Gemological-Mineralogical Characteristic of Spinels from Sri Lanka

Detailed mineralogical and gemological records were conducted on 340 unheated spinels from the Horana, Eheliyagoda, Ratnapura, and Okkampatiya mining areas in Sri Lanka. The color of Sri Lankan spinel varies greatly: in addition to the mainstream pink to purple pink, green and blue can also be seen. Compared with spinel from other regions such as Myanmar, Vietnam, and Tanzania, Sri Lanka's spinel has more abundant inclusions: several mining areas generally have inclusions such as dolomite, apatite, zircon, and chondrodite. Minerals such as graphite and forsterite are also found in spinel produced in the Horana region;graphite and rutile have been found in spinel produced in the Okkamptiya region. Partially healed fissures are most common in spinel in the Okkampatiya mining area;Unlike Vietnamese spinel, dislocations and growth structures are almost absent in Sri Lankan spinel. The LA-ICP-MS analysis results showed that there were no significant differences among the mining areas. LA-ICP-MS analysis of 5 Sri Lankan cobalt blue spinels showed a variation of 11 to 120 ppm in this chromogenic element. The UV visible absorption spectrum results show that Sri Lankan spinel has a combination spectra with variable ratios of the spectral components Cr 3+ , V 3+ and Fe 2+ from pink to red, orange, purple to purple, and blue-green. The results of infrared spectroscopy and laser Raman spectroscopy analysis showed that all samples showed no indications for heat treatment.

Domestic and International Research Progress on the Career Plateau of Nurses

Career plateau is a new phenomenon that occurs in an individual’s career.It also refers to the period when an individual employee enters a“stagnant”period of career development,which can have huge negative impacts on individuals and organizations.Based on reviewing relevant domestic and international literature,this paper organizes,summarizes,and synthesizes information from three key aspects of the overview of nurses’career plateau,research population,and research countermeasures,so as to provide theoretical references for managers to take effective measures to improve the current career situation of nurses.

A Phytophthora capsid Effector Targets ACD11 Binding Partners that Regulate ROS-Mediated Defense Response in Arabidopsis

Reactive oxygen species (ROS) play a vital role in plant immune response, but the genes involved in the regulation of ROS are scantily reported. Phytophthora pathogens produce a large number of effectors to promote infection, but the modes of action adopted are largely unknown. Here, we report that RxLR207 could activate ROS-mediated cell death in Nicotiana benthamiana and was essential for virulence of P. capsici. We found that this effector targeted BPA1 (binding partner of ACD11) and four members of BPLs (BPAl-Like proteins)in Arabidopsis, and the bpa1 and bpl mutants had enhanced ROS accumulation and cell death under biotic or abiotic stresses. Furthermore, we showed that BPA1 and several BPLs functioned redundantly in plant immunity to P. capsici. We discovered that BPA1 and all six BPLs interacted with ACD11, and stabilization of ACD11 was impaired in the bpa 1, bpl2, bpl3, and bpl4 mutants. RxLR207 could promote the degradation of BPA1, BPL1, BPL2, and BPL4 to disrupt ACD11 stabilization in a 26S proteasome-dependent manner. Taken together, these fin dings indicate the important roles of Arabidopsis BPA1 and its homologs in ROS homeostasis and defense response, highlighting the usefulness of a pathogen effector-directed approach as a promising strategy for the discovery of novel plant immune regulators.

Total Synthesis of Three Families of Natural Antibiotics:Anthrabenzoxocinones,Fasamycins/Naphthacemycins,and Benastatins

Due to the critical bacterial resistance and antibiotic crisis,the discovery of new antibiotics is an urgent need in the clinic than ever.Naturally occurring antibiotics have proven to be an indispensable source of the development of new antibacterial agents.Herein,we report the total synthesis of three families of biogenetically related natural antibiotics,including anthrabenzoxocinones(ABXs),fasamycins/naphthacemycins,and benastatins.The synthesis featured divergent and convergent approaches,which enabled efficient construction of the basic polycyclic skeletons in 6–10 steps on a large-scale,followed by a collective synthesis of 14 natural products and their corresponding analogs.The core scaffold of gemdimethyl-anthracenone,a naturally occurring type II fatty acid-specific condensation enzyme(FabFspecific)antibiotic pharmacophore,was forged via a Ti(Oi-Pr)4-mediated photoenolization/Diels–Alder(PEDA)reaction between 2-isopropyl benzaldehyde and a variety of enones.A scale-up of the PEDA reaction was facilitated in an assembled continuous-flow reactor,which allowed us to overcome the issues associated with batch photochemistry.Subsequently,the synthetic natural antibiotics and their analogs would be utilized in structure–activity relationships(SAR)and mechanism studies,which should enable the discovery of new and leading antibiotic compounds.

Effects of straw mulching on microclimate characteristics and evapotranspiration of drip-irrigated winter wheat in North China Plain

Field experiments were conducted for two consecutive years(2013-2014 and 2014-2015)to study the effects of straw mulching on microclimate characteristics,energy flux,soil evaporation(Es)and evapotranspiration of winter wheat(Triticum aestivum L.)under adequate drip irrigation in North China Plain(NCP).The results revealed that straw mulching affected air temperature(T_(a))and dew point temperature(T_(d))near the soil surface but had little effect on relative humidity(RH)compared with non-mulched fields.Straw mulching increased the turbulent exchange coefficient(K),and K within the canopy was higher than that above the canopy.Straw mulching clearly increased the sensible heat flux(H)distribution in April-June,and part of the energy needed for evapotranspiration was provided by transfer from the warmer air aloft.There was a linear functional relationship between H and T_(a)measured above and within the canopy.The straw mulching decreased Es but increased crop transpiration(T).Mulched treatment(TM)can decrease the total irrigation amount by about 7%-15%compared with the non-mulched treatment(TN).There were no significant differences(p>0.05)in cumulative crop evapotranspiration(ETc)between TM and TN treatments under adequate drip irrigation,and the use of mulch may need to be combined with an optimal deficit drip irrigation schedule for managing the declining water table in NCP.

Synthesis of Clionastatins A and B through Enhancement of Chlorination and Oxidation Levels of Testosterone

A full account of semisyntheses of polychlorinated marine steroids clionastatins A and B is described.We have developed a unique two-stage chlorination-oxidation strategy that enabled concise and divergent semisyntheses of clionastatins A and B in 16 steps from inexpensive testosterone.Key transformations in chlorination stage include(a)conformationally controlled,stereospecific dichlorination through an unusualβ-chloronium intermediate to install the diequatorial C1,C2-dichloride;(b)C4-OH directed C19–H oxygenation followed by challenging neopentyl chlorination to install the C19–Cl.The high oxidation level was constructed through(a)the desaturation at C6–C7 through one-pot photochemical dibromination–reductive debromination;(b)regioselective anti-Markovnikov oxidation of the C6–C7 double bond by photoredox-metal dual catalysis to forge the B ring enone;(c)late-stage desaturation with SeO_(2)to introduce C8–C9 double bond.Wharton transposition was used to forge the D-ring enone,and thermodynamically driven epimerization secured the cis-fused C/D ring system.Furthermore,we also provided a 14-step approach to clionastatin A by optimizing the construction of the D-ring enone with a dehydration-oxidation sequence.

Data fusion and machine learning for ship fuel efficiency modeling:Part Ⅱ-Voyage report data,AIS data and meteorological data

When voyage report data is utilized as the main data source for ship fuel efficiency analysis,its information on weather and sea conditions is often regarded as unreliable.To solve this issue,this study approaches AIS data to obtain the ship's actual detailed geographical positions along its sailing trajectory and then further retrieve the weather and sea condition information from publicly accessible meteorological data sources.These more reliable data about weather and sea conditions the ship sails through is fused into voyage report data in order to improve the accuracy of ship fuel consumption rate models.Eight 8100-TEU to 14,000-TEU containerships from a global shipping company were used in experiments.For each ship,nine datasets were constructed based on data fusion and eleven widely-adopted machine learning models were tested.Experimental results revealed the benefits of fusing voyage report data,AIS data,and meteorological data in improving the fit performances of machine learning models of forecasting ship fuel consumption rate.Over the best datasets,the performances of several decision tree-based models are promising,including Extremely randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG).With the best datasets,their R^(2) values over the training sets are all above 0.96 and mostly reach the level of 0.99-1.00,while their R^(2) values over the test sets are in the range from 0.75 to 0.90.Fit errors of ET,AB,GB,and XG on daily bunker fuel consumption,measured by RMSE and MAE,are usually between 0.8 and 4.5 ton/day.These results are slightly better than our previous study,which confirms the benefits of adopting the actual geographical positions of the ship recorded by AIS data,compared with the estimated geographical positions derived from the great circle route,in retrieving weather and sea conditions the ship sails through.

Data fusion and machine learning for ship fuel efficiency modeling:Part Ⅰ-Voyage report data and meteorological data

The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships'bunker fuel consumption and the accompanying emissions,including speed optimization,trim optimization,weather routing,and the virtual arrival policy.The theoretical foundation of these measures is a model that can accurately forecast a ship's bunker fuel consumption rate according to its sailing speed,displacement/draft,trim,weather conditions,and sea conditions.Voyage report is an important data source for ship fuel efficiency modeling but its information quality on weather and sea conditions is limited by a snapshotting practice with eye inspection.To overcome this issue,this study develops a solution to fuse voyage report data and publicly accessible meteorological data and constructs nine datasets based on this data fusion solution.Eleven widelyadopted machine learning models were tested over these datasets for eight 8100-TEU to 14,000-TEU containerships from a global shipping company.The best datasets found reveal the benefits of fusing voyage report data and meteorological data,as well as the practically acceptable quality of voyage report data.Extremely randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG)present the best fit and generalization performances.Their R^(2) values over the best datasets are all above 0.96 and even reach 0.99 to 1.00 for the training set,and 0.74 to 0.90 for the test set.Their fit errors on daily bunker fuel consumption are usually between 0.5 and 4.0 ton/day.These models have good interpretability in explaining the relative importance of different determinants to a ship's fuel consumption rate.

Data fusion and machine learning for ship fuel efficiency modeling:Part Ⅲ-Sensor data and meteorological data

Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis.However,important information about weather and sea conditions the ship sails through,such as waves,sea currents,and sea water temperature,is often absent from sensor data.This study addresses this issue by fusing sensor data and publicly accessible meteorological data,constructing nine datasets accordingly,and experimenting with widely adopted machine learning(ML)models to quantify the relationship between a ship's fuel consumption rate(ton/day,or ton/h)and its voyage-based factors(sailing speed,draft,trim,weather conditions,and sea conditions).The best dataset found reveals the benefits of fusing sensor data and meteorological data for ship fuel consumption rate quantification.The best ML models found are consistent with our previous studies,including Extremely randomized trees(ET),Gradient Tree Boosting(GB)and XGBoost(XG).Given the best dataset from data fusion,their R^(2) values over the training set are 0.999 or 1.000,and their R^(2) values over the test set are all above 0.966.Their fit errors with RMSE values are below 0.75 ton/day,and with MAT below 0.52 ton/day.These promising results are well beyond the requirements of most industry applications for ship fuel efficiency analysis.The applicability of the selected datasets and ML models is also verified in a rolling horizon approach,resulting in a conjecture that a rolling horizon strategy of“5-month training t 1-month test/applicatoin”could work well in practice and sensor data of less than five months could be insufficient to train ML models.