UV index-based model for predicting synthesis of (pre-)vitamin D3 in the mediterranean basin.

The importance of solar radiation for the body's ability to synthesize Vitamin D3 is well documented, yet the precise amount of sun exposure required to avoid Vitamin D insufficiency is less clear. To address this knowledge gap, this study sought to utilize the sun in a suitable period at the optimum dose by utilizing numerical simulations to determine the amount of Vitamin D3 synthesis in the skin according to season, time of day, and geographical location in Turkey. The study was carried out in three stages; in the first stage, daily, monthly, and annual values were determined in cases where the solar zenith angle has the active UV-B wavelength. The second stage determined the level of Vitamin D that can be synthesized in all skin types at 25% solar radiation exposure. In the third stage, the sun exposure time required for 1000 International Units (IU) for all skin types was calculated. According to the analysis, the yearly period of active synthesis of D3 on Earth lasts from the beginning of March to the third week of October. During the day, it is between 10:00 and 16:00. For 1000 IU/day, the average annual estimated times (minutes) are 5.05 for Type I, 6.3 for Type II, 7.6 for Type III, 11.35 for Type IV, 15.15 for Type V, and 25.25 for Type VI. The results of this paper will impact awareness for academic-medical users.

Assessment of antimicrobial, antidiabetic, and anti-inflammatory properties of acetone extract of Aerva lanata (L.) by in-vitro approach and bioactive compounds characterization.

The antimicrobial, antidiabetic, and anti-inflammatory activities efficiency of Aerva lanata plant extracts (aqueous (Aqu-E), acetone (Ace-E), and ethanol (Eth-E)) were investigated in this study. Furthermore, the active molecules exist in the crude extract were characterized by UV-Visible spectrophotometer, Fourier transform infrared (FTIR), High-performance liquid chromatography (HPLC), and Gas Chromatography-Mass Spectrometry (GC-MS) analyses. The preliminary phytochemical study revealed that the Ace-E restrain more phytochemicals like alkaloids, saponins, anthraquinone, tannins, phenolics, flavonoids, glycosides, terpenoids, amino acid, steroids, protein, coumarin, as well as quinine than Aqu-E and Eth-E. Accordingly to this Ace-E showed considerable antimicrobial activity as the follows: for bacteria S. aureus > E. coli > K. pneumoniae > P. aeruginosa > B. subtilis and for fungi T. viride > A.flavus > C. albicans > A.niger at 30 mg ml concentration. Similarly, Ace-E showed considerable antidiabetic (α-amylase: 71.7 % and α-glucosidase: 70.1 %) and moderate anti-inflammatory (59 % and 49.8 %) activities. The spectral and chromatogram studies confirmed that the Ace-E have pharmaceutically valuable bioactive molecules such as (Nbutyl)-octadecane, propynoic acid, neophytadiene, and 5,14-di (N-butyl)-octadecane. These findings suggest that Ace-E from A. lanata can be used to purify additional bioactive substances and conduct individual compound-based biomedical application research.

Mitigating microplastic pollution: A critical review on the effects, remediation, and utilization strategies of microplastics.

Microplastics are found ubiquitous in the natural environment and are an increasing source of worry for global health. Rapid industrialization and inappropriate plastic waste management in our daily lives have resulted in an increase in the amount of microplastics in the ecosystem. Microplastics that are <150 µm in size could be easily ingested by living beings and cause considerable toxicity. Microplastics can aggregate in living organisms and cause acute, chronic, carcinogenic, developmental, and genotoxic damage. As a result, a sustainable approach to reducing, reusing, and recycling plastic waste is required to manage microplastic pollution in the environment. However, there is still a significant lack of effective methods for managing these pollutants. As a result, the purpose of this review is to convey information on microplastic toxicity and management practices that may aid in the reduction of microplastic pollution. This review further insights on how plastic trash could be converted as value-added products, reducing the load of accumulating plastic wastes in the environment, and leading to a beneficial endeavor for humanity.

Projected decline in the strength of vegetation carbon sequestration under climate change in India.

Tropical vegetation plays a critical role in terrestrial carbon budget and supply many ecological functions such as carbon sequestration. In recent decades, India has witnessed an increase in net primary productivity (NPP), an important measure of carbon sequestration. However, uncertainties persist regarding the sustainability of these land carbon sinks in the face of climate change. The enhanced NPP is driven by the strong CO2 fertilization effect (CFE), but the temporal patterns of this feedback remain unclear. Using the carbon flux data from the Earth System Models (ESMs), an increasing trend in NPP was observed, with projections of NPP to 2.00 ± 0.12 PgCyr-1 (25 % increase) during 2021-2049, 2.36 ± 0.12 PgCyr-1 (18 % increase) during 2050-2079, and 2.67 ± 0.07 PgCyr-1 (13 % increase) during 2080-2099 in Indian vegetation under SSP585 scenario. This suggests a significant decline in the NPP growth rate. To understand the feedback mechanisms driving NPP, the relative effects of CFE and warming were analyzed. Comparing simulations from the biogeochemically coupled model (BGC) with the fully coupled model, the BGC model projected a 74.7 % increase in NPP, significantly higher than the 55.9 % increase projected by the fully coupled model by the end of the century. This indicates that the consistent increase in NPP was associated with CO2 fertilization. More importantly, results reveal that the decrease in the NPP growth rate was due to the declining contribution of CFE at a rate of -0.62 % per 100 ppm CO2 increase. This decline could be attributed to factors such as nutrient limitations and high temperatures. Additionally, significant shifts in the strength of carbon sinks in offsetting the CO2 emissions were identified, decreasing at a rate of -1.15 % per decade. This decline in the strength of vegetation carbon sequestration may increase the societal dependence on mitigation measures to address climate change.

PDMS nanoparticles-decorated PDMS substrate promotes adhesion, proliferation and differentiation of skin cells.

Polydimethylsiloxane (PDMS) is known to be a common substrate for various cell culture-based applications. However, native PDMS is not very conducive for cell culture and hence, surface modification via cell adhesion moieties is generally needed to make it suitable especially for long-term cell culture. To address this issue, we propose to coat PDMS nanoparticles (NPs) on the surface of PDMS film to improve adhesion, proliferation and differentiation of skin cells. The proposed modification strategy introduces necessary nanotopography without altering the surface chemical properties of PDMS. Due to resemblance in the mechanical properties of PDMS with skin, PDMS NPs can recreate the native extracellular nanoenvironment of skin on the PDMS surface and provide anchoring sites for skin cells to adhere and grow. Human keratinocytes, representing 95% of the epidermal skin cells maintained their characteristic well-spread morphology with the formation of interconnected cell-sheets on this coated PDMS surface. Moreover, our in vitro immunofluorescence studies confirmed expression of distinctive epidermal protein markers on the coated surface indicating close resemblance with the native skin epidermis. Conclusively, our findings suggest that introducing nanotopography via PDMS NPs can be an effective strategy for emulating the native cellular functions of keratinocytes on PDMS based cell culture devices.

Predictive value of the thrombotic risk criteria proposed in the 2023 ESC guidelines for the management of ACS: insights from a large PCI registry.

AIM: To assess the value of the thrombotic risk criteria proposed in the 2023 guidelines of the European Society of Cardiology (ESC) for the management of acute coronary syndrome (ACS) to predict the ischaemic risk after percutaneous coronary intervention (PCI). METHODS AND RESULTS: Consecutive patients with acute or chronic coronary syndrome undergoing PCI at a large tertiary-care center from 2014 to 2019 were included. Patients were stratified into low, moderate, or high thrombotic risk based on the ESC criteria. The primary endpoint was major adverse cardiovascular events (MACEs) at 1 year, a composite of all-cause death, myocardial infarction (MI), and stroke. Secondary endpoints included major bleeding. Among 11 787 patients, 2641 (22.4%) were at low-risk, 5286 (44.8%) at moderate risk, and 3860 (32.7%) at high-risk. There was an incremental risk of MACE at 1 year in patients at moderate (hazard ratios (HR) 2.53, 95% confidence interval (CI) 1.78-3.58) and high-risk (HR 3.39, 95% CI 2.39-4.80) as compared to those at low-risk, due to higher rates of all-cause death and MI. Major bleeding rates were increased in high-risk patients (HR 1.59, 95% CI 1.25-2.02), but similar between the moderate and low-risk group. The Harrell's C-index for MACE was 0.60. CONCLUSION: The thrombotic risk criteria of the 2023 ESC guidelines for ACS enable to stratify patients undergoing PCI in categories with an incremental 1 year risk of MACE; however, their overall predictive ability for MACE is modest. Future studies should confirm the value of these criteria to identify patients benefiting from an extended treatment with a second antithrombotic agent.

Sugar cane bagasse hydrolysate (SBH) as a lucrative carbon supplement to upgrade the lipid and fatty acid production in Chlorococcum sp. for biodiesel through an optimized binary solvent system.

Cost is the crucial impediment in commercializing microalgal biodiesel. Therefore, cultivating microalgae in cost-effective nutrients reduces the upstream process cost remarkably. Thus, in this study, sugar cane bagasse hydrolysate (SBH) as a lucrative carbon supplement for Chlorococcum sp. and subsequent lipid extraction via an optimized solvent system for biodiesel production was investigated. Characterization of SBH revealed the presence of various monosaccharides and other sugar derivatives such as glucose, fructose, xylose, arabinose, etc. The maximum dry cell weight of 1.7 g/L was estimated in cultures grown in 10 mL SBH. Different solvents such as diethyl ether (DEE), chloroform (CHL), ethyl acetate (ETA), hexane (HEX), methanol (MET), ethanol (ETOH), acetone (ACE) and also combination of solvents (2:1 ratio) such as DEE: MET, CHL: MET, HEX: MET, HEX: ETOH was tested for lipid extraction efficacy. Among solvents used, 12.3% and 18.4% of lipids were extracted using CHL and CHL: MET, respectively, from 10 mL SBH amended cultures. However, the biodiesel yield was found to be similar at about 70.16 % in both SBH and no SBH-added cultures. The fatty acid profile of the biodiesel shows palmitic, oleic, linoleic, linolenic, and arachidonic acid as principal fatty acids. Further, the levels of SFAs, MUFAs, and PUFAs in 10 mL SBH-added cells were 24.67, 12.89, and 34.24%, respectively. Eventually, the fuel properties of Chlorococcum sp. biodiesel, satisfying international biodiesel standards, make the biodiesel a viable diesel substitute in the future.

Macrocyclic Sulfur Ligand Stabilized Trans-Palladium Dichloride Complex: Syntheses, Structure, Chlorine Rotation, and Application in α-Olefination of Nitriles by Primary Alcohols.

Herein, we have reported the synthesis of a macrocyclic organosulfur ligand (L1) having a seventeen-membered macrocyclic ring. Subsequently, the corresponding trans-palladium complex (C1) of bulky macrocyclic organosulfur ligand (L1) was synthesized by reacting it with PdCl2 (CH3 CN)2 salt. The newly synthesized ligand and complex were characterized using various analytical and spectroscopic techniques. The complex showed a square planar geometry with trans orientation of two ligands around the palladium center. The complex possesses intramolecular SCH…Cl interactions of 2.648 Šbetween the macrocyclic ligand and palladium dichloride. The potential energy surface (PES) for the rotational process of C1 suggested a barrier of ~23.81 kcal/mol for chlorine rotation. Furthermore, the bulky macrocyclic organosulfur ligand stabilized palladium complex (C1) was used as a catalyst (2.5 mol %) for α-olefination of nitriles by primary alcohols. The α,ß-unsaturated nitrile compounds were found to be the major product of the reaction (57-78 % yield) with broad substrate scope and large functional group tolerance. Notably, the saturated nitrile product was not observed during the reaction. The mechanistic studies suggested the formation of H2 and H2 O as only by-products of the reaction, thereby making the protocol greener and sustainable.

Pharmacoscreening, molecular dynamics, and quantum mechanics of inermin from Panax ginseng: a crucial molecule inhibiting exosomal protein target associated with coronary artery disease progression.

Background: Exosomes, microvesicles, carry and release several vital molecules across cells, tissues, and organs. Epicardial adipose tissue exosomes are critical in the development and progression of coronary artery disease (CAD). It is hypothesized that exosomes may transport causative molecules from inflamed tissue and deliver to the target tissue and progress CAD. Thus, identifying and inhibiting the CAD-associated proteins that are being transported to other cells via exosomes will help slow the progression of CAD. Methods: This study uses a systems biological approach that integrates differential gene expression in the CAD, exosomal cargo assessment, protein network construction, and functional enrichment to identify the crucial exosomal cargo protein target. Meanwhile, absorption, distribution, metabolism, and excretion (ADME) screening of Panax ginseng-derived compounds was conducted and then docked against the protein target to identify potential inhibitors and then subjected to molecular dynamics simulation (MDS) to understand the behavior of the protein-ligand complex till 100 nanoseconds. Finally, density functional theory (DFT) calculation was performed on the ligand with the highest affinity with the target. Results: Through the systems biological approach, Mothers against decapentaplegic homolog 2 protein (SMAD2) was determined as a potential target that linked with PI3K-Akt signaling, Ubiquitin mediated proteolysis, and the focal adhesion pathway. Further, screening of 190 Panax ginseng compounds, 27 showed drug-likeness properties. Inermin, a phytochemical showed good docking with -5.02 kcal/mol and achieved stability confirmation with SMAD2 based on MDS when compared to the known CAD drugs. Additionally, DFT analysis of inermin showed high chemical activity that significantly contributes to effective target binding. Overall, our computational study suggests that inermin could act against SMAD2 and may aid in the management of CAD.

Advanced 3D Through-Si-Via and Solder Bumping Technology: A Review.

Three-dimensional (3D) packaging using through-Si-via (TSV) is a key technique for achieving high-density integration, high-speed connectivity, and for downsizing of electronic devices. This paper describes recent developments in TSV fabrication and bonding methods in advanced 3D electronic packaging. In particular, the authors have overviewed the recent progress in the fabrication of TSV, various etching and functional layers, and conductive filling of TSVs, as well as bonding materials such as low-temperature nano-modified solders, transient liquid phase (TLP) bonding, Cu pillars, composite hybrids, and bump-free bonding, as well as the role of emerging high entropy alloy (HEA) solders in 3D microelectronic packaging. This paper serves as a guideline enumerating the current developments in 3D packaging that allow Si semiconductors to deliver improved performance and power efficiency.

From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology.

Prognostic survival prediction in colorectal cancer (CRC) plays a crucial role in guiding treatment decisions and improving patient outcomes. In this research, we explore the application of deep learning techniques to predict survival outcomes based on histopathological images of human colorectal cancer. We present a retrospective multicenter study utilizing a dataset of 100,000 nonoverlapping image patches from hematoxylin & eosin-stained histological images of CRC and normal tissue. The dataset includes diverse tissue classes such as adipose, background, debris, lymphocytes, mucus, smooth muscle, normal colon mucosa, cancer-associated stroma, and colorectal adenocarcinoma epithelium. To perform survival prediction, we employ various deep learning architectures, including convolutional neural network, DenseNet201, InceptionResNetV2, VGG16, VGG19, and Xception. These architectures are trained on the dataset using a multicenter retrospective analysis approach. Extensive preprocessing steps are undertaken, including image normalization using Macenko's method and data augmentation techniques, to optimize model performance. The experimental findings reveal promising results, demonstrating the effectiveness of deep learning models in prognostic survival prediction. Our models achieve high accuracy, precision, recall, and validation metrics, showcasing their ability to capture relevant histological patterns associated with prognosis. Visualization techniques are employed to interpret the models' decision-making process, highlighting important features and regions contributing to survival predictions. The implications of this research are manifold. The accurate prediction of survival outcomes in CRC can aid in personalized medicine and clinical decision-making, facilitating tailored treatment plans for individual patients. The identification of important histological features and biomarkers provides valuable insights into disease mechanisms and may lead to the discovery of novel prognostic indicators. The transparency and explainability of the models enhance trust and acceptance, fostering their integration into clinical practice. Research demonstrates the potential of deep learning models for prognostic survival prediction in human colorectal cancer histology. The findings contribute to the understanding of disease progression and offer practical applications in personalized medicine. By harnessing the power of deep learning and histopathological analysis, we pave the way for improved patient care, clinical decision support, and advancements in prognostic prediction in CRC.

Comparative Efficacy of Metformin and Glimepiride in Modulating Pharmacological Network to Increase BDNF Levels and Benefit Type 2 Diabetes-Related Cognitive Impairment.

Cognitive impairment is anotable complication of type 2 diabetes (T2DM), accompanied by reduced brain-derived neurotrophic factor (BDNF) in the brain and blood. Anti-diabetic drugs reduce hyperglycemia, yet their effect on cognitive improvement is unknown. We aimed to investigate the effect of anti-diabetic drugs regulating BDNF in T2DM through computational and case-control study design. We obtained T2DMproteins viatext-mining to construct a T2DMprotein network. From the T2DMnetwork, the metformin and glimepiride interactomes and their crucial shortest-path-stimulating BDNF were identified. Using qRTPCR, the genes encoding the shortest-path proteins were assessed in four groups (untreated-T2DM, metformin-treated, glimepiride-treated, and healthy controls). Finally, ELISA was used to assess serum BDNF levels to validate drug efficacy. As a result of this investigation, aT2DMnetwork was constructed with 3683 text-mined proteins. Then, the T2DMnetwork was explored to generate a metformin and glimepiride interactome that establishes the critical shortest-path for BDNF stimulation. Metformin stimulates BDNF via APP binding to the PRKAB1 receptor. Whereas, glimepiride increases BDNF by binding to KCNJ11 via AP2M1 and ESR1 proteins. Both drug shortest-path encoding genes differed significantly between the groups. Unlike metformin, BDNF gene and protein expression rise significantly with glimepiride. Overall, glimepiride can effectively increase BDNF, which could benefit T2DM patients with cognitive deterioration.

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.

Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.

The Effectiveness of Polypill for the Prevention of Cardiovascular Disease: A Meta-Analysis of Randomized Controlled Trials.

A significant global health concern, cardiovascular disease (CVD) is characterized by a rising prevalence and accompanying mortality rates. It is crucial to implement primary and secondary prevention strategies, particularly in resource-scarce settings. Polypills, which combine blood pressure, cholesterol, and homocysteine drugs, hold significant potential for lowering the risk of CVD. This study follows PRISMA meta-analysis guidelines. Two researchers conducted an extensive literature search. Inclusion criteria encompassed RCT design, polypill use, a four-week duration, and one meta-analysis outcome. Primary outcomes included MACE and CV mortality, while secondary outcomes encompassed SBP and LDL-C changes. Data extraction was performed independently, and conflicts were resolved. Review Manager 5.4 with random effects was employed for statistical analysis, and ROB 2.0 bias evaluation was conducted. The study reported CVD mortality and MACE risk ratios (RRs) with 95% CIs, as well as SBP and LDL-C weighted mean differences (MD). A total of 24 trials were included in this meta-analysis. The results revealed that the polypill was associated with a decreased risk of CVD mortality and major adverse cardiovascular events (MACE). Additionally, a significant reduction in systolic blood pressure (SBP) and low-density lipoprotein cholesterol (LDL-C) was observed. This meta-analysis showed that polypill is a viable medication for reducing the risk of CVD mortality and MACE. It is also a beneficial medication for lowering LDL-C levels and SBP.

Genomic epidemiology analysis of drug-resistant Mycobacterium tuberculosis distributed in Mexico.

Genomics has significantly revolutionized pathogen surveillance, particularly in epidemiological studies, the detection of drug-resistant strains, and disease control. Despite its potential, the representation of Latin American countries in the genomic catalogues of Mycobacterium tuberculosis (Mtb), the bacteria responsible for Tuberculosis (TB), remains limited. In this study, we present a whole genome sequencing (WGS)-based analysis of 85 Mtb clinical strains from 17 Mexican states, providing insights into local adaptations and drug resistance signatures in the region. Our results reveal that the Euro-American lineage (L4) accounts for 94% of our dataset, showing 4.1.2.1 (Haarlem, n = 32), and 4.1.1.3 (X-type, n = 34) sublineages as the most prevalent. We report the presence of the 4.1.1.3 sublineage, which is endemic to Mexico, in six additional locations beyond previous reports. Phenotypic drug resistance tests showed that 34 out of 85 Mtb samples were resistant, exhibiting a variety of resistance profiles to the first-line antibiotics tested. We observed high levels of discrepancy between phenotype and genotype associated with drug resistance in our dataset, including pyrazinamide-monoresistant Mtb strains lacking canonical variants of drug resistance. Expanding the Latin American Mtb genome databases will enhance our understanding of TB epidemiology and potentially provide new avenues for controlling the disease in the region.

The Effects of Restrictive Fluid Resuscitation on the Clinical Outcomes in Patients with Sepsis or Septic Shock: A Meta-Analysis of Randomized-Controlled Trials.

This study aims to assess the impact of a restrictive resuscitation strategy on the outcomes of patients with sepsis and septic shock. This meta-analysis was conducted in accordance with the recommendations from the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) guidelines. A systematic search was performed in databases, including PubMed, Web of Science, EMBASE, and the Cochrane Library, covering the period from the inception of the database to August 2023, with no limitations on the language of publication. Outcomes assessed in the meta-analysis included mortality, duration of intensive care unit (ICU) stay in days, duration of mechanical ventilation in days, acute kidney injury (AKI) or the need for renal replacement therapy (RRT), and length of hospital stay in days. Overall, 12 studies met the inclusion criteria and were included in the present meta-analysis. The findings of this study indicate that although the risk of mortality was lower in fluid restriction compared to the control group, the difference was statistically insignificant (risk ratio (RR): 0.98; 95% confidence interval (CI): 0.9-1.05; P value: 0.61). Additionally, the duration of mechanical ventilation was significantly shorter in the restrictive fluid group compared to its counterparts (mean difference (MD): -1.02; 95% CI: -1.65 to -0.38; P value: 0.003). There were no significant differences found in relation to the duration of ICU stays, the incidence of AKI, the requirement for RRT, or the length of hospital stays measured in days.

Network-Derived Radioresistant Breast Cancer Target with Candidate Inhibitors from Brown Algae: A Sequential Assessment from Target Selection to Quantum Chemical Calculation.

Despite significant progress in early detection and treatment, a few aggressive breast cancers still exhibit resistance to therapy. This study aimed to identify a therapeutic target for radioresistant breast cancer (RRbc) through a protein network from breast cancer genes and to evaluate potent phytochemicals against the identified target. Our approach includes the integration of differential expression genes from expression datasets to create a protein network and to use survival analysis to identify the crucial RRbc protein in order to discover a therapeutic target. Next, the phytochemicals sourced from brown algae were screened through molecular docking, ADME (absorption, distribution, metabolism, and excretion), molecular dynamics (MD) simulation, MM-GBSA, and quantum mechanics against the identified target. As a result of our protein network investigation, the proto-oncogene c-KIT (KIT) protein was identified as a potent radioresistant breast cancer target. Further, phytochemical screening establishes that nahocol-A1 from brown algae has high binding characteristics (-8.56 kcal/mol) against the KIT protein. Then, quantum chemical analysis of nahocol-A1 provided insights into its electronic properties favorable for protein binding. Also, MD simulation comprehends the conformational stability of the KIT-nahocol-A1 complex. Overall, our findings suggest nahocol-A1 could serve as a promising therapeutic candidate for radioresistant breast cancer.

Subcycle surface electron emission driven by strong-field terahertz waveforms.

The advent of intense terahertz (THz) sources opened a new era when the demonstration of the acceleration and manipulation of free electrons by THz pulses became within reach. THz-field-driven electron emission was predicted to be confined to a single burst due to the single-cycle waveform. Here we demonstrate the confinement of single-cycle THz-waveform-driven electron emission to one of the two half cycles from a solid surface emitter. Either the leading or the trailing half cycle was active, controlled by reversing the field polarity. THz-driven single-burst surface electron emission sources, which do not rely on field-enhancement structures, will impact the development of THz-powered electron acceleration and manipulation devices, all-THz compact electron sources, THz waveguides and telecommunication, THz-field-based measurement techniques and solid-state devices.

Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture.

Agriculture encompasses the study, practice, and discipline of plant cultivation. Agriculture has an extensive history dating back thousands of years. Depending on climate and terrain, it began independently in various locations on the planet. In comparison to what could be sustained by foraging and gathering, agriculture has the potential to significantly increase the human population. Throughout the twenty-first century, precision farming (PF) has increased the agricultural output. precision agriculture (PA) is a technology-enabled method of agriculture that assesses, monitors, and evaluates the needs of specific fields and commodities. The primary objective of this farming method, as opposed to conventional farming, is to increase crop yields and profitability through the precise application of inputs. This work describes in depth the development and function of artificial intelligence (AI) and the internet of things (IoT) in contemporary agriculture. Modern day-to-day applications rely extensively on AI and the IoT. Modern agriculture leverages AI and IoT for technological advancement. This improves the accuracy and profitability of modern agriculture. The use of AI and IoT in modern smart precision agricultural applications is highlighted in this work and the method proposed incorporates specific steps in PF and demonstrates superior performance compared to existing classification methods. It achieves a remarkable accuracy of 98.65%, precision of 98.32%, and recall rate of 97.65% while retaining competitive execution time of 0.23 s, when analysing PF using the FAOSTAT benchmark dataset. Additionally, crucial equipment and methods used in PF are described and the vital advantages and real-time tools utilised in PA are covered in detail.

Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique.

Rice is one of the most widely consumed foods all over the world. Various diseases and deficiency disorders impact the rice crop's growth, thereby hampering the rice yield. Therefore, proper crop monitoring is very important for the early diagnosis of diseases or deficiency disorders. Diagnosis of diseases and disorders requires specialized manpower, which is not scalable and accessible to all farmers. To address this issue, machine learning and deep learning (DL)-driven automated systems are designed, which may help the farmers in diagnosing disease/deficiency disorders in crops so that proper care can be taken on time. Various studies have used transfer learning (TL) models in the recent past. In recent studies, further improvement in rice disease and deficiency disorder diagnosis system performance is achieved by performing the ensemble of various TL models. However, in all these DL-based studies, the segmentation of the region of interest is not done beforehand and the infected-region extraction is left for the DL model to handle automatically. Therefore, this article proposes a novel framework for the diagnosis of rice-infected leaves based on DL-based segmentation with bitwise logical AND operation and DL-based classification. The rice diseases covered in this study are bacterial leaf blight, brown spot, and leaf smut. The rice nutrient deficiencies like nitrogen (N), phosphorous (P), and potassium (K) were also included. The results of the experiment conducted on these datasets showed that the performance of DeepBatch was significantly improved as compared to the conventional technique.