Miracle-dz/UniFAR-FTUs huggingface dataset:
Phytolith assemblages of Shorea robusta Gaertn. (Sal) from West Bengal part of the Bengal Basin, India and their implications in palaeoclimate reconstructions

ABSTRACT Phytolith assemblages of Shorea robusta Gaertn. (Dipterocarpaceae), an important constituent of tropical deciduous/semi-evergreen forests were studied from six different districts of West Bengal, the western margin of the Bengal Basin. Since S. robusta grows in the tropical mixed dry to wet deciduous and semi-evergreen forests and can tolerate a wide variability in temperature and precipitation regimes, we aim to understand if these climatic parameters drive any changes in phytolith production pattern of the plant. Eleven different phytolith morphotypes retrieved from 13 plant specimens collected along the temperature and precipitation gradients show considerable variations among the specimens. Phytolith assemblages thus retrieved have been subjected to detrended correspondence analysis (DCA), which has helped to understand indirectly the influence of climatic parameter(s) on the phytolith production pattern of S. robusta, if any. Further, canonical correspondence analysis (CCA) performed on the phytolith data reveals how temperature and precipitation influence the variations in phytolith assemblages of the plant. The data provide a basis to understand how phytolith spectra of non-grass plants capture signal of climatic variability of any region and could be used in regional palaeoclimate reconstructions if recovered in fossil sediments.
How Did It Go? A Comparison of Experience and Outcome-Focused Online Reviews on Treatment Expectations

Abstract Previous research indicated that valence of qualitative consumer reviews (positive versus negative) on a mental health therapist’s provider website caused participants to ignore high or low numerical ratings (participants were more influenced by qualitative reviews than by numerical ratings). Our current study extended this research by manipulating the type of commentary presented (experience- or outcome-focused qualitative reviews). Participants reviewed a fictitious therapist’s online profile and indicated their intent to seek professional help, compliance intent, potential bond, and expectations of client health outcomes. Results indicated participants were more sensitive to experience comments than outcome comments when comments were negative.
CCR4 and CCR7 differentially regulate thymocyte localization with distinct outcomes for central tolerance

Central tolerance ensures autoreactive T cells are eliminated or diverted to the regulatory T cell lineage, thus preventing autoimmunity. To undergo central tolerance, thymocytes must enter the medulla to test their TCRs for autoreactivity against the diverse self-antigens displayed by antigen presenting cells (APCs). While CCR7 is known to promote thymocyte medullary entry and negative selection, our previous studies implicate CCR4 in these processes, raising the question of whether CCR4 and CCR7 play distinct or redundant roles in central tolerance. Here, synchronized positive selection assays, 2-photon timelapse microscopy, and quantification of TCR-signaled apoptotic thymocytes, demonstrate that CCR4 and CCR7 promote medullary accumulation and central tolerance of distinct post-positive selection thymocyte subsets. CCR4 is upregulated within hours of positive selection signaling and promotes medullary entry and clonal deletion of immature post-positive selection thymocytes. In contrast, CCR7 is expressed several days later and is required for medullary localization and negative selection of mature thymocytes. In addition, CCR4 and CCR7 differentially enforce self-tolerance, with CCR4 enforcing tolerance to self-antigens presented by activated APCs, which express CCR4 ligands. Our findings show that CCR7 expression is not synonymous with medullary localization and support a revised model of central tolerance in which CCR4 and CCR7 promote early and late stages of negative selection, respectively, via interactions with distinct APC subsets.
Rheology of Growing Axons

The growth of axons is a key process in neural system development, which relies upon a subtle balance between external mechanical forces and remodeling of cellular constituents. A key problem in the biophysics of axons is therefore to understand the overall response of the axon under stretch, which is often modeled phenomenologically using morphoelastic or viscoelastic models. Here, we develop a microscopic mixture model of growth and remodeling based on protein turnover and damage to obtain the macroscopic rheology of axonal shafts. First, we provide an estimate for the instantaneous elastic response of axons. Second, we demonstrate that under moderate traction velocities, axons behave like a viscoelastic Maxwell material. Third, for larger velocities, we show that failure takes place due to extensive damage.
Editorial

Despite the world having been once again impacted by the COVID-19 pandemic in 2021, research in botany has been wealthier than ever. Our journal has received a total of 296 manuscripts submitted, with an acceptance rate of approximately 14%. Forty-eight articles have been published in the past year, which is more than in 2020. Thanks to the efficiency of our editorial board, we are constantly improving publication speed. There are in average 4 days in average from submission to first decision, 43 days from submission to first postreview decision, and 22 days from acceptance to online publication. The increasing success of Botany Letters translates into the impact factor, which has once again increased, reaching 1.744. Our journal is now ranking 132/235 in the Plant Sciences-SCIE JCR category. Almost 540,000 articles were downloaded from the website of Botany Letters, which is 26% higher than downloads received in 2020. The most downloaded article was “Genetic variation underlies the plastic response to shade of snapdragon plants (Antirrhinum majus L.)” by Mousset et al. 2021 cover an impressive variety of topics, taxa, and methods. We wish to thank all the authors that helped us make our journal a steady reference in botany in a broad sense and of course our readers. As usual, a special issue of Botany Letters was devoted to diatoms, with the publication of the proceedings from the 2019 meeting of the ADLaF, the French-speaking diatoms community, thanks to the active work of editor Benoît Schoefs (issue 168(1)). A second special issue was devoted to investigating pollination strategies in a changing world, organized by editor Guillaume Besnard and the guest editors Bertrand Schatz and Mathilde Dufaÿ (issue 168(3)). Studying pollination is more than ever crucial in the present context of pollinator decline and global change, and we are very proud to have hosted such an interesting and timely collection of articles in our journal. Three additional articles from the series “Monographs on Invasive Plants in Europe” have been published in 2021, adding up to the general knowledge on the plant species that put ecosystems at threat. We are pleased to welcome a new member in our very active editorial board. Lilian Eggers, from the Paris National Museum of Natural History, brings her expertise in systematics and taxonomy, particularly on monocotyledons. In addition to our proficient editors, the success of Botany Letters relies also very much on the major contribution of the many experts, anonymous for most of them, who help us select the best articles and help the authors improve their manuscripts. On behalf of the Société Botanique de France, we wish to warmly thank them all for their contribution in 2021. The list of experts is published as an online-only document on the website of Botany Letters (https://www.tandfonline.com/toc/tabg21/cur rent). Those who need a certificate to testify to this activity may contact Henrietta Thompson (henrietta. thompson@tandf.co.uk) to obtain a reviewer certificate from Taylor & Francis.
Life Cycle Analysis

Of all the infrastructure elements, pavement performance has probably received more research than any. Similarly, there has been more application of pavement research. The concepts that are developing into practice in the pavement field will have similar applications with all other infrastructure elements. Thus, for the purpose of addressing a few specific concepts, the remaining discussion will refer to pavements only with the understanding that the concepts have similar applications to other aspects of the infrastructure.
CD169+ macrophages orchestrate plasmacytoid dendritic cell arrest and retention for optimal priming in the bone marrow of malaria-infected mice

Plasmacytoid dendritic cells (pDC) are the most potent producer of type I interferon (IFN), but how pDC are primed in vivo is poorly defined. Using a mouse model of severe malaria, we have previously established that upon priming by CD169+ macrophages (MP), pDC initiate type I IFN-I secretion in the bone marrow (BM) of infected mice via cell-intrinsic TLR7 sensing and cell-extrinsic STING sensing. Herein we show that CD169+ MP and TLR7-sensing are both required for pDC arrest during priming, suggesting CD169+ MP are the source of TLR7 ligands. We establish that TLR7 sensing in pDC and chemotaxis are both required for pDC arrest and functional clustering with CD169+ MP in the BM. Lastly, we demonstrate that STING-sensing in CD169+ MP control pDC initiation of type I IFN production while also regulating pDC clustering and egress from the BM. Collectively, these results link pDC acquisition of type I IFN secreting capacity with changes in their motility, homing and interactions with CD169+ MP during infection. Thus, targeting this cellular interaction may help modulate type I IFN to improve outcomes of microbial infections and autoimmune diseases.
Whole-brain comparison of rodent and human brains using spatial transcriptomics

The ever-increasing use of mouse models in preclinical neuroscience research calls for an improvement in the methods used to translate findings between mouse and human brains. Previously, we showed that the brains of primates can be compared in a direct quantitative manner using a common reference space built from white matter tractography data (Mars et al., 2018b). Here, we extend the common space approach to evaluate the similarity of mouse and human brain regions using openly accessible brain-wide transcriptomic data sets. We show that mouse-human homologous genes capture broad patterns of neuroanatomical organization, but the resolution of cross-species correspondences can be improved using a novel supervised machine learning approach. Using this method, we demonstrate that sensorimotor subdivisions of the neocortex exhibit greater similarity between species, compared with supramodal subdivisions, and mouse isocortical regions separate into sensorimotor and supramodal clusters based on their similarity to human cortical regions. We also find that mouse and human striatal regions are strongly conserved, with the mouse caudoputamen exhibiting an equal degree of similarity to both the human caudate and putamen.
A Review of Improved CMOS Dynamic Comparators

This paper reviews some CMOS dynamic comparators that achieving high performances on different features. Based on conventional structures, a comparator with a dynamic floating inverter amplifier optimize their circuit by a floating reservoir capacitor, which achieves the best energy efficiency while is insensitive to common-mode voltage. The optimization of the Elzakker's comparators increases the pre-amplifier stage's structures by adding a tail capacitor and a tail transistor. And three-stage comparator with extra parallel feedback is discussed, decreasing delay significantly. The classic comparator comprises a differential input stage, two regenerative flip-flops, and an S-R latch is also mentioned.
Word Normalization Information Systems and Improved Learning Representation for Ontology Matching

Biomedical ontology matching can find similar corresponds between biomedical ontologies to enable data integration. There are many ontology matching methods. Featurebased methods mainly focus on concept features to find mappings, ignoring semantic information and domain-specific morphology. On the other hand, learning representation methods think about the semantic information for hidden mappings but don't make full use of ontology structure. In this research, we propose a strategy for increasing performance by integrating the representation learning method with the feature-based method of additional word normalization. Our experiment is based on AgreementMakerLight (AML) and shows promising results.
Conceptual Design of a Gait Rehabilitation Robot

Article history: Received: December 29, 2014. Received in revised form: April 9, 2015. Accepted: May 11, 2015. Gait rehabilitation using body weight support on a treadmill is a recommended rehabilitation technique for neurological injuries, such as spinal cord injury. In this paper, a new robotic orthosis is presented for treadmill training. In the presented design the criteria such as low inertia of robot components, backdrivability, high safety and degrees of freedom based on human walking are considered. This robot is composed of a leg exoskeleton for leg control and a segment for pelvis control. In the exoskeleton two degrees of freedom are considered for the hip joint and one for the knee joint. Also two degrees of freedom are considered for the pelvis joints. The inertia of moving components and the required force for the robot motion are measured to evaluate the robot backdrivability and transparency. Further, a walking algorithm is implemented on the robot and is tested on a human subject. Evaluation of the design showed that the robot is suitable for gait rehabilitation exercises.
A 10Î¼W Hybrid OTA with Bulk-Biasing Driving 20nF Capacitive Load with 115 dB DC-gain and 1.68MHz GBW

—For better area and power efficient, single stage amplifiers are a better replacement of their multi-stage cmplifies ounterparts, particularly for display applications that imply massive buffer amplifier in their column drivers. This paper employs a signal-current enhancer technique for a single-stage amplifier to achieve significant enhancement of DC gain, gain-bandwidth product (GBW) and slew-rate. Ana bulk-biasing technique employed to minimize the internal node capacitances and area of OTA. Analytical treatments of the signal-current enhancer technique in terms of performance limits and robustness reveals that hybrid OTA can suppress the fundamental power-efficiency limit set by the basic differential-pair amplifier. The proposed hybrid OTA is implemented in a standard PTM 65 nm CMOS technology. Working under a 0.5V supply and proven by simulation results, the resultant OTA when driving an output capacitor of 20nF attained 115 dB DC gain, a GBW of 1.68 MHz and an Slewrate of 0.15 V/µs, consuming only 10µw of current and requiring no compensation capacitor.
Business value creation : estimation and measurement in E-Business agile projects

E-businesses are increasingly adapting agile software development methods to develop and improve their software. Iterative cycles make it possible to prioritize features early in development that quickly deliver the most business value to the customer. However, without a complete business value determination approach, agile practitioners are trusting on their own intuition to estimate the potential business value, and management does not know how their organization is performing regarding its delivery. Therefore, a design science research process was set up to design an artefact in a large Dutch e-commerce organization, involving a systematic literature review and internal and external semi-structured interviews. The resulted artefact consists of a model to represent the business value of a feature in conjunction with a proposed set of estimation and measurement guidelines. The results contribute to the literature by providing new insights in how the determination should be carried out exactly. Furthermore, the artefact enables organizations to develop the most valuable features first in a structural manner. It should also offer more objectivity and transparency during and after the prioritization process, and may help practitioners to work towards a more sustainable agile organization by managing and anticipating on the performance regarding business value creation.
CIDOC: Conceptual Reference Model: oder: das Schweizer Taschenmesser für die Museums- und Kulturinformatik

Until now documentation within the museum has very often mainly been focused on administrational information. This kind of information was handled very similar to bibliographical information. But collecting scientific information about objects is different in character, range and aims. The CIDOC Conceptual Reference Model (CRM) supports these extended needs in many respects in handling complex data structures in a controlled but open way. The CIDOC CRM is technically seen a so called ontology created under the object oriented paradigm, specialized on the needs of contents of museums. The current CHIOS project aims at introducing the CIDOC-CRM as a standard within the International Organisation for Standardization (ISO).
Navigating the Marketing Technology Landscape

Rapid technology advancements have changed the way customers research products and make purchases. This has led to an almost immeasurable increase in the number of customer touchpoints along the customer journey and a corresponding need for a richer set of digital tools that find and engage people on a global scale. The result has been a plethora of options that overwhelm most marketing teams. It is time for us to more carefully consider our approach to the tech frameworks we are creating in marketing.
Improving Effectiveness of Different Deep Transfer Learning-Based Models for Detecting Brain Tumors From MR Images

Early classification of brain tumors from magnetic resonance imaging (MRI) plays an important role in the diagnosis of such diseases. There are many diagnostic imaging methods used to identify tumors in the brain. MRI is commonly used for such tasks because of its unmatched image quality. The relevance of artificial intelligence (AI) in the form of deep learning (DL) has revolutionized new methods of automated medical image diagnosis. This study aimed to develop a robust and efficient method based on transfer learning technique for classifying brain tumors using MRI. In this article, the popular deep learning architectures are utilized to develop brain tumor diagnostic system. The pre-trained models such as Xception, NasNet Large, DenseNet121 and InceptionResNetV2 are used to extract the deep features from brain MRI. The experiment was performed using two benchmark datasets that are openly accessible from the web. Images from the dataset were first cropped, preprocessed, and augmented for accurate and fast training. Deep transfer learning models are trained and tested on a brain MRI dataset using three different optimization algorithms (ADAM, SGD, and RMSprop). The performance of the transfer learning models is evaluated using performance metrics such as accuracy, sensitivity, precision, specificity and F1-score. From the experimental results, our proposed CNN model based on the Xception architecture using ADAM optimizer is better than the other three proposed models. The Xception model achieved accuracy, sensitivity, precision specificity, and F1-score values of 99.67%, 99.68%, 99.68%, 99.66%, and 99.68% on the MRI-large dataset, and 91.94%, 96.55%, 87.50%, 87.88%, and 91.80% on the MRI-small dataset, respectively. The proposed method is superior to the existing literature, indicating that it can be used to quickly and accurately classify brain tumors.
Risk management models for sustainable nanomanufacturing capacity expansion decisions

As nanotechnologies rapidly proliferate, new companies that produce nanomaterials or nanodevices are emerging. However, nanotechnology companies face uncertainties from several fronts including environmental, health, and safety (EHS) risk of nanomaterials, regulations, future market conditions for nano-enabled products, and technology advancements. These uncertainties can prove to be a challenge for planning the production capacity expansion of engineered nanomaterials or nano-enabled products. Moreover, these uncertainties will also impact the decisions for environmentally benign nanomanufacturing. Risk management methods can be useful for the investors to make sustainable capacity/production planning decisions in order to minimize the high investment and sustainability risks. A comprehensive review of environmental, health, and safety risk analysis studies related to nanomaterials, nanomanufacturing, and nano-enabled products was first conducted. Network analysis approach was applied to analyze the emerging research trends and determine the modeling tools commonly applied in EHS risk analysis studies. The EHS risk analysis studies were reviewed, and research needs, possible future directions, and opportunities for nano research community were discussed. A Monte Carlo simulation model was developed to analyze the risk factors that influence the total production expenditure over time in CNT production. The simulation model examines how the expenditures (including the lost revenues) of CNTs manufacturing companies vary when companies face uncertainties in 1) demand for CNTs, 2) technological evolution for CNT production, and 3) occupational safety regulations. The model also investigates how the total expenditure is affected by different investment
Simulation Research of Full-Ocean-Depth Manned Submersible Based on Experimental Data

The motion simulation of manned submersible plays an important role in the whole submersible development. During the actual operation of manned submersible, it is affected by the factors such as deep ocean current, model uncertainty and unmodeled dynamics, which make the traditional simulation results differ greatly from the actual results. Taking “Fen Douzhe” manned submersible as the research object, the kinematic and dynamic models were established, and the system disturbance was estimated by using long and short term memory neural network, and the model was verified by simulation calculation with the experimental data of “Fen Douzhe”. The effectiveness of the simulation method is verified by comparing the simulation results with the actual experimental data.
Localization Approach for Forced Oscillation Source Based on Synchrosqueezed Wavelet

The localization of forced oscillation sources is of great significance for power system stability. This paper proposes a localization approach based on synchrosqueezed wavelet. First, the wide-area measurement data is processed by synchrosqueezed wavelet to extract oscillation mode parameters. Moreover, the dissipated energy flow of each generator is calculated based on the extracted mode parameters. In addition, according to the direction of dissipated energy flow, the forced oscillation source is localized. Finally, the WECC 240-bus system is used to verified the effectiveness of the proposed approach.