 0.11 g/mL). The serum β2-microglobulin level was used to categorize the patients with SCI into four groups. As serum β2-microglobulin levels increased, the MoCA score reduced (p < 0.05). After adjustment of baseline data, further regression analysis showed that serum β2-microglobulin level remained an independent risk factor for post-SCI cognitive impairment. Conclusions Patients with SCI had higher serum levels of β2-microglobulin, which may be a biomarker for cognitive decline following SCI

GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception

Existing algorithms for enhancing low-light images predominantly focus on the low-light region, which leads to over-enhancement of the glare region, and the high complexity of the algorithm makes it difficult to apply it to embedded devices. In this paper, a GS-AGC algorithm based on regional luminance perception is proposed. The indirect perception of the human eye’s luminance vision was taken into account. All similar luminance pixels that satisfied the luminance region were extracted, and adaptive adjustment processing was performed for the different luminance regions of low-light images. The proposed method was evaluated experimentally on real images, and objective evidence was provided to show that its processing effect surpasses that of other comparable methods. Furthermore, the potential practical value of GS-AGC was highlighted through its effective application in road pedestrian detection and face detection. The algorithm in this paper not only effectively suppressed glare but also achieved the effect of overall image quality enhancement. It can be easily combined with the embedded hardware FPGA for acceleration to improve real-time image processing

Cell counting for in vivo flow cytometry signals with baseline drift

In biomedical research fields, the in vivo flow cytometry (IVFC) is a widely used technology which is able to monitor target cells dynamically in living animals. Although the setup of IVFC system has been well established, baseline drift is still a challenge in the process of quantifying circulating cells. Previous methods, i.e., the dynamic peak picking method, counted cells by setting a static threshold without considering the baseline drift, leading to an inaccurate cell quantification. Here, we developed a method of cell counting for IVFC data with baseline drift by interpolation fitting, automatic segmentation and wavelet-based denoising. We demonstrated its performance for IVFC signals with three types of representative baseline drift. Compared with non-baseline-correction methods, this method showed a higher sensitivity and specificity, as well as a better result in the Pearson’s correlation coefficient and the mean-squared error (MSE)

Risk Factors for Recurrent Colorectal Polyps

The recurrence of colorectal polyps is caused by various factors and leads to the carcinogenesis of colorectal cancer, which ranks third in incidence and fourth in mortality among cancers worldwide. The potential risk factors for colorectal polyp recurrence have been demonstrated in multiple trials. However, an article that pools and summarizes the various results is needed. This review enumerates and analyzes some risk factors in terms of patient characteristics, procedural operations, polyp characteristics, and dietary aspects to propose some effective prophylactic measures. This review aimed to provide a reference for clinical application and guide patients to prevent colorectal polyp recurrence in a more effective manner