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"Kumar, K. S."
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Molecular characterization and antifungal activity of lipopeptides produced from Bacillus subtilis against plant fungal pathogen Alternaria alternata
Over 380 host plant species have been known to develop leaf spots as a result of the fungus
Alternaria alternata
. It is an aspiring pathogen that affects a variety of hosts and causes rots, blights, and leaf spots on different plant sections. In this investigation, the lipopeptides from the B. subtilis strains T3, T4, T5, and T6 were evaluated for their antifungal activities. In the genomic DNA, iturin, surfactin, and fengycin genes were found recovered from
B. subtilis
bacterium by PCR amplification. From different
B. subtilis
strains, antifungal Lipopeptides were extracted, identified by HPLC, and quantified with values for T3 (24 g/ml), T4 (32 g/ml), T5 (28 g/ml), and T6 (18 g/ml). To test the antifungal activity, the isolated lipopeptides from the B. subtilis T3, T4, T5, and T6 strains were applied to
Alternaria alternata
at a concentration of 10 g/ml. Lipopeptides were found to suppress
Alternaria alternata
at rates of T3 (75.14%), T4 (75.93%), T5 (80.40%), and T6 (85.88%). The T6 strain outperformed the other three by having the highest antifungal activity against
Alternaria alternata
(85.88%).
Journal Article
Engineering hydrology : an introduction to processes, analysis, and modeling
This comprehensive engineering textbook offers a thorough overview of all aspects of hydrology and shows how to apply hydrologic principles for effective management of water resources. It presents detailed explanations of scientific principles along with real-world applications and technologies.
Book
A Chronicle Review of In-Silico Approaches for Discovering Novel Antimicrobial Agents to Combat Antimicrobial Resistance
Antimicrobial resistance (AMR) poses a foremost threat to global health, necessitating innovative strategies for discovering antimicrobial agents. This review explores the role and recent advances of
in-silico
techniques in identifying novel antimicrobial agents and combating AMR giving few briefings of recent case studies of AMR.
In-silico
techniques, such as homology modeling, virtual screening, molecular docking, pharmacophore modeling, molecular dynamics simulation, density functional theory, integrated machine learning, and artificial intelligence, are systematically reviewed for their utility in discovering antimicrobial agents. These computational methods enable the rapid screening of large compound libraries, prediction of drug-target interactions, and optimization of drug candidates. The review discusses integrating
in-silico
approaches with traditional experimental methods and highlights their potential to accelerate the discovery of new antimicrobial agents. Furthermore, it emphasizes the significance of interdisciplinary collaboration and data-sharing initiatives in advancing antimicrobial research. Through a comprehensive discussion of the latest developments in
in-silico
techniques, this review provides valuable insights into the future of antimicrobial research and the fight against AMR.
Graphical Abstract
Journal Article
Machine learning-assisted ammonium detection using zinc oxide/multi-walled carbon nanotube composite based impedance sensors
We report a machine learning approach to accurately correlate the impedance variations in zinc oxide/multi walled carbon nanotube nanocomposite (F-MWCNT/ZnO-NFs) to NH
4
+
ions concentrations. Impedance response of F-MWCNT/ZnO-NFs nanocomposites with varying ZnO:MWCNT compositions were evaluated for its sensitivity and selectivity to NH
4
+
ions in the presence of structurally similar analytes. A decision-making model was built, trained and tested using important features of the impedance response of F-MWCNT/ZnO-NF to varying NH
4
+
concentrations. Different algorithms such as kNN, random forest, neural network, Naïve Bayes and logistic regression are compared and discussed. ML analysis have led to identify the most prominent features of an impedance spectrum that can be used as the ML predictors to estimate the real concentration of NH
4
+
ion levels. The proposed NH
4
+
sensor along with the decision-making model can identify and operate at specific operating frequencies to continuously collect the most relevant information from a system.
Journal Article
Stimulated Raman adiabatic passage in a three-level superconducting circuit
The adiabatic manipulation of quantum states is a powerful technique that opened up new directions in quantum engineering—enabling tests of fundamental concepts such as geometrical phases and topological transitions, and holding the promise of alternative models of quantum computation. Here we benchmark the stimulated Raman adiabatic passage for circuit quantum electrodynamics by employing the first three levels of a transmon qubit. In this ladder configuration, we demonstrate a population transfer efficiency >80% between the ground state and the second excited state using two adiabatic Gaussian-shaped control microwave pulses. By doing quantum tomography at successive moments during the Raman pulses, we investigate the transfer of the population in time domain. Furthermore, we show that this protocol can be reversed by applying a third adiabatic pulse, we study a hybrid nondiabatic–adiabatic sequence, and we present experimental results for a quasi-degenerate intermediate level.
The precise control and manipulation of the states of a multi-level quantum system are fundamental for quantum information processing. Here, the authors demonstrate the robust adiabatic manipulation of the quantum states of a superconducting circuit via stimulated Raman adiabatic passage.
Journal Article
AI‐Driven TENGs for Self‐Powered Smart Sensors and Intelligent Devices
Triboelectric nanogenerators (TENGs) are emerging as transformative technologies for sustainable energy harvesting and precision sensing, offering eco‐friendly power generation from mechanical motion. They harness mechanical energy while enabling self‐sustaining sensing for self‐powered devices. However, challenges such as material optimization, fabrication techniques, design strategies, and output stability must be addressed to fully realize their practical potential. Artificial intelligence (AI), with its capabilities in advanced data analysis, pattern recognition, and adaptive responses, is revolutionizing fields like healthcare, industrial automation, and smart infrastructure. When integrated with TENGs, AI can overcome current limitations by enhancing output, stability, and adaptability. This review explores the synergistic potential of AI‐driven TENG systems, from optimizing materials and fabrication to embedding machine learning and deep learning algorithms for intelligent real‐time sensing. These advancements enable improved energy harvesting, predictive maintenance, and dynamic performance optimization, making TENGs more practical across industries. The review also identifies key challenges and future research directions, including the development of low‐power AI algorithms, sustainable materials, hybrid energy systems, and robust security protocols for AI‐enhanced TENG solutions. Triboelectric nanogenerators (TENGs) enable sustainable energy harvesting and self‐powered sensing but face challenges in material optimization, fabrication, and stability. Integrating artificial intelligence (AI) enhances TENG performance through machine learning, improving energy output, adaptability, and predictive maintenance. This review explores AI‐driven TENG advancements, key challenges, and future research directions for practical applications.
Journal Article
Management of relapsed and refractory multiple myeloma: novel agents, antibodies, immunotherapies and beyond
Despite enormous advances, management of multiple myeloma (MM) remains challenging. Multiple factors impact the decision to treat or which regimen to use at MM relapse/progression. Recent major randomized controlled trials (RCTs) showed widely varying progression-free survivals (PFS), ranging from a median of 4 months (MM-003) to 23.6 months (ASPIRE). Based on these RCTs, next-generation proteasome inhibitors (carfilzomib and ixazomib), next-generation immunomodulatory agent (pomalidomide), and monoclonal antibodies (elotuzumab and daratumumab) were approved for relapsed and refractory MM. Daratumumab, targeting CD38, has multiple mechanisms of action including modulation of the immunosuppressive bone marrow micro-environment. In addition to the remarkable single agent activity in refractory MM, daratumumab produced deep responses and superior PFS in MM when combined with lenalidomide/dexamethasone, or bortezomib/dexamethasone. Other anti-CD38 antibodies, such as isatuximab and MOR202, are undergoing assessment. Elotuzumab, targeting SLAMF7, yielded superior response rates and PFS when combined with lenalidomide/dexamethasone. New combinations of these next generation novel agents and/or antibodies are undergoing clinical trials. Venetoclax, an oral BH3 mimetic inhibiting BCL2, showed single agent activity in MM with t(11;14), and is being studied in combination with bortezomib/dexamethasone. Selinexor, an Exportin-1 inhibitor, yielded promising results in quad- or penta-refractory MM including patients resistant to daratumumab. Pembrolizumab, an anti-PD1 check-point inhibitor, is being tested in combination with lenalidomide/dexamethasone or pomalidomide/dexamethasone. Chimeric antigen receptor-T cells targeting B-cell maturation antigen have yielded deep responses in RRMM. Finally, salvage autologous stem cell transplantation (ASCT) remains an important treatment in MM relapsing/progressing after a first ASCT. Herein, the clinical trial data of these agents are summarized, cautious interpretation of RCTs highlighted, and algorithm for salvage treatment of relapse/refractory MM proposed.
Journal Article
Continued improvement in survival in multiple myeloma: changes in early mortality and outcomes in older patients
Therapy for multiple myeloma (MM) has markedly changed in the past decade with the introduction of new drugs, but it is not clear whether the improvements have been sustained. We studied 1038 patients diagnosed between 2001 and 2010, grouping patients into two 5-year periods by diagnosis, 2001–2005 and 2006–2010. The median estimated follow-up for the cohort was 5.9 years with 47% alive at the last follow-up. The median overall survival (OS) for the entire cohort was 5.2 years: 4.6 years for patients in the 2001–2005 group compared with 6.1 years for the 2006–2010 cohort (
P
=0.002). The improvement was primarily seen among patients over 65 years, the 6-year OS improving from 31 to 56%,
P
<0.001. Only 10% of patients died during the first year in the latter group, compared with 16% in the earlier cohort (
P
<0.01), suggesting improvement in early mortality. The improved outcomes were linked closely to the use of one or more new agents in initial therapy. The current results confirm continued survival improvement in MM and highlight the impact of initial therapy with novel agents. Most importantly, we demonstrate that the improved survival is benefitting older patients and that early mortality in this disease has reduced considerably.
Journal Article
N-glycosylation of monoclonal light chains on routine MASS-FIX testing is a risk factor for MGUS progression
Our group previously demonstrated that M-protein light chain (LC) glycosylation can be detected on routine MASS-FIX testing. Glycosylation is increased in patients with immunoglobulin LC amyloidosis (AL) and rarely changes over the course of a patient’s lifetime. To determine the rates of progression to AL and other plasma cell disorders (PCDs), we used residual serum samples from the Olmsted monoclonal gammopathy of undetermined significance (MGUS) screening cohort. Four-hundred and fourteen patients with known MGUS were tested by MASS-FIX, and 25 (6%) were found to have glycosylated LCs. With a median follow-up of surviving patients of 22.2 years, the 20-year progression rates to a malignant PCD were 67% (95% CI 29%, 84%) and 13% (95% CI 9%, 18%) for patients with and without glycosylated LCs, respectively. The risk of progression was independent of Mayo MGUS risk score. The respective rates of progression to AL at 20 years were 21% (95% CI 0.0%, 38%) and 3% (95% CI 0.6%, 5.5%). In summary, monoclonal LC glycosylation is a potent risk factor for progression to AL, myeloma, and other PCDs, an observation which could lead to earlier diagnoses and potentially reduced morbidity and mortality.
Journal Article