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Machine learning is a means to derive artificial intelligence by discovering patterns in existing data. Here, we show that applying machine learning to ordinary human language results in human-like semantic biases. We replicated a spectrum of known biases, as measured by the Implicit Association Test, using a widely used, purely statistical machine-learning model trained on a standard corpus of text from the World Wide Web. Our results indicate that text corpora contain recoverable and accurate imprints of our historic biases, whether morally neutral as toward insects or flowers, problematic as toward race or gender, or even simply veridical, reflecting the status quo distribution of gender with respect to careers or first names. Our methods hold promise for identifying and addressing sources of bias in culture, including technology.

Using word embeddings from 850 billion words in English-language Google Books, we provide an extensive analysis of historical change and stability in social group representations (stereotypes) across a long timeframe (from 1800 to 1999), for a large number of social group targets (Black, White, Asian, Irish, Hispanic, Native American, Man, Woman, Old, Young, Fat, Thin, Rich, Poor), and their emergent, bottom-up associations with 14,000 words and a subset of 600 traits. The results provide a nuanced picture of change and persistence in stereotypes across 200 y. Change was observed in the top-associated words and traits: Whether analyzing the top 10 or 50 associates, at least 50% of top associates changed across successive decades. Despite this changing content of top-associated words, the average valence (positivity/negativity) of these top stereotypes was generally persistent. Ultimately, through advances in the availability of historical word embeddings, this study offers a comprehensive characterization of both change and persistence in social group representations as revealed through books of the English-speaking world from 1800 to 1999.

In this work, a Python framework for characteristic feature extraction is developed and applied to gene expression data of human fibroblasts. Unlabeled feature selection objectively determines groups and minimal gene sets separating groups. ML explainability methods transform the features correlating with phenotypic differences into causal reasoning, supported by further pipeline and visualization tools, allowing user knowledge to boost causal reasoning. The purpose of the framework is to identify characteristic features that are causally related to phenotypic differences of single cells. The pipeline consists of several data science methods enriched with purposeful visualization of the intermediate results in order to check them systematically and infuse the domain knowledge about the investigated process. A specific focus is to extract a small but meaningful set of genes to facilitate causal reasoning for the phenotypic differences. One application could be drug target identification. For this purpose, the framework follows different steps: feature reduction (PFA), low dimensional embedding (UMAP), clustering ((H)DBSCAN), feature correlation (chi-square, mutual information), ML validation and explainability (SHAP, tree explainer). The pipeline is validated by identifying and correctly separating signature genes associated with aging in fibroblasts from single-cell gene expression measurements: PLK3, polo-like protein kinase 3; CCDC88A, Coiled-Coil Domain Containing 88A; STAT3, signal transducer and activator of transcription-3; ZNF7, Zinc Finger Protein 7; SLC24A2, solute carrier family 24 member 2 and lncRNA RP11-372K14.2. The code for the preprocessing step can be found in the GitHub repository https://github.com/AC-PHD/NoLabelPFA , along with the characteristic feature extraction https://github.com/LauritzR/characteristic-feature-extraction .

Purpose This study aims to reveal and prioritize the main barriers and challenges in front of the Logistics 4.0 transformation, which is the extension of Industry 4.0. Also, this study presents a roadmap for a company operating in developing countries to reduce and eliminate challenges and hurdles for each link in their supply chain. Design/methodology/approach A two-stage methodology was used in this study. First, a detailed literature review was conducted to identify the barriers to innovations compatible with Industry 4.0. Hence, barriers have been identified, including nine from the literature review. The best–worst method (BWM) is then used to determine these barriers’ weights and order of importance. To implement BWM, two-stage e-surveys are applied to experts. Findings The “Managerial and Economic Challenges” dimension is the most important, and “Regulatory and social challenges” is the least essential dimension among the main dimension. Moreover, financial constraints or capitals are the most critical barriers among the sub-barriers. This study gives the reader a comprehensive insight into how detected barriers affect digitalization performance. Therefore, this framework is a roadmap designed with a holistic view to guide manufacturers, logistics parties and even policy and decision-makers. Originality/value Theoretically and empirically identifies the potential barriers and challenges in the digital transformation of logistics is already missing at the desired level. From this point of view, to the best of the authors’ knowledge, this study is the first research that determines barriers based on the Logistics 4.0 model with an industrial perspective. One of the most important limitations of this study is that a total of nine dimensions were examined under only three basic barriers. Different alternatives can be identified for future studies.

Studying the molecular basis of intestinal infections caused by enteric pathogens at the tissue level is challenging, because most human intestinal infection models have limitations, and results obtained from animals may not reflect the human situation. Infections with Salmonella enterica serovar Typhimurium (STm) have different outcomes between organisms. 3D tissue modeling of primary human material provides alternatives to animal experimentation, but epithelial co‐culture with immune cells remains difficult. Macrophages, for instance, contribute to the immunocompetence of native tissue, yet their incorporation into human epithelial tissue models is challenging. A 3D immunocompetent tissue model of the human small intestine based on decellularized submucosa enriched with monocyte‐derived macrophages (MDM) is established. The multicellular model recapitulated in vivo‐like cellular diversity, especially the induction of GP2 positive microfold (M) cells. Infection studies with STm reveal that the pathogen physically interacts with these M‐like cells. MDMs show trans‐epithelial migration and phagocytosed STm within the model and the levels of inflammatory cytokines are induced upon STm infection. Infected epithelial cells are shed into the supernatant, potentially reflecting an intracellular reservoir of invasion‐primed STm. Together, the 3D model of the human intestinal epithelium bears potential as an alternative to animals to identify human‐specific processes underlying enteric bacterial infections. In this work, a human primary cell‐based immunocompetent model is established to investigate early responses to STm infection. Intestinal fibroblasts and monocyte‐derived macrophages are embedded in decellularized small intestinal submucosa (SIS) from domestic pigs, followed by the development of an enteroid monolayer. The generated model highlights human responses and can further serve as a platform in enteric pathogen research.

Since ancient times aging has also been regarded as a disease, and humankind has always strived to extend the natural lifespan. Analyzing the genes involved in aging and disease allows for finding important indicators and biological markers for pathologies and possible therapeutic targets. An example of the use of omics technologies is the research regarding aging and the rare and fatal premature aging syndrome progeria (Hutchinson-Gilford progeria syndrome, HGPS). In our study, we focused on the in silico analysis of differentially expressed genes (DEGs) in progeria and aging, using a publicly available RNA-Seq dataset (GEO dataset GSE113957) and a variety of bioinformatics tools. Despite the GSE113957 RNA-Seq dataset being well-known and frequently analyzed, the RNA-Seq data shared by Fleischer et al. is far from exhausted and reusing and repurposing the data still reveals new insights. By analyzing the literature citing the use of the dataset and subsequently conducting a comparative analysis comparing the RNA-Seq data analyses of different subsets of the dataset (healthy children, nonagenarians and progeria patients), we identified several genes involved in both natural aging and progeria (KRT8, KRT18, ACKR4, CCL2, UCP2, ADAMTS15, ACTN4P1, WNT16, IGFBP2). Further analyzing these genes and the pathways involved indicated their possible roles in aging, suggesting the need for further in vitro and in vivo research. In this paper, we (1) compare “normal aging” (nonagenarians vs. healthy children) and progeria (HGPS patients vs. healthy children), (2) enlist genes possibly involved in both the natural aging process and progeria, including the first mention of IGFBP2 in progeria, (3) predict miRNAs and interactomes for WNT16 (hsa-mir-181a-5p), UCP2 (hsa-mir-26a-5p and hsa-mir-124-3p), and IGFBP2 (hsa-mir-124-3p, hsa-mir-126-3p, and hsa-mir-27b-3p), (4) demonstrate the compatibility of well-established R packages for RNA-Seq analysis for researchers interested but not yet familiar with this kind of analysis, and (5) present comparative proteomics analyses to show an association between our RNA-Seq data analyses and corresponding changes in protein expression.

The oncogenic cytoplasmic p21 contributes to cancer aggressiveness and chemotherapeutic failure. However, the molecular mechanisms remain obscure. Here, we show for the first time that cytoplasmic p21 mediates 5-Fluorouracil (5FU) resistance by shuttling p-Chk2 out of the nucleus to protect the tumor cells from its pro-apoptotic functions. We observed that cytoplasmic p21 levels were up-regulated in 5FU-resistant colorectal cancer cells in vitro and the in vivo Chorioallantoic membrane (CAM) model. Kinase array analysis revealed that p-Chk2 is a key target of cytoplasmic p21. Importantly, cytoplasmic form of p21 mediated by p21T145D transfection diminished p-Chk2-mediated activation of E2F1 and apoptosis induction. Co-immunoprecipitation, immunofluorescence, and proximity ligation assay showed that p21 forms a complex with p-Chk2 under 5FU exposure. Using in silico computer modeling, we suggest that the p21/p-Chk2 interaction hindered the nuclear localization signal of p-Chk2, and therefore, the complex is exported out of the nucleus. These findings unravel a novel mechanism regarding an oncogenic role of p21 in regulation of resistance to 5FU-based chemotherapy. We suggest a possible value of cytoplasmic p21 as a prognosis marker and a therapeutic target in colorectal cancer patients.

Purpose In recent years, the business of container lines has faced severe challenges such as overcapacity and low profitability. To survive in such a competitive market, container lines need to maintain long-term customer relationships by enhancing the satisfaction and loyalty of customers. The purpose of this paper is to adopt a social exchange theory (SET) approach and investigate the impact of relational bonding strategies on the satisfaction and loyalty of customers in container shipping. Design/methodology/approach Drawing on SET, a theoretical model that specifies the relationships between relational bonding strategies, customer satisfaction and loyalty was proposed. Survey data were collected from 175 freight forwarders. The obtained data were analyzed using structural equation modelling. Findings The results indicate that financial bonding strategies have the most significant direct effects on customer satisfaction, while social bonding strategies have the strongest direct impact on customer loyalty. Financial bonding strategies, on the other hand, have the strongest total effects on customer loyalty. Intermodal and basic operations are found to have the equal total effects on customer loyalty. Research limitations/implications By identifying the most effective relational bonding strategies for enhancing customer satisfaction and loyalty, this study’s findings allow container lines to better allocate their resources and implement effective relational marketing policies to satisfy and retain their customers. Originality/value This research analyses and validates the determinants of customer satisfaction and loyalty from a relational lens and empirically contributes to the field of relational marketing in the container shipping industry.

This study aimed to determine reporting compliance by measuring the extent to which Integrated Reporting Framework content element is linked to value creation. The sample of the logistics industry study was gathered from Integrated Reporting Examples Database. Sentence-by-sentence content analysis was conducted on 11 integrated reports of logistics companies using a multi-weighted scoring tool and an Integrated Reporting Value Creation Checklist (IRVC) based on the literature and the International Integrated Reporting Council (IIRC) Integrated Reporting (IR) framework. Additionally, descriptive statistics were performed for subtitles of IRVC, Value Creation Scores, and the Integrated Report Specific Feature Scores. In the end, IR content disclosures of logistics companies were presented. The scoring results of the Content Analysis were interpreted in terms of each content element and each company scores. Additionally, descriptive statistics were applied for IRVC scores. The findings indicated that content items were highly aligned with value creation links, with the highest scores being determined in \"business model\" and the lowest scores in \"performance,\" \"outlook,\" and \"risks and opportunities.\" In addition, it has been observed that there are noticeable differences among the company value creation scores in the current practice.

Abstract Representativeness is a relevant but unexamined property of stereotypes in language models. Existing auditing and debiasing approaches address the direction of stereotypes, such as whether a social category (e.g. men, women) is associated more with incompetence vs. competence content. On the other hand, representativeness is the extent to which a social category's stereotypes are about a specific content dimension, such as Competence, regardless of direction (e.g. as indicated by how often dimension-related words appear in stereotypes about the social category). As such, two social categories may be associated with competence (vs. incompetence), yet one category's stereotypes are mostly about competence, whereas the other's are mostly about alternative content (e.g. Warmth). Such differentiability would suggest that direction-based auditing may fail to identify biases in content representativeness. Here, we use a large sample of social categories that are salient in American society (based on gender, race, occupation, and others) to examine whether representativeness is an independent feature of stereotypes in the ChatGPT chatbot and SBERT language model. We focus on the Warmth and Competence stereotype dimensions, given their well-established centrality in human stereotype content. Our results provide evidence for the construct differentiability of direction and representativeness for Warmth and Competence stereotypes across models and target stimuli (social category terms, racialized name exemplars). Additionally, both direction and representativeness uniquely predicted the models' internal general valence (positivity vs. negativity) and human stereotypes. We discuss implications for the use of AI in the study of human cognition and the field of fairness in AI.