Transcriptional-Readthrough RNAs Reflect the Phenomenon of “A Gene Contains Gene(s)” or “Gene(s) within a Gene” in the Human Genome, and Thus Are Not Chimeric RNAs

Tens of thousands of chimeric RNAs, i.e., RNAs with sequences of two genes, have been identified in human cells. Most of them are formed by two neighboring genes on the same chromosome and are considered to be derived via transcriptional readthrough, but a true readthrough event still awaits more evidence and trans-splicing that joins two transcripts together remains as a possible mechanism. We regard those genomic loci that are transcriptionally read through as unannotated genes, because their transcriptional and posttranscriptional regulations are the same as those of already-annotated genes, including fusion genes formed due to genetic alterations. Therefore, readthrough RNAs and fusion-gene-derived RNAs are not chimeras. Only those two-gene RNAs formed at the RNA level, likely via trans-splicing, without corresponding genes as genomic parents, should be regarded as authentic chimeric RNAs. However, since in human cells, procedural and mechanistic details of trans-splicing have never been disclosed, we doubt the existence of trans-splicing. Therefore, there are probably no authentic chimeras in humans, after readthrough and fusion-gene derived RNAs are all put back into the group of ordinary RNAs. Therefore, it should be further determined whether in human cells all two-neighboring-gene RNAs are derived from transcriptional readthrough and whether trans-splicing truly exists

Designing a Stable g-C₃N₄/BiVO₄-Based Photoelectrochemical Aptasensor for Tetracycline Determination

The excessive consumption of tetracycline (TC) could bring a series of unpredictable health and ecological risks. Therefore, it is crucial to develop convenient and effective detection technology for TC. Herein, a “signal on” photoelectrochemical (PEC) aptasensor was constructed for the stable detection of TC. Specifically, the g-C3N4/BiVO4 were used to promote the migration of photo-generated charges to an enhanced photocurrent response. TC aptamer probes were stably fixed on the g-C3N4/BiVO4/FTO electrode as a recognition element via covalent bonding interaction. In the presence of TC, the aptamer probes could directly recognize and capture TC. Subsequently, TC was oxidized by the photogenerated holes of g-C3N4/BiVO4, causing an enhanced photocurrent. The “signal on” PEC aptasensor displayed a distinguished detection performance toward TC in terms of a wide linear range from 0.1 to 500 nM with a low detection limit of 0.06 nM, and possessed high stability, great selectivity, and good application prospects

Suppressing photoinduced charge recombination at the BiVO4||NiOOH junction by sandwiching an oxygen vacancy layer for efficient photoelectrochemical water oxidation

Nickel oxyhydroxide (NiOOH) is regarded as one of the promising cocatalysts to enhance the catalytic performance of photoanodes but suffers from serious interfacial charge-carrier recombination at the photoanode|| NiOOH interface. In this work, surface-engineered BiVO4 photoanodes are fabricated by sandwiching an oxygen vacancy (Ovac) interlayer between BiVO4 and NiOOH. The surface Ovac interlayer is introduced on BiVO4 by a chemical reduction treatment using a mild reducing agent, sodium hypophosphite. The induced Ovac can alleviate the interfacial charge-carrier recombination at the BiVO4||NiOOH junction, resulting in efficient charge separation and transfer efficiencies, while an outer NiOOH layer is coated to prevent the Ovac layer from degradation. As a result, the as-prepared NiOOH-P-BiVO4 photoanode exhibits a high photocurrent density of 3.2 mA cm�2 at 1.23 V vs. RHE under the irradiatio

Moxibustion using different habitat moxa floss for moderate to severe primary knee osteoarthritis: study protocol for a three-armed randomized, double-blinded, sham-controlled trial

Abstract Background According to the traditional Chinese medicine theory, moxa floss is the best material for moxibustion; the effect of moxibustion is closely related to the habitats of moxa floss, among which Qichun County, Hubei Province, China, is considered as the genuine origin. However, this view has not been validated by clinical studies. Moxibustion has been proven effective in alleviating pain and improving physical function and quality of life for patients with knee osteoarthritis (KOA). This trial aims to determine whether the habitat of moxa floss contributes to the effect of moxibustion and to validate the effectiveness of moxibustion for KOA. Methods This is a three-armed, randomized, double-blinded, sham-controlled trial. A total of 350 patients with moderate to severe primary KOA will be randomly allocated to groups A, B, or C with a 2:2:1 ratio. Moxa stick moxibustion using moxa floss from different habitats will be applied in two experimental groups: group A, moxa floss from the habitat of Qichun County, Hubei Province, China; and group B, moxa floss from the habitat of Nanyang County, Henan Province. Group C will use non-moxa floss for sham moxibustion as control. Patients will be treated for 20 min per session, for three sessions per week for 2 weeks, and followed up for 4 weeks. The primary outcome will be the change from baseline in the pain score of the Western Ontario and McMaster Osteoarthritis Index (WOMAC) at week 2. Secondary outcomes will include a change in the WOMAC pain score at week 6; the visual analogue scale for knee pain, the total WOMAC score, the WOMAC stiffness score, the WOMAC function score, the patient global assessment, and the responder criteria at weeks 2 and 6. Adverse events will be assessed throughout the study. Discussion This trial will help to identify the effectiveness of moxibustion for KOA and whether the habitat of moxa floss contributes to the effect of moxibustion. Trial registration Acupuncture-Moxibustion Clinical Trial Registry: AMCTR-IOR-16000007. Registered on 29 February 2016

Recent progress on post-synthetic treatments of photoelectrodes for photoelectrochemical water splitting

For the global energy demand and climate change challenges, seeking renewable, sustainable energy sources is of great significance. Photoelectrochemical (PEC) water splitting is one of the promising technologies for converting intermittent solar energy into storable hydrogen energy, to tackle these issues. As the core component in a PEC system, photoelectrodes have been modified by various strategies including nanostructuring, facet-engineering, elemental doping, and heterostructured engineering. Apart from these techniques, numerous effective post-synthetic treatments have also been used to facilely and powerfully boost the physicochemical properties of photoelectrodes, for the enhancement of their PEC performance. Among them, a number of post-treatments can selectively influence photoelectrode surface and subsurface areas, altering surface states that play crucial roles in the hydrogen/oxygen evolution reaction. In virtue of such post-treatments, we summarize recently reported post-synthetic treatments for enhanced PEC applications. Post-treatment methods are classified into three sections: chemical treatments, electrochemical and irradiation-based treatments, and post-annealing treatments. In the end, a summary and outlook section regarding the utilization of post-treatments for PEC applications have been provided. This review can provide inspiration for further studies about post-treatments, not only in the PEC water splitting field, but also in other aspects, such as electrolysis

New data‐driven method for estimation of net ecosystem carbon exchange at meteorological stations effectively increases the global carbon flux data

Abstract The eddy covariance (EC) flux stations have great limitations in the evaluation of the global net ecosystem carbon exchange (NEE) and in the uncertainty reduction due to their sparse and uneven distribution and spatial representation. If the EC stations are linked with widely distributed meteorological stations using machine learning (ML) and remote sensing, it will play a big role in effectively improving the accuracy of the global NEE assessment and reducing uncertainty. In this study, we developed a framework for estimating NEE at meteorological stations. We first optimized the hyperparameters and input variables of the ML model based on the optimization method called an adaptive genetic algorithm. Then, we developed 566 random forest (RF)‐based NEE estimation models by the strategy of spatial leave‐out‐one cross‐validation. We innovatively established the Euclidean distance‐based accuracy projection algorithm of the R square (R2), which could test the accuracy of each model to estimate the NEE of the specific flux at the weather station. Only the model with the highest R2 was selected from the models with a prediction accuracy of R2 > 0.5 for the specific meteorological stations to estimate its NEE. 4674 out of 10,289 weather stations around the world might match at least one of the 566 NEE estimation models with a projected accuracy of R2 > 0.5. The NEE estimation models we screened for the meteorological stations showed a reliable performance and a higher accuracy than the former studies. The NEE values of the most (96.9%) screened meteorological stations around the world are negative (carbon sink) and most (65.3%) of those showed an increasing trend in the mean annual NEE (carbon sink). The NEE dataset produced at the meteorological stations could be used as a supplement to the EC observations and quasi‐observation data to assess the NEE products of the global grid. The NEE dataset is publicly available via the figshare with https://doi.org/10.6084/m9.figshare.20485563.v1

Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation

Net ecosystem exchange (NEE) is an important indicator of carbon cycling in terrestrial ecosystems. Many previous studies have combined flux observations and meteorological, biophysical, and ancillary predictors using machine learning to simulate the site-scale NEE. However, systematic evaluation of the performance of such models is limited. Therefore, we performed a meta-analysis of these NEE simulations. A total of 40 such studies and 178 model records were included. The impacts of various features throughout the modeling process on the accuracy of the model were evaluated. Random forests and support vector machines performed better than other algorithms. Models with larger timescales have lower average R-2 values, especially when the timescale exceeds the monthly scale. Half-hourly models (average R-2 = 0.73) were significantly more accurate than daily models (average R-2 = 0.5). There are significant differences in the predictors used and their impacts on model accuracy for different plant functional types (PFTs). Studies at continental and global scales (average R-2 = 0.37) with multiple PFTs, more sites, and a large span of years correspond to lower R 2 values than studies at local (average R-2 = 0.69) and regional (average R-2 = 0.7) scales. Also, the site-scale NEE predictions need more focus on the internal heterogeneity of the NEE dataset and the matching of the training set and validation set