Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic predisposition of LEPR (rs1137101) gene polymorphism related to type 2 diabetes mellitus – a meta-analysis

Abstract Background: Type 2 diabetes mellitus (T2DM) is a multifaceted disease appropriate to elevated blood glucose levels resulting from decreased insulin and beta-cell activity. Using a case–control methodology, researchers have examined the relationship between polymorphisms in LEPR and T2DM in a population from south India. Materials and Methods: We conducted a genetic analysis of 311 participants, and results were accomplished using a case-control study, a meta-analysis of previous studies on LEPR was conducted, and type 2 diabetes genotype distribution across various geographical regions Malaysians, Chinese Han, Kuwait, Iran, Mongolia, and Han Chinese, Greece, Saudi, India (North India, Punjabi), (South India, Tamilnadu). The study involved 254 prospective investigations, and nine association studies were preferred according to preset criteria. Studies were assessed for quality using the Hardy-Weinberg equilibrium (HWE) and the Newcastle–Ottawa Scale (NOS). An analysis of the genetic models was conducted to determine their relationship, statistical analysis was utilized to calculate odds ratios (ORs) and matching 95% confidence intervals (CIs). Results: The LEPR-rs1137101 polymorphism in the case-control study was associated with a significant increase in the risk of type 2 diabetes. A meta-analysis revealed a connection between LEPR gene polymorphism (rs1137101) and type 2 diabetes risk. Investigators might gain a more profound thought on the significance of the identified genetic variation and its impact on the chance of developing type 2 diabetes by verifying and strengthening previously reported findings. The model of fixed effects was chosen due to the low heterogeneity, and significant associations were observed in the allelic (OR = 0.79, 95% CI [0.70–0.87]), homozygote (OR = 0.58, 95% CI [0.46–0.72]), dominant (OR = 0.66, 95% CI [0.56–0.79]), and recessive (OR = 0.83, 95% CI [0.71–0.96]) genetic models. A Begg’s funnel plot and Egger’s test indicated no publication bias. These findings suggest that the rs1137101 variant in the LEPR gene has been linked to a higher risk of T2DM. Conclusions: A larger sample size, however, is required for further research, and consideration of potential confounding factors is needed to validate these associations. Understanding the implications of LEPR gene polymorphisms in T2DM susceptibility may contribute to personalized treatment strategies for patients with T2DM.
Transcriptional landscape of the pMP7017 megaplasmid and its impact on the Bifidobacterium breve UCC2003 transcriptome

Abstract The 190 kb megaplasmid pMP7017 of Bifidobacterium breve JCM7017 represents the first conjugative and largest plasmid characterised within this genus to date. In the current study, we adopted an integrated approach combining transcriptomics, whole genome comparative analysis and metagenomic data mining to understand the biology of pMP7017 and related megaplasmids, and to assess the impact of plasmid‐carriage on the host strain. The data generated revealed variations within basic features of promoter elements which correlate with a high level of transcription on the plasmid and highlight the transcriptional activity of genes encoding both offensive and defensive adaptations, including a Type IIL restriction‐modification system, an anti‐restriction system and four Type II toxin‐antitoxin systems. Furthermore, a highly transcribed tmRNA, which likely provides translational support to the host strain, was identified, making pMP7017 the first plasmid of the Bifidobacterium genus and the smallest plasmid known to express a tmRNA. Analyses of synteny and variability among pMP7017 and related plasmids indicate substantial diversity in gene organisation and accessory gene cargo highlighting diverse (co‐)evolution and potential host‐specific rearrangements and adaptations. Systematic analysis of the codon usage profile of transcriptionally active pMP7017‐encoded genes suggests that pMP7017 originated from (sub)species of Bifidobacterium longum. Furthermore, mining of metagenomic data suggests the presence of pMP7017‐homologues in ~10% of microbiome samples, mostly infants and/or mothers from various geographical locations. Comparative transcriptome analysis of the B. breve UCC2003 chromosome in the presence or absence of pMP7017 revealed differential expression of genes representing 8% of the total gene pool. Genes involved in genetic information processing were exclusively upregulated, while altered expression of genes involved in biofilm production and polysaccharide biosynthesis was also observed.
Effect of variable confining pressure on cyclic behaviour of granular soil under triaxial tests

Previous studies were performed to use combined cyclic deviatoric stress and confining pressure to simulate cyclic vertical and horizontal normal stresses under traffic loading. The effect of variable confining pressure (VCP) on the permanent deformation of soils was investigated. However, some studies concluded VCP could promote the development of permanent deformation compared to the tests with constant confining pressure (CCP), while others drew the opposite conclusions. In this study, three types of CCP and VCP tests with identical maximum stress, identical average stress, and identical initial stress were conducted. Test results showed VCP tests accumulated more permanent strains when CCP and VCP tests had identical maximum or average stress, and the permanent strains increased with the decrease in the inclinations of stress paths, while similar permanent strains were generated when CCP and VCP tests had identical initial stress. In addition, larger permanent strains were generated with the increase .
Conflicting and complementary notions of responsibility in caregiver’s and health care workers’ vaccination narratives in the Philippines

Background Vaccine hesitancy (VH) continues to pose a public health threat globally. Understanding the attitudes and perceptions about vaccination of key stakeholders in vaccine decision-making (such as health care workers (HCWs) and caregivers) about vaccination can pave the way toward novel approaches to bolster vaccine confidence. In this study, we explored the role of notions of responsibilities among HCWs and caregivers in shaping vaccination interactions and decision-making in the Philippines. Methods We conducted in-depth interviews (IDIs) and focus group discussions (FGDs) with 44 vaccine-hesitant caregivers, seven HCWs, and 20 community health workers (barangay health workers) in the Philippines between August 2020 and March 2021. The interviews and focus groups were conducted online, transcribed verbatim, and analysed through the reflexive thematic analysis approach. Results Caregivers highlighted responsibility in terms of being a good caregiver, managing risk to one’s own child, and seeking and validating information. Meanwhile, HCWs highlighted responsibility as: being a good HCW, managing risk to children and to the community, and providing and transforming information. Our findings suggest that responsibility manifests differently in HCWs’ and caregivers’ narratives, and that these notions can be both conflicting and complementary, shaping the interaction between stakeholders and, ultimately, their vaccine decision-making. We also found that fostering a good relationship between HCWs and caregivers through communication techniques such as motivational interviewing could help bridge the gap created by mistrust in vaccinations. HCWs sharing their own experiences as parents who vaccinate their own children also resonate with caregivers. Conclusions Notions of responsibility can underpin collaborative and divisive interactions between HCWs and caregivers. Public health messaging and interventions related to vaccination must consider strategies that align with these notions to address VH.
Clinical and Echocardiographic Predictors for the Presence of Late Gadolinium Enhancement on Cardiac Magnetic Resonance Imaging in Patients with Carbon Monoxide Poisoning

Late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMRI) reflects the burden of myocardial damage in carbon monoxide (CO) poisoning. This study aimed to identify the clinical and echocardiographic parameters that can predict myocardial LGE on CMRI in CO poisoning. This prospective observational study included patients who presented with acute CO poisoning and elevated troponin I and underwent echocardiography and CMRI to identify myocardial damage at a tertiary university hospital between August 2017 and May 2019 and August 2020 and July 2022. Based on the CMRI findings, participants were categorized into LGE and non-LGE groups. The median age of the 155 patients was 51.0 years, and 98 (63.2%) were males. Median times from emergency department arrival to either CMRI or echocardiography were 3.0 days each. The LGE group included 99 (63.9%) patients with LGE positivity on CMRIs. Time from rescue to hyperbaric oxygen therapy >4 h (odds ratio (OR): 3.31, 95% confidence interval (CI): 1.28–8.56, p = 0.01); serum lactate levels >2 mmol/L (OR: 2.62, 95% CI: 1.20–5.73, p = 0.02); and left ventricular global longitudinal strain >−16% (OR: 2.95, 95% CI: 1.35–6.47, p = 0.007) were significant predictors of LGE positivity. The area under the curve of these predictors was 0.711. Our prediction model, which combines the clinical parameters with left ventricular global longitudinal strain, may be helpful in the early detection of LGE positivity.
Machine Learning Applications in Traumatic Brain Injury Diagnosis and Prognosis: A Spotlight on Mild TBI and CT Imaging.

Traumatic Brain Injury (TBI) poses a significant global public health challenge, contributing to high morbidity and mortality rates and placing a substantial economic burden on healthcare systems worldwide. The diagnosis and prognosis of TBI relies on a combination of clinical and imaging data often acquired using a Computed Tomography (CT) scanner. Addressing the multifaceted challenges posed by TBI requires innovative, data driven approaches, for this complex condition. As such, we provide a summary of the state-of-the-art Machine Learning (ML) and Deep Learning (DL) techniques applied to clinical and images in TBI, with a particular focus on mild TBI (mTBI). We explore the rich spectrum of ML and DL techniques used and highlight their impact in TBI . We categorize ML and DL methods by TBI severity and showcase their application in mTBI and moderate-to-severe TBI scenarios. Finally, we emphasize the role of ML and DL in mTBI diagnosis, where conventional methods often fall short, and comment on the potential of CT-based ML applications in TBI. This review may serve as a source of inspiration for future research endeavours aimed at improving the diagnosis and prognosis of TBI.
Approximate probability density function for nonlinear surging in irregular following seas

The broaching that follows the surf-riding is a dangerous phenomenon that can lead to the capsizing of a vessel due to its violent yaw motion. Most of the previous studies on surf-riding phenomena in irregular waves have been conducted by replacing irregular waves with regular waves. In contrast, this study provides suggestions on how to directly calculate nonlinear surge motion in irregular seas. In this study, the statistical aspects of the surf-riding phenomenon are first presented. Then, under several approximations, we show how to calculate the probability density function theoretically. Although the results obtained are based on strong approximations, it is found that the nonlinear surge oscillations in irregular following seas can be explained from a qualitative point of view.
Automated Assessment of Students' Code Comprehension using LLMs

Assessing student's answers and in particular natural language answers is a crucial challenge in the field of education. Advances in machine learning, including transformer-based models such as Large Language Models(LLMs), have led to significant progress in various natural language tasks. Nevertheless, amidst the growing trend of evaluating LLMs across diverse tasks, evaluating LLMs in the realm of automated answer assesment has not received much attention. To address this gap, we explore the potential of using LLMs for automated assessment of student's short and open-ended answer. Particularly, we use LLMs to compare students' explanations with expert explanations in the context of line-by-line explanations of computer programs. For comparison purposes, we assess both Large Language Models (LLMs) and encoder-based Semantic Textual Similarity (STS) models in the context of assessing the correctness of students' explanation of computer code. Our findings indicate that LLMs, when prompted in few-shot and chain-of-thought setting perform comparable to fine-tuned encoder-based models in evaluating students' short answers in programming domain.
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation

Recent works demonstrate that using reinforcement learning (RL) with quality rewards can enhance the quality of generated images in text-to-image (T2I) generation. However, a simple aggregation of multiple rewards may cause over-optimization in certain metrics and degradation in others, and it is challenging to manually find the optimal weights. An effective strategy to jointly optimize multiple rewards in RL for T2I generation is highly desirable. This paper introduces Parrot, a novel multi-reward RL framework for T2I generation. Through the use of the batch-wise Pareto optimal selection, Parrot automatically identifies the optimal trade-off among different rewards during the RL optimization of the T2I generation. Additionally, Parrot employs a joint optimization approach for the T2I model and the prompt expansion network, facilitating the generation of quality-aware text prompts, thus further enhancing the final image quality. To counteract the potential catastrophic forgetting of the original user prompt due to prompt expansion, we introduce original prompt centered guidance at inference time, ensuring that the generated image remains faithful to the user input. Extensive experiments and a user study demonstrate that Parrot outperforms several baseline methods across various quality criteria, including aesthetics, human preference, image sentiment, and text-image alignment.
Light rings on stationary axisymmetric spacetimes: Blind to the topology and able to coexist

It has been established that Black Hole (BH) spacetimes obeying some general set of assumptions always possess, at least, one light ring (per rotation sense) [arXiv:2003.06445]. This theorem was originally established for asymptotically flat, stationary, axial symmetric, 1+3 dimensional circular spacetimes harbouring a non-extremal and topologically spherical Killing horizon. Following the mantra that a theorem is only as strong as its assumptions in this work we extend this theorem to non topologically spherical (toroidal) BHs and to spacetimes harbouring more than one BH. As in [arXiv:2003.06445], we show that each BH still contributes with, at least, one LR (per rotation sense).
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization

While machine learning models are typically trained to solve prediction problems, we might often want to use them for optimization problems. For example, given a dataset of proteins and their corresponding fluorescence levels, we might want to optimize for a new protein with the highest possible fluorescence. This kind of data-driven optimization (DDO) presents a range of challenges beyond those in standard prediction problems, since we need models that successfully predict the performance of new designs that are better than the best designs seen in the training set. It is not clear theoretically when existing approaches can even perform better than the naive approach that simply selects the best design in the dataset. In this paper, we study how structure can enable sample-efficient data-driven optimization. To formalize the notion of structure, we introduce functional graphical models (FGMs) and show theoretically how they can provide for principled data-driven optimization by decomposing the original high-dimensional optimization problem into smaller sub-problems. This allows us to derive much more practical regret bounds for DDO, and the result implies that DDO with FGMs can achieve nearly optimal designs in situations where naive approaches fail due to insufficient coverage of the offline data. We further present a data-driven optimization algorithm that inferes the FGM structure itself, either over the original input variables or a latent variable representation of the inputs.
Inferring Intentions to Speak Using Accelerometer Data In-the-Wild

Humans have good natural intuition to recognize when another person has something to say. It would be interesting if an AI can also recognize intentions to speak. Especially in scenarios when an AI is guiding a group discussion, this can be a useful skill. This work studies the inference of successful and unsuccessful intentions to speak from accelerometer data. This is chosen because it is privacy-preserving and feasible for in-the-wild settings since it can be placed in a smart badge. Data from a real-life social networking event is used to train a machine-learning model that aims to infer intentions to speak. A subset of unsuccessful intention-to-speak cases in the data is annotated. The model is trained on the successful intentions to speak and evaluated on both the successful and unsuccessful cases. In conclusion, there is useful information in accelerometer data, but not enough to reliably capture intentions to speak. For example, posture shifts are correlated with intentions to speak, but people also often shift posture without having an intention to speak, or have an intention to speak without shifting their posture. More modalities are likely needed to reliably infer intentions to speak.
Loss it right: Euclidean and Riemannian Metrics in Learning-based Visual Odometry

This paper overviews different pose representations and metric functions in visual odometry (VO) networks. The performance of VO networks heavily relies on how their architecture encodes the information. The choice of pose representation and loss function significantly impacts network convergence and generalization. We investigate these factors in the VO network DeepVO by implementing loss functions based on Euler, quaternion, and chordal distance and analyzing their influence on performance. The results of this study provide insights into how loss functions affect the designing of efficient and accurate VO networks for camera motion estimation. The experiments illustrate that a distance that complies with the mathematical requirements of a metric, such as the chordal distance, provides better generalization and faster convergence. The code for the experiments can be found at https://github.com/remaro-network/Loss_VO_right
Video Anomaly Detection and Explanation via Large Language Models

Video Anomaly Detection (VAD) aims to localize abnormal events on the timeline of long-range surveillance videos. Anomaly-scoring-based methods have been prevailing for years but suffer from the high complexity of thresholding and low explanability of detection results. In this paper, we conduct pioneer research on equipping video-based large language models (VLLMs) in the framework of VAD, making the VAD model free from thresholds and able to explain the reasons for the detected anomalies. We introduce a novel network module Long-Term Context (LTC) to mitigate the incapability of VLLMs in long-range context modeling. We design a three-phase training method to improve the efficiency of fine-tuning VLLMs by substantially minimizing the requirements for VAD data and lowering the costs of annotating instruction-tuning data. Our trained model achieves the top performance on the anomaly videos of the UCF-Crime and TAD benchmarks, with the AUC improvements of +3.86\% and +4.96\%, respectively. More impressively, our approach can provide textual explanations for detected anomalies.
Improving the energy density and flexibility of PMN-0.3PT based piezoelectric generator by composite designing

Ceramics based piezoelectric generators are known for their high energy density and poor flexibility. In this work, v_r-PMN-0.3PT/PDMS 2-2 composite with optimum PMN-0.3PT content (v_r) was designed that demonstrated enhanced output energy density and superior mechanical flexibility under dynamic mechanical excitation. v_r-PMN-0.3PT/PDMS 2-2 composite with different PMN-PT reinforcement content (v_r) and two different reinforcement configurations were fabricated and characterized for effective electro-elastic properties and energy harvesting response. Parallelly, using the finite element method and analytical models, effective electromechanical properties were calculated. Composites with parallel connectivity of the reinforcement phase demonstrated enhanced piezoelectric charge coefficient even with low PMN-0.3PT content whereas the relative permittivity and elastic modulus exhibited a linearly increasing trend with reinforcement volume fraction. At a compressive load of 50 N and 5 Hz frequency, a piezoelectric generator (PG) based on a v_r = 0.2, v_r- PMN-0.3PT/PDMS 2-2 composite with parallel connectivity produced a maximum short-circuit current density of 69 nA/cm2 and an open-circuit electric field of 189 V/cm, translating to a maximum output power density of ~13 {\mu}W/cm3 higher than that of pristine PMN-0.3 PT based piezoelectric generator. Estimated mechanical flexibility was found to be ~53 % higher than that of pristine PMN-0.3PT.
Computing NMHV Gravity Amplitudes at Infinity

In this note we show how the solutions to the scattering equations in the NMHV sector fully decompose into subsectors in the $z\to \infty$ limit of a Risager deformation. Each subsector is characterized by the punctures that coalesce in the limit. This naturally decomposes the $E(n-3,1)$ solutions into sets characterized by partitions of $n-3$ elements so that exactly one subset has more than one element. We present analytic expressions for the leading order of the solutions in an expansion around infinite $z$ for any $n$. We also give a simple algorithm for numerically computing arbitrarily high orders in the same expansion. As a consequence, one has the ability to compute Yang-Mills and gravity amplitudes purely from this expansion around infinity. Moreover, we present a new analytic computation of the residue at infinity of the $n=12$ NMHV tree-level gravity amplitude which agrees with the results of Conde and Rajabi. In fact, we present the analytic form of the leading order in $1/z$ of the Cachazo-Skinner-Mason/CHY formula for graviton amplitudes for each subsector and to all multiplicity. As a byproduct of the all-order algorithm, one has access to the numerical value of the residue at infinity for any $n$ and hence to the corrected CSW (or MHV) expansion for NMHV gravity amplitudes.