Slice Level Research Articles

REDD1 Is Involved in Amyloid β-Induced Synaptic Dysfunction and Memory Impairment.

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by neurological dysfunction, including memory impairment, attributed to the accumulation of amyloid β (Aβ) in the brain. Although several studies reported possible mechanisms involved in Aβ pathology, much remains unknown. Previous findings suggested that a protein regulated in development and DNA damage response 1 (REDD1), a stress-coping regulator, is an Aβ-responsive gene involved in Aβ cytotoxicity. However, we still do not know how Aβ increases the level of REDD1 and whether REDD1 mediates Aβ-induced synaptic dysfunction. To elucidate this, we examined the effect of Aβ on REDD1-expression using acute hippocampal slices from mice, and the effect of REDD1 short hairpin RNA (shRNA) on Aβ-induced synaptic dysfunction. Lastly, we observed the effect of REDD1 shRNA on memory deficit in an AD-like mouse model. Through the experiments, we found that Aβ-incubated acute hippocampal slices showed increased REDD1 levels. Moreover, Aβ injection into the lateral ventricle increased REDD1 levels in the hippocampus. Anisomycin, but not actinomycin D, blocked Aβ-induced increase in REDD1 levels in the acute hippocampal slices, suggesting that Aβ may increase REDD1 translation rather than transcription. Aβ activated Fyn/ERK/S6 cascade, and inhibitors for Fyn/ERK/S6 or mGluR5 blocked Aβ-induced REDD1 upregulation. REDD1 inducer, a transcriptional activator, and Aβ blocked synaptic plasticity in the acute hippocampal slices. REDD1 inducer inhibited mTOR/Akt signaling. REDD1 shRNA blocked Aβ-induced synaptic deficits. REDD1 shRNA also blocked Aβ-induced memory deficits in passive-avoidance and object-recognition tests. Collectively, these results demonstrate that REDD1 participates in Aβ pathology and could be a target for AD therapy.

A multi-label classification model for full slice brain computerised tomography image

BackgroundScreening of the brain computerised tomography (CT) images is a primary method currently used for initial detection of patients with brain trauma or other conditions. In recent years, deep learning technique has shown remarkable advantages in the clinical practice. Researchers have attempted to use deep learning methods to detect brain diseases from CT images. Methods often used to detect diseases choose images with visible lesions from full-slice brain CT scans, which need to be labelled by doctors. This is an inaccurate method because doctors detect brain disease from a full sequence scan of CT images and one patient may have multiple concurrent conditions in practice. The method cannot take into account the dependencies between the slices and the causal relationships among various brain diseases. Moreover, labelling images slice by slice spends much time and expense. Detecting multiple diseases from full slice brain CT images is, therefore, an important research subject with practical implications.ResultsIn this paper, we propose a model called the slice dependencies learning model (SDLM). It learns image features from a series of variable length brain CT images and slice dependencies between different slices in a set of images to predict abnormalities. The model is necessary to only label the disease reflected in the full-slice brain scan. We use the CQ500 dataset to evaluate our proposed model, which contains 1194 full sets of CT scans from a total of 491 subjects. Each set of data from one subject contains scans with one to eight different slice thicknesses and various diseases that are captured in a range of 30 to 396 slices in a set. The evaluation results present that the precision is 67.57%, the recall is 61.04%, the F1 score is 0.6412, and the areas under the receiver operating characteristic curves (AUCs) is 0.8934.ConclusionThe proposed model is a new architecture that uses a full-slice brain CT scan for multi-label classification, unlike the traditional methods which only classify the brain images at the slice level. It has great potential for application to multi-label detection problems, especially with regard to the brain CT images.

Scattered slice SHARD reconstruction for motion correction in multi-shell diffusion MRI

Diffusion MRI offers a unique probe into neural microstructure and connectivity in the developing brain. However, analysis of neonatal brain imaging data is complicated by inevitable subject motion, leading to a series of scattered slices that need to be aligned within and across diffusion-weighted contrasts. Here, we develop a reconstruction method for scattered slice multi-shell high angular resolution diffusion imaging (HARDI) data, jointly estimating an uncorrupted data representation and motion parameters at the slice or multiband excitation level. The reconstruction relies on data-driven representation of multi-shell HARDI data using a bespoke spherical harmonics and radial decomposition (SHARD), which avoids imposing model assumptions, thus facilitating to compare various microstructure imaging methods in the reconstructed output. Furthermore, the proposed framework integrates slice-level outlier rejection, distortion correction, and slice profile correction. We evaluate the method in the neonatal cohort of the developing Human Connectome Project (650 scans). Validation experiments demonstrate accurate slice-level motion correction across the age range and across the range of motion in the population. Results in the neonatal data show successful reconstruction even in severely motion-corrupted subjects. In addition, we illustrate how local tissue modelling can extract advanced microstructure features such as orientation distribution functions from the motion-corrected reconstructions.

Expiratory CT: What is Good Enough?

This study obtained approval from the Institutional Review Board. The craniocaudal dimensions of inspiratory and expiratory chest CT scans of 83 patients were examined by an experienced radiologist (MS with 20 years of experience). The difference between the two measurements, called the Slice Level Difference (SLD), was calculated. Mosaic attenuation on inspiratory images and air trapping on expiratory images were quantified, as well as the change in grade between the two series. Age and gender were compared to degree of expiratory effort.

Reduction in types I and III collagen gene expression by an alphavbeta1 integrin inhibitor correlates with a decrease in PRO-C1 and PRO-C3 levels in precision-cut liver tissue slices from cirrhotic non-alcoholic steatohepatitis patients

Reduction in types I and III collagen gene expression by an alphavbeta1 integrin inhibitor correlates with a decrease in PRO-C1 and PRO-C3 levels in precision-cut liver tissue slices from cirrhotic non-alcoholic steatohepatitis patients

Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans.

The COVID-19 pneumonia is a global threat since it emerged in early December 2019. Driven by the desire to develop a computer-aided system for the rapid diagnosis of COVID-19 to assist radiologists and clinicians to combat with this pandemic, we retrospectively collected 206 patients with positive reverse-transcription polymerase chain reaction (RT-PCR) for COVID-19 and their 416 chest computed tomography (CT) scans with abnormal findings from two hospitals, 412 non-COVID-19 pneumonia and their 412 chest CT scans with clear sign of pneumonia are also retrospectively selected from participating hospitals. Based on these CT scans, we design an artificial intelligence (AI) system that uses a multi-scale convolutional neural network (MSCNN) and evaluate its performance at both slice level and scan level. Experimental results show that the proposed AI has promising diagnostic performance in the detection of COVID-19 and differentiating it from other common pneumonia under limited number of training data, which has great potential to assist radiologists and physicians in performing a quick diagnosis and mitigate the heavy workload of them especially when the health system is overloaded. The data is publicly available for further research at https://data.mendeley.com/datasets/3y55vgckg6/1https://data.mendeley.com/datasets/3y55vgckg6/1.

UV-C Treatment Applied Alone or Combined with Orange Juice to Improve the Bioactive Properties, Microbiological, and Sensory Quality of Fresh-Cut Strawberries

The effects of UV-C treatment (5.8 kJ m−2) alone and combined with the addition of orange juice on the bioactive properties, microbiological, and sensory quality of fresh-cut strawberries for 12 days at 0 °C were studied. During storage, the contents of ascorbic acid (AA), total phenols, flavonoids, and anthocyanins were enhanced in strawberry slices only treated with UV-C (UV slices), whereas ellagic acid slightly decreased in correlation with the antioxidant capacity. After 12 days of storage, AA level in UV slices was 10% higher than those in the other samples studied. In addition, UV slices showed higher contents of phenols (about 14%), flavonoids (about 23%), and anthocyanins (11–41%) than untreated slices during storage. However, fresh-cut strawberries UV-C treated and immersed in juice showed slight but significant AA decrease due to its oxidation to dehydroascorbic acid. Also, phenolic compounds decreased that was associated with their diffusion to the juice and correlated with the antioxidant capacity. Although both UV-C treatments alone and combined with juice immersion reduced microbial load, UV-C applied alone allowed obtaining the highest microbial reductions. After 8 days of storage, the most significant reductions were found, 1.4 log units for mesophilic bacteria and 1.3 log units for molds and yeasts. In addition, UV-C applied alone allowed maintaining firmness and obtaining a product with the best sensory quality throughout storage. Hence, the irradiation at 5.8 kJ m−2 applied alone was the most effective preserving treatment and may be useful to obtain a value-added product.

Development of gelatin/chitosan film incorporated with lemon essential oil with antioxidant properties

This study aims to evaluate the physical, chemical, and mechanical properties of gelatin/chitosan (Ge/Ch) films enriched with different ratios of lemon essential oil (LEO; 0.25–0.50–0.75–1%) and Ge/Ch (60%/40%) based film without LEO as control. Total phenolic compound content (TPCC) of film samples enriched with LEO were found between 120.3 mg/kg and 276 mg/kg. Antioxidant activity values of LEO containing Ge/Ch films were found between 13.12% and 69.99%. As a result, it was determined that with increased LEO ratio, TPCC and antioxidant activity increased. In general, the higher LEO concentration-incorporated Ge/Ch films had better mechanical properties than the Ge/Ch film. The higher LEO concentration-incorporated Ge/Ch films were opaque and more resistant to light transmission. Considering the effect of LEO on consumers’ preference, the optimum LEO ratio has been determined as 0.75%. Fresh red apple slices were coated with Ge/Ch based film, Ge/Ch based film containing 0.75% LEO, and some samples were not coated for control purposes. All these samples were stored at + 4 °C in the refrigerator for 7 days. Total mesophilic aerobic bacteria count (TMABC) was not at detectable levels in apple slices coated with Ge/Ch films containing 0.75% LEO during storage. The Ge/Ch film incorporated with 0.75% LEO showed the best overall properties. This composite material could be a good preference for extending the shelf life of apples and decreasing food waste. Ge/Ch films incorporated with LEO have good physical, mechanical, and chemical properties therefore this natural material combination could be a green alternative to synthetic compounds for food packaging application.

Assimilation of atmospheric infrasound data to constrain tropospheric and stratospheric winds

AbstractThis data assimilation study exploits infrasound from explosions to probe an atmospheric wind component from the ground up to stratospheric altitudes. Planned explosions of old ammunition in Finland generate transient infrasound waves that travel through the atmosphere. These waves are partially reflected back towards the ground from stratospheric levels, and are detected at a receiver station located in northern Norway at 178 km almost due north from the explosion site. The difference between the true horizontal direction towards the source and the backazimuth direction (the horizontal direction of arrival) of the incoming infrasound wavefronts, in combination with the pulse propagation time, are exploited to provide an estimate of the average cross‐wind component in the penetrated atmosphere. We perform offline assimilation experiments with an ensemble Kalman filter and these observations, using the ERA5 ensemble reanalysis atmospheric product as background (prior) for the wind at different vertical levels. We demonstrate that information from both sources can be combined to obtain analysis (posterior) estimates of cross‐winds at different vertical levels of the atmospheric slice between the explosion site and the recording station. The assimilation makes greatest impact at the 12–60 km levels, with some changes with respect to the prior of the order of 0.1–1.0 m·s−1, which is a magnitude larger than the typical standard deviation of the ERA5 background. The reduction of background variance in the higher levels often reached 2–5%. This is the first published study demonstrating techniques to implement assimilation of infrasound data into atmospheric models. It paves the way for further exploration in the use of infrasound observations – especially natural and continuous sources – to probe the middle atmospheric dynamics and to assimilate these data into atmospheric model products.

Study on the Variations of Image Density and Morphologic Index of Multidetector Row Computed Tomography for Healthy Lungs

We selected several image density and morphologic indices to establish CAD for prediction of biopsy in lung diseases. Our results suggested that significant difference was not observed in the image density indices between bilateral lungs at each slice level. Our own designed image indices may be reliable parameters in the establishment of CAD system and pathologic diagnosis for lung disease.

Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.

To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images. This institutional review board-approved retrospective study evaluated CECT in 315 patients with 77 benign (57 oncocytomas, and 20 fat-poor angiomyolipoma) and 238 malignant (RCC: 123 clear cell, 69 papillary, and 46 chromophobe subtypes) tumors identified consecutively between 2015 and 2017. We employed a decision fusion-based model to aggregate slice level predictions determined by convolutional neural network (CNN) via a majority voting system to evaluate renal masses on CECT. The CNN-based model was trained using 7023 slices with renal masses manually extracted from CECT images of 155 patients, cropped automatically around kidneys, and augmented artificially. We also examined the fully automated approach for renal mass evaluation on CECT. Moreover, a 3D CNN was trained and tested using the same datasets and the obtained results were compared with those acquired from slice-wise algorithms. For differentiation of RCC versus benign solid masses, the semi-automated majority voting-based CNN algorithm achieved accuracy, precision, and recall of 83.75%, 89.05%, and 91.73% using 160 test cases, respectively. Fully automated pipeline yielded accuracy, precision, and recall of 77.36%, 85.92%, and 87.22% on the same test cases, respectively. 3D CNN reported accuracy, precision, and recall of 79.24%, 90.32%, and 84.21% using 160 test cases, respectively. A semi-automated majority voting CNN-based methodology enabled accurate classification of RCC from benign neoplasms among solid renal masses on CECT. • Our proposed semi-automated majority voting CNN-based algorithm achieved accuracy of 83.75% for the diagnosis of RCC from benign solid renal masses on CECT images. • A fully automated CNN-based methodology classified solid renal masses with moderate accuracy of 77.36% using the same test images. • Employing 3D CNN-based methodology yielded slightly lower accuracy for renal mass classification compared with the semi- automated 2D CNN-based algorithm (79.24%).

The PHY-NGSC-Based ORT Run Length Encoding Scheme for Video Compression

This paper proposes a compression algorithm using octonary repetition tree (ORT) based on run length encoding (RLE). Generally, RLE is one type of lossless data compression method which has duplication problem as a major issue due to the usage of code word or flag. Hence, ORT is offered instead of using a flag or code word to overcome this issue. This method gives better performance by means of compression ratio, i.e. 99.75%. But, the functioning of ORT is not good in terms of compression speed. For that reason, physical- next generation secure computing (PHY-NGSC) is hybridized with ORT to raise the compression speed. It uses an MPI-open MP programming paradigm on ORT to improve the compression speed of encoder. The planned work achieves multiple levels of parallelism within an image such as MPI and open MP for parallelism across a group of pictures level and slice level, respectively. At the same time, wide range of data compression like multimedia, executive files and documents are possible in the proposed method. The performance of the proposed work is compared with other methods like accordian RLE, context adaptive variable length coding (CAVLC) and context-based arithmetic coding (CBAC) through the implementation in Matlab working platform.

Stimulation of the β2 adrenergic receptor rapidly increases phosphorylation of the Na+‐Clcotransporter (NCC)

Adrenergic signaling is proposed to modulate activity of the thiazide‐sensitive Na+‐Clcotransporter (NCC) by direct effects on the renal distal convoluted tubule (DCT). However, there are conflicting data on whether, α1, β1 or β2‐adrenoceptors mediate these effects by activation of the with‐no‐lysine kinases (WNKs) and STE20/SPS1‐related proline alanine‐rich kinase (SPAK) kinase pathway or by other signaling pathways. Here, we determined using RT‐PCR that the β2‐adrenergic receptor was abundantly expressed in mpkDCT cells (a model of the DCT) and in DCT cells isolated from parvalbumin EGFP mice by fluorescence‐activated cell sorting. In mice administered salbutamol (β2 adrenergic receptor agonist used to treat asthma) for 30 min, phosphorylated NCC levels increased, indicating increased activity. However, mice developed hypokalemia, a condition that also increases NCC phosphorylation. To investigate direct effects of β2 adrenergic signaling on DCT cells independent of K+, the effects of salbutamol on mpkDCT cells and in kidney slices/tubule suspensions were examined. Salbutamol gradually increased NCC phosphorylation in the pharmacologically relevant concentration range of 0.01–10 μM, an effect observed after 15 min and maintained at 60 min. Changes in NCC phosphorylation were associated with increased cAMP levels in tubule suspensions, kidney slices and mpkDCT cells. A role for the NCC modulatory PKA/I‐1/PP1 pathway was suggested by attenuated effects of salbutamol on phosphorylated NCC during PKA/PP1 pathway inhibition or in slices from I‐1 KO mice. Inhibition of the WNK‐SPAK pathway with a WNK inhibitor also attenuated, but did not prevent, the salbutamol‐induced phosphorylation of NCC. In conclusion, β2 adrenergic receptor signaling modulates NCC phosphorylation in vivo and ex vivo via PKA and WNK‐dependent pathways. We speculate that this response may be a compensatory mechanism to limit electrogenic renal K+ secretion during β2 adrenergic receptor stimulation of muscle. Furthermore, frequent use of salbutamol in e.g. asthmatic patients may lead to renal NaCl retention and influence blood pressure.Support or Funding InformationThe Novo Nordisk Foundation, The Leducq Foundation and the Danish Medical Research Council.

Effect of Ultrasound and Cellulase Pre-treatment on the Water Distribution, Physical Properties, and Nutritional Components of Lentinula edodes Chips

This study aimed to understand the effect of ultrasound pre-treatment (UP), cellulase pre-treatment (CP) and their combination (UP+CP) pre-treatments on freeze-dried Lentinula edodes chips. The water distribution, microstructure, physical properties (colour, texture and shrinkage) and the main nutritional components (polysaccharides, protein and phenolic compounds) were investigated. The low-field nuclear magnetic resonance spectroscopy results showed that UP+CP improved the water mobility and level in mushroom slices. The chips subjected to UP+CP were found to have no free water. In addition, compared with other pre-treatment, UP+CP chips achieved better physical properties, which were characterized by the texture, colour and pore structures. All these attributors could contribute to a rapidly water transfer and improve the crisp characteristics. However, no significant difference was found in three pre-treatment products in polysaccharide and protein contents, though UP+CP products obtained higher phenolic content. Overall, the application of UP+CP was a potential pre-treatment approach for improving the quality of mushroom chips.

Resource slicing and customization in RAN with dueling deep Q-Network

The emerging future generation 5G technology is expected to support service-oriented virtualized networks where different network applications provide unique services. 5G networks have the potential to allow completely different slices to co-exist in a substrate network and satisfy the differentiated requirements of various users. In networks with heterogeneous traffics, operators are required to provide services in isolation since each operator has its own defined performance requirements. However, achieving an efficient resource provisioning mechanism for such traffics is very challenging. This paper proposes a coarse resource provisioning scheme and a dynamic resource slicing refinement scheme based on dueling deep reinforcement learning for virtualized radio access network. Firstly, coarse resource provisioning scheme provisions and allocates radio resource to slices based on preferences and weights at different base stations. Secondly, reinforcement learning based slicing refinement adjusts the resource allocated to slices autonomously in order to balance satisfaction and resource utilization. The proposed dueling DQN algorithm unifies two objectives (QoS satisfaction and resource utilization) by weights to indicate the importance of each factor in the reward function. After the dueling DQN algorithm has output actions to provision resource at slice level, BS-level resource update is performed. Also, a common learning agent is used to control the activities of all the slices in the network. Then, a shape-based resource allocation algorithm is proposed to customize the diverse requirements of users to improve user satisfaction and resource utilization. Finally, a comprehensive performance evaluation is conducted against state-of-the-art solutions based on OFDMA air-interface design. The results reveal that the proposed algorithm balances satisfaction and resource utilization with 80% of the available resources. The algorithm also provides performance isolation such that, a sudden change in user population in one slice does not affect the others.

Statistical Analysis of Shrinkage Levels of Human Brain Slices Preserved by Sheet Plastination Technique With Polyester Resin

Statistical Analysis of Shrinkage Levels of Human Brain Slices Preserved by Sheet Plastination Technique With Polyester Resin

Abstract WP59: Cerebral Blood Flow Increase After Endovascular Thrombectomy on Perfusion Weighted Image is Associated With Hemorrhagic Transformation

Introduction: The role of increased CBF after endovascular thrombectomy in post-ischemic hyperperfusion has not been studied in detail. We aimed to investigate the timing of CBF increases on PWI after thrombectomy in association with hemorrhagic transformation. Methods: We analyzed prospectively collected data in consecutive patients treated with endovascular thrombectomy. Inclusion criteria were: (1) patients with ICA or M1 occlusion, and (2) PWI and GRE obtained within 12 hours and 12-48 hours after thrombectomy. We compared each rCBF with early hemorrhage (within 12 hours after thrombectomy), late hemorrhage (12-48 hour) and non-hemorrhage in basal ganglia (BG) and MCA cortical or subcortical (CS) region. In each PWI dataset, ROIs were placed in two slice levels of the BG and three slice levels of the CS region. Results: Fifty-three patients met inclusion criteria. Early BG hemorrhages were noted in 13 patients, with 4 late BG hemorrhage, 8 early CS hemorrhage and 3 late CS hemorrhage. There were no significant differences on rCBF in PWI within 12 hours after thrombectomy between early hemorrhage, late hemorrhage and non-hemorrhage groups. In contrast, rCBF on 12-48 hours PWI in the BG region was significantly higher in the early BG hemorrhage than non-BG hemorrhage (lower BG slice 1.36 vs 1.01, p<0.001, upper BG slice 1.33 vs 0.96, p<0.001) and rCBF in CS region were significantly higher in early CS hemorrhage than non-CS hemorrhage (lower CS slice 1.55 vs 0.98, p=0.001, middle CS slice 1.31 vs 0.92, p=0.018). There were no significant differences in rCBF on 12-48 hours PWI between the late hemorrhage and non-hemorrhage group. Conclusions: Most intracerebral hemorrhages after thrombectomy were seen within 12 hours after intervention. A rCBF increase in hemorrhage cases was not seen on PWI within 12 hours after thrombectomy. rCBF increases on PWI 12-48 hours after thrombectomy, however, was associated with post-thrombectomy hemorrhage within 12 hours.

Novel Volumetric Sub-region Segmentation in Brain Tumors.

A novel deep learning based model called Multi-Planar Spatial Convolutional Neural Network (MPS-CNN) is proposed for effective, automated segmentation of different sub-regions viz. peritumoral edema (ED), necrotic core (NCR), enhancing and non-enhancing tumor core (ET/NET), from multi-modal MR images of the brain. An encoder-decoder type CNN model is designed for pixel-wise segmentation of the tumor along three anatomical planes (axial, sagittal, and coronal) at the slice level. These are then combined, by incorporating a consensus fusion strategy with a fully connected Conditional Random Field (CRF) based post-refinement, to produce the final volumetric segmentation of the tumor and its constituent sub-regions. Concepts, such as spatial-pooling and unpooling are used to preserve the spatial locations of the edge pixels, for reducing segmentation error around the boundaries. A new aggregated loss function is also developed for effectively handling data imbalance. The MPS-CNN is trained and validated on the recent Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018 dataset. The Dice scores obtained for the validation set for whole tumor (WT :NCR/NE +ET +ED), tumor core (TC:NCR/NET +ET), and enhancing tumor (ET) are 0.90216, 0.87247, and 0.82445. The proposed MPS-CNN is found to perform the best (based on leaderboard scores) for ET and TC segmentation tasks, in terms of both the quantitative measures (viz. Dice and Hausdorff). In case of the WT segmentation it also achieved the second highest accuracy, with a score which was only 1% less than that of the best performing method.

Weakly-Supervised Network for Detection of COVID-19 in Chest CT Scans.

Deep Learning-based chest Computed Tomography (CT) analysis has been proven to be effective and efficient for COVID-19 diagnosis. Existing deep learning approaches heavily rely on large labeled data sets, which are difficult to acquire in this pandemic situation. Therefore, weakly-supervised approaches are in demand. In this paper, we propose an end-to-end weakly-supervised COVID-19 detection approach, ResNext+, that only requires volume level data labels and can provide slice level prediction. The proposed approach incorporates a lung segmentation mask as well as spatial and channel attention to extract spatial features. Besides, Long Short Term Memory (LSTM) is utilized to acquire the axial dependency of the slices. Moreover, a slice attention module is applied before the final fully connected layer to generate the slice level prediction without additional supervision. An ablation study is conducted to show the efficiency of the attention blocks and the segmentation mask block. Experimental results, obtained from publicly available datasets, show a precision of 81.9% and F1 score of 81.4%. The closest state-of-the-art gives 76.7% precision and 78.8% F1 score. The 5% improvement in precision and 3% in the F1 score demonstrate the effectiveness of the proposed method. It is worth noticing that, applying image enhancement approaches do not improve the performance of the proposed method, sometimes even harm the scores, although the enhanced images have better perceptual quality.

Physico-chemical conditions of crystallization and ore-bearing of granitoids of the border zone of the Southern and Middle Urals

The geological structure of the border zone of the Southern and Middle Urals (Kunashak area), as well as the composition and conditions for the formation of granitoid complexes developed here, whose age varies from Devonian to Triassic, is briefly considered. Thermobarometric studies of quartz of medium-grained biotite granites showed that in the age range ― Poletaev (D3), Shalkar (P1), Sultayev (P1), and Yugokonev (P1T2) complexes ― water pressure during the formation of melt inclusions was 2.22.7, 2.53.0, 3.94.4 and 3.64.5 kbar, and the crystallization temperature of rocks ― 800840, 900940, 840900 and 940980С. This indicates a consistent increase in the crystallization depth of the granite massifs in this series from the hypabyssal-subsurface to the hypabyssal-abyssal facies. At the same time, in the melt-fluid inclusions, a decrease in fluid volume is noted ― 14.216.8, 8.114.3, 6.27.9 and 4.36.8 vol.%, Water concentrations ― 3.74.8, 2.13.9, 1.82.3 and 1.31.9 wt.% as well as chlorine ― 0.040.08, 0.030.07, 0.030.06 and 0.020.05 wt.%.
 It is shown that the granitoids of the Sultayev and Yugokonev complexes are very promising for tantalum, niobium, tungsten, molybdenum and beryllium. Of the greatest interest in rare-metal mineralization are the leucocratic aplitic and greisen granites of the final phases of the formation of intrusions. The level of erosional slice of various structural and formation zones of the Kunashak area relative to each other has been established.