Miracle-dz/UniFAR-FTUs huggingface dataset:
Hydrogel microenvironments for cancer spheroid growth and drug screening

Man-made biomimetic hydrogels with tunable biophysical and biochemical properties can support the growth of microtumors. Multicellular cancer spheroids (MCSs) have emerged as a promising in vitro model that replicates many features of solid tumors in vivo. Biomimetic hydrogel scaffolds for MCS growth offer a broad spectrum of biophysical and biochemical cues that help to recapitulate the behavior of natural extracellular matrix, essential for regulating cancer cell behavior. This perspective highlights recent advances in the development of hydrogel environments for MCS growth, release, and drug screening. We review the use of different types of hydrogels for MCS growth, the effect of biophysical and biochemical cues on MCS fate, the isolation of MCSs from hydrogel scaffolds, the utilization of microtechnologies, and the applications of MCSs grown in hydrogels. We conclude with the discussion of new research directions in the development of hydrogels for MCS growth.
New Genetic and Linguistic Analyses Show Ancient Human Influence on Baobab Evolution and Distribution in Australia

This study investigates the role of human agency in the gene flow and geographical distribution of the Australian baobab, Adansonia gregorii. The genus Adansonia is a charismatic tree endemic to Africa, Madagascar, and northwest Australia that has long been valued by humans for its multiple uses. The distribution of genetic variation in baobabs in Africa has been partially attributed to human-mediated dispersal over millennia, but this relationship has never been investigated for the Australian species. We combined genetic and linguistic data to analyse geographic patterns of gene flow and movement of word-forms for A. gregorii in the Aboriginal languages of northwest Australia. Comprehensive assessment of genetic diversity showed weak geographic structure and high gene flow. Of potential dispersal vectors, humans were identified as most likely to have enabled gene flow across biogeographic barriers in northwest Australia. Genetic-linguistic analysis demonstrated congruence of gene flow patterns and directional movement of Aboriginal loanwords for A. gregorii. These findings, along with previous archaeobotanical evidence from the Late Pleistocene and Holocene, suggest that ancient humans significantly influenced the geographic distribution of Adansonia in northwest Australia.
Quantitative Abundance Estimates From Bidirectional Reflectance Measurements

A simplified approach to the problem of determining the relative proportion of minerals in a mixture from a reflectance spectrum of the mixture is presented. Fundamental to this approach is a priori information concerning reflectance spectra of the minerals in the mixture and some estimate of the particle sizes of the mixture components. Reflectance spectra of intimate mixtures are a systematic but nonlinear combination of the spectra of the minerals in the mixtures. Equations for bidirectional reflectance are used to linearize the systematics of spectral mixing for intimate mixtures. The equations are simplified by assuming that particulate media scatter light isotropically at phase angles between 15 o and 40 o. This method for linearizing the mixing systematics is used to determine mineral relative geometric cross sections (proportional to mass fraction/density x particle diameter) from reflectance spectra of mixtures of igneous rock-forming minerals (oilvine, magnetite, enstatite, and anorthite) and to determine endmember relative geometric cross sections from reflectance spectra of mixtures of terrestrial desert soils. Since particle diameters are known, the mass fractions of the mixture components are also calculated. For materials without strongly absorbing components, the accuracy of abundance determinations is better than 5%. The results indicate that the method presented can be used to accurately determine the relative proportions of components (minerals or complex endmembers) in a mixture from a reflectance spectrum of the mixture given information of the endmembers in the mixture, reflectance spectra of the endmembers, and an estimate of particle sizes of the respective mixture components.
Ontogenetic Shape Change in the Chicken Brain: Implications for Paleontology

Paleontologists have investigated brain morphology of extinct birds with little information on post-hatching changes in avian brain morphology. Without the knowledge of ontogenesis, assessing brain morphology in fossil taxa could lead to misinterpretation of the phylogeny or neurosensory development of extinct species. Hence, it is imperative to determine how avian brain morphology changes during post-hatching growth. In this study, chicken brain shape was compared at various developmental stages using three-dimensional (3D) geometric morphometric analysis and the growth rate of brain regions was evaluated to explore post-hatching morphological changes. Microscopic MRI (μMRI) was used to acquire in vivo data from living and post-mortem chicken brains. The telencephalon rotates caudoventrally during growth. This change in shape leads to a relative caudodorsal rotation of the cerebellum and myelencephalon. In addition, all brain regions elongate rostrocaudally and this leads to a more slender brain shape. The growth rates of each brain region were constant and the slopes from the growth formula were parallel. The dominant pattern of ontogenetic shape change corresponded with interspecific shape changes due to increasing brain size. That is, the interspecific and ontogenetic changes in brain shape due to increased size have similar patterns. Although the shape of the brain and each brain region changed considerably, the volume ratio of each brain region did not change. This suggests that the brain can change its shape after completing functional differentiation of the brain regions. Moreover, these results show that consideration of ontogenetic changes in brain shape is necessary for an accurate assessment of brain morphology in paleontological studies.
Classification of Compressed Human Face Images by using Principle Components

This paper describes the novel approach of classifying the humans on the basis of their compressed face images. The compression of the face images is performed using Discrete Wavelet Transform (DWT). While the classification encompass the use of Principal Components Analysis (PCA). Classification technique utilizes PCA in some different way. Only first principal component is used as feature vector out of 92 components (since image size is 112×92), causing a better results of 87.39%. The Euclidean distance is used as distance metric. In the end our results are compared to our previous research of classifying the uncompressed images.
The Default Mode Network and the Working Memory Network Are Not Anti-Correlated during All Phases of a Working Memory Task

Introduction The default mode network and the working memory network are known to be anti-correlated during sustained cognitive processing, in a load-dependent manner. We hypothesized that functional connectivity among nodes of the two networks could be dynamically modulated by task phases across time. Methods To address the dynamic links between default mode network and the working memory network, we used a delayed visuo-spatial working memory paradigm, which allowed us to separate three different phases of working memory (encoding, maintenance, and retrieval), and analyzed the functional connectivity during each phase within and between the default mode network and the working memory network networks. Results We found that the two networks are anti-correlated only during the maintenance phase of working memory, i.e. when attention is focused on a memorized stimulus in the absence of external input. Conversely, during the encoding and retrieval phases, when the external stimulation is present, the default mode network is positively coupled with the working memory network, suggesting the existence of a dynamically switching of functional connectivity between “task-positive” and “task-negative” brain networks. Conclusions Our results demonstrate that the well-established dichotomy of the human brain (anti-correlated networks during rest and balanced activation-deactivation during cognition) has a more nuanced organization than previously thought and engages in different patterns of correlation and anti-correlation during specific sub-phases of a cognitive task. This nuanced organization reinforces the hypothesis of a direct involvement of the default mode network in cognitive functions, as represented by a dynamic rather than static interaction with specific task-positive networks, such as the working memory network.
Simple and Efficient k-Coverage Verification without Location Information

Wireless sensor networks (WSNs) have recently emerged as a prominent technology for environmental monitoring and hazardous event detection. Yet, their success depends considerably on their ability to ensure reliable event detection. Such guarantees can be provided only if the target field monitored by a WSN does not contain coverage holes that are not monitored by any sensor. Currently, the coverage-holes detection solutions require accurate knowledge of the sensors locations, which cannot be easily obtained, or they cannot provide guarantees on the coverage quality. In this study we address the challenge of designing an accurate k-coverage verification scheme, without using location information, for a predefined kges1. To this end, we present an efficient, distributed and localized k-coverage verification scheme with proven guarantees on its coverage detection quality. Our simulations show that the scheme accurately detects coverage holes of various sizes.
Stepwise Connectivity of the Modal Cortex Reveals the Multimodal Organization of the Human Brain

How human beings integrate information from external sources and internal cognition to produce a coherent experience is still not well understood. During the past decades, anatomical, neurophysiological and neuroimaging research in multimodal integration have stood out in the effort to understand the perceptual binding properties of the brain. Areas in the human lateral occipitotemporal, prefrontal, and posterior parietal cortices have been associated with sensory multimodal processing. Even though this, rather patchy, organization of brain regions gives us a glimpse of the perceptual convergence, the articulation of the flow of information from modality-related to the more parallel cognitive processing systems remains elusive. Using a method called stepwise functional connectivity analysis, the present study analyzes the functional connectome and transitions from primary sensory cortices to higher-order brain systems. We identify the large-scale multimodal integration network and essential connectivity axes for perceptual integration in the human brain.
Constrained School Choice

Recently, several school districts in the US have adopted or consider adopting the Student-Optimal Stable mechanism or the Top Trading Cycles mechanism to assign children to public schools. There is evidence that for school districts that employ (variants of) the so-called Boston mechanism the transition would lead to efficiency gains. The first two mechanisms are strategy-proof, but in practice student assignment procedures typically impede a student to submit a preference list that contains all his acceptable schools. We study the preference revelation game where students can only declare up to a fixed number of schools to be acceptable. We focus on the stability and efficiency of the Nash equilibrium outcomes. Our main results identify rather stringent necessary and sufficient conditions on the priorities to guarantee stability or efficiency of either of the two mechanisms. This stands in sharp contrast with the Boston mechanism which has been abandoned in many US school districts but nevertheless yields stable Nash equilibrium outcomes.
Design-Time Compliance of Service Compositions in Dynamic Service Environments

In order to improve the flexibility of information systems, an increasing amount of business processes is being automated by implementing tasks as modular services in service compositions. As organizations are required to adhere to laws and regulations, with this increased flexibility there is a demand for automated compliance checking of business processes. Model checking is a technique which exhaustively and automatically verifies system models against specifications of interest, e.g. A finite state machine against a set of logic formulas. When model checking business processes, existing approaches either cause large amounts of overhead, linearize models to such an extent that activity parallelization is lost, offer only checking of runtime execution traces, or introduce new and unknown logics. In order to fully benefit from existing model checking techniques, we propose a mapping from workflow patterns to a class of labeled transition systems known as Kripke structures. With this mapping, we provide pre-runtime compliance checking using well-known branching time temporal logics. The approach is validated on a complex abstract process which includes a deferred choice, parallel branching, and a loop. The process is modeled using the Business Process Model and Notation (BPMN) standard, converted into a colored Petri net using the workflow patterns, and subsequently translated into a Kripke structure, which is then used for verification.
Relyzer: exploiting application-level fault equivalence to analyze application resiliency to transient faults

Future microprocessors need low-cost solutions for reliable operation in the presence of failure-prone devices. A promising approach is to detect hardware faults by deploying low-cost monitors of software-level symptoms of such faults. Recently, researchers have shown these mechanisms work well, but there remains a non-negligible risk that several faults may escape the symptom detectors and result in silent data corruptions (SDCs). Most prior evaluations of symptom-based detectors perform fault injection campaigns on application benchmarks, where each run simulates the impact of a fault injected at a hardware site at a certain point in the application's execution (application fault site). Since the total number of application fault sites is very large (trillions for standard benchmark suites), it is not feasible to study all possible faults. Previous work therefore typically studies a randomly selected sample of faults. Such studies do not provide any feedback on the portions of the application where faults were not injected. Some of those instructions may be vulnerable to SDCs, and identifying them could allow protecting them through other means if needed. This paper presents Relyzer, an approach that systematically analyzes all application fault sites and carefully picks a small subset to perform selective fault injections for transient faults. Relyzer employs novel fault pruning techniques that prune faults that need detailed study by either predicting their outcomes or showing them equivalent to other faults. We find that Relyzer prunes about 99.78% of the total faults across twelve applications studied here, reducing the faults that require detailed simulation by 3 to 5 orders of magnitude for most of the applications. Fault injection simulations on the remaining faults can identify SDC causing faults in the entire application. Some of Relyzer's techniques rely on heuristics to determine fault equivalence. Our validation efforts show that Relyzer determines fault outcomes with 96% accuracy, averaged across all the applications studied here.
Optimized routing in organization-aware multihop wireless networks (ORION)

In our previous works, we defined a novel routing metric for organization-aware multihop wireless networks [1]. We demonstrated that our proposed routing protocol with this new metric provides the much needed survivability and efficiency in battlefield environments when keeping the cross-organization data transfer at a low level [2]. In this paper, we take one step further and propose ORION, a framework for quality-of-service (QoS) routing in organization-aware wireless networks. Here, we formulate routing as a constrained optimization problem with a new routing metric to address QoS and security/policy requirements of traffic flows as well as node-specific needs, while taking the organizational constraints into account. Since the problem is not solvable in polynomial time, a near optimal solution procedure based on Lagrangian decomposition and subgradient method is proposed and studied to solve the constrained optimization problem, and such a solution renders itself to distributed implementations. Simulation results show that the proposed method provides high QoS while keeping the cross-organization data transfer at a specified level satisfying strict organizational security constraints.
The Potential Biomarkers to Identify the Development of Steatosis in Hyperuricemia

Hyperuricemia (HU) often progresses to combine with non-alcoholic fatty liver disease (NAFLD) in the clinical scenario, which further exacerbates metabolic disorders; early detection of biomarkers, if obtained during the HU progression, may be beneficial for preventing its combination with NAFLD. This study aimed to decipher the biomarkers and mechanisms of the development of steatosis in HU. Four groups of subjects undergoing health screening, including healthy subjects, subjects with HU, subjects with HU combined with NAFLD (HU+NAFLD) and subjects with HU initially and then with HU+NAFLD one year later (HU→HU+NAFLD), were recruited in this study. The metabolic profiles of all subjects’ serum were analyzed by liquid chromatography quadruple time-of-flight mass spectrometry. The metabolomic data from subjects with HU and HU+NAFLD were compared, and the biomarkers for the progression from HU to HU+NAFLD were predicted. The metabolomic data from HU→HU+NAFLD subjects were collected for further verification. The results showed that the progression was associated with disturbances of phospholipase metabolism, purine nucleotide degradation and Liver X receptor/retinoic X receptor activation as characterized by up-regulated phosphatidic acid, cholesterol ester (18:0) and down-regulated inosine. These metabolic alterations may be at least partially responsible for the development of steatosis in HU. This study provides a new paradigm for better understanding and further prevention of disease progression.
Dis-S2V: Discourse Informed Sen2Vec

Vector representation of sentences is important for many text processing tasks that involve clustering, classifying, or ranking sentences. Recently, distributed representation of sentences learned by neural models from unlabeled data has been shown to outperform the traditional bag-of-words representation. However, most of these learning methods consider only the content of a sentence and disregard the relations among sentences in a discourse by and large. In this paper, we propose a series of novel models for learning latent representations of sentences (Sen2Vec) that consider the content of a sentence as well as inter-sentence relations. We first represent the inter-sentence relations with a language network and then use the network to induce contextual information into the content-based Sen2Vec models. Two different approaches are introduced to exploit the information in the network. Our first approach retrofits (already trained) Sen2Vec vectors with respect to the network in two different ways: (1) using the adjacency relations of a node, and (2) using a stochastic sampling method which is more flexible in sampling neighbors of a node. The second approach uses a regularizer to encode the information in the network into the existing Sen2Vec model. Experimental results show that our proposed models outperform existing methods in three fundamental information system tasks demonstrating the effectiveness of our approach. The models leverage the computational power of multi-core CPUs to achieve fine-grained computational efficiency. We make our code publicly available upon acceptance.
Providing a Triangular Model for Gap Analysis Case Study: Iran Khodro Company

The investigation of current situation of an organization is one of the most important steps for implementation of knowledge management (KM). Also, gap analysis is among techniques proposed for evaluation of present state of the organization. This paper is an attempt to provide a new framework for gap analysis within implementation phase of KM project. In this way, we first introduce effects of three important factors of Organizational Culture (OC), Information Technology (IT), a nd KM mechanisms (human based tools) on gap analysis. Therefore, the status of each of these gaps will be investigated in Iran Khodro Company (IKCO) by using statistical methods and data mining techniques. Then, the triangular model of gap analysis is provided considering new researches in this area and also the findings of investigation of gap analysis in aforementioned company. Finally, a framework is proposed for planning and establishment of KM project in organizations.
Scrub typhus in the northern provinces of Vietnam: an observational study of admissions to a national referral hospital

Background Scrub typhus is a common cause of fever in parts of South East and Southern Asia. Little is known about the disease burden in Vietnam. Methods A 2-year observational study of scrub typhus at a tertiary referral hospital in northern Vietnam was carried out. Diagnosis was based on a single serological test in patients with suggestive clinical symptoms. Results Scrub typhus was diagnosed in 3.5% (251/7226) of admissions. Cases occurred throughout the year, with incidence highest in the summer. Although complications were common, mortality was low (1.2%; 3/251). Conclusions These data suggest that scrub typhus is common, with a seasonal distribution in northern Vietnam.
A Neural Algorithm of Artistic Style

In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.
Development of a Kemp’s ridley sea turtle stock assessment model

BENNY J. GALLAWAY, WILLIAM J. GAZEY, CHARLES W. CAILLOUET, JR., PAMELA T. PLOTKIN, F. ALBERTO ABREU GROBOIS, ANTHONY F. AMOS, PATRICK M. BURCHFIELD, RAY R. CARTHY, MARCO A. CASTRO MARTÍNEZ, JOHN G. COLE, ANDREW T. COLEMAN, MELISSA COOK, STEVEN DIMARCO, SHERYAN P. EPPERLY, MASAMI FUJIWARA, DANIEL GOMEZ GAMEZ, GARY L. GRAHAM, WADE L. GRIFFIN, FRANCISCO ILLESCAS MARTÍNEZ, MARGARET M. LAMONT, REBECCA L. LEWISON, KENNETH J. LOHMANN, JAMES M. NANCE, JONATHAN PITCHFORD, NATHAN F. PUTMAN, SCOTT W. RABORN, JEFFREY K. RESTER, JACK J. RUDLOE, LAURA SARTI MARTÍNEZ, MARK SCHEXNAYDER, JEFFREY R. SCHMID, DONNA J. SHAVER, CHRISTOPHER SLAY, ANTON D. TUCKER, MANDY TUMLIN, THANE WIBBELS, AND BLANCA M. ZAPATA NAJERA