Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Theobroma cacao L. on the Efficacy and Toxicity of Doxorubicin in Mice Bearing Ehrlich Ascites Carcinoma

Background and objective: Doxorubicin is a widely used chemotherapeutic agent that causes oxidative stress leading to cardiotoxicity, hepatotoxicity, and nephrotoxicity. In contrast, Theobroma cacao L. has been recorded as an anticancer agent and found to be protective against multiple chemical-induced organ injuries, including heart, liver, and kidney injuries. The present study investigated the possible role of extracts from T. cacao beans for organ-protective effects in doxorubicin-induced toxicity in mice bearing Ehrlich ascites carcinoma (EAC). Methodology: After survival analysis in rodents, cocoa bean extract (COE) was investigated for its efficacy against EAC-induced carcinoma and its organ-protective effect against doxorubicin-treated mice with EAC-induced carcinoma. Results: Significant reductions in EAC and doxorubicin-induced alterations were observed in mice administered the COE, either alone or in combination with doxorubicin. Furthermore, COE treatment significantly increased the mouse survival time, life span percentage, and antioxidant defense system. It also significantly improved cardiac, hepatic, and renal function biomarkers and markers for oxidative stress, and it also reduced doxorubicin-induced histopathological changes. Conclusion: COE acted against doxorubicin-induced organ toxicity; potent antioxidant and anticancer activities were also reflected by the COE itself. The COE may therefore serve as an adjuvant nutraceutical in cancer chemotherapy.
Genome-Wide Association Study of Six Forage Traits in Ramie (Boehmeria nivea L. Gaud)

Genome-wide association study (GWAS) of six forage traits using whole-genome sequencing data generated from 301 ramie accessions found that traits were continuously distributed; the maximum variant coefficient was fresh weight per clump (FWPC) (2019) and individual plant height (IPH) (2019) minimum. Correlation analysis demonstrated that 2019 and 2020 results were similar; all traits were correlated. GWAS analysis demonstrated that six traits exhibited consistent and precise association signals. Of the latter, 104 were significant and detected in 43 genomic regions. By screening forage trait-associated single nucleotide polymorphisms and combining Manhattan map with genome annotation, signals were categorized according to functional annotations. One loci associated with fresh weight per plant (FWP) (chromosome 5; Bnt05G007759), two associated with FWPC (chromosome 13; Bnt13G018582, and Bnt13G018583), and two associated with leaf dry weight per plant (LDWP) and dry weight per plant (DWP) (chromosome 4; Bnt04G005779 and Bnt04G005780), were identified. We describe forage trait candidate genes that are highly correlated with FWP and FWPC; Bnt05G007759 may be involved in nitrogen metabolism, while Bnt13G018582 and Bnt13G018583 may encode TEOSINTE branch 1/CYCLOIDEA/proliferating cytokine 1 (TCP) domains. Bnt04G005779 and Bnt04G005780, which may regulate growth and development, are highly related to LDWP and DWP. These genomic resources will provide a basis for breeding varieties.
FEW SHOT CROP MAPPING USING TRANSFORMERS AND TRANSFER LEARNING WITH SENTINEL-2 TIME SERIES: CASE OF KAIROUAN TUNISIA

Abstract. In this paper, we present an approach to land cover mapping from Sentinel-2 (S-2) satellite image time series using deep learning methods in the context of few shots in agricultural areas which aims to learn a classifier to recognize unseen classes during training with limited labelled examples. In many countries, there is a lack of Land Parcel Information Systems (LPIS) and thus of agricultural crop type annotations. Annotations are still based on fastidious digitization of parcels and in-field observations that are available in few numbers. Our idea is to transfer learning from pre-trained models on existing LPIS in France and apply them to a different geographical area in Kairouan in Central Tunisia. We build on work employing multi-headed self-attention mechanisms that have contributed to results that outperform other deep learning algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) in agricultural context using S-2 Time series. We used two transformer-based deep learning models PSE-TAE (Pixel-Set Encoders + Temporal Self-Attention) and PSE-LTAE (Pixel-Set Encoders + Lightweight Temporal Self-Attention). We first studied their generalisation capacity in a few shot context and on different geographical study site. Then, by transferring the knowledge of these models and adapting them to the Tunisian context with the transfer learning techniques we have demonstrated experimentally that the adaptation of these methods is efficient for land cover mapping in agricultural areas with few in-field observations in terms of accuracy with an overall accuracy for both models reaching almost 93% for a detailed classification level with 17 classes.
Diabetic Retinopathy Detection Using Convolutional Neural Networks

— Diabetes is one of the most common health issues that we come across in our lives. Most of the people are unaware of the consequences of not taking care of the same. Diabetes leads to a lot of health dilemmas, one of which being Diabetic Retinopathy. Diabetes leads to rise in blood sugar level which in turn becomes the source for the development of Diabetic Retinopathy and having too much sugar can damage the retina over time along with damage of optical nerve in the long run. Diabetic Retinopathy can even lead to blindness if not treated properly in the initial stages. Convolutional Neural Networks can be used efficiently for the detection of Diabetic Retinopathy. The fundus images of retina can be tested using a well-trained network and the disease as well as its current stage can be determined. Some of the existing networks namely-VGG16, Resnet50 and Inception V3 have been trained with the datasets from kaggle. The most efficient network being Inception V3 is selected and has been trained and tested with the external dataset collected from Government Medical College, Thrissur. Hyperparameter tuning has also been studied with the parameters learning rate, epoch, decay and momentum, giving a wider picture of how effective CNN can be in diagnosing Diabetic Retinopathy.
The Potential of Carbon Nanoparticles as a Stimulant to Improve the Propagation of Native Boreal Forest Species: A Mini-Review

Boreal forests across Canada and other geographic areas globally have vast networks or densities of seismic lines, pipelines, access roads, utility corridors, and multipurpose trails collectively termed “linear disturbances” or “linear features. ” Additionally, large areas of disturbances attributed to resource harvesting represent a major anthropogenic impact on the global boreal forest ecosystem. Restoration of these disturbed areas is currently a significant component of global boreal forest management strategies. A key to successful restoration or re-vegetation of these disturbed sites is the availability of highly adaptive native planting materials to grow and establish on the disturbed sites, particularly in varying abiotic stressors or severe environmental conditions. Abiotic stress includes non-living environmental factors, including salinity, drought, waterlogging or extreme temperatures, adversely affecting plant growth, development, and establishment on field sites. Herein, we present the concept of nanopriming native boreal seeds with microgram concentrations of carbon nanoparticles (CNPs) as an effective approach to improve the propagation and vigor of native boreal forest species. Priming refers to the technique of hydrating seeds in solutions or in combination with a solid matrix to enhance the rate at which they germinate and their germination uniformity. Seed priming has been shown to increase seed vigor in many plant species. In this mini-review, we will provide a brief overview of the effect of nanopriming on seed germination and seed vigor in agricultural plants and native boreal forest species, indicating the potential future applications of CNPs on native boreal species for use in forest reclamation or restoration.
The Isolation, Cloning, Characterization and Conservation among the Psittacidae of a Sex-Specific, Tandemly Repeated DNA Component from the African Grey Parrot (Psittacus erithacus)

Digestion of DNA of the female African grey parrot (Psittacus erithacus) with the restriction endonuclease Msp1 revealed the presence of a tandemly repeated, female-specific component with a repeat monomer length of approx. 628 nts and a repeat copynumber of approx. 1.3 x 10 4 . The nucleotide sequence of a cloned 628 bp repeat monomer, is characterized by short oligo dA2-6 segments which occur with an average periodicity of 10.5 bp. The sequence shows therefore some similarity with the W-specific repeat of the chicken, which is characterized by alternating dT3-5 and dA3-5 oligonucleotides occurring each with a periodicity of 21 bp. Similar to the chicken sequence the psittacine monomers show a strong reduction in electrophoretic mobility in polyacrylamide gels. The component occurs widespread in females of many members of different subfamilies of the Psittacidae. Minor amounts of cross hybridizing material were found in males of most species tested. Only in a few species, including the Eclectus parrot (Eclectus roratus) and the Bourke’s parakeet (Neopsephotus bourkii), DNA of males contained, in addition to an abundant female specific component, significant amounts of non-sex-specific PARsex-1Arch Vet Sci Med 2022; 5 (2):004-023 DOI: 10.26502/avsm.032 Archives of Veterinary Science and Medicine 5 like material, whereas DNA of male and female keas (Nestor notabilis) contains almost nothing.
A Splice Site Mutation Associated with Congenital CD59 Deficiency

Congenital CD59 deficiency is a recently described rare autosomal recessive disease associated with CD59 gene mutations that lead to deficient or dysfunctional CD59 protein on the cell surface. The disease is characterized by the early onset of chronic hemolysis, relapsing peripheral demyelinating neuropathy, and recurrent ischemic strokes. To date, there are 14 patients with 4 exon mutations reported globally. A young boy with early onset peripheral neuropathy and atypical hemolytic uremic syndrome is presented. Next-generation sequencing (NGS) identified a homozygous splice site variant in intron 1 of the CD59 gene (c.67 + 1G > T). This variant alters a consensus donor splicing site. Quantitative reverse transcription PCR showed that CD59 mRNA expression in the patient is significantly reduced to 0.017-fold compared to the controls. Flow cytometry showed the lack of CD59 protein on the surface of the patient’s red blood cells. This variant is the first splice site mutation reported to be associated with congenital CD59 deficiency.
Modeling, Simulation & Implementation of Li-ion Battery Powered Electric and Plug-in Hybrid Vehicles

The modeling, simulation and hardware implementation of a Li-ion battery powered electric vehicle are presented in this thesis. The results obtained from simulation and experiments were analyzed to develop the control algorithm for the vehicle. An existing electric vehicle was reengineered with a new Li-ion battery pack and an advanced control strategy. The implementation platform is a Solectria E-10 electric vehicle which was originally powered with a Lead acid battery pack. A simulation model of the Solectria E-10 electric vehicle was first developed for vehicle simulation using various standard drive cycles. The supervisory control strategy was first developed and tested in simulation before it was implemented on actual hardware. A Power Save feature was developed to limit the vehicle power demand upon the driver request to increase the zero emissions vehicle range of the electric vehicle. Simulations were used to demonstrate the effectiveness of Power Save feature and it was shown that the range of the electric vehicle can be extended. The feasibility for plug-in hybrid vehicle conversion of the developed electric vehicle to extend the range of the electric vehicle beyond its zero emissions driving range was studied through simulation. A control algorithm for plug-in hybrid vehicle was developed and simulated.
Report on the use of sampling frames in European studies

Obtaining good probability samples is a key challenge for European cross-national studies in order to represent the population. This report gives an overview of the sampling frames which are used in countries participating in the four cross-European surveys cooperating in SERISS: the European Social Survey (ESS), the European Values Study (EVS), the Gender and Generations Program (GGP), and the Survey of Health, Ageing, and Retirement in Europe (SHARE). The overview will show where possibilities exist to jointly build and share sampling frames and where studies not using an existing population register can profit from the experience of other studies which do have access to such a register in the same country. It provides a valuable knowledge database of national sampling procedures and accessible population registers across Europe and in addition offers a way to improve harmonization of sampling frames and sample data across European surveys.
Claim Category in Indonesian Coffee Discourse Argument

The objective of this research is to describe the construction of claims in the strategies used in the coffee discourse. The claim in the argument can show cognition about coffee that is implanted by the producer. his research takes data from the online discourse of Indonesian coffee. The data of this research are statements that are contextually interpreted as claims and then examined from the Toulmin concept. By studying the types of claims, types of claims and its strategy to connect claims and ground, the researcher describes the arguments in the coffee discourse. From the results of the qualitative study, it is obtained that there are three types of claims, namely fact-based claims, judgment and value claims, and claim-based policies. From the three types of claims it can be concluded that there are. From the results and discussion it can be stated that there are subjective and objective claims formed in the coffee discourse. Objective claims are proven by geographical location, research, knowledge and environmental conservation in industrial agriculture (future knowledge). Subjective claims are shown by personality and support quality. Regarding history, as an aspect of judgment and value claims, it is subjective and objective. From these claims, it can be seen that the coffee discourse contains cognitions about coffee companies with historical authority, personality, and taste; future knowledge and research references; taste, packaging, and good process.
Ex-ante versus ex-post buyback pricing: An experimental study

Abstract We investigate behaviors and performance of a supply chain, in which a supplier uses buyback contracts to transact with a retailer under stochastic market demand. We compare ex-ante versus ex-post buyback pricing—the supplier decides the buyback price before demand realization in the former, but after demand realization in the latter. Theoretical analyses predict that the retailer order quantity and the supply chain efficiency are lower under ex-post buyback pricing. However, in experiments with human subjects playing both the supplier and retailer, we observe the opposite: the retailer order quantity and the supply chain efficiency are significantly higher under ex-post buyback pricing. Several behaviors are found significant. First, the supplier exhibits weaker advantageous fairness concern under ex-ante buyback pricing than under ex-post buyback pricing. Second, the supplier over-weights the buyback cost under the former, but under-weights (reciprocates) under the latter. Third, the retailer’s fairness concern is weaker under the latter. Fourth, the retailer exhibits trust behavior with the supplier under the latter. These drivers lead to less rejection and higher order quantity under ex-post buyback pricing than under ex-ante buyback pricing. Our results shed light on how human behaviors change with the decision timing and benefit supply chain performance.
Keynote: Radar Probabilistic Data Association Filter with GPS Aiding for Target Selection and Relative Position Determination

In this thesis, Global Positioning System (GPS) and radar measurements are utilized in a multi-sensor architecture to achieve a confident relative positioning solution between two vehicles. A GPS solution providing a three-dimensional positioning vector is determined using pseudorange and carrier phase measurements. The carrier phase measurements make sub-meter level accuracy achievable. However, the carrier phase ambiguity must be resolved before estimating the relative position vector. A Dynamic Base Real-Time Kinematic (DRTK) positioning algorithm using differential GPS methods is used to achieve highly precise relative positioning between the two GPS antennas. A comparison of the performance of the DRTK algorithm using either single frequency (L1 or L2 frequency only) or dual frequency (L1 and L2 frequency) measurements is introduced. The radar measurements including range, range rate, and bearing are utilized in a probabilistic data association filter (PDAF). The PDAF determines which of the radar channels’ solutions are considered valid, and the weighted mean of these solutions is used as the selected target measurements. The PDAF algorithm is discussed in great detail, and the performance of the PDAF algorithm using radar measurements and the performance of the DRTK solution are compared and presented demonstrating that the radar PDAF solution tracks the desired target with reasonable accuracy as long as the lead vehicle is in line of sight. Finally, the DRTK algorithm is extended to incorporate the radar PDAF solution to increase solution availability, output rate, and reliability of the algorithm’s solution. The PDAF algorithm’s solution using the radar measurements can be utilized during GPS outages. The update rate of the radar measurements is ten times faster than the rate of the GPS receiver. The resultant combined system produces estimates at a much higher output rate.
PENGUATAN INDUSTRI GARAM NASIONAL MELALUI PERBAIKAN TEKNOLOGI BUDIDAYA DAN DIVERSIFIKASI PRODUK

Penelitian bertujuan : mendesain sistem produksi garam yang berkualitas dan diversifikasi produk garam. Penelitian mempergunakan pendekatan kualitatif dan kuantitatif dengan locus PT Garam Mas. Data dianalisis secara interaktif dan uji laboratorium. Hasil penelitian menunjukan bahwa hasil produksi garam menggunakan membran HDPE lebih putih dan bersih daripada produk garam konvensional “solar evaporation”. Garam konsumsi bermutu tinggi memiliki kandungan NaCl 97%, kadar air dibawah 0,05%, warna putih bersih, butiran kristal halus, digunakan untuk garam meja, penyedap makanan, camilan, industri sosis dan keju, serta industri minyak goreng. Garam konsumsi kelas menengah memiliki kadar NaCl 94,7-97% dan kadar air 3-7% untuk garam dapur, industri kecap, tahu, pakan ternak.Garam konsumsi mutu rendah memiliki kadar NaCl 90-94,7%, kadar air 5-10%, warna putih kusam, digunakan untuk pengasinan ikan dan pertanian. Kualitas dan kuantitas garam dipengaruhi oleh temperatur, iklim/cuaca, kekentalan air yang digunakan dan kedisiplinan petani.Penggunaan air kurang dari 23 Be berakibat produk garam tidak berkualitas. Optimasi proses pengolahan garam bahan baku menjadi garam konsumsi beriodium perlu dilakukan, sehingga memenuhi standar SNI. Sedangkan untuk membangun kelembagaan di tingkat petani diperlukan integrasi kebijakan antar stakeholder yang terkait melalui pembentukan kawasan minapolitan garam. o
Comparative Transcriptome Analysis of Purple and Green Non-Heading Chinese Cabbage and Function Analyses of BcTT8 Gene

Non-heading Chinese cabbage (Brassica campestris ssp. chinensis) is an important vegetative crop in the south of China. As an antioxidant, anthocyanin is the major quality trait for vegetables with purple leaves or petioles. However, the molecular biosynthetic mechanism of anthocyanin in non-heading Chinese cabbage has not been explained exclusively. In this study, two non-heading Chinese cabbage with contrasting colors in the leaves were used as the materials for RNA-seq. A total of 906 DEGs were detected, and we found that the anthocyanin and flavonoid biosynthetic pathways are significantly enriched in the purple NHCC. The transcriptome result was verified by RT-qPCR. Though bioinformatics analysis, BcTT8 was selected as the candidate gene for the regulation of anthocyanin synthesis, and the characterization of BcTT8 was elucidated by the functional analyses. The results proved that BcTT8 is a nucleus protein and phylogenetically close to the TT8 protein from Brassica. After silencing BcTT8, the total anthocyanin content of pTY-BcTT8 plants decreased by 42.5%, and the relative expression levels of anthocyanin pathway genes BcDFR, BcLODX and BcUF3GT-1 were significantly downregulated, while the transcription level of BcFLS was significantly upregulated. Compared with the wild type, the transgenic Arabidopsis showed obvious violet in the cotyledons part, and the anthocyanin biosynthetic genes such as AtDFR and AtLODX were significantly upregulated. In conclusion, BcTT8 is critical in the anthocyanin synthesis process of non-heading Chinese cabbage. Our findings illustrated the molecular mechanism of anthocyanin biosynthesis in non-heading Chinese cabbage.
SPATIOTEMPORAL RECOVERY OF HIMAWARI-8 HOURLY AEROSOL OPTICAL DEPTH PRODUCTS VIA THE NESTED BAYESIAN MAXIMUM ENTROPY METHOD

Abstract. Satellite-derived aerosol optical depth (AOD) is an indispensable parameter when conducting studies related to atmospheric environment, climate change, and biogeochemical cycle. However, current satellite-derived AOD products are limited in related applications due to the large proportion of missing data, and the existed methods mainly concentrate on recovering AOD from polar-orbit satellite sensors. In order to solve these issues and take full use of the preponderance of geostationary satellite sensors in high frequency observation, we propose a spatiotemporal AOD recovery framework integrating multi-time scale AOD products based on the nested Bayesian maximum entropy methodology (NBME), aimed to obtain satellite-derived AOD datasets with low data missing and high accuracy. The experiment results show that the spatial coverage of AOD datasets increases from 20.5% to 70.0%, and the R2 and RMSE of the recovered AOD against ground-based AERONET AOD are approximately 0.62 and 0.19, respectively. Moreover, the further simulated experiments indicate that the proposed method also performs better relatively when comparing with other popular recovery methods. Therefore, the proposed NBME recovery method can obtain a more convincing product both in applicable accuracy and visual quality.