Miracle-dz/UniFAR-FTUs huggingface dataset:
Fermion Numbers 1/2 of Sphalerons from Spectral Mirror Symmetry

Motivated by the pioneering work of Jackiw and Rebbi, we explore the properties of fermionic Hamiltonians under discrete transformations in the background of the $SU(2)_L$ S sphaleron of the electroweak standard model. We find that CP is not a symmetry of the system along the noncontractible loop of field configurations that connects topologically distinct vacua and includes the sphaleron. By augmenting the CP transformation with an additional operation, we observe that the Dirac Hamiltonian is odd under the new transformation precisely at the sphaleron, and this ensures the mirror symmetry of the spectrum. This symmetry also indicates that the zero mode is self-conjugate. As a consistency check, we show that the fermionic zero mode discovered by Ringwald in the sphaleron background is invariant under the new transformation. This symmetry is broken elsewhere along the loop. For the vacua, this symmetry is ensured by the usual CP invariance. The fermion numbers $\frac{1}{2}$ of the sphaleron then follow immediately from the reasonings presented by Jackiw and Rebbi or equivalently from the spectral deficiency $\frac{1}{2}$ of the Dirac sea. The relevance of this analysis to other solutions is also discussed.
Improving Mental Health Service Utilization Among Men: A Systematic Review and Synthesis of Behavior Change Techniques Within Interventions Targeting Help-Seeking

Compared to women, men are less likely to seek help for mental health difficulties. Despite considerable interest, a paucity in evidence-based solutions remains to solve this problem. The current review sought to synthesize the specific techniques within male-specific interventions that may contribute to an improvement in psychological help-seeking (attitudes, intentions, or behaviors). A systematic review identified 6,598 potential articles from three databases (MEDLINE, EMBASE, and PsycINFO). Nine studies were eligible. A meta-analysis was problematic due to disparate interventions, outcomes, and populations. The decision to use an innovative approach that adopted the Behavior Change Technique (BCT) taxonomy to synthesize each intervention’s key features likely to be responsible for improving help-seeking was made. Of the nine studies, four were engagement strategies (i.e., brochures/documentaries), two randomized controlled trials (RCTs), two pilot RCTs, and one retrospective review. Regarding quality assessment, three were scored as “strong,” five as “moderate,” and one as “weak. ” Key processes that improved help-seeking attitudes, intentions, or behaviors for men included using role models to convey information, psychoeducational material to improve mental health knowledge, assistance with recognizing and managing symptoms, active problem-solving tasks, motivating behavior change, signposting services, and, finally, content that built on positive male traits (e.g., responsibility and strength). This is the first review to use this novel approach of using BCTs to summarize and identify specific techniques that may contribute to an improvement in male help-seeking interventions, whether engagement with treatment or the intervention itself. Overall, this review summarizes previous male help-seeking interventions, informing future research/clinical developments.
Classical and Non-classical Presentations of Complement Factor I Deficiency: Two Contrasting Cases Diagnosed via Genetic and Genomic Methods

Deficiency of complement factor I is a rare immunodeficiency that typically presents with increased susceptibility to encapsulated bacterial infections. However, non-infectious presentations including rheumatological, dermatological and neurological disease are increasingly recognized and require a high-index of suspicion to reach a timely diagnosis. Herein, we present two contrasting cases of complement factor I deficiency: one presenting in childhood with invasive pneumococcal disease, diagnosed using conventional immunoassays and genetics and the second presenting in adolescence with recurrent sterile neuroinflammation, diagnosed via a genomic approach. Our report and review of the literature highlight the wide spectrum of clinical presentations associated with CFI deficiency and the power of genomic medicine to inform rare disease diagnoses.
Detecting Weak Signals by Combining Small P-Values in Genetic Association Studies

We approach the problem of combining top-ranking association statistics or P-values from a new perspective which leads to a remarkably simple and powerful method. Statistical methods, such as the Rank Truncated Product (RTP), have been developed for combining top-ranking associations and this general strategy proved to be useful in applications for detecting combined effects of multiple disease components. To increase power, these methods aggregate signals across top ranking SNPs, while adjusting for their total number assessed in a study. Analytic expressions for combined top statistics or P-values tend to be unwieldy, which complicates interpretation, practical implementation, and hinders further developments. Here, we propose the Augmented Rank Truncation (ART) method that retains main characteristics of the RTP but is substantially simpler to implement. ART leads to an efficient form of the adaptive algorithm, an approach where the number of top ranking SNPs is varied to optimize power. We illustrate our methods by strengthening previously reported associations of µ-opioid receptor variants with sensitivity to pain.
Stable Rank Normalization for Improved Generalization in Neural Networks and GANs

Exciting new work on the generalization bounds for neural networks (NN) given by Neyshabur et al. , Bartlett et al. closely depend on two parameter-depenedent quantities: the Lipschitz constant upper-bound and the stable rank (a softer version of the rank operator). This leads to an interesting question of whether controlling these quantities might improve the generalization behaviour of NNs. To this end, we propose stable rank normalization (SRN), a novel, optimal, and computationally efficient weight-normalization scheme which minimizes the stable rank of a linear operator. Surprisingly we find that SRN, inspite of being non-convex problem, can be shown to have a unique optimal solution. Moreover, we show that SRN allows control of the data-dependent empirical Lipschitz constant, which in contrast to the Lipschitz upper-bound, reflects the true behaviour of a model on a given dataset. We provide thorough analyses to show that SRN, when applied to the linear layers of a NN for classification, provides striking improvements-11.3% on the generalization gap compared to the standard NN along with significant reduction in memorization. When applied to the discriminator of GANs (called SRN-GAN) it improves Inception, FID, and Neural divergence scores on the CIFAR 10/100 and CelebA datasets, while learning mappings with low empirical Lipschitz constants.
Disentangling Genetic Variation for Resistance and Endurance to Scuticociliatosis in Turbot Using Pedigree and Genomic Information

Selective breeding for improving host responses to infectious pathogens is a promising option for disease control. In fact, disease resilience, the ability of a host to survive or cope with infectious challenge, has become a highly desirable breeding goal. However, resilience is a complex trait composed of two different host defence mechanisms, namely resistance (the ability of a host to avoid becoming infected or diseased) and endurance (the ability of an infected host to survive the infection). While both could be targeted for genetic improvement, it is currently unknown how they contribute to survival, as reliable estimates of genetic parameters for both traits obtained simultaneously are scarce. A difficulty lies in obtaining endurance phenotypes for genetic analyses. In this study, we present the results from an innovative challenge test carried out in turbot whose design allowed disentangling the genetic basis of resistance and endurance to Philasterides dicentrarchi, a parasite causing scuticociliatosis that leads to substantial economic losses in the aquaculture industry. A noticeable characteristic of the parasite is that it causes visual signs that can be used for disentangling resistance and endurance. Our results showed the existence of genetic variation for both traits (heritability = 0.26 and 0.12 for resistance and endurance, respectively) and for the composite trait resilience (heritability = 0.15). The genetic correlation between resistance and resilience was very high (0.90) indicating that both are at a large extent the same trait, but no significant genetic correlation was found between resistance and endurance. A total of 18,125 SNPs obtained from 2b-RAD sequencing enabled genome-wide association analyses for detecting QTLs controlling the three traits. A candidate QTL region on linkage group 19 that explains 33% of the additive genetic variance was identified for resilience. The region contains relevant genes related to immune response and defence mechanisms. Although no significant associations were found for resistance, the pattern of association was the same as for resilience. For endurance, one significant association was found on linkage group 2. The accuracy of genomic breeding values was also explored for resilience, showing that it increased by 12% when compared with the accuracy of pedigree-based breeding values. To our knowledge, this is the first study in turbot disentangling the genetic basis of resistance and endurance to scuticociliatosis.
Differential Effects of Valence and Encoding Strategy on Internal Source Memory and Judgments of Source: Exploring the Production and the Self-Reference Effect

Item memory studies show that emotional stimuli are associated with improved memory performance compared to neutral ones. However, emotion-related effects on source memory are less consistent. The current study probed how emotional valence and specific encoding conditions influence internal source memory performance and judgments of source (JOSs). In two independent experiments, participants were required to read silently/aloud (Experiment 1) or to perform self-reference/common judgments (Experiment 2) on a list of negative/neutral/positive words. They also performed immediate JOSs ratings for each word. The study phase was followed by a test phase in which participants performed old-new judgments. In Experiment 1, the production effect was replicated for item memory, but the effects of valence on item and source memory were not significant. In Experiment 2, self-referential processing effects on item and source memory differed as a function of valence. In both experiments, JOSs ratings were sensitive to valence and encoding conditions, although they were not predictive of objective memory performance. These findings demonstrate that the effects of valence on internal source memory and JOSs are modulated by encoding strategy. Thus, the way information is encoded can shed light on how emotion might enhance, impair or exert no influence on source memory.
DNA Methylation Readers and Cancer: Mechanistic and Therapeutic Applications

DNA methylation is a major epigenetic process that regulates chromatin structure which causes transcriptional activation or repression of genes in a context-dependent manner. In general, DNA methylation takes place when methyl groups are added to the appropriate bases on the genome by the action of “writer” molecules known as DNA methyltransferases. How these methylation marks are read and interpreted into different functionalities represents one of the main mechanisms through which the genes are switched “ON” or “OFF” and typically involves different types of “reader” proteins that can recognize and bind to the methylated regions. A tightly balanced regulation exists between the “writers” and “readers” in order to mediate normal cellular functions. However, alterations in normal methylation pattern is a typical hallmark of cancer which alters the way methylation marks are written, read and interpreted in different disease states. This unique characteristic of DNA methylation “readers” has identified them as attractive therapeutic targets. In this review, we describe the current state of knowledge on the different classes of DNA methylation “readers” identified thus far along with their normal biological functions, describe how they are dysregulated in cancer, and discuss the various anti-cancer therapies that are currently being developed and evaluated for targeting these proteins.
Mesh adaptivity for quasi‐static phase‐field fractures based on a residual‐type a posteriori error estimator

In this work, we consider adaptive mesh refinement for a monolithic phase‐field description for fractures in brittle materials. Our approach is based on an a posteriori error estimator for the phase‐field variational inequality realizing the fracture irreversibility constraint. The key goal is the development of a reliable and efficient residual‐type error estimator for the phase‐field fracture model in each time‐step. Based on this error estimator, error indicators for local mesh adaptivity are extracted. The proposed estimator is based on a technique known for singularly perturbed equations in combination with estimators for variational inequalities. These theoretical developments are used to formulate an adaptive mesh refinement algorithm. For the numerical solution, the fracture irreversibility is imposed using a Lagrange multiplier. The resulting saddle‐point system has three unknowns: displacements, phase‐field, and a Lagrange multiplier for the crack irreversibility. Several numerical experiments demonstrate our theoretical findings with the newly developed estimators and the corresponding refinement strategy.
CTHRC1: A New Candidate Biomarker for Improved Rheumatoid Arthritis Diagnosis

Background: The purpose of this study was to determine whether plasma levels of the collagen triple helix repeat containing 1 (CTHRC1) protein can serve as a blood-based biomarker for improved diagnosis of rheumatoid arthritis (RA) patients and monitoring of RA disease activity. Methods: We measured levels of CTHRC1 in the plasma of patients diagnosed with RA, osteoarthritis (OA), reactive arthritis (ReA), as well as in healthy individuals. We then assessed the correlation between CTHRC1 protein and a range of indices including the 28-joint disease activity score (DAS28), rheumatoid factor (RF), C-reactive protein (CRP), anti-citrullinated protein antibodies (ACPA), erythrocyte sedimentation rate (ESR), as well as a panel of cytokines, including interleukin 1 beta (IL-1β), interleukin 6 (IL-6), interleukin 8 (IL-8), and interferon gamma (IFNγ). Receiver operating characteristic (ROC) analysis was further performed to assess the diagnostic value of CTHRC1. Results: CTHRC1 plasma levels were significantly elevated in RA patients compared to healthy individuals, OA and ReA patients. ROC curve and risk score analysis suggested that plasma CTHRC1 can accurately discriminate patients with RA from healthy controls and may have practical value for RA diagnosis. CTHRC1 levels were positively associated with RF, ACPA, CRP, and disease activity based on the combined index of DAS28 with CRP (DAS28-CRP), and also strongly correlated with IL-1β, IL-6, IL-8, and IFNγ. Conclusion: Our studies show that CTHRC1 is a sensitive and easy-to-measure plasma marker that differentiates between RA and healthy status and also distinguishes between RA and other forms of arthritis, such as OA and ReA. At the current level of understanding, plasma CTHRC1 levels may improve the diagnosis of RA and these findings warrant confirmation in a larger, more comprehensive patient population.
Safety and efficacy analysis of long-term follow up real-world data with ibrutinib monotherapy in 58 patients with CLL treated in a single-center in Greece

Abstract Ibrutinib (IB) revealed high efficacy and safety profile in phase 2/3 chronic lymphocytic clinical trials. Emerging real-world-data shows similar response and survival, but higher discontinuation rates due to adverse events (AEs). We present retrospective real-world data from 58 chronic lymphocytic leukemia (CLL) patients (August 2014–January 2019) treated with IB monotherapy, according to standard instructions, in a Greek single-center, focusing on safety and efficacy. Eleven untreated first line (1st L) and 47 relapsed/refractory(R/R) CLL patients received IB for 6.6(0.7–46.8) and 16.3(0.4–53.7) months, respectively. Nine percent of 1stL and 10.6% of R/R patients discontinued IB due to AEs. Atrial fibrillation (AF) was the most common discontinuation AE cause (3.5% of patients). Thirteen patients (24.5%) discontinued due to disease progression: 6 Richter transformation (RT) cases, after 10.6 months (1–35.9) and 7 CLL-progression cases, after 30.3 months (5.4–43.4) of IB initiation. IB had minimal impact on immunoglobulin G (IgG)-levels, CLL-related autoimmunity, and second primary malignancies (SPM). Our real-world data show that CLL patients present similar to clinical trials’ outcomes if treated homogenously according to standard guidelines, resulting in fewer unneeded discontinuations and shrinkage of treatment armamentarium.
Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding

Active learning is a popular methodology in text classification - known in the legal domain as "predictive coding" or "Technology Assisted Review" or "TAR" - due to its potential to minimize the required review effort to build effective classifiers. In this study, we use extensive experimentation to examine the impact of popular seed set selection strategies in active learning, within a predictive coding exercise, and evaluate different active learning strategies against well-researched continuous active learning strategies for the purpose of determining efficient training methods for classifying large populations quickly and precisely. We study how random sampling, keyword models and clustering based seed set selection strategies combined together with top-ranked, uncertain, random, recall inspired, and hybrid active learning document selection strategies affect the performance of active learning for predictive coding. We use the percentage of documents requiring review to reach 75% recall as the "benchmark" metric to evaluate and compare our approaches. In most cases we find that seed set selection methods have a minor impact, though they do show significant impact in lower richness data sets or when choosing a top-ranked active learning selection strategy. Our results also show that active learning selection strategies implementing uncertainty, random, or 75% recall selection strategies has the potential to reach the optimum active learning round much earlier than the popular continuous active learning approach (top-ranked selection). The results of our research shed light on the impact of active learning seed set selection strategies and also the effectiveness of the selection strategies for the following learning rounds. Legal practitioners can use the results of this study to enhance the efficiency, precision, and simplicity of their predictive coding process.
Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization

I review unsupervised or self-supervised neural networks playing minimax games in game-theoretic settings. (i) Adversarial Curiosity (AC, 1990) is based on two such networks. One network learns to probabilistically generate outputs, the other learns to predict effects of the outputs. Each network minimizes the objective function maximized by the other. (ii) Generative Adversarial Networks (GANs, 2010-2014) are an application of AC where the effect of an output is 1 if the output is in a given set, and 0 otherwise. (iii) Predictability Minimization (PM, 1990s) models data distributions through a neural encoder that maximizes the objective function minimized by a neural predictor of the code components. We correct a previously published claim that PM is not based on a minimax game.
Stability and Metastability of Traffic Dynamics in Uplink Random Access Networks

We characterize the stability, metastability, and the stationary regime of traffic dynamics in a single-cell uplink wireless system. The traffic is represented in terms of spatial birth-death processes, in which users arrive as a Poisson point process in time and space, each with a file to transmit to the base station. The service rate of each user is based on its signal to interference plus noise ratio, where the interference is from other active users in the cell. Once the file is fully transmitted, the user leaves the cell. We derive the necessary and sufficient condition for network stability, which is independent of the specific bounded path loss function. A novel observation is that for a certain range of arrival rates, the network appears stable for a possibly long time, and then suddenly exhibits instability. This property, which is known in statistical physics but rarely observed in wireless communication, is called metastability. Finally, we propose two heuristic characterizations based on mean-field interpretation, of the network steady-state regime when it exists. The first-order approximation is very simple to compute, but loose in some regimes, whereas the second-order approximation is more sophisticated but tight for the whole range of arrival rates.
The Human TET2 Gene Contains Three Distinct Promoter Regions With Differing Tissue and Developmental Specificities

Tet methylcytosine dioxygenase 2 (TET2) is a tumor suppressor gene that is inactivated in a wide range of hematological cancers. TET2 enzymatic activity converts 5-methylcytosine (5-mC) into 5-hydroxymethylcytosine (5-hmC), an essential step in DNA demethylation. Human TET2 is highly expressed in pluripotent cells and down-regulated in differentiated cells: however, transcriptional regulation of the human TET2 gene has not been investigated in detail. Here we define three promoters within a 2.5 kb region located ∼ 87 kb upstream of the first TET2 coding exon. The three promoters, designated as Pro1, Pro2, and Pro3, generate three alternative first exons, and their presence in TET2 mRNAs varies with cell type and developmental stage. In general, all three TET2 transcripts are more highly expressed in human tissues rich in hematopoietic stem cells, such as spleen and bone marrow, compared to other tissues, such as brain and kidney. Transcripts from Pro2 are expressed by a broad range of tissues and at a significantly higher level than Pro1 or Pro3 transcripts. Pro3 transcripts were highly expressed by embryoid bodies generated from the H9 ES cell line, and the major Pro3 transcript is an alternatively spliced mRNA isoform that produces a truncated TET2 protein lacking the catalytic domain. Our study demonstrates distinct tissue-specific mechanisms of TET2 transcriptional regulation during early pluripotent states and in differentiated cell types.