Case report of a Li-Fraumeni syndrome-like phenotype with a de novo mutation in <i>CHEK2</i>

BACKGROUND: Cases of multiple tumors are rarely reported in China. In our study, a 57-year-old female patient had concurrent squamous cell carcinoma, mucoepidermoid carcinoma, brain cancer, bone cancer, and thyroid cancer, which has rarely been reported to date. METHODS: To determine the relationship among these multiple cancers, available DNA samples from the thyroid, lung, and skin tumors and from normal thyroid tissue were sequenced using whole exome sequencing. RESULTS: The notable discrepancies of somatic mutations among the 3 tumor tissues indicated that they arose independently, rather than metastasizing from 1 tumor. A novel deleterious germline mutation (chr22:29091846, G->A, p.H371Y) was identified in CHEK2, a Li–Fraumeni syndrome causal gene. Examining the status of this novel mutation in the patient's healthy siblings revealed its de novo origin. CONCLUSION: Our study reports the first case of Li–Fraumeni syndrome-like in Chinese patients and demonstrates the important contribution of de novo mutations in this type of rare disease

Wheat Ear Detection Algorithm Based on Improved YOLOv4

The continuously growing population requires improving the efficiency of agricultural production. Wheat is one of the most wildly cultivated crops. Intelligent wheat ear monitoring is essential for crop management and crop yield prediction. Although a variety of methods are utilized to detect or count wheat ears, there are still some challenges both from the data acquisition process and the wheat itself. In this study, a computer vision methodology based on YOLOv4 to detect wheat ears is proposed. A large receptive field allows viewing objects globally and increases the connections between the image points and the final activation. Specifically, in order to enhance the receptive field, additional Spatial Pyramid Pooling (SPP) blocks are added to YOLOv4 at the feature fusion section to extract multi-scale features. Pictures of wheat ears taken at different growth stages from two different datasets are used to train the model. The performance of the proposed methodology was evaluated using various metrics. The Average Precision (AP) was 95.16% and 97.96% for the two datasets, respectively. By fitting the detected wheat ear numbers and true wheat ear numbers, the R2 value was 0.973. The results show that the proposed method outperforms YOLOv4 in wheat ear detection. It indicates that the proposed method provides a technical reference for agricultural intelligence

Wheat Ear Detection Algorithm Based on Improved YOLOv4

The continuously growing population requires improving the efficiency of agricultural production. Wheat is one of the most wildly cultivated crops. Intelligent wheat ear monitoring is essential for crop management and crop yield prediction. Although a variety of methods are utilized to detect or count wheat ears, there are still some challenges both from the data acquisition process and the wheat itself. In this study, a computer vision methodology based on YOLOv4 to detect wheat ears is proposed. A large receptive field allows viewing objects globally and increases the connections between the image points and the final activation. Specifically, in order to enhance the receptive field, additional Spatial Pyramid Pooling (SPP) blocks are added to YOLOv4 at the feature fusion section to extract multi-scale features. Pictures of wheat ears taken at different growth stages from two different datasets are used to train the model. The performance of the proposed methodology was evaluated using various metrics. The Average Precision (AP) was 95.16% and 97.96% for the two datasets, respectively. By fitting the detected wheat ear numbers and true wheat ear numbers, the R2 value was 0.973. The results show that the proposed method outperforms YOLOv4 in wheat ear detection. It indicates that the proposed method provides a technical reference for agricultural intelligence

Pinellia ternata agglutinin produced in Bombyx mori cells exhibits bioactivity

Pinellia ternata agglutinin (PTA) is highly homologous to many other monocot mannose-binding lectins which reportedly possess antitumor activities. Its production in silkworm cells has great application potential because the baculovirus expression system can produce post-translationally modified proteins at low cost. In the current study, the pta gene was cloned and expressed in silkworm cells, and the expressed protein was analyzed using a hemagglutination assay. A preliminary in vitro study on its anti-proliferative activity was performed. The results show that the recombinant PTA with an apparent molecular mass of 29 kDa can hemagglutinate rabbit erythrocytes and this activity can be inhibited by D-mannan at a low concentration. In addition, the recombinant hemagglutinin exhibited a dose-dependent anti-proliferative activity on hepatoma cells. The results of the current study suggest that PTA and other important bioactive proteins could be produced by silkworm bioreactor for biomedicine research and application

Spatiotemporal Evolution and Influencing Factors of Electricity Consumption in the Yangtze River Delta Region

Electricity consumption accounts for a considerable part of the final energy consumption, and it is important for economic development and human life. This study explores the spatiotemporal evolution characteristics and influencing factors of electricity consumption in the Yangtze River Delta region in China from 2006 to 2019, using the gravity model and Logarithmic Mean Divisia Index method, respectively. The results show that: (1) The centers of gravity for the total final, industrial and residential electricity consumptions have a trend of migration towards the west. (2) The distance of migration of the center of gravity for residential electricity consumption is the highest, and the trend of migration of the center of gravity for industrial and total final electricity consumptions are synchronous. (3) Economic development is the main reason for the growth in regional electricity consumption, and the decrease in the investment electricity consumption intensity inhibits the growth of electricity consumption. This study provides references to restrain the excessive increase in electricity consumption and improve the layout of power facilities at the regional level

High-temperature ferromagnetism of helical carbon nanotubes

We report the experimental results on the magnetism of curvature-induced helical carbon nanotubes (HCNTs). It is demonstrated that without any magnetic impurities in the sample, the as-prepared HCNTs show clear ferromagnetism with a Curie point as high as 970 K

Modeling the current and future distribution of Brucellosis under climate change scenarios in Qinghai Lake basin, China

Bruce llosis is a bacterial disease caused by various Brucella species, which infect primarily cattle, swine, goats, sheep, and dogs. The disease is typically transmitted to humans through direct contact with diseased animals, consumption of contaminated animal products, or inhalation of airborne pollutants. The majority of cases are caused by consuming unpasteurized goat or sheep milk or cheese. Based on observed Brucellosis occurrence data and ecogeographic variables, a MaxEnt algorithm was used to model the current and future distribution of Brucellosis in Qinghai Lake basin, P.R. China. Our model showed the Brucellosis current distribution and predicts suitable habitat shifts under future climate scenarios. In the new representatives; SSP 2.6 and SSP 4.5 for the year 2050s and 2070s, our model predicts an expansion in the current suitable areas. This indicates that under the possible climate changes in the future, the living space of Brucellosis in Qinghai Lake basin China will expand significantly. Ecogeographic variables that contributed significantly to the distribution of Brucellosis in Qinghai Lake basin are revealed by our model. The results of our study will promote comparisons with future research and provide a new perspective to inform decision-making in the field of public health in Qinghai province

Development and Experimental Analysis of a Fuzzy Grey Control System on Rapeseed Cleaning Loss

One of the most important means of improving the mechanization of rapeseed harvests and increasing farmers&rsquo; income is to reduce the cleaning loss of rapeseed. In this study, a fuzzy grey control system was developed using an assembled cleaning loss sensor. Based on experimental data, the relationship between the cleaning loss and the opening of the louver sieve in the cleaning device was obtained. The fuzzy control scheme was established by combining grey prediction and the fuzzy control principle. Secondly, a microcontroller unit (MCU) was used as the controller, and the opening of the louver sieve was automatically regulated by detecting the signal of the cleaning loss. Finally, the performance and robustness of the control system was evaluated in field tests. Different experiments were conducted under different speed conditions to reflect the variable throughput. Results showed that using the grey prediction control system can realize the adjustment of the louver sieve opening in real time. The cleaning loss could be maintained within the ideal setpoint interval, compared with the operation with the control system switched off. These findings indicate that the application of the grey fuzzy control system reduces cleaning loss, and the nonlinear, time-variable and time delay problems in cleaning devices can be solved effectively