Miracle-dz/UniFAR-FTUs huggingface dataset:
Longitudinal Predictors of Neurodevelopmental Outcomes in Children with Down Syndrome

ABSTRACT This study examined longitudinal predictors of neurodevelopmental outcomes in children with Down syndrome (DS). Participants were assessed at Wave 1 during infancy on measures of looking behavior and caregivers provided infant sensory ratings. At Wave 2, child-age participants completed a developmental assessment and caregivers provided ratings of executive function, ADHD symptoms, and autism symptoms. Longer looking durations and greater sensory dysregulation during infancy were predictive of higher ADHD symptom ratings and other neurodevelopmental outcomes during childhood. The findings suggest that early indicators of neurodevelopmental dysregulation may be detectable during infancy in DS.
CT-based radiomics nomogram analysis for assessing BRCA mutation status in patients with high-grade serous ovarian cancer

Background Radiomics nomogram analysis is widely preoperatively used to assess gene mutations in various tumors. Purpose To explore the value of computed tomography (CT)-based radiomics nomogram analysis for assessing BRCA gene mutation status of patients with high-grade serous ovarian cancer (HGSOC). Material and Methods In total, 96 patients with HGSOC were retrospectively screened and randomly divided into primary (n = 68) and validation cohorts (n = 28). The clinical model was constructed based on clinical features and CT morphological features using univariate and multivariate logistic analyses. Maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were performed for feature dimensionality reduction and radiomics score was calculated. The nomogram model combining the clinical model and the radiomics score was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curves were generated to assess models’ performance. The calibration analysis and decision curve analysis (DCA) were also performed. Results The clinical model consisted of CA125 level and supradiaphragmatic lymphadenopathy and yielded an area under the curve (AUC) of 0.69 (primary cohort) and 0.81 (validation cohort). The radiomics model was built with seven selected features and showed an AUC of 0.87 (primary cohort) and 0.81 (validation cohort). The nomogram finally showed the highest AUC of 0.89 (primary cohort) and 0.87 (validation cohort). The nomogram presented favorable calibrations in both the primary and validation cohorts. DCA further confirmed the clinical benefits of the constructed nomogram. Conclusion CT-based radiomics nomogram provides a non-invasive method to discriminate BRCA gene mutation status of HGSOC and potentially helps develop precise medical strategies.
Time‐restricted eating for chronodisruption‐related chronic diseases

The circadian timing system enables organisms to adapt their physiology and behavior to the cyclic environmental changes including light–dark cycle or food availability. Misalignment between the endogenous circadian rhythms and external cues is known as chronodisruption and is closely associated with the development of metabolic and gastrointestinal disorders, cardiovascular diseases, and cancer. Time‐restricted eating (TRE, in human) is an emerging dietary approach for weight management. Recent studies have shown that TRE or time‐restricted feeding (TRF, when referring to animals) has several beneficial health effects, which, however, are not limited to weight management. This review summarizes the effects of TRE/TRF on regulating energy metabolism, gut microbiota and homeostasis, development of cardiovascular diseases and cancer. Furthermore, we will address the role of circadian clocks in TRE/TRF and propose ways to optimize TRE as a dietary strategy to obtain maximal health benefits.
Liver fat as risk factor of hepatic and cardiometabolic diseases

Non‐alcoholic fatty liver disease (NAFLD) is a disorder characterized by excessive accumulation of fat in the liver that can progress to liver inflammation (non‐alcoholic steatohepatitis [NASH]), liver fibrosis, and cirrhosis. Although most efforts for drug development are focusing on the treatment of the latest stages of NAFLD, where significant fibrosis and NASH are present, findings from studies suggest that the amount of liver fat may be an important independent risk factor and/or predictor of development and progression of NAFLD and metabolic diseases. In this review, we first describe the current tools available for quantification of liver fat in humans and then present the clinical and pathophysiological evidence that link liver fat with NAFLD progression as well as with cardiometabolic diseases. Finally, we discuss current pharmacological and non‐pharmacological approaches to reduce liver fat and present open questions that have to be addressed in future studies.
Thermostability enhancement of polyethylene terephthalate degrading PETase using self‐ and nonself‐ligating protein scaffolding approaches

Polyethylene terephthalate (PET) hydrolase enzymes show promise for enzymatic PET degradation and green recycling of single‐use PET vessels representing a major source of global pollution. Their full potential can be unlocked with enzyme engineering to render activities on recalcitrant PET substrates commensurate with cost‐effective recycling at scale. Thermostability is a highly desirable property in industrial enzymes, often imparting increased robustness and significantly reducing quantities required. To date, most engineered PET hydrolases show improved thermostability over their parental enzymes. Here, we report engineered thermostable variants of Ideonella sakaiensis PET hydrolase enzyme (IsPETase) developed using two scaffolding strategies. The first employed SpyCatcher‐SpyTag technology to covalently cyclize IsPETase, resulting in increased thermostability that was concomitant with reduced turnover of PET substrates compared to native IsPETase. The second approach using a GFP‐nanobody fusion protein (vGFP) as a scaffold yielded a construct with a melting temperature of 80°C. This was further increased to 85°C when a thermostable PETase variant (FAST PETase) was scaffolded into vGFP, the highest reported so far for an engineered PET hydrolase derived from IsPETase. Thermostability enhancement using the vGFP scaffold did not compromise activity on PET compared to IsPETase. These contrasting results highlight potential topological and dynamic constraints imposed by scaffold choice as determinants of enzyme activity.
The Worldwide Prevalence of Herpes Simplex Virus Encephalitis and Meningitis: A Systematic Review and Meta-Analysis

Given the relatively high frequency of central nervous system infections and considerable mortality and morbidity reported to be caused by herpes simplex viruses among the other viral agents, having a clear knowledge about their epidemiological profile seems necessary. This systematic review and meta-analysis aimed to determine the relative frequency and prevalence of herpes simplex encephalitis and meningitis in patients tested for viral etiologies. A comprehensive systematic review was performed in PubMed, Scopus, and Web of Science databases, searching for studies on the prevalence and relative frequency of herpes simplex virus 1 and herpes simplex virus 2 encephalitis and meningitis. Seventy-one studies were included. Overall, the prevalence of herpes simplex virus encephalitis among patients tested was 8% (95% confidence interval, 6%-11%; I 2 = 98%) and the prevalence of herpes simplex virus meningitis among aseptic patients tested was 4% (95% confidence interval, 3%-7%; I 2 = 95%), and a significant difference was observed by region. The results of our subgroup analysis for herpes simplex virus encephalitis revealed a prevalence of 8% for pediatric patients and adolescents and 12% for adults. The results for herpes simplex virus meningitis showed a prevalence of 4% for pediatric patients and adolescents and 9% for adults. We observed significant differences in the frequency of herpes simplex virus 1 and herpes simplex virus 2 detection rates by region. Having high rates of missed cases due to inadequate, highly sensitive paraclinical tests performed on patients with suspected viral central nervous system infection is one of the possible factors. More studies are needed to detect the possible flaws in the process of diagnosis in different regions.
TPL2 kinase activity regulates microglial inflammatory responses and promotes neurodegeneration in tauopathy mice

Tumor progression locus 2 (TPL2) (MAP3K8) is a central signaling node in the inflammatory response of peripheral immune cells. We find that TPL2 kinase activity modulates microglial cytokine release and is required for microglia-mediated neuron death in vitro. In acute in vivo neuroinflammation settings, TPL2 kinase activity regulates microglia activation states and brain cytokine levels. In a tauopathy model of chronic neurodegeneration, loss of TPL2 kinase activity reduces neuroinflammation and rescues synapse loss, brain volume loss, and behavioral deficits. Single-cell RNA sequencing analysis indicates that protection in the tauopathy model was associated with reductions in activated microglia subpopulations as well as infiltrating peripheral immune cells. Overall, using various models, we find that TPL2 kinase activity can promote multiple harmful consequences of microglial activation in the brain including cytokine release, iNOS (inducible nitric oxide synthase) induction, astrocyte activation, and immune cell infiltration. Consequently, inhibiting TPL2 kinase activity could represent a potential therapeutic strategy in neurodegenerative conditions.
Ostracods as Indicators of Natural and Anthropogenically-Induced Changes in Coastal Marine Environments

A rapid increase in urbanization and commercial development of coastal areas has occurred worldwide over the last two centuries. Industrial, agricultural and technological activities have triggered changes in natural ecosystems at considerably accelerated rates. The industrial revolution, growth in human population and the onset of modern agricultural development have all resulted in increased nutrient loading of rivers and lakes, and the alteration of natural freshwater supply drainage basins. Rivers have become the conduits for anthropogenic byproducts that reach the coastal ecosystems affecting water quality and amplifying eutrophication on the continental shelves. As a result, decreasing oxygen levels in the water column have impacted marine fauna and flora. Most cases of eutrophication are related to massive nutrient flux from rivers and anthropogenic addition of nitrogen and phosphorous to the watershed.
Evaluating the Impact of Obesity and Different Metabolic Statuses on the Prognosis of Hospitalized Patients with Inflammatory Bowel Disease: A Cohort Study

Introduction: Obesity is associated with an increased risk of inflammatory bowel disease (IBD), whereas not all obese individuals have the same effect. In individuals with obesity, the role of metabolic status in the readmission of IBD remains unclear. Our study aimed to evaluate the association between different obesity metabolic phenotypes and the prognosis of IBD patients. Methods: We conducted a longitudinal cohort study using Nationwide Readmissions Database (NRD) (2018 sample). Out of 12,928,231 discharge records, 63,748 records with a discharge diagnosis of IBD were identified for analysis. Cox proportional hazard ratio (HR) with 95% confidence interval (CI) was calculated, adjusting for potential confounders. Results: During a 180-day follow-up in IBD patients with different obesity metabolic phenotypes, all-cause readmission rate, inpatient mortality rate, unplanned readmission rate, total charge, hospitalized length of stay were statistically different (all p < 0.001). After multivariate Cox regression analysis, IBD patients with metabolically unhealthy nonobese (MUNO) had higher risk of readmission (all-cause and unplanned) (HR 1.04, 95% CI: 1.00–1.08 and HR 1.06, 95% CI: 1.02–1.10), and those with metabolically unhealthy obesity (MUO) had higher risk of unplanned readmission (HR 1.08, 95% CI: 1.02–1.15). In subgroup analysis, both the MUNO group and MUO group had higher risk of readmission (all-cause and unplanned) in the ulcerative colitis (UC) subgroup, but only the MUNO group had higher risk of readmission (all-cause and unplanned) (HR 1.05, 95% CI: 1.00–1.10 and HR 1.06, 95% CI: 1.01–1.12) in the Crohn’s disease (CD) subgroup. Conclusion: Metabolic abnormalities were associated with an increased risk of readmission in patients with IBD, regardless of obesity.
HSD-PAM: High Speed Super Resolution Deep Penetration Photoacoustic Microscopy Imaging Boosted by Dual Branch Fusion Network

Photoacoustic microscopy (PAM) is a novel implementation of photoacoustic imaging (PAI) for visualizing the 3D bio-structure, which is realized by raster scanning of the tissue. However, as three involved critical imaging parameters, imaging speed, lateral resolution, and penetration depth have mutual effect to one the other. The improvement of one parameter results in the degradation of other two parameters, which constrains the overall performance of the PAM system. Here, we propose to break these limitations by hardware and software co-design. Starting with low lateral resolution, low sampling rate AR-PAM imaging which possesses the deep penetration capability, we aim to enhance the lateral resolution and up sampling the images, so that high speed, super resolution, and deep penetration for the PAM system (HSD-PAM) can be achieved. Data-driven based algorithm is a promising approach to solve this issue, thereby a dedicated novel dual branch fusion network is proposed, which includes a high resolution branch and a high speed branch. Since the availability of switchable AR-OR-PAM imaging system, the corresponding low resolution, undersample AR-PAM and high resolution, full sampled OR-PAM image pairs are utilized for training the network. Extensive simulation and in vivo experiments have been conducted to validate the trained model, enhancement results have proved the proposed algorithm achieved the best perceptual and quantitative image quality. As a result, the imaging speed is increased 16 times and the imaging lateral resolution is improved 5 times, while the deep penetration merit of AR-PAM modality is still reserved.
Adeno-associated virus gene therapy prevents progression of kidney disease in genetic models of nephrotic syndrome

Gene therapy for kidney diseases has proven challenging. Adeno-associated virus (AAV) is used as a vector for gene therapy targeting other organs, with particular success demonstrated in monogenic diseases. We aimed to establish gene therapy for the kidney by targeting a monogenic disease of the kidney podocyte. The most common cause of childhood genetic nephrotic syndrome is mutations in the podocyte gene NPHS2, encoding podocin. We used AAV-based gene therapy to rescue this genetic defect in human and mouse models of disease. In vitro transduction studies identified the AAV-LK03 serotype as a highly efficient transducer of human podocytes. AAV-LK03–mediated transduction of podocin in mutant human podocytes resulted in functional rescue in vitro, and AAV 2/9–mediated gene transfer in both the inducible podocin knockout and knock-in mouse models resulted in successful amelioration of kidney disease. A prophylactic approach of AAV 2/9 gene transfer before induction of disease in conditional knockout mice demonstrated improvements in albuminuria, plasma creatinine, plasma urea, plasma cholesterol, histological changes, and long-term survival. A therapeutic approach of AAV 2/9 gene transfer 2 weeks after disease induction in proteinuric conditional knock-in mice demonstrated improvement in urinary albuminuria at days 42 and 56 after disease induction, with corresponding improvements in plasma albumin. Therefore, we have demonstrated successful AAV-mediated gene rescue in a monogenic renal disease and established the podocyte as a tractable target for gene therapy approaches. Description Adeno-associated virus gene therapy partially rescues kidney function in genetic models of nephrotic syndrome both in vitro and in vivo. Editor’s summary Mutations in the podocin-encoding NPHS2 gene can cause steroid-resistant nephrotic syndrome in children, with no effective targeted treatments available. Here, Ding and colleagues demonstrated the effectiveness of adeno-associated virus (AAV)-LK03 at transducing human podocytes in vitro. They then developed an AAV-based gene therapy to treat NPHS2 mutations using a minimal human nephrin promoter and tested it using AAV serotype 2/9, which can transduce murine podocytes, in two different mouse models of disease. Treatment before induction of disease and after disease onset reduced albuminuria in nephrotic mice, providing proof-of-concept that gene therapy might be a viable treatment strategy for patients with monogenic causes of nephrotic syndrome. —Melissa Norton
Unified Matrix Factorization with Dynamic Multi-view Clustering

Matrix factorization (MF) is a classical collaborative filtering algorithm for recommender systems. It decomposes the user-item interaction matrix into a product of low-dimensional user representation matrix and item representation matrix. In typical recommendation scenarios, the user-item interaction paradigm is usually a two-stage process and requires static clustering analysis of the obtained user and item representations. The above process, however, is time and computationally intensive, making it difficult to apply in real-time to e-commerce or Internet of Things environments with billions of users and trillions of items. To address this, we propose a unified matrix factorization method based on dynamic multi-view clustering (MFDMC) that employs an end-to-end training paradigm. Specifically, in each view, a user/item representation is regarded as a weighted projection of all clusters. The representation of each cluster is learnable, enabling the dynamic discarding of bad clusters. Furthermore, we employ multi-view clustering to represent multiple roles of users/items, effectively utilizing the representation space and improving the interpretability of the user/item representations for downstream tasks. Extensive experiments show that our proposed MFDMC achieves state-of-the-art performance on real-world recommendation datasets. Additionally, comprehensive visualization and ablation studies interpretably confirm that our method provides meaningful representations for downstream tasks of users/items.
Automated Driving Without Ethics: Meaning, Design and Real-World Implementation

The ethics of automated vehicles (AV) has received a great amount of attention in recent years, specifically in regard to their decisional policies in accident situations in which human harm is a likely consequence. After a discussion about the pertinence and cogency of the term 'artificial moral agent' to describe AVs that would accomplish these sorts of decisions, and starting from the assumption that human harm is unavoidable in some situations, a strategy for AV decision making is proposed using only pre-defined parameters to characterize the risk of possible accidents and also integrating the Ethical Valence Theory, which paints AV decision-making as a type of claim mitigation, into multiple possible decision rules to determine the most suitable action given the specific environment and decision context. The goal of this approach is not to define how moral theory requires vehicles to behave, but rather to provide a computational approach that is flexible enough to accommodate a number of human 'moral positions' concerning what morality demands and what road users may expect, offering an evaluation tool for the social acceptability of an automated vehicle's decision making.
A Spin on the Bulk Locality of Slightly Broken Higher Spin Theories

In this paper, we investigate if it is possible to express correlation functions in Large N Chern-Simons (CS) matter theories/ Slightly Broken Higher Spin (SBHS) theories purely in terms of single trace twist conformal blocks (TCBs). For this, we first develop the machinery for spinning TCBs. We do this both by explicitly solving the spinning TCB eigenvalue equation taking into account consistency with the operator product expansion (OPE) and crossing symmetry, and also by employing weight shifting and spin raising operators and acting with them on scalar seeds. Using these results we show that spinning correlators in theories with exact higher spin symmetry can be entirely expressed in terms of single trace TCBs. However, when the higher spin symmetry is slightly broken at large- N, even though the scalar four-point function is given by single-trace TCBs, the spinning correlators in general, are not. Our results suggest that it may be possible to identify a sub-sector of SBHS theory which has a local bulk dual.
ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning Problems

Recently, uncertainty-aware deep learning methods for multiclass labeling problems have been developed that provide calibrated class prediction probabilities and out-of-distribution (OOD) indicators, letting machine learning (ML) consumers and engineers gauge a model’s confidence in its predictions. However, this extra neural network prediction information is challenging to scalably convey visually for arbitrary data sources under multiple uncertainty contexts. To address these challenges, we present ScatterUQ, an interactive system that provides targeted visualizations to allow users to better understand model performance in context-driven uncertainty settings. ScatterUQ leverages recent advances in distance-aware neural networks, together with dimensionality reduction techniques, to construct robust, 2-D scatter plots explaining why a model predicts a test example to be (1) in-distribution and of a particular class, (2) in-distribution but unsure of the class, and (3) out-of-distribution. ML consumers and engineers can visually compare the salient features of test samples with training examples through the use of a “hover callback” to understand model uncertainty performance and decide follow up courses of action. We demonstrate the effectiveness of ScatterUQ to explain model uncertainty for a multiclass image classification on a distance-aware neural network trained on Fashion-MNIST and tested on Fashion-MNIST (in distribution) and MNIST digits (out of distribution), as well as a deep learning model for a cyber dataset. We quantitatively evaluate dimensionality reduction techniques to optimize our contextually driven UQ visualizations. Our results indicate that the ScatterUQ system should scale to arbitrary, multiclass datasets. Our code is available at https://github.com/mit-ll-responsible-ai/equine-webapp.
A Comparative Study of Code Generation using ChatGPT 3.5 across 10 Programming Languages

Large Language Models (LLMs) are advanced Artificial Intelligence (AI) systems that have undergone extensive training using large datasets in order to understand and produce language that closely resembles that of humans. These models have reached a level of proficiency where they are capable of successfully completing university exams across several disciplines and generating functional code to handle novel problems. This research investigates the coding proficiency of ChatGPT 3.5, a LLM released by OpenAI in November 2022, which has gained significant recognition for its impressive text generating and code creation capabilities. The skill of the model in creating code snippets is evaluated across 10 various programming languages and 4 different software domains. Based on the findings derived from this research, major unexpected behaviors and limitations of the model have been identified. This study aims to identify potential areas for development and examine the ramifications of automated code generation on the evolution of programming languages and on the tech industry.