PfCRT mutations conferring piperaquine resistance in falciparum malaria shape the kinetics of quinoline drug binding and transport.

The chloroquine resistance transporter (PfCRT) confers resistance to a wide range of quinoline and quinoline-like antimalarial drugs in Plasmodium falciparum, with local drug histories driving its evolution and, hence, the drug transport specificities. For example, the change in prescription practice from chloroquine (CQ) to piperaquine (PPQ) in Southeast Asia has resulted in PfCRT variants that carry an additional mutation, leading to PPQ resistance and, concomitantly, to CQ re-sensitization. How this additional amino acid substitution guides such opposing changes in drug susceptibility is largely unclear. Here, we show by detailed kinetic analyses that both the CQ- and the PPQ-resistance conferring PfCRT variants can bind and transport both drugs. Surprisingly, the kinetic profiles revealed subtle yet significant differences, defining a threshold for in vivo CQ and PPQ resistance. Competition kinetics, together with docking and molecular dynamics simulations, show that the PfCRT variant from the Southeast Asian P. falciparum strain Dd2 can accept simultaneously both CQ and PPQ at distinct but allosterically interacting sites. Furthermore, combining existing mutations associated with PPQ resistance created a PfCRT isoform with unprecedented non-Michaelis-Menten kinetics and superior transport efficiency for both CQ and PPQ. Our study provides additional insights into the organization of the substrate binding cavity of PfCRT and, in addition, reveals perspectives for PfCRT variants with equal transport efficiencies for both PPQ and CQ.

Responses of intraspecific metabolic scaling to temperature and activity differ between water- and air-breathing ectothermic vertebrates.

Metabolism underpins all life-sustaining processes and varies profoundly with body size, temperature and locomotor activity. A current theory explains some of the size-dependence of metabolic rate (its mass exponent, b) through changes in metabolic level (L). We propose two predictive advances that: (a) combine the above theory with the evolved avoidance of oxygen limitation in water-breathers experiencing warming, and (b) quantify the overall magnitude of combined temperatures and degrees of locomotion on metabolic scaling across air- and water-breathers. We use intraspecific metabolic scaling responses to temperature (523 regressions) and activity (281 regressions) in diverse ectothermic vertebrates (fish, reptiles and amphibians) to show that b decreases with temperature-increased L in water-breathers, supporting surface area-related avoidance of oxygen limitation, whereas b increases with activity-increased L in air-breathers, following volume-related influences. This new theoretical integration quantitatively incorporates different influences (warming, locomotion) and respiration modes (aquatic, terrestrial) on animal energetics.

DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.

BACKGROUND: P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data. RESULTS: We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors). CONCLUSIONS: Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients.

Enhanced RhoA signalling stabilizes E-cadherin in migrating epithelial monolayers.

Epithelia migrate as physically coherent populations of cells. Previous studies have revealed that mechanical stress accumulates in these cellular layers as they move. These stresses are characteristically tensile in nature and have often been inferred to arise when moving cells pull upon the cell-cell adhesions that hold them together. We now report that epithelial tension at adherens junctions between migrating cells also increases due to an increase in RhoA-mediated junctional contractility. We found that active RhoA levels were stimulated by p114 RhoGEF (also known as ARHGEF18) at the junctions between migrating MCF-7 monolayers, and this was accompanied by increased levels of actomyosin and mechanical tension. Applying a strategy to restore active RhoA specifically at adherens junctions by manipulating its scaffold, anillin, we found that this junctional RhoA signal was necessary to stabilize junctional E-cadherin (CDH1) during epithelial migration and promoted orderly collective movement. We suggest that stabilization of E-cadherin by RhoA serves to increase cell-cell adhesion to protect against the mechanical stresses of migration. This article has an associated First Person interview with the first author of the paper.

The 2022 Massive Open Online Course (MOOC) to train physiotherapists in the management of people with spinal cord injuries: a qualitative and quantitative analysis of learners' experiences and its impact.

STUDY DESIGN: Observational. OBJECTIVES: To determine the learners' experience and the impact of a Massive Open Online Course (MOOC) conducted to teach physiotherapists about the management of people with spinal cord injuries (SCI). METHODS: A SCI MOOC for physiotherapists was run in 5 different languages at the end of 2022. Qualitative and quantitative data were collected from different sources including registration details, pre- and post-MOOC Knowledge Assessments, a post-MOOC Evaluation, social media posts and online tracking of websites and emails. The data were used to answer four key questions: (i) what was the reach of the MOOC, (ii) what did participants think about the MOOC (iii) did the MOOC change participants' knowledge and/or confidence, and (iv) did the MOOC change participants' clinical practice or the way they teach others? RESULTS: 25,737 people from 169 countries registered for the MOOC. 98% of participants who completed the Evaluation (n = 2281) rated the MOOC as either "good" or "very good". Participants' knowledge improved by a median (IQR) of 25% (10 to 45%) (n = 4016 participants) on the MOOC Knowledge Assessment. Participants reported changes in confidence, and intentions to change clinical practice and incorporate what they had learnt into the way they teach others in response to the MOOC. CONCLUSION: The MOOC provided an efficient way to increase physiotherapists' knowledge about the physiotherapy management of people with SCI. Participants enjoyed the MOOC, and indicated an intention to change clinical practice and the way they taught others.

On the Fitness Functions Involved in Genetic Algorithms and the Cryptanalysis of Block Ciphers.

There are many algorithms used with different purposes in the area of cryptography. Amongst these, Genetic Algorithms have been used, particularly in the cryptanalysis of block ciphers. Interest in the use of and research on such algorithms has increased lately, with a special focus on the analysis and improvement of the properties and characteristics of these algorithms. In this way, the present work focuses on studying the fitness functions involved in Genetic Algorithms. First, a methodology was proposed to verify that the closeness to 1 of some fitness functions' values that use decimal distance implies decimal closeness to the key. On the other hand, the foundation of a theory is developed in order to characterize such fitness functions and determine, a priori, if one method is more effective than another in the attack to block ciphers using Genetic Algorithms.

Complexity Reduction in Analyzing Independence between Statistical Randomness Tests Using Mutual Information.

The advantages of using mutual information to evaluate the correlation between randomness tests have recently been demonstrated. However, it has been pointed out that the high complexity of this method limits its application in batteries with a greater number of tests. The main objective of this work is to reduce the complexity of the method based on mutual information for analyzing the independence between the statistical tests of randomness. The achieved complexity reduction is estimated theoretically and verified experimentally. A variant of the original method is proposed by modifying the step in which the significant values of the mutual information are determined. The correlation between the NIST battery tests was studied, and it was concluded that the modifications to the method do not significantly affect the ability to detect correlations. Due to the efficiency of the newly proposed method, its use is recommended to analyze other batteries of tests.

Disparities among crop species in the evolution of growth rates: the role of distinct origins and domestication histories.

Growth rates vary widely among plants with different strategies. For crops, evolution under predictable and high-resource environments might favour rapid resource acquisition and growth, but whether this strategy has consistently evolved during domestication and improvement remains unclear. Here we report a comprehensive study of the evolution of growth rates based on comparisons among wild, landrace, and improved accessions of 19 herbaceous crops grown under common conditions. We also examined the underlying growth components and the influence of crop origin and history on growth evolution. Domestication and improvement did not affect growth consistently, that is growth rates increased or decreased or remained unchanged in different crops. Crops selected for fruits increased the physiological component of growth (net assimilation rate), whereas leaf and seed crops showed larger domestication effects on morphology (leaf mass ratio and specific leaf area). Moreover, climate and phylogeny contributed to explaining the effects of domestication and changes in growth. Crop-specific responses to domestication and improvement suggest that selection for high yield has not consistently changed growth rates. The trade-offs between morpho-physiological traits and the distinct origins and histories of crops accounted for the variability in growth changes. These findings have far-reaching implications for our understanding of crop performance and adaptation.

Regulating life after death: how mechanical communication mediates the epithelial response to apoptosis.

It is increasingly evident that cells in tissues and organs can communicate with one another using mechanical forces. Such mechanical signalling can serve as a basis for the assembly of cellular communities. For this to occur, there must be local instabilities in tissue mechanics that are the source of the signals, and mechanisms for changes in mechanical force to be transmitted and detected within tissues. In this review, we discuss these principles using the example of cell death by apoptosis, when it occurs in epithelia. This elicits the phenomenon of apical extrusion, which can rapidly eliminate apoptotic cells by expelling them from the epithelium. Apoptotic extrusion requires that epithelial cells detect the presence of nearby apoptotic cells, something which can be elicited by the mechanotransduction of tensile instabilities caused by the apoptotic cell. We discuss the central role that adherens junctions can play in the transmission and detection of mechanical signals from apoptotic cells.

Spatiotemporal pattern of specialization of sunbird-plant networks on Mt. Cameroon.

Differences in interaction specializations between nectarivorous birds and plants across continents serve as common examples of evolutionary trajectory specificity. While New World hummingbird-plant networks have been extensively studied and are considered highly specialized, knowledge on the network specialization of their Old World counterparts, sunbirds (Nectariniidae), remains limited. A few studies from tropical Africa indicate that sunbird-plant networks are rather generalized. Unfortunately, these studies are limited to dry seasons and high elevations at the tree line, environments where niche-based hypotheses also often predict lower resource partitioning. In our study, we explored the specialization of sunbird-plant networks and their spatiotemporal variability on Mt. Cameroon (Cameroon). Using a combination of automatic video recordings and personal observations, we constructed eight comprehensive sunbird-plant networks in four forest types at different elevations in both the dry and wet seasons. As reported in previous studies, the montane forest plants, birds and whole networks were highly generalized. Nevertheless, we observed a much higher specialization in forests at lower elevations. Except at the lowest altitude, the wet season was also characterized by higher specialization. While less specialized flowering trees dominated in the dry season networks, more specialized herbs and shrubs were visited by birds during the wet season. As our findings do not support the generally accepted assumption that Old World bird-plant networks are rather generalized, we need further studies to understand the differences in bird-plant specializations on individual continents.

Detection of DIAG and LINE Patterns in PassPoints Graphical Passwords Based on the Maximum Angles of Their Delaunay Triangles.

An alternative authentication method to traditional alphanumeric passwords is graphical password authentication, also known as graphical authentication, for which one of the most valuable cued-recall techniques is PassPoints. This technique stands out for its security and usability. However, it can be violated if the user follows a predefined pattern when selecting the five points in an image as their passwords, such as the DIAG and LINE patterns. Dictionary attacks can be built using these two patterns to compromise graphical passwords. So far, no reports have been found in the state of the art about any test capable of detecting graphical passwords with DIAG or LINE patterns in PassPoints. Studies carried out in other scenarios have shown the effectiveness of the characteristics of Delaunay triangulations in extracting information about the dependence between the points. In this work, graphical passwords formed by five randomly selected points on an image are compared with passwords whose points contain patterns of the DIAG or LINE type. The comparison is based on building for each password its Delaunay triangulation and calculating the mean value of the maximum angles of the triangles obtained; such a mean value is denoted by amadt. It is experimentally shown that in passwords containing DIAG and LINE patterns, the value of amadt is higher than the one obtained in passwords formed by random dots. From this result, it is proposed to use this amadt value as a statistic to build a test of means. This result constitutes the work's main contribution: The proposal of a spatial randomness test to detect weak graphic passwords that contain DIAG and LINE type patterns. The importance and novelty of this result become evident when two aspects are taken into account: First, these weak passwords can be exploited by attackers to improve the effectiveness of their attacks; second, there are no prior criteria to detect this type of weak password. The practical application of said test contributes to increasing PassPoints security without substantially affecting its efficiency.

A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma.

BACKGROUND: Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking. METHODS: We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions. RESULTS: We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival. CONCLUSIONS: This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: https://github.com/amin20/GBM_WSSM .

Translation and linguistic validation of the Female Genitourinary Pain Index in Spanish.

INTRODUCTION AND HYPOTHESIS: We sought to develop a Spanish translation of the Female Genitourinary Pain Index (GUPI) and to validate this instrument in US Latina women. METHODS: Translation back-translation was performed to create the initial Spanish version. Bilingual women with pelvic and/or genitourinary pain were recruited from clinical sites and social media. Participants reported demographics and completed the Female GUPI in both English and Spanish. Agreement was assessed for each item, subscale and total score. Additionally, we performed cognitive debriefing interviews to further test face validity. A consensus group of bilingual physicians and healthcare personnel utilized comments from the interviews to create a final Spanish version. RESULTS: Thirty-four participants completed the questionnaire. Their average age was 33 years, 80% reported attending some college, and 20% reported an undergraduate degree or higher. Most were born in mainland USA (57%) or Mexico (27%). Agreement for the pain, urinary and quality of life subscales between the English and Spanish versions of the measure were excellent (0.91, 0.89 and 0.92, respectively) with 0.96 agreement for the measure as a whole. Despite favorable psychometrics, preferences for alternate wording were reported over 50 times. Based on that feedback, a consensus group was formed, which recommended changes to 13 of the 15 items, 3 of which required complete rewriting. CONCLUSIONS: The Spanish Female GUPI is strongly correlated with the English original; however, participants reported the language was overly complex. Translation and validation should include review of the measure and feedback by the target audience for optimal clarity and readability.

New Sensor Device to Accurately Measure Cable Tension in Cable-Driven Parallel Robots.

Cable-driven parallel robots are a special type of robot in which an end-effector is attached to a fixed frame by means of several cables. The position and orientation of the end-effector can be controlled by controlling the length of the cables. These robots present a wide range of advantages, and the control algorithms required have greater complexity than those in traditional serial robots. Measuring the cable tension is an important task in this type of robot as many control algorithms rely on this information. There are several well-known approaches to measure cable tension in cable robots, where a trade-off between complexity and accuracy is observed. This work presents a new device based on strain gauges to measure cable tension specially designed to be applied in cable-driven parallel robots. This device can be easily mounted on the cable near the fixed frame, allowing the cable length and orientation to change freely, while the measure is taken before the cable passes through the guiding pulleys for improved accuracy. The results obtained from the device show a strong repeatability and linearity of the measures.

A Drug Screening Pipeline Using 2D and 3D Patient-Derived In Vitro Models for Pre-Clinical Analysis of Therapy Response in Glioblastoma.

Glioblastoma is one of the most common and lethal types of primary brain tumor. Despite aggressive treatment with chemotherapy and radiotherapy, tumor recurrence within 6-9 months is common. To overcome this, more effective therapies targeting cancer cell stemness, invasion, metabolism, cell death resistance and the interactions of tumor cells with their surrounding microenvironment are required. In this study, we performed a systematic review of the molecular mechanisms that drive glioblastoma progression, which led to the identification of 65 drugs/inhibitors that we screened for their efficacy to kill patient-derived glioma stem cells in two dimensional (2D) cultures and patient-derived three dimensional (3D) glioblastoma explant organoids (GBOs). From the screening, we found a group of drugs that presented different selectivity on different patient-derived in vitro models. Moreover, we found that Costunolide, a TERT inhibitor, was effective in reducing the cell viability in vitro of both primary tumor models as well as tumor models pre-treated with chemotherapy and radiotherapy. These results present a novel workflow for screening a relatively large groups of drugs, whose results could lead to the identification of more personalized and effective treatment for recurrent glioblastoma.

Information Theory Based Evaluation of the RC4 Stream Cipher Outputs.

This paper presents a criterion, based on information theory, to measure the amount of average information provided by the sequences of outputs of the RC4 on the internal state. The test statistic used is the sum of the maximum plausible estimates of the entropies H(jt|zt), corresponding to the probability distributions P(jt|zt) of the sequences of random variables (jt)t∈T and (zt)t∈T, independent, but not identically distributed, where zt are the known values of the outputs, while jt is one of the unknown elements of the internal state of the RC4. It is experimentally demonstrated that the test statistic allows for determining the most vulnerable RC4 outputs, and it is proposed to be used as a vulnerability metric for each RC4 output sequence concerning the iterative probabilistic attack.

Selecting an Effective Entropy Estimator for Short Sequences of Bits and Bytes with Maximum Entropy.

Entropy makes it possible to measure the uncertainty about an information source from the distribution of its output symbols. It is known that the maximum Shannon's entropy of a discrete source of information is reached when its symbols follow a Uniform distribution. In cryptography, these sources have great applications since they allow for the highest security standards to be reached. In this work, the most effective estimator is selected to estimate entropy in short samples of bytes and bits with maximum entropy. For this, 18 estimators were compared. Results concerning the comparisons published in the literature between these estimators are discussed. The most suitable estimator is determined experimentally, based on its bias, the mean square error short samples of bytes and bits.

Metaheuristics in the Optimization of Cryptographic Boolean Functions.

Generating Boolean Functions (BFs) with high nonlinearity is a complex task that is usually addresses through algebraic constructions. Metaheuristics have also been applied extensively to this task. However, metaheuristics have not been able to attain so good results as the algebraic techniques. This paper proposes a novel diversity-aware metaheuristic that is able to excel. This proposal includes the design of a novel cost function that combines several information from the Walsh Hadamard Transform (WHT) and a replacement strategy that promotes a gradual change from exploration to exploitation as well as the formation of clusters of solutions with the aim of allowing intensification steps at each iteration. The combination of a high entropy in the population and a lower entropy inside clusters allows a proper balance between exploration and exploitation. This is the first memetic algorithm that is able to generate 10-variable BFs of similar quality than algebraic methods. Experimental results and comparisons provide evidence of the high performance of the proposed optimization mechanism for the generation of high quality BFs.

Measuring Independence between Statistical Randomness Tests by Mutual Information.

The analysis of independence between statistical randomness tests has had great attention in the literature recently. Dependency detection between statistical randomness tests allows one to discriminate statistical randomness tests that measure similar characteristics, and thus minimize the amount of statistical randomness tests that need to be used. In this work, a method for detecting statistical dependency by using mutual information is proposed. The main advantage of using mutual information is its ability to detect nonlinear correlations, which cannot be detected by the linear correlation coefficient used in previous work. This method analyzes the correlation between the battery tests of the National Institute of Standards and Technology, used as a standard in the evaluation of randomness. The results of the experiments show the existence of statistical dependencies between the tests that have not been previously detected.

Reduced accumulation of defective viral genomes contributes to severe outcome in influenza virus infected patients.

Influenza A virus (IAV) infection can be severe or even lethal in toddlers, the elderly and patients with certain medical conditions. Infection of apparently healthy individuals nonetheless accounts for many severe disease cases and deaths, suggesting that viruses with increased pathogenicity co-circulate with pandemic or epidemic viruses. Looking for potential virulence factors, we have identified a polymerase PA D529N mutation detected in a fatal IAV case, whose introduction into two different recombinant virus backbones, led to reduced defective viral genomes (DVGs) production. This mutation conferred low induction of antiviral response in infected cells and increased pathogenesis in mice. To analyze the association between low DVGs production and pathogenesis in humans, we performed a genomic analysis of viruses isolated from a cohort of previously healthy individuals who suffered highly severe IAV infection requiring admission to Intensive Care Unit and patients with fatal outcome who additionally showed underlying medical conditions. These viruses were compared with those isolated from a cohort of mild IAV patients. Viruses with fewer DVGs accumulation were observed in patients with highly severe/fatal outcome than in those with mild disease, suggesting that low DVGs abundance constitutes a new virulence pathogenic marker in humans.