Modular Set Research Articles

The MAXLab aggression and bystander intervention scenario set (MAXLab_ABISS): A modular scenario set for studying decision making in situations of interpersonal violence in virtual reality

Abstract Objective This Research Note describes the MAXLab Aggression and Bystander Intervention Scenario Set (MAXLab_ABISS) and provides empirical examples of its applicability. We describe the scenarios, elaborate on the production process, provide technical specifications, and explain how materials can be obtained. Method This modular stimulus set, which is currently available in the English, German and Dutch language, includes 360° videos for studying decision-making in interpersonal violence. It offers researchers a complimentary method to collect data on emotions, perceptions, and reactions to violent situations using immersive technology. Results Analysis from two samples (n = 101 and n = 55) show the immersive scenarios of the stimulus set are effective at evoking targeted emotional experiences at the moment of a crime decision. Conclusion MAXLab_ABISS is a safe and effective tool for studying decision making in real-life, emotion-laden criminogenic settings. We offer insights for how researchers can further use the materials to study decision making related to crime and violence.

Modulo ℓ distinction problems

Let $F$ be a non-archimedean local field of characteristic different from 2 and residual characteristic $p$ . This paper concerns the $\ell$ -modular representations of a connected reductive group $G$ distinguished by a Galois involution, with $\ell$ an odd prime different from $p$ . We start by proving a general theorem allowing to lift supercuspidal $\overline {\mathbf {F}}_{\ell }$ -representations of $\operatorname {GL}_n(F)$ distinguished by an arbitrary closed subgroup $H$ to a distinguished supercuspidal $\overline {\mathbf {Q}}_{\ell }$ -representation. Given a quadratic field extension $E/F$ and an irreducible $\overline {\mathbf {F}}_{\ell }$ -representation $\pi$ of $\operatorname {GL}_n(E)$ , we verify the Jacquet conjecture in the modular setting that if the Langlands parameter $\phi _\pi$ is irreducible and conjugate-selfdual, then $\pi$ is either $\operatorname {GL}_n(F)$ -distinguished or $(\operatorname {GL}_{n}(F),\omega _{E/F})$ -distinguished (where $\omega _{E/F}$ is the quadratic character of $F^\times$ associated to the quadratic field extension $E/F$ by the local class field theory), but not both, which extends one result of Sécherre to the case $p=2$ . We give another application of our lifting theorem for supercuspidal representations distinguished by a unitary involution, extending one result of Zou to $p=2$ . After that, we give a complete classification of the $\operatorname {GL}_2(F)$ -distinguished representations of $\operatorname {GL}_2(E)$ . Using this classification we discuss a modular version of the Prasad conjecture for $\operatorname {PGL}_2$ . We show that the ‘classical’ Prasad conjecture fails in the modular setting. We propose a solution using non-nilpotent Weil–Deligne representations. Finally, we apply the restriction method of Anandavardhanan and Prasad to classify the $\operatorname {SL}_2(F)$ -distinguished modular representations of $\operatorname {SL}_2(E)$ .

Vast Parameter Space Exploration of the Virtual Brain: A Modular Framework for Accelerating the Multi-Scale Simulation of Human Brain Dynamics

Global neural dynamics emerge from multi-scale brain structures, with nodes dynamically communicating to form transient ensembles that may represent neural information. Neural activity can be measured empirically at scales spanning proteins and subcellular domains to neuronal assemblies or whole-brain networks connected through tracts, but it has remained challenging to bridge knowledge between empirically tractable scales. Multi-scale models of brain function have begun to directly link the emergence of global brain dynamics in conscious and unconscious brain states with microscopic changes at the level of cells. In particular, adaptive exponential integrate-and-fire (AdEx) mean-field models representing statistical properties of local populations of neurons have been connected following human tractography data to represent multi-scale neural phenomena in simulations using The Virtual Brain (TVB). While mean-field models can be run on personal computers for short simulations, or in parallel on high-performance computing (HPC) architectures for longer simulations and parameter scans, the computational burden remains red heavy and vast areas of the parameter space remain unexplored. In this work, we report that our HPC framework, a modular set of methods used here to implement the TVB-AdEx model for the graphics processing unit (GPU) and analyze emergent dynamics, notably accelerates simulations and substantially reduces computational resource requirements. The framework preserves the stability and robustness of the TVB-AdEx model, thus facilitating a finer-resolution exploration of vast parameter spaces as well as longer simulations that were previously near impossible to perform. Comparing our GPU implementations of the TVB-AdEx framework with previous implementations using central processing units (CPUs), we first show correspondence of the resulting simulated time-series data from GPU and CPU instantiations. Next, the similarity of parameter combinations, giving rise to patterns of functional connectivity, between brain regions is demonstrated. By varying global coupling together with spike-frequency adaptation, we next replicate previous results indicating inter-dependence of these parameters in inducing transitions between dynamics associated with conscious and unconscious brain states. Upon further exploring parameter space, we report a nonlinear interplay between the spike-frequency adaptation and subthreshold adaptation, as well as previously unappreciated interactions between the global coupling, adaptation, and propagation velocity of action potentials along the human connectome. Given that simulation and analysis toolkits are made public as open-source packages, this framework serves as a template onto which other models can be easily scripted. Further, personalized data-sets can be used for for the creation of red virtual brain twins toward facilitating more precise approaches to the study of epilepsy, sleep, anesthesia, and disorders of consciousness. These results thus represent potentially impactful, publicly available methods for simulating and analyzing human brain states.

Modular vector assembly enables rapid assessment of emerging CRISPR technologies

The diversity of CRISPR systems, coupled with scientific ingenuity, has led to an explosion of applications; however, to test newly described innovations in their model systems, researchers typically embark on cumbersome, one-off cloning projects to generate custom reagents that are optimized for their biological questions. Here, we leverage Golden Gate cloning to create the Fragmid toolkit, a modular set of CRISPR cassettes and delivery technologies, along with a web portal, resulting in a combinatorial platform that enables scalable vector assembly within days. We further demonstrate that multiple CRISPR technologies can be assessed in parallel in a pooled screening format using this resource, enabling the rapid optimization of both novel technologies and cellular models. These results establish Fragmid as a robust system for the rapid design of CRISPR vectors, and we anticipate that this assembly approach will be broadly useful for systematic development, comparison, and dissemination of CRISPR technologies.

Ultrasound COVID-19 Classification Based on the Novel Module-Based Dual-Path Network

With a large-scale novel coronavirus pneumonia (COVID-19) outbreak, more and more researchers have acquired convenient and efficient COVID-19 infection status through medical imaging. Here, due to the excellent features of zero-radiation and rapid clinical examination, ultrasound images have been used to assist doctors in COVID-19 diagnosis. To effectively identify the pathological differences between common pneumonia and novel coronavirus pneumonia, an ultrasound COVID-19 classification based on the novel module-based dual-path network (MD-DPNet) is proposed. Specifically, this paper effectively improves the generalization ability by adding the progressive heatmaps intuitively representing the lesion density with the original ultrasound images. Meanwhile, the proposed algorithm creates regular modular sets to reduce the calculating loads and the coupling between each module, which takes advantage of the fact that most pathological features are concentrated in relatively small regions. Furthermore, the proposed MD-DPNet model divides the complete task into multiple sub-tasks to alleviate interclass disequilibrium and the lesion artifact localization is obtained by an unsupervised method. In this paper, experiments show the effectiveness of the proposed innovation, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Dice reach 0.99, 0.99, 0.99, 0.98, and 0.99, respectively. Experiments on the proposed mixed dataset demonstrate satisfactory results on all the considered tasks, which can provide a new idea for effectively assisting doctors in COVID-19 diagnosis and follow-up treatment.

NLU-V: A Family of Instruction Set Extensions for Efficient Symmetric Cryptography on RISC-V

Cryptographic primitives nowadays are not only implemented in high-performance systems but also in small-scale systems, which are increasingly powered by open-source processors, such as RISC-V. In this work, we leverage RISC-V’s modular base instruction set and architecture to propose a generic instruction set extension (ISE) for symmetric cryptography. We adapt the work from Engels et al. in ARITH’13, the non-linear/linear instruction set extension (NLU), which presents a generic hardware/software co-design solution for efficient symmetric crypto implementations through a hardware unit extending the 8-bit AVR instruction set. These new instructions realize non-linear and linear layers, which are widely used to implement the block ciphers in symmetric cryptography. Our proposal modifies and extends the NLU instructions to a 32-bit RISC-V architecture; hence, we call the proposed ISE ‘NLU-V’. The proposed architecture is integrated into the open-source RISC-V implementation ‘Icicle’ and synthesized on a Xilinx Kintex-7 XC7K160T FPGA. The area overhead for the proposed NLU-V ISE is 1088 slice registers and 4520 LUTs. As case studies, the PRESENT and AES block ciphers are implemented using the new ISE on RISC-V in assembly. Our evaluation metric to showcase the performance gain, Z ‘time-area-product (TAP)’ (the execution time in clock cycles times code memory consumption), reflects the impact of the proposed family of instructions on the performance of the cipher implementations. The simulations show that the NLU-V achieves 89% gain for PRESENT and 68% gain for AES. Further, the NLU-V requires 44% less lines of code for the PRESENT and 23% less for the AES implementation.

An Updated Modular Set of Synthetic Spectral Energy Distributions for Young Stellar Objects

Measured properties of young stellar objects (YSOs) are key tools for research into pre-main-sequence stellar evolution. YSO properties are commonly measured by comparing observed radiation to existing grids of template YSO spectral energy distributions (SEDs) calculated by radiative transfer. These grids are often sampled and constructed using simple models of mass assembly/accretion over time. However, because we do not yet have a complete theory of star formation, the choice of model sets the tracked parameters and range of allowed values. By construction, then, the assumed model limits the measurements that can be made using the grid. Radiative transfer models not constrained by specific accretion histories would enable assessment of a wider range of theories. We present an updated version of the Robitaille set (2017) of YSO SEDs, a collection of models with no assumed evolutionary theory. We outline our newly calculated properties: envelope mass, weighted-average dust temperature, disk stability, and circumstellar A V. We also convolve the SEDs with new filters, including JWST, and provide users the ability to perform additional convolutions. We find a correlation between the average temperature and millimeter-wavelength brightness of optically thin dust in our models and discuss its ramifications for mass measurements of pre- and protostellar cores. We also compare the positions of YSOs of different observational classes and evolutionary stages in IR color space and use our models to quantify the extent to which class and stage may be confused due to observational effects. Our updated models are released to the public.

IST1 regulates select recycling pathways.

ESCRTs (Endosomal Sorting Complex Required for Transports) are a modular set of protein complexes with membrane remodeling activities that include the formation and release of intraluminal vesicles (ILVs) to generate multivesicular endosomes. While most of the 12 ESCRT-III proteins are known to play roles in ILV formation, IST1 has been associated with a wider range of endosomal remodeling events. Here, we extend previous studies of IST1 function in endosomal trafficking and confirm that IST1, along with its binding partner CHMP1B, contributes to scission of early endosomal carriers. Functionally, depleting IST1 impaired delivery of transferrin receptor from early/sorting endosomes to the endocytic recycling compartment and instead increased its rapid recycling to the plasma membrane via peripheral endosomes enriched in the clathrin adaptor AP-1. IST1 is also important for export of mannose 6-phosphate receptor from early/sorting endosomes. Examination of IST1 binding partners on endosomes revealed that IST1 interacts with the MIT domain-containing sorting nexin SNX15, a protein previously reported to regulate endosomal recycling. Our kinetic and spatial analyses establish that SNX15 and IST1 occupy a clathrin-containing subdomain on the endosomal perimeter distinct from those previously implicated in cargo retrieval or degradation. Using live-cell microscopy, we see that SNX15 and CHMP1B alternately recruit IST1 to this subdomain or the base of endosomal tubules. These findings indicate that IST1 contributes to a subset of recycling pathways from the early/sorting endosome.

The MAXLab Aggression and Bystander Intervention Scenario Set (MAXLab_ABISS): A modular scenario set for studying decision making in situations of interpersonal violence in virtual reality (preprint)

The MAXLab Aggression and Bystander Intervention Scenario Set (MAXLab_ABISS): A modular scenario set for studying decision making in situations of interpersonal violence in virtual reality (preprint)

Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions

Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill, allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

Systematic Part Transfer by Extending a Modular Toolkit to Diverse Bacteria.

It is impractical to develop a new parts collection for every potential host organism. It is well-established that gene expression parts, like genes, are qualitatively transferable, but there is little quantitative information defining transferability. Here, we systematically quantified the behavior of a parts set across multiple hosts. To do this, we developed a broad host range (BHR) plasmid system compatible with the large, modular CIDAR parts collection for E.coli, which we named openCIDAR. This enabled testing of a library of DNA constructs across the Pseudomonadota─Escherichia coli, Pseudomonas putida, Cupriavidus necator, and Komagataeibacter nataicola. Part performance was evaluated with a standardized characterization procedure that quantified expression in terms of molecules of equivalent fluorescein (MEFL), an objective unit of measure. The results showed that the CIDAR parts enable graded gene expression across all organisms─meaning that the same parts can be used to program E.coli, P.putida, C.necator, and K.nataicola. Most parts had a similar expression trend across hosts, although each organism had a different average gene expression level. The variability is enough that to achieve the same MEFL in a different organism, a lookup table is required to translate a design from one host to another. To identify truly divergent parts, we applied linear regression to a combinatorial set of promoters and ribosome binding sites, finding that the promoter J23100 behaves very differently in K.nataicola than in the other hosts. Thus, it is now possible to evaluate any CIDAR compatible part in three other hosts of interest, and the diversity of these hosts implies that the collection will also be compatible with many other Proteobacteria (Pseudomonadota). Furthermore, this work defines an approach to generalize modular synthetic biology parts sets beyond a single host, implying that only a few parts sets may be needed to span the tree of life. This will accelerate current efforts to engineer diverse species for environmental, biotechnological, and health applications.

Deep learning denoising of digital breast tomosynthesis: Observer performance study of the effect on detection of microcalcifications in breast phantom images.

The noise in digital breast tomosynthesis (DBT) includes x-ray quantum noise and detector readout noise. The total radiation dose of a DBT scan is kept at about the level of a digital mammogram but the detector noise is increased due to acquisition of multiple projections. The high noise can degrade the detectability of subtle lesions, specifically microcalcifications (MCs). We previously developed a deep-learning-based denoiser to improve the image quality of DBT. In the current study, we conducted an observer performance study with breast radiologists to investigate the feasibility of using deep-learning-based denoising to improve the detection of MCs in DBT. We have a modular breast phantom set containing seven 1-cm-thick heterogeneous 50% adipose/50% fibroglandular slabs custom-made by CIRS, Inc. (Norfolk, VA). We made six 5-cm-thick breast phantoms embedded with 144 simulated MC clusters of four nominal speck sizes (0.125-0.150, 0.150-0.180, 0.180-0.212, 0.212-0.250mm) at random locations. The phantoms were imaged with a GE Pristina DBT system using the automatic standard (STD) mode. The phantoms were also imaged with the STD+ mode that increased the average glandular dose by 54% to be used as a reference condition for comparison of radiologists' reading. Our previously trained and validated denoiser was deployed to the STD images to obtain a denoised DBT set (dnSTD). Seven breast radiologists participated as readers to detect the MCs in the DBT volumes of the six phantoms under the three conditions (STD, STD+, dnSTD), totaling 18 DBT volumes. Each radiologist read all the 18 DBT volumes sequentially, which were arranged in a different order for each reader in a counter-balanced manner to minimize any potential reading order effects. They marked the location of each detected MC cluster and provided a conspicuity rating and their confidence level for the perceived cluster. The visual grading characteristics (VGC) analysis was used to compare the conspicuity ratings and the confidence levels of the radiologists for the detection of MCs. The average sensitivities over all MC speck sizes were 65.3%, 73.2%, and 72.3%, respectively, for the radiologists reading the STD, dnSTD, and STD+ volumes. The sensitivity for dnSTD was significantly higher than that for STD (p<0.005, two-tailed Wilcoxon signed rank test) and comparable to that for STD+. The average false positive rates were 3.9±4.6, 2.8±3.7, and 2.7±3.9 marks per DBT volume, respectively, for reading the STD, dnSTD, and STD+ images but the difference between dnSTD and STD or STD+ did not reach statistical significance. The overall conspicuity ratings and confidence levels by VGC analysis for dnSTD were significantly higher than those for both STD and STD+ (p≤0.001). The critical alpha value for significance was adjusted to be 0.025 with Bonferroni correction. This observer study using breast phantom images showed that deep-learning-based denoising has the potential to improve the detection of MCs in noisy DBT images and increase radiologists' confidence in differentiating noise from MCs without increasing radiation dose. Further studies are needed to evaluate the generalizability of these results to the wide range of DBTs from human subjects and patient populations in clinical settings.

Modular Set of Reagents in Lateral Flow Immunoassay: Application for Antibiotic Neomycin Detection in Honey

A scheme of modular competitive immunochromatography with an analyte-independent test strip and changeable specific immunoreactants has been proposed. Native (detected) and biotinylated antigens interact with specific antibodies during their preincubation in solution, that is, without the immobilization of reagents. After this, the detectable complexes on the test strip are formed by the use of streptavidin (which binds biotin with high affinity), anti-species antibodies, and immunoglobulin-binding streptococcal protein G. The technique was successfully applied for the detection of neomycin in honey. The visual and instrumental detection limits were 0.3 and 0.014 mg/kg, respectively, and the degree of neomycin revealed in honey samples varied from 85% to 113%. The efficiency of the modular technique with the use of the same test strip for different analytes was confirmed for streptomycin detection. The proposed approach excludes the necessity of finding the condition of immobilization for each new specific immunoreactant and transferring the assay to other analytes by a simple choice of concentrations for preincubated specific antibodies and the hapten-biotin conjugate.

Automatic Exercise Generation for Exploring Connections between Mathematics and Music

Abstract Mathematics and Music are closely connected and their multifaceted relationship has been explored since ancient times. Pythagoras was one of the first who discovered and formalized one of those connections by studying the arithmetic of musical intervals, expressing them by numerical ratios and relating ratios to consonance/dissonance notions, being the Pythagorean tuning and scale based on arithmetic principles. Another interesting link between Mathematics and Music is the geometric approach to musical composition. Geometric patterns are present in different musical style compositions and some composing techniques, such as the 12-tone technique, use geometric transformations, involving also applications of modular arithmetic and set theory to Music. The aim of this work is to present existing relations between Mathematics and Music and to propose automatic exercise generation for exploring and studying those relations. The exercises are generated using the system MVGEN and the LaTeX package MusiXTeX. The generation process and the automatically generated exercises can be used for creating learning and assessment materials for education in Mathematics and Music, linking Science and Art.

Development of Undergraduate Teaching Laboratories in the Analysis of Maple Syrup Antioxidants and Phenolics

To address the perpetual need for STEM education resources that foster an interest in scientific research, promote a positive public perception of science, and create an appreciation for the relevance of scientific data in decision-making, we developed a modular set of undergraduate laboratory exercises influenced by ongoing basic research towards elucidating the chemical composition of pure maple syrups. Maple syrup has garnered attention in the chemical literature due to the abundance of phenolic and antioxidant compounds that impart both flavor and therapeutic potential. These experiments feature liquid-liquid extraction techniques and instrumental analysis using Gas Chromatography-Mass Spectrometry (GC-MS) as well as UV-visible spectroscopy. These lab modules can be adapted to different levels of technical rigor for use in courses designed for chemistry majors as well as those designed as introductory courses for nonmajors. Two versions of these exercises were implemented in the spring of 2023: one in a majors-level sophomore organic chemistry, and another in a 100-level food chemistry course. Learning outcomes were specified and survey data was collected from students to assess the ability of the exercises to facilitate student learning.

DIGITALIZED MODULES THROUGH KOTOBEE APPLICATION: A TEACHING INTERVENTION ON IMPROVING PUPILS’ PERFORMANCE LEVEL IN SCIENCE SUBJECT

Accessible lesson inputs serve as motivation and basis for grasping science concepts, especially in online classes. Pupils fail to review these teacher-given inputs in blended modular and synchronous settings. This study sought to determine the effectiveness of digitalizing modules through Kotobee Author and Reader application on pupils’ performance level in science. Using a quantitative approach and a one-group pretest-posttest design, twenty-six (26) Grade 4 pupils of Jimenez Central School (JCS) enrolled in the school year 2020-2021 participated in the study The Division evaluated and validated Science quarterly assessment was used as the instrument for this study. Utilizing statistical tools such as Actual Mean, Hypothetical Mean, the z-test, and the t-test, the instrument's data were analyzed and interpreted. Results of the study showed the following: (1) the performance level of the pupils before the intervention did not meet expectations, (2) the performance level of the pupils after the intervention was satisfactory, and (3) there was an improvement in the pupils’ performance level in science when Kotobee-digitalized modules were used. The researchers recommend the following: (1) teachers could conduct a follow-up study to improve pupils’ academic performance level in other subject areas, (2) teachers could initiate a similar study on pupils from other grade levels, and (3) researchers could determine the perceptions of pupils, teachers and parents in the utilization of the Kotobee Author and Reader application.

On the existence and construction of modular difference sets

The concept of a modular difference set was originally motivated by the cognate notion of modular Hadamard matrices, which have been researched extensively. We initiate the study of the repetition-parameter set in a modular difference set, and we relate the repetition-parameter set to integer partitions and Diophantine equations. By example, we show how a computational study of integer partitions can improve the upper bound on the size of such repetition-parameter set. All previously known examples of modular difference sets in a direct product of groups are concerned with a product of just two groups. We present new constructions of modular difference sets in a direct product of [Formula: see text] groups. These new constructions suggest that the size of the repetition-parameter set is intimately related to the group’s structure. A generalization of difference sets, partial difference sets, and relative difference sets, modular difference sets have been used to construct both modular symmetric designs and equiangular tight frames in finite fields.

Modular computations of standard bases for subalgebras

We develop a theory, algorithms and implementations to compute sagbi bases for local orderings in a parallel and modular setting. By this method, we can calculate sagbi basis with large size coefficients efficiently by avoiding intermediate coefficients growth. We have implemented these algorithms including modular and parallel implementations and give timings. We also discuss the verification of the modular algorithm to compute sagbi bases.

Worldwide host associations of the tick genus Ixodes suggest relationships based on environmental sharing rather than on co-phylogenetic events

BackgroundThis study aims to capture how ticks of the genus Ixodes gained their hosts using network constructs. We propose two alternative hypotheses, namely, an ecological background (ticks and hosts sharing environmentally available conditions) and a phylogenetic one, in which both partners co-evolved, adapting to existing environmental conditions after the association took place.MethodsWe used network constructs linking all the known pairs of associations between each species and stage of ticks with families and orders of hosts. Faith’s phylogenetic diversity was used to evaluate the phylogenetic distance of the hosts of each species and changes occurring in the ontogenetic switch between consecutive stages of each species (or the extent of the changes in phylogenetic diversity of hosts for consecutive stages of the same species).ResultsWe report highly clustered associations among Ixodes ticks and hosts, supporting the influence of the ecological adaptation and coexistence, demonstrating a lack of strict tick-host coevolution in most cases, except for a few species. Keystone hosts do not exist in the relationships between Ixodes and vertebrates because of the high redundancy of the networks, further supporting an ecological relationship between both types of partners. The ontogenetic switch of hosts is high for species with enough data, which is another potential clue supporting the ecological hypothesis. Other results suggest that the networks displaying tick-host associations are different according to the biogeographical realms. Data for the Afrotropical region reveal a lack of extensive surveys, while results for the Australasian region are suggestive of a mass extinction of vertebrates. The Palearctic network is well developed, with many links demonstrating a highly modular set of relationships.ConclusionsWith the obvious exceptions of Ixodes species restricted to one or a few hosts, the results point to an ecological adaptation. Even results on species linked to groups of ticks (such as Ixodes uriae and the pelagic birds or the bat-tick species) are suggestive of a previous action of environmental forces.Graphical

English Writing Errors, Challenges, and Strategies in Modular Learning: A Multiple-Case Study

Writing especially using the English language, is a very important skill that an individual should acquire as it is very beneficial in different aspects of life. Many understand its significance in schools, workplaces and the social world in general. However, it is notable that the English writing proficiency of Filipino learners continuously deteriorates even before the pandemic up until the present. Therefore, it is important to explore the common English writing errors committed along with the challenges experienced as schools shifted to modular settings in learning writing as a compromising strategy to continue learning, as well as the possible strategies adopted by the learners in this context. Using a Multiple-Case study design, the researchers unveiled the writing errors of Grade 7 learners from three public High Schools in Cebu, Philippines. Findings revealed that the students have common writing errors in terms of punctuation, spelling, and capitalization. It is also discovered that the perceived challenges of the students include a lack of guidance from teachers or adults, understanding difficulty, text construction difficulty, and physical distractions. The students’ perceived strategies include writing exercises like note-taking, reading materials, checking and validating, and asking for guidance from peers. It was concluded that the modular setting, as a compromising academic response to minimize the spread of the COVID-19 pandemic, also demands proper and appropriate strategies to compensate for the required learning competencies for English writing.