Observations with a global coverage are very important for space physics research and space weather monitoring. However, due to the technical limitations, it would be very expensive or even ...impossible to achieve a seamless global coverage even with advanced observational devices. It would be useful to fill missing data gaps to create a global map from the available data, but up until now this has been very challenging. Fortunately, the deep learning method, a recent breakthrough in artificial intelligence, may provide an effective way to solve this problem by making full use of data from reliable observations. In this paper, a promising deep learning algorithm, deep convolutional generative adversarial network (DCGAN), is investigated to fill the missing data of total electron content (TEC) map images. The direct use of DCGAN fails to fill missing data for the completion of TEC maps because there are always missing TEC data in some regions, such as oceans, where the features vary with time and geophysical conditions. Thus, no useful information can be utilized by DCGAN to achieve a meaningful image completion. In order to overcome this shortcoming of the original DCGAN method, a novel regularized DCGAN (R‐DCGAN) is proposed by adding an extra discriminator and some widely used reference TEC maps from the International Global Navigation Satellite Systems Service Ionosphere Working Group. The proposed R‐DCGAN method generates satisfactory ionospheric peak structures at different times and geomagnetic conditions, which demonstrate its effectiveness on filling the missing data in TEC maps. The proposed R‐DCGAN framework can be readily extended to a broad application in other fields of space sciences, particularly for addressing the missing observation data issues.
Plain Language Summary
This paper proposes an improved deep learning algorithm, regularized deep convolutional generative adversarial network (R‐DCGAN), for the image completion of total electron content (TEC) maps. The traditional DCGAN (which is a very popular and powerful deep learning algorithm for image completion, such as human face images) needs the training data to be completed observations. Since there is lack of distinct features in the missing data part of the training data that can be utilized by DCGAN to fill these missing values, DCGAN fails to directly learn the observation with data missing. In order to overcome the shortcoming of the original DCGAN method, an improved algorithm, R‐DCGAN, is proposed to fulfill missing data completion for the Massachusetts Institute of Technology‐TEC maps. The R‐DCGAN is designed from DCGAN, with an extra discriminator and the reference TECs. The R‐DCGAN produces satisfactory ionospheric peak structures at different times and geomagnetic conditions, and the results demonstrate that the deep learning algorithm is promising to fill the missing data.
Key Points
This study proposes an improved deep learning algorithm to deal with common missing observation data issues
The result generated by the algorithm can show satisfactory ionospheric peak structures at different times and geomagnetic conditions
The traditional DCGAN fails to directly learn the observation with data missing; in order to overcome this, DCGAN extended to a broader application
From the last decade, pharmaceutical companies are facing difficulties in tracking their products during the supply chain process, allowing the counterfeiters to add their fake medicines into the ...market. Counterfeit drugs are analyzed as a very big challenge for the pharmaceutical industry worldwide. As indicated by the statistics, yearly business loss of around $200 billion is reported by US pharmaceutical companies due to these counterfeit drugs. These drugs may not help the patients to recover the disease but have many other dangerous side effects. According to the World Health Organization (WHO) survey report, in under-developed countries every 10th drug use by the consumers is counterfeit and has low quality. Hence, a system that can trace and track drug delivery at every phase is needed to solve the counterfeiting problem. The blockchain has the full potential to handle and track the supply chain process very efficiently. In this paper, we have proposed and implemented a novel blockchain and machine learning-based drug supply chain management and recommendation system (DSCMR). Our proposed system consists of two main modules: blockchain-based drug supply chain management and machine learning-based drug recommendation system for consumers. In the first module, the drug supply chain management system is deployed using Hyperledger fabrics which is capable of continuously monitor and track the drug delivery process in the smart pharmaceutical industry. On the other hand, the N-gram, LightGBM models are used in the machine learning module to recommend the top-rated or best medicines to the customers of the pharmaceutical industry. These models have trained on well known publicly available drug reviews dataset provided by the UCI: an open-source machine learning repository. Moreover, the machine learning module is integrated with this blockchain system with the help of the REST API. Finally, we also perform several tests to check the efficiency and usability of our proposed system.
Recurrent gene fusions comprise a class of viable genetic targets in solid tumors that have culminated several recent breakthrough cancer therapies. Their role in breast cancer, however, remains ...largely underappreciated due to the complexity of genomic rearrangements in breast malignancy. Just recently, we and others have identified several recurrent gene fusions in breast cancer with important clinical and biological implications. Examples of the most significant recurrent gene fusions to date include (1) ESR1::CCDC170 gene fusions in luminal B and endocrine‐resistant breast cancer that exert oncogenic function via modulating the HER2/HER3/SRC Proto‐Oncogene (SRC) complex, (2) ESR1 exon 6 fusions in metastatic disease that drive estrogen‐independent estrogen‐receptor transcriptional activity, (3) BCL2L14::ETV6 fusions in a more aggressive form of the triple‐negative subtype that prime epithelial–mesenchymal transition and endow paclitaxel resistance, (4) the ETV6::NTRK3 fusion in secretory breast carcinoma that constitutively activates NTRK3 kinase, (5) the oncogenic MYB‐NFIB fusion as a genetic driver underpinning adenoid cystic carcinomas of the breast that activates MYB Proto‐Oncogene (MYB) pathway, and (6) the NOTCH/microtubule‐associated serine–threonine (MAST) kinase gene fusions that activate NOTCH and MAST signaling. Importantly, these fusions are enriched in more aggressive and lethal breast cancer presentations and appear to confer therapeutic resistance. Thus, these gene fusions could be utilized as genetic biomarkers to identify patients who require more intensive treatment and surveillance. In addition, kinase fusions are currently being evaluated in breast cancer clinical trials and ongoing mechanistic investigation is exposing therapeutic vulnerabilities in patients with fusion‐positive disease.
With the development of radiotherapeutic oncology, computer technology and medical imaging technology, radiation therapy has made great progress. Research on the impact and the specific mechanism of ...radiation on tumors has become a central topic in cancer therapy. According to the traditional view, radiation can directly affect the structure of the DNA double helix, which in turn activates DNA damage sensors to induce apoptosis, necrosis, and aging or affects normal mitosis events and ultimately rewires various biological characteristics of neoplasm cells. In addition, irradiation damages subcellular structures, such as the cytoplasmic membrane, endoplasmic reticulum, ribosome, mitochondria, and lysosome of cancer cells to regulate various biological activities of tumor cells. Recent studies have shown that radiation can also change the tumor cell phenotype, immunogenicity and microenvironment, thereby globally altering the biological behavior of cancer cells. In this review, we focus on the effects of therapeutic radiation on the biological features of tumor cells to provide a theoretical basis for combinational therapy and inaugurate a new era in oncology.
"Daqu" is a saccharifying and fermenting agent commonly used in the traditional solid-state fermentation industry (e.g., baijiu and vinegar). The patterns of microbial community succession and flavor ...formation are highly similar among batches, yet the mechanisms promoting temporal succession in the Daqu microbial ecology remain unclear. Here, we first correlated temporal profiles of microbial community succession with environmental variables (temperature, moisture, and titratable acidity) in medium temperature Daqu (MT-Daqu) throughout fermentation. Temperature dynamics significantly correlated (
< 0.05) with the quick succession of MT-Daqu microbiota in the first 12 d of fermentation, while the community structure was relatively stable after 12 d. Then, we explored the effect of temperature on the MT-Daqu community assembly. In the first 4 d of fermentation, the rapid propagation of most bacterial taxa and several fungal taxa, including
,
, and unclassified
and
species, significantly increased MT-Daqu temperature to 55°C. Subsequently, sustained bio-heat generated by microbial metabolism (53 to 56°C) within MT-Daqu inhibited the growth of most microbes from day 4 to day 12, while thermotolerant taxa, including
, unclassified
,
,
,
, and
survived or kept on growing. Furthermore, temperature as a major driving force on the shaping of MT-Daqu microbiota was validated. Lowering the fermentation temperature by placing the MT-Daqu in a 37°C incubator resulted in decreased relative abundances of thermotolerant taxa, including
,
, and
, in the MT-Daqu microbiota. This study revealed that bio-heat functioned as a primary endogenous driver promoting the formation of functional MT-Daqu microbiota.
Humans have mastered the Daqu preparation technique of cultivating functional microbiota on starchy grains over thousands of years, and it is well known that the metabolic activity of these microbes is key to the flavor production of Chinese baijiu. The pattern of microbial community succession and flavor formation remains highly similar between batches, yet mechanistic insight into these patterns and into microbial population fidelity to specific environmental conditions remains unclear. Our study revealed that bio-heat was generated within Daqu bricks in the first 4 d of fermentation, concomitant with rapid microbial propagation and metabolism. The sustained bio-heat may then function as a major endogenous driving force promoting the formation of the MT-Daqu microbiota from day 4 to day 12. The bio-heat-driven growth of thermotolerant microorganisms might contribute to the formation of flavor metabolites. This study provides useful information for the temperature-based modulation of microbiota function during the fermentation of Daqu.
Phytotherapy offers obvious advantages in the intervention of Coronary Artery Disease (CAD), but it is difficult to clarify the working mechanisms of the medicinal materials it uses. DGS is a natural ...vasoprotective combination that was screened out in our previous research, yet its potential components and mechanisms are unknown. Therefore, in this study, HPLC-MS and network pharmacology were employed to identify the active components and key signaling pathways of DGS. Transgenic zebrafish and HUVECs cell assays were used to evaluate the effectiveness of DGS. A total of 37 potentially active compounds were identified that interacted with 112 potential targets of CAD. Furthermore, PI3K-Akt, MAPK, relaxin, VEGF, and other signal pathways were determined to be the most promising DGS-mediated pathways. NO kit, ELISA, and Western blot results showed that DGS significantly promoted NO and VEGFA secretion via the upregulation of VEGFR2 expression and the phosphorylation of Akt, Erk1/2, and eNOS to cause angiogenesis and vasodilation. The result of dynamics molecular docking indicated that Salvianolic acid C may be a key active component of DGS in the treatment of CAD. In conclusion, this study has shed light on the network molecular mechanism of DGS for the intervention of CAD using a network pharmacology-driven strategy for the first time to aid in the intervention of CAD.
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•The microbial ecosystem of Chinese liquor cellar was studied as a whole.•Significantly distinctive microbial communities were identified in jiupei and pit mud.•The metabolic ...contribution in organic acid synthesis of jiupei and pit mud microbiota was explored.•Acetate and lactate accumulated in jiupei were further converted to butyrate and hexanoate by pit mud microbiota.•Synergistic cooperation within jiupei and pit mud microbiota drives baijiu’s typical flavor formation.
Mud cellars have long been used as anaerobic bioreactors for the fermentation of Chinese strong-flavor Baijiu, where starchy raw materials (mainly sorghum) are metabolized to ethanol and various flavor compounds by multi-species microorganisms. Jiupei (fermented grains) and pit mud are two spatially linked microbial habitats in the mud cellar, yet their metabolic division of labor remains unclear. Here, we investigated the changes in environmental variables (e.g., temperature, oxygen, pH), key metabolites (e.g., ethanol, organic acids) and microbial communities in jiupei and pit mud during fermentation. Jiupei (low pH, high ethanol) and pit mud (neutral pH) provided two habitats with distinctly different environmental conditions for microbial growth. Lactic acid accumulated in jiupei, while butyric and hexanoic acids were mainly produced by microbes inhabiting the pit mud. Biomass analysis using quantitative real-time PCR showed that bacteria dominated the microbial consortia during fermentation, moreover cluster and principal coordinate analysis (PCoA) analysis showed that the bacterial communities of jiupei and pit mud were significantly divergent. The bacterial community diversity of jiupei decreased significantly during the fermentation process, and was relatively stable in pit mud. Lactobacillus dominated the jiupei bacterial community, and its relative abundance reached 98.0% at the end of fermentation. Clostridia (relative abundance: 42.9–85.5%) was the most abundant bacteria in pit mud, mainly distributed in the genus Hydrogenispora (5.3–68.4%). Fungal communities of jiupei and pit mud showed a similar succession pattern, and Kazachstania, Aspergillus and Thermoascus were the predominant genera. PICRUSt analysis demonstrated that enzymes participating in the biosynthesis of acetic and lactic acid were mainly enriched in jiupei samples, while the bacterial community in the pit mud displayed greater potential for butyric and hexanoic acid synthesis. Assays from an in vitro simulated fermentation further validated the roles of jiupei microbiota in acetic and lactic acid production, and these acids were subsequently metabolized to butyric and hexanoic acid by the pit mud microbiota. This work has demonstrated the synergistic cooperation between the microbial communities of jiupei and pit mud for the representative flavor formation of strong-flavor Baijiu.
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•Smelting plants cause heavy vanadium pollution in local area.•Vanadium pollution posed serious impact on soil microbes over multiple gradients.•Vanadium(V)-reducing related bacteria ...might play a core role in community response.•Relation between vanadium(V)-reducing related bacteria and vanadium was explored.
The mining and smelting of navajoite has resulted in a serious vanadium pollution in regional geological environments and significant influence on soil microorganisms. However, the core microbiome responsible for adjusting community response to vanadium pollution and the driving pattern have been kept unclear. In this study, a suite of surface and profile soil samples over multiple gradients were collected in four directions and distances of 10–2000 m from a vanadium smelting plant in Panzhihua, China. The indigenous microbial communities and vanadium(V)-reducing related bacteria (VRB) were profiled by 16S rRNA gene high-throughput sequencing technique. Five VRB were detected in the original collected soil samples including Bacillus, Geobacter, Clostridium, Pseudomonas and Comamonadaceae based on high-throughput sequencing data analysis, and their abundances were significantly related with the content of vanadium. Low vanadium concentration promoted the growth of VRB, while high vanadium concentration would inhibit VRB multiplication. The Gaussian equation could be used to quantitatively describe the nonlinear relationship between VRB and vanadium. Network analysis demonstrated that the microbial communities were significantly influenced by VRB assemblage, and 1.32–52.77% of microbes in the community showed a close association with VRB. A laboratory incubation experiment also confirmed the core role of VRB to drive community response to vanadium pressure.
•Impact of biochar addition on performance of DGW composting process was studied.•Reduced nitrogen loss and accelerated composting process were achieved.•Adding 10% biochar allowed the lowest N loss ...of 25.69% vs 40.01% in control.•Inhibiting denitrification by biochar was the main reason for reducing N loss.•Biochar promoted composting process by accelerating the microbial succession.
This study evaluated the impact of biochar addition on nitrogen (N) loss and the process period during distilled grain waste (DGW) composting. Results from the five treatments (0, 5, 10, 15, and 20% biochar addition) indicated that 10% biochar addition (DB10) was optimal, resulting in the lowest N loss, 25.69% vs. 40.01% in the control treatment. Moreover, the DGW composting period was shortened by approximately 14 days by biochar addition. The composition of the microbial community was not significantly altered with biochar addition in each phase, however, it did accelerate the microbial succession during DGW composting. N metabolism pathway prediction revealed that biochar addition enhanced nitrification and inhibited denitrification, and the latter phenomenon was the main reason for reducing N loss during DGW composting. Based on the above results, a potential mechanism model for biochar addition to reduce N loss during the DGW composting process was established.
Image logs provide important information on lithology, sedimentary textures, paleoflow directions, fractures and in situ stress analysis. Lacking of criteria used for establishing the image log ...facies limits the applications of image logs in sedimentary reservoir interpretation. This paper provides a thoroughgoing review focusing on the recent applications of borehole image logs for sedimentological and structural description and interpretation, and aims to establish image log facies which can provide guidelines in sedimentary reservoir interpretation. This paper firstly summarizes the principles and basic characteristics of various imaging logging tools, and then briefly introduces the pre-processing workflow of the image log data. Then the generated images are used for depth and orientation shifts of cores by calibrating individual sedimentary and structural features. Descriptive and concise image log facies are established based on combinations of image textures including dip type, dip pattern, and color scheme, and the characteristics as well as physical criteria for each individual image facies are summarized. The established image log facies are then interpreted in terms of structural and sedimentological features such as lithology, sedimentary structures, vugs, fractures and faults. The image log facies and its stacking patterns are then used to interpret the lithofacies associations by calibrating with cores and conventional logs. Natural fractures and induced fractures are recognized by image logs, and the principles of breakouts and drilling-induced fractures for in-situ stress analysis are reviewed. Then the applications of image logs in investigation of fracture attitudes and states, as well as in computation of fracture parameters are summarized. The procedures to evaluate fracture effectiveness through image logs are discussed. At last, the application of image logs for structural dip analysis is reviewed, and image logs are used for recognizing faults, fracture sets, and attitudes of stratum through the dip patterns. The basic procedures for paleocurrent reconstruction, which includes dip picking of cross beddings, structural dip determination and structural dip removal, are reviewed. Then the paleocurrent directions of the Lower Cretaceous Bashijiqike Formation in the Kuqa depression were reconstructed, which could help further understanding of the depositional systems. This review will help extend the utility of image logs in interpreting small to large -scale sedimentary and structural features, and bridges the gaps between well log analysis and sedimentary and structural interpretation.
•The principles and basic characteristics of imaging logging tools were summarized.•Descriptive and concise image log facies are established.•The procedures to evaluate fracture effectiveness through image logs are discussed.•Image logs are used for structural dip analysis and paleocurrent reconstruction.
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