Miracle-dz/UniFAR-FTUs huggingface dataset:
Cancer Immune Evasion Through Loss of MHC Class I Antigen Presentation

Major histocompatibility class I (MHC I) molecules bind peptides derived from a cell's expressed genes and then transport and display this antigenic information on the cell surface. This allows CD8 T cells to identify pathological cells that are synthesizing abnormal proteins, such as cancers that are expressing mutated proteins. In order for many cancers to arise and progress, they need to evolve mechanisms to avoid elimination by CD8 T cells. MHC I molecules are not essential for cell survival and therefore one mechanism by which cancers can evade immune control is by losing MHC I antigen presentation machinery (APM). Not only will this impair the ability of natural immune responses to control cancers, but also frustrate immunotherapies that work by re-invigorating anti-tumor CD8 T cells, such as checkpoint blockade. Here we review the evidence that loss of MHC I antigen presentation is a frequent occurrence in many cancers. We discuss new insights into some common underlying mechanisms through which some cancers inactivate the MHC I pathway and consider some possible strategies to overcome this limitation in ways that could restore immune control of tumors and improve immunotherapy.
Multidimensional Face Representation in a Deep Convolutional Neural Network Reveals the Mechanism Underlying AI Racism

The increasingly popular application of AI runs the risks of amplifying social bias, such as classifying non-white faces to animals. Recent research has attributed the bias largely to data for training. However, the underlying mechanism is little known, and therefore strategies to rectify the bias are unresolved. Here we examined a typical deep convolutional neural network (DCNN), VGG-Face, which was trained with a face dataset consisting of more white faces than black and Asian faces. The transfer learning result showed significantly better performance in identifying white faces, just like the well-known social bias in human, the other-race effect (ORE). To test whether the effect resulted from the imbalance of face images, we retrained the VGG-Face with a dataset containing more Asian faces, and found a reverse ORE that the newly-trained VGG-Face preferred Asian faces over white faces in identification accuracy. In addition, when the number of Asian faces and white faces were matched in the dataset, the DCNN did not show any bias. To further examine how imbalanced image input led to the ORE, we performed the representational similarity analysis on VGG-Face’s activation. We found that when the dataset contained more white faces, the representation of white faces was more distinct, indexed by smaller ingroup similarity and larger representational Euclidean distance. That is, white faces were scattered more sparsely in the representational face space of the VGG-Face than the other faces. Importantly, the distinctiveness of faces was positively correlated with the identification accuracy, which explained the ORE observed in the VGG-Face. In sum, our study revealed the mechanism underlying the ORE in DCNNs, which provides a novel approach of study AI ethics. In addition, the face multidimensional representation theory discovered in human was found also applicable to DCNNs, advocating future studies to apply more cognitive theories to understand DCNN’s behavior.
Behavioral and Neurodynamic Effects of Word Learning on Phonotactic Repair

Processes governing the creation, perception and production of spoken words are sensitive to the patterns of speech sounds in the language user’s lexicon. Generative linguistic theory suggests that listeners infer constraints on possible sound patterning from the lexicon and apply these constraints to all aspects of word use. In contrast, emergentist accounts suggest that these phonotactic constraints are a product of interactive associative mapping with items in the lexicon. To determine the degree to which phonotactic constraints are lexically mediated, we observed the effects of learning new words that violate English phonotactic constraints (e.g., srigin) on phonotactic perceptual repair processes in nonword consonant-consonant-vowel (CCV) stimuli (e.g., /sre/). Subjects who learned such words were less likely to “repair” illegal onset clusters (/sr/) and report them as legal ones (/∫r/). Effective connectivity analyses of MRI-constrained reconstructions of simultaneously collected magnetoencephalography (MEG) and EEG data showed that these behavioral shifts were accompanied by changes in the strength of influences of lexical areas on acoustic-phonetic areas. These results strengthen the interpretation of previous results suggesting that phonotactic constraints on perception are produced by top-down lexical influences on speech processing.
Unbiasing Genome-Based Analyses of Selection: An Example Using Iconic Shark Species

Whilemostly confined tomarine environments, species in the taxon Chondrichthyes (cartilaginous fishes) exhibit a wide variety of body size, longevity, and habitat depth. Despite their diversity, genome analysis has concentrated on species with high public visibility (Read et al., 2017; Marra et al., 2019). Previously, a study reporting whole genome sequencing of the white shark, Carcharodon carcharias (Marra et al., 2019) suggested unique molecular evolution accounted for the maintenance of genome stability and the elongated lifespan of the whale shark, Rhincodon typus as well as the white shark. The results reported by Marra et al. (2019) included positive selection of dozens of protein-coding genes potentially involved in genome stability and wound healing. We performed a reanalysis of some of the data presented in Marra et al. (2019) using the genome resources of other shark species and report our opinion based the results of the reanalysis.
Impact of Rocky Desertification Control on Soil Bacterial Community in Karst Graben Basin, Southwestern China

Microorganisms play critical roles in belowground ecosystems, and karst rocky desertification (KRD) control affects edaphic properties and vegetation coverage. However, the relationship between KRD control and soil bacterial communities remains unclear. 16S rRNA gene next-generation sequencing was used to investigate soil bacterial community structure, composition, diversity, and co-occurrence network from five ecological types in KRD control area. Moreover, soil physical-chemical properties and soil stoichiometry characteristics of carbon, nitrogen and phosphorus were analyzed. Soil N and P co-limitation decreased in the contribution of the promotion of KRD control on edaphic properties. Though soil bacterial communities appeared strongly associated with soil pH, soil calcium, soil phosphorus and plant richness, the key factor to determine their compositions was the latter via changed edaphic properties. The co-occurrence network analysis indicated that soil bacterial network complexity in natural ecosystem was higher than that in additional management ecosystem. Candidatus Udaeobacter, Chthoniobacterales, and Pedosphaeraceae were recognized as the key taxa maintaining karst soil ecosystems in KRD control area. Our results indicate that natural recovery is the suitable way for restoration and rehabilitation of degraded ecosystems, and thus contribute to the ongoing endeavor to appraise the interactions among soil-plant ecological networks.
ZNF213 Facilitates ER Alpha Signaling in Breast Cancer Cells

Background Breast cancer is the most common women malignancy worldwide, while estrogen receptor alpha positive type accounts for two third of all breast cancers. Although ER alpha positive breast cancer could be effectively controlled by endocrine therapy, more than half of the cases could develop endocrine resistance, making it an important clinical issue in breast cancer treatment. Thus, decoding the detailed mechanism, which controls ER alpha signaling activation and ER alpha protein stability, is of great importance for the improvement of breast cancer therapy. Several zinc finger proteins were shown to mediate the ubiquitination process and modulate protein stability. Thus, we further explore the function of Zinc finger protein 213 on ER alpha protein stability and tamoxifen resistance. Methods CCK8 and Edu assay was used to measure cell proliferation. RNA sequence was performed by Ingenuity pathway analysis. The ER alpha signaling activities were measured with luciferase assay, real-time quantitative PCR, and western blotting. Protein stability assay and ubiquitin assay were used to determine ER alpha protein degradation and ubiquitination. The immuno-precipitation was utilized to determine ER alpha and ZNF213 interaction. The ubiquitin-based immuno-precipitation assay was sued to detect specific ubiquitination manner on ER alpha. Results We identified ZNF213 as a novel zinc finger protein, which modulated ER alpha protein. ZNF213 expression correlated with poor outcome in endocrine treated patients. ZNF213 depletion inhibited ER alpha signaling and proliferation in breast cancer cells. Further mechanistic studies showed ZNF213 located in cytosol and nuclear, which modulated ER alpha stability via inhibiting ER alpha K48-linked ubiquitination. Conclusions Our study reveals an interesting post-translational mechanism between ER alpha and ZNF213 in breast cancer. Targeting ZNF213 could be an appealing strategy for ER alpha positive breast cancer.
Three-Complex Numbers and Related Algebraic Structures

Three-complex numbers are introduced for using a geometric vector product in the three-dimensional Euclidean vector space R3 and proving its equivalence with a spherical coordinate product. Based upon the definitions of the geometric power and geometric exponential functions, some Euler-type trigonometric representations of three-complex numbers are derived. Further, a general l23−complex algebraic structure together with its matrix, polynomial and variable basis vector representations are considered. Then, the classes of lp3-complex numbers are introduced. As an application, Euler-type formulas are used to construct directional probability laws on the Euclidean unit sphere in R3.
Cognitive analytics management of the customer lifetime value: an artificial neural network approach

PurposeThe purpose of this study is to show that the use of CAM (cognitive analytics management) methodology is a valid tool to describe new technology implementations for businesses.Design/methodology/approachStarting from a dataset of recipes, we were able to describe consumers through a variant of the RFM (recency, frequency and monetary value) model. It has been possible to categorize the customers into clusters and to measure their profitability thanks to the customer lifetime value (CLV). FindingsAfter comparing two machine learning algorithms, we found out that self-organizing map better classifies the customer base of the retailer. The algorithm was able to extract three clusters that were described as personas using the values of the customer lifetime value and the scores of the variant of the RFM model.Research limitations/implicationsThe results of this methodology are strictly applicable to the retailer which provided the data.Practical implicationsEven though, this methodology can produce useful information for designing promotional strategies and improving the relationship between company and customers.Social implicationsCustomer segmentation is an essential part of the marketing process. Improving further segmentation methods allow even small and medium companies to effectively target customers to better deliver to society the value they offer.Originality/valueThis paper shows the application of CAM methodology to guide the implementation and the adoption of a new customer segmentation algorithm based on the CLV.
Impact of emerging technologies in banking and finance in Europe: A volatility spillover and contagion approach

The empirical evidence suggests that stock returns in the emerging technology environment exhibit high stock return volatility. The fundamental aim of the article is to investigate the dynamic, time series properties of the correlations between daily log returns and magnitude of the volatility transmissions from the emerging technologies environment to the Spanish banking sector, the Spanish market portfolio and the finance industry in the EU area. Using daily log returns for the performance variables and an equally weighted index was constructed as proxy to represent the emerging technology phenomena covering a period from the 7th of July of 2015 to the 20th of September of 2019. The study applies generalized autoregressive conditional heteroskedasticity GARCH followed by the diagonal BEKK approach. One key finding is that the emerging technology environment is important in capturing volatility of Spanish banking sector, the Spanish market portfolio and the finance industry in the EU area through significant volatility clustering, volatility spillover and volatility persistence. Results exhibit very large GARCH and relatively low ARCH effects indicating a long persistence of resulting shocks over volatility. Broadly, the Spanish banking sector seems to be the most exposed to volatility spillover. Nevertheless, it is the finance industry across the EU which is more affected by the volatility persistence from emerging technology shocks in terms of volatility and cross – volatility point of view. Additionally, high volatility periods provide insights about an increased integration and volatility spillover. From an investor perspective, one important implication is that adding stocks from different emerging technologies to a portfolio does not necessarily lead to risk reduction.
Bayesian Surrogate Learning for Uncertainty Analysis of Coupled Multidisciplinary Systems

Engineering systems are often composed of many subsystems that interact with each other. These subsystems, referred to as disciplines, contain many types of uncertainty and in many cases are feedback-coupled with each other. In designing these complex systems, one needs to assess the stationary behavior of these systems for the sake of stability and reliability. This requires the system level uncertainty analysis of the multidisciplinary systems, which is often computationally intractable. To overcome this issue, techniques have been developed for capturing the stationary behavior of the coupled multidisciplinary systems through available data of individual disciplines. The accuracy and convergence of the existing techniques depend on a large amount of data from all disciplines, which are not available in many practical problems. Toward this, we have developed an adaptive methodology that adds the minimum possible number of samples from individual disciplines to achieve an accurate and reliable uncertainty propagation in coupled multidisciplinary systems. The proposed method models each discipline function via Gaussian process (GP) regression to derive a closed-form policy. This policy sequentially selects a new sample point that results in the highest uncertainty reduction over the distribution of the coupling design variables. The effectiveness of the proposed method is demonstrated in the uncertainty analysis of an aerostructural system and a coupled numerical example.
On (2-d)-Kernels in the Tensor Product of Graphs

In this paper, we study the existence, construction and number of (2-d)-kernels in the tensor product of paths, cycles and complete graphs. The symmetric distribution of (2-d)-kernels in these products helps us to characterize them. Among others, we show that the existence of (2-d)-kernels in the tensor product does not require the existence of a (2-d)-kernel in their factors. Moreover, we determine the number of (2-d)-kernels in the tensor product of certain factors using Padovan and Perrin numbers.
A supervised scheme for aspect extraction in sentiment analysis using the hybrid feature set of word dependency relations and lemmas

Due to the massive progression of the Web, people post their reviews for any product, movies and places they visit on social media. The reviews available on social media are helpful to customers as well as the product owners to evaluate their products based on different reviews. Analyzing structured data is easy as compared to unstructured data. The reviews are available in an unstructured format. Aspect-Based Sentiment Analysis mines the aspects of a product from the reviews and further determines sentiment for each aspect. In this work, two methods for aspect extraction are proposed. The datasets used for this work are SemEval restaurant review dataset, Yelp and Kaggle datasets. In the first method a multivariate filter-based approach for feature selection is proposed. This method support to select significant features and reduces redundancy among selected features. It shows improvement in F1-score compared to a method that uses only relevant features selected using Term Frequency weight. In another method, selective dependency relations are used to extract features. This is done using Stanford NLP parser. The results gained using features extracted by selective dependency rules are better as compared to features extracted by using all dependency rules. In the hybrid approach, both lemma features and selective dependency relation based features are extracted. Using the hybrid feature set, 94.78% accuracy and 85.24% F1-score is achieved in the aspect category prediction task.
Correct and stable sorting for overflow streaming data with a limited storage size and a uniprocessor

Tremendous quantities of numeric data have been generated as streams in various cyber ecosystems. Sorting is one of the most fundamental operations to gain knowledge from data. However, due to size restrictions of data storage which includes storage inside and outside CPU with respect to the massive streaming data sources, data can obviously overflow the storage. Consequently, all classic sorting algorithms of the past are incapable of obtaining a correct sorted sequence because data to be sorted cannot be totally stored in the data storage. This paper proposes a new sorting algorithm called streaming data sort for streaming data on a uniprocessor constrained by a limited storage size and the correctness of the sorted order. Data continuously flow into the storage as consecutive chunks with chunk sizes less than the storage size. A theoretical analysis of the space bound and the time complexity is provided. The sorting time complexity is O (n), where n is the number of incoming data. The space complexity is O (M), where M is the storage size. The experimental results show that streaming data sort can handle a million permuted data by using a storage whose size is set as low as 35% of the data size. This proposed concept can be practically applied to various applications in different fields where the data always overflow the working storage and sorting process is needed.
On Generalizations of Sampling Theorem and Stability Theorem in Shift-Invariant Subspaces of Lebesgue and Wiener Amalgam Spaces with Mixed-Norms

In this paper, we establish generalized sampling theorems, generalized stability theorems and new inequalities in the setting of shift-invariant subspaces of Lebesgue and Wiener amalgam spaces with mixed-norms. A convergence theorem of general iteration algorithms for sampling in some shift-invariant subspaces of Lp→(Rd) are also given.
Evaluation of Rust code verbosity, understandability and complexity

Rust is an innovative programming language initially implemented by Mozilla, developed to ensure high performance, reliability, and productivity. The final purpose of this study consists of applying a set of common static software metrics to programs written in Rust to assess the verbosity, understandability, organization, complexity, and maintainability of the language. To that extent, nine different implementations of algorithms available in different languages were selected. We computed a set of metrics for Rust, comparing them with the ones obtained from C and a set of object-oriented languages: C++, Python, JavaScript, TypeScript. To parse the software artifacts and compute the metrics, it was leveraged a tool called rust-code-analysis that was extended with a software module, written in Python, with the aim of uniforming and comparing the results. The Rust code had an average verbosity in terms of the raw size of the code. It exposed the most structured source organization in terms of the number of methods. Rust code had a better Cyclomatic Complexity, Halstead Metrics, and Maintainability Indexes than C and C++ but performed worse than the other considered object-oriented languages. Lastly, the Rust code exhibited the lowest COGNITIVE complexity of all languages. The collected measures prove that the Rust language has average complexity and maintainability compared to a set of popular languages. It is more easily maintainable and less complex than the C and C++ languages, which can be considered syntactically similar. These results, paired with the memory safety and safe concurrency characteristics of the language, can encourage wider adoption of the language of Rust in substitution of the C language in both the open-source and industrial environments.
Computational Techniques for Investigating Information Theoretic Limits of Information Systems

Computer-aided methods, based on the entropic linear program framework, have been shown to be effective in assisting the study of information theoretic fundamental limits of information systems. One key element that significantly impacts their computation efficiency and applicability is the reduction of variables, based on problem-specific symmetry and dependence relations. In this work, we propose using the disjoint-set data structure to algorithmically identify the reduction mapping, instead of relying on exhaustive enumeration in the equivalence classification. Based on this reduced linear program, we consider four techniques to investigate the fundamental limits of information systems: (1) computing an outer bound for a given linear combination of information measures and providing the values of information measures at the optimal solution; (2) efficiently computing a polytope tradeoff outer bound between two information quantities; (3) producing a proof (as a weighted sum of known information inequalities) for a computed outer bound; and (4) providing the range for information quantities between which the optimal value does not change, i.e., sensitivity analysis. A toolbox, with an efficient JSON format input frontend, and either Gurobi or Cplex as the linear program solving engine, is implemented and open-sourced.