Miracle-dz/UniFAR-FTUs huggingface dataset:
From haemadin to haemanorm: Synthesis and characterization of full‐length haemadin from the leech Haemadipsa sylvestris and of a novel bivalent, highly potent thrombin inhibitor (haemanorm)

Hirudin from Hirudo medicinalis is a bivalent α‐Thrombin (αT) inhibitor, targeting the enzyme active site and exosite‐I, and is currently used in anticoagulant therapy along with its simplified analogue hirulog. Haemadin, a small protein (57 amino acids) isolated from the land‐living leech Haemadipsa sylvestris, selectively inhibits αT with a potency identical to that of recombinant hirudin (KI = 0.2 pM), with which it shares a common disulfide topology and overall fold. At variance with hirudin, haemadin targets exosite‐II and therefore (besides the free protease) it also blocks thrombomodulin‐bound αT without inhibiting the active intermediate meizothrombin, thus offering potential advantages over hirudin. Here, we produced in reasonably high yields and pharmaceutical purity (>98%) wild‐type haemadin and the oxidation resistant Met5 → nor‐Leucine analogue, both inhibiting αT with a KI of 0.2 pM. Thereafter, we used site‐directed mutagenesis, spectroscopic, ligand‐displacement, and Hydrogen/Deuterium Exchange‐Mass Spectrometry techniques to map the αT regions relevant for the interaction with full‐length haemadin and with the synthetic N‐ and C‐terminal peptides Haem(1–10) and Haem(45–57). Haem(1–10) competitively binds to/inhibits αT active site (KI = 1.9 μM) and its potency was enhanced by 10‐fold after Phe3 → β‐Naphthylalanine exchange. Conversely to full‐length haemadin, haem(45–57) displays intrinsic affinity for exosite‐I (KD = 1.6 μM). Hence, we synthesized a peptide in which the sequences 1–9 and 45–57 were joined together through a 3‐Glycine spacer to yield haemanorm, a highly potent (KI = 0.8 nM) inhibitor targeting αT active site and exosite‐I. Haemanorm can be regarded as a novel class of hirulog‐like αT inhibitors with potential pharmacological applications.
Nursery habitats for ladyfi sh, Elops saurus, along salinity gradients in two Florida estuaries

Many fi sh species use estuaries of the southeastern United States as nurseries (e.g. Skud and Wilson, 1960; Gunter, 1967), but coastal habitat degradation threatens many of the economically important fi sheries that rely on estuaries (Gilmore, 1995). The re-enacted Magnuson-Stevens Fishery Conservation and Management Act (MSFCMA) was developed to protect or enhance fi sheries habitats, by fi rst requiring information regarding the value of coastal habitats to the survival of marine organisms (e.g. Schmitten, 1996). A simple approach to ranking relative habitat value is to compare intraspecifi c fi sh distributions with respect to habitat in different estuaries. Associations between abundance and habitat can assist in predicting the response of coastal fi sh populations to changes in these habitats. Remarkably, such information is rarely available except for the most economically valuable species (Haedrich, 1983). Freshwater infl ows to estuaries of the southeast United States have been severely altered in the last 150 years and coastal development continues to divert more water away from estuaries (Stickney, 1984). The MSFCMA’s Essential Fish Habitat mandate provides a policy framework for identifying the effects of reduced freshwater infl ows on estuarine-dependent species. Yet researchers and managers often characterize species as estuarine-dependent more on intuition than rigorous examination of data. Able and Fahay (1998) outlined three criteria for defi ning estuarine-dependence: 1) predictable use Nursery habitats for ladyfi sh, Elops saurus, along salinity gradients in two Florida estuaries
Unawareness of Primary Aldosteronism as a Common Cause of Hypokalemia – Insights from the IPAHK + Trial (Incidence of Primary Aldosteronism in Patients with Hypokalemia)

Abstract Hypokalemia plays an important role in the diagnosis and management of primary aldosteronism (PA). While the hypokalemic variant of the disease accounts for about one third of all cases, little is known about the incidence of PA in hypokalemic populations. The IPAHK+ study is an epidemiological, cross-sectional trial to provide evidence on the incidence of PA in hypokalemic patients from a university hospital outpatient population. Recruitment of outpatients with hypokalemia≤3 mmol/l is carried out on a continuous referral-basis through an automated data delivery system. Up to an interim data closure, 66 patients underwent the study protocol. The mean age of the participants was 52.9±1.5 years with an equal sex ratio of 1:1 women to men, a mean potassium value of 2.78±0.31 mmol/l [1.8;3.0] and a prevalence of arterial hypertension of 72.7%. PA was diagnosed in 46.6% of all participants, all of whom had a history of hypertension. Incidence of PA increased continuously with decreasing potassium levels with proportions of 26.7%, 50% and 57.1% in the subgroups of 3.0 mmol/l (n=15), 2.8–2.9 mmol/l (n=22) and≤2.7 mmol/l (n=21), respectively. Prior to testing, 59.1% of all patients presented at least with one plausible other cause of hypokalemia. The incidence of PA in the investigated outpatient population was more than 4 out of 10 and inversely correlated with baseline potassium levels. Moderate or severe hypokalemia, regardless of its cause, should therefore prompt evaluation for PA in hypertensive individuals. Normotensive hypokalemic PA was not observed in this cohort.
Defining phases of bedload transport using piecewise regression

Differences in the transport rate and size of bedload exist for varying levels of flow in coarse‐grained channels. For gravel‐bed rivers, at least two phases of bedload transport, with notably differing qualities, have been described in the literature. Phase I consists primarily of sand and small gravel moving at relatively low rates over a stable channel surface. Transport rates during Phase II are considerably greater than Phase I and more coarse grains are moved, including material from both the channel surface and subsurface. Transition from Phase I to Phase II indicates initiation and transport of grains comprising the coarse surface layer common in steep mountain channels. While the existence of different phases of transport is generally acknowledged, the threshold between them is often poorly defined. We present the results of the application of a piecewise regression analysis to data on bedload transport collected at 12 gravel‐bed channels in Colorado and Wyoming, USA. The piecewise regression recognizes the existence of different linear relationships over different ranges of discharge. The inflection, where the fitted functions intersect, is interpreted as the point of transition from Phase I to Phase II transport; this is termed breakpoint. A comparison of grain sizes moved during the two phases shows that coarse gravel is rarely trapped in the samplers during Phase I transport, indicating negligible movement of grains in this size range. Gravel larger than about D16 of the channel surface is more consistently trapped during Phase II transport. The persistence of coarse gravel in bedload samples provides good evidence that conditions suitable for coarse grain transport have been reached, even though the size of the sediment approaches the size limits of the sampler (76 mm in all cases). A relative breakpoint (Rbr) was defined by the ratio between the discharge at the breakpoint and the 1·5‐year flow (a surrogate for bankfull discharge) expressed as a percentage. The median value of Rbr was about 80 percent, suggesting that Phase II begins at about 80 percent of the bankfull discharge, though the observed values of Rbr ranged from about 60 to 100 percent. Variation in this value appears to be independent of drainage area, median grain size, sorting of bed materials, and channel gradient, at least for the range of parameters measured in 12 gravel‐bed channels. Published in 2002 by John Wiley & Sons, Ltd.
HYDROMETEOROLOGICAL CONDITIONS AND THEIR RELATION TO LANDSLIDE PROCESSES IN THE POLISH FLYSCH CARPATHIANS (AN EXAMPLE OF SZYMBARK AREA)

Hydrometeorological conditions in Szymbark region are analysed, and then the influence of these conditions on the displacements of experimental landslide slopes is discussed. The investiga- tions, on which the analysis is based, were carried out in the period of 09.2004-08.2007. Relations betwe- en precipitation totals and fluctuations of ground water level as well as between these characteristics and underground displacements measured on experimental slopes by inclinometric method were con- sidered.
Correlations between inflammatory cytokine levels and degree of pigmentation in acral melanomas

Cutaneous melanoma, a highly aggressive skin tumor, is characterized by complex signaling pathways in terms of its pathogenesis and progression. Although the degree of pigmentation in melanoma determines its progression, metastasis, and prognosis, its association with inflammatory cytokines remains unclear. Thus, we evaluated the associations between melanoma pigmentation and plasma levels of inflammatory cytokines; furthermore, we investigated the potential variations in this relationship across the primary anatomic sites of melanoma. We enrolled patients with cutaneous melanoma who visited Chonnam National University Hwasun Hospital between January 2021 and December 2021. The anatomical sites of melanoma were categorized as acral and non-acral sites. The degree of pigmentation was quantified using computer software. In total, nine inflammatory cytokines were analyzed, including interleukin (IL)-2, IL-4, IL-5, IL-10, IL-12, IL-13, granulocyte-macrophage colony-stimulating factor (GM-CSF), interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α). This study included 80 melanoma patients. Of these, 53 had acral melanoma and 27 had non-acral melanoma. Overall, plasma concentrations of IL-2, IL-4, IL-5, GM-CSF, and IFN-γ demonstrated significant correlations with diminished pigmentation. Furthermore, in the acral melanoma patients group, plasma concentrations of IL-2, IL-4, IL-5, GM-CSF, IFN-γ, and TNF-α revealed significant correlations with diminished pigmentation. Our results reveal significant associations between melanoma pigmentation and various cytokine levels, particularly in acral melanoma patients; these associations can be influenced by factors related to acral melanoma, such as physical stress or trauma. These correlations may also provide directions for the treatment of acral melanoma.
ATGNN: Audio Tagging Graph Neural Network

Deep learning models such as CNNs and Transformers have achieved impressive performance for end-to-end audio tagging. Recent works have shown that despite stacking multiple layers, the receptive field of CNNs remains severely limited. Transformers on the other hand are able to map global context through self-attention, but treat the spectrogram as a sequence of patches which is not flexible enough to capture irregular audio objects. In this letter, we treat the spectrogram in a more flexible way by considering it as graph structure and process it with a novel graph neural architecture called ATGNN. ATGNN not only combines the capability of CNNs with the global information sharing ability of Graph Neural Networks, but also maps semantic relationships between learnable class embeddings and corresponding spectrogram regions. We evaluate ATGNN on two audio tagging tasks, where it achieves 0.585 mAP on the FSD50 K dataset and 0.335 mAP on the AudioSet-balanced dataset, achieving comparable results to Transformer based models with significantly lower number of learnable parameters.
Approximate Multiagent Reinforcement Learning for On-Demand Urban Mobility Problem on a Large Map (extended version)

In this paper, we focus on the autonomous multiagent taxi routing problem for a large urban environment where the location and number of future ride requests are unknown a-priori, but can be estimated by an empirical distribution. Recent theory has shown that a rollout algorithm with a stable base policy produces a near-optimal stable policy. In the routing setting, a policy is stable if its execution keeps the number of outstanding requests uniformly bounded over time. Although, rollout-based approaches are well-suited for learning cooperative multiagent policies with considerations for future demand, applying such methods to a large urban environment can be computationally expensive due to the large number of taxis required for stability. In this paper, we aim to address the computational bottleneck of multiagent rollout by proposing an approximate multiagent rollout-based two phase algorithm that reduces computational costs, while still achieving a stable near-optimal policy. Our approach partitions the graph into sectors based on the predicted demand and the maximum number of taxis that can run sequentially given the user's computational resources. The algorithm then applies instantaneous assignment (IA) for re-balancing taxis across sectors and a sector-wide multiagent rollout algorithm that is executed in parallel for each sector. We provide two main theoretical results: 1) characterize the number of taxis $m$ that is sufficient for IA to be stable; 2) derive a necessary condition on $m$ to maintain stability for IA as time goes to infinity. Our numerical results show that our approach achieves stability for an $m$ that satisfies the theoretical conditions. We also empirically demonstrate that our proposed two phase algorithm has equivalent performance to the one-at-a-time rollout over the entire map, but with significantly lower runtimes.
Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations Datasets

The evaluation of the fidelity of eXplainable Artificial Intelligence (XAI) methods to their underlying models is a challenging task, primarily due to the absence of a ground truth for explanations. However, assessing fidelity is a necessary step for ensuring a correct XAI methodology. In this study, we conduct a fair and objective comparison of the current state-of-the-art XAI methods by introducing three novel image datasets with reliable ground truth for explanations. The primary objective of this comparison is to identify methods with low fidelity and eliminate them from further research, thereby promoting the development of more trustworthy and effective XAI techniques. Our results demonstrate that XAI methods based on the backpropagation of output information to input yield higher accuracy and reliability compared to methods relying on sensitivity analysis or Class Activation Maps (CAM). However, the backpropagation method tends to generate more noisy saliency maps. These findings have significant implications for the advancement of XAI methods, enabling the elimination of erroneous explanations and fostering the development of more robust and reliable XAI.
Should Under-parameterized Student Networks Copy or Average Teacher Weights?

Any continuous function $f^*$ can be approximated arbitrarily well by a neural network with sufficiently many neurons $k$. We consider the case when $f^*$ itself is a neural network with one hidden layer and $k$ neurons. Approximating $f^*$ with a neural network with $n<k$ neurons can thus be seen as fitting an under-parameterized"student"network with $n$ neurons to a"teacher"network with $k$ neurons. As the student has fewer neurons than the teacher, it is unclear, whether each of the $n$ student neurons should copy one of the teacher neurons or rather average a group of teacher neurons. For shallow neural networks with erf activation function and for the standard Gaussian input distribution, we prove that"copy-average"configurations are critical points if the teacher's incoming vectors are orthonormal and its outgoing weights are unitary. Moreover, the optimum among such configurations is reached when $n-1$ student neurons each copy one teacher neuron and the $n$-th student neuron averages the remaining $k-n+1$ teacher neurons. For the student network with $n=1$ neuron, we provide additionally a closed-form solution of the non-trivial critical point(s) for commonly used activation functions through solving an equivalent constrained optimization problem. Empirically, we find for the erf activation function that gradient flow converges either to the optimal copy-average critical point or to another point where each student neuron approximately copies a different teacher neuron. Finally, we find similar results for the ReLU activation function, suggesting that the optimal solution of underparameterized networks has a universal structure.
A Proof of the Kashaev Signature Conjecture

In 2018 Kashaev introduced a diagrammatic link invariant conjectured to be twice the Levine-Tristram signature. If true, the conjecture would provide a simple way of computing the Levine-Tristram signature of a link by taking the signature of a real symmetric matrix associated with a corresponding link diagram. This paper establishes the conjecture using the Seifert surface definition of the Levine-Tristram signature on the disjoint union of an oriented link and its reverse. The proof also reveals yet another formula for the Alexander polynomial.
Topological inference on brain networks across subtypes of post-stroke aphasia

Persistent homology (PH) characterizes the shape of brain networks through the persistence features. Group comparison of persistence features from brain networks can be challenging as they are inherently heterogeneous. A recent scale-space representation of persistence diagram (PD) through heat diffusion reparameterizes using the finite number of Fourier coefficients with respect to the Laplace-Beltrami (LB) eigenfunction expansion of the domain, which provides a powerful vectorized algebraic representation for group comparisons of PDs. In this study, we advance a transposition-based permutation test for comparing multiple groups of PDs through the heat-diffusion estimates of the PDs. We evaluate the empirical performance of the spectral transposition test in capturing within- and between-group similarity and dissimilarity with respect to statistical variation of topological noise and hole location. We also illustrate how the method extends naturally into a clustering scheme by subtyping individuals with post-stroke aphasia through the PDs of their resting-state functional brain networks.
Trust-Preserved Human-Robot Shared Autonomy enabled by Bayesian Relational Event Modeling

Shared autonomy functions as a flexible framework that empowers robots to operate across a spectrum of autonomy levels, allowing for efficient task execution with minimal human oversight. However, humans might be intimidated by the autonomous decision-making capabilities of robots due to perceived risks and a lack of trust. This paper proposed a trust-preserved shared autonomy strategy that allows robots to seamlessly adjust their autonomy level, striving to optimize team performance and enhance their acceptance among human collaborators. By enhancing the relational event modeling framework with Bayesian learning techniques, this paper enables dynamic inference of human trust based solely on time-stamped relational events communicated within human-robot teams. Adopting a longitudinal perspective on trust development and calibration in human-robot teams, the proposed trust-preserved shared autonomy strategy warrants robots to actively establish, maintain, and repair human trust, rather than merely passively adapting to it. We validate the effectiveness of the proposed approach through a user study on a human-robot collaborative search and rescue scenario. The objective and subjective evaluations demonstrate its merits on both task execution and user acceptability over the baseline approach that does not consider the preservation of trust.
Mitigating Framing Bias with Polarity Minimization Loss

Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events. Media outlets with divergent political stances often use polarized language in their reporting of the same event. We propose a new loss function that encourages the model to minimize the polarity difference between the polarized input articles to reduce framing bias. Specifically, our loss is designed to jointly optimize the model to map polarity ends bidirectionally. Our experimental results demonstrate that incorporating the proposed polarity minimization loss leads to a substantial reduction in framing bias when compared to a BART-based multi-document summarization model. Notably, we find that the effectiveness of this approach is most pronounced when the model is trained to minimize the polarity loss associated with informational framing bias (i.e., skewed selection of information to report).
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation

Recently, large-scale pre-trained models such as Segment-Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP) have demonstrated remarkable success and revolutionized the field of computer vision. These foundation vision models effectively capture knowledge from a large-scale broad data with their vast model parameters, enabling them to perform zero-shot segmentation on previously unseen data without additional training. While they showcase competence in 2D tasks, their potential for enhancing 3D scene understanding remains relatively unexplored. To this end, we present a novel framework that adapts various foundational models for the 3D point cloud segmentation task. Our approach involves making initial predictions of 2D semantic masks using different large vision models. We then project these mask predictions from various frames of RGB-D video sequences into 3D space. To generate robust 3D semantic pseudo labels, we introduce a semantic label fusion strategy that effectively combines all the results via voting. We examine diverse scenarios, like zero-shot learning and limited guidance from sparse 2D point labels, to assess the pros and cons of different vision foundation models. Our approach is experimented on ScanNet dataset for 3D indoor scenes, and the results demonstrate the effectiveness of adopting general 2D foundation models on solving 3D point cloud segmentation tasks.
Satellite mapping of conservation tillage adoption in the Little River experimental watershed, Georgia

Conservation tillage is a commonly adopted best management practice for improving soil quality and reducing erosion. However, there are currently no methods in place to monitor conservation tillage adoption at the watershed scale. The primary objective of this study was to evaluate the usefulness of Landsat TM data as a tool to depict conservation tillage in a small Coastal Plain watershed. Satellite imagery was used to calculate four commonly used indices: Normalized Difference Vegetation Index, Crop Residue Cover Index, Normalized Difference Tillage Index, and the Simple Tillage Index. Ground truth data consisted of a windshield survey, assigning each site a tillage regime (conventional or conservation tillage) at 138 locations throughout the watershed and surrounding areas. A logistical regression approach was used on two subsets of the data set (n = 20 or n = 44) to determine the influence of the number of ground control points on the success of modeling the occurrence of conservation tillage. The most accurate model was re-applied to the satellite image and evaluated using an independent sample of 94 survey sites. Results indicate that the normalized difference tillage and simple tillage indices performed best, with an overall accuracy of 71% and 78% for models developed using n = 20 and n = 44 sample locations, respectively. Errors were typically in the form of commission. Results are encouraging and suggest that currently available satellite imagery can be used for rapid assessment of conservation tillage adoption using minimal a priori information.