Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Temperature Performance of Leading Bioenergy Crops Utilizing the C4 Photosynthetic Pathway

The use of bioenergy has been considered one of the most important solutions for the reduction in the dependence on fossil fuels and to mitigate global climate change. C4 perennial grasses stands as the best alternative for biofuel feedstock and Miscanthus x giganteus has shown outstanding performance in cool climates. In addition, sugarcane has a history of high productivity and success in the biofuel industry of Brazil. The high photosynthetic rate at low temperature (14o to 20oC) and radiation use efficiency of M. x giganteus allow this plant to build a vast canopy early in the spring, allowing it to intercept high amounts of solar radiation once temperature rise in summer, obtaining high yields of biomass by the end of growing season. It is unclear, however, how Miscanthus will tolerate the severe winter cold and frequent episodic frost in the spring of higher latitudes, like Canada. Here, I show that rhizomes of diploid Miscanthus can tolerate temperatures above -14oC if sub-zero acclimation is allowed, while allopolyploid Miscanthus are tolerant to temperatures above -6.5oC. Also, in contrary to M. sinensis, shoots of Miscanthus hybrids were killed when exposed to sunlight at temperatures below 10oC in the spring. However, triploid Miscanthus showed great recovery and three weeks
Progress in Whole-Tree Architectural Studies for Apple Cultivar Characterization at INRA, France - Contribution to the Ideotype Approach

In France, the determination of fruiting type and tree ideotype has been a first step in the description of the variability of the whole-tree shape in relation to fruiting behaviour. Although the ranking into four ideotypes gives an easy-to-use criterion to discriminate between cultivars, it does not clearly address the various strategies of growth, branching and fruiting among apple cultivars. Over the last decade, detailed architectural investigations have been developed at trunk and branch levels. Both frequencies and spatial distribution of (i) types (vegetative sylleptic or proleptic, flowering, latent); and (ii) length of laterals (spur, long shoot) on 1-year old wood and during further development have been shown to follow specific patterns depending on the cultivar. This paper will discuss the interest of these architectural analyses for cultivar characterization with the objective to provide the apple breeder with an apple tree ideotype for low-input regular-bearing cultivar.
Methodologies for Estimating Traffic Flow on Freeways Using Probe Vehicle Trajectory Data

METHODOLOGIES FOR ESTIMATING TRAFFIC FLOW ON FREEWAYS USING PROBE VEHICLE TRAJECTORY DATA Khairul Azfi Anuar Old Dominion University, 2016 Director: Dr. Mecit Cetin Probe vehicle data are increasingly becoming the primary source of traffic data. As probe vehicle data become more widespread, it is imperative that methods are developed so that traffic state estimators such as flow, density, and speed can be derived from such data. In this dissertation three different methodologies are proposed for predicting traffic flow or volume on a freeway. All of the proposed methodologies exploit several different traffic flow theories in conjunction with probe vehicle data to predict traffic flow. The first methodology takes advantage of the fundamental diagram or speed-flow relationship. The relationship states that flow can be estimated when speed is known. In this case, flow is traffic volume and speed comes from probe vehicles. Flow is predicted for four different models of fundamental diagrams and is analyzed at different time aggregation intervals. Results show that of the four fundamental diagrams, Van Aerde’s Model is the best performing model with the lowest average percent error. It is also observed that flow prediction is more accurate during low speed (congestion) compared to high speed (free-flow) conditions. The second methodology exploits the shockwave theory, which pertains to the propagation of a change (discontinuity) in traffic flow. From probe vehicle trajectories, shockwave is estimated as the boundary between free-flow and congested regimes of traffic flow. After clustering the traffic regimes into free-flow and congested periods, the traffic flow during congestion is estimated using the Northwestern congested-regime fundamental diagram. From this estimation, the flow during free-flow is then predicted. Analyses show that the percent error of the predicted flow during free-flow ranges from -9 to 1%. The third methodology is the car-following approach which relies on the spacing or distance between a leader and follower which can be directly measured from the trajectories. Based on a set of known probability distributions, the position of the follower vehicle with respect to the lead vehicle is estimated given that the spacing between the two random probe vehicles is known. A framework is developed to automatically process probe trajectories to extract relevant probe data under stop-and-go traffic conditions. The model is tested based on NGSIM datasets. The results show that when vehicle spacing is small the prediction of follower position is very accurate. As spacing increases the error in predicted follower position also increases. Though there exists some estimation error, all three approaches can reasonably predict flow for freeways using probe vehicle data.
Measurement of high accuracy flatness using a commercial interferometer

Higher accuracy of optical surface measurement is needed with the rapid development of optical industry and technology, especially in the field of optical manufacture and optical metrology. Fizeau interferometery is widely recognized as one of the most important measurement techniques currently in use. Results of the Fizeau interferometeric testing contain the reference surface errors and test surface errors. The test accuracy is restricted by the error of reference surface. As a result, so-called absolute flatness testing technology must be used to eliminate the system error such as reference surface error and higher accuracy of the surface profile testing. In this paper, the theory formula of even and odd functions is deduced, and the method are programmed for simulation. Based on HOOL phase-shifting interferometer, experiments are done to achieve high accuracy flatness measurement. The experiment shows that with high-accuracy interferometer, absolute flatness testing technology can effectively calibrate reference surface and improve the accuracy of flatness surface testing. An important result of this experiment is that the accuracy of the test surface can be reached λ/50.
MaSln1, a Conserved Histidine Protein Kinase, Contributes to Conidiation Pattern Shift Independent of the MAPK Pathway in Metarhizium acridum

The productivity and quality of conidia are both crucial for mycopesticides. In this study, we systematically analyzed the roles of MaSln1 in fungal pathogens. ABSTRACT As a conserved sensor kinase in the HOG-MAPK pathway, Sln1 plays distinct functions in different fungi. In this study, the roles of MaSln1 in Metarhizium acridum were analyzed using gene knockout and rescue strategies. Deletion of MaSln1 did not affect conidial germination, conidial yield, or resistance to chemical agents. However, fungal tolerance to heat shock and UV-B were significantly reduced after deletion of MaSln1. Insect bioassays showed that fungal pathogenicity was significantly impaired when MaSln1 was deleted. Further studies showed that MaSln1 did not affect either germination or appressorium formation of M. acridum on locust wings, but it significantly increased appressorium turgor pressure. In addition, disruption of MaSln1 resulted in a conidiation pattern shift in M. acridum. Microscopic observation revealed, however, that some genes located in the MAPK signaling pathway, including MaSho1, MaHog1, MaMk1, and MaSlt2, were not involved in the conidiation pattern shift on SYA medium (microcycle medium). Meanwhile, of the 143 differently expressed genes (DEGs) identified by RNA-seq, no genes related to the MAPK pathway were found, suggesting that MaSln1 regulation of the conidiation pattern shift was probably independent of the conserved MAPK signaling pathway. It was found that 22 of the 98 known DEGs regulated by MaSln1 were involved in mycelial growth, cell division, and cytoskeleton formation, indicating that MaSln1 likely regulates the expression of genes related to cell division and morphogenesis, thus regulating the conidiation pattern shift in M. acridum. IMPORTANCE The productivity and quality of conidia are both crucial for mycopesticides. In this study, we systematically analyzed the roles of MaSln1 in fungal pathogens. Most importantly, our results revealed that deletion of MaSln1 resulted in a conidiation pattern shift in M. acridum. However, some other genes, located in the MAPK signaling pathway, were not involved in the conidiation pattern shift. RNA-seq revealed no genes related to the MAPK pathway, suggesting that the regulation of the conidiation pattern shift by MaSln1 was probably independent of the conserved MAPK signaling pathway. This study provided a new insight into the functions of Sln1 and laid a foundation for exploring the mechanisms of conidiation pattern shifts in M. acridum.
Lung fibrosis is a novel therapeutic target to suppress lung metastasis of osteosarcoma

The prognosis of patients with metastatic and recurrent osteosarcoma has not improved over the last 30 years because no effective treatment strategy has been established for lung metastases. Although molecular‐targeted drugs that modify the extracellular environment, such as antifibrotic agents, have been developed for cancer treatment, the suppressive effects of antifibrotic agents on osteosarcoma lung metastasis are unclear. Osteosarcomas need to adapt to considerable changes with respect to the stiffness of the environment and fibrosis during lung metastasis and may thus be vulnerable to fibrotic suppression as they originate at the site of a stiff bone with considerable fibrosis. In our study, we investigated whether fibrosis was a therapeutic target for suppressing osteosarcoma metastasis. Lung tissue samples from patients and a mouse model (LM8‐Dunn model) showed that lung metastatic colonization of osteosarcoma cells proceeded with massive lung fibrosis. Metastatic osteosarcoma LM8 cells proliferated in a scaffold‐dependent manner; the proliferation was less dependent on YAP‐mediated mechanotransduction on soft polyacrylamide gels. The antifibrotic agents pirfenidone and nintedanib suppressed lung metastasis in the LM8‐Dunn model. The osteosarcoma cells did not show increased proliferation, as reported in breast cancer, after continuous culture in a soft environment. We speculated that the antifibrotic agents were effective because the osteosarcoma cells remained scaffold‐dependent in the soft tissue environment. Thus, antifibrotic strategies may be useful in suppressing lung metastasis of bone and soft tissue tumors with stiff primary sites such as those in osteosarcoma.
High performance work systems and workplace safety : a multilevel approach

Research in workplace safety has focused on either the physical approach that emphasizes ergonomic design of the environment and work-related equipment, an environmental approach that emphasizes potential hazards such as noise, toxins, and temperature, or a behavioural approach that emphasizes changing employee behaviours that are deemed to be responsible for workplace incidents such as accidents, injuries, fatalities, and safetyrelated events. I built on this research (i) to develop and validate an HPWS for safety scale, and (ii) to propose and test a model of processes through which unit level HPWS for safety influence individual level safety outcomes. This thesis reports the findings of four studies. Three of these studies focused on the development and validation of an HPWS for safety scale while the fourth study used the validated scale to examine the processes through which HR practices influence workplace safety. Data were analyzed using multilevel structural equation modelling (MSEM) with Mplus version 7.3. The findings revealed that (i) experienced HPWS for safety directly relates to safety behaviours and also indirectly through safety knowledge and safety motivation, (ii) experienced HPWS for safety relates to both safety-specific events and workplace injuries through a mediational chain of safety knowledge and safety compliance, (iii) experienced HPWS for safety relates to workplace injuries through a mediational chain of safety knowledge and safety initiative, (iv) experienced HPWS for safety relates to both safety-related events and workplace injuries via safety motivation and safety compliance, (v) the use of HPWS for safety significantly related to unit safety climate, (vi) the use of HPWS for safety significantly related to experienced HPWS for safety while unit-level safety climate moderated the safety knowledge-safety compliance relationship. Lastly, the findings provide support for the psychometric properties of the scale. I also discussed the theoretical and practical implications of the findings, highlighted the study’s strengths and limitations, and then mapped out some directions for future research.
Associations between perceived everyday discrimination and health among college students at a Hispanic-serving institution

Despite efforts by universities to promote racial/ethnic, gender, and socioeconomic diversity, college students continue to report discrimination. In two studies, we examined the frequency, predictors, and health consequences of experiencing everyday discrimination at a Hispanic-Serving Institution. Findings show the majority of students reported experiencing discrimination at the university, with most experiences attributed to their gender and aspects of their physical appearance. More frequent discrimination was associated with poorer physical and psychological health. Furthermore, most participants cited other students as the source of their discrimination. These findings offer important insight into students’ experiences of everyday discrimination at a diverse setting.
Systematic training in master swimmer athletes increases serum insulin growth factor-1 and decreases myostatin and irisin levels

Abstract During ageing, anabolic status is essential to prevent the decrease in quantity and quality of skeletal muscle mass (SMM). Exercise modulates endocrine markers of muscle status. We studied the differences of endocrine markers for muscle status in 62 non-sarcopenic Mexican swimmer adults aged 30–70 y/o, allocated into two groups: the systematic training (ST) group including master athletes with a physical activity level (PAL) >1.6, and the non-systematic training group (NST) composed by subjects with a PAL <1.5. Body composition, diet, biochemical and endocrine markers were analyzed. The ST group showed lower myostatin (MSTN) and irisin (IRI) levels, two strong regulators of SMM. The insulin growth factor-1 (IGF-1) was higher in the ST. This is consistent with most of the evidence in young athletes and resistance training programs, where IGF-1 and IRI seem to play a crucial role in maintaining anabolic status in master athletes.
Effects of Gradient and Speed on Uphill Running Gait Variability

Background: The aim of this study was to investigate the effects of gradient and speed on running variability (RV) and local dynamic stability (LDS) during uphill running. Hypotheses: (1) Both gradient and speed increase metabolic effort, in terms of heart rate (HR) and perceived exertion (CR10), in line with the contemporary literature, and (2) gradient increases RV and impairs LDS. Study design: “Crossover” observational design. Level of Evidence: Level 3. Methods: A total of 25 runners completed 10-minute running trials in 3 different conditions and in a randomized order: gradient at 0% (0CON), 2% (2CON), and 2% at isoefficiency speed (2IES). 0CON and 2CON speeds were calculated as the “best 10-km race performance” minus 1 km·h−1, whereas 2IES speed was adjusted to induce the same metabolic expenditure as 0CON. HR and perceived exertion as well as running kinematic variables were collected across all trials and conditions. Running variability was calculated as the standard deviation of the mean stride-to-stride intervals over 100 strides, while LDS was expressed by the Lyapunov exponent (LyE) determined on running cycle time over different running conditions. Results: Increases in HR and CR10 were observed between 0CON and 2CON (P < 0.001) and between 2IES and 2CON (P < 0.01). Higher RV was found in 2CON compared with 0CON and 2IES (both P < 0.001). Finally, the largest LyE was observed in 2IES compared with 0CON and 2CON (P = 0.02 and P = 0.01, respectively). Conclusion: Whereas RV seems to be dependent more on metabolic effort, LDS is affected by gradient to a greater extent. Clinical Relevance: Running variability could be used to monitor external training load in marathon runners.
Complete Genome Sequence of Enterococcus faecalis Siphophage Sigurd

Enterococcus faecalis is associated with antibiotic-resistant infections, and this study presents E. faecalis siphophage Sigurd. The 41,811-bp Sigurd genome is divided into two arms defined by long convergent predicted transcription units that are separated by a bidirectional rho-independent terminator. Sigurd has a small terminase that is closely related to Bacillus subtilis cos phage phi105. ABSTRACT Enterococcus faecalis is associated with antibiotic-resistant infections, and this study presents E. faecalis siphophage Sigurd. The 41,811-bp Sigurd genome is divided into two arms defined by long convergent predicted transcription units that are separated by a bidirectional rho-independent terminator. Sigurd has a small terminase that is closely related to Bacillus subtilis cos phage phi105.
A Novel Neuromorphic Processors Realization of Spiking Deep Reinforcement Learning for Portfolio Management

The process of constantly reallocating budgets into financial assets, aiming to increase the anticipated return of assets and minimizing the risk, is known as portfolio management. Processing speed and energy consumption of portfolio management have become crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces and environment uncertainty, which their limited onboard resources cannot offset. Emerging neuromorphic chips inspired by the human brain increase processing speed by up to 500 times and reduce power consumption by several orders of magnitude. This paper proposes a spiking deep reinforcement learning (SDRL) algorithm that can predict financial markets based on unpredictable environments and achieve the defined portfolio management goal of profitability and risk reduction. This algorithm is optimized for Intel's Loihi neuromorphic processor and provides 186x and 516x energy consumption reduction compared to a high-end processor and GPU, respectively. In addition, a 1.3x and 2.0x speed-up is observed over the high-end processors and GPUs, respectively. The evaluations are performed on cryptocurrency market benchmark between 2016 and 2021.
Exploring Self-Attention for Visual Intersection Classification

Self-attention has recently emerged as a technique for capturing non-local contexts in robot vision. This study introduced a self-attention mechanism into an intersection recognition system to capture non-local contexts behind the scenes. This mechanism is effective in intersection classification because most parts of the local pattern (e.g., road edges, buildings, and sky) are similar; thus, the use of a non-local context (e.g., the angle between two diagonal corners around an intersection) would be effective. This study makes three major contributions to existing literature. First, we proposed a self-attention-based approach for intersection classification. Second, we integrated the self-attention-based classifier into a unified intersection classification framework to improve the overall recognition performance. Finally, experiments using the public KITTI dataset showed that the proposed self-attention-based system outperforms conventional recognition based on local patterns and recognition based on convolution operations.
Demonstrating CAT: Synthesizing Data-Aware Conversational Agents for Transactional Databases

Databases for OLTP are often the backbone for applications such as hotel room or cinema ticket booking applications. However, developing a conversational agent (i.e., a chatbot-like interface) to allow end-users to interact with an application using natural language requires both immense amounts of training data and NLP expertise. This motivates CAT , which can be used to easily create conversational agents for transactional databases. The main idea is that, for a given OLTP database, CAT uses weak supervision to synthesize the required training data to train a state-of-the-art conversational agent, allowing users to interact with the OLTP database. Furthermore, CAT provides an out-of-the-box integration of the resulting agent with the database. As a major difference to existing conversational agents, agents synthesized by CAT are data-aware. This means that the agent decides which information should be requested from the user based on the current data distributions in the database, which typically results in markedly more efficient dialogues compared with non-data-aware agents. We publish the code for CAT as open source.
SuperMVS: Non-Uniform Cost Volume For High-Resolution Multi-View Stereo

Different from most state-of-the-art (SOTA) algorithms that use static and uniform sampling methods with a lot of hypothesis planes to get ﬁne depth sampling. In this paper, we propose a free-moving hypothesis plane method for dynamic and non-uniform sampling in a wide depth range to build the cost volume, which not only greatly reduces the number of planes but also ﬁners sampling, for both of reducing computational cost and improving accuracy, named Non-Uniform Cost Volume . We present the SuperMVS network to implement Multi-View Stereo with Non-Uniform Cost Volume. SuperMVS is a coarse-to-ﬁne framework with four cascade stages. It can output higher resolution and accurate depth map. Our SuperMVS achieves the SOTA results with low memory, low runtime, and fewer planes on the DTU datasets and Tanks & Temples dataset.
Continual learning of quantum state classification with gradient episodic memory

Continual learning is one of the many areas of machine learning research. For the goal of strong artiﬁcial intelligence that can mimic human-level intelligence, AI systems would have the ability to adapt to ever-changing scenarios and learn new knowledge continuously without forgetting previously acquired knowledge. A phenomenon called catastrophic forgetting emerges when a machine learning model is trained across multiple tasks. The model’s performance on previously learned tasks may drop dramatically during the learning process of the newly seen task. Some continual learning strategies have been proposed to address the catastrophic forgetting problem. Recently, continual learning has also been studied in the context of quantum machine learning. By leveraging the elastic weight consolidation method, a single quantum classiﬁer can perform multiple tasks after being trained consecutively on those tasks. In this work, we incorporate the gradient episodic memory method to train a variational quantum classiﬁer. The gradient of the current task is projected to the closest gradient, avoiding the increase of the loss at previous tasks, but allowing the decrease. We use six quantum state classiﬁcation tasks to benchmark this method. Numerical simulation results show that better performance is obtained compared to the elastic weight consolidation method. Furthermore, positive transfer of knowledge to previous tasks is observed, which means the classiﬁer’s performance on previous tasks is enhanced rather than compromised while learning a new task.