Case Report: Whole-exome sequencing identified two novel COMP variants causing pseudoachondroplasia

Pseudoachondroplasia (PSACH) is a rare, dominant genetic disorder affecting bone and cartilage development, characterized by short-limb short stature, brachydactyly, loose joints, joint stiffness, and pain. The disorder is caused by mutations in the COMP gene, which encodes a protein that plays a role in the formation of collagen fibers. In this study, we present the clinical and genetic characteristics of PSACH in two Chinese families. Whole-exome sequencing (WES) analysis revealed two novel missense variants in the COMP gene: NM_000095.3: c.1319G>T (p.G440V, maternal) and NM_000095.3: c.1304A>T (p.D435V, paternal-mosaic). Strikingly, both the G440V and D435V mutations were located in the same T3 repeat motif and exhibited the potential to form hydrogen bonds with each other. Upon further analysis using Missense3D and PyMOL, we ascertained that these mutations showed the propensity to disrupt the protein structure of COMP, thus hampering its functioning. Our findings expand the existing knowledge of the genetic etiology underlying PSACH. The identification of new variants in the COMP gene can broaden the range of mutations linked with the condition. This information can contribute to the diagnosis and genetic counseling of patients with PSACH

Preliminary investigation of the diagnosis and gene function of deep learning PTPN11 gene mutation syndrome deafness

Syndromic deafness caused by PTPN11 gene mutation has gradually come into the public’s view. In the past, many people did not understand its application mechanism and role and only focused on non-syndromic deafness, so the research on syndromic deafness is not in-depth and there is a large degree of lack of research in this area. In order to let the public know more about the diagnosis and gene function of deafness caused by PTPN11 gene mutation syndrome, this paper used deep learning technology to study the diagnosis and gene function of deafness caused by syndrome with the concept of intelligent medical treatment, and finally drew a feasible conclusion. This paper provided a theoretical and practical basis for the diagnosis of deafness caused by PTPN11 gene mutation syndrome and the study of gene function. This paper made a retrospective analysis of the clinical data of 85 deaf children who visited Hunan Children’s Hospital,P.R. China from January 2020 to December 2021. The conclusion were as follows: Children aged 1–6 years old had multiple syndrome deafness, while children under 1 year old and children aged 6–12 years old had relatively low probability of complex deafness; girls were not easy to have comprehensive deafness, but there was no specific basis to prove that the occurrence of comprehensive deafness was necessarily related to gender; the hearing loss of patients with Noonan Syndrome was mainly characterized by moderate and severe damage and abnormal inner ear and auditory nerve; most of the mutation genes in children were located in Exon1 and Exon3, with a total probability of 57.65%. In the course of the experiment, it was found that deep learning was effective in the diagnosis of deafness with PTPN11 gene mutation syndrome. This technology could be applied to medical diagnosis to facilitate the diagnosis and treatment of more patients with deafness with syndrome. Intelligent medical treatment was also becoming a hot topic nowadays. By using this concept to analyze and study the pathological characteristics of deafness caused by PTPN11 gene mutation syndrome, it not only promoted patients to find diseases in time, but also helped doctors to diagnose and treat such diseases, which was of great significance to patients and doctors. The study of PTPN11 gene mutation syndrome deafness was also of great significance in genetics. The analysis of its genes not only enriched the gene pool, but also provided reference for future research

Controllable ingestion and release of guest components driven by interfacial molecular orientation of host liquid crystal droplets

Controllable construction and manipulation of artificial multi-compartmental structures are crucial in understanding and imitating smart molecular elements such as biological cells and on-demand delivery systems. Here, we report a liquid crystal droplet (LCD) based three-dimensional system for controllable and reversible ingestion and release of guest aqueous droplets (GADs). Induced by interfacial thermodynamic fluctuation and internal topological defect, microscale LCDs with perpendicular anchoring condition at the interface would spontaneously ingest external components from the surroundings and transform them as radially assembled tiny GADs inside LCDs. Landau–de Gennes free-energy model is applied to describe and explain the assembly dynamics and morphologies of these tiny GADs, which presents a good agreement with experimental observations. Furthermore, the release of these ingested GADs can be actively triggered by changing the anchoring conditions at the interface of LCDs. Since those ingestion and release processes are controllable and happen very gently at room temperature and neutral pH environment without extra energy input, these microscale LCDs are very prospective to provide a unique and viable route for constructing hierarchical 3D structures with tunable components and compartments

Heterogeneous Nitrate Production Mechanisms in Intense Haze Events in the North China Plain

Abstract Studies of wintertime air quality in the North China Plain (NCP) show that particulate-nitrate pollution persists despite rapid reduction in NOx emissions. This intriguing NOx-nitrate relationship may originate from non-linear nitrate-formation chemistry, but it is unclear which feedback mechanisms dominate in NCP. In this study, we re-interpret the wintertime observations of 17O excess of nitrate (∆17O(NO3−)) in Beijing using the GEOS-Chem (GC) chemical transport model to estimate the importance of various nitrate-production pathways and how their contributions change with the intensity of haze events. We also analyze the relationships between other metrics of NOy chemistry and [PM2.5] in observations and model simulations. We find that the model on average has a negative bias of −0.9‰ and −3617O(NO3−) and [Ox,major] (≡ [O3] + [NO2] + [p-NO3−]), respectively, while overestimating the nitrogen oxidation ratio ([NO3−]/([NO3−] + [NO2])) by +0.12 in intense haze. The discrepancies become larger in more intense haze. We attribute the model biases to an overestimate of NO2-uptake on aerosols and an underestimate in wintertime O3 concentrations. Our findings highlight a need to address uncertainties related to heterogeneous chemistry of NO2 in air-quality models. The combined assessment of observations and model results suggest that N2O5 uptake in aerosols and clouds is the dominant nitrate-production pathway in wintertime Beijing, but its rate is limited by ozone under high-NOx-high-PM2.5 conditions. Nitrate production rates may continue to increase as long as [O3] increases despite reduction in [NOx], creating a negative feedback that reduces the effectiveness of air pollution mitigation

The Separation and Purification of Ellagic Acid from Phyllanthus urinaria L. by a Combined Mechanochemical-Macroporous Resin Adsorption Method

Ellagic acid is a phenolic compound that exhibits both antimutagenic and anticarcinogenic activity in a wide range of assays in vitro and in vivo. It occurs naturally in some foods such as raspberries, strawberries, grapes, and black currants. In this study, a valid and reliable method based on mechanochemical-assisted extraction (MCAE) and macroporous adsorption resin was developed to extract and prepare ellagic acid from Phyllanthus urinaria L. (PUL). The MCAE parameters, acidolysis, and macroporous adsorption resin conditions were investigated. The key MCAE parameters were optimized as follows: the milling time was 5 min, the ball mill speed was 100 rpm, and the ball mill filling rate was 20.9%. Sulfuric acid with a concentration of 0.552 mol/L was applied for the acidolysis with the optimized acidolysis time of 30 min and acidolysis temperature of 40 °C. Additionally, the XDA-8D macroporous resin was chosen for the purification work. Both the static and dynamic adsorption tests were carried out. Under the optimized conditions, the yield of ellagic acid was 10.2 mg/g, and the content was over 97%. This research provided a rapid and efficient method for the preparation of ellagic acid from the cheaply and easily obtained PUL. Meanwhile, it is relatively low-cost work that can provide a technical basis for the comprehensive utilization of PUL

Multiplicative Weight for Sparse Generalized Linear Model

Sparse generalized linear model is useful in many fields. In the research, the researchers will learn sparse generalized linear model using different algorithms. The paper determines the better algorithm for learning this model by comparing the convergence rate of mirror descent and projected gradient descent. By implementing the two algorithms and comparing the results, the researchers conclude that the mirror descent converges much faster than the projected gradient descent for learning the sparse generalized linear model. This means the mirror descent algorithm is better for learning this model

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

As a fundamental issue in robotics academia and industry, indoor autonomous mobile robots (AMRs) have been extensively studied. For AMRs, it is crucial to obtain information about their working environment and themselves, which can be realized through sensors and the extraction of corresponding information from the measurements of these sensors. The application of sensing technologies can enable mobile robots to perform localization, mapping, target or obstacle recognition, and motion tasks, etc. This paper reviews sensing technologies for autonomous mobile robots in indoor scenes. The benefits and potential problems of using a single sensor in application are analyzed and compared, and the basic principles and popular algorithms used in processing these sensor data are introduced. In addition, some mainstream technologies of multi-sensor fusion are introduced. Finally, this paper discusses the future development trends in the sensing technology for autonomous mobile robots in indoor scenes, as well as the challenges in the practical application environments

The Reshaping of Neighboring Social Networks after Poverty Alleviation Relocation in Rural China: A Two-Year Observation

As one of China’s key poverty-reduction initiatives, poverty alleviation relocation (PAR) unavoidably results in the reshaping of neighboring social networks. This study equally focused on the changes in the scope of social interaction and in the intergroup social support of the two primary stakeholders of PAR in a rural–rural relocation context: the migrant and local groups. In 2019 and 2021, two surveys were conducted in four different types of resettlements: centralized, adjacent, enclave, and infill. To provide decision makers with broad references for sustainable PAR planning, the social changes were compared by groups, types, and years. In general, the migrant group had more significant scope expansion or narrowing in social interaction than the local group, and they were more willing to seek intergroup social support. Specifically, the centralized type was the superior choice since it was well-expanded and group-balanced; the adjacent type was also a good choice in the long term because of its rapid improvement in the later phase; the enclave type should be a last resort because of its persistently negative impact; and the infill type was a good option in the short term, as it rarely improved in the later stage. Furthermore, the personal socioeconomic attributes associated with the above social changes, claims laid to the spaces, and economic benefits and limitations were explored for a more comprehensive understanding

Artificial Neural Network Modeling for Predicting and Evaluating the Mean Radiant Temperature around Buildings on Hot Summer Days

In recent years, the phenomenon of urban warming has become increasingly serious, and with the number of urban residents increasing, the risk of heatstroke in extreme weather has become higher than ever. In order to mitigate urban warming and adapt to it, many researchers have been paying increasing attention to outdoor thermal comfort. The mean radiant temperature (MRT) is one of the most important variables affecting human thermal comfort in outdoor urban spaces. The purpose of this paper is to predict the distribution of MRT around buildings based on a commonly used multilayer neural network (MLNN) that is optimized by genetic algorithms (GA) and backpropagation (BP) algorithms. Weather data from 2014 to 2018 together with the related indexes of the grid were selected as the input parameters for neural network training, and the distribution of the MRT around buildings in 2019 was predicted. This study obtained very high prediction accuracy, which can be combined with sensitivity analysis methods to analyze the important input parameters affecting the MRT on hot summer days (the days with the highest air temperature over 30 °C). This has significant implications for the optimization strategies for future building and urban designers to improve the thermal conditions around buildings