The Structural Basis of Coenzyme A Recycling in a Bacterial Organelle.

Bacterial Microcompartments (BMCs) are proteinaceous organelles that encapsulate critical segments of autotrophic and heterotrophic metabolic pathways; they are functionally diverse and are found across 23 different phyla. The majority of catabolic BMCs (metabolosomes) compartmentalize a common core of enzymes to metabolize compounds via a toxic and/or volatile aldehyde intermediate. The core enzyme phosphotransacylase (PTAC) recycles Coenzyme A and generates an acyl phosphate that can serve as an energy source. The PTAC predominantly associated with metabolosomes (PduL) has no sequence homology to the PTAC ubiquitous among fermentative bacteria (Pta). Here, we report two high-resolution PduL crystal structures with bound substrates. The PduL fold is unrelated to that of Pta; it contains a dimetal active site involved in a catalytic mechanism distinct from that of the housekeeping PTAC. Accordingly, PduL and Pta exemplify functional, but not structural, convergent evolution. The PduL structure, in the context of the catalytic core, completes our understanding of the structural basis of cofactor recycling in the metabolosome lumen

A Comparison of The Mathematical Processes Embedded in The Content Standards of Turkey and Singapore

This study compares Turkey's and Singapore's mathematics content standards in terms of the highligthed mathematical processes. A mathematical processes framework was employed to analyze the content standards drawing on the standards for mathematical practice defined by the Common Core State Standards for Mathematics. The standards for mathematical practice include make sense of problems and persevere in solving them, reason abstractly and quantitatively, construct viable arguments and critique the reasoning of others, model with mathematics, use appropriate tools strategically, attend to precision, look for and make use of structure, look for and express regularity in repeated reasoning. The data sources are 2013 mathematics curriculum standards of Turkey and 2013 mathematics syllabus of Singapore for grades 7 and 8. Data analysis revealed that the two countries reflected mathematical processes differently in their content standards. Some mathematical processes are not identified in Turkey's content standards  while all mathematical processes are observed in Singapore's content standards. The distribution of the observed mathematical processes are also different in the two countries. Suggestions for future content standards revisions are shared in the paper

The Effects of a Professional Development Program for Technology Integrated Algebra Teaching

This study examined technological pedagogical content knowledge change of middle school mathematics teachers who participated in a professional development program designed to integrate technology into teaching algebra. Twenty-eight middle school teachers from 20 different schools participated in the study. The data collection tools were technological pedagogical content knowledge survey, reflective journals, lesson plans, and program evaluation forms. The data analysis showed that the participants’ technological pedagogical content knowledge increased significantly. In addition, participants wrote lesson plans that included effective use of technology to teach algebra contents. The positive effects of the professional development program seem to be related to the following components of the program: the usability of the program activities in middle school classrooms, program’s focus on using technology in teaching algebra, the introduction of new technological tools and software related to mathematics, and the interactive nature of the program

Drying techniques differentially affect bark beetle weight change

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Training-based Model Refinement and Representation Disagreement for Semi-Supervised Object Detection

Semi-supervised object detection (SSOD) aims to improve the performance and generalization of existing object detectors by utilizing limited labeled data and extensive unlabeled data. Despite many advances, recent SSOD methods are still challenged by inadequate model refinement using the classical exponential moving average (EMA) strategy, the consensus of Teacher-Student models in the latter stages of training (i.e., losing their distinctiveness), and noisy/misleading pseudo-labels. This paper proposes a novel training-based model refinement (TMR) stage and a simple yet effective representation disagreement (RD) strategy to address the limitations of classical EMA and the consensus problem. The TMR stage of Teacher-Student models optimizes the lightweight scaling operation to refine the model's weights and prevent overfitting or forgetting learned patterns from unlabeled data. Meanwhile, the RD strategy helps keep these models diverged to encourage the student model to explore complementary representations. Our approach can be integrated into established SSOD methods and is empirically validated using two baseline methods, with and without cascade regression, to generate more reliable pseudo-labels. Extensive experiments demonstrate the superior performance of our approach over state-of-the-art SSOD methods. Specifically, the proposed approach outperforms the baseline Unbiased-Teacher-v2 (& Unbiased-Teacher-v1) method by an average mAP margin of 2.23, 2.1, and 3.36 (& 2.07, 1.9, and 3.27) on COCO-standard, COCO-additional, and Pascal VOC datasets, respectively.Comment: Under revie

Türkiye’nin İlkokul ve Ortaokul Matematik Öğretim Programlarının Genel Konu İzleme Haritası ile İncelenmesi

The purpose of this study was to conduct a comparative analysis for Turkey’s elementary and middle school mathematics standards using the general topic trace mapping method. Turkey’s standards were compared with the Common Core State Standards for Mathematics in the United States and also with the mathematics curriculum of the countries that ranked high in international mathematics achievement tests. The curriculum comparison was conducted from three perspectives: Number of topics at each grade level, topic repetition, and organization of mathematics topics. Data analysis showed that compared to the other countries included in the study, Turkey’s elementary school standards include more topics, whereas the middle school standards include fewer topics. The topic repetition analysis yielded an average of 3.97 years for both Turkey’s standards and the CCSS for mathematics. With respect to topic organization, a three-tier pattern was observed in Turkey’s mathematics standards. The article discusses possible revisions in Turkey’s standards for improvement.Bu çalışmada, Türkiye’nin ilkokul ve ortaokul matematik dersi öğretim programları genel konu izleme haritası yöntemiyle analiz edilmiştir. Genel konu izleme haritası yöntemiyle oluşturulan tablo kullanılarak, Türkiye’nin matematik öğretim programları uluslararası matematik sınavlarında başarılı olan ülkelerin öğretim programlarıyla ve Amerika Birleşik Devletleri’nde birçok eyalet tarafından uygulamaya koyulan ortak matematik öğretim programıyla karşılaştırılmıştır. Program analizi ve karşılaştırması, her yıla düşen toplam konu sayısı, konu tekrarı ve matematik konularının organizasyonu bakış açılarından yapılmıştır. Veri analizi sonuçlarına göre, Türkiye’nin ilkokul programının karşılaştırılan ülkelere göre daha fazla, ortaokul programının ise daha az konu içerdiği tespit edilmiştir. Programlardaki ortalama konu tekrarı hem Türkiye hem de Amerika Birleşik Devletleri için 3,97 yıl olarak hesaplanmıştır. Matematik konularının organizasyonu incelendiğinde, Türkiye’nin programında 3 kademeli bir yapı gözlenmiştir. Makalede, programın geliştirilmesi yönünde atılabilecek adımlar tartışılmıştır

Resource availability and repeated defoliation mediate compensatory growth in trembling aspen (Populus tremuloides) seedlings

Plant ecologists have debated the mechanisms used by plants to cope with the impact of herbivore damage. While plant resistance mechanisms have received much attention, plant compensatory growth as a type of plant tolerance mechanisms has been less studied. We conducted a greenhouse experiment to evaluate compensatory growth for trembling aspen (Populus tremuloides) seedlings under varying intensities and frequencies of simulated defoliation, with or without nutrient enriched media. For the purpose of this study, changes in biomass production and non-structural carbohydrate concentrations (NSC) of roots and leaves were considered compensatory responses. All defoliated seedlings showed biomass accumulation under low defoliation intensity and frequency, regardless of resource availability; however, as defoliation intensity and frequency increased, compensatory growth of seedlings was altered depending on resource availability. Seedlings in a resource-rich environment showed complete compensation, in contrast responses ranged from undercompensation to complete compensation in a resource-limited environment. Furthermore, at the highest defoliation intensity and frequency, NSC concentrations in leaves and roots were similar between defoliated and non-defoliated seedlings in a resource-rich environment; in contrast, defoliated seedlings with limited resources sustained the most biomass loss, had lower amounts of stored NSC. Using these results, we developed a new predictive framework incorporating the interactions between frequency and intensity of defoliation and resource availability as modulators of plant compensatory responses

Crown-CAM: Interpretable Visual Explanations for Tree Crown Detection in Aerial Images

Visual explanation of ``black-box'' models allows researchers in explainable artificial intelligence (XAI) to interpret the model's decisions in a human-understandable manner. In this paper, we propose interpretable class activation mapping for tree crown detection (Crown-CAM) that overcomes inaccurate localization & computational complexity of previous methods while generating reliable visual explanations for the challenging and dynamic problem of tree crown detection in aerial images. It consists of an unsupervised selection of activation maps, computation of local score maps, and non-contextual background suppression to efficiently provide fine-grain localization of tree crowns in scenarios with dense forest trees or scenes without tree crowns. Additionally, two Intersection over Union (IoU)-based metrics are introduced to effectively quantify both the accuracy and inaccuracy of generated explanations with respect to regions with or even without tree crowns in the image. Empirical evaluations demonstrate that the proposed Crown-CAM outperforms the Score-CAM, Augmented Score-CAM, and Eigen-CAM methods by an average IoU margin of 8.7, 5.3, and 21.7 (and 3.3, 9.8, and 16.5) respectively in improving the accuracy (and decreasing inaccuracy) of visual explanations on the challenging NEON tree crown dataset.Comment: Accepted manuscript in IEEE Geoscience and Remote Sensing Letters (GRSL

Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review

Bark beetle outbreaks can result in a devastating impact on forest ecosystem processes, biodiversity, forest structure and function, and economies. Accurate and timely detection of bark beetle infestations is crucial to mitigate further damage, develop proactive forest management activities, and minimize economic losses. Incorporating remote sensing (RS) data with machine learning (ML) (or deep learning (DL)) can provide a great alternative to the current approaches that rely on aerial surveys and field surveys, which are impractical over vast geographical regions. This paper provides a comprehensive review of past and current advances in the early detection of bark beetle-induced tree mortality from three key perspectives: bark beetle & host interactions, RS, and ML/DL. We parse recent literature according to bark beetle species & attack phases, host trees, study regions, imagery platforms & sensors, spectral/spatial/temporal resolutions, spectral signatures, spectral vegetation indices (SVIs), ML approaches, learning schemes, task categories, models, algorithms, classes/clusters, features, and DL networks & architectures. This review focuses on challenging early detection, discussing current challenges and potential solutions. Our literature survey suggests that the performance of current ML methods is limited (less than 80%) and depends on various factors, including imagery sensors & resolutions, acquisition dates, and employed features & algorithms/networks. A more promising result from DL networks and then the random forest (RF) algorithm highlighted the potential to detect subtle changes in visible, thermal, and short-wave infrared (SWIR) spectral regions.Comment: Under review, 33 pages, 5 figures, 8 Table

The Effect of Coding Classes on Mathematics Achievement of Preschool Students

The problem that inspired this study is that Turkish students’ mathematics rankings on global exams such as The Program for the International Students Assessment (PISA) and Trends in International Mathematics and Science Study (TIMMS) had been below the average for many years, a problem that may have rooted in children’s early experiences with mathematics. The purpose of this quantitative pretest posttest quasi-experimental control group study was to determine the effect of computer-programming classes with Code Studio on mathematics scores of preschool students. Siemens’s connectivism theory for the digital age formed the foundation for this research. A single research question regarding the effect of computer coding classes on mathematics scores of preschool students over one academic year guided this study. The sample of this study consisted of randomly selected 128 students’ mathematics scores who are attending two preschools. One of the preschools implemented computer programming classes with Code Studio, experiment group and the other did not, control group. The dependent variable (posttest) was mathematics scores of the both groups taken in June. Pretest mathematics scores was the covariate and was administered in September of previous year. An ANCOVA test was used to analyze the data. The results indicated a statistically significant difference in mathematics post scores in favor of preschool students who were taught computer coding compared to students who were not taught computer coding. The social change implication of this study is that widely adopted early coding instruction may increase early mathematics achievement, which may lead higher mathematics achievement for longer term