Miracle-dz/UniFAR-FTUs huggingface dataset:
The integration of artificial intelligence models to augment imaging modalities in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a limited number of effective treatments. Using emerging technologies such as arti ﬁ cial intelligence (AI) to facilitate the earlier diagnosis and decision-making process represents one of the most promising areas for investigation. The integration of AI models to augment imaging modalities in PDAC has made great progression in the past 5 years, especially in organ segmentation, AI-aided diagnosis, and radiomics based individualized medicine. In this article, we review the developments of AI in the ﬁ eld of PDAC and the present clinical position. We also examine the barriers to future development and more widespread application which will require increased familiarity of the underlying technology among clinicians to promote the necessary enthusiasm and collaboration with computer professionals.
Identifying forecast uncertainties for biogenic gases in the Po Valley related to model configuration in EURAD-IM during PEGASOS 2012

Abstract. Forecasts of biogenic trace gases in the planetary boundary layer (PBL) are highly affected by simulated emission and transport processes. The Po region during the PEGASOS campaign in summer 2012 provides challenging, yet common, conditions for simulating biogenic gases in the PBL. This study identifies and quantifies principal sources of forecast uncertainties induced by various model configurations under these conditions. Specifically, the effects of model configuration on different processes affecting atmospheric distributions of biogenic trace gas distributions are analyzed based on a priori available information. The investigation is based on the EURopean Air pollution Dispersion – Inverse Model (EURAD-IM) chemistry transport model employing the Model for Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN 2.1) biogenic emission module and Regional Atmospheric Chemistry Mechanism – Mainz Isoprene Mechanism (RACM-MIM) as the gas phase chemistry mechanism. Two major sources of forecast uncertainties are identified in this study. Firstly, biogenic emissions appear to be exceptionally sensitive to land surface properties inducing total variations in local concentrations of up to 1 order of magnitude. Moreover, these sensitivities are found to be highly similar for different gases and almost constant during the campaign, varying only diurnally. Secondly, the model configuration also highly influences regional flow patterns with significant effects on pollutant transport and mixing. This effect was corroborated by diverging source regions of a representative air mass and thus applies also to non-biogenic gases. As a result, large sensitivities to model configuration are found for surface concentrations of isoprene, as well as OH, affecting reactive atmospheric chemistry. Especially in areas with small-scale emission patterns, changes in the model configuration are able to induce significantly different local concentrations. The amount and complexity of sensitivities found in this study demonstrate the need to consider forecast uncertainties in chemical transport models with a special focus on biogenic emissions and pollutant transport.
Traceability and ownership claim of data on big data marketplace using blockchain technology

ABSTRACT In the era of big data, modern data marketplaces have received much attention as they allow not only large enterprises but also individuals to trade their data. This new paradigm makes the data prone to various threats, including piracy, illegal reselling, tampering, illegal redistribution, ownership claiming, forgery, theft, misappropriation, etc. Although digital watermarking is a promising technique to address the above-mentioned challenges, the existing solutions in the literature are deemed to be incompetent in big data scenarios due to the following factors: V's of big data, involvement of multiple owners, incremental watermarking, large cover-size and limited watermark-capacity, non-interference, etc. In this paper, we propose a novel big data watermarking technique that leverages the power of blockchain technology and provides a transparent immutable audit trail for data movement in big data monetizing scenarios. In this context, we address all the crucial challenges mentioned above. We present a prototype implementation of the system as a proof of concept using Solidity on Ethereum platform, and we perform experimental evaluation to demonstrate its feasibility and effectiveness in terms of execution gas costs. To the best of our knowledge, this is the first proposal which deals with watermarking issues in the context of big data.
Neural Synaptic Plasticity-Like Computing: An Ultra-Low Cost Approach for Artificial Neural Networks Implementation

Artificial neural networks (ANNs) have gained state-of-the-art results in classification and regression tasks. However, there is still great gap between ANNs and human brain in terms of computation efficiency. In this work, we proposed the neural synaptic plasticity-like computing (NSPC) to simulate the neural network activity for inference task with ultra-simple logic gates. The multiplication of weight in traditional ANNs is transformed by the wire connectivity in NSPC, which requires only bundle of wires without any logics. To this end, the NSPC imitates the structure of neural synaptic plasticity from a circuit wires connection perspective. The proposed NSPC exhibits comparable inference accuracy with low hardware cost. According to the implementation results, the NSPC requires only 28% logic gate resources of conventional ANNs scheme, 114% throughput improvement and 8.454 times better hardware efficiency on the average.
Seismic complex ray tracing in 2D/3D viscoelastic anisotropic media by a modified shortest-path method

In a viscoelastic anisotropic medium, velocity anisotropy and wave energy attenuation occur and are often observed in seismic data applications. Numerical investigation of seismic wave propagation in complex viscoelastic anisotropic media is very helpful in understanding seismic data and reconstructing subsurface structures. Seismic ray tracing is an effective means to study the propagation characteristics of high-frequency seismic waves. Unfortunately, most seismic ray-tracing methods and traveltime tomographic inversion algorithms only deal with elastic media and ignore the effect of viscoelasticity on the seismic raypath. We have developed a method to find the complex ray velocity that gives the seismic ray speed and attenuation in an arbitrary viscoelastic anisotropic medium, and we incorporate them with the modified shortest-path method to determine the raypath and calculate the real and imaginary traveltime (wave energy attenuation) simultaneously. We determine that the complex ray-tracing method is applicable to arbitrary 2D/3D viscoelastic anisotropic media in a complex geologic model and the computational errors of the real and imaginary traveltime are less than 0.36% and 0.59%, respectively. The numerical examples verify that the new method is an effective and powerful tool for accomplishing seismic complex ray tracing in heterogeneous viscoelastic anisotropic media.
Production of nisin and lactic acid from corn stover through simultaneous saccharification and fermentation

ABSTRACT The aim of this work was to demonstrate the feasibility of nisin and lactic acid production from corn stover through simultaneous saccharification and fermentation using Lactococcus lactis subsp. lactis ATCC 11454. The optimum conditions for nisin and lactic acid production were cellulose concentration of 30 g/L, a cellulase loading of 25 FPU/g cellulose and an initial pH of 6.0 in a 10 g/L yeast extract and 10 g/L peptone broth. The optimized bioprocess achieved nisin titer of 1635 IU/mL and lactic acid concentration of 15.2 g/L. The results indicated that simultaneous saccharification and fermentation with corn stover is a promising bioprocess for the economic production of nisin and lactic acid simultaneously.
The Antibacterial Activity and Phytochemical Compounds of Citrus grandis Extract Against Human Infective Bacteria

Background: With the presence of secondary metabolites that have antimicrobial properties and bacterial resistance against antibiotics, medicinal plants have attracted the attention of researchers in the pharmaceutical industry. Objectives: The present study aimed to investigate the antibacterial and antioxidant activity and phytochemical compounds of Citrus grandis extract against human infection bacteria in-vitro. Methods: Samples of Citrus grandis were collected from the north of Iran and assessed in terms of the antibacterial activity, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) using the agar well diffusion method. In addition, the anti-radical activity was determined by DPPH, total phenols were measured using the Folin-Ciocalteu method, and flavonoids were determined using aluminum chloride. The presence of alkaloid, saponin, and tannin was also investigated. Data analysis was performed in SPSS. Results: The colored and white skin methanol extract of C. grandis indicated more significant inhibitory effects on Bacillus cereus, Staphylococcus aureus, and Micrococcus luteus. The total phenol content of the colored and white skin was estimated at 79.71 and 71.63 mgGA/g, and the flavonoid level was determined to be 3.63 and 4.06 mgQ/g, respectively. The IC50 of the colored and white skin methanol extract and ascorbic acid were estimated at 0.1251, 0.1376, and 0.1095 mg.mL-1, respectively. The methanol extract of the colored skin showed the presence of alkaloid, while the white skin showed the presence of alkaloid and saponin. Conclusions: According to the results, the C. grandis extract had antioxidant and antibacterial properties due to the presence of secondary metabolites. Therefore, it is recommended that natural and rare drugs be produced to control pathogenic bacteria in the pharmaceutical industry.
Physicochemical and computational insight of 19F NMR and emission properties of meso-(o-aryl)-BODIPYs

A series of electronic and physicochemical parameters were explored to determine their effect on experimental spectroscopic and photophysical data. Through a systematic obtention of a series of meso-(o-aryl)-BODIPYs, 19F NMR spectra were analyzed and their fluorescence quantum yields in several solvents were measured. Experimental values of 19F chemical shift difference ΔδF correlate well with σ-Hammett constants, which is indicative of the inductive nature of the functional groups on the fluorine atoms. A computational DFT exploration of rotational energy barriers, electrostatic potential maps, group electronegativity, charge partitions and hardness/softness provided insight into how those traits can be directly related to the measured features. Expanded understanding of such characteristics provides design arguments and a structure–property relationship, which in a more advantageous way, would help to understand the properties of the synthesized molecules and of future attempts that are structurally related.
Responsive make‐to‐order supply chain network design

In this article, we address two network design problems for a responsive supply chain that consists of make‐to‐order (make‐to‐assemble) facilities facing stochastic demand and service time. The response time to customer orders, critical to the success of the supply chain, is the sum of the flow time in the facility and the delivery time to the customers. The response time performance in our models is measured by the probability that the response time is shorter than a constant. This nonlinear performance measure makes the models less or not tractable. The objective of both problems is to minimize the expected network cost that consists of the cost of delivery to customers as a function of the time of delivery (or the mode of transportation), the fixed cost of locating facilities and the capacity cost as a linear function of the processing capacity. The main decision variables are the number and locations of facilities, the resources allocated to them and the delivery mode selected between facilities and demand. In the first problem, a constraint is imposed to ensure an acceptable response time level. In the second problem, a penalty is charged on the number of days that each unit is delivered later than the targeted response time and is incorporated into the objective function. In the first problem, the probabilistic constraint on the flow time can be linearized and the problem can be formulated as an integer linear programming model. In the second problem, we propose an approximation approach to linearize the objective function and an iterative search and cut algorithm to combine linear approximation with neighborhood search. A multi‐start meta‐heuristic is also suggested. Computational experiments are conducted to evaluate the performance of these solution procedures. Recommendations are made on the basis of the computational results. This appears to be the first study in the area of locating capacitated facilities with stochastic demand to incorporate the delivery mode choice decision and to evaluate the expected congestion cost as a function of the actual flow time in the facilities, instead of the average one. The models developed enable the decision maker to investigate the effect of a combination of facility location, capacity allocation, and delivery mode decisions on the expected network cost and the response time. In addition, the findings of the computational experiments shed light on tuning parameters of approximation algorithms.
Cornet: Composite-Regularized Neural Network For Convolutional Sparse Coding

Sparse recovery via composite regularization is an interesting approach proposed recently in the literature. One could design nonconvex regularizers through a convex combination of sparsitypromoting penalties with known proximal operators. We develop a new algorithm, namely, convolutional proximal-averaged thresholding algorithm (C-PATA) for composite-regularized convolutional sparse coding (CR-CSC) based on the recently proposed idea of proximal averaging. We develop an autoencoder structure based on the deep-unfolding of C-PATA iterations into neural network layers, which results in the composite-regularized neural network (CoRNet) architecture. The convolutional learned iterative soft-thresholding algorithm becomes a special case of CoRNet. We demonstrate the efficacy of CoRNet considering applications to image denoising and inpainting, and compare the performance with state-of-the-art techniques such as BM3D, convolutional LISTA, and fast and flexible convolutional sparse coding (FFCSC).
Dynamic virulence-related regions of the fungal plant pathogen Verticillium dahliae display remarkably enhanced sequence conservation

Selection pressure impacts genomes unevenly, as different genes adapt with differential speed to establish an organism’s optimal fitness. Plant pathogens co-evolve with their hosts, which implies continuously adaption to evade host immunity. Effectors are secreted proteins that mediate immunity evasion, but may also typically become recognized by host immune receptors. To facilitate effector repertoire alterations, in many pathogens, effector genes reside in dynamic genomic regions that are thought to display accelerated evolution, a phenomenon that is captured by the two-speed genome hypothesis. The genome of the vascular wilt pathogen Verticillium dahliae has been proposed to obey to a similar two-speed regime with dynamic, lineage-specific regions that are characterized by genomic rearrangements, increased transposable element activity and enrichment in in planta-induced effector genes. However, little is known of the origin of, and sequence diversification within, these lineage-specific regions. Based on comparative genomics among Verticillium spp. we now show differential sequence divergence between core and lineage-specific genomic regions of V. dahliae. Surprisingly, we observed that lineage-specific regions display markedly increased sequence conservation. Since single nucleotide diversity is reduced in these regions, host adaptation seems to be merely achieved through presence/absence polymorphisms. Increased sequence conservation of genomic regions important for pathogenicity is an unprecedented finding for filamentous plant pathogens and signifies the diversity of genomic dynamics in host-pathogen co-evolution.
Genomic evaluation of male reproductive adaptations and responses to dehydration in Peromyscus eremicus (Cactus mouse)

Research elucidating the genetic architecture of physiological mechanisms enabling survival and reproduction in extreme environments is becoming prominent in evolutionary biology. The desert, in particular, poses numerous challenges for its endemic species, and mammals (and often, rodents) have been the focus for survival adaptations pertaining to water-limitation. However, desert rodent adaptation research has focused predominantly on survival, while potential physiological reproductive adaptations to dehydration have received less attention, aside from research evaluating water as reproductive cue. The fact that we do not know the physiological mechanisms enabling reproduction during dehydration is surprising, as desert rodents must possess adaptations to successfully reproduce in their water-limited habitats. The cactus mouse (Peromyscus eremicus), a desert-specialist in the Southwest United States, is the focus of my genetic exploration of reproductive adaptations to dehydration. My dissertation describes three research studies that 1) characterize male cactus mouse reproductive tissue transcriptomes and find signatures of positive selection in these tissues relative to other rodent species, 2) describe differential expression of genes responding to water-limitation within testes, providing candidate genes for future studies exploring the impacts of acute and chronic drought on P. eremicus reproduction, and 3) generate a seminal vesicle proteome representative of proteins present in hydrated and dehydrated conditions experienced by the cactus mouse. These three studies contribute comprehensive genetic data critical to future research exploring the effects of water-limitation on reproduction as well as the genetic mechanisms for potential male reproductive adaptations in this desertadapted rodent. Research on desert adaptations is particularly timely, as climate change will result in more frequent stochastic drought events and elevated temperatures. These increasing abiotic shifts will exacerbate clinical challenges for global health; thus, an enhanced understanding of mammalian desert-specialist adaptations may improve our ability to address these physiological demands in humans.
Using estimates of undocumented immigrants to study the immigration-crime relationship

ABSTRACT The debate about undocumented immigration and its potential relation to crime continues to boil in the United States. We study this relationship by using two sets of estimates for the 2014 undocumented foreign-born population in U.S. metropolitan areas acquired from the Pew Research Center and the Migration Population Institute, 2013-2015 FBI Uniform Crime Report data, and 2011-2015 American Community Survey data from the U.S. Census Bureau, to model the association between undocumented immigration and violent and property crime. Findings are consistent across all estimates of metropolitan undocumented populations. Net of relevant covariates, we find negative effects of undocumented immigration on the overall property crime rate, larceny, and burglary; effects in models using violent crime measures as the outcomes are statistically non-significant. Although the results are based on cross-sectional data, they mirror other research findings that immigration either reduces or has no impact on crime, on average, and contribute to a growing literature on the relationship between immigration and crime.
Identification of biological mechanisms by semantic classifier systems

The interpretability of a classification model is one of its most essential characteristics. It allows for the generation of new hypotheses on the molecular background of a disease. However, it is questionable if more complex molecular regulations can be reconstructed from such limited sets of data. To bridge the gap between complexity and interpretability, we replace the de novo reconstruction of these processes by a hybrid classification approach partially based on existing domain knowledge. Using semantic building blocks that reflect real biological processes these models were able to construct hypotheses on the underlying genetic configuration of the analysed phenotypes. As in the building process, also these hypotheses are composed of high-level biology-based terms. The semantic information we utilise from gene ontology is a vocabulary which comprises the essential processes or components of a biological system. The constructed semantic multi-classifier system consists of expert base classifiers which each select the most suitable term for characterising their assigned problems. Our experiments conducted on datasets of three distinct research fields revealed terms with well-known associations to the analysed context. Furthermore, some of the chosen terms do not seem to be obviously related to the issue and thus lead to new, hypotheses to pursue. Author summary Data mining strategies are designed for an unbiased de novo analysis of large sample collections and aim at the detection of frequent patterns or relationships. Later on, the gained information can be used to characterise diagnostically relevant classes and for providing hints to the underlying mechanisms which may cause a specific phenotype or disease. However, the practical use of data mining techniques can be restricted by the available resources and might not correctly reconstruct complex relationships such as signalling pathways. To counteract this, we devised a semantic approach to the issue: a multi-classifier system which incorporates existing biological knowledge and returns interpretable models based on these high-level semantic terms. As a novel feature, these models also allow for qualitative analysis and hypothesis generation on the molecular processes and their relationships leading to different phenotypes or diseases.
Determinación de edades en Otariidae y Phocidae (Carnivora) sobre piezas dentarias, técnicas alternativas

The periodic depositing of layers of cement and dentine on the mammal’s teeth it is very useful to determine the absolute age. The different events produced during the life cycle as moult, sexual maturity, nursing and feeding are reflected on the amount and type of cement and dentine deposits on the teeth. The aim of this contribution is to determine through the application of different techniques the age of specimens of Phocidae and Otariidae (Mammalia) housed on different collections in Argentina; in order to compare the results obtained with the use of different techniques and to incorporate a crucial datum to each studied specimen. The different techniques were tested, improved and then were performed on 335 specimens of Arctocephalus gazella, Arctocephalus australis, Otaria byronia, Mirounga leonina, Leptonychotes weddellii, Lobodon carcinophagus e Hydrurga leptonyx . The techniques used were: root external rings counting, dentine and cement line counting, both undecalsified as decalsified. Our results showed that not all techniques are effective for all species and for all teeth, effectiveness depends i.e. on the teeth size, on the way on the cement is deposited. Finally, we recommend which techniques are more effective for each species. The external ring and dentine line counting techniques with and without decalsification are more suitable for Arctocephalus , the dentine line counting by decalsification is recommendable for O. byronia, while in focids is better to used the line cements counting with previuos decalcificaction in postcanines.