bluuebunny/crossref_metadata_2025 huggingface dataset:
Models for Analyzing Cryptocurrency Market Dynamics with Behavioral Metrics of Stakeholders Based on Social Media Data
Authors:   V. N. Karazin Kharkiv National University, Guryanova Lidiya S. , Lutsenko Rostyslav R. ,   V. N. Karazin Kharkiv National University
Published: December 2024

The use of data mining techniques in the context of the behavioral economy of virtual assets based on social media data allows for a more accurate assessment of the price movements of cryptocurrencies. The study builds models for forecasting prices in the cryptocurrency market, taking into account the behavioral factors of stakeholders based on social data from the TikTok platform. The data for this study were obtained using social media application programming interfaces. The main stages of the study included data collection, processing and aggregation, scaling and correlation analysis, building and evaluating the relevant models. As a result of the study, key behavioral metrics of social networks are identified. Correlation analysis showed strong linear links between TikTok’s social performance and weak links to the price of bitcoin. The study builds linear models, polynomial regression, Decision Tree and Random Forest. Behavioral metrics such as the number of shares, likes, comments, shares, and views are used. Models are evaluated by testing with use of MSE and MAE metrics. The results suggest the limited effectiveness of linear regression for predicting the prices of cryptocurrencies due to the non-linear nature of the market. The Decision Tree model has shown some success in predicting bitcoin prices, however, deviations in forecasts have increased over time, especially in the face of market fluctuations. Polynomial regression and the Random Forest model demonstrate higher accuracy in forecasts. Based on the comparison of MSE and MAE indicators, the Random Forest turned out to be the most effective model for predicting bitcoin prices among those considered.
Stability of Equilibrium and Bifurcation Behavior of the Production and Economic System
Authors:   Simon Kuznets Kharkiv National University of Economics, Malуarets Lyudmyla M. , Voronin Anatolii V. ,   Simon Kuznets Kharkiv National University of Economics, Lebedeva Irina L. ,   Simon Kuznets Kharkiv National University of Economics
Published: December 2024

This study considers some key problems of analysis of nonlinear dynamic systems on the example of production and economic objects. The construction of a mathematical model of systems, functioning in the market environment, is aimed at qualitative forecasting (along the development trajectory) of the behavioral properties of such a system. The conceptual orientation of the study involves the analysis of structural instability of equilibrium states within the framework of the proposed model by the presence of characteristic combinations of the most important economic parameters that have a significant impact on the static and dynamic characteristics of the production and economic system. Critical modes of functioning of the object have been identified and its stability area has been built in three-dimensional space depending on significant parameters. An example of such dynamic modes, which were revealed in the process of analysis using the proposed model, is an unstable boundary cycle that provokes the so-called «hard» mechanism of excitation of self-oscillations around the equilibrium state of the «focus» type. Also in this system, the global bifurcation of the saddle joint in the presence of a loop of the «saddle» separatrix around the state of equilibrium was found and studied. Such modes are rather dangerous, since there are cyclic processes with very long periods, which significantly affects the accuracy of predicting the behavior of the object under study.
Prototipe Sistem Keamanan Buka Pintu dan Jendela Menggunakan Aplikasi Telegram
Authors: Tiffany Nivika , Bakti Zainal Yuda , Indrayanto Ricki , Charisma Atik , Haz Fauzia
Published: December 2024

Tindak kriminal seperti pencurian dan pembobolan rumah merupakan masalah serius yang berkaitan dengan keamanan. Masalah ini sering terjadi ketika pemilik rumah sedang keluar kota atau meninggalkan rumah untuk beberapa hari. Oleh karena itu, dibutuhkan sistem keamanan yang efektif agar rumah tidak mudah dibobol dan dapat dipantau melalui smartphone oleh pemiliknya. Untuk meminimalkan tingkat kejahatan, kami merancang alat pengunci pintu otomatis berbasis mikrokontroler. Sistem otomatis ini dapat mengontrol solenoid door lock dan berfungsi sebagai pengaman rumah. Penelitian ini bertujuan untuk mengembangkan sistem keamanan door lock dan window security berbasis mikrokontroler dan Internet of Things. Sistem ini memanfaatkan RFID untuk membuka pintu yang diproses oleh Arduino, serta Sensor PIR untuk keamanan ganda yang diproses menggunakan NodeMCU ESP8266. Smartphone berfungsi sebagai media untuk memantau dan membuka pintu. Mikrokontroler NodeMCU ESP8266 pada Sensor PIR bertugas memproses data sehingga ketika sensor mendeteksi adanya gerakan, buzzer akan berbunyi dan mengirim notifikasi ke smartphone. Hasil penelitian menunjukkan bahwa pintu otomatis door lock dapat diakses melalui RFID dan aplikasi di smartphone. Sistem dapat mendeteksi RFID pada jarak 3 cm dengan waktu respon 2-5 detik, dan berhasil mendeteksi gerakan pada manusia dan hewan dengan tingkat akurasi 86,67%
Potential of Freshwater Microalgae in Biodegradation of Disposable Face Masks
Authors: Deviany Deviany , Nainggolan Gabriela , Br Pelawi Luluinaita
Published: December 2024

The use of disposable face masks can reduce the transmission of Covid-19 through saliva splashes from infected patients. However, the use of these masks causes waste generation that has the potential to release microplastic fibers into the environment. Biodegradation by freshwater microalgae can be a solution to handling disposable face mask waste. Microalgae were isolated from retention ponds in Institut Teknologi Sumatera using Walne fertilizer and pineapple liquid waste (LCN) as alternative growth media. Selected isolate was used to degrade three-ply mask pieces with Walne:LCN media ratios of 1:0,; 0:1, and 1:1 as well as Walne media without the addition of isolate as a control. Analysis of the masks degradation results was carried out using gravimetric and Fourier Transform Infraredmethods. Gravimetric analysis showed a decrease in mask mass after degradation for the control, 1:0, 0:1, 1:1 treatments, which were 0.0004grams, 0.0054grams, 0.0088grams and 0.0193grams, respectively. FTIR analysis showed new functional groups (O-H), increased C=C, and decreased C-H % transmittance of the masks after degradation. It can be concluded from the analysis that microalgae isolated from ITERA’s retention basin was able to degrade disposable masks, which is characterized by a decrease in mask mass and changes in % transmittance of C=C and C-H functional groups. The addition of LCN did not show a significant effect on mask degradation compared to variations without LCN, as evidenced by the FTIR results in the 1:1 variation where no alkene groups appeared and the alkyl group spectrum peaks were not much different from the control
Delignification of Lignocellulosic Content of Sugarcane Bagasse (Saccarum officinarium) with Variations in Size and Pretreatment Time
Authors: Yusuf Andi Asdiana Irma Sari , Abadi Sakinah Islamiati , Sariwahyuni
Published: December 2024

Sugarcane bagasse (Saccarum officinarium) is a byproduct of the sugar production process and holds potential as a raw material for bioethanol production due to its lignocellulose content. Lignocellulose contains important components such as cellulose, hemicellulose, and lignin. This study focuses on the delignification of sugarcane bagasse (Saccarum officinarium) through a combination of mechanical and autoclave treatments, with variations in particle size and heating time. The goal of this study is to reduce the lignin content and increase the proportion of cellulose and hemicellulose, key components for producing valuable products  for instance bioethanol. In this research, sugarcane bagasse was processed with particle sizes of 60, 70, and 80 mesh, and heated in an autoclave for 30, 45, and 60 minutes. The Chesson-Datta method was used to analyze the lignocellulose content of the bagasse after the delignification process. The results showed that the best outcome was achieved with a particle size of 80 mesh and a heating time of 60 minutes, yielding the lowest lignin content at 14.27%, while cellulose and hemicellulose reached 44.3% and 26.75%, respectively. This indicates that variations in particle size and heating duration significantly affect the effectiveness of the delignification process. Optimizing these two parameters is crucial for increasing cellulose and hemicellulose content while reducing lignin. Further research is expected to develop more effective pretreatment techniques to enhance the efficiency of sugarcane bagasse biomass conversion for industrial applications
Crack Identification and Quantification of Bridge Concrete Based on YOLOX and Image Processing Techniques
Authors: Li Bixiong , Liu Yunjun , Kuang Guixing
Published: December 2024

The investigation and analysis of bridge distress are critical for the assessment and maintenance of bridge safety, necessitating precise information regarding the condition of the bridge surface. In this study, a deep learning framework for automatically identifying bridge concrete cracks is proposed based on comparing the detection performance of YOLOX, SSD, and Faster R-CNN. The deep learning model YOLOX_s is initially trained and employed to identify bridge concrete cracks, and the detection results demonstrate that the bridge concrete crack identification accuracy rate of the YOLOX_s is 91.77% and much higher than that of SSD and Faster R-CNN, which are 88.09% and 86.57% separately. To perform bridge concrete crack quantification, several image processing techniques are applied. The process begins with the cropping of the identified cracks obtained by YOLOX_s followed by binarization using Otsu's method. Subsequently, the Zhang-Suen thinning algorithm is applied to extract the crack skeleton, while the Canny edge detection algorithm outlines the crack boundaries. Finally, a pixel accumulation-based method is implemented to calculate the crack dimensions. The findings indicate that the proposed method for measuring crack length and the maximum width achieves high accuracy levels of 96.6% and 95.86%, respectively.
Treatment of high risk myelodysplastic syndrome
Authors: Kröger Nicolaus
Published: December 2024

The myelodysplastic syndrome (MDS) is considered to be a heterogeneous myeloid malignancy with a common origin in the hematopoietic stem cell compartment, generally divided into lower and higher risk. While treatment goal for lower risk MDS (LR-MDS) is to decrease transfusion burden and transformation into acute leukemia major aim for high risk MDS is to prolong survival and ultimately cure. While novel agents such as luspatercept or imetelstat have recently been approved as new treatment options for LR-MDS, hypomethylating agents (HMA) remain currently the only approved non-transplant option for HR-MDS and is the standard of care for non-transplant-eligible patients. Combinations with other drugs as first-line treatment has to date not proven more efficacious than monotherapy in HR-MDS, and outcome after HMA failure is poor. The only potential cure and standard of care for eligible patients is allogeneic stem cell transplantation (HSCT) and even if the number of transplanted – especially older – MDS patients increased over time due to a better management and donor availability the majority of MDS patients will not be eligible for this curative approach. Current challenges encompass to decrease the relapse risk, the main cause of HSCT failure. This review will summarize current knowledge of options of transplant- and non-transplant treatment approaches for these patients and demonstrate the unmet clinical need for more effective therapies.
Acid ceramidase controls proteasome inhibitor resistance and is a novel therapeutic target for the treatment of relapsed / refractory multiple myeloma
Authors: Bishop Ryan T. , Li Tao , Sudalagunta Praneeth , Nasr Mostafa , Nyman Karl J. , Alugubelli Raghunandan R. , Meads Mark , Frieling Jeremy , Nerlakanti Niveditha , Tauro Marilena , Fang Bin , Grant Steven , Koomen John , Silva Ariosto S. , Shain Kenneth H. , Lynch Conor C.
Published: December 2024

Multiple myeloma (MM) patients are often refractory to targeted therapies including proteasome inhibitors (PIs). Here, analysis of RNA sequencing data derived from 672 patients with newly diagnosed or relapsed/refractory disease identified the acid ceramidase, ASAH1, as a key regulator of PI resistance. Genetic or pharmacological blockade of ASAH1 remarkably restored PI sensitivity and protected mice from resistant MM progression in vivo. Mechanistically, ASAH1 depletion of ceramide promoted SET inhibition of PP2A phosphatase activity thus facilitating the increased expression and activity of the pro-survival proteins, MCL-1, and BCL-2. We corroborated these findings in human MM datasets, and in ex vivo patient MM cells. These preclinical studies suggest that ASAH1 may be a potential therapeutic target for the treatment of relapsed/refractory MM (RRMM).
Complete remission of NUP98 fusion-positive acute myeloid leukemia with the covalent menin inhibitor BMF-219, icovamenib
Authors: Carraway Hetty E. , Nakitandwe Joy , Cacovean Alexandru , Ma Yan , Munneke Brian , Waghmare Ganesh , Mandap Clarissa , Ahmed Uzma , Kowalczyk Nicole , Butler Thomas , Morris Stephan W.
Published: December 2024

Not available.
Kinh nghiệm quốc tế về dự báo thị trường lao động trong bối cảnh chuyển đổi số và bài học cho Việt Nam
Authors: Lê Thị Thu Hương , Phạm Ngọc Toàn
Published: December 2024

Trong bối cảnh chuyển đổi số, dự báo thị trường lao động cần thiết phải thay đổi để đáp ứng yêu cầu thực tiễn. Phương pháp dự báo thị trường lao động ở Việt Nam còn nhiều hạn chế. Bài viết này thực hiện phân tích và tổng hợp kinh nghiệm của một số quốc gia trên thế giới về dự báo thị trường lao động, trên cơ sở đó chỉ ra Việt Nam cần xây dựng một mô hình, phương pháp dự báo thị trường lao động cần áp dụng các công nghệ tiên tiến như phân tích dữ liệu lớn (Big Data), trí tuệ nhân tạo (AI) và học máy (Machine Learning) để nâng cao độ chính xác và hiệu quả của dự báo.
EFFECT OF SHORT ENGLISH WRITING ACTIVITY ON FACEBOOK ON SCIENCE AND NON-SCIENCE STUDENTS’ PERFORMANCE
Authors: Adi Kasuma Shaidatul Akma , Ganapathy Malini , Tan Debbita Ai Lin , Mohamad Saleh Mohamad Saifudin
Published: December 2024

This study compares the Science and Non-Science university students’ performance in a short English writing activity on Facebook. In this mainly quantitative study, 86 Common European Framework of Reference (CEFR) Independent and Basic English Users from 4 classes (2 Sciences and 2 Non-Sciences), joined 4 separate Facebook Groups, managed by two class teachers. Each week, the teachers posted a pre-selected topic in the groups and the students were instructed to respond to it. The mean performance of the groups was compared by performing a paired samples t-test and an independent samples t-test of the pre-test and post-test. The post-test revealed a significant improvement in the students’ short writing performance (within groups), but no significant difference was observed between the Science and Non-Science groups’ performance (between groups). Individual interviews with the teachers revealed that both groups of students committed similar grammatical, structural, and vocabulary mistakes, however, the Science students were more active and confident in using the English language in the classrooms. In line with the constructivist view of language learning, the study highlights the importance of teachers’ instructions, feedback, monitoring, and encouragement in improving students’ short writing ability and consistency in writing practice.
IMPACT OF TASK-BASED LANGUAGE TEACHING (TBLT) ON ENGLISH SPOKEN PRODUCTION AND INTERACTION AMONG MALAYSIAN PRE-UNIVERSITY STUDENTS
Authors: Abd Rahman Nadia
Published: December 2024

This study aims to explore the impact of Task-Based Language Teaching on English-spoken production and interaction among English as a Second Language pre-university students in Malaysia. The quasi-experimental research design involved 63 pre-university ESL students and two ESL teachers from the Centre of Foundation Studies in Management at Universiti Utara Malaysia (UUM), Sintok, Kedah. Pre- and post-tests were conducted before and after the Task-Based Language Teaching intervention respectively. Data were also collected using semi-structured interviews, and online questionnaires. Data analysis procedures included a paired sample test, text analysis, thematic analysis and descriptive analysis. The results from the paired sample test revealed no statistically significant difference between the experimental and control groups. However, the experimental group indicated considerable development in spoken production and interaction in terms of fluency through reduction of fillers and improved speech quality. The thematic and descriptive analysis found that TBLT encouraged the students to enhance their spoken abilities. The findings suggest that although TBLT has the potential to foster greater fluency and deepen students’ engagement in language tasks.
KONTAK DAN PERUBAHAN DIALEK: TINJAUAN TERHADAP MALAYIK HULU SEMENANJUNG
Authors: Hasrah Mohd Tarmizi , Abd Ghani Mohammad Khairulanwar
Published: December 2024

Pemodenan Malaysia telah mempercepatkan perubahan dialek tradisional, dan ini dicerap menerusi peralihan struktur linguistiknya daripada yang kompleks kepada yang umum dan mudah. Berpandukan perspektif kontak dialek menerusi proses pelevelan, makalah ini membincangkan perubahan yang dialami oleh beberapa dialek tradisional Malayik hulu Semenanjung akibat kontak dengan dialek hilir. Antara dialek yang dianalisis ialah dialek hulu Kelantan, dialek hulu Dungun, dialek hulu Kedah dan dialek hulu Pahang. Makalah ini menegaskan bahawa faktor terpenting yang melatari perubahan dalam dialek yang dikaji ialah kontak yang ternatijah daripada migrasi daripada kawasan hilir ke kawasan hulu dan pedalaman. Secara tabii, migrasi tidak sekadar membawa batang tubuh manusia, tetapi yang turut ikut serta ialah bahasa yang dituturkan. Justeru, migrasi berdaya menghasilkan kontak dialek. Kemudian, kontak mewujudkan peminjaman antara dialek. Proses pelevelan beroperasi serempak dengan peminjaman, iaitu apabila dialek yang meminjam menstrukturkan sistem linguistiknya untuk disesuaikan dengan sistem linguistik dialek peminjam. Keempat-empat dialek yang dikaji sememangnya mematuhi peraturan ini, justeru menampakkan trajektori perubahan yang menuruti sistem linguistik dialek yang dipersepsikan lebih berprestij di sesebuah kawasan, iaitu dialek hilir, berbanding dengan bahasa piawai atau standard. Tuntasnya, makalah ini menawarkan kelihan yang menguntaikan fenomena perubahan dialek dengan kontak akibat migrasi. Kelihan ini menampilkan tafsiran baru tentang trajektori difusi dialek berdasarkan model heteronomi.
UNVEILING STUDENTS’ VOICE ON LECTURERS’ CLASSROOM MANAGEMENT IN VIRTUAL ENGLISH MEDIUM INSTRUCTION
Authors: Lestari Sri , Yuliasri Issy , Fitriati Sri Wuli , Widhiyanto
Published: December 2024

A private university in Indonesia has implemented English Medium Instruction (EMI) classes in order to support internationalisation programmes. This policy entails using English as the instructional language in the classroom. This case study examined the teaching practices of seven content lecturers from the Biology, Physics, Mathematics, and Primary Education Departments in EMI classes, focusing on classroom management. Additionally, it explored 208 students’ perceptions in this setting. Data were collected through class observations, questionnaires, and interviews. The findings suggest that effective classroom management by lecturers leads to increased student participation and engagement. The lecturers employed various organisational, teaching management, and teacher-student relationship strategies to manage their classrooms. These findings provide valuable insights into maximising classroom management in an EMI setting, which can enhance critical reflection and support the implementation of EMI classes. The study also highlighted the students' perception of participation and interaction in the class, confirming that lecturers who lack effective teaching skills and multimodal strategies result in passive learning among students.
Cloggs Cave pollen sequences, GunaiKurnai Country, East Gippsland (SE Australia): 25,000 years of cultural plant use and changing environments
Authors: Grono Elle , David Bruno , Stevenson Janelle , Fresløv Joanna , Mullett Russell , Keaney Benedict , Graham Catriona , Ash Jeremy ,   GunaiKurnai Land and Waters Aboriginal Corporation, McDowell Matthew C. , Petchey Fiona , Delannoy Jean-Jacques , Rogers Ashleigh J. , Kennedy David M.
Published: December 2024

In southeastern Australia, GunaiKurnai caves are known by current Aboriginal Elders and from nineteenth century ethnographic documents as special places used by mulla-mullung (“clever men” and “clever women”) for the practice of magic and medicine. Pollen analysis conducted on sediments from one such cave, Cloggs Cave, reveals an unusually well-preserved and well-stratified pollen sequence extending back >25,000 years, with much of the pollen introduced into the cave by people carrying flowering plants. High concentrations of pollen, rare for limestone cave settings, were recovered, including pollen clumps of individual taxa representing deposition of in situ flowering material. These taxa are dominated by plants known through GunaiKurnai knowledge and ethnography to have special cultural uses and that match the plants known to have been used by mulla-mullung, and some edible plants. These include taxa such as Banksia spp., Pimelea spp. (rice flower), and Plantago spp. (plantain) and the plant families of Asteraceae (daisy) and Poaceae (grass). The largely anthropogenic pollen assemblage also signals the presence of plants from cooler and drier climates dominated by more open vegetation during the Last Glacial Maximum than that observed around Cloggs Cave in recent times. The Early Holocene pollen then reflects a warmer and wetter climate that supported the expansion of woodland elements. Together, the pollen record of Cloggs Cave provides remarkable insights into two articulating histories: the cultural practices of the GunaiKurnai Old Ancestors in a special, secluded cave; and the environmental history of Country.
Immune-checkpoint HLA-G gene polymorphisms, 3’-UTR types and their association with hepatocellular carcinoma and treatment response in Indian population
Authors: Nadda Neeti , Yadav Renu , Roy Neelanjana , Singh Nita , Kumar Sonu , Paul Shashi Bala , Gamanagatti Shivanand , Saraya Anoop , Shalimar  , Nayak Baibaswata
Published: December 2024

Background

Human leukocyte antigen-G (HLA-G) is a cancer-associated immune checkpoint protein implicated in tumor-driven immune escape mechanisms. This study was undertaken to determine genetic variations at the 3’-UTR of the HLA-G gene that may alter its expression, identify risk alleles and genotypes for their association with hepatocellular carcinoma (HCC), and treatment responses in the Indian population.

Objectives

Case-control genetic association study of HLA-G gene UTR polymorphisms with HCC and response to locoregional therapy (LRT).

Methods

HCC cases (n = 100) and healthy controls (n = 110) were recruited for the genetic association study, of which 88 patients received LRT. Single nucleotide polymorphisms (SNPs) at the HLA-G 3’-UTR gene were genotyped by sequencing and PCR-RFLP. The genetic association of 14 SNPs with HCC and LRT responses was determined using population genetic approaches.

Results

Three of the 14 SNPs (rs1707, rs1710, and rs1063320) were found to be genetically associated with HCC risk and treatment responses. These three UTR SNPs are important for miRNA binding. We did not observe significant association of the most studied SNP, rs371194629 (INDEL, +2960), with HCC or treatment response. Serum sHLA-G levels were found to be significantly (p = 0.027) higher in HCC patients as compared to healthy controls. Highly prevalent UTR haplotypes in Indian HCC patients were UTR-4, -1, and -7 whereas in healthy controls it was UTR-3, and 15 as determined by a linkage disequilibrium (LD) plot using 8 SNPs.

Conclusion

HLA-G SNPs are genetically associated with HCC and treatment response. Haplotypes associated with high levels of HLA-G expression are more prevalent in HCC than in healthy controls.

Core tip

Population genetic approaches were used to study HLA-G gene polymorphisms in the Indian population for its genetic association with HCC risk, treatment response and altered gene expression. Out of the 14 SNPs studied for HLA-G UTR, three were linked to HCC and response to locoregional therapy. Linkage disequilibrium and UTR haplotyping analysis show that the UTR-4 haplotype linked to high HLA-G levels, is more common in HCC patients, while the UTR-3 haplotype, linked to low HLA-G levels, is more common in healthy controls. This study is the first to look at the UTR types based on HLA-G gene polymorphisms of Indian HCC patients and their response to therapy.
The use of a benign fast-growing cyanobacterial species to control microcystin synthesis from Microcystis aeruginosa
Authors: Lee Hakyung , Xu Vincent , Diao Jinjin , Zhao Runyu , Chen Moshan , Moon Tae Seok , Liu Haijun , Parker Kimberly M. , Jun Young-Shin , Tang Yinjie J.
Published: December 2024

Introduction

Microcystis aeruginosa (M. aeruginosa), one of the most abundant blue-green algae in aquatic environments, produces microcystin by causing harmful algal blooms (HABs). This study investigated the combined effects of nutrients and competition among cyanobacterial subpopulations on the synthesis of microcystin-LR.

Methods

Under varying nitrogen and phosphorus concentrations, cyanobacterial coculture, and the presence of algicidal DCMU, the growth was monitored by optical density analysis or microscopic counting, and the microcystin production was analyzed using high-performance liquid chromatography-UV. Furthermore, growth and toxin production were predicted using a kinetic model.

Results and discussion

First, coculture with the fast-growing cyanobacterium Synechococcus elongatus UTEX 2973 (S. elongatus) reduced M. aeruginosa biomass and microcystin production at 30°C. Under high nitrogen and low phosphorus conditions, S. elongatus was most effective, limiting M. aeruginosa growth and toxin synthesis by up to 94.7% and 92.4%, respectively. Second, this biological strategy became less effective at 23°C, where S. elongatus grew more slowly. Third, the photosynthesis inhibitor DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea) inhibited M. aeruginosa growth (at 0.1 mg/L) and microcystin production (at 0.02 mg/L). DCMU was also effective in controlling microcystin production in S. elongatus–M. aeruginosa cocultures. Based on the experimental results, a multi-substrate, multi-species kinetic model was built to describe coculture growth and population interactions.

Conclusion

Microcystin from representative toxin-producing M. aeruginosa can be controlled by coculturing fast-growing benign cyanobacteria, which can be made even more efficient if appropriate algicide is applied. This study improved the understanding of the biological control of microcystin production under complex environmental conditions.
Comparative analysis of the gut microbiota composition and diversity in Erinaceus amurensis from the Wandashan Mountain range area based on metagenomics
Authors: Jia Xiuqi , Li Qiang , Yang Xinyu , Li Dianwei , Jin Zhimin
Published: December 2024

This study aimed to preliminarily explore the composition and diversity of intestinal bacteria in Erinaceus amurensis during breeding period, aiding in the field rescue and population conservation efforts of Erinaceus amurensis. This also provides foundational data for further research on the prevention and screening of Emerging Zoonotic Infectious Diseases and the experimental animalization of wild Erinaceus amurensis. Between April and July 2023, we collected 13 fresh fecal samples from Erinaceus amurensis at the Sishan Forest Farm in Jidong County, Heilongjiang Province, situated within the Wandashan Mountain range. Utilizing metagenomic sequencing technology, we conducted a comparative analysis of the gut microbiota composition and diversity in wild Erinaceus amurensis across different genders and between adult and fetal individuals within the same habitat. Our results revealed significant differences (P < 0.01) in the classification and diversity of gut microbiota between genders and between adult and fetal Erinaceus amurensis. Specifically, the dominant bacterial groups in the gut of Erinaceus amurensis were Pseudomonas, Proteobacteria, and Enterobacteriaceae. In male and female Erinaceus amurensis, the dominant bacterial groups were Pseudomonas, Bacteroides, and Firmicutes, with variations in bacterial abundance and diversity. While male and female Erinaceus amurensis exhibited similar microbial compositions, they displayed significant differences in specific bacterial classifications. The dominant bacterial group in fetal Erinaceus amurensis was Proteobacteria, which demonstrated lower diversity and abundance compared to the adult group. Furthermore, the types and abundance of pathogenic or opportunistic pathogens in the gut of fetal Erinaceus amurensis and male Erinaceus amurensis were higher than those in female Erinaceus amurensis. The analysis of experimental results indicates that Erinaceus amurensis in this region either have or are at risk of developing inflammation related to the intestinal and urinary tracts, as well as skin-related issues. Consequently, it is advised that forestry and wildlife conservation personnel in this area prioritize treatment against these specific pathogens when conducting rescue operations for Erinaceus amurensis in the wild.
NUF2 is associated with cancer stem cell characteristics and a potential drug target for prostate cancer
Authors: Zhang Dongxu , Liang Pu , Wang Qi , Xia Bowen , Yu Liqian , Hu Xiaopeng
Published: December 2024

Background

Cancer stem cells are characterized by self-renewal, clonal tumor initiation capacity, and treatment resistance, which play essential roles in the tumor progression of prostate cancer (PCa). In this study, we aim to explore the features of cancer stemness and characterize the expression of stem cell-related genes for PCa.

Methods

We downloaded RNA-seq data and related clinical information from The Cancer Genome Atlas (TCGA) database. The mRNA stemness index (mRNAsi) was analyzed for various clinical features, overall survival (OS), and disease-free survival (DFS), and a weighted gene co-expression network analysis (WGCNA) was performed to identify crucial gene modules and key genes, which may play a role in CSCs. The key gene functions were verified using multiple databases, including the TCGA and Gene Expression Omnibus database (GEO). Next, we explored the potential function of the modules and genes obtained using WGCNA using an enrichment analysis. Finally, we performed in vitro experiments for further verification.

Results

We found that mRNAsi were higher in PCa tissues than in normal tissues, and the mRNAsi were closely related to the clinical characteristics of PCa. A total of 16 key genes associated with the mRNAsi scores were identified by WGCNA analysis, including NCAPG, NEK2, DLGAP5, CENPA, CENPF, TPX2, GTSE1, KIF4A, NEIL3, CDC25C, UBE2C, CDCA5, MELK, SKA3, NUF2, and BIRC5. These genes were explicitly highly expressed in PCa across TCGA cancers and were validated in 3 independent GEO PCa datasets. The functional annotations of the key genes were linked with the cell proliferation processes. NUF2 may be a potential biomarker for PCa. In vitro experiments showed that knockdown NUF2 reduced the proliferation and migration of PCa cells.

Conclusion

The 16 key genes identified in this study significantly correlate with PCa stem cell characteristics and showed prognosis-oriented effects in PCa patients. Further, the NUF2 gene may be used as a drug target for treating PCa.
Mass balance, metabolic disposition, and pharmacokinetics of a single IV dose of [14C]CA102N in HT-29 xenograft athymic nude mice
Authors: Tchaparian Eskouhie , Lin Hua-Yang , Chen Yuchih , Hunter J. Neil , Yin Sindy , Ng Huey , Wu Albert
Published: December 2024

Introduction

CA102N is a novel anticancer drug developed by covalently linking H-Nim (N-(4-Amino-2-phenoxyphenyl methanesulfonamide) to Hyaluronic Acid to target CD44 receptor-rich tumors. The proposed approach seeks to enhance the efficacy and overcome limitations associated with H-Nim, including poor solubility and short half-life.

Methods

The study aimed to evaluate the pharmacokinetics, biodistribution, metabolism, and tumor permeability of [14C] CA102N in xenograft mice following a single intravenous dose of 200 mg/kg. Liquid scintillation counting analysis was used for the pharmacokinetics and mass balance analysis. Metabolite profiling was assessed by HPLC-MS coupled with a radio flow-through detector. Quantitative Whole-Body Autoradiography was used to determine tissue distribution. Concentrations of CA102N and its metabolites were measured using total radioactivity data from urine, feces, and tissue samples.

Results

About 94.9% of the administered dose was recovered at 240 h post-dose. The primary route of radioactivity elimination was through urine, accounting for an average of 77% of the dose with around 13.2% excreted in the feces. Tissue distribution showed rapid accumulation within 0.5 h post-administration, followed by a fast decline in most tissues except for the tumor, where slow elimination was observed. CA102N/metabolites exhibited a two-phase pharmacokinetic profile, characterized by an initial rapid distribution phase and a slower terminal elimination, with a half-life (t1/2) of 22 h. The mean maximum concentration (Cmax) of 1798.586 µg equivalents per ml was reached at 0.5 h (Tmax). Most of the radioactivity in plasma was attributed to CA102N, while small-molecule hydrolysis products dominated the excreta and tissue samples. Metabolite profiling revealed two major hydrolysis products: H-Nim-disaccharide and H-Nim-tetrasaccharide. No unchanged [14C] CA102N was detected in urine or feces, suggesting that CA102N undergoes extensive metabolism before excretion.

Conclusion

The current data provided valuable insights into the pharmacokinetics, metabolism, and tissue/tumor distribution of CA102N in mice. These findings demonstrated that metabolic clearance is the primary elimination pathway for CA102N and that the drug exhibits tumor retention, supporting its development as an anticancer therapy. Our results provided a strong pharmacological basis for the advancement of CA102N into the clinic.
Slant spacelike ruled surfaces and their Bertrand offsets
Authors: Almoneef Areej A. , Abdel-Baky Rashad A.
Published: December 2024

In this work, we investigate the synthesis problem of slant spacelike ruled surfaces and associated Bertrand offsets (BO) in E13 (Minkowsk 3-space). We provide the parametric equation for a non-developable spacelike ruled surface (SLRS) by using the Blaschke frame (BF). This results in the amplitude to control a family of curvature functions defining the domestic form of this SLRS. Therefore, we found the appropriate SLRS criteria to be slant SLRS. Thus, several new Bertrand offsets (BO) for slant SLRS are investigated and constructed.
Can the digital economy help reduce agricultural carbon emissions? An empirical test based on the moderated mediating effect model
Authors: Xu Xiaodong , Wang Shuo , Qin Xutian , Han Yufei
Published: December 2024

Considering agriculture's substantial carbon emissions, it is essential to examine how the digital economy can stimulate green innovation and low-carbon development by impacting these emissions. This article employs panel data from 31 provinces in China, covering the period from 2011 to 2020, to empirically analyze the effects of digital economy development on agricultural carbon emissions. The results indicate that: (1) Agricultural green total factor productivity has a partial mediating effect in the impact of the digital economy on agricultural carbon emissions, and green finance has a negative regulatory effect. (2) Green finance positively regulates the inhibitory effect of agricultural green total factor productivity on agricultural carbon emissions. (3) The development of the digital economy can inhibit agricultural carbon emissions, and this negative inhibitory effect follows a “U” shape. Our research offers a scientific foundation for the promotion of green and low-carbon development within Chinese agriculture, while also providing valuable references and insights for the global integration and advancement of agricultural emissions reduction alongside the digital economy.
Robust Object Detection Under Smooth Perturbations in Precision Agriculture
Authors: Mahmoud Nesma Talaat Abbas , Virro Indrek , Zaman A. G. M. , Lillerand Tormi , Chan Wai Tik , Liivapuu Olga , Roy Kallol , Olt Jüri
Published: December 2024

Machine learning algorithms are increasingly used to enhance agricultural productivity cost-effectively. A critical task in precision agriculture is locating a plant’s root collar. This is required for the site-specific fertilization of the plants. Though state-of-the-art machine learning models achieve stellar performance in object detection, they are often sensitive to noisy inputs and variation in environment settings. In this paper, we propose an innovative technique of smooth perturbations to improve the robustness of root collar detection tasks using the YOLOv5 neural network model. We train a YOLOv5 model on blueberry image data for root collar detection. A small amount noise is added as a smooth perturbation to the bounding box of dimensions 50× 50, and this perturbed image is fed for training. Furthermore, we introduce an additional test set that represents the out-of-distribution (O.O.D.) case by applying Gaussian blur on test images to simulate particle situation. We use three different image datasets to train our model, the (i) Estonian blueberry, (ii) Serbian blueberry image, and (iii) public dataset sourced from Roboflow datasets, of sample size 118, 2779, and 2993, respectively. We achieve an overall precision of 0.886 on perturbed blueberry images compared to 0.871 on original (unperturbed) images for the O.O.D. test set. Similarly, our smooth perturbation training has achieved an mAP of 0.828, which significantly increases against the result of normal training, which only reaches 0.794. The result proves that our proposed smooth perturbation is an effective method to increase the robustness and generalizability of the object detection task.
Using Data-Driven Computer Vision Techniques to Improve Wheat Yield Prediction
Authors: Smajlhodžić-Deljo Merima , Hundur Hiyari Madžida , Gurbeta Pokvić Lejla , Merdović Nejra , Bećirović Faruk , Spahić Lemana , Grbović Željana , Stefanović Dimitrije , Miličić Ivana , Marko Oskar
Published: December 2024

Accurate ear counting is essential for determining wheat yield, but traditional manual methods are labour-intensive and time-consuming. This study introduces an innovative approach by developing an automatic ear-counting system that leverages machine learning techniques applied to high-resolution images captured by unmanned aerial vehicles (UAVs). Drone-based images were captured during the late growth stage of wheat across 15 fields in Bosnia and Herzegovina. The images, processed to a resolution of 1024 × 1024 pixels, were manually annotated with regions of interest (ROIs) containing wheat ears. A dataset consisting of 556 high-resolution images was compiled, and advanced models including Faster R-CNN, YOLOv8, and RT-DETR were utilised for ear detection. The study found that although lower-quality images had a minor effect on detection accuracy, they did not significantly hinder the overall performance of the models. This research demonstrates the potential of digital technologies, particularly machine learning and UAVs, in transforming traditional agricultural practices. The novel application of automated ear counting via machine learning provides a scalable, efficient solution for yield prediction, enhancing sustainability and competitiveness in agriculture.
Early Detection and Classification of Diabetic Retinopathy: A Deep Learning Approach
Authors: Youldash Mustafa , Rahman Atta , Alsayed Manar , Sebiany Abrar , Alzayat Joury , Aljishi Noor , Alshammari Ghaida , Alqahtani Mona
Published: December 2024

Background—Diabetes is a rapidly spreading chronic disease that poses a significant risk to individual health as the population grows. This increase is largely attributed to busy lifestyles, unhealthy eating habits, and a lack of awareness about the disease. Diabetes impacts the human body in various ways, one of the most serious being diabetic retinopathy (DR), which can result in severely reduced vision or even blindness if left untreated. Therefore, an effective early detection and diagnosis system is essential. As part of the Kingdom of Saudi Arabia’s Vision 2030 initiative, which emphasizes the importance of digital transformation in the healthcare sector, it is vital to equip healthcare professionals with effective tools for diagnosing DR. This not only ensures high-quality patient care but also results in cost savings and contributes to the kingdom’s economic growth, as the traditional process of diagnosing diabetic retinopathy can be both time-consuming and expensive. Methods—Artificial intelligence (AI), particularly deep learning, has played an important role in various areas of human life, especially in healthcare. This study leverages AI technology, specifically deep learning, to achieve two primary objectives: binary classification to determine whether a patient has DR, and multi-class classification to identify the stage of DR accurately and in a timely manner. The proposed model utilizes six pre-trained convolutional neural networks (CNNs): EfficientNetB3, EfficientNetV2B1, RegNetX008, RegNetX080, RegNetY006, and RegNetY008. In our study, we conducted two experiments. In the first experiment, we trained and evaluated different models using fundus images from the publicly available APTOS dataset. Results—The RegNetX080 model achieved 98.6% accuracy in binary classification, while the EfficientNetB3 model achieved 85.1% accuracy in multi-classification, respectively. For the second experiment, we trained the models using the APTOS dataset and evaluated them using fundus images from Al-Saif Medical Center in Saudi Arabia. In this experiment, EfficientNetB3 achieved 98.2% accuracy in binary classification and EfficientNetV2B1 achieved 84.4% accuracy in multi-classification, respectively. Conclusions—These results indicate the potential of AI technology for early and accurate detection and classification of DR. The study is a potential contribution towards improved healthcare and clinical decision support for an early detection of DR in Saudi Arabia.
Embleporicin: A Novel Class I Lanthipeptide from the Actinobacteria Embleya sp. NF3
Authors: Roblero-Mejía Dora Onely , García-Ausencio Carlos , Rodríguez-Sanoja Romina , Guzmán-Chávez Fernando , Sánchez Sergio
Published: December 2024

Genome mining has emerged as a revolutionary tool for discovering new ribosomally synthesized and post-translationally modified peptides (RiPPs) in various genomes. Recently, these approaches have been used to detect and explore unique environments as sources of RiPP-producing microorganisms, particularly focusing on endophytic microorganisms found in medicinal plants. Some endophytic actinobacteria, especially strains of Streptomyces, are notable examples of peptide producers, as specific biosynthetic clusters encode them. To uncover the genetic potential of these organisms, we analyzed the genome of the endophytic actinobacterium Embleya sp. NF3 using genome mining and bioinformatics tools. Our analysis led to the identification of a putative class I lanthipeptide. We cloned the core biosynthetic genes of this putative lanthipeptide, named embleporicin, and expressed them in vitro using a cell-free protein system (CFPS). The resulting product demonstrated antimicrobial activity against Micrococcus luteus ATCC 9341. This represents the first RiPP reported in the genus Embleya and the first actinobacterial lanthipeptide produced through cell-free technology.
Antimicrobial Usage and Antimicrobial Resistance in Commensal Escherichia coli from Broiler Farms: A Farm-Level Analysis in West Java, Indonesia
Authors: Anwar Sani Rianna , Sunandar Sunandar , Rachmawati Annisa , Pertela Gian , Susanti Oli , Rahayu Kanti Puji , Allamanda Puttik , Suandy Imron , Nurbiyanti Nofita , Jahja Elvina J. , Purwanto Budi ,   on behalf of CORNERSTONE Group, Velkers Francisca C. , Dinar Tagrid , Wagenaar Jaap A. , Speksnijder David C.
Published: December 2024

Background/Objectives: Antimicrobial resistance (AMR) is a global public health threat, with antimicrobial use (AMU) in livestock recognized as a significant driver. This study examines farm-level AMU and AMR as well as the relationship between AMU and AMR on broiler farms in Indonesia. Methods: Data were collected from 19 farms in West Java between 2019 and 2021 to examine AMU in depth across four to five successive production cycles. The correlation between AMU and AMR in commensal Escherichia coli (E. coli) was investigated. AMU was recorded as treatment days per 30-day production cycle, and antimicrobial susceptibility was assessed using epidemiological cut-off (ECOFF) values to differentiate wildtype (WT) and non-wildtype (NWT) E. coli. Results: The average AMU was 12 treatment days per 30-day production cycle, with a wide range of 4 to 22 days. On average, E. coli isolates from each farm exhibited NWT phenotypes, reflecting AMR levels, for 6 out of 14 antimicrobials tested. This included notable levels for the highest priority critically important antimicrobials (HPCIAs) ciprofloxacin (93%) and nalidixic acid (64%). A significant correlation (Spearman ρ = 0.67, p < 0.05) was observed between the total farm-level AMU and the number of antimicrobials for which NWT E. coli isolates were found. However, no significant correlation was found between AMU and AMR for the five most frequently used antimicrobials, likely due to a high baseline prevalence of NWT E. coli isolates and relatively few independent observations. Conclusions: These findings highlight the urgent need to reduce AMU in general, specifically the use of (HP)CIAs, to mitigate AMR on Indonesian broiler farms.