bluuebunny/crossref_metadata_2025 huggingface dataset:
Post-stroke seizure (PSS) following intravenous thrombolysis with recombinant tissue plasminogen activator (rtPA): A retrospective study
Authors: Tabatabaee Seyedehnarges , Soleimani Sevim , Eissazade Negin , Abbasi Mohammad Hossein , Esmaeili Sara , Sheibani Fatemeh , Pirouz Mahdi Saberi , Mokhtari Mahisa , Alavi Seyedeh Niloufar Rafiei , Mostafavi Keihan , Khotbehsara Samaneh Tanhapour , Siahpoosh Fateme , Hatam Jaber , Mirzaasgari Zahra
Published: February 2024

Background Recombinant tissue plasminogen activator (rtPA), the gold standard treatment of acute ischemic stroke, can reportedly result in seizures, known as post-stroke seizure (PSS). PSS is categorized as early PSS (EPSS), occurring within the first week of stroke onset, and late PSS (LPSS), occurring after the first week of stroke onset. Therefore, we aimed to evaluate the incidence of PSS after intravenous thrombolysis (IVT) with rtPA and to identify the potentially associated risk factors.
Methods This retrospective cohort study included 955 patients who were admitted between January 2017 and October 2018. Demographics, medical records, and paraclinical data were recorded and analyzed.
Results Out of the 955 patients included, 176 (18.42%) had received rtPA, of which 93 (69.88%) were male. Focal to bilateral tonic-clonic seizure was the most frequent seizure type in our study. We found that the use of rtPA was not significantly associated with the incidence of PSS. EPSS was significantly associated with a positive history of ischemic stroke (P = 0.019), cortical involvement (P = 0.002), cardioembolic stroke (P = 0.013), and HT (P = 0.009). LPSS was significantly associated with DM (P = 0.021), cortical involvement (P = 0.049) and higher mRS scores (P = 0.007). Conclusions rtPA did not significantly impact the incidence of PSS. Further research is needed to strengthen the body of evidence for the relationship between IVT with rtPA and PSS and to identify the associated risk factors.
Rice Production in Tamilnadu Using Bayesian Predictive Models
Authors: Saranyadevi M. , Mohideen A. Kachi
Published: February 2024

In this paper, prediction for Rice production in Tamilnadu using Bayesian model average and model selection with Zellner's g-prior approach. The methods were performed through MCMC sampling procedures for Bayesian Model Selection and Model Average, Bayesian Predictive Model and Markov Chain Monte Carlo Sampling Method. This posterior distribution can be used to simply select the “best” model BMA performs better than adopting a single best model for prediction. The results in discussed in rice production inclusion Probabilities and p-value normalizing this product, one can infer the PMPs and model weighted posterior distribution for any coefficients.
Targeted protein editing with an antibody-based system
Authors: Vendruscolo Michele , Rimon Oded , Konc Juraj , Ali Montader , Chowdhury Vaidehi Roy , Sormanni Pietro , Bernardes Gonçalo
Published: February 2024

The chemical modification of proteins is one of the major mechanisms used to regulate the properties and functions of these macromolecules in the cell. It is therefore of great interest to develop tools to exploit this type of modifications for applications in molecular biology, medicine and biotechnology. Here we present a method of using antibodies to perform post-translational covalent modifications of endogenous proteins in complex environments by exploiting proximity-driven chemistry. The method is based on the ability of antibodies to hold a weakly reactive group close to its intended site of reaction by binding the target protein on a nearby epitope. We characterise this approach by modifying the green fluorescent protein in increasingly complex environments, and illustrate its applicability by targeting the disease-associated protein beta-2 microglobulin.
Psychological, social, and health-related factors predict risk for financial exploitation
Authors: Smith David , Yang Yi , Hackett Katherine , Katta Srikar , Ludwig Rita , Jarcho Johanna , Giovannetti Tania , Fareri Dominic
Published: February 2024

People lose tens of billions of dollars a year to financial exploitation in the United States alone. Few studies have examined how preferences for trust and fairness in economic activities may contribute to risk for financial exploitation. Furthermore, few studies have examined the interaction between risk factors. In three studies, we attempt to address these gaps by surveying 1918 demographically and socioeconomically diverse participants to examine putative risk factors for self-reported financial exploitation. We focused on: 1) how trust in others and fairness preferences during economic games are associated with self-reported financial exploitation; and 2) how sociodemographic and health-related factors interact with psychosocial factors to confer risk for financial exploitation. We found participants with lower socioeconomic status and poor emotion regulation skills were at the greatest risk for financial exploitation. We also found associations between greater risk for financial exploitation and poorer physical health, more severe cognitive decline, increased persuadability, and increased insensitivity to trustworthiness cues. Our findings suggest that risk for financial exploitation is dependent upon a combination of psychosocial, sociodemographic and health factors, which may lead to interventions that protect vulnerable individuals.
Risk factors associated to a positive screening for Postpartum Depression in Mexican women with elective cesarean delivery: an analytical cross-sectional study during the early postoperative stage
Authors: Hernández-Muñoz Adrián Enrique , Fletes-Reyes Ana Lilia , López-Jiménez José de Jesús , Cuevas-Gómez Gloria Noemí , Herrera-Godina Melva Guadalupe , Cabrera-Pivaral Carlos Enrique , Rangel Miguel A.
Published: February 2024

Background. It has been reported that the Postpartum Depression (PPD)’s symptomatology increases during the first 72 hours after delivery. Although PPD’s screening is recommended during this period, there is few evidence about the associated Risk Factors (RF) that determine this disorder in women who exclusively underwent an Elective Cesarean Delivery (ECD). Hence, our objective was to determine the RF for PPD’s positive screening in the early postoperative stage after an ECD.
Methods. An analytical cross-sectional study was conducted in 123 women, rooming-in at Guadalajara’s Civil Hospital Fray Antonio Alcalde, during the average hospital length of stay after an ECD (2.1±1.02 days). To screen the PPD’s symptomatology, we applied the Edinburgh Postpartum Depression Scale (EPDS). To identify possible RF, we checked the women’s sociodemographic characteristics, obstetric/pregnancy complications, and neonatal comorbidities in their Medical Records. Subsequently, we carried out Odds Ratio (OR) analysis, followed by logistic regression that determined adjusted OR (aOR) and 95% Confidence Intervals (95% CI).
Results. By applying the EPDS, we identified 32.5% of PPD’s positive screening, significantly associated with miscarriages (OR 2.6, 95% CI: 1.06-6.30), multiparity (OR 2.6, 95% CI: 1.08-6.43) and age ≥30 years (OR 2.3, 95% CI: 1.04-5.20). Other associations were found with less than four prenatal visits (OR 2.4, 95% CI: 1.10-5.35), unplanned pregnancy (OR 2.7, 95%CI: 1.29-5.98), newborn comorbidities (OR 2.7, 95% CI: 1.18-6.03), and obstetric complications (OR 3.1, 95% CI: 1.37-7.07). Nevertheless, the logistic regression determined that previous history of depression (aOR 8.8, 95% CI: 1.62-13.8), obstetric complications (aOR 8.8, 95% CI: 1.58-9-43), and newborn comorbidities (aOR 9.9, 95% CI: 1.86-14.7) were RF for PPD.
Conclusions. Newborn and obstetric complications, along with previous depression, were main causes for the presence of depressive symptoms in women with an ECD. By considering not only the latter, but also that the prevalence for PPD’s positive screening that we found was higher than the results from national reports, we suggest that clinical and surgical professionals seek these RF during this early postoperative period, in order to treat and prevent any negative long-term effects on the mother’s mental well-being.
A Mendelian randomization study suggests that human skin color is an intermediate factor in the influence of mental disorders
Authors: Cao Junhua , Xu Xiaoyu , Zhao Jingru , Liu Jiuming , Chen Yanyan , Pu Youwei , Yang Zhaohui
Published: February 2024

Background
Mental disorders are a group of clinical diseases that result in varying degrees of impairment of a person's cognitive, emotional, volitional and behavioral mental activities. Previous studies have demonstrated variations in disease susceptibility among different groups. In addition to differences in disease pathogenesis, differences in human phenotypes among populations may also be intermediate factors affecting disease development, such as the diversity of human skin color. Whether there is a genetic link between skin color and mental disorders remains unknown. Therefore, our aim is to investigate whether skin color is associated with the risk of mental disorders.
Methods
We conducted two-sample Mendelian randomization study to analyze the effect of skin color on eight prevalent mental disorders (schizophrenia, bipolar disorder, attention deficit hyperactivity disorder, anxiety, depression, anorexia nervosa, obsessive-compulsive disorder, autism spectrum disorder) as well as a closely related trait (insomnia). Analyses were conducted using the "TwoSampleMR" and "MRPRESSO" packages in R.
Results
MR results suggest that higher genetically predicted skin color was associated with an increased risk of schizophrenia by the IVW method [odds ratios (OR) = 1.132; 95% confidence interval (CI): 1.046–1.225; P = 0.002]. We also observed that skin color was positively associated with bipolar disorder by the weighted median method [OR = 1.176; 95% CI: 1.028–1.345; P = 0.0147]. However, higher genetically predicted skin color was significantly associated with a decreased risk of attention deficit hyperactivity disorder [IVW: OR = 0.840, 95% CI: 0.730–0.966, P = 0.014; weighted median: OR = 0.797, 95% CI: 0.648–0.980, P = 0.034].
Conclusion
Our study establishes a relationship between skin color and mental disorders, such as schizophrenia, bipolar disorder and attention deficit hyperactivity disorder. These findings provide new evidence that human common phenotypes have an impact on mental disorders.
Synthesis, structural elucidation, DNA-binding and anti-oxidant activities of centrosymmetric paddlewheel copper carboxylate complexes
Authors: Mushtaq Afifa , Iqbal Muhammad , Rashid Zahid , Shahid Khadija , Tahir Muhammad Nawaz , Ali Saqib
Published: February 2024

Synthesis, structural characterization and preliminary biological relevance of four new copper carboxylate complexes (1-4) has been presented here. The complexes have been synthesized by direct treatment of the substituted phenyl acetate and pyridine ligands in aqueous medium. The complexes were stable indefinitely with excellent yield and were characterized using spectroscopic and single crystal XRD techniques. FTIR spectroscopy revealed the bridging bidentate coordination mode for the carboxylate moiety in accordance to the actual structure revealed by XRD. Moreover, UV-Visible spectroscopic and cyclic voltammetric studies helped in their characterization and yielded signals which were typical of the copper(II) complexes. Successfully solved single crystal XRD data showed binuclear paddlewheel structures for all the complexes with both copper ions linked through four OCO bridges of ortho-methoxy phenyl acetate (1-3) and ortho-methyl-meta-nitrophenyl acetate (4). The geometry around each copper was distorted square pyramidal where the apical positions are occupied by meta-bromopyridine (1), meta-methylpyridine (2) and DMSO (3 and 4) molecules. The complexes exhibited excellent DNA-binding activity majorly via intercalation as revealed by four experimental techniques in line with the in silico studies. Their anti-oxidant activity was also comparable to that of the ascorbic acid. Complexes 1 and 4 exhibited significant anti-bacterial activity against Micrococcus luteus and Staphylococcus aureus. These preliminary findings indicated the biological potential of the synthesized complexes.
Density of Limnoperna fortunei larval stages in a subtropical basin: spatiotemporal variation and environmental influences
Authors: Lopes M. N. , Pinheiro-Silva L. , Ribolli J. , Ávila-Simas S. , Lopes C. A. , Cardoso G. F.M. , Guereschi R. M. , Kavaguichi-Leite N. , Petrucio M. M. , Nuñer A. P.O.
Published: February 2024

The golden mussel (Limnoperna fortunei) is an invasive bivalve that has established itself in several South American river systems, impacting ecosystem functioning. Reservoir cascades provide their larvae with the means of rapid dispersal, but the relationship between environmental variables and larval stage structure remains unclear. In this study, the density of four L. fortunei larval stages was assessed in a cascade of five reservoirs in the upper Uruguay River Basin and associated with spatiotemporal variation in environmental parameters. Larval density increases were more related to fluctuating environmental parameters than to the expected effect of a cascade system, as the highest larval densities did not occur in the downstream reservoirs. Smaller D-shaped larvae predominated, suggesting that larger umbonated larvae either attempted to settle on the substrate (prior to metamorphosis to pediveligers) or were naturally preyed upon. The relationships between environmental parameters were evaluated using a multivariate model. Water temperature and dissolved oxygen had a significant effect on larval density, but did not respond to density variations independently. However, during winter, larval density decreased significantly. Other variables also contribute to species activity and survival in the upper Uruguay River Basin reservoirs and may be considered limiting factors. The interaction between basin area and rainfall strongly influenced the increase in larval density because runoff enriched the reservoirs with nutrients essential for the leading development of larval phases. Our findings provide insights on larval development in golden mussels, which can be used as a tool to manage further dispersal of this invasive species.
Carbon sequestration potential of Qimbaba forest; Implications of managing forests for climate change mitigation
Authors: Kassa Getachew , Molla Eyayu
Published: February 2024

Climate change poses one of the most significant challenges humanity has ever encountered, potentially underpinning the ongoing global warming phenomenon. Tropical forests play a crucial role in mitigating climate change by absorbing and storing carbon from the atmosphere. However, there has been limited research on carbon sequestration and emission reduction in Ethiopia. This study aimed to assess the carbon stock of the Qimbaba forest non-destructively, utilizing 40 circular sample plots and an allometric equation. The results indicated that the total carbon stock of the plantation forest was estimated at 24.98 t/ha-1, with 20.34 ± 0.18 t/ha-1 attributed to natural trees and 4.64 ± 0.1 t/ha-1 to nurse trees. Above-ground carbon was calculated at 16.01 ± 0.14 t/ha-1 for natural forest and 3.65 ± 0.08 t/ha-1 for nurtured trees in the plantation forest. Below-ground carbon values were determined to be 4.32 ± 0.04 t/ha-1 for natural forests and 0.99 ± 0.21 t/ha-1 for nurse trees in plantation forests. The findings suggest that the Qimbaba forest significantly contributes to climate change mitigation. To capitalize on this potential and receive financial incentives, it is recommended to integrate the forest into programs such as REDD+ and CDM of the Kyoto Protocol.
Triple Band Diamond-shaped Polarization Insensitive Plasmonic Nano Emitter for Thermal Camouflage and Radiative Cooling
Authors: Şanlı Atıf Kerem , Tabaru Timuçin Emre , Kılıç Veli Tayfun
Published: February 2024

This study proposes the design of a novel Metal-Insulator-Metal (MIM) nano-infrared emitter that uses a unique diamond-shaped grating to achieve selective infrared absorption. Diamond-shaped nano emitter (DNE) structure exhibits four narrow resonant peaks within key absorption windows such as short-wave infrared (SWIR) and mid-wave infrared (MWIR), alongside a wide absorption band in the Non-Transmissive Infrared Range (NTIR) for thermal camouflage applications compatible with radiative cooling. Moreover, the proposed DNE is polarization insensitive as it has an in-plane symmetric design. Using the 3D Finite-Difference Time-Domain (FDTD) simulations, we demonstrate the nano emitter's superior performance characterized by its high absorption rates and tuned effective impedance matching. As of our knowledge, the findings suggest that this is the first time that a MIM structure achieved multiple narrow resonance peaks, located in SWIR and MWIR simultaneously, with a wide absorption range in the NTIR. Represented DNE stands as a significant innovation in the field of stealth technology, providing a tunable, high-efficiency solution for managing and controlling thermal emissions across diverse applications.
Centralized Smart Energy Monitoring System for Legacy Home Appliances
Authors: Ahmad Shahed S. , Almasalha Fadi , Qutqut Mahmoud H. , Hijjawi Mohammad
Published: February 2024

As people increasingly rely on electrical devices for their daily tasks. As a result, energy consumption rates have sharply risen, leading to higher household electricity bills. This has produced a growing demand for energy monitoring systems that can accurately estimate energy usage, especially for older residential appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity accurately. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we propose a centralized smart energy monitoring system that utilizes prediction algorithms to calculate the power consumption of legacy home appliances. The primary goal of our proposed system is to overcome the limitations of legacy home appliances that require infrastructure upgrades. The system consists of two layers: a hardware layer that includes an Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors, and a software layer that includes Artificial Intelligence (AI) proposed predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted experiments on real home appliances to evaluate the proposed model. The accuracy of the proposed models showed positive results after several modifications and hard tuning of several parameters in the hardware devices, specifically for Jordanian power plants.
Hydrogeological and speleological characterization of a karstic spring: Cokragan Cave System
Authors: BAYKARA Mehmet Oruç , Gemici Ünsal
Published: February 2024

Karstic landscapes, shaped by water dissolving rock, are unique ecosystems with complex water systems. Karst aquifers, vital for over 25% of the world's drinking water, offer a sustainable resource but are vulnerable to human activities. Their diverse plant, animal, and geological features contribute significantly to our planet's ecosystem. Notably, 49% of European geoparks contain karst, highlighting their importance in geoheritage and geotourism. Studying karst requires precise mapping of springs, ponors, channels, and caves to understand their unique hydrogeological processes. Here, we present a rare integrated study of Cokragan Cave (Spring) using both speleology and hydrogeology. This complex 2,050-meter cave system reveals multiple past groundwater levels through its geometry, with elevation differences suggesting tectonic influence. From 2003 to 2007, Cokragan Spring discharged and recharged 63.5 and 62.37 million cubic meters annually, respectively. Measured discharge ranged from a maximum of 1.488 cubic meters per second to a minimum of 0.108 cubic meters per second. In situ measurements and analysis of 13 samples revealed the groundwater's physicochemical characteristics, including major ions like calcium, magnesium, and bicarbonate, and trace elements like iron, manganese, and zinc.
The effect of Sambucus nigra fruit extract in three chemotropic cycles of cisplatin drug on male reproductive system of mice
Authors: Esmaeli Mojtaba , Ghanbari Ali , Roshankhah Shiva , Barazesh Mahdi
Published: February 2024

Background
Administration of chemotherapeutic agents, such as cisplatin, induces infertility as a side effect in men of childbearing age, while consumption of antioxidants can recover this process. Therefore, the present study was designed to evaluate the protective effect of Sambucus nigra (SNA) on cisplatin alone and in combination in reducing the toxicity of cisplatin on the reproductive system of mice.
Materials & methods
In this study, 30 adult balb/c mice were randomly divided into 5 groups (n = 6). Mice in the groups received cisplatin (0.4 mg/kg) and SNA (200 mg/kg) intraperitoneally for three periods of six days with an interval of 10 days for a total of 48 days. The positive and negative control groups received normal saline and no drug, respectively. At the end of the treatment period, various parameters such as semen analysis, changes in testicular histological structures, changes in hormone levels, expression of apoptotic genes and the amount of malondialdehyde (MDA), total superoxide dismutase (T-SOD), glutathione peroxidase (GSH-Px) and catalase (CAT) were evaluated.
Results
The results of the present study showed that SNA could significantly reduce abnormalities of sperm and testis due to cisplatin administration. In addition, SNA was able to reduce the expression of genes involved in the apoptotic pathway compared to the cisplatin group. Meanwhile, SNA reduced serum MDA rate and raised rate T-SOD, GSH-Px and CAT rate compared to the cisplatin group.
Conclusion
The findings of this study suggest that SNA may have a protective effect against cisplatin in testicular tissue via its antioxidant properties.
Functional investigation and two-sample Mendelian randomization study of Early gastric cancer (EGC) hub genes obtained by WGCNA analysis
Authors: Ren Xiao-Jun , Zhang Man-Ling , Zhao Hui-Hui , Shi Zhao-Hong
Published: February 2024

Objective: This study aimed to identify differentially expressed genes associated with early gastric cancer and analyze their potential functions through bioinformatics analysis. Additionally, we sought to validate the genetic causality of identified differential molecules related to early gastric cancer using Mendelian randomization.
Methods: We analyzed the expression profiles of 19 gastritis specimens and 20 early gastric cancer specimens (high-grade intraepithelial neoplasia) from the GEO database using combined differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). Hub genes most relevant to early gastric cancer were selected and subjected to functional enrichment and signaling pathway analysis using GO and KEGG enrichment. The diagnostic efficacy of the predictive model based on the top five ranked hub genes was evaluated using ROC curves. Finally, Mendelian randomization analysis was employed to assess genetic causality between hub genes and early gastric cancer.
Results: A total of 755 hub genes were identified after intersecting the most relevant modular genes from WGCNA with genes exhibiting significant expression differences. GO and KEGG pathway enrichment analyses revealed significant differential expression of hub genes in responses to xenobiotic stimuli, hormone transport, apical cell parts, and oxidoreductase activity targeting the CH-OH group of donors, as well as involvement in pluripotency of stem cells, hepatocellular carcinoma, and axon guidance. The top five core genes—IL6, CLU, UGT2B15, NANOG, and NFE2L2—were analyzed using Cytoscape software. The column-linear graph prediction model demonstrated excellent performance in predicting the risk of early gastric cancer, as evidenced by ROC analysis. In the inverse variance weighting (IVW) method, we found that the core gene CLU was associated with an increased risk of early gastric cancer (OR = 1.157, 95% CI = 1.043-1.283, p = 0.0057).
Conclusion: Our bioinformatics analysis identified the CLU gene as genetically causally associated with early gastric cancer, suggesting its potential as a diagnostic or therapeutic target for this condition.
Response of Branched Broomrape (Phelipanche ramosa (L.) Pomel) to Amino Acid Treatments
Authors: Albayrak Hilal , Isik Doğan , Arslan Mehmet
Published: February 2024

Phelipanche ramose(branched broomrape), a root holoparasitic weed causes great trouble in agriculture along Mediterranean, Europe, Asia, Africa, North and South America, cannot be easily controlled by the known management practices applied for non-parasitic weeds. Determination of novel compounds with higher herbicidal activity against P. ramosa can provide effective control for this parasitic weed. Certain amino acids have potential to inhibit early development of broomrape without any injury to host plants. A series of pot and in vitro studies were conducted to identify amino acids with higher inhibition efficacy on broomrape germination and growth. Germination inhibition rates of amino acids were significantly different. The highest inhibitory rates were obtained from alanine, lysine and tryptophan, while the lowest was obtained from theanine, leucine and isoleucine under the in vitro conditions. The inhibitory effect of amino acids on germination enhanced with the increasing concentrations. In the polyethylene bag studies, the number of germinated seed per plant varied between 46.00 and 205.50. The lowest and the highest germinated seeds were obtained from proline and theanine, respectively. Tubercle number per plant varied between 0.00 and 3.50. Glutamic acid had the highest tubercle number, while leucine, glycine, tryptophan and isoleucine had the lowest tubercle number per plant. In general, lysine, proline, phenylalanine, asparagine are thought to the most effective inhibitors of brunched broomrape germination. Further studies are needed to evaluate the usefulness of amino acids in P. ramose control under field conditions.
RDMS: Reverse Distillation with Multiple Students of Different Scales for Anomaly Detection
Authors: Chen Ziheng , Lyu Chenzhi , Zhang Lei , Li ShaoKang , Xia Bin
Published: February 2024

Unsupervised anomaly detection is a critical task in computer vision, often approached as a one-class classification problem. Knowledge distillation has shown promising results in this area, particularly with the emergence of reverse distillation networks using encoder-decoder architectures, which further enhance anomaly detection accuracy. In this study, we propose a novel reverse knowledge distillation network with multiple scale student decoders, called RDMS. RDMS integrates a pretrained teacher encoding module, a trainable multi-level feature fusion connection module (MFFCM), and a student decoding module composed of three mutually independent decoders. Each student decoder is tasked with distilling a specific feature from the teacher encoder, effectively mitigating the overfitting issue that occurs when the student and teacher structures are similar or identical. Our model achieves an average of 99.3% image-level AUROC and 98.34% pixel-level AUROC on the publicly available dataset MVTec-AD, and also achieves state-of-the-art performance on the more challenging BTAD dataset. The proposed RDMS model demonstrates high accuracy for anomaly detection and local-ization, highlighting the potential of multi-student reverse distillation for improving unsupervised anomaly detection capabilities. Source code is available at https://github.com/zihengchen777/RDMS
Deep learning tool：Reconstruction of long missing climate data based on multilayer perceptron(MLP)
Authors: Zhang Yan , Xu Tianxin , Zhang Chenjia , Daokun Ma
Published: February 2024

Long-term monitoring of climate data is significant for grasping the law and development trend of climate change and guaranteeing food security. However, some weather stations lack monitoring data for even decades. In this study, 62 years of historical monitoring data from 105 weather stations in Xinjiang were used for missing sequence prediction, validating proposed data reconstruction tool. First of all, study area was divided into three parts according to the climatic characteristics and geographical locations. A deep learning tool based on multilayer perceptron (MLP) was established to reconstruct meteorological data with three time scales（Short term, cycle and long term）and one spatio dimension as inputing, filling in long sequence blank data. By designing an end-to-end model to autonomously detect the locations of missing data and make rolling predictions,we obtained complete meteorological monitoring data of Xinjiang from 1961 to 2022. Seven kinds of parameter reconstructed include maximum temperature (Max_T), minimum temperature (Min_T), mean temperature (Ave _ T), average water vapor pressure (Ave _ WVP), relative humidity (Ave _ RH), average wind speed (10 m Ave _ WS), and sunshine duration (Sun_H). The quality of reconstructed data was evaluated by calculating correlation coefficient with the monitored sequences of nearest station. Results show that,proposed model reached satisfied average correlation coefficient for Max_T, Min_T, Ave _ T and Ave _ WVP parameters are 0.969, 0.961, 0.971 and 0.942 respectively. The average correlation coefficient of Sun_H and Ave _ RH are 0.720 and 0.789. Although it is difficult to predict extreme values, it can still capture the period and trend; the reconstruction effect of 10 m Ave _ WS is poor, with the average similarity of 0.488. Finally, we published the trained parameter files and prediction codes as a micro service on the Agricultural Smart Brain platform, which provides firstly a deep learning tool for rapid and reliable reconstruction of meteorological monitoring data.
Structural characterization and bioactive screening of two homogeneity proteins from Tianshan red deer abomasum
Authors: Yang Zi , Wali Ahmidin , Wang Yahui , Arken Amina , Eli Yusupjan , Gao Yanhua , Yili Abulimiti , Aisa HajiAkber
Published: February 2024

In this study, response surface methodology (RSM) and Box-Behnken design (BBD) were used to optimize the process of extracting abomasum protein from Tianshan red deer. The final extraction conditions were as follows: ultrasonic power: 445 W, extraction temperature: 43 °C, and extraction solution salt concentration: 0.57 mol/L. Gel chromatography and anion exchange chromatography were applied to isolate two homogeneity proteins named A1 and A2 from the abomasum of Tianshan red deer. The protein contents of A1 and A2 were 71.57% ± 5.65 and 68.83%±4.35 respectively. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) determined the molecular weights of two homogeneity proteins to be 63 kDa and 55 kDa. The secondary structures of two homogeneity proteins are primarilyβ-folds and α-helixes. In addition, activity screening revealed the anti-inflammatory property of A1 (78.67% ± 2.23 inhibition of cyclooxygenase 2 (COX-2) at 0.1 mg/ml) and the anti-tumor activity of A2 (79.38% ± 3.52 and 85.89% ± 3.37 inhibition of human colon cancer cells (HCT-116) and human gastric cancer cells (HGC-27), respectively, at 1 mg/ml). These findings suggest that protein from the abomasum of Tianshan red deer may be useful as an efficacy factor in foods and nutraceuticals.
A cross-sectional analysis of severe SARS cases evolution in a brazilian municipality using data mining techniques
Authors: Júnior Silvano Herculano da Luz , Oliveira William Farias Carvalho , Neto Luis Cesar de Albuquerque , Souza Hugo Araujo , de Sant’Anna Yúri Faro Dantas , Adeodato Paulo Jorge Leitão
Published: February 2024

The first Severe Acute Respiratory Syndrome (SARS) outbreak occurred in China in November 2002. Since then, other coronavirus variants have emerged worldwide, such as Middle East Respiratory Syndrome (MERS) in 2012, 2019-nCOV in 2019, and Omicron in 2020. Several studies have been published, demonstrating the use of data mining (DM) to create relevant classification and decision systems for analyzing clinical data of patients with SARS. However, most of these studies lack the depth of consideration for the socioeconomic factors of the patients, such as income, education levels, race, among others, which could be relevant for classification algorithms. This study demonstrates the application of the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework and the use of DM techniques and tools, employing binary classification and decision algorithms to predict the progression of severe cases in SARS patients residing in the municipality of Recife, Pernambuco, Brazil. It is a cross-sectional study conducted using open datasets, considering various attributes related to symptoms, pre-existing conditions, and socioeconomic factors, including income, literacy rate, and patient domicile location. The analysis involved three healthcare experts (physicians). The results highlighted that the apriori algorithm performed better in rule induction, and the decision tree showed slightly better performance compared to logistic regression. Furthermore, the analysis brought to light interesting correlations between the progression of severe cases and the socioeconomic data of patients.
Identification of Hidrogeochemical Processes in the Montebello Lagunar System by Inverse Modeling in PHREEQC.
Authors: Villanueva Luis Antonio García , LozadaTrujillo José Abel , Rivas Dionisio Arturo Cabrera , Hernández Denise Michel Villarreal , Moctezuma Gustavo Alejandro Méndez , Moctezuma Alan Humberto Méndez
Published: February 2024

The aim of this article is to address a current problematic, which is concentrated in one of the most iconic locations in the state of Chiapas, Mexico. It is about a series of lakes found out at the Parque Nacional Lagunas de Montebello where has presented important alterations detected since the beginning of the 21st century. Making use of inverse modeling hydrogeochemical, it seeks to identify the predominant hydrogeochemical process in the place, since the chemical condition of the water will be known in greater detail in its constant interaction with geological conditions of the site to determine the degree of alteration owing anthropogenic activities developed around it, such as agriculture, industrial discharges and sewage that reach the lakes. Finding due to geological framework of the karst region, where there are rocks made of dolomites, limestone and plaster formations, one of the principal processes determinated by the modelation and comprobated by the comparation in site, is the precipitation of the plaster proper to the low solubility, reactions of oxidation and reduction of the organic matter, precipitation or dissolution of nitrogenous elements and salts that came from the unloading done to the bed of the great river that feed the study site.
Image-Driven Laser Ablation Optimization
Authors: Bukhtoyarov Vladimir V. , Ageev Daniel A. , Malashin Ivan P. , Tynchenko Vadim S. , Borodulin Aleksei S. , Neluyb Vladimir A.
Published: February 2024

This study presents the development of optimal nanostructures on surfaces post-laser ablation processinga software solution aimed at enhancing the efficiency of this process. Leveraging image processing techniques, particularly images from Scanning Electron Microscopy (SEM), the software optimizes laser system parameters to achieve desired nanostructure morphology. By integrating image analysis with laser system optimization, this approach offers a solution for improving the precision and efficacy of nanostructure fabrication. The proposed methodology holds significant promise for advancing laser ablation processes in material engineering and nanophotonics.
Thermomechanical Behaviour of Concrete Reinforced With Synthetic And Organic Fibers.
Authors: sahnoun Ilhem , Toumi Belgacem
Published: February 2024

The aim of this research work is to study the thermomechanical behavior of concrete with organic fibers such as camel fibers, chicken feather fibers and compare it with concrete without fibers and with synthetic fibers (polypropylene fibers). In order to study the influence of the introduction of fibers on the behavior of concretes subjected to high temperatures, tests are carried out to characterize the various physical and mechanical properties of concretes at high temperatures. Concrete formulated with different fiber content (0.5% chicken feather fiber (OCC2), 1.5% camel hair fiber (OCC3) and 0.9% polypropylene fiber (OCC4)) and ordinary concrete taken as control concrete (OCC1) are subjected to heating/cooling cycles at a rate of 1°C/min at 105°C,150°C, 250°C, 500°C and 750°C. The results showed that chicken feather fibers can be used to reinforce concrete by improving its behavior in the face of increased temperature and by having a better impact on the environment.
PSC Diffusion: Patch-based Simplified Conditional Diffusion Model for Low-light Image Enhancement
Authors: Wan Fei , Xu Bingxin , Pan Weiguo , Liu Hongzhe
Published: February 2024

Low-light image enhancement is pivotal for augmenting the utility and recognition of visuals captured under inadequate lighting conditions. Generative models are widely recognized as a mainstream approach by framing the challenge as an image-to-image translation task. This paper propose the Patch-based Simplified Conditional Diffusion Model (PSC Diffusion) for low-light image enhancement due to the outstanding performance of diffusion models in image generation. Specifically, recognizing the potential issue of gradient vanishing in extremely low-light images due to smaller pixel values, we design a simplified U-Net architecture with SimpleGate and Parameter-free attention (SimPF) block to predict noise. This architecture utilizes parameter-free attention mechanism and fewer convolutional layers to reduce multiplication operations across feature maps, resulting in a 12%-51% reduction in parameters compared to U-Nets used in several prominent diffusion models, which also accelerates the sampling speed. In addition, preserving intricate details in images during the diffusion process is achieved through employing a patch-based diffusion strategy, integrated with global structure-aware regularization, which effectively enhances the overall quality of the enhanced images. Experiments show that the method proposed in this paper achieves richer image details and better perceptual quality, while the sampling speed is over 35% faster than similar diffusion model-based methods.
In-Situ UNet-Based Heliostat Beam Characterization Method for Precise Flux Calculation Using the Camera-Target Method
Authors: Kuhl Mathias , Pargmann Max , Cherti Mehdi , Jitsev Jenia , Quinto Daniel Maldonado , Pitz-Paal Robert
Published: February 2024

Concentrated Solar Power (CSP) utilizing solar tower power plants exhibits significant potential for dispatchable sustainable power generation and fuel production, by concentrating up to hundred thousand focal spots of individual mirrors onto one surface. The irradiance on the receiver is a critical parameter for power plant operators, not only to prevent component-damaging temperature peaks and gradients but also to optimize the distribution of heliostat’s focal spots. Therefore, recent approaches aim to exploit images of isolated focal spots on a Lambertian target, taken on a day-to-day basis in most commercial power plants, to achieve more accurate flux predictions. State-of-the-art methods for flux measurements require additional equipment and are mostly time-intensive. This study introduces a novel methodology to derive single heliostat irradiance profiles using simple digital camera images, employing an image-to-image neural network. The model we propose is capable of generating accurate, high-resolution flux distributions from single images. It uniquely accounts for the target surface’s irregularities and reflectivity as well as background irradiation, factors critical for precise measurements of individual focal spots. By incorporating these factors, our methodology offers a new scaling approach for the measured flux density based on a calculated total flux, obviating the need for additional equipment such as flux gauges or moving bars. This enables to effortlessly be integrated into the daily calibration routine in-situ using the cameratarget method. This seamless integration not only supports existing flux prediction methodologies but also paves the way for future enhancements by potentially improving the accuracy of overall flux prediction.
Large-scale fermentation of Lactiplantibacillus pentosus 292 for the production of lactic acid and the storage strategy based on molasses as a preservative
Authors: chen xing , Wei Zhirong , Feng Ziqiao , Ren Wei , Che Yuhan , Wang Xinyi , Long Hao , Cai Xiaoni , Xie Zhenyu
Published: February 2024

A cheap medium containing 30 g/L glucose, 15 g/L yeast powder, 8 g/L K2HPO4, and 10 g/L tween-80 was developed for Lactiplantibacillus pentosus 292, and the lactic acid (LA) produced in this medium was up to 16.24 g/L increased by 83.48% compared with that in MRS medium after 24 h fermentation under optimal condition (temperature 35 ℃, pH 6, shaking speed 100 rpm, and inoculation amount 3%). Subsequently, the large-scale fermentation kinetics models of bacterial growth, substrate consumption, and product generation in a 200-L fermenter were built using better-fitting models of Logistic, Luedeking, and Luedeking-Piret, respectively. Then, the preservation strategy of fermentation broth with 1–3% molasses as preservative and temperature acceleration experiment were studied. This work besides constructing the large-scale fermentation kinetics models of L. pentosus 292, also developed a storage strategy based on the molasses as a preservative, thereby facilitating the application of 292-strain for large-scale production of lactic acid bacteria (LAB) preparation and its products.
Optimization of the selective effect of zeolite in the synthesis of Cu2O Nanoparticles
Authors: Valenzuela-Martínez H. , Hurtado R. Britto , Navarro-Badilla A. , Santacruz-Ortega H. , Brown Francisco , Munguía-Arvayo R. , Flores-López N. S. , Roldan Manuel A. , Cortez-Valadez M. , Flores-Acosta M.
Published: February 2024

In this study, we exhibit a novel method for synthesizing Cu2O nanoparticles by employing ion exchange within the synthetic nanozeolite 4A matrix. The nanoparticles showed a consistent particle size, predominantly around 6 nm, with a narrow distribution. Nanosizing of the zeolite was achieved through high-energy milling treatments, thereby enhancing the surface-to-volume ratio. A band close to 700 cm− 1 was observed in the FTIR spectrum, potentially indicating an aspect of zeolite downsizing related to symmetric stretching of the Si-O bond post-milling. Furthermore, a distinctive band corresponding to Cu(I)-O stretching vibrations was identified around 600 cm− 1. Additionally, optical absorption analyses in the UV-Vis spectrum revealed two characteristic bands attributable to Cu2O nanoparticles, positioned at 370 nm and 470 nm, respectively. These findings lead to advancements in resource use and promote sustainable and environmentally friendly production practices.
Pollution pressure drives microbial assemblages that improve the phytoremediation potential of heavy metals by Ricinus communis
Authors: Rubio-Noguez Daniela , Breton-Deval Luz , Salinas-Peralta Ilse , Juárez Katy , Galicia Leopoldo
Published: February 2024

Due to the rapid expansion of industrial activity, soil pollution has intensified. Plants growing in these polluted areas have developed a rhizobiome uniquely and specially adapted to thrive in such environments. However, it remains uncertain whether pollution acts as a sufficiently selective force to shape the rhizobiome, and whether these adaptations endure over time, potentially aiding in long-term phytoremediation. Therefore, in the present study, we aimed to compare whether the microbiome associated with roots from plants germinated in polluted riverbanks will improve the phytoremediation of Cd and Pb under mesocosm experiments compared with plants germinating in a greenhouse. The experimental design was a factorial 2 x 2, i.e., the origin of the plant and the presence or absence of 100 mg/L of Cd and 1000 mg/L of Pb. Our results showed that plants germinated in polluted riverbanks have the capacity to accumulate twice the amount of Pb and Cd during mesocosm experiments. The metagenomic analysis showed that plants from the river exposed to heavy metals at the end of mesocosm experiments were rich in Rhizobium_sp_AC44_96 and Enterobacter sp. EA_1, Enterobacter soli, Pantoea rwandensis, Pantoea endophytica. In addition, those plants were uniquely associated with Rhizobium grahamii, which likely contributed to the differences in the levels of phytoremediation achieved. Furthermore, the functional analysis revealed an augmented functional potential related to hormones, metallothioneins, dismutases, and reductases; meanwhile, the plants germinated in the greenhouse showed an unspecific strategy to exceed heavy metal stress. In conclusion, pollution pressure drives microbial assemblages that improve the phytoremediation process.
Surveillance via wastewater monitoring and nasal self-collection of specimens (The SWANSS Study): A CFIR-informed qualitative study with key carceral and healthcare stakeholders
Authors: Moore Kyler N. , Bircher Patrick M. , Ogutu Emily A. , Kennedy Shanika S. , Brown Victoria M. , Freeman Matthew C. , Phillips Victoria L. , Spaulding Anne C. , Akiyama Matthew J.
Published: February 2024

Background
Implementing public health interventions can be challenging in carceral settings. Jails are institutions with the shortest lengths of stay, resulting in frequent turnover and constantly shifting populations. Jails had particularly acute challenges during the early stages of the COVID-19 pandemic, when incarcerated persons were highly susceptible to infection and severe disease. The purpose of this study was to identify barriers and facilitators to wastewater-based surveillance (WBS) and nasal self-testing (NST) as a combined strategy of COVID-19 surveillance in Fulton County Jail (FCJ), Atlanta, Georgia. Implications for efficient and effective infectious disease testing among this susceptible population are relevant for ongoing surveillance for current endemic pathogens and future epidemics.
Methods
We utilized a multilevel, theory-informed qualitative approach to conduct semi-structured one-on-one and small group interviews with nine distinct jail stakeholder groups: jail custody leadership, administrators, officers, maintenance workers, Georgia Department of Health’s COVID-19 testing contractor, jail health care leaders, nursing staff, laboratory leadership and staff. Interview guides and analyses were informed by the Consolidated Framework for Implementation Research (CFIR). We used directed content analysis to identify domains within CFIR reflecting barriers and facilitators to WBS combined with NST in large urban jail settings.
Results
Twelve interviews were conducted with administrative, healthcare, maintenance, custody, and laboratory personnel from July 2022 to November 2022. Seven CFIR constructs were identified as barriers, sixteen as facilitators, and one as a neutral factor for the implementation of WBS combined with NST. Jail stakeholders underscored the relative advantage for self-testing, highlighted limited resources, and expressed concerns for sustainability of WBS due to competing priorities. Many of the stakeholders within the jail setting that were interviewed had hierarchical organizational structures, which made decision-making processes regarding WBS and NST complex and challenging to implement.
Conclusions
Given the political, structural, and organizational factors in a jail setting, innovations such as NST and WBS require a rigorous implementation strategy supported by ongoing engagement and collaboration with a multitude of stakeholders.