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We consider likelihood score-based methods for causal discovery in structural causal models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecification in terms of non-Gaussian error distribution. We present a surprising negative result for Gaussian likelihood scoring in combination with nonparametric regression methods.

In a microscopic model of the photoelectric effect, it becomes clear that the conservation of energy is exclusively determined by Doppler shift processes, i.e., the whole energy of the photon vanishes by means of Doppler redshifts. Accordingly, if a photon is generated, the energy is won by Doppler blueshifts. This is supposed to be valid for all processes with energy conservation. An experiment is carried out to make this Doppler energy flow visible by means of interactions with probes. The result of this experiment is that a weak force is measurable in the vicinity of processes with energy conservation. With the aid of a twisted rubber-driven low-power device (P˜=10W\tilde{P}=10\hspace{.5em}\text{W}), periodic accelerations and decelerations of about 10−6 m/s2 are measurable. In the close vicinity of the device, accelerations with values up to 10−3 m/s2 can be concluded. The consequences that result from this force are discussed.

Genetic TNFAIP3 (A20) inactivation is a classical somatic lymphoma lesion and the genomic trait in haploinsufficiency of A20 (HA20). In a cohort of 34 patients with HA20, we show that heterozygous TNFAIP3 loss skews immune repertoires toward lymphocytes with classical self-reactive antigen receptors typically found in B and T cell lymphomas. This skewing was mediated by a feed-forward tumor necrosis factor (TNF)/A20/nuclear factor κB (NF-κB) loop that shaped pre-lymphoma transcriptome signatures in clonally expanded B (CD81, BACH2, and NEAT1) or T (GATA3, TOX, and PDCD1) cells. The skewing was reversed by anti-TNF treatment but could also progress to overt lymphoma. Analysis of conditional TNFAIP3 knock-out mice reproduced the wiring of the TNF/A20/NF-κB signaling axis with permissive antigen receptors and suggested a distinct regulation in B and T cells. Together, patients with the genetic disorder HA20 provide an exceptional window into A20/TNF/NF-κB-mediated control of immune homeostasis and early steps of lymphomagenesis that remain clinically unrecognized. [ABSTRACT FROM AUTHOR]

The classification of B cell lymphomas—mainly based on light microscopy evaluation by a pathologist—requires many years of training. Since the B cell receptor (BCR) of the lymphoma clonotype and the microenvironmental immune architecture are important features discriminating different lymphoma subsets, we asked whether BCR repertoire next-generation sequencing (NGS) of lymphoma-infiltrated tissues in conjunction with machine learning algorithms could have diagnostic utility in the subclassification of these cancers. We trained a random forest and a linear classifier via logistic regression based on patterns of clonal distribution, VDJ gene usage and physico-chemical properties of the top-n most frequently represented clonotypes in the BCR repertoires of 620 paradigmatic lymphoma samples—nodular lymphocyte predominant B cell lymphoma (NLPBL), diffuse large B cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL)—alongside with 291 control samples. With regard to DLBCL and CLL, the models demonstrated optimal performance when utilizing only the most prevalent clonotype for classification, while in NLPBL—that has a dominant background of non-malignant bystander cells—a broader array of clonotypes enhanced model accuracy. Surprisingly, the straightforward logistic regression model performed best in this seemingly complex classification problem, suggesting linear separability in our chosen dimensions. It achieved a weighted F1-score of 0.84 on a test cohort including 125 samples from all three lymphoma entities and 58 samples from healthy individuals. Together, we provide proof-of-concept that at least the 3 studied lymphoma entities can be differentiated from each other using BCR repertoire NGS on lymphoma-infiltrated tissues by a trained machine learning model. Author summary: Lymphoma, a complex group of malignant blood cancers, poses a significant diagnostic challenge due to its diverse subtypes. Yet, precise classification is crucial for tailored treatment. In our research, we developed a machine learning algorithm and conducted comprehensive validation to discern distinct B cell lymphoma subtypes. We therefore leveraged B cell repertoires of lymphoma-infiltrated tissue, as ascertained through next-generation sequencing. Our data offers three key insights: We detail the creation and training of our machine learning algorithm, explaining how we selected features and designed the model. We demonstrate the algorithm's diagnostic precision using sequencing data from a test-set of patient samples. Moreover, through a deep dive into the most distinguishing aspects of our algorithm, we unveil distinctive disease-related patterns present within the malignant B cell and its surrounding environment. This analysis showed that both the malignant lymphoma cell, but also healthy bystander immune cells contribute to the distinctive architecture that characterizes a specific lymphoma subtype. We hope our work will contribute towards creating tools to diagnose lymphoma more easily and accurately ultimately leading to better outcomes for patients with this type of cancer. [ABSTRACT FROM AUTHOR]

We introduce a simple diagnostic test for assessing the overall or partial goodness of fit of a linear causal model with errors being independent of the covariates. In particular, we consider situations where hidden confounding is potentially present. We develop a method and discuss its capability to distinguish between covariates that are confounded with the response by latent variables and those that are not. Thus, we provide a test and methodology for partial goodness of fit. The test is based on comparing a novel higher-order least squares principle with ordinary least squares. In spite of its simplicity, the proposed method is extremely general and is also proven to be valid for high-dimensional settings. for this article are available online. [ABSTRACT FROM AUTHOR]

Multisystem inflammatory syndrome in children (MIS-C), a hyperinflammatory syndrome associated with SARS-CoV-2 infection, shares clinical features with toxic shock syndrome, which is triggered by bacterial superantigens. Superantigen specificity for different V[beta] chains results in V[beta] skewing, whereby T cells with specific V[beta] chains and diverse antigen specificity are overrepresented in the T cell receptor (TCR) repertoire. Here, we characterized the TCR repertoire of MIS-C patients and found a profound expansion of TCR[beta] variable gene 11-2 (TRBV11-2), with up to 24% of clonal T cell space occupied by TRBV112 T cells, which correlated with MIS-C severity and serum cytokine levels. Analysis of TRBJ gene usage and complementarity-determining region 3 (CDR3) length distribution of MIS-C expanded TRBV11-2 clones revealed extensive junctional diversity. Patients with TRBV11-2 expansion shared HLA class I alleles A02, B35, and C04, indicating what we believe is a novel mechanism for CDR3-independent T cell expansion. In silico modeling indicated that polyacidic residues in the V[beta] chain encoded by TRBV11-2 (V[beta]21.3) strongly interact with the superantigen- like motif of SARS-CoV-2 spike glycoprotein, suggesting that unprocessed SARS-CoV-2 spike may directly mediate TRBV11-2 expansion. Overall, our data indicate that a CDR3-independent interaction between SARS-CoV-2 spike and TCR leads to T cell expansion and possibly activation, which may account for the clinical presentation of MIS-C., Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19), which started as an epidemic in China and culminated in a global pandemic. [...]

A considerable fraction of B cells recognize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with germline-encoded elements of their B cell receptor, resulting in the production of neutralizing and nonneutralizing antibodies. We found that antibody sequences from different discovery cohorts shared biochemical properties and could be retrieved across validation cohorts, confirming the stereotyped character of this naive response in coronavirus disease 2019 (COVID-19). While neutralizing antibody sequences were found independently of disease severity, in line with serological data, individual nonneutralizing antibody sequences were associated with fatal clinical courses, suggesting detrimental effects of these antibodies. We mined 200 immune repertoires from healthy individuals and 500 repertoires from patients with blood or solid cancers--all acquired prior to the pandemic--for SARS-CoV-2 antibody sequences. While the largely unmutated B cell rearrangements occurred in a substantial fraction of immune repertoires from young and healthy individuals, these sequences were less likely to be found in individuals over 60 years of age and in those with cancer. This reflects B cell repertoire restriction in aging and cancer, and may to a certain extent explain the different clinical courses of COVID-19 observed in these risk groups. Future studies will have to address if this stereotyped B cell response to SARS-CoV-2 emerging from unmutated antibody rearrangements will create long-lived memory., Introduction Severe acute respiratory syndrome coronavirus 2-specific (SARS-CoV-2-specific) B cell immunity and antibody responses are being actively investigated, since early neutralization of the virus may play a decisive role in [...]