Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal deep learning for liver cancer applications: a scoping review

Background Hepatocellular carcinoma is a malignant neoplasm of the liver and a leading cause of cancer-related deaths worldwide. The multimodal data combines several modalities, such as medical images, clinical parameters, and electronic health record (EHR) reports, from diverse sources to accomplish the diagnosis of liver cancer. The introduction of deep learning models with multimodal data can enhance the diagnosis and improve physicians' decision-making for cancer patients. Objective This scoping review explores the use of multimodal deep learning techniques (i.e., combining medical images and EHR data) in diagnosing and prognosis of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA). Methodology A comprehensive literature search was conducted in six databases along with forward and backward references list checking of the included studies. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review guidelines were followed for the study selection process. The data was extracted and synthesized from the included studies through thematic analysis. Results Ten studies were included in this review. These studies utilized multimodal deep learning to predict and diagnose hepatocellular carcinoma (HCC), but no studies examined cholangiocarcinoma (CCA). Four imaging modalities (CT, MRI, WSI, and DSA) and 51 unique EHR records (clinical parameters and biomarkers) were used in these studies. The most frequently used medical imaging modalities were CT scans followed by MRI, whereas the most common EHR parameters used were age, gender, alpha-fetoprotein AFP, albumin, coagulation factors, and bilirubin. Ten unique deep-learning techniques were applied to both EHR modalities and imaging modalities for two main purposes, prediction and diagnosis. Conclusion The use of multimodal data and deep learning techniques can help in the diagnosis and prediction of HCC. However, there is a limited number of works and available datasets for liver cancer, thus limiting the overall advancements of AI for liver cancer applications. Hence, more research should be undertaken to explore further the potential of multimodal deep learning in liver cancer applications.
A Note on Bias Correction for the Standard Two‐Sided Power Distribution

It is verified that the maximum likelihood (ML) estimators of the standard two‐sided power distribution (STSP) parameters introduced by van Dorp and Kotz on the interval (0,1) can be severely biased. Since the usual analytical methods of bias correction cannot be applied in such a parametric distribution, bootstrap bias correction methods are proposed. The numerical study favors a particular bootstrap estimator based on parametric resampling. Real data applications are also considered to illustrate the impact of bias correction of the usual maximum likelihood estimators of the standard two‐sided power distribution parameters when the sample size is small.
Cooperative Deep Reinforcement Learning for Dynamic Pollution Plume Monitoring Using a Drone Fleet

Monitoring pollution plumes is a key issue, given the harmful effects they cause. The dynamic of these plumes, which may be important due to meteorological conditions, makes their study difficult. Real-time monitoring in order to obtain an accurate mapping of the pollution dispersion is helpful and valuable to mitigate risks. In this work, we consider a fleet of cooperative drones carrying pollution sensors and operating in order to assess a pollution plume. The latter is assumed to follow a Gaussian process (GP) with varying parameters. For this use case, we propose an efficient approach to characterize spatially and temporarily the plume while optimizing the path planning of drones. In our approach, drones are guided by a deep reinforcement learning (DRL) model called the categorical deep $Q$ -network (Categorical DQN) to maximize the plume coverage while considering budget constraints. Specifically, we develop a scalable independent $Q$ -learning (IQL) scheme that shares team rewards based on each drone’s deployment relevance and, therefore, ensures cooperation. We evaluate the performance of the plume parameter estimation as well as the maps generated by the GP regression. By testing our framework on several plume scenarios, we show that it offers good results in terms of both estimation quality and runtime efficiency.
Pol-SAR Image Simulation of the Lunar Surface With Data Analysis of Chandrayaan-2 and Mini-RF

Dual-frequency synthetic aperture radar (DFSAR) onboard Chandrayaan-2 and Mini-RF onboard Lunar Reconnaissance Orbiter have acquired fully polarized and compact polarized SAR data of the lunar surface. However, there are few numerical simulations of lunar SAR images. Here, a fully polarized SAR images simulation method of lunar rough surfaces is presented. Scatterings from lunar surfaces are numerically simulated with the Kirchhoff approximation and small perturbation model. The Range Doppler algorithm is used to produce the SAR images. SAR images of lunar craters are simulated and compared with the DFSAR and Mini-RF data. The influence of co-polarization ratio $\sigma _{\text{HH}}^0/\sigma _{\text{VV}}^0$ on its relative phase are analyzed and validated with simulation and data. The enhanced polarized scatterings from undulating topography is simulated and validated with Mini-RF data at S-band and optical image of the crater Galilei E. Due to small incidence angles and radar parallax, strong backscattering caused by rocks at the crater wall facing away from the radar occupies more pixels in range direction in DFSAR images than Mini-RF images. DFSAR dual-frequency images of Apollo 17 landing site are specifically analyzed with assistances of simulations, optical images and high-resolution digital terrain model. Dual-frequency SAR images from Chandrayaan-2 reveal different geologic units of lunar surface, which are invisible in optical images. In addition, the inversion of orientation angles is analyzed as well. The simulation and analysis in this paper will be helpful in lunar SAR data interpretation.
The Contribution of Hippocampal All-Trans Retinoic Acid (ATRA) Deficiency to Alzheimer’s Disease: A Narrative Overview of ATRA-Dependent Gene Expression in Post-Mortem Hippocampal Tissue

There is accumulating evidence that vitamin A (VA) deficiency contributes to the pathogenesis and progression of Alzheimer’s disease (AD). All-trans retinoic acid (ATRA), a metabolite of VA in the brain, serves distinct roles in the human hippocampus. Agonists of retinoic acid receptors (RAR), including ATRA, promote activation of the non-amyloidogenic pathway by enhancing expression of α-secretases, providing a mechanistic basis for delaying/preventing amyloid beta (Aβ) toxicity. However, whether ATRA is actually deficient in the hippocampi of patients with AD is not clear. Here, using a publicly available human transcriptomic dataset, we evaluated the extent to which ATRA-sensitive genes are dysregulated in hippocampal tissue from post-mortem AD brains, relative to age-matched controls. Consistent with ATRA deficiency, we found significant dysregulation of many ATRA-sensitive genes and significant upregulation of RAR co-repressors, supporting the idea of transcriptional repression of ATRA-mediated signaling. Consistent with oxidative stress and neuroinflammation, Nrf2 and NfkB transcripts were upregulated, respectively. Interestingly, transcriptional targets of Nrf2 were not upregulated, accompanied by upregulation of several histone deacetylases. Overall, our investigation of ATRA-sensitive genes in the human hippocampus bolsters the scientific premise of ATRA depletion in AD and that epigenetic factors should be considered and addressed as part of VA supplementation.
Spatial Heterogeneity of Ecosystem Metabolism in a Shallow Wetland

Spatial Heterogeneity of Ecosystem Metabolism in a Shallow Wetland D. Riley Rackliffe Department of Biology, BYU Master of Science Spatial heterogeneity in ecosystem metabolism may play a critical role in determining ecosystem functions. Variation in ecosystem metabolism between macrophyte patches in shallow wetlands at the extremes of freshwater habitats has not been investigated. We estimated ecosystem metabolism in mesocosms containing different macrophytes using 24-hour oxygen curves to test our hypotheses: (1) net aquatic production (NAP) during spring and summer would be similar among algal patches (metaphyton and Chara), (2) NAP in algal patches would be greater than patches dominated by the vascular plant Potamogeton foliosus, (3) heterotrophy and anaerobiosis would be greatest in patches dominated by Lemna, and (4) the pond would be autotrophic in the spring and fall but heterotrophic in the summer. We found that different patches generated differences in NAP but not always as we predicted. NAP was different among algal patches in the spring and summer, and only metaphyton was more heterotrophic than P. foliosus. In the summer Chara and Lemna patches were heterotrophic and metaphyton became autotrophic. As predicted, the pond was net autotrophic in the spring and heterotrophic in the summer with an absence of patchiness in fall attributed to the dominance of Lemna. This research suggests the importance of macrophyte patchiness in wetlands in determining patterns of ecosystem metabolism despite challenges in measuring 24 hour oxygen curves (e.g. oxygen supersaturation). Consequently, macrophyte traits may be important in determining spatial heterogeneity of ecosystem metabolism in shallow ponds.
The Strategic Role of Paediatricians in Protecting Children’s Oral and Dental Health

Dental caries and oral health problems are a major public health concern in our country and around the world. Society needs to improve tooth brushing habits, reduce sugar and carbohydrate intake and provide adequate fluoride to achieve dental and oral health goals. Additionally, we must reach out to disadvantaged and socio-culturally backward populations. Although scans that detect the issue could be significant, tooth brushing sessions with role models who turn behavioral changes into habits are even more crucial in solving the problem. Families should be taught the triangle of brushing teeth, reading books and then sleeping.
The Possible Influence Channel of Sub-surface Sea Temperature Anomaly in the North Pacific on China Summer Rainfall

By use of Godas monthly sub-surface sea temperature (ST),the similarity of ST among different layers is analyzed,and ST is layered in the North Pacific region for winter,spring,and summer.The time-lag correlations among the 500-hPa geopotential height field,sub-surface sea temperature field in the North Pacific,and the summer rainfall in China are further studied.Results show that the sub-surface ST in spring is an important factor linking the anterior and posterior atmospheric circulation.Atmospheric circulation in winter has a most remarkable influence on the ST in the North Pacific in spring,and the ST in spring also has persistent effects on atmospheric circulation for the corresponding period and the later summer.The interactions between atmospheric circulation and the sea surface,sub-surface temperature anomalies respectively lead to the opposite anomalous pattern of precipitation for the Yangtze River basin with North China and South China in summer.
Comments on the Revision of the Specifications for Hydrographic Survey

Based on more than 15 years' implement of the GB12327-1998 Specifications for Hydrographic Survey and on the development of hydrographic surveying and charting technologies,studies and discussions are carried out on the guidelines,composition,technological methods and specifications. Suggestions of revision are made so as to guarantee the advanced nature,scientific nature and authority of the Specifications for Hydrographic Survey.
Age, growth, and biomass projections of red porgy Pagrus pagrus (Teleostei, Sparidae) after the fishery collapse in southern Brazil

A formerly unexploited stock of red porgy that was intensely fished along southern Brazil in the 1970s collapsed in less than a decade. Subsequently, population dynamics or stock status has not been reported, so we updated growth parameters by analysis of otolith annual‐increments and micro‐increments. Growth did not change after the stock collapsed, which suggested no density‐dependent effect. Given the lack of growth changes and data‐limitation, we used an age‐structured assessment model set until 1984 to forecast relative spawning biomass and assess recovery possibilities. Projections showed that a 19 years moratorium would have been necessary for the stock to rebuild. The model suggested an annual catch of at least 170 t in the post‐collapse period because the stock did not recover, and a small‐scale hand‐line fishery with unreported landings continued fishing. Our results reinforced the need for more curated and up‐to‐date data collection to properly assess and manage this formerly abundant stock.
Analytical solutions for deformable elliptical inclusions in general shear

SUMMARY Using Muskhelishvili’s method, we present closed-form analytical solutions for an isolated elliptical inclusion in general shear far-field flows. The inclusion is either perfectly bonded to the matrix or, as in the case of a circular inclusion, to a possible intermediate layer. The solutions are valid for incompressible all-elastic or all-viscous systems. The actual values of the shear modulus or viscosity in the inclusion, mantle and matrix can be different and no limits are imposed on the possible material property contrasts. The solutions presented are complete 2-D solutions and the parameters that can be analysed include all kinematic (e.g. stream functions, velocities, strain rates, strains) and dynamic parameters (e.g. pressure, maximum shear stress, etc.). We refrain from giving the tedious derivation of the presented solutions, and instead we focus on how to use the solutions, how to extract the parameters of interest and how to apply and verify them. In order to demonstrate the usefulness of Muskhelishvili’s method for slow viscous flow problems, we apply our results to a clast in a shear zone and obtain important new insights, even for simple, circular inclusions. Another important application is the benchmarking of numerical codes for which the presented solutions are most suitable due to the infinite range of viscosity contrasts and the strong local gradients of properties and results. To stimulate a broader use of Muskhelishvili’s method, all solutions are implemented in MATLAB and downloadable from the web.
Advanced Unknown Malicious Code Detection Model

As IT technology increases, it became part of our life. Because of change in numbers, the files anddata used in IT also increased. With simple data and file, malicious codes also increased in greatnumber. This malicious code leads too many problems in the society. In fact, recently, new maliciouscodes that have not been detected yet are used in attacks such as APT (Advanced PersistentThreat). These codes became problem and lead to great damages. Thus, the speed of finding theundefined malicious code and making countermeasures became one of the important key words ofthe security. However, finding new malicious codes that is currently in action seems impossible. Inorder to find the malicious codes faster, there are researches on finding the special properties of themalicious code’s file or action pattern. Through finding the properties of the codes, detecting themalicious codes became more effective and the results are satisfactory. This research will find therelation of malicious code’s file property, behavior property, and property of each group or groupas a hole in order to effectively detect the code that is suspicious of being malignant effectively andprecisely. Thus, this paper will present a way to apply priority when it comes to detecting maliciouscode.
Wind‐driven spreading of fresh surface water beneath ice shelves in the Eastern Weddell Sea

Solar heated, fresh Antarctic Surface Water (ASW) is a permanent feature along the Eastern Weddell Sea (EWS) coast in summer down to a depth of roughly 200 m. Recently, ASW has been observed beneath the Fimbul Ice Shelf, suggesting that it might play an important role in basal melting. We propose that wind-driven coastal downwelling is the main mechanism that spreads ASW beneath the ice shelf in this sector of Antarctica. We validate this hypothesis with observations, scaling analyses, and numerical modeling, along three principle lines: (i) data analyses of about 1500 salinity profiles collected by instrumented seals indicate that the observed freshening of the coastal water column is likely explained by the on-shore Ekman transport and subsequent downwelling of ASW; (ii) an analytical model of the coastal momentum balance indicates that wind-driven downwelling is capable of depressing the buoyant surface water to a depth similar to the ice shelf draft; and (iii) simulations from both idealized and regional eddy-resolving numerical ice shelf/ocean models support our proposition. Our main conclusion is that wind-driven spreading of ASW beneath the ice shelf occurs when downwelling exceeds the depth of the ice shelf base. Furthermore, our study adds to the understanding of the oceanic processes at the Antarctic Slope Front in the EWS, with possible implications for other sectors of Antarctica.
The Dysfunction of Ca2+ Channels in Hereditary and Chronic Human Heart Diseases and Experimental Animal Models

Chronic heart diseases, such as coronary heart disease, heart failure, secondary arterial hypertension, and dilated and hypertrophic cardiomyopathies, are widespread and have a fairly high incidence of mortality and disability. Most of these diseases are characterized by cardiac arrhythmias, conduction, and contractility disorders. Additionally, interruption of the electrical activity of the heart, the appearance of extensive ectopic foci, and heart failure are all symptoms of a number of severe hereditary diseases. The molecular mechanisms leading to the development of heart diseases are associated with impaired permeability and excitability of cell membranes and are mainly caused by the dysfunction of cardiac Ca2+ channels. Over the past 50 years, more than 100 varieties of ion channels have been found in the cardiovascular cells. The relationship between the activity of these channels and cardiac pathology, as well as the general cellular biological function, has been intensively studied on several cell types and experimental animal models in vivo and in situ. In this review, I discuss the origin of genetic Ca2+ channelopathies of L- and T-type voltage-gated calcium channels in humans and the role of the non-genetic dysfunctions of Ca2+ channels of various types: L-, R-, and T-type voltage-gated calcium channels, RyR2, including Ca2+ permeable nonselective cation hyperpolarization-activated cyclic nucleotide-gated (HCN), and transient receptor potential (TRP) channels, in the development of cardiac pathology in humans, as well as various aspects of promising experimental studies of the dysfunctions of these channels performed on animal models or in vitro.
Classified Early-warning and Nowcasting of Hail Weather Based on Radar Products and Random Forest Algorithm

Hail is a kind of severe convective weather with high possibility to cause serious disasters, but it is hard to be early-warned and nowcasted for meteorological operations. This study classified and nowcasted the hail disaster weather and its accompanying severe convective weather based on random forest (RF) algorithm and C-band radar products. The RF model was generated and processed using the observations of severe convective weather in 2008-2017 as the training set firstly. Evaluation results of RF model show a low generalization error of 4.2%. We then applied the model on hail weather observations in 2018-2019, and found that the false identical percentage of no severe convective weather is 15.6%, and the mean false identical percentage of four types of hail weather (the hail, hail with strong wind, hail with short-time heavy precipitation, hail with strong wind and short-time heavy precipitation) is 26.7%. For the nowcast of hail weather in 15 min to 1 h using the RF model and the storm identification and tracking products (SCIT), the mean probability of detection (POD) of four types of hail weather is 74.8%, the mean critical success index (CSI) is 60.8% and the mean false alarm ratio (FAR) is 24.4%. Therefore, the RF model can well classify and nowcast the hail disaster weather and its accompanying severe convective weather, and is appropriate to be applied in the automatic weather forecast operations.
indiCAdorEs dE ClimA ÉtiCo nAs orGAniZAÇÕEs: um Estudo dE CAso Em umA orGAniZAÇÃo do tErCEiro sEtor etHiCal Climate indiCators in orGaniZations: a Case study in a tHird seCtor orGaniZation

This article is the result of a “Research Experience”, a course in the master of business administration teaching program offered by Faculdade Novos Horizontes, which intended to conduct a study, based on Navran’s Model, about the ethical level of a third sector organization located in the State of Minas Gerais. The principle guiding this Model states that both employees’ individual performance and the reach of the organization’s results are directly linked with the congruence between the organization’s values and beliefs,
More Productive and Profitable Peach Planting Systems

Peach trees in NY Statehave a relatively shortproductive life (15 years),and low production levelscompared to other peachproducing areas withwarmer climates. Our re-search has shown that high-density peach orchards canbe successfully managed atdensities of 400-600 trees/acre. These systems aremuch more productive andprofitable than traditionallow-density systems. Peachgrowers in NY should planthigher densities per acreto be competitive withpeach growers in moresouthern climates.
Groundwater quality assessment using Water Quality Index (WQI) approach - Case study in a coastal region of Tamil Nadu, India

Over exploitation of ground water has become a major challenge not only to the present civilization and also for the future generations. The more groundwater is exploited, the lower the quality obtainable. Quality of groundwater is equally important to its quantity owing to the suitability of water for various purposes. Especially people who are living in the vicinity of coastal regions are struggling lot to fulfill their day-to-day water need. Vedaranyam is one such region of TamilNadu, India, which is very sensitive to natural resources which abundant in all aspects. Water quality threat is also high due to its proximity to the special accreditional coastline. In this work, an effort has been taken to understand the ground water quality of Vedaranyam Taluk by using water quality index (WQI) approach. Forty-four groundwater samples were collected from bore and tube wells. This sample collection was carried out both in summer and post-monsoon seasons. This study revealed that very few areas of South-West region of the study area are having consumable quality standards.