Palladium catalyzed carbon-carbon bond formation under reductive, oxidative and redox neutral conditions

De organische chemie houdt zich bezig met reacties en eigenschappen van koolstof-gebaseerde verbindingen. Het is verantwoordelijk voor veel van de wonderen van de chemie die we waarnemen in ons dagelijkse leven zoals geneesmiddelen, plastics etc. Centraal binnen de studie van organische chemie staan reacties die de formatie van koolstof-koolstof bindingen mogelijk maken. In de afgelopen jaren, is het metaal palladium verrezen als een uitstekende katalysator voor de formatie van deze koolstof-koolstof bindingen. Dit proefschrift is toegewijd aan het onderzoek naar twee van zulke reacties, de Heck reactie en de geconjugeerde additie. De Heck reactie is erkend met de Nobel prijs in 2010. Dit proefschrift verkent beide reacties onder verschillende condities en de relatie tussen beide. Deze kennis staat ons toe om verschillende industrieel relevante reacties, die plaatsvinden met de formatie van grote hoeveelheden met metaal vervuild afval, te vervangen met schonere en goedkopere alternatieven. Daarnaast is het onderzoek in dit proefschrift gericht op de vorming van "benzylische quaternaire stereocentrums" via palladium-gekatalyseerde geconjugeerde additie reacties, een voorheen onbekende toepassing van deze reactie. Belangrijke eigenschappen van deze reactie werden ontdekt. Verschillende uitdagingen werden geïdentificeerd en hun oplossingen aangedragen. De voordelen van deze reactie is dat het reacties vervangt die drastische condities nodig hebben (gecontroleerde reactie-atmosfeer en erg lage temperaturen) of reacties die erg dure metal nodig hebben zoals rhodium. Een toepassing van deze nieuwe ontwikkeling is de synthese van het natuurproduct (–) - α - cuparenon in slecht 2 stappen, een molecuul dat voorheen tussen de 5 en 17 stappen vereiste

Understanding and Predicting Image Memorability at a Large Scale

Progress in estimating visual memorability has been limited by the small scale and lack of variety of benchmark data. Here, we introduce a novel experimental procedure to objectively measure human memory, allowing us to build LaMem, the largest annotated image memorability dataset to date (containing 60,000 images from diverse sources). Using Convolutional Neural Networks (CNNs), we show that fine-tuned deep features outperform all other features by a large margin, reaching a rank correlation of 0.64, near human consistency (0.68). Analysis of the responses of the high-level CNN layers shows which objects and regions are positively, and negatively, correlated with memorability, allowing us to create memorability maps for each image and provide a concrete method to perform image memorability manipulation. This work demonstrates that one can now robustly estimate the memorability of images from many different classes, positioning memorability and deep memorability features as prime candidates to estimate the utility of information for cognitive systems. Our model and data are available at: http://memorability.csail.mit.edu.National Science Foundation (U.S.) (Grant 1532591)McGovern Institute for Brain Research at MIT. Neurotechnology (MINT) ProgramMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory. MIT Big Data InitiativeGoogle (Firm)Xerox Corporatio

Environmental impact of green house gas emissions from the tea industries of northeastern states of India

MotivationTea, derived from the Camellia sinensis plant, holds the position of being the most widely consumed manufactured beverage globally. Its cultivation necessitates specific agro-climatic conditions, leading to its production being confined to select regions, notably including India. India contributes about 20.81% to the world’s tea output. The production and processing of tea leaves to final product consume energy in terms of machinery, fertilizer, irrigation etc. The energy consumption involved in tea production is a pressing concern, given the associated high costs and CO2 emissions resulting from fossil fuel usage. To achieve a net-zero carbon balance, there is need to pay attention towards promoting renewable energy technologies as a means to mitigate the CO2 emissions stemming from fossil fuels in India’s tea sector.ObjectivesAligned with the objective of sustainability through the integration of renewable energy sources, a pilot study was conducted in the primary tea-growing regions of northeastern India during 2021–22. The primary aims of this study were twofold: to gauge the quantity of CO2 emissions originating from conventional energy sources and to explore the feasibility of incorporating renewable energy sources as viable substitutes.Data and methodsData on various inputs used in tea production were collected from Assam and West Bengal states of India by using a stratified random sampling method with equal probability and without replacement.ResultsThe findings of this investigation underscore a noteworthy potential for the adoption of renewable energy, particularly solar energy, within the tea estates situated in the north eastern region of India. Such a transition would yield benefits for both the tea estates themselves and the overall environment

A trial sequential meta-analysis of TNF-α –308G\u3eA (rs800629) gene polymorphism and susceptibility to colorectal cancer

© 2019 The Author(s). Purpose: Tumor necrosis factor-α (TNF-α), secreted by the activated macrophages, may participate in the onset and progression of colorectal cancer (CRC). The association of TNF-α –308 G\u3eA (rs1800629) single-nucleotide polymorphism (SNP) with CRC risk has been investigated by many studies but the results are inconclusive. A trial sequential meta-analysis was performed for precise estimation of the relationship between TNF-α –308 G\u3eA gene polymorphism with CRC risk. Methods: Medline (PubMed), EMBASE (Excerpta-Medica) and Google Scholar were mined for relevant articles. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to estimate the significance of association. Results: The pooled analysis indicated no risk associated with TNF-α –308 G\u3eA SNP and overall CRC risk in five genetic comparison models, i.e. allelic (A vs. G: P = 0.524; OR = 1.074, 95% CI = 0.863–1.335), homozygous (AA vs. GG: P = 0.489; OR = 1.227, 95% CI = 0.688–2.188), heterozygous (AG vs. GG: P = 0.811; OR = 1.024, 95% CI = 0.843–1.244), dominant (AA+AG vs. GG: P = 0.630; OR = 1.055, 95% CI = 0.849–1.311) and recessive (AA vs. AG+GG: P = 0.549; OR = 1.181, 95% CI = 0.686–2.033). Subgroup analysis revealed that TNF-α –308 G\u3eA SNP is associated with reduced risk of CRC in Asian ethnicity. The study showed no publication bias. Conclusions: No association of TNF-α –308 G\u3eA SNP with overall CRC risk was found. This SNP is likely to be protective against CRC in Asian population when compared with Caucasian population. Larger prospective-epidemiological studies are warranted to elucidate the roles of TNF-α –308 G\u3eA SNP in the etiology of CRC and to endorse the present findings