Miracle-dz/UniFAR-FTUs huggingface dataset:
Origins of weedy rice revealed by polymorphisms of chloroplast DNA sequences and nuclear microsatellites

Conspecific weeds that permanently infest worldwide agroecosystems are evolved from their crop species. These weeds cause substantial problems for crop production by competing for resources in agricultural fields. Weedy rice represents such a conspecific weed infesting rice ecosystems, and causing tremendous rice yield losses owing to its strong competitiveness and abundant genetic diversity, likely resulted from its complex origins. Here, we report the use of chloroplast DNA (cpDNA) fingerprints to determine whether weedy rice is evolved from its wild (exo‐feral) or cultivated (endo‐feral) rice progenitor as the maternal donor in recent hybridization events. In addition, we also applied nuclear simple sequence repeat (SSR) markers to confirm the exo‐feral or endo‐feral origins of weedy rice accessions determined by the cpDNA fingerprints. We found that the studied weedy rice accessions evolved either from their wild or cultivated rice progenitor, as the maternal donor, based on the cpDNA network and structure analyses. Combined analyses of cpDNA and nuclear SSR markers indicated that a much greater proportion of weedy rice accessions had the endo‐feral origin. In addition, results from the genetic structure of nuclear SSR markers indicated that weedy rice accessions from the endo‐feral pathway are distinctly associated with either indica or japonica rice cultivars, suggesting their complex origins through crop–weed introgression. The complex pathways of origin and evolution could greatly promote genetic diversity of weedy rice. Therefore, innovative methods should be developed for effective weedy rice control.
A genetically encoded GRAB sensor for measuring serotonin dynamics in vivo

Serotonin (5-HT) is a phylogenetically conserved monoamine neurotransmitter modulating a variety of processes in the brain. To directly visualize the dynamics of 5-HT, we developed a genetically encoded GPCR-Activation-Based 5-HT (GRAB5-HT) sensor with high sensitivity, selectivity, and spatiotemporal resolution. GRAB5-HT, detected 5-HT release in multiple physiological and pathological conditions in both flies and mice, and thus provides new insights into the dynamics and mechanisms of 5-HT signaling.
Trainer Development of the Electrical and Electronics Course as a Learning Media in Electrical Department SMKN 1 Tilatang Kamang

Tilatang Kamang Village, which is in the rural area, causes most students to have limited access to technological developments, especially in terms of practical equipment in the laboratory. This limitation causes students to be unable to think quickly, and has an impact on the speed and accuracy of students in practicing in the laboratory. Existing teaching aids are ineffective, because they only consist of circuit boards that require a long time and make it difficult for students to operate. Low ability and limited experience of students at SMKN 1 Tilatang in using laboratory equipment are also the causes of ineffective learning, especially in the learning of electrical and electronic circuits. This type of research is Research and Development which uses the Borg and Gall research procedure. The research subject is the electric circuit media trainer and the respondent was a teacher of Basic Electricity and Electronics and an electrical student at SMKN 1 Tilatang Kamang. The research instrument is the trainer questionnaire validation, practicality, and effectiveness of the trainer. Data analysis includes trainer validity, practicality, and trainer effectiveness. The results showed that the trainer’s validity was 90% with a valid category. The practicality of the trainer is viewed from the teacher’s side by 96% with very practical criteria, while the average practice in terms of students is 90% with very practical criteria. The average effectiveness of trainers is 86.9% with effective criteria.
An Improved Authentication Scheme for Remote Data Access and Sharing Over Cloud Storage in Cyber-Physical-Social-Systems

Cyber-physical-social systems (CPSSs) epitomize an evolving paradigm, including the social, physical, and cyber world. The vital goal of CPSSs is to offer personalized, high-quality, and proactive services for the end-users. An ingenious framework for reliable services is required for CPSSs to achieve this purpose. In this regard, the cloud storage environment of cloud computing (having a great connection with the physical, cyber, and social world) requires a reliable framework for secure communication between cloud and users. Cloud storage provides various services that need scalable, cost-effective, and secure facilities of data management. Public cloud storage bound its users to maintain strict security considerations that are offered by cloud service providers. On the other hand, an opportunity for users is offered by private cloud storage to construct a controlled and self-managed model of data security. This mobile model is responsible for managing the sharing and accessing of data privately. Despite that, it induces new challenges of data security. One censorious problem is to ensure the authenticated and secure model of data-storage for accessing the data under the controlled environment of data accessibility. To tackle this challenge, many protocols have been developed. The problem is that all these protocols are unable to fulfill the required security efficiency and are susceptible to various security attacks. Recently, Tiwari et al. presented an authentication scheme for data sharing and access with a biometric feature. They claimed that their scheme resists significant security attacks. However, in this article, we show that the claim of Tiwari et al. for developing a secure scheme is not valid, and their protocol is insecure against user and server impersonation attacks. Moreover, the protocol of Tiwari et al. do not provide user anonymity. Therefore, we present an enhanced, secure, and convenient scheme for data access. Besides, in order to add the flexible distribution of data that is controlled by data-owner, our protocol provides proxy re-encryption in which the cloud server utilizes the proxy re-encryption key. Then, the data-owner generates the credential token during decryption for controlling user’s accessibility. The security analysis determines that our proposed protocol resists numerous security attacks. Furthermore, performance analysis determines that our protocol has practical computation, communication, and storage costs as compared to various related protocols. Consequently, our introduced protocol achieves not only the security goals but also has performance efficiency comparable to numerous relevant protocols of cloud storage.
Methods as Interpretation and Glocalization, Not Application: Water Far Away Will Not Put out Nearby Fires.

Much research shows that regardless of where teachers are trained, there is the disjuncture between training and practice. As a result, teachers reject new approaches because they are not aligned to contexts in which they eventually find themselves. This research, however, shines a light on the efforts of Chinese English language teachers in Yunnan Province to make the connection between new and external approaches into their classrooms. Findings demonstrate that the teachers' interpretation and glocalization decision-making is based on whether it empowers teachers (e.g. strengthens teachers’ professional position); empowers their teaching (e.g. improves their course design); engenders critical thinking (e.g. helps teachers to critically evaluate practice); enables appropriation and transformation (e.g. provides room for teachers to incorporate local practices). The findings result in the development of the “Critical Glocalization Teaching Framework” to help teachers to critically reflect on the utility of their teacher training experiences in light of their immediate teaching contexts. While the specifics of the teachers’ contexts in the study differ from that of teachers’ elsewhere, the outcomes of the research reflect not only many issues shared by but also the teacher agency of English-asa-Second/Foreign language teachers across the board.
Interactional Justice

By drawing on extensive ethnographic fieldnotes and interviews with lawyers, this sociological study brings their loyalty work to life and reveals to the reader the unwritten rules of emotional interactions. It presents how defence lawyers socially construct their duty of loyalty by negotiating informal and implicit professional and social expectations. This accomplishment demands emotion work and face work in order to perform a role which includes defending clients accused of heinous crimes and "losing" the majority of cases. As the defence team is central to this, the ways of doing teamwork with their joint efforts is explored.
Enzyme‐Mediated Tumor Starvation and Phototherapy Enhance Mild‐Temperature Photothermal Therapy

Compared with conventional tumor photothermal therapy (PTT), mild‐temperature PTT brings less damage to normal tissues, but also tumor thermoresistance, introduced by the overexpressed heat shock protein (HSP). A high dose of HSP inhibitor during mild‐temperature PTT might lead to toxic side effects. Glucose oxidase (GOx) consumes glucose, leading to adenosine triphosphate supply restriction and consequent HSP inhibition. Therefore, a combinational use of an HSP inhibitor and GOx not only enhances mild‐temperature PTT but also minimizes the toxicity of the inhibitor. However, a GOx and HSP inhibitor‐encapsulating nanostructure, designed for enhancing its mild‐temperature tumor PTT efficiency, has not been reported. Thermosensitive GOx/indocyanine green/gambogic acid (GA) liposomes (GOIGLs) are reported to enhance the efficiency of mild‐temperature PTT of tumors via synergistic inhibition of tumor HSP by the released GA and GOx, together with another enzyme‐enhanced phototherapy effect. In vitro and in vivo results indicate that this strategy of tumor starvation and phototherapy significantly enhances mild‐temperature tumor PTT efficiency. This strategy could inspire people to design more delicate platforms combining mild‐temperature PTT with other therapeutic methods for more efficient cancer treatment.
Enhanced stress tolerance through reduction of G3BP and suppression of stress granules

Stress granules (SG) are membrane-less ribonucleoprotein condensates that form in response to various stress stimuli via phase separation. SG act as a protective mechanism to cope with acute stress, but persistent SG have cytotoxic effects that are associated with several age-related diseases. Here, we demonstrate that the testis-specific protein, MAGE-B2, increases cellular stress tolerance by suppressing SG formation through translational inhibition of the key SG nucleator G3BP. MAGE-B2 reduces G3BP protein levels below the critical concentration for phase separation and suppresses SG initiation. Importantly, knockout of the MAGE-B2 mouse ortholog confers hypersensitivity of the male germline to heat stress in vivo. Thus, MAGE-B2 provides cytoprotection to maintain mammalian spermatogenesis, a highly thermo-sensitive process that must be preserved throughout reproductive life. These results demonstrate a mechanism that allows for tissue-specific resistance against stress through fine-tuning phase separation and could aid in the development of male fertility therapies.
Essays on structural change tests under long memory

This thesis contains six essays on tests for structural change under long memory with applications to economic time series. Chapter 1 introduces structural change tests and the concept of long memory. The main problem examined in Chapters 2 to 5 is that standard change-in-mean tests are invalid in long-memory time series. Chapter 2 reviews the literature on long-memory robust extensions of standard testing principles for a change in mean. Apart from giving a systematic review, an extensive Monte Carlo study is conducted to compare the relative performance of the introduced methods. Special attention is paid to the interaction the test results have with the estimation of the long-memory parameter. Furthermore, it is shown that the power of self-normalized test statistics can be improved considerably by using an estimator that is robust to mean shifts. Chapter 3 introduces a simple test on structural change in long-memory time series. In contrast to the testing principles introduced in the previous chapter, it is much easier to implement in statistical software and has a limiting distribution that does not depend on the degree of memory. The test is based on the idea that the test statistic of the standard CUSUM test retains its asymptotic distribution if it is applied to fractionally differenced data. It is proven that the approach is asymptotically valid if the memory is estimated consistently under the null hypothesis. Therefore, the well-known CUSUM test can be used on the differenced data without any further modification. In simulations, the proposed CUSUM test on the differenced series is compared with a CUSUM test on structural change that is specifically constructed for long-memory time series. It is observed that the new approach performs reasonably well. The two chapters described previously find that self-normalized tests on change in mean are robust against size distortions in finite samples and that the CUSUM testing principle tends to be the most powerful. Based on these results, Chapter 4 proposes a new family of self-normalized CUSUM tests for structural change under long memory. The test statistics apply non-parametric kernel-based long-run variance estimators and have well-defined limiting distributions that only depend on the long-memory parameter. A Monte Carlo simulation shows that these tests provide finite sample size control while outperforming the competing procedures, which are presented in the previously described chapters, in terms of power. Chapter 5 presents the memochange package which offers implementations of all methods reviewed and proposed in Chapters 2 to 4 in the programming language R. The package
On Nonadiabatic Dynamical Simulations near Conical Intersections via Surface Hopping Methods

On Nonadiabatic Dynamical Simulations near Conical Intersections via Surface Hopping Methods Rami Gherib Doctor of Philosophy Graduate Department of Chemistry University of Toronto 2019 Many chemical processes involve nuclear dynamics on multiple potential energy surfaces. These often involve conical intersections, which cause nonadiabatic couplings and diagonal Born-Oppenheimer corrections to diverge and thus a complete breakdown of the Born-Oppenheimer approximation. Furthermore, conical intersections make electronic and nuclear wavefunctions double-valued, requiring proper consideration of the boundary conditions of molecular wavefunctions. In this work we rationalize why the fewest switches surface hopping method, despite not involving nuclear wavefunctions nor any consideration for diagonal Born-Oppenheimer corrections, can quantitatively approximate timescales of initial population decays of exact quantum dynamics through conical intersection-induced interstate crossings. In addition, we identify regimes when it is beneficial to include diagonal Born-Oppenheimer corrections in surface hopping dynamics through the phase-space formalism. Lastly, we consider the use of neural networks within the surface hopping framework, to approximate the nonadiabatic dynamics of fewest switches surface hopping by emulating its hopping criterion. We show that a properly trained “neural network-induced surface hopping” algorithm can approximately simulate population dynamics without the need to propagate electronic wavefunctions.
Quantifying Extreme Precipitation Forecasting Skill in High-Resolution Models Using Spatial Patterns: A Case Study of the 2016 and 2018 Ellicott City Floods

Recent historic floods in Ellicott City, MD, on 30 July 2016 and 27 May 2018 provide stark examples of the types of floods that are expected to become more frequent due to urbanization and climate change. Given the profound impacts associated with flood disasters, it is crucial to evaluate the capability of state-of-the-art weather models in predicting these hydrometeorological events. This study utilizes an object-based approach to evaluate short range (<12 h) hourly forecast precipitation from the High-Resolution Rapid Refresh (HRRR) versus observations from the National Centers for Environmental Prediction (NCEP) Stage IV precipitation analysis. For both datasets, a binary precipitation field is delineated using thresholds that span trace to extreme precipitation rates. Next, spatial metrics of area, perimeter, solidity, elongation, and fragmentation, as well as centroid positions for the forecast and observed fields are calculated. A Mann–Whitney U-test reveals biases (using a confidence level of 90%) related to the spatial attributes and locations of model forecast precipitation. Results indicate that traditional pixel-based precipitation verification metrics are limited in their ability to quantify and characterize model skill. In contrast, an object-based methodology offers encouraging results in that the HRRR can skillfully predict the extreme precipitation rates that are anticipated with anthropogenic climate change. Yet, there is still room for improvement, since model forecasts of extreme convective rainfall tend to be slightly too numerous and fragmented compared with observations. Lastly, results are sensitive to the HRRR model’s representation of synoptic-scale and mesoscale processes. Therefore, detailed surface analyses and an “ingredients-based” approach should remain central to the process of forecasting excessive rainfall.
Thidiazuron (TDZ) Induced in Vitro Axillary Shoot Regeneration of Desi Chikpea (Cicer arietinum L.)

Desi Chcikpea (Cicer auritinum L.) is samll sized chickpea of blackish color with rough seed coat. Desi Chcikpea is very popular in Indian subcontinent due to its use as in making food and also as medicinal plant especially against Diabetes. Desi chickpea is also cultivatedcrop in SouthEastern Anatolian region of Turkey. shoot tip and cotyledonary node explants excised from in vitro germinated seeds were inoculated on MS medium fortified with different TDZ concentrations (0.25-3.0 mg/L). Multiple shoot formation without any callusing was evidented after four weeks of culture. Both explants and interactive effect of explants X TDZ concentration was insignificant. Whereas, TDZ concentrations yielded 93.33-100.00% shoot formation frequency. Contrarily, explants, TDZ concentration and interactive effect of explants X TDZ concentration was statistically significant. Shoot tip explant yielded 12.54 shoots compared to 11.46 shoots for cotyledonary node. Thehighest shoot counts of 17.90 were recorded for medium augmented with 0.25 mg/L TDZ. Similarly, maximum shoot counts from explants X TDZ concentration were also recorded on medium augmented with 0.25 mg/L TDZ for both explants. In vitro regenerated shoots were shorter in length and failed to root on the rooting medium enrched with 0.25-1.00 mg/l IBA. The results revealed that both explants can be used for multiple shoot formation of desi chickpea. However, it is recommended to use other explants and plant growth regulators to obatin normal shoots for rooting and acclimatization.
The Influence of Social Insurance on Wages in China: An Empirical Study Based on Chinese Employee-Employer Matching Data

ABSTRACT This article uses China Employee-Employer Matching Survey data (CEES) to estimate the influence of social insurance contributions on workers’ wages. The results indicate firms may transfer the increased burden of social insurance onto their workers, and the negative effect of firms’ actual contribution rate is greater for poorly educated workers, migrant workers, non-manager group (manufacturing worker or clerks) and workers in the private sector, small firms, and labor-intensive firms than for their counterparts.
Research on Garbage Image Classification Method Based on Convolutional Neural Network

: The issue of garbage classification has aroused great concern for residents recently. The traditional garbage classification method has a low classification accuracy rate, high cost, and large personnel demand. With the increasing amount of garbage, it is difficult for traditional methods to effectively separate waste. Therefore, consider applying deep learning to the garbage classification problem. This paper uses two types of Convolutional Neural Networks (CNNs)--Inception V3 and Inception V4 to train Huawei's public garbage data set (Garbage Date) and establish a garbage classification model. After experiments, the classification results are compared and the performance of the model is tested. In this paper, by observing the changes in the accuracy rate and cross-entropy loss function of the two models on the training and test sets in the experiment, it is found that both models can obtain higher accuracy of garbage classification The network model using Inception V4 is more stable and accurate than the network model using Inception V3. The experimental results also show that this method can improve the accuracy of garbage classification in daily life and the efficiency of recyclable garbage collection.
Neurotensin:a candidate for peptide-guideddrug delivery

Modern pharmacotherapy, especially with regards to the treatment of cancer, has been faced with one major obstacle for many years: poor drug specificity. The use of many biotherapeutic/chemotherapeutic drugs for the treatment of cancer is largely dose-limited due to their highly toxic nature and largely unspecific mode of action, causing 'off target' drug interactions, inadequate drug concentrations at 'on target' areas and poor patient compliance due to the necessity to receive large amounts of a drug that hinder quality of life. Currently available research into the development of recombinant biotherapeutic drugs, targeted to specific ligand receptors, has presented a new mode of drug delivery, which could potentially avoid these problems – drug delivery selective to the tissue of choice with minimal damage to physiologically normal cells. Neurotensin (NT) is a tridecapeptide known to exert a variety of effects on the human body; it has a dual role as both a neuromodulator in the central nervous system and as a local hormone in the periphery. The actions of neurotensin are mediated by specific interactions with one of three neurotensin receptors: NTS1, NTS2 and NTS3, all of which are known to internalise upon interaction with neurotensin. NTS1 in particular is thought to play the biggest role in eliciting the actions of neurotensin and is the main focus of this study; as NTS1 is known to be up regulated in a variety of different cancer types, including pancreatic and breast tumours, it could potentially serve as a target for neurotensin-mediated drug-delivery. The data presented in this study characterises the extent of expression of NTS1 in both polarised and non-polarised Caco-2, and HEK293 cell lines. The uptake and transport of two NT-conjugated fluorophores (GFP and fluorescein) were compared to evaluate the affect of cargo size on cellular uptake. Investigation into uptake and transport of the two fluorophores, elicited by neurotensin, showed both to be internalised in a receptor mediated fashion with the smaller of the two (fluorescein) exhibiting internalisation and transport to a greater extent than the larger (GFP). Visualisation by confocal microscopy showed fluorophore localisation close to the nucleus of the cell. From a more general perspective, this work aims to elucidate the potential for neurotensin to be employed to enhance the delivery of bio-therapeutics to treat 'difficult' cancers, such as pancreatic cancer, which is notorious for poor survival rates and is known to overexpress receptors for neurotensin. If successful, neurotensin-conjugation of bio-therapeutics could improve survival rates by lowering necessary dosages and increasing cancer cell specificity.