Miracle-dz/UniFAR-FTUs huggingface dataset:
The International Hip Dysplasia Institute (IHDI) classification is more informative than the Tönnis classification

Background A new, supposedly more reproducible radiographic classification, set to replace the Tönnis classification of hip dislocations, was proposed in 2015: the International Hip Dysplasia Institute (IHDI) classification. Purpose To compare the IHDI classification with the Tönnis classification when evaluating the severity of hip dislocations as well as their respective inter- and intra-observer reliability. Material and Methods Since January 2000, Swedish-born children with a hip dislocation were prospectively registered. From this registry, radiographs of 97 hips in 79 patients (91% girls; median age = 7 months), born in 2000–2009, were analyzed. Two observers, one consultant and one resident, classified each hip both by IHDI and Tönnis twice. Results The IHDI classification had a more even distribution of grades with the majority in grade 2–3. The Tönnis classification graded the majority (77%) of the patients as grade 2 and equally among the other grades. There was moderate inter-observer agreement using both methods calculated with Kappa, 0.61 (95% confidence interval [CI] = 0.44–0.79) for Tönnis and 0.62 (95% CI = 0.49–0.74) for IHDI. The resident calculated Tönnis with weak intra-observer reliability of 0.57 (95% CI = 0.40–0.74) compared to high intra-observer reliability of 0.86 (95% CI = 0.74–0.98) for the consultant. Both observers graded IHDI with high intra-observer reliability. Conclusion IHDI is more discriminative than the Tönnis classification when evaluating the severity of a hip dislocation in infants.
Indirect and Out-of-Pocket Disease-associated Costs in Pediatric Inflammatory Bowel Disease: A Cross-sectional Analysis

Objectives: Data on pediatric inflammatory bowel disease (IBD)-associated indirect and out-of-pocket (OOP) costs are limited. We aimed to estimate indirect (lost work hours and productivity) and OOP pediatric IBD-associated costs in Canada. Methods: In a nation-wide cross-sectional analysis, caregivers of children with IBD were invited to complete a questionnaire on lost work hours and OOP costs related to IBD in the 4 weeks prior to the survey. Participants were reinvited to periodically answer the same questionnaire every 3–9 months for 2 years. Lost productivity was calculated using the Human Capital method. Costs were reported in 2018 inflation-adjusted Canadian dollars. Predictors of high cost users (top 25%) were examined using binary logistic regression. Results: Consecutive 243 (82 incident cases) of 262 (92.7%) approached participants completed the first survey with a total of 450 surveys longitudinally completed over 2 years. The median annual indirect cost per patient was $5966 (IQR $1809–$12,676), with $5721 (IQR $1366–$11,545) for Crohn’s disease (CD) and $7007 (IQR $2428–$14,057) for ulcerative colitis (UC) (P = 0.11). The annual median per patient OOP costs were $4550 with $4550 for CD and $5038 for UC (P = 0.53). Longer travel distance to clinic was associated with higher OOP costs (odds ratio = 4.55; P < 0.0001; 95% confidence interval: 1.99–10.40). Conclusions: Indirect and OOP IBD-associated costs are substantial and more likely to affect families living in remote communities.
Clinical Significance of Dual-Block Elastic Stain Evaluating Visceral Pleural Invasion in Peripheral Non-Small Cell Lung Cancer

Visceral pleural invasion (VPI) is a critical component in the staging of peripheral non-small cell lung carcinoma (NSCLC). We aim to investigate whether dual-block elastic stain increases visceral pleural invasion positivity compared with single-block elastic stain. We further analyze the potential predictors of visceral pleural invasion. 8419 peripheral NSCLC patients (including 6008 patients with tumor size≤3 cm in stage I) were divided into a cohort using one paraffin block (single-block group, n = 5184) and a cohort using dual paraffin blocks (dual-block group, n = 3235) for elastic stain. The VPI-positive rate demonstrated by the dual-block elastic stains group was significantly higher than that of the single-block elastic stain group (17.7% (573/3235) versus 9.1% (474/5184), respectively, P < . 001). The presence of visceral pleural invasion in T1 (≤3 cm) patients detected by single- and dual-block elastic stain was 6.3% (235/3730) and 12.0% (273/2278), respectively (P < .001). 5.7% of T1 patients (stage IA) were additionally upstaged to T2a (stage IB) by dual-block elastic stain. However, the incidence of visceral pleural invasion in pT2a patients showed no significant difference between the single-block group and the dual-block group (16.8% vs. 17.1%, P = .916). Lymphovascular invasion, lymph node metastasis, dedifferentiated carcinomas, the presence of spread through airspaces (STAS) and a poorly differentiated adenocarcinomatous growth pattern could be significant predictors of visceral pleural invasion (P < .001). Our results indicate that using dual-block elastic stain identifies more visceral pleural invasion positive T1 NSCLC patients who are upstaged to T2a, and who could benefit from optimal management post-operatively. The application of dual-block elastic stain is an efficient and practical method to detect visceral pleural invasion status.
ER‐phagy: selective autophagy of the endoplasmic reticulum

Eukaryotic cells adequately control the mass and functions of organelles in various situations. Autophagy, an intracellular degradation system, largely contributes to this organelle control by degrading the excess or defective portions of organelles. The endoplasmic reticulum (ER) is an organelle with distinct structural domains associated with specific functions. The ER dynamically changes its mass, components, and shape in response to metabolic, developmental, or proteotoxic cues to maintain or regulate its functions. Therefore, elaborate mechanisms are required for proper degradation of the ER. Here, we review our current knowledge on diverse mechanisms underlying selective autophagy of the ER, which enable efficient degradation of specific ER subdomains according to different demands of cells.
A LASSO-based approach to sample sites for phylogenetic tree search

Abstract Motivation In recent years, full-genome sequences have become increasingly available and as a result many modern phylogenetic analyses are based on very long sequences, often with over 100 000 sites. Phylogenetic reconstructions of large-scale alignments are challenging for likelihood-based phylogenetic inference programs and usually require using a powerful computer cluster. Current tools for alignment trimming prior to phylogenetic analysis do not promise a significant reduction in the alignment size and are claimed to have a negative effect on the accuracy of the obtained tree. Results Here, we propose an artificial-intelligence-based approach, which provides means to select the optimal subset of sites and a formula by which one can compute the log-likelihood of the entire data based on this subset. Our approach is based on training a regularized Lasso-regression model that optimizes the log-likelihood prediction accuracy while putting a constraint on the number of sites used for the approximation. We show that computing the likelihood based on 5% of the sites already provides accurate approximation of the tree likelihood based on the entire data. Furthermore, we show that using this Lasso-based approximation during a tree search decreased running-time substantially while retaining the same tree-search performance. Availability and implementation The code was implemented in Python version 3.8 and is available through GitHub (https://github.com/noaeker/lasso_positions_sampling). The datasets used in this paper were retrieved from Zhou et al. (2018) as described in section 3. Supplementary information Supplementary data are available at Bioinformatics online.
NSUN2-mediated RNA m5C modification modulates uveal melanoma cell proliferation and migration

ABSTRACT RNA 5-methylcytosine (m5C) is a widespread post-transcriptional modification involved in diverse biological processes through controlling RNA metabolism. However, its roles in uveal melanoma (UM) remain unknown. Here, we describe the biological roles and regulatory mechanisms of RNA m5C in UM. Initially, we identified significantly elevated global RNA m5C levels in both UM cells and tissue specimens using ELISA assay and dot blot analysis. Meanwhile, NOP2/Sun RNA methyltransferase family member 2 (NSUN2) was upregulated in both types of these samples, whereas NSUN2 knockdown significantly decreased RNA m5C level. Such declines inhibited UM cell migration and suppressed cell proliferation through cell cycle G1 arrest. Furthermore, bioinformatic analyses, m5C-RIP-qPCR, and luciferase assay identified β-Catenin (CTNNB1) as a direct target of NSUN2-mediated m5C modification in UM cells. Additionally, overexpression of miR-124a in UM cells diminished NSUN2 expression levels indicating that it is an upstream regulator of this response. Our study suggests that NSUN2-mediated RNA m5C methylation provides a potential novel target to improve the therapeutic management of UM pathogenesis.
Accelerating and improving radiochromic film calibration by utilizing the dose ratio in photon and proton beams

Abstract Purpose Radiochromic films are versatile 2D dosimeters with high‐resolution and near tissue equivalence. To assure high precision and accuracy, a time‐consuming calibration process is required. To improve the time efficiency, a novel calibration method utilizing the ratio of the same dose profile measured at different monitor units (MUs) is introduced and tested in a proton and photon beam. Methods The calibration procedure employs the dose ratio of film measurements of the same relative profile for different absolute dose values. Hence, the ratio of the dose is constant at any point of the profile, but the ratio of the net optical densities is not constant. The key idea of the method is to optimize the calibration function until the ratio of the calculated doses is constant. The proposed method was tested in the dose range between 0.25–12 and 1–6 Gy in a proton and photon beam, respectively. A radial symmetric profile and a rectangular profile were created, both having a central plateau region of about 3 cm diameter and a dose falloff of about 1.5 cm at larger distances. The dose falloff region was used as input for the optimization method and the central plateau region served as dose reference points. Only the plateau region of the highest dose entered the optimization as an additional objective. The measured data were randomly split into differently sized training and test sets. The optimization was repeated 1000 times with random start value initialization using the same start values for the standard and the gradient method. Finally, a proton plan with four dose levels was created, which were separated spatially, to test the possibility of a full calibration within a single measurement. Results Parameter estimation was possible with as low as one dose ratio used for optimization in both the photon and the proton case, yet exhibiting a high sensitivity on the dose level. The root mean squared deviation (RMSD) of the dose was less than 1% when the dose ratio was in the order of 20, whereas the median RMSD of all optimizations was 1.7%. Using four dose levels for optimization resulted in a median RMSD of 1% when randomly selecting the dose levels. Having at least one dose ratio of about 20 included in the optimization considerably improved the RMSD of the calibration function. Using six or eight dose levels reduced the sensitivity on the dose level selection and the median RMSD was 0.8%. A full calibration was possible in a single measurement having four dose levels in one plan but spatially separated. Conclusions The number of measurements required to obtain an EBT3 film calibration function could be reduced using the proposed dose ratio method while maintaining the same accuracy as with the standard method.
Body size responses to the combined effects of climate and land use changes within an urban framework

Alterations in body size can have profound impacts on an organism's life history and ecology with long‐lasting effects that span multiple biological scales. Animal body size is influenced by environmental drivers, including climate change and land use change, the two largest current threats to biodiversity. Climate warming has led to smaller body sizes of many species due to impacts on growth (i.e., Bergmann's rule and temperature‐size rule). Conversely, urbanization, which serves as a model for investigating the effects of land use changes, has largely been demonstrated to cause size increases, but few studies have examined the combined influences of climate and land use changes on organism size. We present here the background theory on how each of these factors is expected to influence body size, summarize existing evidence of how size has recently been impacted by climate and land use changes, and make several recommendations to guide future research uniting these areas of focus. Given the rapid pace of climate change and urbanization, understanding the combined effects of climate and land use changes on body size is imperative for biodiversity preservation.
Microsatellite instability in rectal cancer: what does it mean? A study of two randomized trials and a systematic review of the literature

Currently, compelling evidence illustrates the significance of determining microsatellite instability (MSI) in colorectal cancer (CRC). The association of MSI with proximal CRC is well established, however, its implications in patients with rectal cancer remain undefined. We therefore aimed to determine the role of MSI with respect to incidence and outcome in patients with rectal cancer.
A review on the mechanistic details of OXA enzymes of ESKAPE pathogens

ABSTRACT The production of β-lactamases is a prevalent mechanism that poses serious pressure on the control of bacterial resistance. Furthermore, the unavoidable and alarming increase in the transmission of bacteria producing extended-spectrum β-lactamases complicates treatment alternatives with existing drugs and/or approaches. Class D β-lactamases, designated as OXA enzymes, are characterized by their activity specifically towards oxacillins. They are widely distributed among the ESKAPE bugs that are associated with antibiotic resistance and life-threatening hospital infections. The inadequacy of current β-lactamase inhibitors for conventional treatments of ‘OXA’ mediated infections confirms the necessity of new approaches. Here, the focus is on the mechanistic details of OXA-10, OXA-23, and OXA-48, commonly found in highly virulent and antibiotic-resistant pathogens Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterobacter spp. to describe their similarities and differences. Furthermore, this review contains a specific emphasis on structural and computational perspectives, which will be valuable to guide efforts in the design/discovery of a common single-molecule drug against ESKAPE pathogens.
The validity and reliability of the Turkish version of the 12-item Örebro musculoskeletal screening questionnaire (ÖMSQ-12-TR)

Abstract Purpose The 12-item Örebro Musculoskeletal Screening Questionnaire (ÖMSQ-12) is a multidimensional questionnaire assessing general musculoskeletal problems. This study aimed to investigate its construct validity and reliability. Materials and methods Confirmatory factor analysis (CFA) was performed for construct validity. The Tampa Scale for Kinesiophobia (TSK) and the SF-12 and Pain Numerical Rating Scale (P-NRS) were used for convergent validity. Reliability (ICC), internal consistency (Cronbach’s alpha), reproducibility, and known-group validity were assessed. The cut-off value was measured. Results A total of n = 378 individuals (aged 35.7 ± 12.4 years, female = 73.3%) with a musculoskeletal problem participated in the study. P-NRS score of the individuals was 5. Results showed that a 3-factor model did fit well under CFA (χ 2/df = 2.76 ≤ 3). The questionnaire had good reliability (ICC = 0.865) and internal consistency (α = 0.810). There were no floor or ceiling effects (<%15). Total ÖMSQ-12-TR scores had a correlation with the TSK, SF-12 and P-NRS (r = 0.303–0.609). The AUC for the risk of absenteeism from work was obtained as 0.738 (p < 0.001). The risk of absenteeism was high in individuals with an ÖMSQ-12-TR score of ≥57.5. Conclusions The ÖMSQ-12-TR is a valid and reliable questionnaire that can be used in determining the risk of absenteeism in musculoskeletal disorders and is convenient for online use. Clinical Trial Number NCT04723615
Stochastic recurrence equation with diagonal matrices

We consider the stochastic equation X d = AX + B where A is a random diagonal matrix and X , B are random vectors, X , A are independent and the equation is meant in law. We prove that X is regularly varying in a multivariate nonstandard sense. The results are applicable to stochastic recursions with diagonal matrices, in particular, to multivariate autoregressive models like CCC-GARCH or BEKK-ARCH.
DECODE: a computational pipeline to discover T cell receptor binding rules

Abstract Motivation Understanding the mechanisms underlying T cell receptor (TCR) binding is of fundamental importance to understanding adaptive immune responses. A better understanding of the biochemical rules governing TCR binding can be used, e.g. to guide the design of more powerful and safer T cell-based therapies. Advances in repertoire sequencing technologies have made available millions of TCR sequences. Data abundance has, in turn, fueled the development of many computational models to predict the binding properties of TCRs from their sequences. Unfortunately, while many of these works have made great strides toward predicting TCR specificity using machine learning, the black-box nature of these models has resulted in a limited understanding of the rules that govern the binding of a TCR and an epitope. Results We present an easy-to-use and customizable computational pipeline, DECODE, to extract the binding rules from any black-box model designed to predict the TCR-epitope binding. DECODE offers a range of analytical and visualization tools to guide the user in the extraction of such rules. We demonstrate our pipeline on a recently published TCR-binding prediction model, TITAN, and show how to use the provided metrics to assess the quality of the computed rules. In conclusion, DECODE can lead to a better understanding of the sequence motifs that underlie TCR binding. Our pipeline can facilitate the investigation of current immunotherapeutic challenges, such as cross-reactive events due to off-target TCR binding. Availability and implementation Code is available publicly at https://github.com/phineasng/DECODE. Supplementary information Supplementary data are available at Bioinformatics online.
Fermion sign bounds theory in quantum Monte Carlo simulation

Sign problem in fermion quantum Monte Carlo (QMC) simulation appears to be an extremely hard problem. Traditional lore passing around for years tells people that when there is a sign problem, the average sign in QMC simulation approaches zero exponentially fast with the space-time volume of the conﬁgurational space. We, however, analytically show this is not always the case and manage to ﬁnd physical bounds for the average sign. Our understanding is based on a direct connection between the sign bounds and a well-deﬁned partition function of reference system and could distinguish when the bounds have the usual exponential scaling, and when they are bestowed on an algebraic scaling at low temperature limit. We analytically explain such algebraic sign problems found in ﬂat band moir´e lattice models at low temperature limit. At ﬁnite temperature, a domain size argument based on sign bounds also explains the connection between sign behavior and ﬁnite temperature phase transition. Sign bounds, as a well-deﬁned observable, may have ability to ease or even make use of the sign problem.
Personalized rTMS for Depression: A Review

Personalized treatments are gaining momentum across all fields of medicine. Precision medicine can be applied to neuromodulatory techniques, where focused brain stimulation treatments such as repetitive transcranial magnetic stimulation (rTMS) are used to modulate brain circuits and alleviate clinical symptoms. rTMS is well-tolerated and clinically effective for treatment-resistant depression (TRD) and other neuropsychiatric disorders. However, despite its wide stimulation parameter space (location, angle, pattern, frequency, and intensity can be adjusted), rTMS is currently applied in a one-size-fits-all manner, potentially contributing to its suboptimal clinical response (~50%). In this review, we examine components of rTMS that can be optimized to account for inter-individual variability in neural function and anatomy. We discuss current treatment options for TRD, the neural mechanisms thought to underlie treatment, differences in FDA-cleared devices, targeting strategies, stimulation parameter selection, and adaptive closed-loop rTMS to improve treatment outcomes. We suggest that better understanding of the wide and modifiable parameter space of rTMS will greatly improve clinical outcome.
Distinguishing Learning Rules with Brain Machine Interfaces

Despite extensive theoretical work on biologically plausible learning rules, clear evidence about whether and how such rules are implemented in the brain has been difficult to obtain. We consider biologically plausible supervised- and reinforcement-learning rules and ask whether changes in network activity during learning can be used to determine which learning rule is being used. Supervised learning requires a credit-assignment model estimating the mapping from neural activity to behavior, and, in a biological organism, this model will inevitably be an imperfect approximation of the ideal mapping, leading to a bias in the direction of the weight updates relative to the true gradient. Reinforcement learning, on the other hand, requires no credit-assignment model and tends to make weight updates following the true gradient direction. We derive a metric to distinguish between learning rules by observing changes in the network activity during learning, given that the mapping from brain to behavior is known by the experimenter. Because brain-machine interface (BMI) experiments allow for precise knowledge of this mapping, we model a cursor-control BMI task using recurrent neural networks, showing that learning rules can be distinguished in simulated experiments using only observations that a neuroscience experimenter would plausibly have access to.
Extracting Large Scale Spatio-Temporal Descriptions from Social Media

The ability to track large-scale events as they happen is essential for understanding them and coordinating reactions in an appropriate and timely manner. This is true, for example, in emergency management and decision-making support, where the constraints on both quality and latency of the extracted information can be stringent. In some contexts, real-time and large-scale sensor data and forecasts may be available. We are exploring the hypothesis that this kind of data can be augmented with the ingestion of semi-structured data sources, like social media. Social media can diffuse valuable knowledge, such as direct witness or expert opinions, while their noisy nature makes them not trivial to manage. This knowledge can be used to complement and confirm other spatio-temporal descriptions of events, highlighting previously unseen or undervalued aspects. The critical aspects of this investigation, such as event sensing, multilingualism, selection of visual evidence, and geolocation, are currently being studied as a foundation for a unified spatio-temporal representation of multi-modal descriptions. The paper presents, together with an introduction on the topics, the work done so far on this line of research, also presenting case studies relevant to the posed challenges, focusing on emergencies caused by natural disasters.