Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of personality on the quality of vision after multifocal intraocular lens implantation

Objective To assess the possible correlation between patients’ personality traits and subjective perception of quality of vision (QoV), after multifocal intraocular lens (mIOL) implantation. Methods patients who had bilateral implantation of a non-diffractive X-WAVE or a trifocal lens were assessed 6 months postoperatively. Patients answered the NEO-Five Factor Inventory (NEO-FFI-20) questionnaire (“Big Five five-factor personality model”) to examine their personality. Six months following surgery, patients were asked to fill a QoV questionnaire where they graded the frequency of 10 common visual symptoms. Primary outcomes were to evaluate the correlation between personality scores and the reported frequency of visual disturbances. Results The study comprised 20 patients submitted to bilateral cataract surgery, 10 with a non-diffractive X-WAVE lens (AcrySof® IQ Vivity) and 10 with a trifocal lens (AcrySof® IQ PanOptix). Mean age was 60.23 (7.06) years. Six months following surgery, patients with lower scores of conscientiousness and extroversion reported a higher frequency of visual disturbances (blurred vision, P = .015 and P = .009, seeing double images P = .018 and P = .006, and having difficulties focusing, P = .027 and P = .022, respectively). In addition, patients with high neuroticism scores had more difficulty focusing (P = .033). Conclusions In this study, personality traits such as low conscientiousness and extroversion and high neuroticism significantly influenced QoV perception 6 months after bilateral multifocal lens implantation. Patients’ personality questionnaires could be a useful preoperative assessment test to a mIOL.
Identification of a nonlinear model between control and structural deflections of an F/A-18 aircraft

The aim of this paper is to determine the mathematical model between control deflections and structural deflections of the F/A-18 modified aircraft in the Active Aeroelastic Wing AAW program. Five excited sources were provided by NASA DFRC (Dryden Flight Research Center) from Flight Flutter Test (FFT). These excitations are: differential and collective ailerons, collective and differential stabilizers and rudders. We choose to use the Neural Network (NN) and fuzzy logic algorithms in order to identify the MIMO (Multi Input Multi Output) system for the F/A-18 aircraft. One of main contributions in this paper consists in the conversion of fuzzy logic algorithm results into neural network data. Then, these methods (NN and Fuzzy logic) were applied for the model identification and validation for sixteen flight conditions where Mach number varied from 0.85 to 1.30 and altitudes from 5,000 ft to 25,000 ft. Accurate results, expressed in terms of fit coefficients between estimated and measured signals higher than 99% were obtained, that led to the conclusion that the new methods here developed are very efficient.
FSTIF-UNet: A Deep Learning-Based Method Towards Automatic Segmentation of Intracranial Aneurysms in Un-Reconstructed 3D-RA

Segmentation of intracranial aneurysms (IAs) is an important step for the diagnosis and treatment of IAs. However, the process by which clinicians manually recognize and localize IAs is overly labor intensive. This study aims to develop a deep-learning-based framework (defined as FSTIF-UNet) towards IAs segmentation in un-reconstructed 3D Rotational Angiography (3D-RA) images. 3D-RA sequences from 300 patients with IAs from Beijing Tiantan Hospital are enrolled. Inspired by radiologists' clincial skills, a Skip-Review attention mechanism is proposed to repeatedly fuse the long-term spatiotemporal features of several images with the most obvious IA's features (sellected by a pre-detection network). Then, a Conv-LSTM is used to fuse the short-term spatiotemporal features of the selected 15 3D-RA images from the equally-spaced viewing angles. The combination of the two modules realizes the full-scale spatiotemporal information fusion of the 3D-RA sequence. FSTIF-UNet achieves DSC, IoU, Sens, Haus, and F1-Score of 0.9109, 0.8586, 0.9314, 1.358 and 0.8883, respectively, and time taken for network segmentation is 0.89 s/case. The results show significant improvement in IA segmentation performance with FSTIF-UNet compared with baseline networks (with DSC from 0.8486 - 0.8794). The proposed FSTIF-UNet establishes a practical method to assist the radiologists in clinical diagnosis.
Improving programme‐led and focused interventions for eating disorders: An experts' consensus statement—A UK perspective

Abstract Objective Eating disorders are associated with significant illness burden and costs, yet access to evidence‐based care is limited. Greater use of programme‐led and focused interventions that are less resource‐intensive might be part of the solution to this demand‐capacity mismatch. Method In October 2022, a group of predominantly UK‐based clinical and academic researchers, charity representatives and people with lived experience convened to consider ways to improve access to, and efficacy of, programme‐led and focused interventions for eating disorders in an attempt to bridge the demand‐capacity gap. Results Several key recommendations were made across areas of research, policy, and practice. Of particular importance is the view that programme‐led and focused interventions are suitable for a range of different eating disorder presentations across all ages, providing medical and psychiatric risk are closely monitored. The terminology used for these interventions should be carefully considered, so as not to imply that the treatment is suboptimal. Conclusions Programme‐led and focused interventions are a viable option to close the demand‐capacity gap for eating disorder treatment and are particularly needed for children and young people. Work is urgently needed across sectors to evaluate and implement such interventions as a clinical and research priority.
LHC Longitudinal Single-Bunch Stability Threshold

The aim of the MD studies presented here was to determine with a reasonable accuracy the single-bunch longitudinal stability threshold in the LHC. The measurements were performed by placing along the ring 8 or 20 ‘single’ bunches with different intensities but similar longitudinal emittances. Then they were accelerated and bunch stability was observed at arrival to flat top. Combining the results of two measurement sessions, the single bunch stability threshold is estimated to be (2:4 0:2) 10 11 ppb for an emittance of 1.89 eVs (1.0 ns) at 6.5 TeV with 12 MV RF voltage.
Dual representation of choice and aspirational preferences

We consider choice over a set of monetary acts (random variables) and study a general class of preferences. These preferences favor diversification, except perhaps on a subset of sufficiently disliked acts, over which concentration is instead preferred. This structure encompasses a number of known models in this setting. We show that such preferences can be expressed in dual form in terms of a family of measures of risk and a target function. Specifically, the choice function is equivalent to selection of a maximum index level such that the risk of beating the target function at that level is acceptable. This dual representation may help to uncover new models of choice. One that we explore in detail is the special case of a bounded target function. This case corresponds to a type of satisficing and has descriptive relevance. Moreover, the model results in optimization problems that may be efficiently solved in large-scale.
Energization of Outer Membrane Transport by the ExbB ExbD Molecular Motor

The outer membranes (OM) of Gram-negative bacteria contain a class of proteins (TBDTs) that require energy for the import of nutrients and to serve as receptors for phages and protein toxins. Energy is derived from the proton motif force (pmf) of the cytoplasmic membrane (CM) through the action of three proteins, namely, TonB, ExbB, and ExbD, which are located in the CM and extend into the periplasm. ABSTRACT The outer membranes (OM) of Gram-negative bacteria contain a class of proteins (TBDTs) that require energy for the import of nutrients and to serve as receptors for phages and protein toxins. Energy is derived from the proton motif force (pmf) of the cytoplasmic membrane (CM) through the action of three proteins, namely, TonB, ExbB, and ExbD, which are located in the CM and extend into the periplasm. The leaky phenotype of exbB exbD mutants is caused by partial complementation by homologous tolQ tolR. TonB, ExbB, and ExbD are genuine components of an energy transmission system from the CM into the OM. Mutant analyses, cross-linking experiments, and most recently X-ray and cryo-EM determinations were undertaken to arrive at a model that describes the energy transfer from the CM into the OM. These results are discussed in this paper. ExbB forms a pentamer with a pore inside, in which an ExbD dimer resides. This complex harvests the energy of the pmf and transmits it to TonB. TonB interacts with the TBDT at the TonB box, which triggers a conformational change in the TBDT that releases bound nutrients and opens the pore, through which nutrients pass into the periplasm. The structurally altered TBDT also changes the interactions of its periplasmic signaling domain with anti-sigma factors, with the consequence being that the sigma factors initiate transcription.
Pattern formation simulations in reaction-diffusion systems by local integral equation method

A meshless Local Integral Equation (LIE) method is proposed for numerical simulation of 2D pattern formation in nonlinear reaction-diffusion systems. The method works with weak formulation of the differential governing equations on local sub-domains with using the Green function of the Laplace operator as the test function. The Moving Least Square (MLS) approximation is employed for spatial variations of field variables while the time evolution is discretized by using suitable finite difference approximations. The effects of model parameters and conditions are studied by considering the well known Schnakenberg model.
Obesity, Charlson comorbidity index, and neutrophil-to-lymphocyte ratio in chronic obstructive pulmonary disease: relationship to disease severity.

BACKGROUND Chronic obstructive pulmonary disease (COPD) is a common chronic inflammatory lung disease with high mortality and morbidity rates. Obesity, various comorbid diseases, and inflammation often coexist in chronic obstructive pulmonary disease (COPD), exhibiting a complex interaction with disease severity. The aim of the study was to examine the relationship between COPD markers and obesity, the Charlson Comorbidity Index (CCI), and neutrophil/lymphocyte ratio (NLR). METHODS Eighty male patients with stable COPD admitted to the pulmonology unit were included in the study. The presence of comorbidities was investigated in obese and non-obese individuals with COPD. Pulmonary function tests and the mMRC dyspnea scale were examined, and CCI scores were calculated. RESULTS 60.9 % with mild/moderate COPD, and 64.7 % with severe COPD had a comorbid disease. The incidence of hypertension and diabetes was significantly higher in obese patients. The obesity rate was 41.3 % in patients with mild/moderate COPD (FEV1 ≥ 50) and 26.5 % in those with severe COPD (FEV1 < 50). There was a positive and significant correlation between CCI value and BMI and mMRC dyspnea scale. NLR was significantly higher in patients with FEV1 < 50 and mMRC ≥ 2. CONCLUSIONS As a result, it is essential to screen obese patients with COPD, who are among the groups with the highest incidence of comorbidities, in terms of such diseases that exacerbate the symptoms of their disease. Findings may support the potential applicability of simple blood count indices (such as NLR) in the clinical assessment of disease in stable COPD patients (Tab. 4, Fig. 1, Ref. 46).
Unsupervised Deep Learning Can Identify Protein Functional Groups from Unaligned Sequences

Abstract Interpreting protein function from sequence data is a fundamental goal of bioinformatics. However, our current understanding of protein diversity is bottlenecked by the fact that most proteins have only been functionally validated in model organisms, limiting our understanding of how function varies with gene sequence diversity. Thus, accuracy of inferences in clades without model representatives is questionable. Unsupervised learning may help to ameliorate this bias by identifying highly complex patterns and structure from large data sets without external labels. Here, we present DeepSeqProt, an unsupervised deep learning program for exploring large protein sequence data sets. DeepSeqProt is a clustering tool capable of distinguishing between broad classes of proteins while learning local and global structure of functional space. DeepSeqProt is capable of learning salient biological features from unaligned, unannotated sequences. DeepSeqProt is more likely to capture complete protein families and statistically significant shared ontologies within proteomes than other clustering methods. We hope this framework will prove of use to researchers and provide a preliminary step in further developing unsupervised deep learning in molecular biology.
Estimation of exposure to phthalate esters from consumption of powdered infant formula sampled in Turkey

Abstract Phthalate esters (PAEs) adversely affect the human endocrine and reproductive systems. These chemical toxic compounds are used as plasticizers, in particular to improve the mechanical properties of different food packing materials. Daily food consumption is the predominant source of exposure for PAEs, especially for infants. In this study, residue profiles and levels for eight PAEs were determined in 30 infant (stage I, II and special A and B) formulas of 12 brands in Turkey and health risk assessments were performed. The average level of PAEs were different for each formula group and packing type except for BBP (p < 0.01). The highest average mean level of PAEs was detected in paperboard type packing, while the lowest average mean level of PAEs was detected in metal can type packing. The highest average level of PAEs detected was DEHP in special formulas (22.1 ng g−1). The average hazard quotient (HQ) value was calculated as 8.43 × 10–5–8.94 × 10–5 for BBP, 1.49 × 10–3–1.58 × 10–3 for DBP, 2.06 × 10–2–2.18 × 10–2 for DEHP, and 7.21 × 10–4–7.65 × 10–4 for DINP. The average HI values were calculated as 2.29 × 10–2 for 0–6 months old infants, 2.39 × 10–2 for 6–12 months old infants, and 2.43 × 10–2 for 12–36 months old infants. These calculated results show that commercial infant formulas were a source of exposure to PAEs but did not present a significant health risk.
The Role of Alpha-2 Agonists for Attention Deficit Hyperactivity Disorder in Children: A Review

Introduction: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders, characterized by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), which is marked by symptoms such as inappropriate levels of inattention, hyperactivity, and impulsivity that can affect academic, social, and personal functioning in children and adolescents. This review summarizes clinical trials demonstrating the effectiveness of Alpha-2 agonists in reducing symptoms of inattention, hyperactivity, and impulsivity in children with ADHD. Studies were identified through a systematic search of PubMed and Cochrane databases. However, these medications’ long-term safety and efficacy remain uncertain, with a lack of data on their effects on growth, cardiovascular function, and other adverse events. Further studies are required to determine these medications’ optimal dose and treatment duration. Methods: Medications that target the noradrenergic system, such as Alpha-2 agonists, have been increasingly used as a treatment option for ADHD, with guanfacine and clonidine being two of the most commonly used medications. They function by selectively targeting Alpha-2 adrenergic receptors in the brain leading to improved attention and reduced hyperactivity and impulsivity symptoms in children with ADHD. Results: Clinical trials have demonstrated the effectiveness of Alpha-2 agonists in treating ADHD in children by reducing symptoms of inattention, hyperactivity, and impulsivity. However, these medications’ long-term safety and efficacy still need to be completely understood. Due to a lack of information on the effects of Alpha-2 agonists on growth, cardiovascular function, and other long-term adverse events, more studies must investigate the optimal dose and treatment duration for these medications. Conclusions: Despite these concerns, Alpha-2 agonists remain a valuable treatment option for ADHD in children, especially those unable to tolerate stimulant medications or who have coexisting conditions such as tic disorders. Future research should continue to explore the safety and efficacy of Alpha-2 agonists in the long term. In conclusion, Alpha-2 agonists show promise as a treatment for ADHD in children; however, the safety and efficacy of these drugs in the long term are not yet completely understood. Additional studies are required to investigate the optimal dose and treatment duration for these medications in their use as a treatment for this debilitating disease.
A lack of efficacy of continuous theta burst stimulation over the left dorsolateral prefrontal cortex in autism: A double blind randomized sham‐controlled trial

Although previous open‐label trials suggest the therapeutic potential of inhibitory repetitive transcranial magnetic stimulation (rTMS) over the dorsolateral prefrontal cortex (DLPFC) in autism spectrum disorder (ASD), methodological caveats exist. We conducted an 8‐week randomized, double‐blind sham‐controlled trial to investigate the efficacy of inhibitory continuous theta burst stimulation (cTBS, a variant of rTMS) over the left DLPFC in individuals with ASD. Sixty children, adolescents and young adults (aged 8–30 years) with ASD without co‐occurring intellectual disabilities were randomized to a 16‐session 8‐week cTBS versus sham stimulation course, with a follow‐up 4 weeks after the trial. The Active group was not superior to the Sham group in any clinical or neuropsychological metrics at Week 8 or Week 12. Time effects of 8‐week cTBS on symptoms and executive function were remarkable in both Active and Sham groups, with comparable response rates and effect sizes of changes in symptoms/cognition between groups. Our results from a sufficiently powered sample do not endorse the superior efficacy of cTBS over the left DLPFC to the shamed stimulation for children, adolescents and adults with ASD. These findings suggest that earlier positive open‐label trial findings may be generalized by generalized/placebo effects. This highlights the urgent need for more rTMS/TBS studies with rigorous trial designs in ASD.
A recursive least squares implementation for adaptive beamforming under quadratic constraint

Quadratic constraints on the weight vector of an adaptive linearly constrained minimum power (LCMP) beamformer can improve robustness to pointing errors and to random perturbations in sensor parameters. In this paper, we propose a technique for implementing a quadratic inequality constraint with recursive least squares (RLS) updating. A variable diagonal loading term is added at each step, where the amount of loading is found from the solution to a quadratic equation. Simulations under different scenarios demonstrate that this algorithm outperforms both the RLS beamformer with no quadratic constraint, and the RLS beamformer using the scaled projection technique.
Scaling properties of neural

One of the problems in evaluating the usefulness of various classification techniques for real-world applications is the lack of information about their scaling properties: how will the complexity of a given method depend on the number of attributes, classes and cases? In this paper a simple classification task is presented as a challenge for different methods, allowing to determine their scaling properties as well as to evaluate the complexity of the solutions obtained by various methods.
NGC 7654: An interesting cluster to study star formation history

CCD UBV IC photometry in a wide eld around the open cluster NGC 7654 has been carried out for 17 860 stars, down to V 20. The reddening across the cluster region is found to be variable with E(B V )min = 0:46 to E(B V )max =0 :80. The cluster is situated at a distance of 1380 70 pc. The colour magnitude diagrams show a large age spread in the ages. Star formation was biased towards relatively higher masses during the early phase of star formation whereas most of the low mass stars of the cluster were formed during the later phase. The star formation seems to have been a gradual process that proceeded sequentially in mass and terminated with the formation of most massive stars. The present data do not support a uniform mass function (MF) for dierent regions in the cluster. Although for the whole cluster region, the MF in the observed mass range 0:8 M 4:5, can be represented by a single power law with a slope = 1:400:07, however it indicates various features when examined carefully. In three subregions of the cluster the slope , for the mass range 1:5 <M < 4:0, comes out to be 1:07 0:08 and 1:28 0:20 for the inner and intermediate regions and it becomes steeper in the outermost region ( = 2:78 0:21). For stars having masses <1.5 M the MF, in the inner and intermediate regions, can be represented by a power law having a steeper slope, whereas in the outer region a turnover can be seen in the MF at M 1:5 M. The age of NGC 7654 is found to be comparable to its two-body relaxation time-scale; therefore this may also be a reason for the observed mass segregation in the cluster.
Modeling and Simulation Method of Distributed Parameter Switched Systems

The modeling and simulation of distributed parameter switched systems (DPSS) were discussed. By the semigroup theory, the evolution equation model of DPSS was constructed, and the system simulation method was designed. For the numerical solution of switching law related to system state which is difficult to be obtained, the general orthogonal polynomials was used to get approximate switching law and the simulation was realized. Finally, two examples are illustrated to show the feasibility and effectiveness of the proposed method.
Applying a Radiation Therapy Volume Analysis Pipeline to Determine the Utility of Spectroscopic MRI-Guided Adaptive Radiation Therapy for Glioblastoma

Accurate radiation therapy (RT) targeting is crucial for glioblastoma treatment but may be challenging using clinical imaging alone due to the infiltrative nature of glioblastomas. Precise targeting by whole-brain spectroscopic MRI, which maps tumor metabolites including choline (Cho) and N-acetylaspartate (NAA), can quantify early treatment-induced molecular changes that other traditional modalities cannot measure. We developed a pipeline to determine how spectroscopic MRI changes during early RT are associated with patient outcomes to provide insight into the utility of adaptive RT planning. Data were obtained from a study (NCT03137888) where glioblastoma patients received high-dose RT guided by the pre-RT Cho/NAA twice normal (Cho/NAA ≥ 2x) volume, and received spectroscopic MRI scans pre- and mid-RT. Overlap statistics between pre- and mid-RT scans were used to quantify metabolic activity changes after two weeks of RT. Log-rank tests were used to quantify the relationship between imaging metrics and patient overall and progression-free survival (OS/PFS). Patients with lower Jaccard/Dice coefficients had longer PFS (p = 0.045 for both), and patients with lower Jaccard/Dice coefficients had higher OS trending towards significance (p = 0.060 for both). Cho/NAA ≥ 2x volumes changed significantly during early RT, putting healthy tissue at risk of irradiation, and warranting further study into using adaptive RT planning.