Screening Experiments Research Articles

Selectively cross-linked hydrogel-based cocktail drug delivery micro-chip for colon cancer combinatorial drug screening using AI-CSR platform for precision medicine.

Cancer, ranked as the second leading cause of global mortality with a prevalence of 1 in 6 deaths, necessitates innovative approaches for effective treatment. Combinatorial drug therapy for cancer treatment targets several key pathways simultaneously and potentially enhances anti-cancer efficacy without intolerable side effects. However, it demands precise and accurate control of drug-dose combinations and their release. In this study, we demonstrated a selectively cross-linked hydrogel-based platform that can quantify and release drugs simultaneously for in-parallel cocktail drug screening. PDMS was used as the flow channel substrate and the poly (ethylene glycol) diacrylate (PEGDA) hydrogel array was formed by UV exposure using the photomask. Employing our platform, cocktails of anticancer drugs are precisely loaded and simultaneously released in-parallel into HCT-116 colon cancer cells, facilitating combinatorial drug screening. The integration of an artificial intelligence-based complex system response (AI-CSR) platform successfully identifies optimal drug-dose combinations from a pool of ten approved drugs. Notably, our cocktail drug chip demonstrates exceptional efficiency, screening 155 drug-dose combinations within a brief two and a half hours, a marked improvement over traditional methods. Furthermore, the device exhibits low drug consumption, requiring a mere 1 μL per patch of chip. Thus, our developed PDMS drug-loaded hydrogel platform presents a novel and expedited approach to quantifying drug concentrations, promising to be a faster, efficient and more precise approach for conducting cocktail drug screening experiments.

Changes in groundwater and surface water bacterial communities under disinfection processes: Chlorination, ozonization, photo-fenton and ultraviolet radiation

Pathogenic bacteria, introduced in water sources through faecal contamination, have traditionally been investigated as individual species, leading to the establishment of microbial, sanitary, and environmental quality indicators. Recent advancements in our understanding of the microbiome and its intricate interactions within the human-microbiome-environment network advocate for a broader evaluation of the impact of disinfection on the entire microbial community. In this study, we conducted a comprehensive screening experiment involving four disinfection processes; ozone, ultraviolet radiation with wavelengths between 200 – 280 nm (UV-C), photo-Fenton, and chlorination, applied to two distinct water sources; surface (SW) and groundwater (GW). The cells that remained viable after treatment were recovered using Brain Heart Infusion (BHI) broth, and 16S rRNA gene sequencing was used for their identification. Our findings confirmed the presence of faecal contamination in the water sources and revealed distinct effects of each treatment on the recovered bacterial populations. The chlorination of groundwater samples likely had a greater impact on bacteria in a vegetative state than on spores. Consequently, this led to a higher abundance in the BHI cultures of sporulating bacteria such as Bacillus (increasing from 0.36 to 93.62 %), while ozonation led to an elevated recovery of Pseudomonas (increasing from 45.2 to 69.9 %). Conversely, in surface water, calcium hypochlorite and ozone treatments favored the selection of Staphylococcus and Bacillus, whose relative abundance in the cultures increased from 0 to 39.22 % and from 0.35 to 96.6 %, respectively. In groundwater, Pseudomonas was resistant to UV-C radiation and their relative abundance increased from 45.2 % to 93.56 %, while photo-Fenton was effective against this bacterial group decreasing its relative abundance to 0.46 %. However, other genera such as Bacteroides, Aeromonas, and Citrobacter seemed to be less injured by this disinfection process. BHI broth was successful in recovering various bacterial groups that exhibited resistance to sublethal water disinfection.

Design and performance evaluation of low-volatility and low-viscosity absorbent for CO2 capture

Deficiencies such as high viscosity, volatility, and rich phase precipitation limited the engineering application of non-aqueous absorbents. A series of high boiling point solvent screening experiments were conducted to develop...

HcHCR: High-Throughput Single-Cell Imaging of RNA in Human Primary Immune Cells.

Functional genomics and chemical screens can identify and characterize novel cellular factors regulating signaling networks and chemical tools to modulate their function for the treatment of disease. Screening methods have relied primarily on immortalized and/or transformed cancer cell lines, which can limit the generalization of results to more physiologically relevant systems. Most have also relied on immunofluorescence, or on stably expressed recombinant fluorescent proteins, to detect specific protein markers using high-content imaging readouts. In comparison, high-throughput methods to visualize and measure RNA species have been less explored. To address this, we have adapted an isothermal signal amplification chemistry for RNA FISH known as hybridization chain reaction (HCR) to an automated, high-content imaging assay format. We present a detailed protocol for this technique, which we have named high-content HCR (hcHCR). The protocol focuses on the measurement of changes in mRNA abundance at the single-cell level in human primary cells, but it can be applied to a variety of primary cell types and perturbing agents. We anticipate that hcHCR will be most suitable for low- to medium-throughput screening experiments in which changes in transcript abundance are the desired output measure.

Advanced Methods for Constructing 𝑼𝑬(𝑺 𝟐 ) Optimal Supersaturated Designs in Factor Screening Experiments

Advanced Methods for Constructing 𝑼𝑬(𝑺 𝟐 ) Optimal Supersaturated Designs in Factor Screening Experiments

A novel tracking and analysis system for time-lapse cellular imaging of Schizosaccharomyces pombe.

The importance of the parent-progeny relationship tracking technique in single-cell analysis has grown with the passage of time. In this study, fundamental image-processing techniques were combined to develop software capable of inferring cell cycle alterations in fission yeast cells, which exhibit equipartition during division. These methods, exclusively relying on bright-field images as input, could track parent-progeny relationships after cell division by assessing the temporal morphological transformation of these cells. In the application of this technique, the software was employed for calculating intracellular fluorescent dots during every stage of the cell cycle, using a yeast strain expressing EGFP-fused Swi6, which binds to chromatin. The results obtained with this software were consistent with those of previous studies. This software facilitated single-cell-level tracking of parent-progeny relationships in cells exhibiting equipartition during division and enabled the monitoring of spatial fluctuations in a cell cycle-dependent protein. This method, expediting the analysis of extensive datasets, may also empower large-scale screening experiments that cannot be conducted manually.

A Conceptual Basis for Reducing the Number of Complex Multi-Parametric Experiments in Bio-Production Engineering via Artificial Intelligence

The biologicalization of production technology requires a far-reaching transformation from fossil to biogenic raw materials as feedstock for various production processes. The development of sustainable paints, adhesives and plastics made predominantly from biogenic and biodegradable raw materials is extremely time-consuming and therefore cost-intensive, which is counterproductive to the increasing demand for sustainable products. Due to the large number and complexity of biological systems, an unmanageable number of screening experiments would be required to use them in a specific way in products and processes. Combining evolutionary algorithm and artificial intelligence, the effort could be reduced by narrowing the range of variation and random selection of parameters and identifying patterns from parameter settings and evaluation criteria. In this paper, we thus evaluate the role of combining evolutionary algorithms and artificial intelligence for the prediction of experimental results in the context of biologicalization. In doing so, we describe key challenges using an application example, develop a methodological framework and outline an approach for translation into a software solution. The presented approach is intended to serve as a conceptual basis for further development, application and verification in a diverse set of future experiments.

Improving Gas Diffusion Electrodes for Electrochemical CO2 Reduction: Influence of the Ionomer

Electrochemical CO2 reduction is a promising way of harnessing greenhouse gas as an abundant resource for the production of value-added hydrocarbons. By employing copper as the catalyst, CO2 can be converted to multi-carbon compounds such as ethylene. Highly porous gas diffusion electrodes (GDEs) enable efficient contacting of gaseous CO2 and the electrolyte. Copper-coated GDEs are commonly manufactured by depositing a mixture of copper nanoparticles and ionomer on a gas diffusion layer. The ionomer not only acts as a binder, but also influences the GDE’s wetting behavior and can improve mass transport within the catalyst layer due to the combination of a hydrophobic PTFE-backbone and hydrophilic ion groups. Thus, optimizing the type and content of ionomer in the catalyst layer is of utmost importance.In this study, we investigate how the composition of catalyst layers affects the faradaic efficiency (FE) toward ethylene during electrochemical CO2 reduction. For this purpose, we fabricate different GDEs with constant copper loading but varied ionomer content and type (Nafion, Sustainion). To assess the performance of the GDEs, we conduct electrolysis experiments in a flow cell (Flex-E-Cell) setup with KHCO3 as the catholyte. Preliminary screening experiments at up to 50 mA cm-2 reveal a strong effect of the Nafion/copper mass ratio (NCR) on the FE toward ethylene. Within the investigated range (NCRs of 0.1, 0.2, 0.3, 0.4 and 0.5), we identify a sweet spot at an NCR of approximately 0.4. We hypothesize that at higher NCRs negative effects, such as decreasing porosity and an increasingly acidic pH, outweigh the improved mass transport. To assess the performance of the different GDEs at industrially relevant conditions, we perform constant current experiments at ≥ 100 mA cm-2. Here, we corroborate the previously observed trend and find that increasing the NCR from 0.2 to 0.4 doubles the maximum observed FE toward ethylene. When replacing Nafion with an anion exchange ionomer such as Sustainion, the total FE toward carbon products remains nearly unchanged. However, the selectivity shifts from ethylene toward single-carbon species such as CO and formic acid. These results highlight that ionomers play a decisive role in hydrogen evolution mitigation and tuning of the product spectrum during electrochemical CO2 reduction. Figure 1

Catalyst for lactose hydrolysis based on zeolite Y modified with Fe species by ultrasound treatment

Research has focused on biomass as a sustainable energy source, driving biomass valorization to convert carbohydrates. The price of lactose has dropped below the production cost, urging exploration for diverse applications, such as conversion into value-added compounds. Zeolites, especially Y zeolite, enhance hexose conversion when modified with metals. Fe-modified zeolites show promise, with controlled Si/Al ratios impacting acidity and selectivity. The recent challenges remain in active site comprehension, catalyst tunability, and reaction optimization. Therefore, this study explores the selective hydrolysis of lactose, limiting undesired side-product formation catalyzed through a sono-assisted modified zeolite Y with Fe. The obtained series of Fe-modified zeolite Y were evaluated for the hydrolysis of lactose in a screening experiment. The samples with the higher potential were further used to optimize reaction conditions by a response surface methodology (RSM). This revealed that the samples prepared at high pH generated more impurities and that higher temperatures and lactose concentrations favored hydrolysis. Despite their apparent similarities, the selected Fe-modified zeolite Y samples (Fe5Y and FeYII) highlight distinct catalytic behaviors and active sites. Employing UV-Vis spectroscopy, FTIR spectroscopy, and magnetic thermal gravimetric analysis (MTGA), various Fe active sites were detected, including α-Fe(II), FeOx nanoparticles, and iron oxides. These active sites give insight into the mechanisms involved with temperature and time, which play critical roles—evidencing the potential of sono-assisted iron modification of zeolite Y to achieve selective lactose hydrolysis. These findings contribute to understanding metal-zeolite composites as bifunctional catalysts and their application in sustainable processes. Furthermore, this research highlights the complexity of catalytic reactions of modified zeolites and their potential for fine-tuning active sites for various applications, particularly lactose hydrolysis. Such advancements hold the promise of propelling the field of sustainable energy and catalysis towards a more efficient and environmentally friendly future.

Research of the vacuum thermocyclic nitrogen process in a plasma pulsing glow discharge

As a result of the studies, the regularities of the influence of vacuum thermocyclic nitriding in a pulsating glow discharge plasma parameters on the microhardness, the diffusion saturation depth, the magnitude and distribution of residual stresses in the hardened layers of steel surfaces are established. Based on the use of expert assessment methods and the results of a series of screening experiments, optimization criteria (endurance limit and corrosion resistance) and controlled factors for mathematical modeling of the formation of strengthened ion-nitrated surface layers are determined. A mathematical model of the technology of the formation of reinforced surfaces of the vacuum thermocyclic nitriding in a pulsating glow discharge plasma according to the criteria of endurance and corrosion resistance is obtained. An analysis of the studies showed that there are no general conclusions and recommendations on the selection of optimal technological parameters of the vacuum thermocyclic nitriding in a pulsating glow discharge plasma that would be used for the practical application of this technology. These circumstances confirm the need for further study of the vacuum thermocyclic nitriding in a plasma of a pulsating glow discharge of vacuum thermocyclic nitriding in a pulsating glow discharge plasma technology and the feasibility of its optimization.

Response of Tomato Quality and Yield to Elevated Temperatures under Controlled Environment

The experiment was carried out during 2020-2021 at the Department of Plant Physiology, College of Agriculture, Vellayani, Kerala Agricultural University. The experiment was carried out with the aim of characterization of contrasting tomato genotypes for high temperature tolerance under high temperature condition and control condition to identify key quality and yield traits controlling high temperature tolerance in tomato. The experiment was designed in completely randomized design (CRD) with 2 treatment levels- control and high temperature conditions (36+/-2oC) wherein 3 tolerant and 3 susceptible genotypes were selected for the study. These genotypes were selected from the summer varietal screening experiment performed during the summer months of 2021 from March to May. The best performing genotypes were selected in terms of pollen viability, leaf membrane thermo-stability, chlorophyll fluorescence, number of fruits, fruit set %. One set of treatment was maintained under ambient condition and the other set with high temperature stress was maintained under polyhouse facility from transplanting stage to the harvesting stage. The quality parameters and yield traits were analysed at the harvesting stage of the crop. From this study it can be understood that many of the quality parameters like lycopene content, total sugars, flavanol content was found decreasing in both tolerant and susceptible genotypes but the extend of this reduction was considerate in tolerant ones. In case of ascorbic acid content and firmness of fruit at the time of ripening these were found to be improved in heat stress (HS) conditions. Yield related qualities like number of fruits, yield per plant, and root-shoot ratio was found decreasing whereas root dry weight, total dry weight and intensity of fruit drop and flower drop was increased under HS. Therefore, this study focused on tomato genotypes reported to be resilient to high-temperature stress, and comparing them to the susceptible cultivars under stress and control settings for analyzing the variations in terms of quality and yield traits in tropical hot climate regions of India. The study performed here highlights the possibility for future breeding programs utilizing the key quality and yield traits enhancing thermo-tolerance in tomato and to develop new genotypes that can combine good yield performances and fruit nutritional quality at high temperatures.

C. elegans: A potent model for high-throughput screening experiments investigating the FLASH effect

This study explores the effects of UHDR irradiation on Caenorhabditis elegans embryos. UHDR proton and electron beams demonstrate a sparing effect, aligning with literature findings. This highlights C. eleganssuitability as a screening model for studying the LET impact on the FLASH effect, reinforcing its potential in radiation research.

Microbial Fermentation of the Water-Soluble Fraction of Brewers’ Spent Grain for the Production of High-Value Fatty Acids

Brewers’ spent grain (BSG) constitutes the primary by-product of the brewing industry. The valorization of BSG from a circular economy perspective is of high industrial interest. The objective of this study was the exploitation of BSG for the microbial production of branched-chain fatty acids (BCFAs) and polyunsaturated fatty acids (PUFAs), representing two different classes of high-value fatty acids (FAs). In the present study, this waste material underwent treatment with hot water in an autoclave and the resultant extract was utilized for the preparation of a novel liquid medium (BSG medium) to be employed for microbial fermentation. Screening and subsequent scaling-up experiments confirmed the suitability of the BSG medium to support the microbial production of various high-value FAs. In particular, Streptomyces jeddahensis and Conidiobolus heterosporus could be employed for BCFAs production, Pythium ultimum and Mortierella alpina could be used to provide cis-5,8,11,14,17-eicosapentaenoic acid (EPA) and arachidonic acid (ARA), whereas Mucor circinelloides, when grown in a BSG medium, was able to accumulate γ-linolenic acid (GLA).

Cultivation of Arthrospira platensis using different agro-industrial liquid anaerobic digestates diluted with geothermal water: A sustainable culture strategy

The anaerobic digestion sector is undergoing worldwide expansion, and alternatives of using digestate as substrate for microalgae cultivation in order to improve the profitability and environmental impact of biogas plants are important. This work investigates the feasibility of growing Arthrospira platensis (A. platensis) on diluted liquid anaerobic digestates using geothermal water (GW) as alternative source of water. To this purpose, digestates from four industrial biogas plants were collected and used to carry out A. platensis cultivation assays. In the first part of the study, a screening experiment was performed to determine the ideal dilution factor suitable for A. platensis growth using each individual anaerobic digestate diluted with demineralized water (DW). Depending on the type of digestate, the most appropriate dilution factors ranged from 2× to 60× corresponding to optimal initial ammonium and turbidity values of 40–95 mgN.L−1 and 10–70 NTU, respectively. These dilution factors were consequently used to investigate the opportunity to substitute DW by GW for the culture of A. platensis. No significant changes were observed in the growth profiles when DW was replaced by GW for the four digestates tested. Regarding the final concentration reached by the different digestates, values ranged from 0.58 to 0.77 gdry matter.L−1 with DW as compared to values from 0.53 to 0.66 gdry matter.L−1 with GW. Experimental findings also showed the strong nutrient bioremediation potential of A. platensis culture, with removal efficiencies ranging from 96 to 97 %, 78 to 100 %, 45 to 66 % for N-NH4+, P-PO43− and K+, respectively. These promising results indicate that geothermal water could be used as substitute to fresh water for diluting anaerobic digestates and cultivating A. platensis, in a more sustainable way compared to conventional culture approaches using fresh water.

Ex Vivo Drug Sensitivity Evaluation of a ZMYM2::FGFR1 Fusion-Positive 8p11 Myeloproliferative Syndrome (EMS) Leukemia

Introduction : Fibroblast growth factor receptors (FGFRs) are conserved single transmembrane receptor tyrosine kinases (RTKs) with an extracellular ligand-binding domain and a cytoplasmic tyrosine kinase domain. They play vital roles in cellular proliferation, survival, development, fetal organogenesis, metabolism, and tissue repair. 8p11 myeloproliferative syndrome (EMS) is a hematologic malignancy caused by a translocation of the fibroblast growth factor receptor 1 (FGFR1) gene on chromosome 8p11-12. The most common fusion partner of FGFR1 is the zinc-finger domain ZNF198 (also known as ZMYM2) on chromosome 13q12. Pemigatinib is approved for FGFR fusion positive myeloid/lymphoid neoplasms (MLNs), and other FGFR inhibitors are being tested in clinical trials. However, EMS patients still face a poor prognosis and drug resistance, necessitating further research to overcome these challenges. This study evaluated ex vivo drug screening assay data generated from ZMYM2::FGFR1 positive primary leukemia samples throughout the course of an EMS patient's clinical response and validated these findings on a ZMYM2::FGFR1 positive cell line. Methods : Ex vivo and drug screening experiments were performed on freshly isolated primary patient samples and a ZMYM2::FGFR1-expressing cell line. The fusion was confirmed using FISH, and additional clinical specimens were collected with informed patient consent. Ex vivo drug sensitivity assays were conducted on patient mononuclear cells isolated from blood and bone marrow. IL-3 dependent BaF3 cells were infected with retrovirus containing a ZMYM2::FGFR1-V5 construct. ZMYM2::FGFR1 transformed BaF3 cells were evaluated with single agent and drug combination panels. Sanger sequencing of the FGFR portion of the fusion was performed on patient samples to confirm the breakpoint and identify potential mutations affecting sensitivity to Ponatinib throughout the course of treatment. Results : Sample were obtained with informed consent from a 36-year-old man diagnosed with 8p11 myeloproliferative syndrome and a ZMYM2::FGFR1 fusion along with a RUNX1 R201Q mutation. Ex vivo drug screens performed on isolated mononuclear cells demonstrated sensitivity to Ponatinib and Pemigatinib. He underwent treatment with HyperCVAD plus Ponatinib and subsequently HyperCVAD with Ponatinib and Velcade (Bortezomib) to CR1, FISH for FGFR1 rearrangement was undetected. He was scheduled to proceed with hematopoietic stem cell transplant (HSCT), however his pre-transplant bone marrow biopsy showed return of his FGFR1 translocation by FISH. Ex vivo drug screening assays confirmed resistance to Ponatinib, but continued sensitivity to Pemigatinib. He was transitioned to treatment with single agent Pemigatinib and was able to re-clear his FGFR1 fusion by FISH, and has since undergone hematopoietic stem cell transplantation. He remains in remission on post-transplant Pemigatinib maintenance therapy. Sanger sequencing performed at the time of progression on Ponatinib identified a nucleotide alteration resulting in an amino acid substitution (FGFR1 F686L; ZMYM2::FGFR1 F1171L) within the kinase domain that could contribute to decreased Ponatinib sensitivity. Bortezomib and Axitinib exhibited high efficacy on the patient's sample. Ex vivo drug assays performed on ZMYM2::FGFR1 transformed BaF3 cells confirmed the efficacy of Pemigatinib and Ponatinib. Other FGFR inhibitors, including Olverematinib, AZD4547, Axitinib, Cediranib, Dovitinib, and Lenvatinib, also demonstrated exquisite sensitivity. Despite extensive screening, no other single agents or drug combinations exhibited increased effectiveness in the ZMYM2::FGFR1 transformed BaF3 cells except for Trametinib, a MEK inhibitor, and the combination of Belvarafenib (RAF inhibitor) and Gilteritinib (FLT3 inhibitor). Conclusions : Ex vivo drug sensitivity assays demonstrated the highly selective efficacy of FGFR inhibitors in ZMYM2::FGFR1 fusion-positive leukemia cells and a fusion-expressing BaF3 cell line. Mutations in the FGFR1 kinase domain (ZMYM2::FGFR1 F1171L) could contribute to Ponatinib insensitivity. These ex vivo drug screening results provide further support for ongoing clinical trials which are investigating the use of single agent Pemigatinib and other FGFR1 inhibitors for the treatment of patients with FGFR1-fusion positive leukemias.

Construction of mixed-level screening designs using Hadamard matrices

Modern experiments typically involve a very large number of variables. Screening designs allow experimenters to identify active factors in a minimum number of trials. To save costs, only low-level factorial designs are considered for screening experiments, especially two- and three-level designs. In this article, we provide a systematic method to construct screening designs that contain both two- and three-level factors based on Hadamard matrices with the fold-over structure. The proposed designs have good performance in terms of D-optimal and A-optimal criteria, and the estimates of the main effects are unbiased by the second-order effects, making them very suitable for screening experiments. Besides, some theoretical results on D- and A-optimality are obtained as a by-product.

Synergistic Utilization of ε-Polylysine and p-Coumaric Acid as Natural Preservatives for Enhancing the Shelf Life of Fresh-Cut Green Bell Peppers

The utilization of natural preservatives presents a promising avenue for mitigating the spoilage of fresh-cut fruits and vegetables induced by microorganisms, enzymatic browning, and water loss. We have developed an innovative method for preserving fresh-cut green peppers using the combined effects of ε-polylysine (ε-PL) and p-Coumaric acid (p-CA). Through concentration screening experiments, we determined that the optimal concentrations of ε-PL and p-CA were 25 mg/L and 10 mg/L, respectively (ε-p-CA). Treatment with ε-p-CA significantly improved the quality of fresh-cut green peppers. It effectively reduced hardness and weight loss, preserving the texture and appearance of the peppers. Furthermore, ε-p-CA treatment delayed the increase in respiratory rate, electrolyte leakage, and ethylene production, thereby maintaining the structural integrity. Meanwhile, ε-p-CA treatment effectively inhibited the malondialdehyde (MDA) content increase and maintained DPPH radical scavenging activity. The microbial analysis demonstrated the ε-p-CA-treated peppers also showed lower total bacterial, mold, and yeast counts, which prolonged the freshness of fresh-cut peppers. In addition, ε-p-CA treatment improved the retention of phenolics and vitamin C without significantly affecting the color and soluble sugar content of green peppers. Overall, the ε-p-CA treatment showed promise as a natural preservative for extending the shelf life of fresh-cut green peppers.

Drug combinations screening using a Bayesian ranking approach based on dose-response models.

Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose-response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank-based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4-parameter log-logistic (4PLL) model was used to estimate dose-response curves of dose-candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose-response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloidleukemia.

Data driven development of iron-based metallic glasses using artificial neural networks

Metallic glasses exhibit outstanding properties due to their distinctive amorphous structure. Within the realm of metallic glasses, iron-based variants stand out as a cost-effective option. However, the conventional approach to discover metallic glass alloys is inefficient in regards to the required resources. Manufacturing processes for metallic glasses, such as melt spinning as well as the analysis by differential scanning calorimetry (DSC) are cost- and time-intensive. Artificial neural networks (ANN) are used to address this issue. The ANNs can predict the complex, non-linear behaviour of glass formation and therefore save time and resources. The database required for a Machine Learning approach was gathered through a literature research on iron-based metallic glasses. After hyperparameter tuning, the trained ANNs can estimate the Glass-Forming Ability (GFA) parameter γm of various alloy compositions with a Root Mean Squared Error of 0.0296. A Genetic Algorithm (GA) is incorporated in the ANN to find GFA maxima in defined ranges. This approach enables the optimisation of the GFA prior to experimentation and thus enhances the discovery of new iron-based metallic glass alloys. Using an ANN alleviates the need for in depth analysis and screening experimentation while still allowing a fast, resource efficient virtual alloy evaluation.

Dissecting the Genetic Relationship between Root Morphological Traits with Grain Yield of Introgression Lines (ILs) Derived from Wild Rice (Oryza rufipogon Griff) under Low Soil Phosphorous Condition

Soil phosphorous (P) deficiency is one of the limiting factors in rice production in both upland and rainfed lowland ecosystems. Since P is diffusion limited in depleted root zones, understanding the link between root morphological traits and yields is crucial for improving rice productivity. To understand this link, phenotypic screening experiment was conducted for the 38 ILs (BC1F6) derived from the wild rice (Oryza rufipogon Griff) along with the six checks in specialized low P plots and normal soil P (RDF) plot at ICAR-IIRR, Hyderabad during rice growing season Kharif-2020 by adapting augmented block design. The genotypes were screened for grain yield along with root traits such as tiller number per plant (TN), shoot length (SL), root length (RL), root volume (RV), shoot fresh weight (SFW), shoot dry weight (SDW), root fresh weight (RFW), root dry weight (RDW), root to shoot ration on wet weight basis (RSRWW), root to shoot on dry weight basis (RSRDW), SPAD chlorophyll meter reading (SCMR). The results from ANOVA revealed that, MSS due to genotypes, (checks + genotypes) were significant ((p<0.01 and p<0.05)) for most of the root architectural traits and for two different regime of P under investigation. Wide range of genetic variation was recorded for the traits such as RL, RV, SFW, SDW, RFW, RDW, RSRWW, RSRDW and GYP with high GCV and PCV and high h2 coupled with high GAM under both P gradient conditions. Strong inter-correlation among the component traits was observed for the root traits such as RV (0.43**: 0.50**), SFW(0.81**:0.60**), SDW (0.65**: 0.81**), RFW (0.87**:0.64**), RDW (0.83**: 0.87**) and RL (0.36**: 0.43**) along with GYP (0.52**) under low soil P and normal soil P. Principal component analysis with first four PCs revealed existence of 69.40 and 70.30% of total variance and clustering analysis identified the promising genotypes as IL-9-10, IL-11-2, IL-21-8, IL-19-3, IL-22-1, IL-23-2, IL-23-7, IL-31-3, IL-42-3, IL-67-2, IL-69-1, IL-75-2, Swarna and Rasi with root traits RV, RDW, RFW, RL, and GYP are effective traits for rice cultivation.