Clinical features and therapeutic outcomes of GH/TSH cosecreting pituitary adenomas: experience of a single pituitary center

BackgroundGrowth hormone (GH)/thyroid stimulating hormone (TSH) cosecreting pituitary adenoma (PA) is an exceedingly rare kind of bihormonal pituitary neuroendocrine tumors (PitNETs). Its clinical characteristics have rarely been reported.ObjectivesThis study aimed to summarize the clinical characteristics and experience of diagnosis and treatment among patients with mixed GH/TSH PAs from a single center.MethodsWe retrospectively reviewed GH/TSH cosecreting PAs from 2063 patients diagnosed with GH-secreting PAs admitted to Peking Union Medical College Hospital between January 1st, 2010, and August 30th, 2022, to investigate the clinical characteristics, hormone detection, imaging findings, treatment patterns and outcomes of follow-up. We further compared these mixed adenomas with age- and sex-matched cases of GH mono-secreting PAs (GHPAs). The data of the included subjects were collected using electronic records from the hospital’s information system.ResultsBased on the inclusion and exclusion criteria, 21 GH/TSH cosecreting PAs were included. The average age of symptom onset was 41.6 ± 14.9 years old, and delayed diagnosis occurred in 57.1% (12/21) of patients. Thyrotoxicosis was the most common complaint (10/21, 47.6%). The median inhibition rates of GH and TSH in octreotide suppression tests were 79.1% [68.8%, 82.0%] and 94.7% [88.2%, 97.0%], respectively. All these mixed PAs were macroadenomas, and 23.8% (5/21) of them were giant adenomas. Comprehensive treatment strategies comprised of two or more therapy methods were applied in 66.7% (14/21) of patients. Complete remission of both GH and TSH was accomplished in one-third of cases. In the comparison with the matched GHPA subjects, the mixed GH/TSH group presented with a higher maximum diameter of the tumor (24.0 [15.0, 36.0] mm vs. 14.7 [10.8, 23.0] mm, P = 0.005), a greater incidence of cavernous sinus invasion (57.1% vs. 23.8%, P = 0.009) and a greater difficulty of long-term remission (28.6% vs. 71.4%, P <0.001). In addition, higher occurrence rates of arrhythmia (28.6% vs. 2.4%, P = 0.004), heart enlargement (33.3% vs. 4.8%, P = 0.005) and osteopenia/osteoporosis (33.3% vs. 2.4%, P = 0.001) were observed in the mixed PA group.ConclusionThere are great challenges in the treatment and management of GH/TSH cosecreting PA. Early diagnosis, multidisciplinary therapy and careful follow-up are required to improve the prognosis of this bihormonal PA

Automatic Modulation Classification for Underwater Acoustic Communication Signals Based on Deep Complex Networks

Automatic modulation classification (AMC) is an important method for monitoring and identifying any underwater communication interference. Since the underwater acoustic communication scenario is full of multi-path fading and ocean ambient noise (OAN), coupled with the application of modern communication technology, which is usually susceptible to environmental influences, automatic modulation classification (AMC) becomes particularly difficult when it comes to an underwater scenario. Motivated by the deep complex networks (DCN), which have an innate ability to process complex data, we explore DCN for AMC of underwater acoustic communication signals. To integrate the signal processing method with deep learning and overcome the influences of underwater acoustic channels, we propose two complex physical signal processing layers based on DCN. The proposed layers include a deep complex matched filter (DCMF) and deep complex channel equalizer (DCCE), which are designed to remove noise and reduce the influence of multi-path fading for the received signals, respectively. Hierarchical DCN is constructed using the proposed method to achieve better performance of AMC. The influence of the real-world underwater acoustic communication scenario is taken into account; two underwater acoustic multi-path fading channels are conducted using the real-world ocean observation dataset, white Gaussian noise, and real-world OAN are used as the additive noise, respectively. Contrastive experiments show that the AMC based on DCN can achieve better performance than the traditional deep neural network based on real value (the average accuracy of the DCN is 5.3% higher than real-valued DNN). The proposed method based on DCN can effectively reduce the influence of underwater acoustic channels and improve the AMC performance in different underwater acoustic channels. The performance of the proposed method was verified on the real-world dataset. In the underwater acoustic channels, the proposed method outperforms a series of advanced AMC method

A Spatial-Enhanced LSE-SFIM Algorithm for Hyperspectral and Multispectral Images Fusion

The fusion of a hyperspectral image (HSI) and multispectral image (MSI) can significantly improve the ability of ground target recognition and identification. The quality of spatial information and the fidelity of spectral information are normally contradictory. However, these two properties are non-negligible indicators for multi-source remote-sensing images fusion. The smoothing filter-based intensity modulation (SFIM) method is a simple yet effective model for image fusion, which can improve the spatial texture details of the image well, and maintain the spectral characteristics of the image significantly. However, traditional SFIM has a poor effect for edge information sharpening, leading to a bad overall fusion result. In order to obtain better spatial information, a spatial filter-based improved LSE-SFIM algorithm is proposed in this paper. Firstly, the least square estimation (LSE) algorithm is combined with SFIM, which can effectively improve the spatial information quality of the fused image. At the same time, in order to better maintain the spatial information, four spatial filters (mean, median, nearest and bilinear) are used for the simulated MSI image to extract fine spatial information. Six quality indexes are used to compare the performance of different algorithms, and the experimental results demonstrate that the LSE-SFIM based on bilinear (LES-SFIM-B) performs significantly better than the traditional SFIM algorithm and other spatially enhanced LSE-SFIM algorithms proposed in this paper. Furthermore, LSE-SFIM-B could also obtain similar performance compared with three state-of-the-art HSI-MSI fusion algorithms (CNMF, HySure, and FUSE), while the computing time is much shorter

Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition

Underwater acoustic target recognition based on ship-radiated noise belongs to the small-sample-size recognition problems. A competitive deep-belief network is proposed to learn features with more discriminative information from labeled and unlabeled samples. The proposed model consists of four stages: (1) A standard restricted Boltzmann machine is pretrained using a large number of unlabeled data to initialize its parameters; (2) the hidden units are grouped according to categories, which provides an initial clustering model for competitive learning; (3) competitive training and back-propagation algorithms are used to update the parameters to accomplish the task of clustering; (4) by applying layer-wise training and supervised fine-tuning, a deep neural network is built to obtain features. Experimental results show that the proposed method can achieve classification accuracy of 90.89%, which is 8.95% higher than the accuracy obtained by the compared methods. In addition, the highest accuracy of our method is obtained with fewer features than other methods

Spatial–Temporal Distribution of Tropospheric Specific Humidity in the Arid Region of Northwest China

Based on the atmospheric temperature and dew point temperature difference series of mandatory levels in the arid region of northwest China, we calculated the specific humidity of stations at 200, 300, 400, 500, 700, and 850 hPa and analyzed the spatial and temporal distribution. The specific humidity of radiosonde is compared with two sets of reanalysis data (ERA-interim from European Centre for Medium Range Weather Forecasts and Modern Era Retrospective Analysis for Research and Applications: MERRA-2). The annual specific humidity and summer specific humidity show a positive trend in the vertical atmospheric levels during the period 1958–2018. Taking the middle of the 1980s and 2002 as boundaries, the selected levels show the trend of “declining-gentle rising-fluctuation rising”. The maximum specific humidity is observed at the level of 850–700 hPa during the warm months of the year, and the most vertical expansion in specific humidity is in July. In terms of spatial distribution, the specific humidity is greatly influenced by the topography and underlying surface at lower levels. The characteristics of spatial distribution of the trend are well described by the two sets of reanalysis data in the middle and upper levels, but there are some deficiencies in depicting the trend in the lower levels

Hydrochemistry Differences and Causes of Tectonic Lakes and Glacial Lakes in Tibetan Plateau

The Tibetan Plateau has the largest lake cluster in China and in the world. In order to clarify the differences of lake hydrochemistry of Tibetan Plateau, water samples were collected from 32 lakes, including 22 tectonic lakes and 11 glacial lakes, along the Tibetan Plateau road, from September to October 2016. We detected and analyzed the major ion concentrations and characteristics of samples, and discuss the hydrochemistry type, controlling factors, and major ion sources of lake water. The results showed that, firstly, tectonic lake samples on the Tibetan Plateau have much higher physicochemical parameters and ion contents than glacial lakes, and Total Dissolved Solids (TDS) contents fluctuate from high to low latitudes. The variations of ion concentrations in the northern part of the Qiagui Co were more fluctuating and have two obvious peaks, while the variations in the southern part were moderate. The TDS of glacial lakes were low and leveling off in the upper and middle reaches of the basin, while higher and more variable in the lower reaches. Secondly, the tectonic lakes were mainly chloride saline lakes, with Na+ as the major cation, and SO42−, Cl− as the major anions. Glacial lakes were mainly carbonate and sulfate type lakes, Ca2+ and Mg2+ were the major cations, HCO3− was the major anion, and SO42− was the second. Thirdly, the hydrochemistry processes of the tectonic lakes were mainly controlled by evaporation-crystallization, and the ions mainly came from the evaporites of basin. Glacial lake water samples were mainly influenced by the weathering of basin rocks, with ion sources strongly influenced by the weathering of basin carbonates than evaporites, with calcite and dolomite being important sources of Ca2+, Mg2+, and HCO3−

Ship Type Classification by Convolutional Neural Networks with Auditory-Like Mechanisms

Ship type classification with radiated noise helps monitor the noise of shipping around the hydrophone deployment site. This paper introduces a convolutional neural network with several auditory-like mechanisms for ship type classification. The proposed model mainly includes a cochlea model and an auditory center model. In cochlea model, acoustic signal decomposition at basement membrane is implemented by time convolutional layer with auditory filters and dilated convolutions. The transformation of neural patterns at hair cells is modeled by a time frequency conversion layer to extract auditory features. In the auditory center model, auditory features are first selectively emphasized in a supervised manner. Then, spectro-temporal patterns are extracted by deep architecture with multistage auditory mechanisms. The whole model is optimized with an objective function of ship type classification to form the plasticity of the auditory system. The contributions compared with an auditory inspired convolutional neural network include the improvements in dilated convolutions, deep architecture and target layer. The proposed model can extract auditory features from a raw hydrophone signal and identify types of ships under different working conditions. The model achieved a classification accuracy of 87.2% on four ship types and ocean background noise

Glacier Change and Its Influencing Factors in the Northern Part of the Kunlun Mountains

The glaciers in northwest China are mainly distributed in the northern part of the Himalayas, the Kunlun Mountains, and the Tianshan Mountains. Glaciers are an important freshwater resource in the northern part of the Kunlun Mountains, and the melting of glaciers and snow provides an assured source of water for rivers on the southern edge of the Tarim Basin. Based on the first glacier inventory dataset on China (1968), the second glacier inventory dataset on China (2009), and the glacier inventory dataset on Western China in 2018, this study used DEM data, Landsat remote sensing images, and ERA5 atmospheric reanalysis data to investigate glacier change and its influencing factors with respect to the northern part of the Kunlun Mountains. The results showed that there were 9273 glaciers in the northern part of the Kunlun Mountains in 2018, with an area of about 11,762.72 km2, an ice inventory of about 1168.53 km3, and an average length per glacier of about 1.68 km. The glaciers were mainly distributed at altitudes of 5300–6100 m (7574.66 km2). From 1968 to 2018, the number of glaciers in the northern stretch of the Kunlun Mountains increased by 343, while the glacier area decreased by 2452.80 km2 (−0.14%/a). From 2009 to 2018, the glacier area at the altitude of 4900 m to 6100 m decreased in the northern section of the Kunlun Mountains, and the glacier area at the remaining altitude increased slightly (10.67 km2). From 1968 to 2018, the glacier area and glacier length in all river basins decreased. The relative rate of glacier area change in the Qarqan River basin from 2009 to 2018 was five times that of 1968–2009, and this needs significant attention. From 1968 to 2018, both temperature and precipitation increased to varying degrees, and the increase in precipitation was beneficial to the accumulation of glaciers. Therefore, the increase in temperature was the main cause of glacier change in the northern section of the Kunlun Mountains