(5-Bromo-2-chloro­phen­yl)(4-ethoxy­phen­yl)methanone

In the title mol­ecule, C15H12BrClO2, the two benzene rings form a dihedral angle of 69.30 (3)°. In the crystal structure, weak inter­molecular C—H⋯O hydrogen bonds link mol­ecules into chains propagating along the b axis

N,N′-Diacetyl-N′-[(4-nitro­phen­oxy)acetyl]acetohydrazide

The asymmetric unit of the title compound, C14H15N3O7, contains two independent mol­ecules which are linked into a pseudocentrosymmetric dimer by a π–π inter­action, as shown by the short distance of 3.722 (5) Å between the centroids of the benzene rings. An extensive network of weak inter­molecular C—H⋯O hydrogen bonds helps to stabilize the crystal packing

Aromatic Amino Acid Mutagenesis at the Substrate Binding Pocket of Yarrowia lipolytica

The lipase2 from Yarrowia lipolytica (YLLip2) is a yeast lipase exhibiting high homologous to filamentous fungal lipase family. Though its crystal structure has been resolved, its structure-function relationship has rarely been reported. By contrast, there are two amino acid residues (V94 and I100) with significant difference in the substrate binding pocket of YLLip2; they were subjected to site-directed mutagenesis (SDM) to introduce aromatic amino acid mutations. Two mutants (V94W and I100F) were created. The enzymatic properties of the mutant lipases were detected and compared with the wild-type. The activities of mutant enzymes dropped to some extent towards p-nitrophenyl palmitate (pNPC16) and their optimum temperature was 35°C, which was 5°C lower than that of the wild-type. However, the thermostability of I100F increased 22.44% after incubation for 1 h at 40°C and its optimum substrate shifted from p-nitrophenyl laurate (pNPC12) to p-nitrophenyl caprate (pNPC10). The above results demonstrated that the two substituted amino acid residuals have close relationship with such enzymatic properties as thermostability and substrate selectivity

Machine Learning‑Assisted Low‑Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional trial and error method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research

Cost-effectiveness of neoadjuvant pembrolizumab plus chemotherapy with adjuvant pembrolizumab for early-stage non-small cell lung cancer in the United States

IntroductionPerioperative (neoadjuvant and adjuvant) pembrolizumab has shown favorable efficacy in patients with early-stage non-small cell lung cancer (NSCLC). This study aims to evaluate the cost-effectiveness of this treatment from the perspective of the United States healthcare payers.MethodsWe established a Markov model to compare the cost-effectiveness of perioperative pembrolizumab with that of neoadjuvant chemotherapy in 21-day cycles, utilizing data from the phase 3 KEYNOTE-671 trial. Additional data were extracted from other publications or online sources. Sensitivity analyses were conducted to evaluate the robustness of the findings. A willingness-to-pay threshold of $150,000 per quality-adjusted life-years (QALYs) gained was established. The main outcomes of this study were the measurement of QALYs, overall costs, incremental cost-effectiveness ratio (ICER), and net monetary benefit (NMB).ResultsDuring a 10-year time horizon, the total costs of perioperative pembrolizumab and the control treatment were$224,779.1 and $110,026.3, respectively. The QALYs were 4.19 and 2.97 for the two treatments, respectively, which led to an ICER of$94,222.29 per QALY gained. The NMB at the WTP threshold at $150,000 per QALY gained was$67,931.3. One-way sensitivity analysis identified the cost of pembrolizumab as the primary factor influencing cost-effectiveness. Probabilistic sensitivity analysis indicated a 97.7% probability of perioperative pembrolizumab being cost-effective at the WTP threshold.ConclusionsFrom the perspective of the United States healthcare payers, perioperative pembrolizumab is a cost-effective treatment for patients with early-stage NSCLC

Fertilization drives distinct biotic and abiotic factors in regulating functional groups of protists in a 5-year fertilization system

IntroductionProtists play an important role in nutrient cycling, microbiome stability and soil fertility maintenance. However, the driving force of protistan functional groups remains poorly understood in agricultural ecosystems.MethodsWe investigated the impacts of fertilization regimes on the diversity, composition and functional groups of protists and further disentangled the effects of multiple factors shaping the community composition of functional groups in a 5-year fertilization regime (CK, no fertilization; M, organic fertilization; MNPK, combined inorganic and organic fertilization; NPK, inorganic fertilization).ResultsFertilization significantly changed the community composition of protists rather than diversity. The MNPK treatment significantly increased the relative abundance of phototrophs and decreased that of the parasites and consumers. Partial least squares path modeling indicated that fertilization indirectly regulated protistan consumers via changes in the P content, which affected the composition of consumers mainly by regulating fungal community composition. Soil moisture (SM) and available phosphorus (AP) were identified as the top predictors for the composition of parasites, and the composition of phototrophs was mainly affected by SM, indicating that parasites and phototrophs were more sensitive to abiotic factors in the fertilization system.DiscussionTaken together, our findings highlight that fertilization significantly affects the composition of functional groups of protists and their biotic or abiotic regulatory processes, which have implications for the potential changes in their ecosystem functions for soil management systems