Miracle-dz/UniFAR-FTUs huggingface dataset:
Complex 4q35 and 10q26 Rearrangements

Background and Objectives After clinical evaluation, the molecular diagnosis of type 1 facioscapulohumeral dystrophy (FSHD1) relies in most laboratories on the detection of a shortened D4Z4 array at the 4q35 locus by Southern blotting. In many instances, this molecular diagnosis remains inconclusive and requires additional experiments to determine the number of D4Z4 units or identify somatic mosaicism, 4q-10q translocations, and proximal p13E-11 deletions. These limitations highlight the need for alternative methodologies, illustrated by the recent emergence of novel technologies such as molecular combing (MC), single molecule optical mapping (SMOM), or Oxford Nanopore-based long-read sequencing providing a more comprehensive analysis of 4q and 10q loci. Over the last decade, MC revealed a further increasing complexity in the organization of the 4q and 10q distal regions in patients with FSHD with cis-duplication of D4Z4 arrays in approximately 1%–2% of cases. Methods By using MC, we investigated in our center 2,363 cases for molecular diagnosis of FSHD. We also evaluated whether previously reported cis-duplications might be identified by SMOM using the Bionano EnFocus FSHD 1.0 algorithm. Results In our cohort of 2,363 samples, we identified 147 individuals carrying an atypical organization of the 4q35 or 10q26 loci. Mosaicism is the most frequent category followed by cis-duplications of the D4Z4 array. We report here chromosomal abnormalities of the 4q35 or 10q26 loci in 54 patients clinically described as FSHD, which are not present in the healthy population. In one-third of the 54 patients, these rearrangements are the only genetic defect suggesting that they might be causative of the disease. By analyzing DNA samples from 3 patients carrying a complex rearrangement of the 4q35 region, we further showed that the SMOM direct assembly of the 4q and 10q alleles failed to reveal these abnormalities and lead to negative results for FSHD molecular diagnosis. Discussion This work further highlights the complexity of the 4q and 10q subtelomeric regions and the need of in-depth analyses in a significant number of cases. This work also highlights the complexity of the 4q35 region and interpretation issues with consequences on the molecular diagnosis of patients or genetic counseling.
Vitamin E Prevented Hepatic and Renal Tissue Damage in Hypothyroid Rats

Background: Considering antioxidant effects of vitamin E (Vit E), in the present study, the effect of Vit E on liver and kidney functions and oxidative stress parameters in tissues of these organs of hypothyroid (Hypo) rats were reported. Materials and Methods: The animals were included in three groups:(1) control, (2) hypo, and (3) hypo-hypo-Vit E. Hypothyroidism was induced in rats by giving 0.05% propylthiouracil (PTU) in drinking water. Besides PTU, the rats in group 3 were daily injected with Vit E (20 mg/kg) for 42 days. The animals were deeply anesthetized and sacrificed, and the serum of the rats was immediately removed to measure thyroxin level and subsequent analysis. The liver and kidney tissues were also immediately removed for biochemical oxidative stress criteria. Results: PTU administration reduced serum thyroxin level and also thiol content, superoxide dismutase (SOD), and catalase (CAT) activities in the liver and kidney tissues while increasing malondialdehyde (MDA). Hypothyroidism also increased alanine aminotransferase (ALT), blood urea nitrogen (BUN), and creatinine while decreasing albumin. Vit E increased thiol, SOD, and CAT in the liver and kidney tissues while diminished MDA. Vit E also decreased ALT, BUN, and creatinine while increased albumin. Conclusion: The results of this study showed that Vit E prevented liver and renal tissue damage in hypothyroid rats.
Comparison Theorems of Liapunov-Razumikhin Type for NFDEs With Infinite Delay

Abstract Some comparison theorems of Liapunov-Razumikhin type are provided for uniform (asymptotic) stability and uniform (ultimate) boundedness of solutions to neutral functional differential equations with infinite delay with respect to a given phase space pair. Examples are given to illustrate how the comparison theorems and stability and boundedness of solutions depend on the choice(s) of phase space(s) and are related to asymptotic behavior of solutions to some difference and integral equations.
Anti-leishmaniasis Effect of Staphylococcus Aureus Protein A on the Size of the Lesion and Parasitic Load

Background: Many studies in the past have evaluated the role of immune system boosters in the treatment of leishmania major infection. Protein A (PA) is one of the structural components in peptidoglycan cell wall of gram-negative bacteria such as staphylococcus aurous which functions as a stimulator in the cellular immune system. The present study aims to evaluate the anti-inflammatory effect of PA on the recovery of leishmania major infection. Materials and Methods: This study was conducted on 24 female Balb/c-infected mice. The experimental group received PA at a dose of 60 mg/kg for four weeks. There was no intervention for the negative control group; the third group received the solvent of PA and sterile H2O; and the positive control group received Amphotericin B at a dose of 1 mg/kg body weight. At the end of the treatment period, a real-time polymerase chain reaction (PCR) assay was performed to determine parasitic burden, and the size of the lesions was measured by caliper with an accuracy of 0.01 mm. Results: Results showed that PA did slightly decrease the wound spread and growth but not to an extent that can be considered statistically significant. Also, differences in cycle threshold (Ct) values between the treated group and the untreated group was not impressive. Conclusions: Although findings showed that PA isn't such a good candidate for leishmania treatment, it may still be suitable for therapies that use multiple drugs in combination to speed up the healing of leishmaniosis, an issue that merits evaluation in future studies.
Transcriptomic signatures of feline chronic gingivostomatitis are influenced by upregulated IL6

Feline chronic gingivostomatitis (FCGS) is a relatively common and debilitating disease characterized by bilateral inflammation and ulceration of the caudal oral mucosa, alveolar and buccal mucosa, and varying degrees of periodontal disease. The etiopathogenesis of FCGS remains unresolved. In this study, we performed bulk RNA-seq molecular profiling of affected tissues derived from a cohort of client-owned cats with FCGS compared to tissues from unaffected animals, to identify candidate genes and pathways that can help guide future exploration of novel clinical solutions. We complemented transcriptomic findings with immunohistochemistry and in situ hybridization assays to better understand the biological significance of the results and performed RNA-seq validation of biologically relevant differentially expressed genes using qPCR assays to demonstrate technical reproducibility. Transcriptomic profiles of oral mucosal tissues in cats with FCGS are enriched with immune- and inflammation-related genes and pathways that appear to be largely influenced by IL6 , and include NFKB, JAK/STAT, IL-17 and IFN type I and II signaling, offering new opportunities to develop novel clinical applications based on a more rational understanding of the disease.
Exploring football coaches’ views on coach education, role, and practice design: An Australian perspective

Despite the importance placed on the design and delivery of formal coach education programs by Football Australia, there remains a lack of research relating to how formal coach education strategies support Australian football (i.e., soccer) coaches and their coaching practices. Through a series of semi-structured interviews, 20 highly qualified and experienced Australian senior football coaches shared their perspectives on: (i) coach education, (ii) their role as coach, and (iii) practice design. Findings revealed that formal coach education in Australia was largely ineffective in preparing senior coaches for the realities of senior football. Coaches attributed this to a number of factors, including the content’s quality, structure and delivery, which they viewed as rudimentary, outdated, repetitive and lacking in relevance and depth. Coaches also revealed there was an expectation of conformity to the content and practices endorsed by the National Football Curriculum, limiting the value and impact of formal coach education in supporting the development of coaches’ theoretical and practical dispositions. These findings point towards a number of broad and systemic issues relating to the conceptual, theoretical and practical foundations of the National Football Curriculum and subsequent courses. If Football Australia are to reach their goal in designing and delivering effective and meaningful coach education programs that support the highly complex and multifaceted role of senior coaching, formal coach education may need to adapt and evolve in a manner that better supports the multi-dimensional and context-specific needs of Australian senior football coaches.
Ketogenic diet protects MPTP‐induced mouse model of Parkinson's disease via altering gut microbiota and metabolites

Abstract The ketogenic diet (KD) is a low‐carbohydrate, high‐fat regime that is protective against neurodegenerative diseases. However, the impact of KD on Parkinson's disease (PD) and its mechanisms remains unclear. 1‐Methyl‐4‐phenyl‐1,2,3,6‐tetrahydropyridine (MPTP)‐induced mouse model of PD was fed with KD for 8 weeks. Motor function and dopaminergic neurons were evaluated. Inflammation in the brain, plasma, and colon tissue were also measured. Fecal samples were assessed by 16S rDNA gene sequencing and untargeted metabolomics. We found that KD protected motor dysfunction, dopaminergic neuron loss, and inflammation in an MPTP mouse model of PD. 16S rDNA sequencing revealed that MPTP administration significantly increased Citrobacter, Desulfovibrio, and Ruminococcus, and decreased Dubosiella, whereas KD treatment reversed the dysbiosis. Meanwhile, KD regulated the MPTP‐induced histamine, N‐acetylputrescine, d‐aspartic acid, and other metabolites. Fecal microbiota transplantation using feces from the KD‐treated mice attenuated the motor function impairment and dopaminergic neuron loss in antibiotic‐pretreated PD mice. Our current study demonstrates that KD played a neuroprotective role in the MPTP mouse model of PD through the diet–gut microbiota–brain axis, which may involve inflammation in the brain and colon. However, future research is warranted to explore the explicit anti‐inflammatory mechanisms of the gut–brain axis in PD models fed with KD.
Study of the Apoptotic Impacts of Hazelnut Oil on the Colorectal Cancer Cell Line

Background: Hazelnut oil has a unique structure with a high oleic acid content, tocopherol, tocotrienols, and other bioactive compounds, such as phytosterols. These biochemical compounds have been widely studied because of their potential health properties. Understanding the process of apoptosis is the basis of new therapies contributing to cancer cells' death. Recently, the potential role of the evolutionary-reserved bcl-2 protein family in tumor progression and prognosis of some malignancies has been addressed in several studies. The present study is aimed at evaluating the effect of apoptotic properties of hazelnut oil on colorectal cancer cells through the major members of this family (bax and bcl-2). Materials and Methods: MTT assay, apoptotic cell staining (using Annexin V and propidium iodide), flow cytometry, and real-time PCR were used to evaluate the toxicity, percentage of apoptotic cells, and bax and bcl-2 genes' expression after exposing HT29 cells to hazelnut oil. Results: After hazelnut treatment, significant decreases in cell viability, and the gene expression of bax and bcl-2 were observed compared to the control group (P < 0.05). In addition, the total percentage of apoptotic cells after hazelnut oil treatment showed a significant increase in comparison with the negative control group (P < 0.05). Conclusion: Hazelnut oil appears to cause the death of cancerous cells through an apoptotic mechanism.
Surgical outcome and predictors of neonates with esophageal atresia admitted at Tikur Anbesa Specialized Hospital

Background Esophageal atresia (EA) with or without tracheoesophageal fistula (TEF) is the most common congenital anomaly of the esophagus. This anomaly continues to cause considerable morbidity and mortality in Sub-Saharan Africa, presenting various concerns about how to treat esophageal atresia. Esophageal atresia-related neonatal mortality can be reduced by evaluating the surgical outcome and identifying associated factors. Objective This study aimed to assess the surgical outcome and identify predictors of neonates with esophageal atresia admitted at Tikur Anbesa specialized hospital. Methods Retrospective crossectional study design was employed on 212 neonates with esophageal atresia who were undergone surgical intervention in Tikur Anbesa specialized hospital. Data were entered into epi data 4.6 and exported to Stata version 16 software for further analysis. A logistic regression model with Adjusted odds ratio (AOR), confidence interval (CI) and p-value <0.05 were used to identify predictors of poor surgical outcome of neonates with esophageal atresia. Result In this study, 25% of newborns who underwent surgical intervention at TikurAbnbesa specialized hospital had successful surgical outcomes, compared to 75% of neonates with esophageal atresia who had poor surgical outcomes. Significant predictors of the poor surgical outcome of neonates with esophageal atresia were severe thrombocytopenia (AOR = 2.81(1.07–7.34)), timing of surgery (AOR = 3.7(1.34–10.1), aspiration pneumonia (AOR = 2.93(1.17–7.38)) and related abnormalities (AOR = 2.26(1.06–4.82)). Conclusion The results of this study showed that, when compared to other studies, a substantial percentage of newborn children with esophageal atresia had poor surgical outcomes. Early surgical management, aspiration pneumonia and thrombocytopenia prevention and therapy play a big part in improving the surgical prognosis for newborns with esophageal atresia.
A Language Model With Million Sample Context For Raw Audio Using Transformer Architectures

Modeling long-term dependencies for audio signals is a particularly challenging problem, as even small-time scales yield on the order of a hundred thousand samples. With the recent advent of Transformers, neural architectures became good at modeling dependencies over longer time scales, but they suffered from quadratic constraints to scale them. We propose a generative auto-regressive architecture that can model audio waveforms over quite a large context, greater than 500,000 samples. Our work is adapted to learn time dependencies by learning a latent representation by a CNN front-end, and then learning dependencies over these representations using Transformer encoders, fully trained end-to-end: thereby allowing to learn representations as it deems fit for the next sample. Unlike previous works that compared different time scales to show improvement, we use a standard dataset, with the same number of parameters/context to show improvements. We achieve a state-of-the-art performance as compared to other approaches such as Wavenet, SaSHMI, and Sample-RNN on a standard dataset for modeling long-term structure. This work gives very exciting direction for the field, given improvements in context modeling that can be scaled with more data, as well as potentially better results by using billions/trillions of parameters.
A Survey on Zero Pronoun Translation

Zero pronouns (ZPs) are frequently omitted in pro-drop languages (e.g. Chinese, Hungarian, and Hindi), but should be recalled in non-pro-drop languages (e.g. English). This phenomenon has been studied extensively in machine translation (MT), as it poses a significant challenge for MT systems due to the difficulty in determining the correct antecedent for the pronoun. This survey paper highlights the major works that have been undertaken in zero pronoun translation (ZPT) after the neural revolution so that researchers can recognize the current state and future directions of this field. We provide an organization of the literature based on evolution, dataset, method, and evaluation. In addition, we compare and analyze competing models and evaluation metrics on different benchmarks. We uncover a number of insightful findings such as: 1) ZPT is in line with the development trend of large language model; 2) data limitation causes learning bias in languages and domains; 3) performance improvements are often reported on single benchmarks, but advanced methods are still far from real-world use; 4) general-purpose metrics are not reliable on nuances and complexities of ZPT, emphasizing the necessity of targeted metrics; 5) apart from commonly-cited errors, ZPs will cause risks of gender bias.
Nowcasting with signature methods

Key economic variables are often published with a significant delay of over a month. The nowcasting literature has arisen to provide fast, reliable estimates of delayed economic indicators and is closely related to filtering methods in signal processing. The path signature is a mathematical object which captures geometric properties of sequential data; it naturally handles missing data from mixed frequency and/or irregular sampling -- issues often encountered when merging multiple data sources -- by embedding the observed data in continuous time. Calculating path signatures and using them as features in models has achieved state-of-the-art results in fields such as finance, medicine, and cyber security. We look at the nowcasting problem by applying regression on signatures, a simple linear model on these nonlinear objects that we show subsumes the popular Kalman filter. We quantify the performance via a simulation exercise, and through application to nowcasting US GDP growth, where we see a lower error than a dynamic factor model based on the New York Fed staff nowcasting model. Finally we demonstrate the flexibility of this method by applying regression on signatures to nowcast weekly fuel prices using daily data. Regression on signatures is an easy-to-apply approach that allows great flexibility for data with complex sampling patterns.
On Consistency of Signatures Using Lasso

Signature transforms are iterated path integrals of continuous and discrete-time time series data, and their universal nonlinearity linearizes the problem of feature selection. This paper revisits the consistency issue of Lasso regression for the signature transform, both theoretically and numerically. Our study shows that, for processes and time series that are closer to Brownian motion or random walk with weaker inter-dimensional correlations, the Lasso regression is more consistent for their signatures defined by It\^o integrals; for mean reverting processes and time series, their signatures defined by Stratonovich integrals have more consistency in the Lasso regression. Our findings highlight the importance of choosing appropriate definitions of signatures and stochastic models in statistical inference and machine learning.
Balancing Lexical and Semantic Quality in Abstractive Summarization

An important problem of the sequence-to-sequence neural models widely used in abstractive summarization is exposure bias. To alleviate this problem, re-ranking systems have been applied in recent years. Despite some performance improvements, this approach remains underexplored. Previous works have mostly specified the rank through the ROUGE score and aligned candidate summaries, but there can be quite a large gap between the lexical overlap metric and semantic similarity. In this paper, we propose a novel training method in which a re-ranker balances the lexical and semantic quality. We further newly define false positives in ranking and present a strategy to reduce their influence. Experiments on the CNN/DailyMail and XSum datasets show that our method can estimate the meaning of summaries without seriously degrading the lexical aspect. More specifically, it achieves an 89.67 BERTScore on the CNN/DailyMail dataset, reaching new state-of-the-art performance. Our code is publicly available at https://github.com/jeewoo1025/BalSum.
The Noncommutative Edmonds' Problem Re-visited

Let $T$ be a matrix whose entries are linear forms over the noncommutative variables $x_1, x_2, \ldots, x_n$. The noncommutative Edmonds' problem (NSINGULAR) aims to determine whether $T$ is invertible in the free skew field generated by $x_1,x_2,\ldots,x_n$. Currently, there are three different deterministic polynomial-time algorithms to solve this problem: using operator scaling [Garg, Gurvits, Oliveira, and Wigserdon (2016)], algebraic methods [Ivanyos, Qiao, and Subrahmanyam (2018)], and convex optimization [Hamada and Hirai (2021)]. In this paper, we present a simpler algorithm for the NSINGULAR problem. While our algorithmic template is similar to the one in Ivanyos et. al.(2018), it significantly differs in its implementation of the rank increment step. Instead of computing the limit of a second Wong sequence, we reduce the problem to the polynomial identity testing (PIT) of noncommutative algebraic branching programs (ABPs). This enables us to bound the bit-complexity of the algorithm over $\mathbb{Q}$ without requiring special care. Moreover, the rank increment step can be implemented in quasipolynomial-time even without an explicit description of the coefficient matrices in $T$. This is possible by exploiting the connection with the black-box PIT of noncommutative ABPs [Forbes and Shpilka (2013)].
FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention

Diffusion models excel at text-to-image generation, especially in subject-driven generation for personalized images. However, existing methods are inefficient due to the subject-specific fine-tuning, which is computationally intensive and hampers efficient deployment. Moreover, existing methods struggle with multi-subject generation as they often blend features among subjects. We present FastComposer which enables efficient, personalized, multi-subject text-to-image generation without fine-tuning. FastComposer uses subject embeddings extracted by an image encoder to augment the generic text conditioning in diffusion models, enabling personalized image generation based on subject images and textual instructions with only forward passes. To address the identity blending problem in the multi-subject generation, FastComposer proposes cross-attention localization supervision during training, enforcing the attention of reference subjects localized to the correct regions in the target images. Naively conditioning on subject embeddings results in subject overfitting. FastComposer proposes delayed subject conditioning in the denoising step to maintain both identity and editability in subject-driven image generation. FastComposer generates images of multiple unseen individuals with different styles, actions, and contexts. It achieves 300$\times$-2500$\times$ speedup compared to fine-tuning-based methods and requires zero extra storage for new subjects. FastComposer paves the way for efficient, personalized, and high-quality multi-subject image creation. Code, model, and dataset are available at https://github.com/mit-han-lab/fastcomposer.