Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a pigment-based whole-cell biosensor for the analysis of environmental copper

Using engineered microorganisms to detect heavy metals in the environment has proven to be highly effective and robust. This paper reports on the development of a novel microbial sensor for the detection of copper ions. To develop this microbial sensor, we screened and characterized various biological parts, including promoters, output signals, and hosts. In addition, we used the plant pigment betaxanthin to output fluorescent signals in order to reduce the detection time. The resulting whole-cell biosensor presented a good sensitivity when detecting copper ions in environmental samples including freshwater pond and tap water.
Thermodynamic Studies on DNA Unzipping

Prediction of DNA duplex stability and thermodynamics is invaluable for many molecular biology applications. Sequence dependent stability of duplex DNA plays a major role in fundamental processes of the living cell, such as replication and transcription. In these fundamental processes, DNA must undergo dynamical and structural changes. Since these processes require a local opening of the DNA molecule, they resemble DNA denaturation, or DNA melting, which is a considerably simpler process to study theoretically and experimentally. For this, In-vitro we need to understand how the CELL environment affects the equilibrium properties and the dynamics of the DNA molecule. After the discovery of the helix structure, the attention was for a long time directed at the DNA denaturation, specifically the effect of chain sequence and length. In past few years a particular interest has been devoted to the study of mechanical strand separation shading light on the role of thermal fluctuations and sequence. In this work, the denaturation and mechanical unzipping of DNA were explored by numerical simulations applying the PBD model. Although DNA dynamics has gained increased interest during the last decades, there is still a need of more insight and knowledge within this field. This thesis work contains the first quantitative study of DNA denaturation as well as the DNA unzipping in concentrated and crowded solutions through the well-known PBD mesoscopic model. We study the thermal melting of short heterogeneous DNA sequences as well as the complete unzipping of λ-phase DNA at different salt conditions. We also study the unzipping of short DNA molecules having defects or mismatches at different locations in the sequence. The outcome elucidate the role of loop entropy and bubble formation in opening of DNA molecules. In case of force induced unzipping not only the magnitude of the applied force is important, but the nature of force, how and where force is applied, is also important in the understanding of unzipping of dsDNA. How and up-to which length the applied force propagates, when it is applied at different sites in the chain, is introduced. We also find the thermal stability of homogeneous DNA molecule in crowded solution. Through the density profile we clearly show how the crowders block the
Medium band gap conjugated polymers from thienoacene derivatives and pentacyclic aromatic lactam as promising alternatives of poly(3‐hexylthiophene) in photovoltaic application

Two alternating medium band gap conjugated polymers (PBDT-TPTI and PDTBDT-TPTI) derived from 4,8-bis(4,5-dioctylthien-2-yl)benzo[1,2-b:4,5-b′]dithiophene (BDT-T) or 5,10-bis(4,5-didecylthien-2-yl)dithieno[2,3-d:2′,3′-d′]benzo[1,2-b:4,5-b′]dithiophene (DTBDT-T) with pentacyclic aromatic lactam of N,N-didodecylthieno[2′,3′:5,6]pyrido[3,4-g]thieno[3,2-c]-iso-quinoline-5,11-dione (TPTI), are synthesized and characterized. The comparative investigation of the photostabilities of the copolymers revealed that the PDTBDT-TPTI film exhibited the comparable photostability in relative to P3HT. Meanwhile, the inverted photovoltaic cells (i-PVCs) from the blend films of PBDT-TPTI and/or PDTBDT-TPTI with PC71BM, in which poly[(9,9-bis(3′-(N,N-dimethylamino)propyl)-2,7-fluorene)-alt-2,7-(9,9-dioctylfluorene)] were used as cathode modifying interlayer, presented higher power conversion efficiencies (PCEs) of 5.98% and 6.05% with photocurrent response ranging from 300 nm to 650 nm in contrast with the PCEs of 4.48% for the optimal inverted PVCs from P3HT/PC71BM under AM 1.5 G 100 mW/cm2. The PCEs of the i-PVCs from PBDT-TPTI and PDTBDT-TPTI were improved to 7.58% and 6.91% in contrast to that of 0.02% for the P3HT-based i-PVCs, and the photocurrent responses of the devices were extended to 300–792 nm, when the ITIC was used as electron acceptor materials. The results indicate that the PBDT-TPTI and PDTBDT-TPTI can be used as the promising alternatives of notable P3HT in the photovoltaic application. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2017.
Combining the Projective Consciousness Model and Virtual Humans for Immersive Psychological Research: A Proof-of-concept Simulating a ToM Assessment

Relating explicit psychological mechanisms and observable behaviours is a central aim of psychological and behavioural science. One of the challenges is to understand and model the role of consciousness and, in particular, its subjective perspective as an internal level of representation (including for social cognition) in the governance of behaviour. Toward this aim, we implemented the principles of the Projective Consciousness Model (PCM) into artificial agents embodied as virtual humans, extending a previous implementation of the model. Our goal was to offer a proof-of-concept, based purely on simulations, as a basis for a future methodological framework. Its overarching aim is to be able to assess hidden psychological parameters in human participants, based on a model relevant to consciousness research, in the context of experiments in virtual reality. As an illustration of the approach, we focused on simulating the role of Theory of Mind (ToM) in the choice of strategic behaviours of approach and avoidance to optimise the satisfaction of agents’ preferences. We designed a main experiment in a virtual environment that could be used with real humans, allowing us to classify behaviours as a function of order of ToM, up to the second order. We show that agents using the PCM demonstrated expected behaviours with consistent parameters of ToM in this experiment. We also show that the agents could be used to estimate correctly each other’s order of ToM. Furthermore, in a supplementary experiment, we demonstrated how the agents could simultaneously estimate order of ToM and preferences attributed to others to optimize behavioural outcomes. Future studies will empirically assess and fine tune the framework with real humans in virtual reality experiments.
Keeping Promises? Investment Objectives and Impact of Carbon Risk Disclosures

Morningstar’s disclosure of carbon risk (CR) scores in May 2018 showed that (environmentally) sustainable mutual funds in the US had lower CR scores before the disclosure and a greater reduction in their portfolio CR scores after the announcement relative to convention funds. The observed causal impact of disclosures is consistent with funds’ primary investment objectives. Conventional funds that are signatories to UN’s Principles for Responsible Investment (PRI) or those with secondary sustainability mandates behave more like other conventional funds rather than sustainable funds. Such funds appear insensitive to disclosures as their sustainability considerations are superseded by other primary investment criteria. Sustainable funds lower their CR score by reducing exposure to fossil fuels, not by increasing exposure to renewables.
Safe, Deterministic Trajectory Planning for Unstructured and Partially Occluded Environments

Ensuring safe behavior for automated vehicles in unregulated traffic areas poses a complex challenge for the industry. It is an open problem to provide scalable and certifiable solutions to this challenge. We derive a trajectory planner based on model predictive control which interoperates with a monitoring system for pedestrian safety based on cellular automata. The combined planner-monitor system is demonstrated on the example of a narrow indoor parking environment. The system features deterministic behavior, mitigating the immanent risk of black boxes and offering full certifiability. By using fundamental and conservative prediction models of pedestrians the monitor is able to determine a safe drivable area in the partially occluded and unstructured parking environment. The information is fed to the trajectory planner which ensures the vehicle remains in the safe drivable area at any time through constrained optimization. We show how the approach enables solving plenty of situations in tight parking garage scenarios. Even though conservative prediction models are applied, evaluations indicate a performant system for the tested low-speed navigation.
Adjustable 3D magnetic configuration in ferrimagnetic multilayers with competing interactions visualized by soft X-ray vector tomography

Soft X-ray magnetic vector tomography has been used to visualize with unprecedented detail and solely from experimental data the 3D magnetic configuration of a ferrimagnetic Gd12Co88/Nd17Co83/Gd24Co76 multilayer with competing anisotropy, exchange and magnetostatic interactions at different depths. The trilayer displays magnetic stripe domains, arranged in a chevron pattern, which are imprinted from the central Nd17Co83 into the bottom Gd12Co88 layer with a distorted closure domain structure across the thickness. Near the top Gd24Co76 layer, local exchange springs with out-of-plane magnetization reversal, modulated ripple patterns and magnetic vortices and antivortices across the thickness are observed. The detailed analysis of the magnetic tomogram shows that the effective strength of the exchange spring at the NdCo/GdCo interface can be finely tuned by GdxCo1-x composition and anisotropy (determined by sample fabrication) and in-plane stripe orientation (adjustable), demonstrating the capability of 3D magnetic visualization techniques in magnetic engineering research.
Ice mantles on dust grains: dramatic variation of thickness with grain size

We compute the desorption rate of icy mantles on dust grains as a function of the size and composition of both the grain and the mantle. We combine existing models of cosmic ray (CR)-related desorption phenomena with a model of CR transport to accurately calculate the desorption rates in dark regions of molecular clouds. We show that different desorption mechanisms dominate for grains of different sizes and in different regions of the cloud. We then use these calculations to investigate a simple model of the growth of mantles, given a distribution of grain sizes. We find that modest variations of the desorption rate with grain size lead to a strong dependence of mantle thickness on grain size. Furthermore, we show that freeze-out is almost complete in the absence of an external ultraviolet (UV) field, even when photodesorption from CR-produced UV is taken into consideration. Even at gas densities of $10^4\, {\rm cm^{-3}}$, less than 30 per cent of the CO remain in the gas phase after 3 × 105 yr for standard values of the CR ionization rate.
PPT: Pre-trained Prompt Tuning for Few-shot Learning

Prompts for pre-trained language models (PLMs) have shown remarkable performance by bridging the gap between pre-training tasks and various downstream tasks. Among these methods, prompt tuning, which freezes PLMs and only tunes soft prompts, provides an efficient and effective solution for adapting large-scale PLMs to downstream tasks. However, prompt tuning is yet to be fully explored. In our pilot experiments, we find that prompt tuning performs comparably with conventional full-model tuning when downstream data are sufficient, whereas it is much worse under few-shot learning settings, which may hinder the application of prompt tuning. We attribute this low performance to the manner of initializing soft prompts. Therefore, in this work, we propose to pre-train prompts by adding soft prompts into the pre-training stage to obtain a better initialization. We name this Pre-trained Prompt Tuning framework “PPT”. To ensure the generalization of PPT, we formulate similar classification tasks into a unified task form and pre-train soft prompts for this unified task. Extensive experiments show that tuning pre-trained prompts for downstream tasks can reach or even outperform full-model fine-tuning under both full-data and few-shot settings. Our approach is effective and efficient for using large-scale PLMs in practice.
MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction

Neural relation extraction models have shown promising results in recent years; however, the model performance drops dramatically given only a few training samples. Recent works try leveraging the advance in few-shot learning to solve the low resource problem, where they train label-agnostic models to directly compare the semantic similarities among context sentences in the embedding space. However, the label-aware information, i.e., the relation label that contains the semantic knowledge of the relation itself, is often neglected for prediction. In this work, we propose a framework considering both label-agnostic and label-aware semantic mapping information for low resource relation extraction. We show that incorporating the above two types of mapping information in both pretraining and fine-tuning can significantly improve the model performance on low-resource relation extraction tasks.
Comprehensive analysis of spherical bubble oscillations and shock wave emission in laser-induced cavitation

Abstract The dynamics of spherical laser-induced cavitation bubbles in water is investigated by plasma photography, time-resolved shadowgraphs and sensitive single-shot probe beam scattering that portrays the transition from initial nonlinear to late linear oscillations. The frequency of late oscillations yields the bubble's gas content. Numerical simulations with an extended Gilmore model using plasma size as input and oscillation times as fit parameter provide insights into experimentally not accessible bubble parameters and shock wave emission. Model extensions include a term covering the initial shock-driven acceleration of the bubble wall, an automated method determining shock front position and pressure decay and a complete energy balance for the partitioning of absorbed laser energy into vaporization, bubble and shock wave energy and dissipation through viscosity and condensation. These tools are used for analysing a scattering signal covering 102 oscillation cycles from a bubble with 36 μm maximum radius produced by a plasma with 1550 K average temperature. Predicted bubble wall velocities during expansion agree well with experimental data. Upon first collapse, most energy was stored in the compressed liquid around the bubble and radiated away acoustically. The collapsed bubble contained more vapour than gas and had a pressure of 13.5 GPa. The decay of the rebound shock wave pressure with radius r was initially $\mathrm{\ \propto }{r^{ - 1.8}}$, and energy dissipation at the shock front heated the liquid near the bubble wall to temperatures above the superheat limit. The shock-induced temperature rise reduces damping during late bubble oscillations. Damping in first collapse increases significantly for small bubbles with less than 10 μm radius.
SORNet: Spatial Object-Centric Representations for Sequential Manipulation

Sequential manipulation tasks require a robot to perceive the state of an environment and plan a sequence of actions leading to a desired goal state. In such tasks, the ability to reason about spatial relations among object entities from raw sensor inputs is crucial in order to determine when a task has been completed and which actions can be executed. In this work, we propose SORNet (Spatial Object-Centric Representation Network), a framework for learning object-centric representations from RGB images conditioned on a set of object queries, represented as image patches called canonical object views. With only a single canonical view per object and no annotation, SORNet generalizes zero-shot to object entities whose shape and texture are both unseen during training. We evaluate SORNet on various spatial reasoning tasks such as spatial relation classification and relative direction regression in complex tabletop manipulation scenarios and show that SORNet significantly outperforms baselines including state-of-the-art representation learning techniques. We also demonstrate the application of the representation learned by SORNet on visual-servoing and task planning for sequential manipulation on a real robot.
Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems

Human dialogue contains evolving concepts, and speakers naturally associate multiple concepts to compose a response. However, current dialogue models with the seq2seq framework lack the ability to effectively manage concept transitions and can hardly introduce multiple concepts to responses in a sequential decoding manner. To facilitate a controllable and coherent dialogue, in this work, we devise a concept-guided non-autoregressive model (CG-nAR) for open-domain dialogue generation. The proposed model comprises a multi-concept planning module that learns to identify multiple associated concepts from a concept graph and a customized Insertion Transformer that performs concept-guided non-autoregressive generation to complete a response. The experimental results on two public datasets show that CG-nAR can produce diverse and coherent responses, outperforming state-of-the-art baselines in both automatic and human evaluations with substantially faster inference speed.
Analysts’ IFRS Knowledge, Forecast Error, and Sec’S Elimination of the 20-F Reconciliation

Foreign private issues (FPI) with trading shares in the United States needed to reconcile their annual financial reports (20-F) to the U.S. Generally Accepted Accounting Principles (GAAP) if they prepare the statements with International Financial Reporting Standards (IFRS). However, in November 2007, the SEC eliminated the 20-F reconciliation requirement. Prior studies have investigated the consequences of removing the reconciliation from country-level as well as firm-level characteristics, and have found mixed results. In this paper, we test the effect of the elimination on analyst forecast error based on analyst individual characteristics. Specifically, we examine whether the effect varies with analysts’ knowledge of IFRS. If there is any information loss from removing the reconciliation, the negative impact would be stronger for analysts without IFRS expertise. Therefore, these analysts’ forecast error might become larger after the elimination, relative to before the elimination. We test our conjecture with a set of hand-collected data of analysts who follow foreign IFRS filers from 2005 to 2009. Results suggest that, in general, there is no significant change in terms of analysts’ forecast error before and after eliminating the reconciliation. However, for analysts who do not have knowledge of IFRS, their forecast error significantly increased in the post-elimination period, while this change is not observed in analysts with IFRS knowledge. Our results not only provide supporting evidence to prior studies and the SEC’s Final Rule (2007), but also highlight the importance of analysts’ individual characteristics on their forecast properties.
The complete chloroplast genome characteristics of Polygala crotalarioides Buch.-Ham. ex DC. (Polygalaceae) from Yunnan, China

Abstract Polygala crotalarioides Buch. -Ham. ex DC. (Polygalaceae) is a perennial herbaceous plant widely distributed in southwest China. Its chloroplast genome sequence was obtained using Illumina NovaSeq sequencing technology, and then was assembled, annotated, and characterized. The total length of chloroplast genome is 164,269 bp and it contained a large (LCS, 82,697 bp) and a small (SSC, 8083 bp) single copy region, separated by a pair of inverted repeats (IRA/IRB, 36,744 bp). The overall GC content of genome was 36.8% and corresponding values of the LSC, SSC, and IR regions are 34.9%, 29.5%, and 39.7%, respectively. There are 135 genes that were predicted including 89 protein-coding, 38 tRNA, and 8 rRNA genes. The maximum-likelihood (ML) phylogenetic tree reconstructed by IQ-TREE indicated that P. crotalarioides had a strong genetic relationship with P. tenuifolia, P. sibirica, and P. japonica. In summary, the complete chloroplast genome of P. crotalarioides is reported for the first time, which is of great significance for future research on the evolutionary phylogeny and protection of Polygala.
Community engagement in the prevention and control of COVID-19: Insights from Vietnam

Introduction Community engagement (CE) is an effective public health strategy for improving health outcomes. There is limited published knowledge about effective approaches to CE in ensuring effective responses to COVID-19 throughout lockdowns, travel restrictions and social distancing. In this paper, we contribute to bridging this gap by highlighting experience of CE in Vietnam, specifically focusing on migrant workers in Vietnam. Methods A cross-sectional qualitative study design was used with qualitative data collection was carried out during August-October 2020. Two districts were purposefully selected from two large industrial zones. Data was collected using in-depth interviews (n = 36) with individuals and households, migrants and owners of dormitories, industrial zone factory representatives, community representatives and health authorities. Data was analyzed using thematic analysis approach. The study received ethics approval from the Hanoi University Institutional Review Board. Results The government’s response to COVID-19 was spearheaded by the multi-sectoral National Steering Committee for the Prevention and Control of COVID-19, chaired by the Vice Prime Minister and comprised different members from 23 ministries. This structure was replicated throughout the province and local levels and all public and private organizations. Different activities were carried out by local communities, following four key principles of infection control: early detection, isolation, quarantine and hospitalization. We found three key determinants of engagement of migrant workers with COVID-19 prevention and control: availability of resources, appropriate capacity strengthening, transparent and continuous communication and a sense of trust in government legitimacy. Discussion and conclusion Our results support the current literature on CE in infection control which highlights the importance of context and suggests that future CE should consider five key components: multi-sectoral collaboration with a whole-of-community approach to strengthen governance structures with context-specific partnerships; mobilization of resources and decentralization of decision making to encourage self-reliance and building of local capacity; capacity building through training and supervision to local institutions; transparent and clear communication of health risks and sensitization of local communities to improve compliance and foster trust in the government measures; and understanding the urgent needs ensuring of social security and engaging all parts of the community, specifically the vulnerable groups.