Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Peptide Docking with Privileged Knowledge Distillation using Deep Learning

Protein-peptide interactions play a key role in biological processes. Understanding the interactions that occur within a receptor-peptide complex can help in discovering and altering their biological functions. Various computational methods for modeling the structures of receptor-peptide complexes have been developed. Recently, accurate structure prediction enabled by deep learning methods has significantly advanced the field of structural biology. AlphaFold (AF) is among the top-performing structure prediction methods and has highly accurate structure modeling performance on single-chain targets. Shortly after the release of AlphaFold, AlphaFold-Multimer (AFM) was developed in a similar fashion as AF for prediction of protein complex structures. AFM has achieved competitive performance in modeling protein-peptide interactions compared to previous computational methods; however, still further improvement is needed. Here, we present DistPepFold, which improves protein-peptide complex docking using an AFM-based architecture through a privileged knowledge distillation approach. DistPepFold leverages a teacher model that uses native interaction information during training and transfers its knowledge to a student model through a teacher-student distillation process. We evaluated DistPepFold’s docking performance on two protein-peptide complex datasets and showed that DistPepFold outperforms AFM. Furthermore, we demonstrate that the student model was able to learn from the teacher model to make structural improvements based on AFM predictions.
Large Language Models in Law: A Survey

The advent of artificial intelligence (AI) has significantly impacted the traditional judicial industry. Moreover, recently, with the development of AI-generated content (AIGC), AI and law have found applications in various domains, including image recognition, automatic text generation, and interactive chat. With the rapid emergence and growing popularity of large models, it is evident that AI will drive transformation in the traditional judicial industry. However, the application of legal large language models (LLMs) is still in its nascent stage. Several challenges need to be addressed. In this paper, we aim to provide a comprehensive survey of legal LLMs. We not only conduct an extensive survey of LLMs, but also expose their applications in the judicial system. We first provide an overview of AI technologies in the legal field and showcase the recent research in LLMs. Then, we discuss the practical implementation presented by legal LLMs, such as providing legal advice to users and assisting judges during trials. In addition, we explore the limitations of legal LLMs, including data, algorithms, and judicial practice. Finally, we summarize practical recommendations and propose future development directions to address these challenges.
New 1,2,3-triazole/1,2,4-triazole hybrids linked to oxime moiety as nitric oxide donor selective COX-2, aromatase, B-RAFV600E and EGFR inhibitors celecoxib analogs: design, synthesis, anti-inflammatory/anti-proliferative activities, apoptosis and molecular modeling study

Abstract A new series of bis-triazole 19a-l was synthesised for the purpose of being hybrid molecules with both anti-inflammatory and anti-cancer activities and assessed for cell cycle arrest, NO release. Compounds 19c, 19f, 19h, 19 l exhibited COX-2 selectivity indexes in the range of 18.48 to 49.38 compared to celecoxib S.I. = 21.10), inhibit MCF-7 with IC50 = 9–16 μM compared to tamoxifen (IC50 = 27.9 μM). and showed good inhibitory activity against HEP-3B with IC50 = 4.5–14 μM compared to sorafenib (IC50 = 3.5 μM) (HEP-3B). Moreover, derivatives 19e, 19j, 19k, 19 l inhibit HCT-116 with IC50 = 5.3–13.7 μM compared to 5-FU with IC50 = 4.8 μM (HCT-116). Compounds 19c, 19f, 19h, 19 l showed excellent inhibitory activity against A549 with IC50 = 3–4.5 μM compared to 5-FU with IC50 = 6 μM (A549). Compounds 19c, 19f, 19h, 19 l inhibit aromatase (IC50 of 22.40, 23.20, 22.70, 30.30 μM), EGFR (IC50 of 0.112, 0.205, 0.169 and 0.066 μM) and B-RAFV600E (IC50 of 0.09, 0.06, 0.07 and 0.05 μM).
Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception?

Despite the commercial abundance of UAVs, aerial data acquisition remains challenging, and the existing Asia and North America-centric open-source UAV datasets are small-scale or low-resolution and lack diversity in scene contextuality. Additionally, the color content of the scenes, solar-zenith angle, and population density of different geographies influence the data diversity. These two factors conjointly render suboptimal aerial-visual perception of the deep neural network (DNN) models trained primarily on the ground-view data, including the open-world foundational models. To pave the way for a transformative era of aerial detection, we present Multiview Aerial Visual RECognition or MAVREC, a video dataset where we record synchronized scenes from different perspectives -- ground camera and drone-mounted camera. MAVREC consists of around 2.5 hours of industry-standard 2.7K resolution video sequences, more than 0.5 million frames, and 1.1 million annotated bounding boxes. This makes MAVREC the largest ground and aerial-view dataset, and the fourth largest among all drone-based datasets across all modalities and tasks. Through our extensive benchmarking on MAVREC, we recognize that augmenting object detectors with ground-view images from the corresponding geographical location is a superior pre-training strategy for aerial detection. Building on this strategy, we benchmark MAVREC with a curriculum-based semi-supervised object detection approach that leverages labeled (ground and aerial) and unlabeled (only aerial) images to enhance the aerial detection. We publicly release the MAVREC dataset: https://mavrec.github. io.
Associations between liver function and cerebrospinal fluid biomarkers of Alzheimer's disease pathology in non‐demented adults: The CABLE study

Liver function has been suggested as a possible factor in the progression of Alzheimer's disease (AD) development. However, the association between liver function and cerebrospinal fluid (CSF) levels of AD biomarkers remains unclear. In this study, we analyzed the data from 1687 adults without dementia from the Chinese Alzheimer's Biomarker and LifestylE study to investigate differences in liver function between pathological and clinical AD groups, as defined by the 2018 National Institute on Aging‐Alzheimer's Association Research Framework. We also examined the linear relationship between liver function, CSF AD biomarkers, and cognition using linear regression models. Furthermore, mediation analyses were applied to explore the potential mediation effects of AD pathological biomarkers on cognition. Our findings indicated that, with AD pathological and clinical progression, the concentrations of total protein (TP), globulin (GLO), and aspartate aminotransferase/alanine transaminase (ALT) increased, while albumin/globulin (A/G), adenosine deaminase, alpha‐L‐fucosidase, albumin, prealbumin, ALT, and glutamate dehydrogenase (GLDH) concentrations decreased. Furthermore, we also identified significant relationships between TP (β = −0.115, pFDR < 0.001), GLO (β = −0.184, pFDR < 0.001), and A/G (β = 0.182, pFDR < 0.001) and CSF β‐amyloid1–42 (Aβ1–42) (and its related CSF AD biomarkers). Moreover, after 10 000 bootstrapped iterations, we identified a potential mechanism by which TP and GLDH may affect cognition by mediating CSF AD biomarkers, with mediation effect sizes ranging from 3.91% to 16.44%. Overall, our results suggested that abnormal liver function might be involved in the clinical and pathological progression of AD. Amyloid and tau pathologies also might partially mediate the relationship between liver function and cognition. Future research is needed to fully understand the underlying mechanisms and causality to develop an approach to AD prevention and treatment approach.
Profiling fake news spreaders on Twitter using BERT and Naive Bayes model

This paper studies a method and model used to profile fake news spreaders. The main task is to determine, based on the Twitter text, whether its author is keen to be a purveyor of fake news. It aims to distinguish profiling fake news spreaders on social as the first step to prevent Fake news from spreading among online users. This paper regards this task as a binary task, merging the Twitter information of each author, using the BERT pre-training language model to extract semantic features of English and Spanish Twitter news, and combining the credibility rating features of Twitter information to jointly make predictions. The accuracy of this classification model on the English data set is 0.7400, and the accuracy of the Spanish classification is 0.8150.
Potential of GIS and Spatial Knowledge in Health Care and Public Safety

Geographical Information Systems (GIS) are a relatively new tool in health care services and organizations. However, health-care professionals who know how to utilize GIS and other spatial tools get a powerful decision support tool. This chapter presents an overview of the GIS and spatial simulation in the health care environment. In the first section, an introduction to the situation is provided. Then, in the second section, the key terms are introduced: access in health care, GIS, and spatial simulation. In the third section, different cases where GIS supports decision making in the health care services are shown. In the fourth section, two examples of spatial simulation are shown. Finally, future research directions and conclusions are discussed.
Non-canonical NF-κB signaling promotes intestinal inflammation by restraining the tolerogenic β-catenin-Raldh2 axis in dendritic cells

Dendritic cell (DC) dysfunctions exacerbate intestinal pathologies. However, the mechanisms compromising DC-mediated immune controls remain unclear. We found that intestinal DCs from mice subjected to experimental colitis possessed heightened non-canonical NF-κB signaling, which activates the RelB:p52 heterodimer. Genetic inactivation of this pathway in DCs alleviated inflammation in colitogenic mice. Unexpectedly, RelB:p52 deficiency diminished the transcription of Axin1, a critical component of the β-catenin destruction complex. This reinforced β-catenin-driven expression of Raldh2, which imparts tolerogenic DC attributes by promoting retinoic acid (RA) synthesis. Indeed, DC-specific non-canonical NF-κB impairment improved the colonic frequency of Tregs and IgA+ B cells, which fostered luminal IgA and eubiosis. Introducing β-catenin haploinsufficiency in non-canonical NF-κB-deficient DCs moderated Raldh2 activity, reinstating colitogenic sensitivity in mice. Finally, IBD patients displayed a deleterious non-canonical NF-κB signature in intestinal DCs. In sum, we establish a DC network that integrates non-canonical NF-κB signaling to subvert RA metabolic pathway in fueling intestinal inflammation. Significance (100) Distorted dendritic cell (DC) functions have been implicated in aberrant intestinal inflammation; however, the underlying mechanism remains obscure. We discovered that the non-canonical NF-κB pathway exacerbates inflammation in the colitogenic gut by downmodulating β-catenin-driven synthesis of Raldh2 in DCs. Raldh2 represents a key enzyme involved in the production of tolerogenic retinoic acid in intestinal DCs. Beyond regulating immune genes, therefore, non-canonical NF-κB signaling appears to instruct retinoic acid-mediated control of gut health. While we illustrate a DC network integrating immune signaling and micronutrient metabolic pathways in the intestine, our finding may have broad relevance for nutritional interventions in inflammatory ailments. eToC Deka and Kumar et al. illustrate a DC-circuitry that exacerbates intestinal inflammation in IBD patients and colitogenic mice. Non-canonical NF-κB signaling restrains β-catenin in DCs to downmodulate Raldh2, which promotes tolerogenic RA synthesis, leading to diminished Treg and IgA+ cell frequencies in the gut. Highlights Aberrant intestinal inflammation is associated with and exacerbated by non-canonical NF-κB signaling in DCs. Non-canonical signaling restrains the tolerogenic β-catenin-Raldh2 axis in DCs by upregulating Axin1. DC-specific RelB:p52 impairment promotes β-catenin-dependent Treg accumulation in the gut. A DC defect of non-canonical signaling causes β-catenin-dependent increase in luminal sIgA, fostering the gut microbiome. One sentence The non-canonical NF-κB pathway fuels intestinal inflammation by waning the tolerogenic β-catenin-Raldh2-retinoic acid axis in DCs.
Translating from mice to humans: using preclinical blood-based biomarkers for the prognosis and treatment of traumatic brain injury

Rodent models are important research tools for studying the pathophysiology of traumatic brain injury (TBI) and developing potential new therapeutic interventions for this devastating neurological disorder. However, the failure rate for the translation of drugs from animal testing to human treatments for TBI is 100%, perhaps due, in part, to distinct timescales of pathophysiological processes in rodents versus humans that impedes translational advancements. Incorporating clinically relevant biomarkers in preclinical studies may provide an opportunity to calibrate preclinical models to human TBI biomechanics and pathophysiology. To support this important translational goal, we conducted a systematic literature review of preclinical TBI studies in rodents measuring blood levels of clinically used NfL, t-Tau, p-Tau, UCH-L1, or GFAP, published in PubMed/MEDLINE up to June 13th, 2023. We focused on blood biomarker temporal trajectories and their predictive and pharmacodynamic value and discuss our findings in the context of the latest clinical TBI biomarker data. Out of 369 original studies identified through the literature search, 71 met the inclusion criteria, with a median quality score on the CAMARADES checklist of 5 (interquartile range 4-7). NfL was measured in 17 preclinical studies, GFAP in 41, t-Tau in 17, p-Tau in 7, and UCH-L1 in 19 preclinical studies. Data in rodent models show that all blood biomarkers exhibited injury severity-dependent elevations, with GFAP and UCH-L1 peaking within hours after TBI, NfL peaking within days after TBI and remaining elevated up to 6 months post-injury, whereas t-Tau and p-Tau levels were gradually increased many weeks after TBI. Blood NfL levels emerges as a prognostic indicator of white matter loss after TBI, while both NfL and GFAP hold promise for pharmacodynamic studies of neuroprotective treatments. Therefore, blood-based preclinical biomarker trajectories could serve as important anchor points that may advance translational research in the TBI field. However, further investigation into biomarker levels in the subacute and chronic phases will be needed to more clearly define pathophysiological mechanisms and identify new therapeutic targets for TBI.
On-shell approach to (spinning) gravitational absorption processes

We utilize three point amplitudes with (spinning) particles of unequal mass and a graviton to capture the dynamics of absorption processes. We demonstrate that the construction can represent the spheroidal harmonics appearing in the Teukolsky equations. The absolute square of the ``Wilson coefficients'' in this effective description can be fixed by matching to the known absorptive cross-sections. As an application, we compute corrections to the gravitational Compton amplitude from the exchange of states corresponding to such absorption effects. In the super-extremal limit, the corrections generate the non-analytic $|a|$-dependent contribution of the Compton amplitude found in ref. \cite{Bautista:2022wjf}.
Torsion-simple objects in abelian categories

We introduce the notion of torsion-simple objects in an abelian category: these are the objects which are always either torsion or torsion-free with respect to any torsion pair. We present some general results concerning their properties, and then proceed to investigate the notion in various contexts, such as the category of modules over an artin algebra or a commutative noetherian ring, and the category of quasi-coherent sheaves over the projective line.
On the Destabilization of High-Mass Neutron Stars by the Emergence of $d^*$-Hexaquarks

We study the effects of the first nontrivial hexaquark, $d^*$(2380), on the equation of state of dense neutron star matter and investigate the consequences of its existence for neutron stars. The matter in the core regions of neutron stars is described using density-dependent relativistic mean-field theory. Our results show that within the parameter spaces examined in our paper, (i) the critical density at which the $d^*$ condensate emerges lies between 4 and 5 times the nuclear saturation density, (ii) $d^*$ hexaquarks are found to exist only in rather massive neutron stars, (iii) only relatively small fractions of the matter in the core of a massive neutron star may contain hexaquarks.
The value of plasma metanephrine measurements during adrenal vein sampling

Objective The assessment of primary aldosteronism incorporates adrenal vein sampling (AVS) to lateralize aldosterone excess. Current adrenal vein sampling protocols rely on concurrent cortisol measurements to assess successful cannulation and lateralization and may be inaccurate in the setting of autonomous cortisol secretion. We aimed to compare the measurement of plasma cortisol and metanephrine concentrations to assess cannulation and lateralization during AVS. Design This is a diagnostic accuracy study in a tertiary referral endocrinology department. Methods Forty-one consecutive patients with confirmed primary aldosteronism undergoing AVS (49 procedures) were included. None had cortisol autonomy. The use of plasma metanephrine-based ratios were compared with standard cortisol-based ratios to assess cannulation and lateralization during ACTH-stimulated AVS. Results There was strong agreement between a cortisol selectivity index (SI) ≥5.0 and an adrenal vein (AV) to peripheral vein (PV) plasma metanephrine ratio (AVmet–PVmet) of ≥12.0 to indicate successful cannulation of the AV (n = 117, sensitivity 98%, specificity 89%, positive predictive value (PPV) 95%, negative predictive value (NPV) 94%). There was strong agreement between the standard cortisol-based SI and an AV plasma metanephrine-to-normetanephrine ratio (AVmet–AVnormet) of ≥2.0 to indicate successful cannulation (n = 117, sensitivity 93%, specificity 86%, PPV 94%, NPV 84%). There was strong agreement between the cortisol- or metanephrine-derived lateralization index (LI) > 4.0 for determining lateralization (n = 26, sensitivity 100%, specificity 94.1%, PPV 91.6%, NPV 100%). Conclusions Ratios incorporating plasma metanephrines provide comparable outcomes to standard cortisol-based measurements for interpretation of AVS. Further studies are required to assess the use of metanephrine-derived ratios in the context of confirmed cortisol autonomy. Significance statement Primary aldosteronism is a common cause of secondary hypertension, and adrenal vein sampling remains the gold standard test to assess lateralization. Cortisol-derived ratios to assess cannulation and lateralization may be affected by concurrent cortisol dysfunction, which is not uncommon in the context of primary aldosteronism. Our study showed comparable outcomes when using accepted cortisol-derived or metanephrine-derived ratios to determine cannulation and lateralization during adrenal vein sampling. Further research is required to validate these findings and to assess the use of metanephrine-derived ratios in the context of confirmed concurrent cortisol dysfunction.
Disentangling Fact from Grid Cell Fiction in Trained Deep Path Integrators

Work on deep learning-based models of grid cells suggests that grid cells generically and robustly arise from optimizing networks to path integrate, i.e., track one’s spatial position by integrating self-velocity signals. In previous work [27], we challenged this path integration hypothesis by showing that deep neural networks trained to path integrate almost always do so, but almost never learn grid-like tuning unless separately inserted by researchers via mechanisms unrelated to path integration. In this work, we restate the key evidence substantiating these insights, then address a response to [27] by authors of one of the path integration hypothesis papers [32]. First, we show that the response misinterprets our work, indirectly confirming our points. Second, we evaluate the response’s preferred “unified theory for the origin of grid cells” in trained deep path integrators [31, 33, 34] and show that it is at best “occasionally suggestive,” not exact or comprehensive. We finish by considering why assessing model quality through prediction of biological neural activity by regression of activity in deep networks [23] can lead to the wrong conclusions.
MIMo: A Multi-Modal Infant Model for Studying Cognitive Development

Human intelligence and human consciousness emerge gradually during the process of cognitive development. Understanding this development is an essential aspect of understanding the human mind and may facilitate the construction of artificial minds with similar properties. Importantly, human cognitive development relies on embodied interactions with the physical and social environment, which is perceived via complementary sensory modalities. These interactions allow the developing mind to probe the causal structure of the world. This is in stark contrast to common machine learning approaches, e.g., for large language models, which are merely passively ``digesting'' large amounts of training data, but are not in control of their sensory inputs. However, computational modeling of the kind of self-determined embodied interactions that lead to human intelligence and consciousness is a formidable challenge. Here we present MIMo, an open-source multi-modal infant model for studying early cognitive development through computer simulations. MIMo's body is modeled after an 18-month-old child with detailed five-fingered hands. MIMo perceives its surroundings via binocular vision, a vestibular system, proprioception, and touch perception through a full-body virtual skin, while two different actuation models allow control of his body. We describe the design and interfaces of MIMo and provide examples illustrating its use. All code is available at https://github.com/trieschlab/MIMo .
Fundamental groups of log Calabi-Yau surfaces

In this article, we study the orbifold fundamental group $\pi_1^{\rm reg}(X,\Delta)$ of a log Calabi--Yau pair $(X,\Delta)$. We conjecture that the orbifold fundamental group $\pi_1^{\rm orb}(X,\Delta)$ of a $n$-dimensional log Calabi--Yau pair admits a normal solvable subgroup of rank at most $2n$ and index at most $c(n)$. We prove this conjecture in the case that $n=2$. More precisely, for a log Calabi--Yau surface pair $(X,\Delta)$ we show that $\pi_1^{\rm orb}(X,\Delta)$ is the extension of a nilpotent group of length $2$ and rank at most $4$ by a finite group of order at most $7200$. In the previous setting, we show that the group $\pi_1^{\rm orb}(X,\Delta)$ may not be virtually abelian. Further, the rank and the order stated above are optimal. Finally, we show some geometric features of log Calabi--Yau surfaces $(X,\Delta)$ for which $\pi_1^{\rm orb}(X,\Delta)$ is infinite.