Miracle-dz/UniFAR-FTUs huggingface dataset:
Writing Scientific Explanations: Effects of a Cognitive Apprenticeship for Students With LD and English Learners

In the current study, we examined the effect of cognitive apprenticeship with contextualized language instruction on students with LD (SWLDs) and English learners’ (ELs) ability to compose scientific explanations using a multiple-probe, multiple-baseline single-case design. Six middle school students (three in each subgroup) participated in ten 30-min sessions in an after-school program. The intervention was implemented with high fidelity, and all participants believed they benefited from the writing instruction. After instruction, SWLDs and ELs had similar gains. They wrote better explanations, as demonstrated by substantial improvements in the quality of their causal and mechanistic reasoning (τ = 1), grammatical and lexical sophistication (τ = 1), and holistic writing (τ = 0.95–0.96). Changes in writing performance were maintained for 4 or more weeks. We discuss implications of these findings for science classrooms and suggest areas for future writing research.
Measuring returns on social investment beyond here-and-now redistribution: A commentary on Parolin and Van Lancker’s response article

Social policy research is truly interdisciplinary with academics from very different theoretical perspectives working together in fervent open-mindedness towards diverse methodological approaches. The exploration of social investment (SI) welfare provision is a clear example of this spirit of interdisciplinary engagement, having stirred up critical scholarly reception and debate over the past decade. On the one hand, some colleagues underscore the potential of SI policies to improve life chances. On the other hand, some researchers voice concerns about perverse unintended consequences of SI. The most worrying scholarly critique of SI is the conjecture that SI policies reinforce rather than alleviate inequality and poverty, because of the operation of so-called Matthew effects (MEs). Parolin and Van Lancker’s commentary on our article ‘The social investment litmus test: family formation, employment and poverty’ falls within the purview of the ME critique, with some extension to other shortcomings discussed in the literature. These criticisms certainly deserve engagement, and we are grateful to the editorial board of the Journal of European Social Policy for inviting us to do so fully. In our commentary, we commence with the multidimensionality of 21st-century welfare state provision. Subsequently, we turn to the welfare state’s carrying capacity, which we maintain needs to be taken into consideration for leveraging positive feedback mechanisms between the micro and the macro level of welfare provision. By so doing, we elaborate on the implications of our research approach for understanding MEs, with insights as to how they are exacerbated or mitigated through policy (in-)complementarities. We then discuss the importance of considering synergies between policies for an improved understanding of SI returns and possible source(s) of MEs. Finally, we turn to the misconception that capacitating SI policies and compensatory consumption-smoothing and poverty alleviation are somehow in competition with each other, and discuss the normative orientation underlying SI welfare provision.
Yeast autonomously replicating sequence (ARS): Identification, function, and modification

Eukaryotic DNA replication begins with multiple origins of replication (ORIs) and thus exploring and identifying ORIs is essential for further understanding of the DNA replication process. In budding yeast, certain autonomously replicating sequences (ARSs) initiate the replication as ORIs, maintaining the stability of chromosomes and plasmids during genome replication. Currently, ARS identification was further facilitated by the DNA microarray technology and bioinformatics, which is based on the previous experimental methods. The structural and functional properties of ARSs on yeast chromosomes are gradually explored in this field. In addition to the function of initiating replication, there is a growing interest for researchers in the effects of ARSs on gene silencing and expression of genes, particularly the relationship between ARSs and chromosome structure. In this review, we summarized the identification methods of ARSs, especially for the bioinformatics prediction methods over the past few years. The functions of ARSs are discussed in this research, moreover, ARS modification that combined with the high‐throughput sequencing was elaborated as well, shedding further light on the understanding of the roles of ARSs, and providing deep insights towards the optimization of ARSs.
Changes in the Movement and Calling Behavior of Minke Whales (Balaenoptera acutorostrata) in Response to Navy Training

Many marine mammals rely on sound for foraging, maintaining group cohesion, navigation, finding mates, and avoiding predators. These behaviors are potentially disrupted by anthropogenic noise. Behavioral responses to sonar have been observed in a number of baleen whale species but relatively little is known about the responses of minke whales (Balaenoptera acutorostrata). Previous analyses demonstrated a spatial redistribution of localizations derived from passive acoustic detections in response to sonar activity, but the lack of a mechanism for associating localizations prevented discriminating between movement and cessation of calling as possible explanations for this redistribution. Here we extend previous analyses by including an association mechanism, allowing us to differentiate between movement responses and calling responses, and to provide direct evidence of horizontal avoidance responses by individual minke whales to sonar during U.S. Navy training activities. We fitted hidden Markov models to 627 tracks that were reconstructed from 3 years of minke whale (B. acutorostrata) vocalizations recorded before, during, and after naval training events at the U.S. Navy’s Pacific Missile Range Facility, Kauai, Hawaii. The fitted models were used to identify different movement behaviors and to investigate the effect of sonar activity on these behaviors. Movement was faster and more directed during sonar exposure than in baseline phases. The mean direction of movement differed during sonar exposure, and was consistent with movement away from sonar-producing ships. Animals were also more likely to cease calling during sonar. There was substantial individual variation in response. Our findings add large-sample support to previous demonstrations of horizontal avoidance responses by individual minke whales to sonar in controlled exposure experiments, and demonstrate the complex nature of behavioral responses to sonar activity: some, but not all, whales exhibited behavioral changes, which took the form of horizontal avoidance or ceasing to call.
Epitranscriptomic Analysis of m6A Methylome After Peripheral Nerve Injury

N6-methyladenosine (m6A) is one of the most plentiful internal RNA modifications, especially in eukaryotic messenger RNA (mRNA), which plays pivotal roles in the regulation of mRNA life cycle and nerve development. However, the mRNA m6A methylation pattern in peripheral nervous injury (PNI) has not been investigated. In this study, sciatic nerve samples were collected from 7 days after sciatic nerve injury (SNI) and control rats. Quantitative real-time PCR demonstrated that m6A-related methyltransferase/demethylase genes were remarkably upregulated in SNI group compared with control group. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) was performed to reveal the m6A methylation landscape. The results showed that 4,014 m6A peaks were significantly altered, including 2,144 upregulated and 1,870 downregulated m6A peaks, which were corresponded to 1,858 genes. Moreover, 919 differentially expressed genes were identified by the conjoint analysis of MeRIP-seq and RNA-seq. GO and KEGG pathway analyses were performed to determine the biological functions and signaling pathways of the m6A-modified genes. Notably, these genes were mainly related to the immune system process, cell activation, and nervous system development in GO analysis. KEGG pathway analysis revealed that these genes were involved in the cell cycle, B cell receptor signaling pathway, axon guidance pathway, and calcium signaling pathway. Furthermore, the m6A methylation and protein expression levels of autophagy-related gene (Atg7) were increased, together with the activation of autophagy. These findings shed some light on the epigenetic regulation of gene expression, which may provide a new opinion to promote functional recovery after PNI.
Alignment of Selected Veterinary Education Competencies With the Interprofessional Professionalism Assessment

The move toward outcomes and competency-based veterinary education began decades ago but has accelerated in the past few years. While many advances in curriculum structure and delivery are replete in the literature, assessment methods remain challenging and less explored. This is especially true for competency domains that are behavior-based such as professionalism, which has been challenging to merely define let alone assess. The Interprofessionalism Professionalism Assessment (IPA) is a validated tool that is used in veterinary education and is useful in assessing competency domains that have remained challenging for educators. Here we present the challenges and solutions for assessing competency in veterinary education and provide examples and data from tools that may be useful especially in areas such as professionalism.
Molecular adaptation to folivory and the conservation implications for Madagascar’s lemurs

Folivory evolved independently at least three times over the last 40 million years among Madagascar’s lemurs. Many extant lemuriform folivores exist in sympatry in Madagascar’s remaining forests. These species avoid feeding competition by adopting different dietary strategies within folivory, reflected in behavioral, morphological, and microbiota diversity across species. These conditions make lemurs an ideal study system for understanding adaptation to leaf-eating. Most folivorous lemurs are also highly endangered. The significance of folivory for conservation outlook is complex. Though generalist folivores may be relatively well equipped to survive habitat disturbance, specialist folivores occupying narrow dietary niches may be less resilient. Characterizing the genetic bases of adaptation to folivory across species and lineages can provide insights into their differential physiology and potential to resist habitat change. We recently reported accelerated genetic change in RNASE1, a gene encoding an enzyme (RNase 1) involved in molecular adaptation in mammalian folivores, including various monkeys and sifakas (genus Propithecus; family Indriidae). Here, we sought to assess whether other lemurs, including phylogenetically and ecologically diverse folivores, might show parallel adaptive change in RNASE1 that could underlie a capacity for efficient folivory. We characterized RNASE1 in 21 lemur species representing all five families and members of the three extant folivorous lineages: 1) bamboo lemurs (family Lemuridae), 2) sportive lemurs (family Lepilemuridae), and 3) indriids (family Indriidae). We found pervasive sequence change in RNASE1 across all indriids, a dN/dS value > 3 in this clade, and evidence for shared change in isoelectric point, indicating altered enzymatic function. Sportive and bamboo lemurs, in contrast, showed more modest sequence change. The greater change in indriids may reflect a shared strategy emphasizing complex gut morphology and microbiota to facilitate folivory. This case study illustrates how genetic analysis may reveal differences in functional traits that could influence species’ ecology and, in turn, their resilience to habitat change. Moreover, our results support the contention that not all primate folivores are built the same and highlight the need to avoid generalizations about dietary guild in considering conservation outlook, particularly in lemurs where such diversity in folivory has probably led to extensive specialization via niche partitioning.
Bayesian estimation of population size and overlap from random subsamples

Counting the number of species, items, or genes that are shared between two sets is a simple calculation when sampling is complete. However, when only partial samples are available, quantifying the overlap between two sets becomes an estimation problem. Furthermore, to calculate normalized measures of β-diversity, such as the Jaccard and Sorenson-Dice indices, one must also estimate the total sizes of the sets being compared. Previous efforts to address these problems have assumed knowledge of total population sizes and then used Bayesian methods to produce unbiased estimates with quantified uncertainty. Here, we address populations of unknown size and show that this produces systematically better estimates—both in terms of central estimates and quantification of uncertainty in those estimates. We further show how to use species count data to refine estimates of population size in a Bayesian joint model of populations and overlap.
Multivalent designed proteins protect against SARS-CoV-2 variants of concern

Escape variants of SARS-CoV-2 are threatening to prolong the COVID-19 pandemic. To address this challenge, we developed multivalent protein-based minibinders as potential prophylactic and therapeutic agents. Homotrimers of single minibinders and fusions of three distinct minibinders were designed to geometrically match the SARS-CoV-2 spike (S) trimer architecture and were optimized by cell-free expression and found to exhibit virtually no measurable dissociation upon binding. Cryo-electron microscopy (cryoEM) showed that these trivalent minibinders engage all three receptor binding domains on a single S trimer. The top candidates neutralize SARS-CoV-2 variants of concern with IC50 values in the low pM range, resist viral escape, and provide protection in highly vulnerable human ACE2-expressing transgenic mice, both prophylactically and therapeutically. Our integrated workflow promises to accelerate the design of mutationally resilient therapeutics for pandemic preparedness. One-Sentence Summary We designed, developed, and characterized potent, trivalent miniprotein binders that provide prophylactic and therapeutic protection against emerging SARS-CoV-2 variants of concern.
Structural snapshots of human PepT1 and PepT2 reveal mechanistic insights into substrate and drug transport across epithelial membranes

The uptake of peptides in mammals plays a crucial role in nutrition and inflammatory diseases. This process is mediated by promiscuous transporters of the Solute Carrier Family 15, which form part of the Major Facilitator superfamily. Besides the uptake of short peptides, Peptide transporter 1 (PepT1) is a highly abundant drug transporter in the intestine and represents a major route for oral drug delivery. Peptide transporter 2 (PepT2) allows in addition renal drug reabsorption from ultrafiltration and brain-to-blood efflux of neurotoxic compounds. Here we present cryo-EM structures of human PepT1 in an outward open state and of human PepT2 in an inward facing partially occluded state with a bound substrate. The structures reveal the architecture of human peptide transporters and provide mechanistic insights into substrate recognition and conformational transitions during transport. Importantly, this may support future drug design efforts to increase the bioavailability of different drugs in the human body.
What is behavior? No seriously, what is it?

Studying ‘behavior’ lies at the heart of many disciplines. Nevertheless, academics rarely provide an explicit definition of what ‘behavior’ actually is. What range of definitions do people use, and how does that vary across disciplines? To answer these questions we have developed a survey to probe what constitutes ‘behavior’. We find that academics adopt different definitions of behavior according to their academic discipline, animal model that they work with, and level of academic seniority. Using hierarchical clustering, we identify at least six distinct types of ‘behavior’ which are used in seven distinct operational archetypes of ‘behavior’. Individual respondents have clear consistent definitions of behavior, but these definitions are not consistent across the population. Our study is a call for academics to clarify what they mean by ‘behavior’ wherever they study it, with the hope that this will foster interdisciplinary studies that will improve our understanding of behavioral phenomena.
Optimization of the TeraTox Assay for Preclinical Teratogenicity Assessment

Current animal-free methods to assess teratogenicity of drugs under development still deliver high numbers of false negatives, and more sensitive approaches of toxicity prediction are required. To address this issue, we characterized the TeraTox test, a newly developed multi-lineage differentiation assay for human teratogenicity prediction using 3D human induced pluripotent stem cells. TeraTox produces as primary output concentration-dependent data sets for each test compound on cytotoxicity and altered gene expression. These data are then fed into a prediction model based on an interpretable machine-learning approach. The final information obtained relates to the concentration-dependent human teratogenicity potential of drug candidates. We applied TeraTox to profile 33 approved pharmaceuticals and 12 proprietary drug candidates with known in vivo data. This way, it was possible to relate the test predictions to known human or animal toxicity. The TeraTox had an accuracy of 69% (specificity: 53%, sensitivity: 79%). It clearly performed better than two quantitative structure-activity relationship (QSAR) models and it had a higher sensitivity than the murine embryonic stem cell test (mEST) run in the same laboratory. By combining TeraTox and mEST data, the overall prediction accuracy was further improved. The knowledge on the pattern of altered gene expression may provide additional value in grouping toxicologically similar compounds and possibly deducing common modes of action. The assay will thus be a helpful additional tool in drug discovery, and the dataset provided here will be a valuable resource for the field of stem cell-based drug profiling.
Hybrid Beamforming for Wideband OFDM Dual Function Radar Communications

This paper considers the hybrid beamforming design for wideband OFDM-DFRC system serving multiple users (MUs). The analog beamformer for the whole bandwidth and the digital beamformers for each subcarrier and are jointly optimized by considering flexible performance trade-off between the radar and communication. To deal with the resulting optimization problem, a consensus alternating direction method of multipliers (consensus-ADMM) framework based on the weighted mean-square error minimization (WMMSE) approach is proposed. Numerical simulations are provided to demonstrate the effectiveness of the proposed scheme.
Convergence Analysis of the Graph-Topology-Inference Kernel LMS Algorithm

Identifying directed connectivity patterns from nodal measurements is an important problem in network analysis. Recent works proposed to leverage the performance and flexibility of strategies operating in reproducing kernel Hilbert spaces (RKHS) to model nonlinear interactions between network agents. Moreover, several applications require online and efficient solutions, which motivated the consideration of distributed adaptive learning strategies inspired by algorithms such as the kernel least mean square (KLMS). Despite showing good performance, a thorough theoretical understanding of the behavior of such algorithms is still missing. This makes applying them in practice challenging, especially because the set-up of adaptive algorithms involves additional parameters like the step size and a dictionary of kernel functions. In this paper, we present a convergence analysis of the graph-topology-inference KLMS algorithm. Monte Carlo simulations demonstrate the accuracy of the theoretical models.
DURAS: Deep Unfolded Radar Sensing Using Doppler Focusing

Sub-Nyquist sampling is used in modern high-resolution pulse-Doppler radar systems to reduce system resources and improve resolution. Xampling with Doppler focusing is utilized to implement these sub-Nyquist radar systems. Signal recovery involves iterative optimization requiring large computational time that may be prohibitive in real applications. In this paper, we propose Deep Unfolded Radar Sensing (DURAS), a model-based deep learning architecture to address this problem. We utilize the recently introduced complex LISTA (C-LISTA) with recurrent neural network units and complex soft-thresholding to handle the complex-valued measurement signals. We propose a partial Doppler focusing (PDF) framework with ensembling of multiple PDF measurement vectors via a convolutional neural network (CNN). This CNN followed by a complex cardioid activation function is added to the front end of the C-LISTA architecture. Thus, DURAS is a hybrid architecture of partial Doppler focusing, CNN, and C-LISTA that provides considerably improved performance compared to existing methods on target detection in radar systems.
Sound Recovery From Radio Signals

With the proliferation of smart devices, voice interfaces have become an integral part of our lives, which typically senses sound by microphones through converting the changes in air pressure into electrical signals. Sound sensing through another modality can enable various sensing applications in the absence of a microphone. In fact, environmental sound creates tiny vibrations on object surfaces, which could be captured by radio signals. In this work, we model the vibration on object surfaces due to sound for mmWave devices. We propose a method for the recovery of sound and conduct experiments with various materials to investigate the feasibility of sound reconstruction. We further evaluate the effect of distance and placement to understand the practical limits on the sound reconstruction. The results show that, by using a commodity off-the-shelf radar, it is possible to capture a significant amount of sound from the environment.
Dual-Channel and Bidirectional Neural Network for Hypersonic Glide Vehicle Trajectory Prediction

Different from traditional aircraft, hypersonic glide vehicles (HGVs) possess stronger maneuverability and a higher flight speed (generally higher than 5 Mach), making trajectory prediction very complicated. Several works have been conducted in this field, which usually analyze the motion characteristics of the HGV first and then use a Kalman filter to track and predict the trajectory. In this way, the accuracy of prediction depends on how to model the control parameters of the target vehicle. The core idea of this paper mainly concerns treating the HGV trajectory prediction as a multivariate time series forecasting problem since HGV trajectories are special multivariate time series with nonperiodic temporal patterns. Moreover, capturing the hidden dependencies between time steps and different time series ensures the accuracy and robustness of predictions. Recently, recurrent neural networks have been widely used in predicting data with temporal patterns between several time steps; however, they fail in capturing nonperiodic temporal patterns. Therefore, we propose a brand-new model named the dual-channel and bidirectional neural network (DCBNN) to intelligently predict the trajectory of hypersonic vehicles in undetectable areas, especially for those with complex maneuver models. DCBNN is constructed with a nonlinear component and a linear component to collect both nonlinear and linear features from input data to improve robustness. Moreover, a dual-branch architecture is utilized in the nonlinear component to capture the complex and mixed dependencies between long-term and short-term patterns. Experiments reveal that the proposed method is effective and intelligent.