Miracle-dz/UniFAR-FTUs huggingface dataset:
Diffuse correlation spectroscopy measurements of blood flow using 1064 nm light

Abstract. Significance: Diffuse correlation spectroscopy (DCS) is an established optical modality that enables noninvasive measurements of blood flow in deep tissue by quantifying the temporal light intensity fluctuations generated by dynamic scattering of moving red blood cells. Compared with near-infrared spectroscopy, DCS is hampered by a limited signal-to-noise ratio (SNR) due to the need to use small detection apertures to preserve speckle contrast. However, DCS is a dynamic light scattering technique and does not rely on hemoglobin contrast; thus, there are significant SNR advantages to using longer wavelengths (>1000 nm) for the DCS measurement due to a variety of biophysical and regulatory factors. Aim: We offer a quantitative assessment of the benefits and challenges of operating DCS at 1064 nm versus the typical 765 to 850 nm wavelength through simulations and experimental demonstrations. Approach: We evaluate the photon budget, depth sensitivity, and SNR for detecting blood flow changes using numerical simulations. We discuss continuous wave (CW) and time-domain (TD) DCS hardware considerations for 1064 nm operation. We report proof-of-concept measurements in tissue-like phantoms and healthy adult volunteers. Results: DCS at 1064 nm offers higher intrinsic sensitivity to deep tissue compared with DCS measurements at the typically used wavelength range (765 to 850 nm) due to increased photon counts and a slower autocorrelation decay. These advantages are explored using simulations and are demonstrated using phantom and in vivo measurements. We show the first high-speed (cardiac pulsation-resolved), high-SNR measurements at large source–detector separation (3 cm) for CW-DCS and late temporal gates (1 ns) for TD-DCS. Conclusions: DCS at 1064 nm offers a leap forward in the ability to monitor deep tissue blood flow and could be especially useful in increasing the reliability of cerebral blood flow monitoring in adults.
Parameter estimation of linear frequency modulated signals based on a Wigner–Ville distribution complex-valued convolutional neural network

Abstract. Our work aims to address the problem of estimating the parameters of constant-amplitude, time-unsynchronized linear frequency-modulated (LFM) signals that have single or multiple components, which is a crucial task in electronic countermeasure techniques. A method for estimating the parameters, center frequency f0, and chirp rate μ of an LFM signal is proposed; the method is referred to as the Wigner–Ville distribution complex-valued convolutional neural network (WVD-CV-CNN). The method can be regarded as an application of neural networks for extracting parameter features from the signal spectrogram, wherein the CV-CNN is the main body of the network, which takes a complex-valued WVD matrix as the input and outputs several sets of estimated parameters. A performance analysis shows that the estimation accuracy and computational efficiency of the proposed method are significantly improved compared with those of the conventional methods. Further, the proposed method shows strong robustness to changes in modulation parameters. We apply the CV-CNN to other spectrograms and prove compatibility of the WVD and CV-CNN by comparison. We also demonstrate that the estimation accuracy of the proposed method is robust against cross interference on the WVD. Our study shows the advantages of using deep learning systems in signal parameter estimation.
Searching Cubes in Division Property Based Cube Attack: Applications to Round-Reduced ACORN

Recently, division property based cube attack has acheived new progress and some cryptanalytic results against well-known stream ciphers. At EUROCRYPT 2020, Hao et al. proposed a new modeling method for three-subset division property without unknown subset. With this method, the exact expression of the superpoly in cube attack can be recovered. Inthispaper, we propose a method to search good cubes for both distinguishing attacks and key recovery attacks in the division property based cube attack scenario. Our cube searching procedure is based on the algorithm of degree evaluation of the superpoly and the algorithm of superpoly recovery. In the process of cube searching, we mainly use the embedded property to narrow down the searching space. As a result, we find some new cube testers of dimension 126 on 775 -round ACORN. We also find a new key recovery attack on 775 -round ACORN with a 126 -dimensional cube, whose corresponding superpoly is a 2-degree polynomial with respect to key bits.
Tissue-specific accumulation profiles of phorbol esters in Jatropha curcas and gene induction in response to abiotic and biotic stresses

Jatropha curcas L. (J. curcas), a shrub plant of the Euphorbiaceae family, has received enormous attention as a promising biofuel plant for the production of biodiesel and medical potential in ethnopharmacology. However, the tumor-promoter toxin phorbol esters present in J. curcas raises concerns for health and environmental risk as its large-scale cultivation limits the use of meal obtained after oil extraction for animal feed. Here, we determined the variation of phorbol ester profiles and contents in eight J. curcas tissues by high performance liquid chromatography (HPLC) and found phorbol esters present in all parts of the plant except the seed shell. We showed tissue-specific patterns of accumulation of phorbol esters and associated terpenoids at the transcriptomic level with high transcript levels in reproductive and young tissues. Genes involved in the same module of terpenoids biosynthesis were positively correlated. We further present diverse abiotic and biotic stresses that had different effects on the accumulation of transcripts in terpenoids shared and branched terpenoid pathways in plant seedlings. The fine-tuning of terpenoids biosynthesis may link with ecological functions in plants under extreme environments and defense against pathogens.
MOTIF: (Almost) Free Branching in GMW via Vector-Scalar Multiplication

MPC functionalities are increasingly speciﬁed in high-level languages, where control-ﬂow constructions such as conditional statements are extensively used. Today, concretely eﬃcient MPC protocols are circuit-based and must evaluate all conditional branches at high cost to hide the taken branch. The Goldreich-Micali-Wigderson, or GMW, protocol is a foundational circuit-based technique that realizes MPC for p players and is secure against up to p − 1 semi-honest corruptions. While GMW requires communication rounds proportional to the computed circuit’s depth, it is eﬀective in many natural settings.
A Differential and Linear Analysis of the Inversion Mapping in Odd-Characteristic Finite Fields

Substitution boxes (S-boxes) based on the inversion mapping ( S [ x ] = x − 1 ) in even-characteristic ﬁnite ﬁelds are widely used components in the design of cryptographic primitives such as block ciphers (notably the AES cipher). This report focuses on the inversion mapping in ﬁnite ﬁelds GF( p n ) where p is a (small) odd prime and n is a (small) integer. We compare the diﬀerential and linear proﬁles of S-boxes over odd-and even-characteristic ﬁelds, which also motivates the design and analysis of AES variants operating in ﬁelds of odd characteristic. Even for GF(2 n ), the study of S-boxes which are APN permutations (odd-valued n ) already shows resistance to diﬀerential and linear cryptanalysis after three rounds.
Pentapeptide repeat proteins QnrB1 and AlbG require ATP hydrolysis to rejuvenate poisoned gyrase complexes

DNA gyrase, a type II topoisomerase found predominantly in bacteria, is the target for a variety of “poisons”, namely natural product toxins (e.g. albicidin. microcin B17) and clinically important synthetic molecules (e.g. fluoroquinolones). Resistance to both groups can be mediated by pentapeptide repeat proteins (PRPs). Despite long-term studies, the mechanism of action of these protective PRPs is not known. We compared activities of two such proteins, QnrB1 and AlbG in vitro. Each of them provided specific protection against its cognate toxin (fluoroquinolone or albicidin), which strictly required ATP hydrolysis by gyrase. Through a combination of fluorescence anisotropy, pull-downs and photocrosslinking we show that QnrB1 binds to the GyrB protein. We further probed the QnrB1 binding site using site-specific incorporation of a photoreactive amino acid and mapped strong and specific crosslinks to the N-terminal ATPase/transducer domain. We propose a model in which protective PRPs bind to the enzyme as T-segment DNA mimics to promote dissociation of the bound poison molecule.
Ontology-based Reasoning for Critical Incidents in Public Events

One of the most critical challenges during critical incidents in big public events are the absence of real time information as well as the lack of ﬁltering this information to meaningful knowledge in order to provide crisis decision support. Towards addressing those challenges, semantic technologies can provide a semantically data representation of the critical events, along with superior capabilities in reasoning to support the decision making. In this paper we describe the semantic reasoning framework of the DESMOS project, that could constitute the backbone of the decision support systems in public events.
Semantics of a Relational λ-Calculus (Extended Version)

We extend the λ-calculus with constructs suitable for relational and functional–logic programming: non-deterministic choice, fresh variable introduction, and unification of expressions. In order to be able to unify λ-expressions and still obtain a confluent theory, we depart from related approaches, such as λProlog, in that we do not attempt to solve higher-order unification. Instead, abstractions are decorated with a location, which intuitively may be understood as its memory address, and we impose a simple coherence invariant: abstractions in the same location must be equal. This allows us to formulate a confluent small-step operational semantics which only performs first-order unification and does not require strong evaluation (below lambdas). We study a simply typed version of the system. Moreover, a denotational semantics for the calculus is proposed and reduction is shown to be sound with respect to the denotational semantics.
Secure training of decision trees with continuous attributes

Abstract We apply multiparty computation (MPC) techniques to show, given a database that is secret-shared among multiple mutually distrustful parties, how the parties may obliviously construct a decision tree based on the secret data. We consider data with continuous attributes (i.e., coming from a large domain), and develop a secure version of a learning algorithm similar to the C4.5 or CART algorithms. Previous MPC-based work only focused on decision tree learning with discrete attributes (De Hoogh et al. 2014). Our starting point is to apply an existing generic MPC protocol to a standard decision tree learning algorithm, which we then optimize in several ways. We exploit the fact that even if we allow the data to have continuous values, which a priori might require fixed or floating point representations, the output of the tree learning algorithm only depends on the relative ordering of the data. By obliviously sorting the data we reduce the number of comparisons needed per node to O(N log2N) from the naive O(N2), where N is the number of training records in the dataset, thus making the algorithm feasible for larger datasets. This does however introduce a problem when duplicate values occur in the dataset, but we manage to overcome this problem with a relatively cheap subprotocol. We show a procedure to convert a sorting network into a permutation network of smaller complexity, resulting in a round complexity of O(log N) per layer in the tree. We implement our algorithm in the MP-SPDZ framework and benchmark our implementation for both passive and active three-party computation using arithmetic modulo 264. We apply our implementation to a large scale medical dataset of ≈ 290 000 rows using random forests, and thus demonstrate practical feasibility of using MPC for privacy-preserving machine learning based on decision trees for large datasets.
Ets2 frame-shift mutant models express in-frame mRNA by exon skipping that complements Ets2 function in the skin

The Ets2 transcription factor has been implicated in various biological processes. An Ets2 mutant model, which lacks the DNA-binding domain (ETS domain), was previously reported to exhibit embryonic lethality caused by a trophoblast abnormality. This phenotype could be rescued by tetraploid complementation, resulting in pups with wavy hair. Here, we generated new Ets2 mutant models with deletions in exon 8 and with frame-shift mutations using the CRISPR/Cas9 method. Homozygous mutants could not be obtained by natural mating as previously reported. After rescuing with tetraploid complementation, homozygous mutant mice were generated, but these mice did not exhibit wavy hair phenotype. Our newly generated mice exhibited exon 8 skipping, which led to in-frame mutant mRNA expression in the skin and thymus but not in E7.5 embryos. As this in-frame mutation contained the ETS domain, the exon 8-skipped Ets2 mRNA was likely translated into protein in the skin that complemented the Ets2 function. Thus, these Ets2 mutant models, depending on the cell types, exhibited novel phenotypes due to exon skipping and are expected to be useful in several fields of research. Summary statement New Ets2 mutant models showed embryonic lethal phenotype by a placental abnormality but did not exhibit a wavy hair phenotype as a previous model.
Exploring the Eating Disorder Examination Questionnaire, Clinical Impairment Assessment, and Autism Quotient to Identify Eating Disorder Vulnerability: A Cluster Analysis

Eating disorders are very complicated and many factors play a role in their manifestation. Furthermore, due to the variability in diagnosis and symptoms, treatment for an eating disorder is unique to the individual. As a result, there are numerous assessment tools available, which range from brief survey questionnaires to in-depth interviews conducted by a professional. One of the many benefits to using machine learning is that it offers new insight into datasets that researchers may not previously have, particularly when compared to traditional statistical methods. The aim of this paper was to employ k-means clustering to explore the Eating Disorder Examination Questionnaire, Clinical Impairment Assessment, and Autism Quotient scores. The goal is to identify prevalent cluster topologies in the data, using the truth data as a means to validate identified groupings. Our results show that a model with k = 2 performs the best and clustered the dataset in the most appropriate way. This matches our truth data group labels, and we calculated our model’s accuracy at 78.125%, so we know that our model is working well. We see that the Eating Disorder Examination Questionnaire (EDE-Q) and Clinical Impairment Assessment (CIA) scores are, in fact, important discriminators of eating disorder behavior.
Correspondence-aware manifold learning for microscopic and spatial omics imaging: a novel data fusion method bringing MSI to a cellular resolution

High-dimensional molecular measurements are transforming the field of pathology into a data-driven discipline. While H&E stainings are still the gold standard to diagnose disease, the integration of microscopic and molecular information is becoming crucial to advance our understanding of tissue heterogeneity. To this end, we propose a data fusion method that integrates spatial omics and microscopic data obtained from the same tissue slide. Through correspondence-aware manifold learning, we can visualise the biological trends observed in the high-dimensional omics data at microscopic resolution. While data fusion enables the detection of elements that would not be detected taking into account the separate data modalities individually, out-of-sample prediction makes it possible to predict molecular trends outside of the measured tissue area. The proposed dimensionality reduction-based data fusion paradigm will therefore be helpful in deciphering molecular heterogeneity by bringing molecular measurements such as MSI to the cellular resolution.
Extracting the phylogenetic dimension of coevolution reveals hidden functional signal

Despite the structural and functional information contained in the statistical coupling between pairs of residues in a protein, coevolution associated with function is often obscured by artifactual signals such as genetic drift, which shapes a protein’s phylogenetic history and gives rise to concurrent variation between protein sequences that is not driven by selection for function. Here, we introduce a method for explicitly defining a phylogenetic dimension of coevolution signal, and demonstrate that coevolution can occur on multiple phylogenetic timescales within a single protein. Our method, Nested Coevolution (NC), can be applied as an extension to any coevolution metric. We use NC to demonstrate that poorly conserved residues can nonetheless have important roles in protein function. Moreover, NC improved structural-contact prediction over gold-standard coevolution-based methods, particularly in subsampled alignments with fewer sequences. NC also lowered the noise in detecting functional sectors of collectively coevolving residues. Sectors of coevolving residues identified after NC correction were more spatially compact and phylogenetically distinct from the rest of the protein, and strongly enriched for mutations that disrupt protein activity. Our conceptualization of the phylogenetic separation of coevolution represents an advance from previous pragmatic attempts to reduce phylogenetic artifacts in measurements of coevolution. Application of NC broadens the application of protein coevolution measurements, particularly to eukaryotic proteins with fewer naturally available sequences, and further elucidates relationships among protein evolution and genetic diseases.
Modulation of metabolic hormone signaling via a circadian hormone and a biogenic amine in Drosophila melanogaster

In insects, Adipokinetic hormone is the primary hormone responsible for the mobilization of stored energy. While a growing body of evidence has solidified AKH’s role in modulating the physiological and behavioral responses to metabolic stress, little is known about the upstream endocrine circuit that directly regulates AKH release. We evaluated the AKH-expressing cell transcriptome to identify potential regulatory elements controlling AKH cell activity, and found that a number of receptors show consistent expression levels, including all known dopamine receptors, dopamine ecdysone receptor (DopEcR), Dopamine 2-like receptor (D2R), Dopamine 1-like receptor 2 (DopR2), DopR, and the Pigment Dispersing Factor (PDFR). We tested the consequences of targeted genetic knockdown and found that RNAi elements targeting each dopamine receptor caused a significant reduction in survival under starvation. In contrast, PDFR knockdown significantly extended lifespan under starvation whereas expression of a tethered PDF in AKH cells resulted in a significantly shorter lifespan during starvation. These manipulations also caused various changes in locomotor activity under starvation. Specifically, there were higher amounts of locomotor activity in dopamine receptor knockdowns, in both replete and starved states. PDFR knockdown resulted in increased locomotion during replete conditions and locomotion levels that were comparable to wild-type during starvation. Expression of a membrane-tethered PDF led to decreased locomotion under baseline and starvation. Next, we used live-cell imaging to evaluate the acute effects of the ligands for these receptors (dopamine, ecdysone, and Pigment Dispersing Factor) on AKH cell activation. Dopamine application led to a transient increase in intracellular calcium in a sugar-dependent manner. Furthermore, we found that co-application of dopamine and ecdysone led to a complete loss of this response, suggesting that these two hormones are acting antagonistically. We also found that PDF application directly led to an increase in cAMP in AKH cells, and that this response was dependent on expression of the PDFR in AKH cells. Together these results suggest a complex circuit in which multiple hormones act on AKH cells to modulate metabolic state.
Biocontrol potential of grapevine endophytes against grapevine trunk pathogens

Grapevine Trunk Diseases (GTDs) are a major challenge to the grape industry worldwide. GTDs are responsible for considerable loss of quality, production, and vineyard longevity. Seventy five percent of Chilean vineyards are estimated to be affected by GTDs. GTDs are complex diseases caused by several species of fungi, including Neofusicoccum parvum, Diplodia seriata, and Phaeomoniella chlamydospora. In this study, we report the isolation of 169 endophytic and 209 epiphytic fungi from grapevines grown under organic and conventional farming in Chile. Multiple isolates of Clonostachys rosea, Trichoderma sp. , Purpureocillium lilacium, Epiccocum nigrum, Cladosporium sp. , and Chaetomium sp. were evaluated for their potential of biocontrol activity against fungal trunk pathogens. Tests were carried out using two dual-culture-plate methods with multiple media types, including agar containing grapevine wood extract to simulate in planta nutrient conditions. Significant pathogen growth inhibition was observed by all isolates tested. C. rosea showed 98.2% inhibition of all pathogens in presence of grapevine wood extract. We observed 100% pathogen growth inhibition when autoclaved lignified grapevine shoots were pre-inoculated with either C. rosea strains or Trichoderma sp. Overall these results show that C. rosea strains isolated from grapevines are promising biocontrol agents against GTDs.
TFIIH kinase CDK7 antagonizes phenotype switching and emergence of drug tolerance in melanoma

Melanoma cells switch back-and-forth between phenotypes of proliferation and invasion in response to changing microenvironment, driving metastatic progression. We show that inhibition of the TFIIH kinase CDK7 (CDK7i) results in a melanocytic to mesenchymal phenotype switching and acquisition of targeted therapy tolerance. We identify a gene expression program controlled by the transcription factor GATA6, which participates in drug tolerance in mesenchymal-like cells and which is antagonized by CDK7 in melanocytic-like cells. This program emerges concomitantly with loss of melanocyte lineage-specific MITF protein following CDK7i. By dissecting the underlying mechanism, we observe that CDK7 accumulates at the super-enhancer regulating MITF to drive its expression. MITF itself binds to a intronic region of GATA6 to transcriptionally repress it. This molecular cascade antagonizes expression of the GATA6 regulon that only emerges in MITF-low cells of metastatic melanoma. Our work reveals a role for CDK7 in counteracting phenotype switching and activation of a gene expression program mediating multidrug tolerance in melanoma cells.