A novel POF1B variant in a Chinese patient is associated with premature ovarian failure.

A novel mutation of POF1B was identified in a patient with premature ovarian failure.

A de novo Mutation (p.Gln277X) of Cyclin D2 is Responsible for a Child with Megalencephaly-Polymicrogyria-Polydactyly-Hydrocephalus Syndrome.

Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome (MPPH), a type of overgrowth syndrome, is characterized by progressive megalencephaly, cortical brain malformations, and distal limb anomalies. Previous studies have revealed that the overactivity of the phosphatidylinositol 3-kinase-Protein kinase B pathway and the increased cyclin D2 (CCND2) expression were the main factors contributing to this disease. Here, we present the case of a patient who exhibited megalencephaly, polymicrogyria, abnormal neuronal migration, and developmental delay. Serum tandem mass spectrometry and chromosome examination did not detect any metabolic abnormalities or copy number variants. However, whole-exome sequencing and Sanger sequencing revealed a de novo nonsense mutation (NM_001759.3: c.829C>T; p.Gln277X) in the CCND2 gene of the patient. Bioinformatics analysis predicted that this mutation may disrupt the structure and surface charge of the CCND2 protein. This disruption could potentially prevent polyubiquitination of CCND2, leading to its resistance against degradation. Consequently, this could drive cell division and growth by altering the activity of key cell cycle regulatory nodes, ultimately contributing to the development of MPPH. This study not only presents a new case of MPPH and expands the mutation spectrum of CCND2 but also enhances our understanding of the mechanisms connecting CCND2 with overgrowth syndromes.

Case report: A novel variant (H49N) in Myelin Protein Zero gene is responsible for a patient with Charcot-Marie-Tooth disease.

This report presents a case of Charcot-Marie-Tooth dominant intermediate D (CMTDID), a rare subtype of Charcot-Marie-Tooth disease, in a 52 years-old male patient. The patient exhibited mobility impairment, foot abnormalities (pes cavus), and calf muscle atrophy. Whole exome sequencing and Sanger sequencing suggested that a novel variant (NM_000530.8, c.145C>A/p.His49Asn) of MPZ may be the genetic lesion in the patient. The bioinformatic program predicted that the new variant (p.His49Asn), located at an evolutionarily conserved site of MPZ, was neutral. Our study expands the variant spectrum of MPZ and the number of identified CMTDID patients, contributing to a better understanding of the relationship between MPZ and CMTDID.

[Predicting soil property in hilly regions by using landscape and multiscale micro-landform features]. / 基于景观及微地形特征的丘陵区土壤属性预测.

To assess the high-resolution digital soil mapping method for small watersheds in hilly areas, we explored the role of landscape classification and multiscale micro-landform features in predicting soil pH, soil clay content (SCC), and cation exchange capacity (CEC). Geomorphons (GM) terrain classification method was used to create landform units. The traditional digital elevation model (DEM) derivatives and remote sensing variables were employed for different combinations with landscape and micro-landform classification variables, with further compa-rison and analysis being conducted. In addition, three machine learning techniques, including support vector machine (SVM), partial least squares regression (PLSR), and random forest (RF), were used to build prediction models. The best method was then selected, and then combined with regression kriging by modeling spatial structure of the model residuals. The results showed that the application of landscape and multiscale micro-landform classification variables effectively improved the prediction accuracy of pH, SCC, and CEC by 18.8%, 8.2% and 8.7%, respectively. The map of landscape classification that contained vegetation coverage information had greater model contribution than land use data. The GM classification map with 5 m resolution was more suitable for high-precision DSM than those with lower resolution. The composite model of RF performed the best in predicting SCC, while the pH and CEC were not suitable for adding the residual regression kriging on the basis of RF model. Finally, the combination of landscape and multiscale micro-landform classification variables, DEM derivatives and remote sensing variables had the highest prediction accuracy for all the three soil properties. This result indicated that multivariable contained more effective soil information than single data source for rolling areas. The landscape variables composed of GM and surface classified data explained about 40% of the spatial variation of tested soil attributes in hilly area. Therefore, multi-resolution GM and landscape classified variables could be included into the construction of prediction model in research of soil mapping.

[Age- and organ-related variances in fire resistance traits of typical tree species in subtropical China]. / æˆ‘å›½äºšçƒ­å¸¦å ¸åž‹æ ‘ç§æŠ—ç«æ€§çŠ¶éšæž—é¾„å’Œå™¨å®˜çš„å˜å¼‚.

We investigated the fire resistance conferred by different forest age groups (young, middle-age and mature forest) and organs (leaf, branch, and bark) of six typical tree species (Myrica rubra, Schima superba, Symplocos sumuntia, Machilus pingii, Castanopsis eyrei, and Quercus glauca) in Qingshigang national forest farm, Yanling County, Hunan Province, subtropical China. We measured morphological, physical, and chemical properties that could be used as proxies for fire resistance and examined the variances of fire resistance among different organs and age groups in the same tree species. Further, we comprehensively ranked all the tree species by their capacity in fire resistance. We found considerable variation in fire resistance among organs and age groups. Compared with branches and barks, leaves had relatively higher water content (53.7%), higher crude ash content (4.5%), and lower crude fiber content (23.9%). Fire resistance of trees decreased first and then increased with increasing stand age. Trees in middle-aged stage showed the lowest contents of water, crude ash, and crude fiber. The comprehensive scores of fire resistance for diffe-rent organs were significantly different among species. Fire resistance of leaves generally decreased in the order of M. pingii > C. eyrei > S. sumuntia > M. rubra > S. superba > Q. glauca. For branches, M. pingii and C. eyrei showed the strongest fire resistance, followed by M. rubra and S. superba. For barks, S. superba and C. eyrei were relatively stronger in fire resistance than other species, while M. pingii and Q. glauca were the weakest. The comprehensive scores of fire resistance performance of species were different. S. superba (1.033) and M. rubra (0.526) were the most fire-resistant species, while M. pingii (-0.405) and Q. glauca (-1.151) were the least fire-resistant. Therefore, S. superba and M. rubra were the preferred tree species for fire prevention forest belt in forests of subtropical southern China.

[The application of adaptive boosting method in automated spectral classification of active galactic nuclei].

Given a set of low-redshift spectra of active galactic nuclei, the wave bands of spectra in the rest frame were intercepted according to the different features of emission lines of broad-line AGNs and narrow-line AGNs, and an adaptive boosting (Adaboost) method was developed to carry out the classification experiments of feature fusion. As a result, the wave band of Halpha and [N II] was confirmed to be the main discriminative feature between broad-line AGNs and narrow-line AGNs. Then based on the wave band of Halpha and [N II], the Adaboost method was used for the spectral classification. In this method, the "weak classifiers" were increased constantly during training until a scheduled error rate or a maximum cycle times was met, then the classification judgment of the consequent collective classifier was determined by the votes of respective judgments of these "weak classifiers". The Adaboost method needs not to adjust parameters in advance and the results of "weak classifiers" are only required to be better than random guessing, so its algorithm is very simple. As proved by the experiments, the adaboost method achieves good performance in the classification just based on the wave band of Halpha and [N II] so that it could be applied effectively to the automatic classification of large amount of AGN spectra from the large-scale spetral surveys.

[Automated recognition of quasars based on adaptive radial basis function neural networks].

Recognizing and certifying quasars through the research on spectra is an important method in the field of astronomy. This paper presents a novel adaptive method for the automated recognition of quasars based on the radial basis function neural networks (RBFN). The proposed method is composed of the following three parts: (1) The feature space is reduced by the PCA (the principal component analysis) on the normalized input spectra; (2) An adaptive RBFN is constructed and trained in this reduced space. At first, the K-means clustering is used for the initialization, then based on the sum of squares errors and a gradient descent optimization technique, the number of neurons in the hidden layer is adaptively increased to improve the recognition performance; (3) The quasar spectra recognition is effectively carried out by the above trained RBFN. The author's proposed adaptive RBFN is shown to be able to not only overcome the difficulty of selecting the number of neurons in hidden layer of the traditional RBFN algorithm, but also increase the stability and accuracy of recognition of quasars. Besides, the proposed method is particularly useful for automatic voluminous spectra processing produced from a large-scale sky survey project, such as our LAMOST, due to its efficiency.