bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sugarcane stem node detection using computer vision and convolutional transfer learning
Authors: Kumar Arun , Saini Abhishek Kumar , Ashok Alaknanda , Mishra Puneet 
Published: September 2023

Abstract
Quantity of seed needed for sugarcane planting can be reduced by employing the single bud planting method. This approach involves utilizing the stem node or bud extracted from harvested sugarcane for replanting. Currently, this process is manually done using traditional planting techniques. To automate node/bud planting, this study developed a computer vision system based on artificial intelligence. The purpose of this system is to automate the recognition of the stem node, where the sugarcane buds are naturally located. By automating the recognition process, this system can facilitate the automation of node cutting equipment and prevent bud damage during the cutting phase. In this study, a transfer learning-based approach was employed, harnessing the capabilities of a pre-trained convolutional neural network to adapt to the specific application of stem node detection. Transfer learning was chosen to avoid the need for developing and training a new model from scratch, thereby saving time in model development, and enhancing accuracy. Transfer learning allows us to leverage the knowledge acquired from a previously trained machine learning model and apply it to a different but related problem. The goal is to exploit the learned knowledge from one task to improve generalization in another. Specifically, the weights learned by a network during "task A" are transferred to a new "task B." Four different transfer learning models were trained and tested in this work: VGG16, ResNet-50, EfficientNetb7, and MobileNet. Practical tests demonstrated that VGG16 exhibited the best performance in stem node detection accuracy, achieving approximately 98%, with an average image processing time of 0.182 seconds per detection. The developed model and methodology hold broad applicability for automating stem node/bud-based sugarcane replanting.
Unraveling the Interplay of Parasitaemia, Cytokines, and Pregnancy Outcomes in Plasmodium bergheiInfection: Insights from a Murine Model
Authors: Manhas Prem Lata , Sehgal Rakesh , Radotra Bishan Dass , Mewara Abhishek , Malhotra Pankaj 
Published: September 2023

Abstract
Malaria infection during pregnancy presents notable risks to both maternal and fetal health. Present study examines the impact of Plasmodium berghei NK-65 strain infection during different stages of pregnancy in mice. The study categorized mice into non-pregnant and pregnant groups (early, mid, and late pregnancy) and induced infection on specific gestation days. Parasitaemia, cytokine profiles, histopathological changes, and pregnancy outcomes were evaluated. Major findings include severe anemia in mid-pregnancy infection, fetal deaths in early and mid-pregnancy infections, stillbirths in mid-pregnancy infections, and premature deliveries in late-pregnancy infections. Cytokine profiles differed between pregnant and non-pregnant mice, indicating varied immune responses. Plasmodium berghei infection in pregnant mice provides valuable insights into understanding placental malaria and its implications for maternal and fetal health.
Methylation of serotonin regulating genes in cord blood cells: modulation by maternal metabolic parameters and correlation with methylation in peripheral blood cells during childhood and adolescence
Authors: Bečeheli Ivona , Horvatiček Marina , Perić Maja , Nikolić Barbara , Holuka Cyrielle , Klasić Marija , Ivanišević Marina , Starčević Mirta , Desoye Gernot , Hranilović Dubravka , Turner Jonathan D. , Štefulj Jasminka 
Published: September 2023

Abstract
Background
Serotonin (5-hydroxytryptamine, 5-HT) signaling is involved in neurodevelopment, mood regulation, energy metabolism, and other physiological processes. DNA methylation plays a significant role in modulating the expression of genes responsible for maintaining 5-HT balance, such as 5-HT transporter (SLC6A4), monoamine oxidase A (MAOA), and 5-HT receptor type 2A (HTR2A). Maternal metabolic health can influence long-term outcomes in offspring, with DNA methylation mediating these effects. We investigated associations between maternal metabolic parameters – pre-pregnancy body mass index (pBMI), gestational weight gain (GWG), and glucose tolerance status (GTS), i.e., gestational diabetes mellitus (GDM) vs. normal glucose tolerance (NGT) – and cord blood methylation of SLC6A4, MAOA, and HTR2A in participants from our PlaNS birth cohort. CpG sites (15, 9, and 2 in each gene, respectively) were selected based on literature and in silico data. Methylation levels were quantified by bisulfite pyrosequencing. We also examined the stability of methylation patterns in these genes in circulating blood cells from birth to adolescence using longitudinal DNA methylation data from the ARIES database.
Results
None of the 203 PlaNS mothers included in this study had preexisting diabetes, 99 were diagnosed with GDM, and 104 had NGT; all neonates were born at full term by planned Cesarean section. Methylation at most CpG sites differed between male and female newborns. SLC6A4 methylation correlated inversely with maternal pBMI and GWG, while methylation at HTR2A site − 1665 correlated positively with GWG. None of the maternal metabolic parameters statistically associated with MAOA methylation. DNA methylation data in cord blood and peripheral blood at ages 7 and 15 years were available for 808 participants from the ARIES database; 4 CpG sites (2 in SLC6A4 and 2 in HTR2A) overlapped between the PlaNS and ARIES cohorts. A positive correlation between methylation levels in cord blood and peripheral blood at 7 and 15 years of age was observed for both SLC6A4 and HTR2A CpG sites.
Conclusions
Methylation of 5-HT regulating genes in cord blood cells is influenced by neonatal sex, with maternal metabolism playing an additional role. Inter-individual variations present in circulating blood cells at birth are still pronounced in childhood and adolescence.
Age-related differences in primary central nervous system lymphomas based on the SEER database
Authors: Wang Ke , Zheng Chuangjie , Liang Jian , Zhang Cuifen , Chen Xinrong , Ren Yingfang , Zhai Linzhu 
Published: September 2023

Abstract
Objective: The aim of this study was to compare prognostic factors and survival outcomes in patients with primary central nervous system lymphoma (PCNSL) stratified according to age (younger vs older than 60 years of age).
Methods: The Surveillance, Epidemiology, and End Results (SEER) database was queried for all patients diagnosed with PCNSL between 2000 and 2019. Clinical parameters were collected, and patients were stratified and compared according to age.
Results: Between 2000 and 2019, a total of 5812 patients were diagnosed with PCNSL, of whom 2175 (37%) were ≤ 60 years old and 3637 (63%) were > 60 years old, hereafter referred to as “younger” and “older” patients, respectively. The younger patients had a higher rate of being diagnosed with stage I–II PCNSL (64% vs 60%), a higher rate of receiving chemotherapy (68% vs 63%), a higher rate of receiving radiotherapy (34% vs 26%, P < 0.001), a similar rate of receiving surgery, and a longer survival time. The most common histological subtype among PCNSL patients was diffuse large B-cell lymphoma (DLBCL) (PCNS-DLBCL; n = 4458, 77%). Multifactorial Cox regression analysis showed that younger PCNS-DLBCL patients who received surgery and chemotherapy exhibited significantly improved overall survival (OS) and disease-specific mortality (DSM) and that African American patients were associated with poorer OS and DSM. Older patients receiving surgery, chemotherapy, and radiotherapy exhibited significantly improved OS and DSM, and male sex and higher Ann Arbor stage were associated with poorer OS and DSM. We created a nomogram for PCNS-DLBCL to predict OS, with a C-index of 0.6749 (95% confidence interval: 0.6641–0.6857) in the younger cohort and 0.6676 (95% confidence interval: 0.6595–0.6759) in the older cohort, indicating that the nomogram had good predictive value. In the combined therapy analysis, chemotherapy combined with surgical resection had better OS and DSM in all patients.
Conclusions: The two age-stratified cohorts significantly differed in terms of OS and independent influences on OS and DSM. However, chemotherapy combined with surgical resection resulted in better OS and DSM in all patients. Our constructed nomogram exhibited high accuracy in predicting OS in PCNS-DLBCL patients.
Improving the Antibacterial Activity of Tryptophan-Containing Peptide Nanostructures Through Self- Assembly
Authors: Zhao Lei , Liu Zhaoyang , Ji Mingyang 
Published: September 2023

Abstract
Antimicrobial peptides (AMPs) are frequently distributed in the tissues and organs of animals to exhibit broad-spectrum activities against various pathogens, and thus to constitute the first line of defense in the innate immune system of most living organisms. AMPs commonly exert antibiotic activities through nonreceptor-mediated membrane lysis of pathogenic organisms and the mechanism of this AMP-induced membrane lysis is generally attributed to their amphipathic nature. Although physicochemical attributes of AMPs, such as hydrophobicity and charge, have been demonstrated to govern their affinity toward biological membranes, the dimensional attributes evolving with self-assembly have yet to be elucidated for the design principle of synthetic AMPs. This work demonstrates that self-assembly effectively improves the antibacterial performance of Fmoc-capped, tryptophan (Trp)-containing peptides. Compared with nonassembled peptides in their monomeric state, indole chromophores of Trp, which exhibited a highly ordered spatial arrangement and were induced by self-assembly, resulted in stronger interactions between the resulting peptide nanostructures and model phospholipid membrane vesicles; these interactions led to greater activities against gram-positive and gram-negative bacteria. This work highlights that the spatial organization of peptide nanostructures evolve with self-assembly and strongly contribute to antibacterial activity, which enriches the design principles for novel synthetic short peptides with therapeutic use.
The killing capacity analysis of tumor infiltrating cytotoxic lymphocytes and impact on lymph node metastasis in differentiated papillary carcinoma of thyroid with BRAF V600E mutation
Authors: Liu Xiaogang , Liu Honggang , Wang Lu , Han Yubing , Kong Linghong , Zhang Xinpeng 
Published: September 2023

Abstract
Background: Cytotoxic lymphocytes(CLs) express potent toxins,including the perforin(P) and granzyme-B(G),which brings about target cell death.The purpose of this study was to evaluate the killing capacity of tumor infiltrating CLs by means of perforin and granzyme-B analysis,and explore the association with lymph node metastasis in papillary carcinoma of thyroid (PTC) without Hashimoto's Thyroiditis(HT).
Methods: Infiltration of lymphocyts in PTC were observed by frozen section.Both fresh tumor tissues and paracancerous tissues with lymphocytes infiltration were respectively collected and made into signal cell suspension. Flow cytomtry was used to detect the percentage of CD3+P+,CD3+G+,CD8+P+,CD8+G+ T lymphocytes(TLs),and CD16-CD56+P+,CD16-CD56+G+ natural killer(NK) cells.Finally we investigated the differential expression of perforin and granzyme B of NK cells and cytotoxic T lymphocytes (CTLs) in paired tumor tissues (group T,n=44) and paracancerous tissues(group N,n=44) from patients of PTC with BRAF V600E mutation . Further more patients were divided into two groups according to whether exist cervical central lymph node metastasis(CCLNM):group A (with lymph node metastases, n=27) and group B(with non-lymph node metastases,n=17).Patients were divided into three groups according to the total number of positive CCLNM: group B,group C(with low-level lymph node metastases,less than 5,n=17) and group D (with high-level lymph node metastases,no less than 5,n=10).
Results:The percentage of CD3+P+ CTLs was significantly higher in the group N than that of group T(P<0.05). The percentage of CD8+G+ CTLs was significantly higher in the group T than that of group N(P<0.05). The percentage of CD3+G+,CD8+P+CTLs, CD16-CD56+P+and CD16-CD56+G+ NK cells showed no significant difference in both group T and group N(P>0.05).The percentage of CD3+P+ CTLs in group A and group C were significantly higher in the paracancerous tissue than that of tumor tissue(P<0.05).The percentage of CD8+G+ CTLs in group A and group C were significantly higher in the tumor tissues than that of paracancerous tissues(P<0.05).The percentage of CD16-CD56+G+ NK cells in group D was significantly higher in the tumor tissues than that of paracancerous tissues(P<0.05).
Conclusions:The killing capacity of infiltrating CLs in PTC differed between tumor tissues and paracancerous tissues.In cases with CCLNM, higher expression of CD16-CD56+G+ NK cells in tumor tissues may associate with high risk of lymph node metastasis.
A Novel Interpretable Machine Learning Model Approach for the Prediction of TiO 2 Photocatalytic Degradation of Air Contaminants
Authors: Schossler Rodrigo Teixeira , Ojo Samuel , Jiang Zhuoying , Hu Jiajie , Yu Xiong 
Published: September 2023

Abstract
Air contaminants lead to various environmental and health issues. Titanium dioxide (TiO2) features the benefits of autogenous photocatalytic degradation of air contaminants. To evaluate its performance, laboratory experiments are commonly used to determine the kinetics of the photocatalytic-degradation rate, which is labor intensive, time-consuming, and costly. In this study, Machine Learning (ML) models were developed to predict the photo-degradation rate constants of air-borne organic contaminants with TiO2 nanoparticles and ultraviolet irradiation. The hyperparameters of the ML models were optimized, which included Artificial Neural Network (ANN) with Bayesian optimization, gradient booster regressor (GBR) with Bayesian optimization, Extreme Gradient Boosting (XGBoost) with optimization using Hyperopt, and Catboost combined with Adaboost. The organic contaminant was encoded through Molecular fingerprints (MF). A data collection protocol was developed to collect data from published papers to analyze the effects of a variety of contributing factors on the photo-catalytic degradation performance. A large portion of data was found missing values. Imputation methods were used to estimate the missing values that allow the dataset to be fully utilized. Furthermore, a generative ML model Vanilla Gan model was utilized to create synthetic data to further augment the size of available dataset. The results indicated that data imputation allowed to fully utilize the limited amount of available dataset to achieve good ML prediction performance and prevent problems such as overfitting common with small-sized data. Besides, augmentation of experimental data with synthetic data significantly improved the ML prediction accuracy and reduced considerably the overfitting problems. The SHapley Additive exPlanations (SHAP) was employed for ML model interpretability. The results ranked the feature importance and assessed the impacts of different experimental variables on the rate of photo-degradation, which were consistent with physico-chemical laws. Interpretable ML models allows to shed light on the mechanism and present a promising tool in the prediction and assessment of the major contributing factors on the TiO2 photo-degradation rate of air contaminants. Besides direct findings on the potentials of ensembled ML models for TiO2 photocatalytic performance prediction, this study showed that imputation processes in data pre-processing to fill missing values and generative ML model for data augmentation allow to fully utilize the value of data, which is important for successful application of ML model for small and imperfect dataset commonly seen in engineering and science domains.
Machine learning prediction of pathological complete response and overall survival of breast cancer patients in the Montefiore Health System in the Bronx
Authors: Dell’Aquila Kevin , Vadlamani Abhinav , Maldjian Takouhie , Fineberg Susan , Eligulashvili Anna , Chung Julie , Adam Richard , Hodges Laura , Hou Wei , Makower Della , Duong Tim Q 
Published: September 2023

Abstract
Background. Predicting response to treatment and survival in breast cancer patients remains a crucial challenge. This study employed four machine-learning models to predict pathological complete response (pCR) and overall survival (OS) up to 7.5 years. Methods. Demographics, staging, tumor subtypes, income, insurance status, and data from radiology reports were obtained from 475 breast cancer patients on neoadjuvant chemotherapy from 01/01/2017 to 12/31/2021 in the Montefiore Health System in the Bronx. Logistic regression, Neural Network, Random Forest, and Gradient Boosted Regression models were used to predict outcomes (pCR and OS) with five-fold cross validation. Results. pCR was not associated with age, race, ethnicity, differentiation, income, and insurance status (p > 0.05). ER-/HER2 + showed the highest pCR rate, followed by triple negative, ER+/HER2+, and ER+/HER2- (p < 0.05), tumor staging (p = 0.011), tumor size (p < 0.003) and background parenchymal enhancement (BPE) (p < 0.03) were associated with pCR. Machine-learning models ranked ER+/HER2-, ER-/HER2+, tumor size, and BPE as top predictors of pCR (AUC = 0.74–0.76). OS was associated with race, pCR status, tumor subtype, and insurance status (p < 0.05), but not ethnicity and incomes (p > 0.05). Machine-learning models ranked tumor stage, pCR, nodal stage, and triple negative subtype as top predictors of OS (AUC = 0.83–0.85). When grouping race and ethnicity by tumor subtypes, neither OS nor pCR were different due to race and ethnicity for each tumor subtype (p > 0.05). Conclusion. Tumor subtypes and imaging characteristics were top predictors of pCR. Insurance status, race, tumor subtypes and pCR were associated with OS. Machine-learning models accurately predicted pCR and OS using clinicopathological and radiological data.
Under organic farming conditions, response of different cow-based enhancer on chickpea and fenugreek
Authors: Chaudhary M. M. , Patel C. K. , Bawa J. D. , Patel Brijal R. , Makwana J. J. , Patel Foram B. 
Published: September 2023

Abstract
At present, there is little information on the effects of organic amendments on Chickpea and fenugreek particularly in Gujarat, India. The objective was to study the effect of cow-based bio-enhancers on organic cultivation of Chickpea and fenugreek, and executed during the Rabi seasons of 2019-20, 2020-21 and 2021-22 at Organic Farm of CNRM, SDAU, Sardarkrushinagar (Gujarat), India. The experiment was laid out in randomized block design with ten different treatments. The application of vermicompost 1.5 t/ha gave significantly higher grain and Stover yields of Chickpea and fenugreek. It is evident from the data that the highest net realization of 58685 ₹/ha, 70939 ₹/ha with the benefit: cost ratio (BCR) of 1.72, 3.06 for chickpea and fenugreek, respectively was obtained under treatment T1 (panchgavya 3% as foliar spray at 30, 45, 60 DAS). However, treatment T8 (Vermicompost 1.5 t/ha) recorded the highest gross realization.
Validating the definition of lumbar instability –a cross-sectional study with 420 healthy volunteers–
Authors: Suzuki Manabu , Tanaka Yasuhisa , Hashimoto Ko , Tsubakino Takumi , Hoshikawa Takeshi , Takahashi Kohei , Latt Myo , Aizawa Toshimi 
Published: September 2023

Abstract
Lower back pain is thought to be caused by lumbar instability. To date, multiple definitions of radiological lumbar instability have been used without verifying the “normal range” of the lumbar segmental mobility. In addition, normative data for lumbar mobility in healthy individuals are required to establish an acceptable threshold for lumbar instability. This study aims to elucidate i) the prevalence of so-called radiological lumbar instability at each lumbar spine level in conventional criteria and ii) a practical radiological threshold for lumbar instability in healthy individuals. This study included participants who filled out a detailed questionnaire and underwent standard active dynamic radiography of the lumbar spine in the standing position. Participants with an Oswestry Disability Index of ≥ 20% and Roland–Morris Disability Questionnaire score of ≥ 14 points were excluded from the study. Intervertebral range of motion (IROM) and sagittal translation distance (ΔST) were measured at each intervertebral level. Nachemson’s criteria of radiological lumbar instability were applied. This study enrolled 420 participants (249 males and 171 females); 76% (320/420) met the criteria for radiological lumbar instability. The definition of lumbar instability based on IROM and ΔST was achieved by 0.2% and 1.7% of participants at the L5–S level, respectively.
Comparing Charge Dynamics in Organo-Inorganic Halide Perovskite: Solid-State vs Solid-Liquid Junctions
Authors: Lv Wenjing , Zhan Kaidong , Ren Xuecheng , Chen Lu , Wu Fan 
Published: September 2023

Abstract
The photoelectrochemical cell based on the perovskite-electrolyte solid-liquid junction has potential applications in electrolyte gated field effect transistors, photoelectrochemical and photocatalytic cells for water-splitting, supercapacitors, and CO2 capture and reduction devices. While hybrid perovskite materials face challenges due to their instability when in contact with aqueous electrolytes, there have been recent advancements in achieving stability of organo-inorganic halide perovskite films by utilizing specific liquid electrolyte solutions that involve the dissolution of tetrabutylammoniumperchlorate in dichloromethane. Currently, there is limited understanding of the differences in charge and ion kinetics at the perovskite/liquid electrolyte interface compared to the perovskite/solid charge transport layer interface. This study utilizes IMPS, OCVD, and C-F techniques to investigate and compare charge dynamics in perovskite/electrolyte-based and perovskite/solid devices. Additionally, the research explores the disparities between solid-liquid junctions and solid-state junctions, considering mobile ions, electric fields, and electron-hole transport. Furthermore, the study delves into changes in recombination resistance and the charging of the ionic double layer in perovskite-based devices, aiming to provide fundamental insights into the operation mechanisms of perovskite/liquid electrolyte devices and shed light on the underlying kinetics at the hybrid perovskite/electrolyte interface.