bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficacy of Chromafenozide Insecticide to Control Tirathaba rufivena and Its Impacts on Oil Palm Pollinator E. kamerunicus
Authors: Happy Nur’afni R , Saepuloh Apit 
Published: March 2024

Abstract
Newly marketed insecticide known with active ingredient Chromafenozide claimed to be potent against various lepidopterous insects, but at the same time almost non-toxic to non-lepidopterous species, including pollinators, predators and parasitoids. This trial aim to evaluate the effectiveness of Chromafenozide insecticides to control population of Tirathaba rufivena in oil palm planted on peat soil and its impact on pollinator weevil, Elaedobius kamerunicus. The trial was divided into two parts which are field assessment of insecticides efficacy and laboratory assay to know the direct effect of insecticide application on E. kamerunicus. Besides Chromafenozide, insecticides used in this trial Cypermethrin and Bacillus thuringiensis var kurstaky as comparison. The results of efficacy test showed that Tirathaba infestation decreased significantly in the Cypermethrin, B. thuringiensis, and Chromafenozide treatments compared to control. However, among these treatments did not show a significant difference. Nevertheless, each of the tested insecticide caused significantly mortality to E. kamerunicus. E. kamerunicus mortality rate of each insecticide treatment showed that Chromafenozide was less detrimental as compared Cypermethrin, but significantly detrimental to B. thuringiensis. B. thuringiensis was not significantly different compared to control.
Prospective oil palm (Elaeis sp) materials for high tocotrienol content
Authors: Setiowati R D , Ernayunita  , Panjaitan F R , Mardiana C , Lubis M I 
Published: March 2024

Abstract
Crude palm oil (CPO) contains various forms of vitamin E, including tocopherol, tocotrienol and a small amount of tocomonoenol, collectively known as tocols. Among these, tocotrienol is dominant in CPO, constituting approximately 70% of the total tocols; unlike tocopherol, which is known for its anti-ageing properties, tocotrienol exhibits potent antioxidant activities, 60 times stronger than tocopherol. Tocotrienol is recognized for its diverse health benefits as a neuroprotective, nephroprotective, anti-cancer, anti-cholesterol, anti-diabetic, and anti-inflammatory agent. Consequently, tocotrienol holds immense potential for pharmaceutical and nutraceutical applications. The exploration of oil palm materials planted in Kebun Adolina PTPN IV, North Sumatera, has been conducted since 2020, encompassing diverse genetic backgrounds, including commercial palm DxP varieties, an interspecific hybrid of Elaeis oleifera x Elaeis guineensis, wild-type palm materials originated from Angola and Cameroon, and palm clones of commercial DxP varieties. The analysis revealed a wide range of total vitamin E (tocol) content, ranging from 310.78 ppm to 1681.45 ppm. Among the genotypes studied, AD14S311 and AD14S2220 emerged as promising candidates for ortets to develop oil palm varieties with high content, especially tocotrienol. These genotypes demonstrated a high oil extraction rate (OER) and a total vitamin E level above 1000 ppm. These findings remade the potential genotypes as genetic resources for breeding programs in developing oil palm varieties enriched with tocotrienol and fulfilled the commercial value.
Estimation of oil palm’s leaf area index (LAI) using Unmanned Aerial Vehicle (UAV) images
Authors: Pradiko I , Wati D , Wiratmoko D , Darlan N H 
Published: March 2024

Abstract
Leaf Area Index (LAI) is a parameter commonly used to indicate oil palm growth and production. The destructive method is the standard method to estimate LAI. However, this method requires much effort and cost and can only be done at a specific period. Unmanned Aerial Vehicles (UAVs), which have been widely used for mapping and calculating trees in oil palm plantations, have the potential to estimate LAI values quickly, efficiently, and without disturbing the oil palm tree. This research was conducted to develop the model for predicting LAI based on UAV images. Data was collected from six plots of 12-year-old oil palm trees at Adolina Estate, Serdang Bedagai, North Sumatra, Indonesia. The research was conducted on six varieties released by IOPRI, consisting of DxP Yangambi, DxP Langkat, DxP Simalungun, DyP Dumpy, DxP LaMe, and DxP PPKS 540. The estimated canopy cover from the UAV images was employed as an independent factor (x) compared to the calculated LAI from the destructive method, which was used as a dependent factor (y). The results showed that the LAI estimation using UAV imagery followed a linear model with R-squared values ranging from 0.3874-0.9556. In conclusion, despite requiring further research, UAV images could be used as rapid tools to estimate oil palm LAI.
Implementation of environmental policies by six oil palm plantation companies in South Sumatra
Authors: Komarudin N A , Siregar H H 
Published: March 2024

Abstract
South Sumatra Province, situated in Indonesia, ranks as the sixth-largest palm oil producer in the country, boasting an extensive plantation area of 1.2 million hectares. Unfortunately, the province has faced significant environmental challenges in recent times due to inadequate environmental management and recurring forest fires. These issues have resulted in adverse effects on the local ecosystem. Consequently, it becomes crucial to conduct a gap analysis of the existing environmental policies and their implementation by oil palm plantation companies operating in South Sumatra Province. The objective of this research is to assess the environmental management practices of companies operating in the oil palm industry in South Sumatra Province, specifically focusing on their adherence to the Indonesia Sustainable Oil Palm (ISPO) criteria. The study aims to evaluate how effectively these companies have implemented environmental policies and identify any disparities or gaps that may exist between the policies and their actual implementation. To achieve this, the research employs a regulatory gap analysis method. The oil palm plantation company in South Sumatra Province has met 105 out of the 126 environmental indicators specified in the ISPO criteria. In general, the implementation of environmental policies on these plantations has achieved an 85% score, indicating a successful fulfillment of the policies outlined by Indonesia Sustainable Palm Oil (ISPO). The relatively low gap value of 0.15 further confirms this success, signifying that the environmental policies have been effectively and adequately implemented in accordance with ISPO’s guideline..
Fixation method of a single muscle fiber by magnetic force for stretching, transportation, and evaluation of mechanical properties
Authors: Wang Zhaoyu , Masuda Taisuke , Arai Fumihito 
Published: March 2024

Abstract
Engineered muscle fibers are attracting interest in bio-actuator research as they can contribute to the fabrication of actuators with a high power/size ratio for micro-robots. These fibers require to be stretched during culture for functional regulation as actuators and require a fixation on a rigid substrate for stretching in culture and evaluation of mechanical properties, such as Young’s modulus and contraction force. However, the conventional fixation methods for muscle fibers have many restrictions as they are not repeatable and the connection between fixation part and the muscle fibers detaches during culture; therefore, the fixation becomes weak during culture, and direct measurement of the muscle fibers’ mechanical properties by a force sensor is difficult. Therefore, we propose a facile and repeatable fixation method for muscle fibers by mixing magnetite nanoparticles at both ends of the muscle fibers to fabricate magnetic ends. The fiber can be easily attached and detached repeatedly by manipulating a magnet that applies a magnetic force larger than 3 mN to the magnetic ends. Thus, the muscle fiber can be stretched fiber during culture for functional regulation, transported between the culture dish and measurement system, and directly connected to the force sensor for measurement with magnetic ends. The muscle fiber connected with magnetic ends have a long lifetime (∼4 weeks) and the cells inside had the morphology of a skeletal muscle. Moreover, the muscle fiber showed a contraction (specific force of 1.02 mN mm−2) synchronized with electrical stimulation, confirming the muscle fiber fabricated and cultured using our method had similar morphology and function as bio-actuators in previous research. This research demonstrates the advantages of the fixation method using muscle fibers with magnetic ends; the fibers are stretched during culture, and the transportation and force measurement of weak and tiny muscle fibers could be finished in 1 min.
A COMSOL novel micromirror matrix model for digital image display
Authors: Karaoui Fares , Benblidia Nadjia , Bougherira Hamida 
Published: March 2024

Abstract
Digital micromirror devices (DMD) have gained significant attention in various scientific and industrial domains due to their potential for spatial light and laser modulation. However existing micromirrors designs often face limitations in terms of rotation angles, voltage consumption, and stability time for some type of applications. In this paper, we address these issues by exploring micromirror structure materials, more precisely, the materials for the torsion bar. To achieve this, we have used COMSOL Multiphysics knowing to provide accurate predictive simulation results, to design a micromirror model incorporating three inclination modes: unidirectional, bidirectional, and a user-defined free mode. Through extensive simulations, we have compared different materials for the torsion bar of the micromirror and have evaluated their performance in terms of voltage consumption, stability time, rotation angles and electric energy consumption. Aluminum 3008-H18 emerged as the optimal choice, exhibiting a stability time of 40 μs and achieving a maximum tilt of 12.75 degrees. The micromirror system has demonstrated stable position within the voltage range (from 0 volts to 27 volts) and maximum electric energy consumption of 7.72 × 10−8
μJ. Our micromirror design features a 10 × 10 μm reflective element capable of achieving a maximum inclination of ±12.75 degrees. To enhance the capabilities of the micromirror, an 8 × 8 micromirror matrix has been developed, enabling collective and coordinated movements of individual micromirrors. Also, a process for translating digital images into micromirror states has been devised, enabling accurate image display on the matrix. Simulation results demonstrate the effectiveness of the micromirror matrix design and the image processing script, images displayed on the micromirror matrix exhibit high accuracy, faithfully reproducing desired patterns in the primary images. Overall, our proposed micromirror model and micromirror matrix model offer enhanced performance, versatility, and accuracy, enabling a diverse array of simulations across scientific research and industrial domains.
Civic Virtue and State Power
Authors: Bascom Ben 
Published: March 2024

Abstract
This chapter focuses on how race—specifically Blackness—was understood as a barrier to civic belonging and participation. By focusing on Jeffrey Brace’s memoir The Blind African Slave (1810)—a neglected anti-slavery narrative about a Black Revolutionary War veteran—this chapter shows how this narrative challenges white republican narratives of belonging. Brace’s narrative offers an important precursor for nineteenth-century abolitionists for how he challenges the conventions that led to his being pressed to leave the Vermont town he called home. This chapter argues that Brace actively queers the norms of propertied whiteness that exclude him even as he reclaims them for himself.
Eliot’s Statements
Authors: Blanton C D 
Published: March 2024

Abstract
The appearance of T. S. Eliot’s Complete Prose makes available a critical object that was always there in scattered form but remained elusive for a century. It now concentrates the poet’s thought, his statements, in a single edition. It also, I argue, makes visible the deeper tendency of that thought, toward a philosophical system glimpsed in incipient but programmatically coherent form. With this collection, Eliot’s theories of logical reference and of truth are (again) not only formulated but also relentlessly enacted. Critically, those theories bear too on the definition of poetic language, taken to consist not in pseudostatements but in logical judgments.
Integrating network pharmacology and in silico analysis deciphers Withaferin-A’s anti-breast cancer potential via hedgehog pathway and target network interplay
Authors: Srinivasan Mythili , Gangurde Apeksha , Chandane Ashwini Y , Tagalpallewar Amol , Pawar Anil , Baheti Akshay M 
Published: March 2024

Abstract
This study examines the remarkable effectiveness of Withaferin-A (WA), a withanolide obtained from Withania somnifera (Ashwagandha), in encountering the mortiferous breast malignancy, a global peril. The predominant objective is to investigate WA’s intrinsic target proteins and hedgehog (Hh) pathway proteins in breast cancer targeting through the application of in silico computational techniques and network pharmacology predictions. The databases and webtools like Swiss target prediction, GeneCards, DisGeNet and Online Mendelian Inheritance in Man were exploited to identify the common target proteins. The culmination of the WA network and protein–protein interaction network were devised using Stitch and String web tools, through which the drug–target network of 30 common proteins was constructed employing Cytoscape-version 3.9. Enrichment analysis was performed by incorporating Gprofiler, Metascape and Cytoscape plugins. David compounded the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, and enrichment was computed through bioinformatics tools. The 20 pivotal proteins were docked harnessing Glide, Schrodinger Suite 2023-2. The investigation was governed by docking scores and affinity. The shared target proteins underscored the precise Hh and WA network roles with the affirmation enrichment P-value of &lt;0.025. The implications for hedgehog and cancer pathways were profound with enrichment (P &lt; 0.01). Further, the ADMET and drug-likeness assessments assisted the claim. Robust interactions were noticed with docking studies, authenticated through molecular dynamics, molecular mechanics generalized born surface area scores and bonds. The computational investigation emphasized WA’s credible anti-breast activity, specifically with Hh proteins, implying stem-cell-level checkpoint restraints. Rigorous testament is imperative through in vitro and in vivo studies.
New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics
Authors: Mokhtari Majid , Khoshbakht Samane , Ziyaei Kobra , Akbari Mohammad Esmaeil , Moravveji Sayyed Sajjad 
Published: March 2024

Abstract
Bioinformatics has revolutionized biology and medicine by using computational methods to analyze and interpret biological data. Quantum mechanics has recently emerged as a promising tool for the analysis of biological systems, leading to the development of quantum bioinformatics. This new field employs the principles of quantum mechanics, quantum algorithms, and quantum computing to solve complex problems in molecular biology, drug design, and protein folding. However, the intersection of bioinformatics, biology, and quantum mechanics presents unique challenges. One significant challenge is the possibility of confusion among scientists between quantum bioinformatics and quantum biology, which have similar goals and concepts. Additionally, the diverse calculations in each field make it difficult to establish boundaries and identify purely quantum effects from other factors that may affect biological processes. This review provides an overview of the concepts of quantum biology and quantum mechanics and their intersection in quantum bioinformatics. We examine the challenges and unique features of this field and propose a classification of quantum bioinformatics to promote interdisciplinary collaboration and accelerate progress. By unlocking the full potential of quantum bioinformatics, this review aims to contribute to our understanding of quantum mechanics in biological systems.
Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization
Authors: Wang Rui , Wang Tao , Zhuo Linlin , Wei Jinhang , Fu Xiangzheng , Zou Quan , Yao Xiaojun 
Published: March 2024

Abstract
Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section.
Prediction of protein–ligand binding affinity via deep learning models
Authors: Wang Huiwen 
Published: March 2024

Abstract
Accurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has brought new hope for deep learning (DL) models to accurately predict protein–ligand binding affinity. However, the current DL models still face limitations due to the low-quality database, inaccurate input representation and inappropriate model architecture. In this work, we review the computational methods, specifically DL-based models, used to predict protein–ligand binding affinity. We start with a brief introduction to protein–ligand binding affinity and the traditional computational methods used to calculate them. We then introduce the basic principles of DL models for predicting protein–ligand binding affinity. Next, we review the commonly used databases, input representations and DL models in this field. Finally, we discuss the potential challenges and future work in accurately predicting protein–ligand binding affinity via DL models.
KaMRaT: a C++ toolkit for k-mer count matrix dimension reduction
Authors: Xue Haoliang , Gallopin Mélina , Marchet Camille , Nguyen Ha N , Wang Yunfeng , Lainé Antoine , Bessiere Chloé , Gautheret Daniel 
Published: March 2024

Abstract

Motivation
KaMRaT is designed for processing large k-mer count tables derived from multi-sample, RNA-seq data. Its primary objective is to identify condition-specific or differentially expressed sequences, regardless of gene or transcript annotation.

Results
KaMRaT is implemented in C++. Major functions include scoring k-mers based on count statistics, merging overlapping k-mers into contigs and selecting k-mers based on their occurrence across specific samples.

Availability and implementation
Source code and documentation are available via https://github.com/Transipedia/KaMRaT.
SpatialView: an interactive web application for visualization of multiple samples in spatial transcriptomics experiments
Authors: Mohanty Chitrasen , Prasad Aman , Cheng Lingxin , Arkin Lisa M , Shields Bridget E , Drolet Beth , Kendziorski Christina 
Published: March 2024

Abstract

Motivation
Spatial transcriptomics (ST) experiments provide spatially localized measurements of genome-wide gene expression allowing for an unprecedented opportunity to investigate cellular heterogeneity and organization within a tissue. Statistical and computational frameworks exist that implement robust methods for pre-processing and analyzing data in ST experiments. However, the lack of an interactive suite of tools for visualizing ST data and results currently limits the full potential of ST experiments.

Results
To fill the gap, we developed SpatialView, an open-source web browser-based interactive application for visualizing data and results from multiple 10× Genomics Visium ST experiments. We anticipate SpatialView will be useful to a broad array of clinical and basic science investigators utilizing ST to study disease.

Availability and implementation
SpatialView is available at https://github.com/kendziorski-lab/SpatialView (and https://doi.org/10.5281/zenodo.10223907); a demo application is available at https://www.biostat.wisc.edu/˜kendzior/spatialviewdemo/
NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data
Authors: Linderman Michael D , Wallace Jacob , van der Heyde Alderik , Wieman Eliza , Brey Daniel , Shi Yiran , Hansen Peter , Shamsi Zahra , Liu Jeremiah , Gelb Bruce D , Bashir Ali 
Published: March 2024

Abstract

Motivation
Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosity) with short-read genome sequencing data (SRS). Improving SV genotyping accuracy in SRS data, particularly for the many SVs first detected with long-read sequencing, will improve our understanding of genetic variation.

Results
NPSV-deep is a deep learning-based approach for genotyping previously reported insertion and deletion SVs that recasts this task as an image similarity problem. NPSV-deep predicts the SV genotype based on the similarity between pileup images generated from the actual SRS data and matching SRS simulations. We show that NPSV-deep consistently matches or improves upon the state-of-the-art for SV genotyping accuracy across different SV call sets, samples and variant types, including a 25% reduction in genotyping errors for the Genome-in-a-Bottle (GIAB) high-confidence SVs. NPSV-deep is not limited to the SVs as described; it improves deletion genotyping concordance a further 1.5 percentage points for GIAB SVs (92%) by automatically correcting imprecise/incorrectly described SVs.

Availability and implementation
Python/C++ source code and pre-trained models freely available at https://github.com/mlinderm/npsv2.
Shu: visualization of high-dimensional biological pathways
Authors: Carrasco Muriel Jorge , Cowie Nicholas , Taylor Parkins Shannara , Mansouvar Marjan , Groves Teddy , Nielsen Lars Keld 
Published: March 2024

Abstract

Summary
Shu is a visualization tool that integrates diverse data types into a metabolic map, with a focus on supporting multiple conditions and visualizing distributions. The goal is to provide a unified platform for handling the growing volume of multi-omics data, leveraging the metabolic maps developed by the metabolic modeling community. In addition, shu offers a streamlined python API, based on the Grammar of Graphics, for easy integration with data pipelines.

Availability and implementation
Freely available at https://github.com/biosustain/shu under MIT/Apache 2.0 license. Binaries are available in the release page of the repository and the web application is deployed at https://biosustain.github.io/shu.