bluuebunny/crossref_metadata_2025_split huggingface dataset:
Natural Selection Primarily Shaping Codon Usage Bias in Glycine max (soybean) WRKY Genes
Authors: Sinha Krishnendu , Jana Sourav , Pramanik Payel , Bera Bithika 
Published: May 2024

Abstract

Background: Codon usage biases in genomes emerge from the interaction of two prominent evolutionary factors: mutational pressure and natural selection. Studying codon usage bias facilitates the understanding of the fundamental evolutionary mechanisms influencing genetic structure. Glycine max (soybean) holds considerable agricultural and industrial significance as a widely cultivated crop. Despite the pivotal role of WRKY transcription factors in plant regulation and stress responses, investigations of these transcription factors in Glycine max are limited, providing an opportunity to probe evolutionary trends within this gene family.
Results: Examination of the base composition of the Glycine max WRKY genes revealed an average GC content of 44.85% and an average GC3 content of 42.01%, indicating a preference for A/T(U)-ending codons. Relative synonymous codon usage (RSCU) analysis identified 27 codons with RSCU > 1, predominantly ending with A and U bases. The effective number of codons (NC) ranged from 42.17 to 61, with codon adaptation index (CAI) values ranging from 0.713 to 0.904, suggesting a phenomenon of weak codon bias yet high expression levels in WRKY genes. Neutrality analysis indicated a significantly weak correlation between GC12 and GC3, while NC plot analysis revealed the predominant role of natural selection in shaping the codon usage pattern. Parity rule 2 plot (PR2) analysis highlighted disproportionate usage of AT and GC bases. One optimal codon was identified.
Conclusion: Natural selection is the primary driving force of codon usage preferences in Glycine max WRKY genes. These findings provide new insights into the genetic landscape and evolutionary mechanisms of translational efficiency in Glycine max, contributing to the enhancement of genetic resources in soybeans.
The Slow Clock is the Master
Authors: Oliveira Henrique M. , Perestrelo Sara 
Published: May 2024

Abstract

We investigate the synchronization and stability of mutually coupled oscillators through small impacts and explore both master-master and master-slave synchrony. The synchronization behaviour between two oscillators, one operating at a frequency ω and the other at a frequency near a multiple of the first one, nω where n is an integer greater than 1, is investigated. It is observed that such systems tend to exhibit a consistent pattern of master-slave relationship. This phenomenon is geometrically elucidated by considering the interaction dynamics: while the multiple bursts of the faster oscillator tend to cancel each other out upon impacting the slower oscillator, the single burst of the slower oscillator on the faster has a secular effect. We propose that this synchronization pattern, arising from perturbative impacts, is prevalent across various physical systems. Further experimental validation of these findings could contribute to a deeper understanding of synchronization phenomena and their implications in natural and technological domains.
2020 Mathematics Subject Classification. Primary 34D06, Secondary 37E30.
Neuro-intestinal Acetylcholine Signalling Regulates the Mitochondrial Stress Response
Authors: Pocock Roger , Cornell Rebecca , Cao Wei , Harradine Bernie , Handley Ava , Godini Rasoul 
Published: May 2024

Abstract

Neurons coordinate inter-tissue protein homeostasis to systemically manage cytotoxic stress. In response to neuronal mitochondrial stress, specific neuronal signals coordinate the systemic mitochondrial unfolded protein response (UPRmt) to promote organismal survival. Yet, whether chemical neurotransmitters are sufficient to control the UPRmt in physiological conditions is not well understood. Here, we show that gamma-aminobutyric acid (GABA) inhibits, and acetylcholine (ACh) promotes the UPRmt in the Caenorhabditis elegans intestine. GABA controls the UPRmt by regulating extra-synaptic ACh release through metabotropic GABAB receptors GBB-1/2. We find that elevated ACh levels in animals that are GABA-deficient or lack ACh-degradative enzymes induce the UPRmt through ACR-11, an intestinal nicotinic a7 receptor. This neuro-intestinal circuit is critical for non-autonomously regulating organismal survival of oxidative stress. These findings establish chemical neurotransmission as a crucial regulatory layer for nervous system control of systemic protein homeostasis and stress responses.
Dynamic allostery in the peptide/MHC complex enables TCR neoantigen selectivity
Authors: Baker Brian , Ma Jiaqi , Ayres Cory , Brambley Chad , Chandran Smita , Rosales Tatiana , Corcelli Steven , Kovrign Evgenii , Klebanoff Christopher 
Published: May 2024

Abstract

The inherent cross-reactivity of the T cell receptor (TCR) is balanced by high specificity, which often manifests in confounding ways not easily interpretable from static structures. We show here that TCR discrimination between an HLA-A*03:01 (HLA-A3)-restricted public neoantigen derived from mutant PIK3CA and its wild-type (WT) counterpart emerges from motions within the HLA binding groove that vary with the identity of the peptide’s first primary anchor. The motions form a dynamic gate that in the complex with the WT peptide impedes a large conformational change required for TCR binding. The more rigid neoantigen is insusceptible to this limiting dynamic, and with the gate open, is able to transit its central tryptophan residue underneath the peptide backbone to the contralateral side of the HLA-A3 peptide binding groove, facilitating TCR binding. Our findings reveal a novel mechanism driving TCR specificity for a cancer neoantigen that is rooted in the dynamic and allosteric nature of peptide/MHC-I complexes, with implications for resolving long-standing and often confounding questions about the determinants of T cell specificity.
Atlas of tandem kinase proteins across the plant kingdom
Authors: Fahima Tzion , Reveguk Tamara , Fatiukha Andrii , Potapenko Evgenii , Reveguk Ivan , Sela Hanan , Klymiuk Valentyna , Li Yinghui , Pozniak Curtis , Wicker Thomas , Coaker Gitta 
Published: May 2024

Abstract

Plant pathogens pose a continuous threat to global food production. Recent discoveries in plant immunity research unveiled a unique protein family characterized by an unusual resistance protein structure that combines two kinase domains (KD). This study demonstrates the widespread occurrence of tandem-kinase proteins (TKPs) across the plant kingdom. An examination of 104 plant species' genomes uncovered 2,682 TKPs. The majority (95.6%) of these KDs are part of the RLK-Pelle receptor-like kinase family, which is crucial for cell surface responses in plant immunity. Notably, 90% of TKPs comprise dual KDs, with over 50% being pseudokinases. Over 56% of these proteins harbor 127 different integrated domains, and over 47% include a transmembrane domain. TKP pseudokinases and/or integrated domains probably serve as decoys, engaging with pathogen effectors to trigger plant immunity. The TKP Atlas we created sheds light on the mechanisms of TKP convergent molecular evolution and potential function.
Identification of Shallow Foundation In Swampland Banjarmasin
Authors: Agusniansyah Nursyarif 
Published: May 2024

Abstract

The condition of the land in Banjarmasin is swamp land (soft soil) with low bearing capacity so that a special and suitable foundation is needed. This makes the uniqueness and characteristics of the foundations that are often applied in this area. This is an advanced application of the shallow foundation type. This study will be carried out in the form of types including shallow foundations. Field observation studies were carried out at various construction sites in Banjarmasin and the surrounding area, especially objects of low-rise buildings and other uses such as fences and earthfill retaining walls. The purpose of this study is to provide an applicative description of the foundation in the Banjarmasin swamp land with a comparison of the types of foundations in the surrounding area. It will identify the foundation materials that can be applied and how the application techniques are implemented in the field.
Does Economic Growth, External Debt, and Institutional Quality Promote Poverty and Income Inequality in Nigeria?
Authors: Yakubu Magaji Ibrahim , Aladejare Samson Adeniyi 
Published: May 2024

Abstract

This study assessed the effect of economic growth on poverty and inequality in Nigeria between 1990 and 2022. The research applied the ARDL technique in its empirical analysis. Findings from the study indicated that economic growth does not have significant long-run impact on poverty and inequality. However, the short-run relationship showed that economic growth increases inequality in Nigeria. External debt was revealed to further aggravate poverty and inequality in the long-run. Also, while government effectiveness demonstrated an enhancing effect on poverty in the short and long-run periods, its long-run impact on inequality is significantly decelerating. Thus, based on the aforesaid conclusions the study recommends the strengthening of small and medium enterprises through access to finance at lower interest rates and equitable distribution of national wealth through the payment of a living wage, provision of social and economic infrastructure, etc. Also, the agricultural sector should be made more attractive to the youths through encouraging export promotion policies. Leveraging financial technology, and encouraging start-up firms can further reduce the poverty and the inequality level in the country.
Pharmacognostic Study and Standardization of Leaves and Stem of Mentha spicata Linn.
Authors: Solanki Neeta , Saini Sapna , Saini Rajesh , Singh Karnail , Pandey Parijat 
Published: May 2024

Background:
Various Mentha species are cultivated all over the world due to their
aroma and valuable therapeutic constituents. Along with its traditional use as a food flavor, it is
well-established folklore medicine for treating digestive and cardiovascular problems. Mentha
spicata L. is a perennial, aromatic herb belonging to the Lamiaceae family. M. spicata leaves
have been used in traditional systems of medicines for treating various digestive system disorders
since old times.

Aim:
The present research work encompasses the pharmacognostic study, phytochemical screening
and physicochemical parameter estimation of leaves and stem of M. spicata L. for standardization
of Mentha plant.

Materials and Methods:
Morpho-anatomical characterization, physicochemical analysis and preliminary
phytochemical investigation of leaves and stems were performed. These investigations
were done as per WHO guidelines to determine the main diagnostic characteristics for correct
identification and standardization of whole plant and powdered drug of M. spicata L.

Results:
The dimensions, size, color, and surface texture characteristics of fresh crude drug (leaf
and stem) were determined. Compound microscope images of the transverse section (TS) of M.
spicata leaf and stem revealed the cellular arrangement and main identification characters. Phytochemical
and physicochemical analysis of powdered drugs confirmed the presence of important
phytoconstituents such as phytosterol, terpeneoids, phenolic compounds, saponins, volatile oil,
etc.

Conclusion:
The results obtained are helpful for the characterization of the crude drug. Morphoanatomical
characterization and physicochemical analysis are inexpensive methods and serve as a
quality control tool for stem and leaf standardization. The ethanolic and aqueous extract of leaves
and stem showed the presence of valuable medicinally active phytoconstituents.
Exploring Natural Compounds Targeting PD-L1 and STAT3: Toxicogenomic Analysis, Virtual Screening, Molecular Docking, ADMET Evaluation, and Biological Activity Prediction
Authors: Karakuş Fuat , Kuzu Burak , Köstekci Sedat , Tülüce Yasin 
Published: May 2024

Background:
One of the most important targets in cancer immunotherapy is programmed
cell death ligand 1 (PD-L1). Monoclonal antibodies developed for this target have
disadvantages due to their low bioavailability and some immune-related adverse effects. Additionally,
small molecules targeting PD-L1 are still in the experimental stage. At this point, discovering
non-toxic natural compounds that directly or indirectly target PD-L1 is essential. In
this in silico study, a comprehensive literature search was conducted to identify publications
reporting the master regulator of PD-L1, which was suggested as a Signal Transducer and Activator
of Transcription 3 (STAT3). The relationship between STAT3 and PD-L1 was further investigated
through bioinformatic analysis.

Method:
Subsequently, natural compounds targeting PD-L1 and STAT3 were screened, and
compounds with suitable toxicity profiles were docked against both PD-L1 and STAT3. Following
molecular docking, the selected molecules underwent DNA docking, ADMET profile analysis,
and in silico assessment of biological activities. The relationship between PD-L1 and
STAT3 was determined in 52 out of the 453 articles, and it was further demonstrated in genegene
interactions. Following the virtual screening, 76 natural compounds were identified, and
after pre-filtering based on physicochemical properties, drug-likeness, and ADMET profiles, 29
compounds remained.

Result:
Subsequent docking revealed that two compounds, 6-Prenylapigenin, and Gelomulide J,
persisted. ADMET and biological activity prediction results suggested that 6-Prenylapigenin is
non-toxic and has the potential to inhibit PD-L1 and STAT3 in silico. The present study highlights
that STAT3 serves as the master regulator of PD-L1, and it further suggests that 6-
Prenylapigenin exhibits the potential to modulate PD-L1 and/or STAT3.

Conclusion:
This finding could pave the way for the development of small molecules designed
to block the PD-1/PD-L1 interaction by silencing the PD-L1 and/or STAT3 genes or reducing
protein levels.

result:
The relationship between PD-L1 and STAT3 was determined in 52 out of the 453 articles found, and it was further demonstrated in gene-gene interactions. Following virtual screening, 76 natural compounds were identified, and after pre-filtering, 29 compounds remained. Subsequent docking revealed that two compounds, 6-Prenylapigenin and Gelomulide J, persisted. ADMET and biological activity prediction results suggested that 6-Prenylapigenin is non-toxic and has the potential to inhibit PD-L1 and STAT3 in silico.
The Role of Verbal Auditory Hallucinations in Influencing and Retrospectively Predicting Physical Harm Prevalence in Early Psychosis
Authors: Hazell Cassie M , Hasapopoulos Sophia , McGowan Jennifer , Hamza Roman , Ahmed Zareena , Gaughan Ben , Malillos Monica Huerga , Gill Amber , Nomani Amber , Hickson Emily , Koruni Anjeza , Islam Faaisa , Souray Jonathan , Raune David 
Published: May 2024

Background
Research has established a relationship between psychosis and physical harm in the early course of psychosis. However, little is known about the relationship between specific psychosis symptoms, such as hearing voices, and physical harm.

Objective
This study aimed to determine the prevalence and typology of physical harm related to hearing voices, as well as what aspects of the voice-hearing experience retrospectively predicted incidents of harm within an Early Intervention in Psychosis Service (EIPS).

Methods
We conducted a quality improvement project in a single EIPS. We reviewed case notes of patients and extracted information on the cognitive-phenomenological features of the voices patients heard, as well as any incidents of physical harm that were causally linked to these voices.

Results
It was found that 32.2% of EI patients had an actual incident of physical harm in their case notes that was causally linked to hearing voices. The most common type of physical harm was neglect. In terms of cognitive phenomenological binary correlations that retrospectively predicted physical harm in the case notes, patients were 20 and 7 times more likely to have harmed themselves if they heard self-harm commands (i.e., directions to harm themselves physically) and perceived the voice as omnipotent, respectively. Patients were 6 times more likely to have harmed someone else if they heard violent commands.

Conclusion
Verbal auditory hallucinations commonly influence physical harm in the early course of psychosis. Hearing commands and/or believing the voice to be omnipotent are strong retrospective-correlative predictors that may aid in the assessment and therapeutic intervention.
Efficacy and Safety of Semaglutide in Weight Loss of Non-diabetic People
Authors: Song Cai-E , Wang Yan , Dai Xue-Mei , Wu Hong-Yan 
Published: May 2024

Objective:
The study aimed to investigate the efficacy and safety of semaglutide in
weight loss in non-diabetic people.

Methods:
In this study, 84 non-diabetic people who used semaglutide to lose weight in the outpatient
department of our hospital from January 1, 2022, to June 30, 2022, were enrolled and
compared for changes in body weight, waist circumference, Body Mass Index (BMI), fasting
blood glucose, blood pressure, pulse, and body composition (body fat ratio, visceral fat area,
and skeletal muscle) before treatment and 12 weeks after the treatment to analyze the weight
loss efficacy and safety.

Results:
After administering semaglutide 0.25 mg, 0.5 mg, 0.75 mg, or 1.0 mg subcutaneously
once a week for 12 weeks, 84 participants in this study obtained an average weight loss of 5.91
± 3.37 kg, equivalent to 6.15 ± 4.28% of baseline body weight, and there was also a significant
reduction in visceral fat area and a slight reduction in blood pressure. The most common adverse
reactions included gastrointestinal reactions (nausea, vomiting, and diarrhea), which were
mild and subsided within 1-2 days. No severe adverse reaction, such as hypoglycemia and hypotension,
was observed.

Conclusion:
Low-dose semaglutide has been found to be effective and safe for short-term
weight loss in non-diabetic people.
Classification of Biomedical Images with Mined Statistical Features and Dynamic Programming
Authors: Man Xinpeng , Song Yinglei 
Published: May 2024

Background:
In the research and practice of medical sciences, accurate classification of biomedical
images with computer programs may provide an important basis for the study and diagnosis of
many diseases.

Methods:
This paper proposes a new statistical approach that can accurately classify biomedical images
based on their statistical features. In the first step of the proposed approach, a number of SIFT features of
different types are computed for each pixel in a biomedical image and a statistical feature that describes
the distribution of each type of SIFT features is obtained for the image. In the second step, a dynamic programming
approach is used to efficiently analyze the dependence among different statistical features associated
with an image and compute the probability for an image to belong to each possible class; the class
with the largest probability is determined as the result of classification.

Results:
Experimental results show that the proposed approach can lead to classification results with accuracy
higher than that of a few state-of-the-art approaches for the classification of biomedical images

Conclusion:
The proposed approach can achieve classification accuracy comparable to that of several
state-of-the-art classification approaches. It is thus potentially useful for applications where large models
are not appropriate for classification tasks due to limitations in computational or communication resources
Artificial Intelligence–Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study
Authors: Cho Youngjin , Yoon Minjae , Kim Joonghee , Lee Ji Hyun , Oh Il-Young , Lee Chan Joo , Kang Seok-Min , Choi Dong-Ju 
Published: May 2024

Background
Although several biomarkers exist for patients with heart failure (HF), their use in routine clinical practice is often constrained by high costs and limited availability.

Objective
We examined the utility of an artificial intelligence (AI) algorithm that analyzes printed electrocardiograms (ECGs) for outcome prediction in patients with acute HF.

Methods
We retrospectively analyzed prospectively collected data of patients with acute HF at two tertiary centers in Korea. Baseline ECGs were analyzed using a deep-learning system called Quantitative ECG (QCG), which was trained to detect several urgent clinical conditions, including shock, cardiac arrest, and reduced left ventricular ejection fraction (LVEF).

Results
Among the 1254 patients enrolled, in-hospital cardiac death occurred in 53 (4.2%) patients, and the QCG score for critical events (QCG-Critical) was significantly higher in these patients than in survivors (mean 0.57, SD 0.23 vs mean 0.29, SD 0.20; P<.001). The QCG-Critical score was an independent predictor of in-hospital cardiac death after adjustment for age, sex, comorbidities, HF etiology/type, atrial fibrillation, and QRS widening (adjusted odds ratio [OR] 1.68, 95% CI 1.47-1.92 per 0.1 increase; P<.001), and remained a significant predictor after additional adjustments for echocardiographic LVEF and N-terminal prohormone of brain natriuretic peptide level (adjusted OR 1.59, 95% CI 1.36-1.87 per 0.1 increase; P<.001). During long-term follow-up, patients with higher QCG-Critical scores (>0.5) had higher mortality rates than those with low QCG-Critical scores (<0.25) (adjusted hazard ratio 2.69, 95% CI 2.14-3.38; P<.001).

Conclusions
Predicting outcomes in patients with acute HF using the QCG-Critical score is feasible, indicating that this AI-based ECG score may be a novel biomarker for these patients.

Trial Registration
ClinicalTrials.gov NCT01389843; https://clinicaltrials.gov/study/NCT01389843
Optimzing Android Program Malware Classification Using GridSearchCV Optimized Random Forest
Authors: Hakim Luqman , Sari Zamah , Aristyo Ananda Rizaldy , Pangestu Syahrul 
Published: May 2024

The growing number of smartphones, particularly Android powered ones, has increased public awareness of the security concerns posed by malware and viruses. While machine learning models have been studied for malware prediction in this field, methods for precise identification and classification still require improvement for the perfect detection of malwares and minimizing the cracks on machine learning based classification. Detection accuracy that ranges from 93% to 95% has been observed in prior research, indicates room for improvement. In order to maximize the hyperparameters, this paper suggests improving the Random Forest method by introducing the grid search algorithm which isn’t present in previous studies. A significant increase in classification accuracy is the main aim of the research. We exhibit an outstanding 99% accuracy rate in detecting malware contaminated programs, demonstrating the significance of our technique. The proposed method can be seen as a huge improvement over existing models, achieving near perfection in detection, in contrast to which typically obtained by previous models with the accuracy rate of 95% max on the same dataset. Our approach achieves such high accuracy and provides a novel remedy for the limits of the Android based platforms, particularly when program processing resources are limited. This study confirms the effectiveness of our improved Random Forest algorithm, points to a paradigm shift in malware detection, and heightened cybersecurity measures for the rapidly growing smartphone market.