Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Cardiac Dose on Overall Survival in Lung Stereotactic Body Radiotherapy (SBRT) Compared to Conventionally Fractionated Radiotherapy for Locally Advanced Non-Small Cell Lung Cancer (LA-NSCLC)

Purpose: To examine possible association between heart irradiation and Overall Survival (OS) in lung SBRT patients and to compare observed associations with cardiac toxicity models previously derived in LA-NSCLC patient studies. Materials and Methods: 197 Patients treated with lung SBRT at Mayo Clinic Arizona were selected for this IRB-approved study. Multivariate Cox model with Akaike Information Criterion (AIC) was used to select patient specific covariates associated with OS. Heart dosimetry was represented by VD indices, which is a percentage of volume exposed to dose D or greater. Multivariate Cox models with patient specific covariates and single VD index per model was used to find a range of doses which were predictive for OS. A digital subdivision of the heart was further used to determine the spatial distribution of doses which were predictive for OS. A coarse subdivision divided heart into 4 segments, while the fine subdivision divided heart into 64 segments. Knowledge constrained Fused Lasso operator was used to derive a more complete model which correlated heart dosimetry with OS. Results of statistical analysis were compared to predictions of a model of cardiac toxicity in LA-NSCLC patients. Results: Higher age (p < 0.001), higher stage (p < 0.001) and squamous cell histology (p = 0.001) were associated with reduced OS. Whole heart DVH analysis did not reveal associations between heart irradiation and reduced OS. Coarse subdivision of the heart into four segments revealed that the irradiation of two inferior segments of the heart with low doses was associated with reduced OS, V2Gy in the right-inferior segment (HR = 1.012/1%, p = 0.02), and V1Gy in the left-inferior segment (HR = 1.01/1%, p = 0.04). Maximum dose in the right-inferior segment of the heart was also associated with reduced OS (HR = 1.02/Gy, p = 0.02). Fine subdivision of the heart into 64 segments revealed that approximately 25% of heart volume in the inferior part of the heart (15/64 segments), when irradiated to doses in the 1 Gy - 5 Gy range, were predictive for reduced OS (HR = 1.01/1%, p = 0.01). A previously derived model of cardiac toxicity in LA-NSCLC patients did not predict a reduction of OS due to heart irradiation in lung SBRT patients, because of relatively low doses to the heart in most lung SBRT patients. Conclusions: Doses lower than 5 Gy in the inferior segments of the heart may be associated with reduced overall survival in patients treated for lung lesions with SBRT. Stage and histology of the disease, as well as patients’ age, were also associated with overall survival. Comparisons of cardiac toxicity patterns in LA-NSCLC patients and lung SBRT patients suggest different etiology of cardiac toxicity in the two groups.
The Second Skin of Seagrass Leaves: A Comparison of Microalgae Epiphytic Communities Between Two Different Species Across Two Seagrass Meadows in Lesser Sunda Islands

Epiphytes as the important features in the seagrass ecosystems have been studied widely, and their functions as a primary producer, influence rates of herbivory grazer, and prevent seagrass leaf from desiccation is well known. However, patterns and distribution among seagrasses especially in Indonesia, which was known as hotspot marine biodiversity is not well understood. Therefore, this study aimed to examined epiphytic assemblages on two seagrass species with different morphological and longevity, Enhalus acoroides and Cymodocea rotundata, in two different meadows (conservation area and non-conservation area) in Lesser Sunda Islands (Bali and Lombok). A total of 22 taxa of microalgae epiphytes species were identified from eight sites and 2 different species of seagrass. The highest number of collected species between class was from Bacillariophyceae (18), followed by Cyanophyceae (3) and Fragilariophyceae (1). Analysis of similarity (ANOSIM) revealed a significant difference of microalgae epiphytes assemblages between sites and seagrasses. Epiphytes assemblages in conservation area were more abundant than non-conservation area, both in Bali and Lombok. On seagrass comparison, Enhalus acoroides showed higher abundance of epiphytes assemblages than those on Cymodocea rotundata. Based on principal component analysis (PCA), this study highlights the microalgae epiphytic communities strongly influenced by seawater temperature, phosphate’s concentration, and pH in sediment. This study also demonstrated that the assemblages of microalgae epiphytic communities affected by differences of seagrass morphological and longevity.
Incidence and Risk Factors of Thrombocytopenia in Neonates Admitted with Surgical Disorders to Neonatal Intensive Care Unit of Tikur Anbessa Specialized Hospital: A One-Year Observational Prospective Cohort Study from a Low-Income Country

Background Thrombocytopenia is one of the most common hematologic disorders affecting neonates admitted to the neonatal intensive care unit. The aim of this study was to determine the incidence and associated risk factors of neonatal thrombocytopenia in neonates admitted with surgical disorders. Methods An observational prospective cohort study was conducted and all neonates admitted to neonatal intensive care unit of Tikur Anbessa Specialized Hospital with surgical disorders were included. Data were collected using a checklist and analyzed by SPSS version 23. Chi square test and independent sample t- test were used to assess the association among different variables. Results A total of 210 neonates were included in the study, out of which 56.2% were males. The incidence of thrombocytopenia was 55.8%. Among neonates with thrombocytopenia, 90.9% had late onset thrombocytopenia and half were in the severe range (<50,000/µL). The presence of sepsis (P = 0.000) and atresia (P = 0.000) were found to be significantly associated with the development of thrombocytopenia. The mean non feeding hours were found to be significantly longer for patients with thrombocytopenia (t [199], 5.81, P = 0.000). Conclusion The incidence of thrombocytopenia is high in our institution. Prevention methods towards neonatal sepsis should be given due emphasis.
Kava root extracts hinder prostate cancer development and tumorigenesis by involvement of dual inhibition of MAO-A and LSD1

Aim: Here, we aim to evaluate the chemopreventive efficacy of kava root extracts (KRE) in transgenic adenocarcinoma of the mouse prostate (TRAMP) mice and investigate potential molecular targets of kavalactones, the main components of kava. Methods: TRAMP mice were administrated with KRE formulated food for different periods of time, and then the incidences of high-grade prostatic intraepithelial neoplasia (HG-PIN) and adenocarcinomas and tumor burdens were compared between vehicle control and KRE food fed groups. In addition, the inhibitory effect of the KRE and kavalactones on monoamine oxidase A (MAO-A) and lysine-specific demethylase 1 (LSD1) enzyme activities were examined by commercially available inhibitor screening kits. Histone H3 lysine 9 dimethylation was also evaluated in prostate cancer cells and tumor tissues using Western blotting analysis. Results: Dietary feeding of 0.3% and 0.6% KRE to TRAMP mice from ages of 6 weeks to 12 weeks inhibited HG-PIN by 43.5% and 59.7%, respectively, and prostate adenocarcinoma by 53.5% and 66.4%, respectively. In addition, 0.6% KRE fed TRAMP mice from ages of 6 weeks to 24 weeks exhibited a significant reduction of genitourinary weight (a surrogate of tumor burden) by 54.5% and reduced body weight gain. Furthermore, the KRE and kavalactones showed a significant inhibition of LSD1 and MAO-A enzyme activities. Conclusion: Our results suggest that consumption of kava products through diet can delay prostate cancer development and progression and that kavalactones may be a new structure model for developing a potent dual inhibitor of LSD1 and MAO-A.
On some tensor inequalities based on the t-product

In this work, we investigate the tensor inequalities in the tensor t-product formalism. The inequalities involving tensor power are proved to hold similarly as standard matrix scenarios. We then focus on the tensor norm inequalities. The well-known arithmetic-geometric mean inequality, Hölder inequality, and Minkowski inequality are generalized to tensors. Furthermore, we obtain some t-eigenvalue inequalities.
Improving Inference from Simple Instruments through Compliance Estimation

Instrumental variables (IV) regression is widely used to estimate causal treatment effects in settings where receipt of treatment is not fully random, but there exists an instrument that generates exogenous variation in treatment exposure. While IV can recover consistent treatment effect estimates, they are often noisy. Building upon earlier work in biostatistics (Joffe and Brensinger, 2003) and relating to an evolving literature in econometrics (including Abadie et al., 2019; Huntington-Klein, 2020; Borusyak and Hull, 2020), we study how to improve the efficiency of IV estimates by exploiting the predictable variation in the strength of the instrument. In the case where both the treatment and instrument are binary and the instrument is independent of baseline covariates, we study weighting each observation according to its estimated compliance (that is, its conditional probability of being affected by the instrument), which we motivate from a (constrained) solution of the first-stage prediction problem implicit to IV. The resulting estimator can leverage machine learning to estimate compliance as a function of baseline covariates. We derive the large-sample properties of a specific implementation of a weighted IV estimator in the potential outcomes and local average treatment effect (LATE) frameworks, and provide tools for inference that remain valid even when the weights are estimated nonparametrically. With both theoretical results and a simulation study, we demonstrate that compliance weighting meaningfully reduces the variance of IV estimates when first-stage heterogeneity is present, and that this improvement often outweighs any difference between the compliance-weighted and unweighted IV estimands. These results suggest that in a variety of applied settings, the precision of IV estimates can be substantially improved by incorporating compliance estimation.
Understanding the Computational Demands Underlying Visual Reasoning

Abstract Visual understanding requires comprehending complex visual relations between objects within a scene. Here, we seek to characterize the computational demands for abstract visual reasoning. We do this by systematically assessing the ability of modern deep convolutional neural networks (CNNs) to learn to solve the synthetic visual reasoning test (SVRT) challenge, a collection of 23 visual reasoning problems. Our analysis reveals a novel taxonomy of visual reasoning tasks, which can be primarily explained by both the type of relations (same-different versus spatial-relation judgments) and the number of relations used to compose the underlying rules. Prior cognitive neuroscience work suggests that attention plays a key role in humans' visual reasoning ability. To test this hypothesis, we extended the CNNs with spatial and feature-based attention mechanisms. In a second series of experiments, we evaluated the ability of these attention networks to learn to solve the SVRT challenge and found the resulting architectures to be much more efficient at solving the hardest of these visual reasoning tasks. Most important, the corresponding improvements on individual tasks partially explained our novel taxonomy. Overall, this work provides a granular computational account of visual reasoning and yields testable neuroscience predictions regarding the differential need for feature-based versus spatial attention depending on the type of visual reasoning problem.
Salinity Tolerance in Plants: Mechanisms and Regulation of Ion Transport

Life presumably arose in the primeval oceans with similar or even greater salinity than the present ocean, so the ancient cells were designed to withstand salinity. However, the immediate ancestors of land plants most likely lived in fresh, or slightly brackish, water. The fresh/brackish water origins might explain why many land plants, including some cereals, can withstand moderate salinity, but only 1 – 2 % of all the higher plant species were able to re-discover their saline origins again and survive at increased salinities close to that of seawater. From a practical side, salinity is among the major threats to agriculture, having been one of the reasons for the demise of the ancient Mesopotamian Sumer civilisation and in the present time causing huge annual economic losses of over 10 billion USD. The effects of salinity on plants include osmotic stress, disruption of membrane ion transport, direct toxicity of high cytoplasmic concentrations of sodium and chloride on cellular processes and induced oxidative stress. Ion transport is the crucial starting point that determines salinity tolerance in plants. Transport via membranes is mediated mostly by the ion channels and transporters, which ensure selective passage of specific ions. The molecular and structural diversity of these ion channels and transporters is amazing. Obtaining the detailed descriptions of distinct ion channels and transporters present in halophytes, marine algae and salt-tolerant fungi and then progressing to the cellular and the whole organism mechanisms, is one of the logical ways to understand high salinity tolerance. Transfer of the genes from halophytes to agricultural crops is a means to increase salt tolerance of the crops. The theoretical scientific approaches involve protein chemistry, structure-function relations of membrane proteins, synthetic biology, systems biology and physiology of stress and ion homeostasis. At the time of compiling this e-book many aspects of ion transport under salinity stress are not yet well understood. The e-book has attracted researchers in ion transport and salinity tolerance. We have combined our efforts to achieve a wider, more detailed understanding of salt tolerance in plants mediated by ion transport, to understand present and future ways to modify and manipulate ion transport and salinity tolerance and also to find natural limits for the modifications.
High temporal resolution total-body dynamic PET/CT imaging based on third- order Hermite interpolation

Dynamic positron emission tomography (dPET) is currently a widely used medical imaging technique for the clinical diagnosis, staging and therapy guidance of all kinds of human cancers. Higher temporal imaging resolution for the early stage of radiotracer metabolism is desired; however, in this case, the reconstructed images with short frame durations always suffer from a limited image signal-to-noise ratio (SNR), which results in unsatisfactory image spatial resolution. In this work, we proposed a dPET processing method that denoises images with short frame durations via pixel-level time-activity curve (TAC) correction based on third-order Hermite interpolation (Pitch-In). The proposed method was validated using total-body dynamic PET image data and compared to several state-of-the-art methods to demonstrate its superior performance in terms of high temporal resolution dPET image noise reduction and imaging contrast. Higher stability and feasibility of the proposed Pitch-In method for future clinical application with high temporal resolution (HTR) dPET imaging can be expected.
Improved Feature Importance Computations for Tree Models: Shapley vs. Banzhaf

Shapley values are one of the main tools used to explain predictions of tree ensemble models. The main alternative to Shapley values are Banzhaf values that have not been understood equally well. In this paper we make a step towards filling this gap, providing both experimental and theoretical comparison of these model explanation methods. Surprisingly, we show that Banzhaf values offer several advantages over Shapley values while providing essentially the same explanations. We verify that Banzhaf values: (1) have a more intuitive interpretation, (2) allow for more efficient algorithms, and (3) are much more numerically robust. We provide an experimental evaluation of these theses. In particular, we show that on real world instances. Additionally, from a theoretical perspective we provide new and improved algorithm computing the same Shapley value based explanations as the algorithm of Lundberg et al. [Nat. Mach. Intell. 2020]. Our algorithm runs in $O(TLD+n)$ time, whereas the previous algorithm had $O(TLD^2+n)$ running time bound. Here, $T$ is the number of trees, $L$ is the maximum number of leaves in a tree, and $D$ denotes the maximum depth of a tree in the ensemble. Using the computational techniques developed for Shapley values we deliver an optimal $O(TL+n)$ time algorithm for computing Banzhaf values based explanations. In our experiments these algorithms give running times smaller even by an order of magnitude.
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors

In 1-bit compressive sensing, each measurement is quantized to a single bit, namely the sign of a linear function of an unknown vector, and the goal is to accurately recover the vector. While it is most popular to assume a standard Gaussian sensing matrix for 1-bit compressive sensing, using structured sensing matrices such as partial Gaussian circulant matrices is of significant practical importance due to their faster matrix operations. In this paper, we provide recovery guarantees for a correlation-based optimization algorithm for robust 1-bit compressive sensing with partial Gaussian circulant matrices (with random column sign flips) under a generative prior, where the signal to estimate is assumed to belong to the range of a Lipschitz continuous generative model with bounded inputs. Under suitable assumptions, we match guarantees that were previously only known to hold for i.i. d. Gaussian matrices that require significantly more computation.
Human-in-the-loop Extraction of Interpretable Concepts in Deep Learning Models

The interpretation of deep neural networks (DNNs) has become a key topic as more and more people apply them to solve various problems and making critical decisions. Concept-based explanations have recently become a popular approach for post-hoc interpretation of DNNs. However, identifying human-understandable visual concepts that affect model decisions is a challenging task that is not easily addressed with automatic approaches. We present a novel human-in-the-loop approach to generate user-defined concepts for model interpretation and diagnostics. Central to our proposal is the use of active learning, where human knowledge and feedback are combined to train a concept extractor with very little human labeling effort. We integrate this process into an interactive system, ConceptExtract. Through two case studies, we show how our approach helps analyze model behavior and extract human-friendly concepts for different machine learning tasks and datasets and how to use these concepts to understand the predictions, compare model performance and make suggestions for model refinement. Quantitative experiments show that our active learning approach can accurately extract meaningful visual concepts. More importantly, by identifying visual concepts that negatively affect model performance, we develop the corresponding data augmentation strategy that consistently improves model performance.
Left, Right, and Gender: Exploring Interaction Traces to Mitigate Human Biases

Human biases impact the way people analyze data and make decisions. Recent work has shown that some visualization designs can better support cognitive processes and mitigate cognitive biases (i.e., errors that occur due to the use of mental “shortcuts”). In this work, we explore how visualizing a user's interaction history (i.e., which data points and attributes a user has interacted with) can be used to mitigate potential biases that drive decision making by promoting conscious reflection of one's analysis process. Given an interactive scatterplot-based visualization tool, we showed interaction history in real-time while exploring data (by coloring points in the scatterplot that the user has interacted with), and in a summative format after a decision has been made (by comparing the distribution of user interactions to the underlying distribution of the data). We conducted a series of in-lab experiments and a crowd-sourced experiment to evaluate the effectiveness of interaction history interventions toward mitigating bias. We contextualized this work in a political scenario in which participants were instructed to choose a committee of 10 fictitious politicians to review a recent bill passed in the U.S. state of Georgia banning abortion after 6 weeks, where things like gender bias or political party bias may drive one's analysis process. We demonstrate the generalizability of this approach by evaluating a second decision making scenario related to movies. Our results are inconclusive for the effectiveness of interaction history (henceforth referred to as interaction traces) toward mitigating biased decision making. However, we find some mixed support that interaction traces, particularly in a summative format, can increase awareness of potential unconscious biases.
Discovery of a Wind-blown Bubble Associated with the Supernova Remnant G346.6-0.2: A Hint for the Origin of Recombining Plasma

We report on CO and H i studies of the mixed-morphology supernova remnant (SNR) G346.6−0.2. We find a wind-blown bubble along the radio continuum shell with an expansion velocity of ∼10 km s−1, which was likely formed by strong stellar winds from the high-mass progenitor of the SNR. The radial velocities of the CO/H i bubbles at V LSR = −82 to −59 km s−1 are also consistent with those of shock-excited 1720 MHz OH masers. The molecular cloud in the northeastern shell shows a high kinetic temperature of ∼60 K, suggesting that shock heating occurred. The H i absorption studies imply that G346.6−0.2 is located on the farside of the Galactic center from us, and the kinematic distance of the SNR is derived to be 11.1−0.3+0.5 kpc. We find that the CO line intensity has no specific correlation with the electron temperature of recombining plasma, implying that the recombining plasma in G346.6−0.2 was likely produced by adiabatic cooling. With our estimates of the interstellar proton density of 280 cm−3 and gamma-ray luminosity <5.8 × 1034 erg s−1, the total energy of accelerated cosmic rays of W p < 9.3 × 1047 erg is obtained. A comparison of the age–W p relation to other SNRs suggests that most of the accelerated cosmic rays in G346.6−0.2 have escaped from the SNR shell.
The saturation number of $C_6$

A graph $G$ is called $C_k$-saturated if $G$ is $C_k$-free but $G+e$ not for any $e\in E(\overline{G})$. The saturation number of $C_k$, denoted $sat(n,C_k)$, is the minimum number of edges in a $C_k$-saturated graph on $n$ vertices. Finding the exact values of $sat(n,C_k)$ has been one of the most intriguing open problems in extremal graph theory. In this paper, we study the saturation number of $C_6$. We prove that ${4n}/{3}-2 \le sat(n,C_6) \le {(4n+1)}/{3}$ for $n\ge9$, which significantly improves the existing lower and upper bounds for $sat(n,C_6)$.