Miracle-dz/UniFAR-FTUs huggingface dataset:
TNFSF10, an autophagy related gene, was a prognostic and immune infiltration marker in skin cutaneous melanoma

Autophagy exerts a pivotal effect on skin cutaneous melanoma (SKCM). This study was aimed to investigate the expression of autophagy related genes (ARGs) in SKCM as well as its clinical value. Differentially expressed (DE) ARGs were downloaded from the intersection of SKCM data in GEPIA2 database and ARGs in Human Autophagy Database (HADB) database, and were verified in SKCM datasets GSE46517 and GSE15605. DE ARGs were enriched by Metascape online tools. According to GEPIA2 database, tumor necrosis factor-related apoptosis-inducing ligand (TNFSF10) was identified as a closely related factor and prognostic marker of SKCM. Then the correlation analysis of clinicopathological characteristics between TNFSF10 and SKCM was completed by several online tools such as TISCH, HPA, BEST and qRT-PCR. Subsequently, we investigated TNFSF10 related functions and signal pathways with LinkedOmics online tool, and immune infiltration using Assistant for Clinical Bioinformatics online tool. Furthermore, correlation analysis between TNFSF10 expression and immunotherapy response was performed by TIDE algorithm and BEST online tool. And Kaplan-Meier Plotter was used to assessing the prognosis of SKCM patients receiving immunotherapy. Finally, the correlation analysis among TNFSF10 methylation, TNFSF10 expression and patient prognosis was completed by the DiseaseMeth version 2.0, UCSC XENA and qRT-PCR. ARGs are DE in SKCM and participate in the ERBB signaling pathway, as well as the processing and presentation of antigens. Moreover, TNFSF10's expression along with methylation expression were significantly associated with the prognosis. Low expression of TNFSF10 was associated with malignant clinicopathological features, lower immune signal activity and lower immunocytes abundance in patients with SKCM. As an ARG, TNFSF10 has a potential capacity in predicting the prognosis of SKCM patients, meanwhile, may be a novel immunotherapy marker for SKCM.
MODERN HIGHER EDUCATION IN THE UKRAINIAN EDUCATIONAL SYSTEM

Higher education, as one of the most critical components of the education system, forms a certain level of knowledge, skills, and abilities that can be applied to relevant activities. The graded nature of higher education means obtaining different educational and qualification levels (characteristics that determine the degree of knowledge, skills, and abilities of a person that help to perform tasks and duties of a certain level of professional activity) at the appropriate stages (degrees) of higher education. Adopting such a branched scheme is of fundamental importance because it guarantees a person freedom of choice and provides an opportunity to receive education following mental and professional abilities. The education sector currently needs more solutions to such problems as instability and limited funding for educational institutions, social protection of teachers, the decline in material resources, and restrictions on textbook publishing. Rural schools face challenging conditions. There are 14.9 thousand schools in 28.9 thousand population centers, 2.8 thousand requiring significant repairs, and almost 800 schools are in disrepair. In rural areas, only a few thousand students study in new-type institutions, while a thousand urban lyceums, gymnasiums, and colleges have 200,000 students. The aim of the educational process is the overall development of a person as a personality and the highest value of society, the disclosure of their talents and mental and physical abilities, and the formation of morality. Higher education should form a citizen capable of consciously making social decisions and, on this basis, enrich society's creative, intellectual, and cultural potential, thus producing qualified specialists for the national and global economy. This article aims at identifying the main tendencies in the formation and development of modern higher education in terms of its place in the educational system and the role played by higher education in the economic and social space of the country. Methodology. The research used the analytical and bibliographic methods to study the scientific literature on the development of the higher education system, as well as induction, deduction, analysis, synthesis of information, systemic and structural, comparative, logical and linguistic methods, abstraction, idealization for studying and processing data, and an online questionnaire survey conducted by the research authors to practically clarify the most critical issues related to the peculiarities of the higher education system development in Ukraine. Results. Based on the study results, we identified the central, most important theoretical aspects of the work of higher education institutions. We also studied the opinion of scientists, heads of departments, and faculties of HEIs on crucial elements of this issue.
Considering the impact of structural adjustment policies on forests in Bolivia, Cameroon and Indonesia

A preliminary insight into how structural adjustment policies (SAPs) may have affected deforestation and forest degradation in the lowland tropical forests of Bolivia, Cameroon and Indonesia. It presents tentative hypotheses regarding how changes in prices, costs, incomes and government services associated with SAPs affect forests. The indirect effects of SAP's on forests resulting from changes in overall economic growth, inflation rates, employment and consumption are difficult to measure and predict. The experience of Cameroon and Indonesia indicate that SAPs that sucees in creating new off-farm employment opportunities probably reduce deforestation, while those that do not succeed have the opposite effect, but this is still inconclusive. There is little evidence that government spending restrictions associated with SAPs diminished governments' ability to promote sustainable forest management or control deforestation, largerly because there was minimal capacity in the first place. It remains to be seen whether SAPs will favour or hinder the development of such capacity in the future.
How did 'bog oaks' grow? First answers from the excavation of a mire woodland in Ypenburg, The Netherlands

Background Sub-fossil ‘bog’ oaks that grew in former mire woodlands are the main source of datable prehistoric tree-ring patterns (Leuschner 1992). They may serve as an archive to reconstruct natural variations in past climate (Baillie 1996, Leuschner et al., in press). Until now the exact environmental conditions in which the oaks grew (species composition, succession, tree density, the response to hydrology and climate) is largely unknown. The detailed excavation of prehistoric woodland sites will provide information about the development, structure, and dynamics of oak supporting mire woodlands.
Dialogue Generation Conditional on Predefined Stories: Preliminary Results

This paper introduces dialogue generation conditional on predefined stories, which will be called Story2Dialogue. As a starting point for the task, this paper presents benchmark performances using simple but modern baseline methods along with error analysis. The experimental results show that few-shot prompting using large-scale pre-trained language models outperforms human effort in some objective evaluation metrics, but the quality of the generated dialogues is far inferior to humans’ creations in terms of suitability as entertainment content and semantic equivalence to the input story. Regarding suitability as a movie script, human evaluators preferred automatically generated dialogues over those created by human writers in only 20% to 29% of cases. As for semantic equivalence to the input story, 75% to 80% of the automatically generated dialogues were found to be semantically insufficient. The error analysis shows that around 80% of the semantically insufficient dialogues lacked information from the given stories and, conversely, irrelevant utterances (including undefined continuations of conversations) were added in 20% to 26% of the dialogues.
A Comparative Study of Deep Learning and Traditional Methods for Environmental Remote Sensing

Because of the accessibility of massive data from remote sensing data and developments in ML, machine learning (ML) techniques have been extensively applied in environmental remote sensing research. Modern machine learning (ML) frameworks like deep learning (DL) have significantly outperformed older models in terms of performance. This study focuses on the software that uses a traditional neural network (NN) as well as Deep Learning (DL) approaches in environmental remote sensing, which also covers land cover mapping, retrieval of environmental parameters, data fusion, image compression, and information reconstruction and prediction. It is also explained how DL may be used to monitor other aspects of the ecosystem, including the environment, water management, ground and air surface temperatures, transpiration, ultraviolet (UV) rays, and sea color all factors to consider. Following that, the essay explores the challenges and prospective uses of DL in environmental remote sensing.
Impacts of Land Use and Land Cover Change on Vegetation Diversity of Tropical Highland in Ethiopia

Ethiopia has undergone a substantial shift in land use and land cover (LULC), which is home to the majority of the human and animal population. Land degradation has occurred in the Ethiopian highlands as a result of modifications in LULC caused by poor farming methods, high livestock population, and human pressures. Most researchers identified the many LULC drivers and their impact on floristic composition. All of these manifestations have the potential to have major consequences for land users and individuals whose livelihoods rely on the products of a healthy environment. This change in LULC type, combined with poor land management practices in Ethiopia, puts land in jeopardy of erosion, resulting in accelerated soil degradation. All LULC variables, such as the spread of various agricultural activities, the production of fuelwood and charcoal, cutting trees for construction resources, settlements, and revenue growth, are associated with population increase and resettlement. In Ethiopia, the lack of a relevant forest policy implementation on the ground is recognized as a cause pushing deforestation and other landscape changes. This review paper aimed to compile the effects of land use land cover changes on Ethiopian vegetation.
Design and Simulation Analysis of a 3TnC MLC FeRAM Using a Nondestructive Readout and Offset-Canceled Sense Amplifier for High-Density Storage Applications

Hf0.5Zr0. 5O2-based multi-level cell (MLC) ferroelectric random-access memory (FeRAM) has great potential for high-density storage applications. However, it is usually limited by the issues of a small operation margin and a large input offset. The study of circuit design and optimization for MLC FeRAM is necessary to solve these problems. In this work, we propose and simulate a configuration for a Hf0.5Zr0. 5O2-based 3TnC MLC FeRAM macro circuit, which also presents a high area efficiency of 12F2 for each bit. Eight polarization states can be distinguished in a single fabricated Hf0.5Zr0. 5O2-based memory device for potential MLC application, which is also simulated by a SPICE model for the subsequent circuit design. Therein, a nondestructive readout approach is adopted to expand the reading margin to 450 mV between adjacent storage levels, while a capacitorless offset-canceled sense amplifier (SA) is designed to reduce the offset voltage to 20 mV, which improves the readout reliability of multi-level states. Finally, a 4 Mb MLC FeRAM macro is simulated and verified using a GSMC 130 nm CMOS process. This study provides the foundation of circuit design for the practical fabrication of a Hf0.5Zr0. 5O2-based MLC FeRAM chip in the future, which also suggests its potential for high-density storage applications.
Leading Edge Technologies and Perspectives in Industrial Oilseed Extraction

With the increase in the world’s population and per capita wealth, oil producers must not only increase edible oil production but also meet the demand for a higher quality and variety of products. Recently, the focus has shifted from single processing steps to the entire vegetable oil production process, with an emphasis on introducing innovative technologies to improve quality and production efficiency. In this review, conventional methods of oilseed storage, processing and extraction are presented, as well as innovative processing and extraction techniques. Furthermore, the parameters most affecting the products’ yields and quality at the industrial level are critically described. The extensive use of hexane for the extraction of most vegetable oils is undoubtedly the main concern of the whole production process in terms of health, safety and environmental issues. Therefore, special attention is paid to environmentally friendly solvents such as ethanol, supercritical CO2, 2-methyloxolane, water enzymatic extraction, etc. The state of the art in the use of green solvents is described and an objective assessment of their potential for more sustainable industrial processes is proposed.
Regulation of Pol II Pausing during Daily Gene Transcription in Mouse Liver

Simple Summary Clock proteins and their collaborating transcription factors often act as distal enhancers to regulate the rhythmic transcription of gene promoters. Those transcription factors need to interact with the mediator complex and general transcription factors near the transcription start site to finally control transcription. Pol II pausing, which is determined by Pol II recruitment, pause release, and premature transcription termination near the transcription start site, plays a critical role in influencing the final transcription output. However, the way Pol II pausing is regulated during daily transcription and its role in shaping transcription rhythms have not been systematically investigated. We recently carried out a quantitative ChIP-seq study to characterize Pol II pausing across the day in mouse liver. Our analyses suggest that Pol II recruitment, pause release, and premature transcription termination activities exhibit genome-wide changes that can peak at distinct clock phases. Such complexity of Pol II pausing regulation during daily transcription provides new perspectives on the transcription regulation of circadian rhythms and warrants future studies to dissect the regulatory mechanisms of Pol II pausing and their roles in shaping the daily rhythms of gene transcription. Abstract Cell autonomous circadian oscillation is present in central and various peripheral tissues. The intrinsic tissue clock and various extrinsic cues drive gene expression rhythms. Transcription regulation is thought to be the main driving force for gene rhythms. However, how transcription rhythms arise remains to be fully characterized due to the fact that transcription is regulated at multiple steps. In particular, Pol II recruitment, pause release, and premature transcription termination are critical regulatory steps that determine the status of Pol II pausing and transcription output near the transcription start site (TSS) of the promoter. Recently, we showed that Pol II pausing exhibits genome-wide changes during daily transcription in mouse liver. In this article, we review historical as well as recent findings on the regulation of transcription rhythms by the circadian clock and other transcription factors, and the potential limitations of those results in explaining rhythmic transcription at the TSS. We then discuss our results on the genome-wide characteristics of daily changes in Pol II pausing, the possible regulatory mechanisms involved, and their relevance to future research on circadian transcription regulation.
ESTIMATION OF INTERPOLATION ERROR IN DEMS USING STATISTICAL METHODS

SUMMARY To work with DEMs requires a knowledge of their accuracy, to avoid having an error larger that the level of details to achieve the task. Such problems can lead to unplanned costs, resurveys, disputes, for example in flood studies (spurious wells), deformation detection (trying to detect mining subsidence effects smaller than the error), stockpile volume calculations (large discrepancy in estimation by buyer and seller). The error can be divided into two components: the observation error (with its many different aspects resulting from the method of observation) and the interpolation error (where heights are estimated between observations). The total error is thus the estimate of the observation error propagated into the interpolation error. The observation error is often difficult to determine, especially where the sensor is mounted on a platform in motion, such as an aircraft in airborne lidar. It is however more readily estimated for example with theodolites and terrestrial laser scanners. The interpolation error in a DEM is a function of the roughness of the surface and the point density of the DEM. It does not depend on the platform chosen for the observations. This article describes a statistical method to estimate the interpolation error in a DEM, regardless of which method was used for the observation. The error is then modelled on the density, using linear regression. The idea is to compare the interpolation error to the level of error required for the task. The accuracy/density model can be used to plan surveys at the observation phase to obtain a level of error adequate for the task.
8 Right Peripheral Domains, Deixis and Information Structure in Southern Quechua

In many languages, the linguistic expression of information structure (new, old, shared information and the current topic of discourse in a sentence) may be grammaticalized at the phonology/syntax interface (Donati and Nespor 2003, Nespor and Vogel 1986, Pereltsvaig 2004, Selkirk 1984, Zubizarreta 1998), at the syntax component (Horvath 1986, Vallduvi 1995), or at the syntax/morphology interface (Cinque 1999, Sánchez 2010, Speas 2004). In Southern Quechua varieties spoken in Peru, there are overt morphological markers that indicate whether a constituent is a main topic in discourse, new information (focus) and/or provides the hearer with the source of information (direct evidence or hearsay), a grammatical characteristic also known as evidentiality. These markers appear mostly on constituents at the left edge of sentences (Cerrón Palomino 1987, Muysken 1995, Sánchez 2010) and very rarely on constituents at the right edge of sentences (Sánchez 2010). In this paper, I analyze the information status and syntactic status of right margin or right dislocated constituents (RDCs) in Southern Quechua, a verb final language characterized by a canonical Subject Object Verb (SOV) word order. I also analyze the deictic nature of RDCs in discourse. The following examples illustrate two types of sentences in Southern Quechua: (1) a sentence with canonical SOV word order and (2) one with a left margin, morphologically marked constituent and an RDC.1 Canonical SOV sentence:
An Imbalanced Discriminant Alignment Approach for Domain Adaptive SAR Ship Detection

Synthetic aperture radar (SAR) imaging has round-the-clock data acquisition capability regardless of light and climate constraints, so it has been widely used for ship detection. However, SAR images usually suffer lower imaging quality, which may result in indistinct contours and non-negligible noise. Therefore, the manual labeling for SAR images is expensive, leading to a lack of training data in the task of ship detection. In this article, we propose a route by utilizing domain adaptive methods to transfer information from labeled visible images (source domain) to unlabeled SAR images (target domain) for ship detection. To address the distribution mismatch between domains, we develop a novel imbalanced discriminant alignment (IDA) approach to improve the discriminant ability of the network and prevent negative migration. The core of the IDA approach is applying a new loss function called imbalanced prediction consistency (IPC) loss to describe the domain classifier consistency, and we further provide theoretical analysis for the effectiveness of the IPC loss. IDA ensures consistency at the image level and instance level, and focuses on the consistency of the source domain to enhance the feature extraction capability of the adversarial network. The theoretical discussion has proven that a necessary and sufficient condition for convergence of the IPC loss is that the two discriminant probabilities converge to 0 at the discriminant distance we define. Experimental results have indicated the advantage of IDA when compared with other domain adaptation SAR ship detection methods.
Semi-Supervised Normalized Embeddings for Fusion and Land-Use Classification of Multiple View Data

Land-use classification from multiple data sources is an important problem in remote sensing. Data fusion algorithms like Semi-Supervised Manifold Alignment (SSMA) and Manifold Alignment with Schroedinger Eigenmaps (SEMA) use spectral and/or spatial features from multispectral, multimodal imagery to project each data source into a common latent space in which classification can be performed. However, in order for these algorithms to be well-posed, they require an expert user to either directly identify pairwise dissimilarities in the data or to identify class labels for a subset of points from which pairwise dissimilarities can be derived. In this paper, we propose a related data fusion technique, which we refer to as Semi-Supervised Normalized Embeddings (SSNE). SSNE is defined by modifying the SSMA/SEMA objective functions to incorporate an extra normalization term that enables a latent space to be well-defined even when no pairwise-dissimilarities are provided. Using publicly available data from the 2017 IEEE GRSS Data Fusion Contest, we show that SSNE enables similar land-use classification performance to SSMA/SEMA in scenarios where pairwise dissimilarities are available, but that unlike SSMA/SEMA, it also enables land-use classification in other scenarios. We compare the effect of applying different classification algorithms including a support vector machine (SVM), a linear discriminant analysis classifier (LDA), and a random forest classifier (RF); we show that SSMA/SEMA and SSNE robust to the use of different classifiers. In addition to comparing the classification performance of SSNE to SSMA/SEMA and comparing classification algorithm, we utilize manifold alignment to classify unknown views.
Kernel Sufficient Dimension Reduction and Variable Selection for Compositional Data via Amalgamation

Compositional data with a large number of components and an abundance of zeros are frequently observed in many fields recently. Analyzing such sparse high-dimensional compositional data naturally calls for dimension reduction or, more preferably, variable selection. Most existing approaches lack interpretability or cannot handle zeros properly, as they rely on a log-ratio transformation. We approach this problem with sufficient dimension reduction (SDR), one of the most studied dimen-sion reduction frameworks in statistics. Charac-terized by the conditional independence of the data to the response on the found subspace, the SDR framework has been effective for both linear and nonlinear dimension reduction problems. This work proposes a compositional SDR that can handle zeros naturally while incorporating the nonlinear nature and spurious negative correlations among components rigorously. A critical consideration of sub-composition versus amal-gamation for compositional variable selection is discussed. The proposed compositional SDR is shown to be statistically consistent in constructing a sub-simplex consisting of true signal variables. Simulation and real microbiome data are used to demonstrate the performance of the proposed SDR compared to existing state-of-art approaches.