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An industry survey suggests an increasing application of high-displacement helical piles with greater shaft and helix diameters to support various structures. In this paper, a database of 84 static load tests is compiled and analyzed to evaluate the disturbance effect and characterize the model factors that can be used for reliability-based limit state design. The measured capacity is defined as the load at a pile head settlement equal to 5% of helix diameter. For similar helix configurations tested at the same site, the ratio of uplift to compression capacity indicates a low degree of disturbance for very stiff clay (0.8–1) and a medium degree of disturbance for dense sand (0.6–0.8). At the ultimate limit state, the model factor is defined as the ratio between measured and calculated capacity, where three design guidelines are considered. A hyperbolic model with two parameters is used to fit the load–displacement curves. At the serviceability limit state, the model factor can be defined with the hyperbolic parameters. Based on the database, probabilistic distributions of the capacity model factor and hyperbolic parameters are established. Finally, the capacity model statistics are applied to calculate the resistance factor in the load and resistance factor design.

Conventional approaches to studying 5mC marks in single cells or samples with picogram input DNA amounts usually suffer from low genome coverage due to DNA degradation. Many methods have been developed to optimize the library construction efficiency for bisulfite-treated DNA. However, most of these approaches ignored the amplification bias of bisulfite-treated DNA, which leads to shallow genome coverage. In this study, we developed the T7-assisted enzymatic methyl-sequencing method (TEAM-seq), which adopts enzymatic conversion to minimize DNA degradation and T7 polymerase-assisted unbiased amplification. We demonstrate that TEAM-seq delivered, to the best of our knowledge, the highest reported coverage(70% for 100pg, 35% for 20pg) of single cell genomes in whole-genome 5mC sequencing.

Background. Patients with severe coronavirus disease 2019 (COVID-19) who develop acute kidney injury (AKI) in the intensive care unit (ICU) have extremely high rates of mortality. This study evaluated the prognostic impact of AKI duration on in-hospital mortality in elder patients. Methods. We performed a retrospective study of 126 patients with confirmed COVID-19 with severe or critical disease who treated in the ICU from February 4, 2020, to April 16, 2020. AKI was defined according to the Kidney Disease Improving Global Outcomes serum creatinine (Scr) criteria. AKI patients were divided into transient AKI and persistent AKI groups based on whether Scr level returned to baseline within 48 h post-AKI. Results. In total, 107 patients were included in the final analysis. The mean age was 70 (64–78) years, and 69 (64.5%) patients were men. AKI occurred in 48 (44.9%) during their ICU stay. Of these, 11 (22.9%) had transient AKI, and 37 (77.9%) had persistent AKI. In-hospital mortality was 18.6% ( n = 11 ) for patients without AKI, 72.7% ( n = 8 ) for patients with transient AKI, and 86.5% ( n = 32 ) for patients with persistent AKI ( P < 0.001 ). Kaplan–Meier curve analysis revealed that patients with both transient AKI and persistent AKI had significantly higher death rates than those without AKI (log-rank P < 0.001 ). Multivariate Cox regression analysis revealed that transient and persistent AKI were an important risk factor for in-hospital mortality in older patients with severe COVID-19 even after adjustment for variables ( h a z a r d r a t i o H R = 2.582 ; 95% CI: 1.025–6.505; P = 0.044 ; and H R = 6.974 ; 95% CI: 3.334–14.588; P < 0.001 ). Conclusions. AKI duration can be an important predictive parameter in elder patients suffering from COVID-19 and are admitted to ICU. Among these patients, those exhibiting persistent AKI have a lower in-hospital survival rate than those with transient AKI, emphasizing the importance of identifying an appropriate treatment window for early intervention.

Motion tracking systems based on optical sensors typically suffer from poor lighting conditions, occlusion, limited coverage, and may raise privacy concerns. More recently, radiofrequency (RF) based approaches using commercial WiFi devices have emerged which offer low-cost ubiquitous sensing whilst preservin privacy. However, RF sensing systems typically output range-Doppler maps, time-frequency spectrograms, cross-range plots etc which cannot represent human motion intuitively and usually requires further processing. In this study, we propose MDPose, a novel framework for human skeletal motion reconstruction base on WiFi micro-Doppler signatures. MDPose provides an effective solution to represent human activity by reconstructing a skeleton model with 17 key points, which can assist with the interpretation of conventional RF sensing outputs in a more understandable way. Specifically, MDPose is implemented over three sequential stage to address a series of challenges: First, a denoising algorithm is employed to remove any unwanted noise that may affect feature extraction and enhance weak Doppler measurements. Secondly, a convolutional neural network (CNN)-recurrent neural network (RNN) architecture is applied to learn temporal spatial dependenc from clean micro-Doppler signatures and restore velocity information to key points under the supervision of the motion capture (Mocap) system. Finally, a pose optimisation mechanism based on learning optimisation vectors is employed to estimate the initial state of the skeleton and to limit additional errors. We hav conducted a comprehensive set of tests in a variety of environments using numerous subjects with a single receiver radar system to demonstrate the performance of MDPose, and report 29.4mm mean absolute error over all key points positions on several common daily activities, which has performance comparable to that of state-ofthe- art RF-based pose estimation systems.

Coastal lands are often affected by salinization, which leads to a deterioration of soil structure and a decrease in land productivity. As a widely used soil amendment, biochar has been proven to improve poor soil properties and promote crop growth and N adsorption and utilization. However, the effects of biochar on soil fertility, N use efficiency (NUE) and balance in coastal salt-affected soil have rarely been reported. Therefore, we conducted a field micro-plot experiment to study the improvement effects of different biochar rates (0, 13.5, 20.25 and 27 t/ha, corresponding to CK, B1, B2 and B3 treatments, respectively) on coastal salt-affected soil. The results showed that biochar application increased soil water content (SWC) in seasons with abundant rainfall but decreased SWC in seasons with strong evaporation, and the increase or decrease in SWC was greater with the increase in biochar rates. Biochar application increased soil salinity and decreased soil pH, although high rates of biochar increased soil salinity to a lesser extent, while low rates of biochar decreased soil pH most. Biochar application was able to reduce soil bulk density, while B1and B2 treatments decreased it to a higher degree. Moreover, biochar application increased soil macro-aggregates (>0.25 mm) and organic matter, while B2 and B3 treatments increased it to a higher degree. Biochar application improved soil fertility to an extent that crop grain increased yield by 2.84~19.88% in barley season and 12.27~16.74% in maize season. Meanwhile, biochar application also increased NUE because it promoted the increase of yield. In particular, the calculation of N balance between soil and plant systems suggested that biochar application could reduce the apparent N loss during crop planting, and B1 treatment was better at reducing apparent N loss. Overall, our study indicates that biochar application has great potential to improve poor physicochemical properties and N nutrient utilization in coastal salt-affected soil. More importantly, we suggest that biochar application rates should be controlled in coastal salt-affected soil.

Chronic kidney disease (CKD) is associated with higher risk of cardiovascular disease-related ischemic events, which includes peripheral arterial disease (PAD). PAD is a strong predictor of future cardiovascular events, which can cause significant morbidity and mortality. Resistin has been found to be involved in pathological processes leading to CVD. Therefore, we aim to investigate whether resistin level is correlated with PAD in patients with non-dialysis CKD stage 3 to 5. There were 240 CKD patients enrolled in this study. Ankle-brachial index (ABI) values were measured using the automated oscillometric method. An ABI value < 0.9 defined the low ABI group. Serum levels of human resistin were determined using a commercially available enzyme immunoassay. Thirty CKD patients (12.5%) were included in the low ABI group. Patients in the low ABI group were older and had higher resistin levels as well as higher diabetes mellitus, hypertension and habit of smoking, and lower estimated glomerular filtration rate than patients in the normal ABI group. After the adjustment for factors that were significantly associated with PAD by multivariate logistic regression analysis, age and serum resistin level were independent predictors of PAD. A high serum resistin level is an independent predictor of PAD in non-dialysis CKD stage 3 to 5.

Here we describe a powerful method, BIND&MODIFY, for probing histone modifications and transcription factors at single molecular level. Our approach used the recombinant fused protein A-M.EcoGII, which tethers the methyltransferase M.EcoGII to the protein binding sites and locally labels the neighboring DNA regions via artificial methylations. This method could reveal ingle-molecule heterogenous histone modification status and CpG methylation at the same time, and could enable quantify the correlation between the distal elements. Further applications based on this method's concept could be applied to probe multiple protein binding events on the same single molecular DNA. The method proposed herein may soon become an essential tool for third-generation sequencing in the future.

Hepatocellular carcinoma (HCC) is a common malignant tumor that is characterized by aggressiveness and poor prognosis. Accumulating evidence indicates that oxidative stress plays a crucial role in carcinogenesis, whereas the potential mechanism between oxidative stress and carcinogenic effects remains elusive. In recent years, long noncoding RNAs (lncRNAs) in cancers have attracted extensive attention and have been shown to be involved in oxidative stress response and carcinogenesis. Nevertheless, the roles of lncRNA AL033381.2 in regulating the development and progression of HCC still remain unclear. The purpose of our study was to evaluate the potential effects and molecular mechanisms of AL033381.2 that may be involved in oxidative stress response in HCC. Using bioinformatics analyses based on the TCGA database, we screened and identified a novel lncRNA AL033381.2 in HCC, which may be involved in oxidative stress responses. qRT-PCR analysis revealed that AL033381.2 is upregulated in HCC tissues. Through in vitro and in vivo experiments, we found that AL033381.2 dramatically facilitates the growth and metastasis of HCC. Mechanistically, RNA pull-down experiments, mass spectrometry, PathArray™, and RIP were used to determine that AL033381.2 binds to PRKRA and may be involved in AL033381.2-mediated oncogenic functions in HCC cells. Moreover, rescue experiments demonstrated that PRKRA overexpression rescues the abilities of HCC cell proliferation, migration, and invasion that were affected by AL033381.2 knockdown. Furthermore, we produced a nanoparticle-based siRNA delivery system and tested its therapeutic effects in vivo. The results showed that the in vivo growth rate of the tumors treated with the nanoparticle/AL033381.2 siRNA complexes was dramatically lower than those treated with the nanoparticle/scramble siRNA complexes. Taken together, our results suggest that the novel lncRNA AL033381.2 may be involved in oxidative stress response by targeting oxidative stress-related genes in HCC. AL033381.2 plays vital oncogenic roles in HCC progression and may be a novel therapeutic marker for HCC diagnosis and treatment.

The COVID-19 pandemic is a global health disaster. Moreover, emerging mutated virus strains present an even greater challenge for existing vaccines and medications. One possible solution is to design drugs based on the properties of virus epigenome, which are more common among coronaviruses.Here, we reported an FDA-approved drug for myelodysplastic syndrome, azacytidine (5Aza), limited virus infection and protected mice against SARS-CoV-2. We demonstrated that this antiviral effect is related to 5Aza incorporation into viral RNA, which disrupt m5C RNA methylation modification profile. This work suggests that targeting viral epigenomes is a viable therapeutic strategy, potentially opening new pathways for treating COVID-19.

Different from the traditional deep tillage, deep vertical rotary tillage can smash deep soil without disturbing the soil layer, which improves soil water infiltration and promotes salt leaching. This has gradually been applied in the research into saline alkali improvements. However, there is limited knowledge about the effect of deep vertical rotary tillage on the temporal–spatial distributions of soil water and salt under the shallow underground water level. Therefore, a preliminary soil column experiment was carried out to explore the results of water and salt movement under three different tillage methods: traditional rotary tillage depth of 20 cm (XG−20), deep vertical rotary tillage depth of 20 cm (FL−20) and deep vertical rotary tillage depth of 40 cm (FL−40). The temporal–spatial variation in soil water and salt was analyzed. The results showed that the average infiltration rate of FL−40 increased by 1.25 and 0.46 cm h−1 in 0−40 cm soil layer compared with that of XG−20 and FL−20. At the same time, soil water content was also increased, and the order of soil water content was FL−40 > FL−20 > XG−20. With the increase in tillage depth, the desalinization rate and the soil electric conductivity (EC) of FL−40 were increased and decreased, respectively. The FL−40 treatment’s average desalinization rates increased by 16.32% and 13.99% compared with XG−20 and FL−20 treatments’ in 0−60 cm soil layer. In conclusion, FL−40 had a better effect on regulating water and salt. The deep vertical rotary tillage provided an effective method for the control and optimization of water and salt in coastal saline soil.

Meningococcal disease caused by Neisseria meningitidis remains a major global public health concern. Serogroup A, B, C and W135 were the major disease-causing serogroups. It is vital to timely and efficiently detect and differentiate these four serogroups. Herein, we developed multiple cross displacement amplification-lateral flow biosensor (MCDA-LFB) assays targeting ctrA, sacB, siaD, siaD and synG gene respectively for detection and subtyping of four N. meningitidis serogroups. This assay utilizes LFB to detect FITC and biotin-labeled target amplicons produced by MCDA through double antibody sandwich principle, to allow sensitive and specific detection under a constant temperature. The detection limit was as low as 10 fg or 100 fg genomic DNA in pure cultures and 5.5 CFUs or 36 CFUs in spiked cerebrospinal fluid (CSF) specimens, which were overall 100 to 1000-fold more sensitive than conventional PCR. High specificity of these assays was also validated through type strains and clinical isolates, with no cross-reactions. MCDA-LFB testing procedure can be finished within 1 h. In conclusion, the N. meningitidis- and serogroup-MCDA-LFB assays established in this study are simple, rapid and efficient, providing valuable molecular methods for diagnosis and surveillance of meningococcal disease, especially in resource-limited regions and when specimen culture fails.

This work considers the use of a passive WiFi radar (PWR) to monitor human activities. Real-time uncooperative monitoring of people has numerous applications ranging from smart cities and transport to IoT and security. In e-healthcare, PWR technology could be used for ambient assisted living and early detection of chronic health conditions. Large training datasets could drive forward machine-learning-focused research in the above applications. However, generating and labeling large volumes of high-quality, diverse radar datasets is an onerous task. Therefore, we present an open-source motion capture data-driven simulation tool, SimHumalator, that can generate large volumes of human micro-Doppler radar data at multiple IEEE WiFi standards(IEEE 802.11g, n, and ad). We qualitatively compare the micro-Doppler signatures generated through SimHumalator with the measured signatures. To create a more realistic training dataset, we artificially add noise to our clean simulated spectrograms. A noise distribution is directly learned from real radar measurements using a Generative Adversarial Network (GAN). We observe improvements in the classification performances between 3 to 8%. Our results suggest that simulation data can be used to make adequate training data when the available measurement training support is low.

Background This study aimed to investigate the differences in the serum levels of glucose, lipid, and thyroid function markers between unipolar and bipolar depressed patients, as well as the effect of anhedonia and suicidal thoughts on the levels of these biochemical parameters. Methods A total of 287 unmedicated depressed patients from January 2016 to December 2017 were included in this study, including 92 bipolar depressions and 195 unipolar depressions. Anhedonia was determined using the item 32 of Symptom Checklist (SCL-90). Suicide ideation was assessed by item 15 of SCL-90. Results The bipolar group had significantly lower lipid levels (including triglycerides, cholesterol, low-density lipoprotein cholesterol [LDL], very low-density lipoprotein cholesterol [VLDL]) and insulin resistance index but higher levels of prolactin, low triiodothyronine (T3) and free T3 (FT3) as well as higher incidence of anhedonia as compared with the unipolar group. Depressed patients with anhedonia had significantly higher LDL level than those without anhedonia. Depressed patients with suicidal thoughts had cholesterol and high-density lipoprotein cholesterol (HDL) level. The above-mentioned differences were confirmed by logistic regression analysis. Receiver operating characteristic curve (ROC) analysis showed that the area under the ROC curve (AUC) ranged from 0.546 to 0.685. Conclusion Triglycerides, cholesterol, LDL, VLDL T3, FT3 levels were significantly different between unipolar and bipolar depressed patients, which might have the potential to be the markers for differential diagnosis. Patients with anhedonia had lower LDL level, while patients with suicidal thoughts had higher levels of cholesterol and HDL as compared with the corresponding control groups.

For steel H-piles under axial compression, 177 static load tests were used to evaluate the performance of the Chin-Davisson method to (1) interpret pile capacity and (2) characterise the statistics...

This paper summarizes 239 static load tests to evaluate the performance of four static design methods for axial resistance of driven piles in clay. The methods are ISO 19901-4:2016, SHANSEP, ICP-05, and NGI-05. The database is categorized into four groups depending on the load type (compression or uplift) and pile tip condition (open or closed end). The model uncertainty in resistance prediction is quantified as a ratio between measured and calculated resistance, which is called a model factor. The measured resistance is interpreted as a load producing a settlement level of 10% pile diameter. Database studies show that the four methods present a similar accuracy, where the mean and coefficient of variation (COV) of the model factor are around 1 and 0.3, respectively. The COV values are smaller than those for driven piles in sand available in literature. The model statistics determined from the database are applicable to a simplified or full probabilistic form of reliability-based design (RBD) of driven piles in clay. As an illustration, the resistance factors in load and resistance factor design (LRFD, a simplified form of RBD) are calibrated by Monte Carlo simulations.

Artemisinin is an important frontline antimalarial. Fast, accurate detection of artemisinin in human serum is of importance in monitoring its clinical pharmaceutical effect. In this work, a strategy using microsensor array coupled with electrochemiluminescence (ECL) imaging technique was developed for detection of artemisinin. The microsensor array was constructed by integrating a patterned indium tin oxide glass plate with two perforated hydrophobic paper covers. By introducing the reactant of p-aminophenylboronic acid, luminol and artemisinin into the microsensor array, artemisinin would oxidize p-aminophenylboronic acid into p-aminophenol, a product which can efficiently inhibit the ECL of luminol. ECL signals decrease linearly with the increase of artemisinin. Based on the decreased ECL signal, artemisinin can be accurately detected. A good linearity (r = 0.994) was observed for artemisinin detection. The detection sensitivity is 0.48 μM for artemisinin. The detection selectivity and stability were also investigated. Results show that the present method shows a good selectivity and stability towards artemisinin detection. To evaluate the applicability of the present strategy for detecting artemisinin in real samples, the artemisinin content in human serum and Artemisia annua samples were analyzed. Results demonstrated that the present strategy shows excellent selectivity with high sensitivity towards artemisinin detection in real samples.

The Retinex models the human visual system to perceive natural colors, which could improve the contrast and sharpness of the degraded image and also provide color constancy and dynamic range simultaneously. This endows the Retinex exceeding advantages for enhancing the underwater image. Based on the multi-scale Retinex, an efficient enhancement method for underwater image and video is presented in this paper. Firstly, the image is pre-corrected to equalize the pixel distribution and reduce the dominating color. Then, the classical multi-scale Retinex with intensity channel is applied to the pre-corrected images for further improving the contrast and the color. In addition, multi-down-sampling and infinite impulse response Gaussian filtering are adopted to increase processing speed. Subsequently, the image is restored from logarithmic domain and the illumination of the restored image is compensated based on statistical properties. Finally, the color is selectively preserved by the inverted gray world method depending on imaging conditions and application requirements. Five kinds of typical underwater images with green, blue, turbid, dark and colorful backgrounds and two underwater videos are enhanced and evaluated on Jetson TX2, respectively, to verify the effectiveness of the proposed method.

Cilia are cell-surface, microtubule-based organelles that project into extracellular space. Motile cilia are conserved throughout eukaryotes, and their beat induces the flow of fluid, relative to cell surfaces. In mammals, the coordinated beat of motile cilia provides highly specialized functions associated with the movement of luminal contents, as seen with metachronal waves transporting mucus in the respiratory tract. Motile cilia are also present in the male and female reproductive tracts. In the female, wave-like motions of oviductal cilia transport oocytes and embryos toward the uterus. A similar function has been assumed for motile cilia in efferent ductules of the male—i.e., to transport immotile sperm from rete testis into the epididymis. However, we report here that efferent ductal cilia in the male do not display a uniform wave-like beat to transport sperm solely in one direction, but rather exert a centripetal force on luminal fluids through whip-like beating with continual changes in direction, generating turbulence, which maintains immotile spermatozoa in suspension within the lumen. Genetic ablation of two miRNA clusters ( miR-34b/c and -449a/b/c ) led to failure in multiciliogenesis in murine efferent ductules due to dysregulation of numerous genes, and this mouse model allowed us to demonstrate that loss of efferent duct motile cilia causes sperm aggregation and agglutination, luminal obstruction, and sperm granulomas, which, in turn, induce back-pressure atrophy of the testis and ultimately male infertility.

Human micro-Doppler signatures in most passive WiFi radar (PWR) scenarios are captured through real-world measurements using various hardware platforms. However, gathering large volumes of high quality and diverse real radar datasets has always been an expensive and laborious task. This work presents an open-source motion capture data-driven simulation tool SimHumalator that is able to generate human micro-Doppler radar data in PWR scenarios. We qualitatively compare the micro-Doppler signatures generated through SimHumalator with the measured real signatures. Here, we present the use of SimHumalator to simulate a set of human actions. We demonstrate that augmenting a measurement database with simulated data, using SimHumalator, results in an 8% improvement in classification accuracy. Our results suggest that simulation data can be used to augment experimental datasets of limited volume to address the cold-start problem typically encountered in radar research.

To investigate the effect of autophagy expression levels of different weight-bearing states and different stages of osteoarthritis in animal models, as well as the corresponding mechanisms.We used the male Sprague-Dawley (SD) rats (12-week-old, SPF) to establish the OA animal models by modified Hulth method, and grouped animal models according to the length of time after surgery and different weight-bearing areas. RT-qPCR was carried out for detection of autophagy-related genes such as Atg7, Atg12, P62, etc. Western blot analysis was used to detect the expression levels of corresponding autophagy-related proteins such as LC3B, P62, etc. T test was performed for statistical analysis to compare different groups, while the differences were deemed statistically significant with P 0.05. Transmission electron microscopy was used to observe the autophagosome to demonstrate the level of autophagy expression and the status of the chondrocytes.The results of the RT-qPCR testing showed that when the weight-bearing cartilage of the 4-week group (relatively mild) was compared with that of the 10-week group (relatively severe), there were statistically significant differences in all the genes tested, in detail: Atg3 (P 0.01), Atg7 (P 0.01), Atg12 (P 0.01), P62 (P 0.0001). The expression of autophagy-related mRNA in the 4-week group is increased compared with that of the 10-week group. As for the expression of proteins, Western blotting showed that in the comparison between the 4- and the 10-week groups, statistically significant results include Atg12 (P 0.01) in the non-weight-bearing area, with decreased autophagy in the 10-week group compared with that of the 4-week group, while expression of LC3B (P 0.05) protein was significantly higher in the 4-week group than in the control in the non-weight-bearing area. The expression of LC3B (P 0.0001) and P62 (P 0.05) in the 10-week group were higher than that of the control. Transmission electron microscope showed that autophagy in the weight-bearing area is stronger than that in the non-weight-bearing area, and autophagy in the 4-week group is stronger than in the 10-week group for the weight-bearing area.The expression of autophagy varies during different stages of osteoarthritis, in which the autophagy is stronger in the early stage of osteoarthritis, and gradually decreases with the progression of the disease. Autophagy in different weight-bearing areas may also be different.

Background Acute myocardial infarction (AMI) is one of the most common global causes of death. Although considerable progress has been made in AMI diagnosis, there remains an urgent need for novel diagnostic biomarkers for its prevention and treatment. Functional exosomal microRNAs (miRNAs) are recognized as potential biomarkers in many diseases. This study's objective was to identify specific plasma exosomal miRNAs with biomarker potential for early AMI detection. Methods Exosomes from the plasma of 26 coronary artery disease (CAD) patients, 55 AMI patients, and 37 healthy controls were isolated and characterized by transmission electron microcopy (TEM), western blotting, and nanoparticle tracking analysis (NTA). The miRNAs were purified from exosomes, and unique molecular identifier (UMI) small RNA sequencing was performed. The random forest (RF) model was trained to predict potential biomarkers. Results NTA demonstrated that nanoparticle concentration did not change after AMI, while nanoparticle size distribution significantly decreased. The CAD and AMI groups' miRNA expression profiles significantly differed from the healthy group's profile. The RF classifier could be used to distinguish the healthy group from the AMI group, but could not be used to distinguish the CAD group from the other groups, which caused a high classification error rate. Eighteen miRNAs were selected as biomarkers based on their RF classifier significance. The diagnostic accuracy of 18 miRNAs was evaluated using AUC values of 0.93, 0.87, and 0.75 to detect healthy controls, AMI, and CAD, respectively. Conclusions Nanoparticle diameter and the 18 miRNAs may serve as simple and accessible fingerprints for early AMI diagnosis.

As one of the post-transcriptional regulatory mechanisms, transcription and translation’s uncoupling plays an essential role in development and adulthood physiology. However, it remains elusive how thousands of mRNAs get translationally silenced while stability is maintained for up to hours or even days before translation. In addition to oocytes and neurons, developing spermatids have significant uncoupling of transcription and translation for delayed translation. Therefore, spermiogenesis represents an excellentin vivomodel for investigating the mechanism underlying uncoupled transcription and translation. Through full-length poly(A) deep sequencing, we discovered dynamic changes in poly(A) length through deadenylation and re-polyadenylation. Deadenylation appeared to be mediated by microRNAs (miRNAs), and transcripts with shorter poly(A) tails tend to be sequestered into ribonucleoproteins (RNPs) for translational repression and stabilization. In contrast, re-polyadenylation allows for translocation of the translationally repressed transcripts from RNPs to polysomes for translation. Overall, our data suggest that miRNA-dependent poly(A) length control represents a novel mechanism underlying uncoupled translation and transcription in haploid male germ cells.

This work presents an application of Integrated sensing and communication (ISAC) system for monitoring human activities directly related to healthcare. Real-time monitoring of humans can assist professionals in providing healthy living enabling technologies to ensure the health, safety, and well-being of people of all age groups. To enhance the human activity recognition performance of the ISAC system, we propose to use synthetic data generated through our human micro-Doppler simulator, SimHumalator to augment our limited measurement data. We generate a more realistic micro-Doppler signature dataset using a style-transfer neural network. The proposed network extracts environmental effects such as noise, multipath, and occlusions effects directly from the measurement data and transfers these features to our clean simulated signatures. This results in more realistic-looking signatures qualitatively and quantitatively. We use these enhanced signatures to augment our measurement data and observe an improvement in the classification performance by 5% compared to no augmentation case. Further, we benchmark the data augmentation performance of the style transferred signatures with three other synthetic datasets -- clean simulated spectrograms (no environmental effects), simulated data with added AWGN noise, and simulated data with GAN generated noise. The results indicate that style transferred simulated signatures well captures environmental factors more than any other synthetic dataset.

The geo-structures embedded in the multiple variable strata could be significantly affected by the geological uncertainty. The quantitative evaluation of geological uncertainty and its influence on the structural safety of embedded tunnels are seldom studied in the past. This paper aims to analyse the effect of geological uncertainty on the structural performance of tunnel using the proposed stochastic geological modelling framework. The geological uncertainty is characterized using an improved coupled Markov chain model based on sparse limited boreholes. A mapping approach is presented to solve the mesh asymmetry problem between the simulated strata and the numerical tunnel model. The tunnel structural performance analysis is then conducted based on the combined model considering the geological uncertainty and tunnel structure. A geological uncertainty index (GUI) is proposed to quantitatively evaluate the level of uncertainty of each borehole and the whole site. The effect of the borehole layout scheme on uncertainty evaluation of factor of safety of tunnel structure is investigated by a large number of stratigraphic realizations. Boreholes collected from Norway with relatively more considerable variability and from Shanghai with relatively more minor variability are adopted as case studies to illustrate the proposed probabilistic analysis framework. The results show that the boreholes with larger GUI values and closer to tunnel locations have a greater weight to affect the embedded tunnel structural performance in uncertain geological strata.

This paper presents a comprehensive dataset intended to evaluate passive Human Activity Recognition (HAR) and localization techniques with measurements obtained from synchronized Radio-Frequency (RF) devices and vision-based sensors. The dataset consists of RF data including Channel State Information (CSI) extracted from a WiFi Network Interface Card (NIC), Passive WiFi Radar (PWR) built upon a Software Defined Radio (SDR) platform, and Ultra-Wideband (UWB) signals acquired via commercial off-the-shelf hardware. It also consists of vision/Infra-red based data acquired from Kinect sensors. Approximately 8 hours of annotated measurements are provided, which are collected across two rooms from 6 participants performing 6 daily activities. This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities. Furthermore, it can potentially be used to passively track a human in an indoor environment. Such datasets are key tools required for the development of new algorithms and methods in the context of smart homes, elderly care, and surveillance applications., Comment: 17 pages, 7 figures

WiFi sensing has shown promising potentials in a number of applications such as healthcare, smart transportation and home automation. Human activity recognition without the use of any cooperative device such as phones or wearable technologies can be achieved by two WiFi based approaches: Channel State Information (CSI) and Passive WiFi Radar (PWR). CSI systems rely directly on WiFi as a communications system, whilst PWR treats the WiFi signal as an illuminator for use in a radar signal processing. However, there has not been a comprehensive comparative study on the similarities and differences between the two systems. To examine the performance of both systems we implement two hardware platforms for CSI and PWR, and use them concurrently to capture the human movements. In this paper, we present Doppler measurements from the two systems and compare their performance using a dataset obtained from five subjects undergoing six activity classes. It is observed that both systems have very different Doppler signatures, and are sensitive to the transmitter-target-receiver geometries. CSI has a better performance in Line-of-Sight (LoS) configurations, whereas PWR has better performance in bistatic configurations where the WiFi access point and radar receiver are spatially separated. It is envisioned that a more robust system should leverage strengths of both the CSI and PWR systems jointly to maximize their benefits in wireless sensing.

This paper compiles two expanded databases-NUS/SpreadFound/919 and NUS/RockFound/270-to evaluate the calculation methods of foundation capacity, where the number is the number of load tests collated. The mean (bias) and coefficient of variation (COV) (dispersion) of the model factor, defined as a ratio of measured-to-calculated capacity, are computed. With the statistics of a ratio of calculated-to-observed foundation settlement reported in literature, design methods for foundation capacity and settlement are classified according to their ability to match measured values. Such a statistical-based classification provides the designer with a sense of the accuracy of a capacity or settlement model. For the 298 case records in NUS/SpreadFound/919, the uncertainty exhibited by the load-movement curves over their full range of movements is captured by two parameters in a hyperbolic fitting of measured data. Copula analysis is adopted to evaluate the observed correlation structure between the hyperbolic parameters. Finally, the model statistics are applied to calibrate the resistance factors in load and resistance factor design of spread foundations at ultimate and serviceability limit states. (c) 2020 American Society of Civil Engineers.

The molecular mechanism underlying epigenetic inheritance remains largely unknown. Previous studies have shown that transmission of paternal acquired traits through the male germline (i.e., sperm) requires functional DNMT2 to maintain normal profiles of sperm-borne tRNA-derived small RNAs (tsRNAs). Here we report that maternal transmission of a Kit paramutant phenotype (white tail tip) through the female germline (i.e., oocytes) also requires normal function of DNMT2 and normal profiles of DNMT2-dependent tsRNAs and other small noncoding RNAs (sncRNAs) in sperm. Specifically, ablation of DNMT2 leads to aberrant profiles of tsRNAs and other sncRNAs in sperm, which correlate with drastically dysregulated mRNA transcriptome in pronuclear zygotes derived from oocytes carrying the Kit paramutation and a complete blockage of transmission of the paramutant phenotype through the oocytes. Together with previous reports, the present study suggests that both paternal and maternal transmission of epigenetic phenotypes requires DNMT2-dependent tsRNAs in sperm.

Occupancy detection and people counting technologies have important uses in many scenarios ranging from management of human resources, optimising energy use in intelligent buildings and improving public services in future smart cities. Wi-Fi based sensing approaches for these applications have attracted significant attention in recent years because of their ubiquitous nature, and ability to preserve the privacy of individuals being counted. In this paper, we present a Passive Wi-Fi Radar (PWR) technique for occupancy detection and people counting. Unlike systems which exploit the Wi-Fi Received Signal Strength (RSS) and Channel State Information (CSI), PWR systems can directly be applied in any environment covered by an existing WiFi local area network without special modifications to the Wi-Fi access point. Specifically, we apply Cross Ambiguity Function (CAF) processing to generate Range-Doppler maps, then we use Time-Frequency transforms to generate Doppler spectrograms, and finally employ a CLEAN algorithm to remove the direct signal interference. A Convolutional Neural Network (CNN) and sliding-window based feature selection scheme is then used for classification. Experimental results collected from a typical office environment are used to validate the proposed PWR system for accurately determining room occupancy, and correctly predict the number of people when using four test subjects in experimental measurements.

Single-cell isoform sequencing can reveal transcriptomic dynamics in individual cells invisible to bulk- and single-cell RNA analysis based on short-read sequencing. However, current long-read single-cell sequencing technologies have been limited by low throughput and high error rate. Here we introduce HIT-scISOseq for high-throughput single-cell isoform sequencing. This method was made possible by full-length cDNA capture using biotinylated PCR primers, and by our novel library preparation procedure that combines head-to-tail concatemeric full-length cDNAs into a long SMRTbell insert for high-accuracy PacBio sequencing. HIT-scISOseq yields > 10 million high-accuracy full-length isoforms in a single PacBio Sequel II 8M SMRT Cell, providing > 8 times more data output than the standard single-cell isoform PacBio sequencing protocol. We exemplified HIT-scISOseq by first studying transcriptome profiles of 4,000 normal and 8,000 injured corneal epitheliums from cynomolgus monkeys. We constructed dynamic transcriptome landscapes of known and rare cell types, revealed novel isoforms, and identified injury-related splicing and switching events that are previously not accessible with low throughput isoform sequencing. HIT-scISOseq represents a high-throughput, cost-effective, and technically simple method to accelerate the burgeoning field of long-read single-cell transcriptomics.

Background and Aims 3-methyl histidine (3MH) is a non-proteinogenic amino acid and an index of muscle breakdown. Subclinical protein malnutrition is an independent association with arterial stiffness. The aim of this study was to evaluate the relationship between serum 3MH levels and carotid-femoral pulse wave velocity (cfPWV) values in chronic hemodialysis patients. Method Fasting blood samples and baseline characteristics were obtained from 136 chronic hemodialysis patients. Serum 3MH was performed with high-performance liquid chromatography and mass spectrometry. Aortic arterial stiffness was defined as cfPWV values >10 m/s according to the ESH-ESC 2013 guidelines. Results Among 110 chronic hemodialysis patients, 45 patients (40.9%) were in the aortic arterial stiffness group. When compared to those in the control group, the aortic arterial stiffness group had high prevalence of diabetes mellitus (p < 0.001), hypertension (p = 0.006), older age (p = 0.002), higher systolic blood pressure (p = 0.016), and lower serum 3MH level (p = 0.001). Multivariable logistic regression analysis of the factors significantly associated with aortic arterial stiffness revealed that serum 3MH levels (odds ratio (OR): 0.791, 95% confidence interval (CI): 0.691–0.906, p = 0.001) was the independent predictor of aortic arterial stiffness in chronic hemodialysis patients. Multivariable forward stepwise linear regression analysis also showed that logarithmically transformed 3MH level (log-3MH, β = -0.322, adjusted R2 change = 0.127, p < 0.001) was an independent predictor of cfPWV values in chronic hemodialysis patients. The area under the receiver-operating characteristic (ROC) curve indicates the diagnostic power of 3MH at predicting aortic stiffness of chronic hemodialysis patients was 0.691 (95% CI: 0.595-0.775, p = 0.0002). Conclusion Serum-free 3MH level is negatively associated with aortic arterial stiffness in chronic hemodialysis patients.

Background and Aims Walking speed test is a usefulness tool for cardiovascular risk stratification in older adults. The inhibition of myostatin in adult significantly increases muscle mass and confers benefits upon measures of performance and metabolism. The present study evaluated the relationship between walking speed test and serum myostatin levels in renal transplant recipients. Method Fasting blood samples were collected from 84 renal transplant recipients. Handgrip strength (HGS) was measured using a Jamar Plus Digital Hand Dynamometer for assessment of muscle strength. Gait speed was measured by walking 6 meters at the usual speed. Gait speed < 1 m/s was defined as low gait speed group according to the European Working Group on Sarcopenia in Older People (EWGSOP) criteria. Serum myostatin levels were measured using a commercial enzyme-linked immunosorbent assay. Results Thirty-one renal transplant recipients (36.9%) had low gait speed, and they included a lower percentage of use of mycophenolate mofetil (p = 0.003), higher percentage of use of steroid (p = 0.037), older age (p = 0.0090, higher body weight (p = 0.044), body mass index (p = 0.017), skeletal muscle index (p = 0.027), serum triglyceride (p = 0.029), glucose (p = 0.007), blood urea nitrogen (p = 0.041), cystatin C (p = 0.015), while lower estimated glomerular filtration rate from serum creatinine (eGFRcre, p = 0.047) and estimated glomerular filtration rate from serum cystatin C (eGFRcys, p = 0.006) compared with renal transplant recipients with normal gait speed. After adjusting for cofounders associated with low gait speed in these patients by multivariable logistic regression analysis, serum myostatin levels (Odds ratio (OR): 0.943, 95% confidence interval (CI): 0.898–0.990, p = 0.018), and mycophenolate mofetil used (OR: 0.199, 95% CI: 0.050–0.795, p = 0.022) were independently associated with low gait speed in renal transplant recipients. The area under the receiver-operating characteristic (ROC) curve indicates the diagnostic power of serum myostatin levels at predicting low gait speed of renal transplant recipients was 0.769 (95% CI: 0.664-0.854, p < 0.001). Multivariable forward stepwise linear regression analysis also showed that serum myostatin levels (β = 0.353, adjusted R2 change: 0.245, p = 0.001) was positively associated with gait speed values in renal transplant recipients. Conclusion In this study, serum myostatin levels is found to be positively correlated with gait speed values and is identified as serum low myostatin levels is associated with low gait speed in renal transplant patients.

Background and Aims Resistin named for its ability to resist insulin action has an important link between obesity, insulin resistance and diabetes. Moreover, additional reports suggest that resistin has a pathogenic role in the development and progression of atherosclerosis and coronary artery disease. Chronic kidney disease (CKD) accelerates atherosclerosis via augmentation of inflammation and perturbation of lipid metabolism. Peripheral arterial occlusive disease (PAOD) is associated with an increased risk of death in CKD patients. The aim of this study was to evaluate the relationship between serum resistin level and PAOD by ankle-brachial index (ABI) in CKD patients. Method Fasting blood samples and baseline characteristics were obtained from 240 patients with CKD (stage 3-5). ABI values were measured using the automated oscillometric method (VaSera VS-1000). ABI values that were < 0.9 were included in the low ABI group. Concentrations of human serum resistin were determined using a commercially available enzyme immunoassay kit. Results Among the 240 CKD patients, 30 of them (12.5%) were in the low ABI group. Compared with patients in the control group, the patients in the low ABI group had higher prevalence of diabetes (p = 0.033), hypertension (p = 0.023), current smoking (p = 0.013), older age (p < 0.001), higher systolic blood pressure (p = 0.013), glucose (p = 0.022) level and resistin (p < 0.001) levels. In addition, the multivariable logistic regression analysis showed that serum resistin levels (Odds ratio [OR]: 1.140, 95% confidence interval [CI]: 1.054-1.234, p = 0.001) and age (OR: 1.102, 95% CI: 1.051-1.156, p < 0.001) were the independently associated with PAOD in CKD patients. The area under the receiver-operating characteristic (ROC) curve predicting PAOD by serum resistin level in CKD patients was 0.699 (95% CI: 0.636-0.756, p = 0.0001). Conclusion In this study, serum resistin level was found to be associated with PAOD in non-dialysis CKD stage 3-5 patients.

Coronavirus disease 2019 (COVID-19) is a global pandemic and has been widely reported; however, a comprehensive systemic review and meta-analysis has not been conducted. We systematically investigated the clinical characteristics of COVID-19 in mainland China to guide diagnosis and treatment. We searched the PubMed, Embase, Scopus, Web of Science, Cochrane Library, bioRxiv, medRxiv, and SSRN databases for studies related to COVID-19 published or preprinted in English or Chinese from January 1 to March 15, 2020. Clinical studies on COVID-19 performed in mainland China were included. We collected primary outcomes including signs and symptoms, chest CT imaging, laboratory tests, and treatments. Study selection, data extraction, and risk of bias assessment were performed by two independent reviewers. Qualitative and quantitative synthesis was conducted, and random-effects models were applied to pooled estimates. This study is registered with PROSPERO (number CRD42020171606). Of the 3624 records identified, 147 studies (20,662 patients) were analyzed. The mean age of patients with COVID-19 was 49.40 years, 53.45% were male, and 38.52% had at least one comorbidity. Fever and cough were the most common symptoms, followed by fatigue, expectoration, and shortness of breath. Most patients with COVID-19 had abnormal chest CT findings with ground glass opacity (70.70%) or consolidation (29.91%). Laboratory findings shown lymphopenia, increased lactate dehydrogenase, increased infection-related indicators, and fibrinolytic hyperactivity. Antiviral therapy, antibiotic therapy, and corticosteroids were administered to 89.75%, 79.13%, and 35.64% of patients, respectively. Most clinical characteristics of COVID-19 are non-specific. Patients with suspected should be evaluated by virological assays and clinically treated.