Bioleaching of Zn(Ⅱ) and Pb(Ⅱ) from Nigerian sphalerite and galena ores by mixed culture of acidophilic bacteria

Zn（Ⅱ） and Pb（Ⅱ） from Nigerian sphalerite and galena ores were bioleached by a mixed culture of acidophilic bacteria.The influences of pH and ferric ion on the bioleaching rates of sphalerite and galena were examined.The result shows that pH 2.1 and 2.7 are favourable for the leaching of Zn（Ⅱ） and Pb（Ⅱ） from sphalerite and galena,respectively.It was observed that the use of agarose-simulated media caused cells to excrete exopolymers containing ferric ions which enhanced oxidation.The oxidation equilibrium for sphalerite and galena took 3 and 4 d,respectively.About 38.3% sphalerite and 34.2% galena were leached within 1 d and approximately 92.0% Zn（Ⅱ） and 89.0% Pb（Ⅱ） were recovered in 5 d,respectively.The unleached residual products were examined by X-ray diffraction for sphalerite,revealing the presence of elemental sulphur（S）,zinc sulphate（ZnSO4） and few traces of calcium aluminate（Ca3Al2O6）.The XRD pattern also indicates the presence of elemental sulphur（S）,lead sulphate（PbSO4） and few traces of itoite [Pb（S,Ge）（O,OH）4] and cobalt lead silicate [Pb8Co（Si2O7）3] in the unleached galena ore.

Synthesis of hydroxymethylfurfural and furfural from hardwood and softwood pulp using ferric sulphate as catalyst

Hydroxymethylfurfural (HMF) and furfural are promising chemicals for the creation of a bio-based economy. The development of an inexpensive catalytic system for converting cellulosic biomass into these chemicals is an important step in this regard. Ferric sulphate is a common, cheap and non-toxic Lewis acid that has been used to catalyse reactions such as wood depolymerisation. In this work, ferric sulphate was used to help the production of HMF and furfural from hardwood and softwood pulps. It was found that for hardwood pulp, the use of ferric sulphate alone gave a maximum HMF yield of 31.6 mol-%. The addition of the ionic liquid [BMIM]Cl or HCl as co-catalysts did not lead to an increase in the yields obtained. A prior decationisation step, however, resulted in HMF yields of 50.4 mol-%. Softwood pulp was harder to depolymerise than hardwood, with a yield of 28.7% obtained using ferric sulphate alone. The maximum HMF yield from softwood, 37.9 mol-%, was obtained using a combination of ferric sulphate and dilute HCl. It was thus concluded that ferric sulphate is a promising catalyst for HMF synthesis from cellulosic biomass.

Producing Designer Oils in Industrial Microalgae by Rational Modulation of Co-evolving Type-2 Diacylglycerol Acyltransferases

Microalgal oils, depending on their degree of unsaturation, can be utilized as either nutritional supplements or fuels; thus, a feedstock with genetically designed and tunable degree of unsaturation is desirable to maximize process efficiency and product versatility. Systematic profiling of ex vivo （in yeast）, in vitro, and in vivo activities of type-2 diacylglycerol acyltransferases in Nannochloropsis oceanica （NoDGAT2s or NoDGTTs）, via reverse genetics, revealed that NoDGAT2A prefers saturated fatty acids （SFAs）, NoDGAT2D prefers monounsaturated fatty acids （MUFAs）, and NoDGAT2C exhibits the strongest activity toward polyunsaturated fatty acids （PUFAs）. As NoDGAT2A, 2C, and 2D originated from the green alga, red alga, and eukaryotic host ancestral participants of secondary endosymbiosis, respectively, a mecha- nistic model of oleaginousness was unveiled, in which the indigenous and adopted NoDGAT2s formulated functional complementarity and specific transcript abundance ratio that underlie a rigid SFA：MUFA：PUFA hierarchy in triacylglycerol （TAG）. By rationally modulating the ratio of NoDGAT2A＇：2C^D transcripts, a bank of N. oceanica strains optimized for nutritional supplement or fuel production with a wide range of degree of unsaturation were created, in which proportion of SFAs, MUFAs, and PUFAs in TAG varied by 1.3-, 3.7-, and 11.2-fold, respectively. This established a novel strategy to simultaneously improve productivity and quality of oils from industrial microalgae.

Remedial ability of maize(Zea-Mays)on lead contamination under potted condition and non-potted field soil condition

This study presents the remedial ability of maize on lead(Pb)contaminated soil.Soil samples were collected randomly from the site and subjected to physico-chemical tests before experimen-tation.The samples were contaminated artificially at six different concentration levels of lead nitrate(Pb(NO_(3))_(2)).Experimental design was 4-factorial combination(6×6×2×1).The study duration was 10 weeks,and during this period,Pb contents of the soil were analyzed in intervals of two weeks.Analyzed physico-chemical properties of the soil showed that the soil was loamy with pH 6.82,electrical conductivity 1.62 dS/m and adequate macro nutrient elements.The av-erage percentage removal of Pb from the soil was 2.25%and 3.67%for potted and non-potted experiments,respectively.Similarly,the average percentage of Pb in the roots was 1.10%and 1.68%for potted and non-potted experiments,respectively.The result of this study indicated that extraction of Pb by the plant system increased with the increase of lead concentration in the soil as well as in the extent of vegetation attained by the crop.It also clearly showed that the non-potted experiments demonstrated greater influence on removal of Pb from the soil system than the potted experiments.

Eff-3DPSeg:3D Organ-Level Plant Shoot Segmentation Using Annotation-Efficient Deep Learning

Reliable and automated 3-dimensional(3D)plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level.Combining deep learning and point clouds can provide effective ways to address the challenge.However,fully supervised deep learning methods require datasets to be point-wise annotated,which is extremely expensive and time-consuming.In our work,we proposed a novel weakly supervised framework,Eff-3DPSeg,for 3D plant shoot segmentation.First,high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system,and the Meshlab-based Plant Annotator was developed for plant point cloud annotation.Second,a weakly supervised deep learning method was proposed for plant organ segmentation.The method contained(a)pretraining a self-supervised network using Viewpoint Bottleneck loss to learn meaningful intrinsic structure representation from the raw point clouds and(b)fine-tuning the pretrained model with about only 0.5%points being annotated to implement plant organ segmentation.After,3 phenotypic traits(stem diameter,leaf width,and leaf length)were extracted.To test the generality of the proposed method,the public dataset Pheno4D was included in this study.Experimental results showed that the weakly supervised network obtained similar segmentation performance compared with the fully supervised setting.Our method achieved 95.1%,96.6%,95.8%,and 92.2%in the precision,recall,F1 score,and mIoU for stem–leaf segmentation for the soybean dataset and 53%,62.8%,and 70.3%in the AP,AP@25,and AP@50 for leaf instance segmentation for the Pheno4D dataset.This study provides an effective way for characterizing 3D plant architecture,which will become useful for plant breeders to enhance selection processes.The trained networks are available at https://github.com/jieyi-one/EFF-3DPSEG.

Toward Real Scenery:A Lightweight Tomato Growth Inspection Algorithm for Leaf Disease Detection and Fruit Counting

The deployment of intelligent surveillance systems to monitor tomato plant growth poses substantial challenges due to the dynamic nature of disease patterns and the complexity of environmental conditions such as background and lighting.In this study,an integrated cascade framework that synergizes detectors and trackers was introduced for the simultaneous identification of tomato leaf diseases and fruit counting.We applied an autonomous robot with smartphone camera to collect images for leaf disease and fruits in greenhouses.