Difluoromethylation of arylidene Meldrum's acid derivatives
Fluorine-containing compounds gained significant attention during the past decade1. About 20% of novel pharmaceuticals and 40% of novel agrochemicals every year contain at least one fluorine atom in the molecule. For a long time the most frequently used was trifluoromethyl group, but nowadays the most promising is the chemistry of partially-fluorinated groups. For example, the difluoromethyl substituent (CHF2) exhibits unique pharmacoforic properties capable of serving as lipophilic hydrogen bond donor thus being bioisosteric to hydroxyl group2. There are several general approaches for the formation of a required fluorinated fragment, one of them is direct nucleophilic fluoroalkylation. This approach is well-developed for trifluoromethylation reactions, such as addition of CF3-anion equivalents to C=O, C=N and electron-deficient C=C bonds or metal-catalyzed substitution in haloarenes3. However the similar difluoromethylation processes are still quite challenging. Herein we present a novel and convenient protocol for the synthesis of β-CF2H functionalized carbonyl compounds and carbinols by nucleophilic difluoromethylation of electron-deficient olefines. The process is based on a 1,4-addition of in situ generated4 phosphorus ylide Ph3P=CF2 2 to the arylidene Meldrum's acid conjugates 1. The resulting phosphobetaines 3 are hydrolized/protodephosphorilated without isolation, giving β-CF2H substituted carboxylic acids 4. The latter may be easily transformed to the corresponding ethers 5 and alcohols 6 without preliminary purification.
探討抗憂鬱症藥物phenelzine對於發生在小鼠巨噬細胞中的細胞凋亡所產生的保護作用及機制
之前有研究指出，使用一些單胺氧化酶(monoamine oxidase, MAO)的抑制劑如pargyline和clorgyline，皆可以保護serum starvation所導致的細胞凋亡，表示MAO可能在細胞凋亡的路徑中扮演重要的角色。 本研究著重於一個臨床上被拿來當抗憂鬱症藥物的MAO抑制劑苯乙肼(phenelzine, PZE)對於沿著腫瘤壞死因子-α (tumor necrosis factor-α, TNF-α)途徑而產生細胞凋亡的小鼠骨髓巨噬細胞(bone marrow-derived macrophages, BMDM)所產生的保護作用。 本研究的結果顯示PZE的確可以保護循TNF-α途徑死亡的細胞，同時使活性氧化物質(reactive oxygen species, ROS)的量下降。我們推論造成此現象的原因是PZE藉由抑制MAO，使得ROS的量下降，進而保護細胞。
SeedBot: Low-Cost Seeding Robot for Agricultural Applications
This paper presents a semi-autonomous seeding robot which is based on both electrical and mechanical platforms that perform advance agriculture process. SeedBot composed of four components: drilling mechanism, body of robot, seed container and paving mechanism. Other than those components the sensor system and the control system are also discussed. The aim of this study is designing and building a low-cost robotic system to automate and optimize process during farming especially for personal usage. This study demonstrates that semi-autonomous farming has crucial advantages over conventional farming. In addition to that, SeedBot provides safer, requires less manpower and precise farming than usual methods that we have so far.
Biodegrable Roof
It became necessary to implement a project for the use of vegetable waste generated in the process of handling plantain cultivation, harvest and postharvest, since in Mexico at harvest large quantities of vegetable waste is produced, since only the fruit is used wasting the Pseudostem with leaves and spine. Based on this information, you can take advantage of banana fiber as raw materials in the manufacture of biodegradable sheets and support options that are feasible and possible to make alternative. This is an inexpensive process, also friendly with the environment, so that thousands of banana plants that bear fruit after they become sterile and are discarded without realizing their Pseudostem.
Geographic Belts for Hurricane Landfall Location Prediction
When predicting a hurricane’s landfall location, small improvements in accuracy result in large savings of lives, property, and money. The project’s purpose was to apply a breakthrough method that can predict the geographic location of a hurricane’s landfall with high accuracy. Researchers have known for a long time that there are strong correlations between a hurricane’s landfall location and the geographic regions its track passes through. However, no methods have been developed to mathematically and explicitly describe these correlations. Consequently, the correlations can only serve to meteorologists as vague guidelines for their guestimates and are not usable in making practical forecasts. By studying the correlations and performing numerical optimization on historical hurricane data, this research discovered a set of geographic belt regions in the Gulf of Mexico that can be used as landfall location predictors. When a hurricane passes through any one of these belt lines, a prediction can be made by extending the hurricane’s moving direction vector towards land – the intersection point of this extension line with the coastline is the predicted landfall location. This prediction method is simple and straightforward. It only uses basic measurements from meteorological satellites: the hurricane’s real-time locations and moving directions. In conclusion, when compared to existing methods, the predictive belt method (PBM) created in this research provides a landfall location forecast with higher accuracy. Verification with historical hurricane data demonstrated that the PBM’s average error is less than 50% of the National Hurricane Center models’ error.