Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloning and construction of single-chain variable fragments (scFv) to Cucumber mosaic virus and production of transgenic plants

The variable regions of heavy and light chain genes were cloned from the RNA of mouse hybridoma cells that produce monoclonal antibodies specific to Cucumber mosaic virus (CMV). The cloned genes were constructed into single-chain variable fragments (scFv) in combination with various promoters and transferred into Nicotiana benthamiana leaf tissue using Agrobacterium tumefacien. Transgenic plants were generated and selected using the bar gene that confers resistance to phosphinothricin. From purified genomic plant DNA, the scFv was re-cloned after PCR and sequenced to verify the presence of the transgene. Resistance to CMV infection was evaluated by mechanically inoculating plants with purified CMV. Several R0 transgenic plants exhibited some degree of disease resistance in the first screening, showing no symptoms, reduced symptoms or delayed symptom development. In subsequent challenge inoculations of R1 plants with lower dilutions of inoculum, occasionally plants showed resistance when compared with non-transformed plants, but, overall, results were variable. For each of the four promoters, three or four transgenic plants were generated exhibiting some degree of disease resistance.
Non-Parametric Calibration of Probabilistic Regression

The task of calibration is to retrospectively adjust the outputs from a machine learning model to provide better probability estimates on the target variable. While calibration has been investigated thoroughly in classification, it has not yet been well-established for regression tasks. This paper considers the problem of calibrating a probabilistic regression model to improve the estimated probability densities over the real-valued targets. We propose to calibrate a regression model through the cumulative probability density, which can be derived from calibrating a multi-class classifier. We provide three non-parametric approaches to solve the problem, two of which provide empirical estimates and the third providing smooth density estimates. The proposed approaches are experimentally evaluated to show their ability to improve the performance of regression models on the predictive likelihood.
Corporate Governance Against Recommendations: The Cases of the Strong Executive and the Strong Ownership

There are several basic configurations of corporate governance according to the separation of ownership and control ( Jensen's theory). Effective governance is described as a situation when an owner (or group of owners) keeps the right to ratify and monitor strategic decisions while management has the right to initiate and implement those decisions. There are two particular situations how this recommendation is partially broken and both situations are linked to CEO duality. The first case happens when an owner loses or does not exercise the right to monitor management of the organization and is termed as the strong executive. The second case is called the strong ownership and is distinguished by an owner taking over implementations of the deci- sions. The focus of the study was to explore particularly configurations of the strong executive and the strong governance. A mixed method research design was chosen to explore the differ- ences between the basic governance configurations. The sample was chosen by purposive sam - pling and covered a hundred for-profit organizations of all size and from all sectors of economy. The data were collected through interviews with representatives, mainly members of top man- agement. We revealed that both of these configurations can bear good corporate performance but also bigger risks. The strong executive is typical for organizations with dispersed ownership or a publicly owned organization and the performance of the organization is fully dependent on competencies but also personalities of managers. This configuration contains a high risk of misuse of authority. The strong ownership is effective in small organizations while in a larger organization leads to an overexertion of owners and low performance because they usually face problems to keep focus on the strategic issues of the organization.
A Review of the Use of Linear Programming to Optimize Diets, Nutritiously, Economically and Environmentally

The “Diet Problem” (the search of a low-cost diet that would meet the nutritional needs of a US Army soldier) is characterized by a long history, whereas most solutions for comparable diet problems were developed in 2000 or later, during which computers with large calculation capacities became widely available and linear programming (LP) tools were developed. Based on the selected literature (52 papers), LP can be applied to a variety of diet problems, from food aid, national food programmes, and dietary guidelines to individual issues. This review describes the developments in the search for constraints. After nutritional constraints, costs constraints, acceptability constraints and ecological constraints were introduced. The 12 studies that apply ecological constraints were analyzed and compared in detail. Most studies have used nutritional constraints and cost constraints in the analysis of dietary problems and solutions, but such research begin showing weaknesses under situations featuring a small number of food items and/or nutritional constraints. Introducing acceptability constraints is recommended, but no study has provided the ultimate solution to calculating acceptability. Future possibilities lie in finding LP solutions for diets by combining nutritional, costs, ecological and acceptability constraints. LP is an important tool for environmental optimization and shows considerable potential as an instrument for finding solutions to a variety of very complex diet problems.
The mycoparasitic fungus Clonostachys rosea responds with both common and specific gene expression during interspecific interactions with fungal prey

Clonostachys rosea is a necrotrophic mycoparasitic fungus, used for biological control of plant pathogenic fungi. A better understanding of the underlying mechanisms resulting in successful biocontrol is important for knowledge‐based improvements of the application and use of biocontrol in agricultural production systems. Transcriptomic analyses revealed that C. rosea responded with both common and specific gene expression during interactions with the fungal prey species Botrytis cinerea and Fusarium graminearum. Genes predicted to encode proteins involved in membrane transport, biosynthesis of secondary metabolites and carbohydrate‐active enzymes were induced during the mycoparasitic attack. Predicted major facilitator superfamily (MFS) transporters constituted 54% of the induced genes, and detailed phylogenetic and evolutionary analyses showed that a majority of these genes belonged to MFS gene families evolving under selection for increased paralog numbers, with predicted functions in drug resistance and transport of carbohydrates and small organic compounds. Sequence analysis of MFS transporters from family 2.A. 1.3. 65 identified rapidly evolving loop regions forming the entry to the transport tunnel, indicating changes in substrate specificity as a target for selection. Deletion of the MFS transporter gene mfs464 resulted in mutants with increased growth inhibitory activity against F. graminearum, providing evidence for a function in interspecific fungal interactions. In summary, we show that the mycoparasite C. rosea can distinguish between fungal prey species and modulate its transcriptomic responses accordingly. Gene expression data emphasize the importance of secondary metabolites in mycoparasitic interactions.
Implementation of Driver Drowsiness Detection and Accident Avoidance in Vehicles-A Review

Drivers when driving long distances without regular breaks run a high risk of becoming drowsy, a state where even the experts fail to recognize it early enough. Survey tells that around one quarter of all serious motorway accidents occur due to sleepy drivers in need of a rest (drowsiness) which causes more road accidents than drinkdriving. Attention assist can warn of inattentiveness and drowsiness in an extended speed range and notify drivers of their current state of fatigue and the driving time since the last break, offers adjustable sensitivity and, if a warning is emitted, indicates nearby service areas in the COMAND navigation system. A low cost system is developed which provides solution to the existing automotive control issues. This system has two main principle components namely Vehicle to Vehicle Collision Avoidance Unit (VVCAU) is used to avoid crashing between vehicles and Black Box (BB) records the relevant details about a vehicle such as Engine Temperature, Distance from obstacle, Speed of vehicle, Brake status, CO2 Content, Alcohol content, Accident Direction, trip Time and Date. There are several works regarding pre-crash detection & avoidance system from obstacle at present. To priorities crash with human or animals compared to obstacles is lacking. Human lives can be saved from an accident by detecting an accident before it occurs. To solve this problem in this work we need advance accurate human or animal detection and also accident detection capabilities and then priorities human or animal first to obstacles. Car will avoid human or animal first then if possible it will try to avoid obstacles also. The system has accident detection technique with pin point location tracking using GSM if the system is unable to avoid accident.
Gathering, Classifying and Visualizing Results from Social Surveys using OCR and Machine Learning Techniques

Statistical representation of data as graphics is useful for making its interpretation easier, and for helping on decision making processes. When it comes to information that is obtained as result of social surveys, the problem is not only on presenting it, but on gathering the results by different means, and classifying them into categories, considering that for open questions, this is a process that is commonly done in a manual way. This paper is aimed on presenting a software tool for gathering data from social surveys relying on OCR technology or through a web application, classifying it using Machine Learning techniques, and presenting the resulting information in a mostly automatic way. For illustrative purposes, a case of study of a survey that was applied in Puebla, Mexico, in 2013 is considered. Quantitative results were obtained and reported for the classification process, and general user interfaces of the developed application are presented and described.
Anti-Unification and Natural Language Processing

Anti-unification is a well-known method to compute generalizations in logic. Given two objects, the goal of anti-unification is to reflect commonalities between these objects in the computed generalizations, and highlight differences between them.Anti-unification appears to be useful for various tasks in natural language processing. Semantic classification of sentences based on their syntactic parse trees, grounded language learning, semantic text similarity, insight grammar learning, metaphor modeling: This is an incomplete list of topics where generalization computation has been used in one form or another. The major anti-unification technique in these applications is the original method for first-order terms over fixed arity alphabets, introduced by Plotkin and Reynolds in 1970s, and some of its adaptations.The goal of this paper is to give a brief overview about existing linguistic applications of anti-unification, discuss a couple of powerful and flexible generalization computation algorithms developed recently, and argue about their potential use in natural language processing tasks.
Air-Ground Image Matching for MAV Urban Localization CPSC 515 Project , Dec 21 , 2015

In this project, we use a vision based method to globally localize a Micro Aerial Vehicle (MAV) flying in GPS shadowed areas such as urban areas and university campuses. A large database consisted of Google Street View images with their location tags is used as the global map, where we match the MAV captured images to the map images by recognizing visually similar and geometrically consistent discrete places. By adding global geometric constraints to the air-ground matching method proposed by [12], our method achieves a higher recall rate at precision 1.0 on their Zurich dataset. We also compared the two methods on a preliminary UBC dataset collected by ourselves. They performed similarly and both generated reasonable results.
Degeneration and Regeneration in the Vertebrate Retina

The human retina is a complex, layered tissue responsible for the perception of the visual stimuli coming from the external environment. Since the visual inputs account for about 30% of our sensory stimulations, it is not surprising that partial or complete blindness results in a strong decrease of life quality. Several diseases affect the retina, often leading to degeneration of one or several cell types. The damage induced by these diseases is often irreversible, thus leading to a permanent loss of the visual ability. Over the last decades significant progress has been made to elucidate the molecular basis of retinal degenerative diseases. This knowledge is necessary in order to design valid approaches for the treatment of retinopathies. However, the extreme genetic heterogeneity of these diseases, combined with the complex molecular nature of the underlying degenerative processes, has hampered the design of definitive therapies. Indeed, pharmacological treatments are only partially effective and tend to treat the effect of the disease, not the underlying cause. Furthermore, despite the recent technical advances, drug delivery into the retina may reveal challenging (reviewed in Yasukawa et al., 2011). On the other hand, gene therapy reveals as a promising alternative approach for inherited retinopathies. However, although significant progress has been achieved in treating recessive forms of retinopathies, dominant or multi-factorial forms are more difficult to treat, revealing the current limits of gene-based therapies (Farrar et al., 2010). Thus, other strategies are needed to treat these diseases. One interesting approach, which will not be covered in this review, is the stem cell-based therapy, where stem cells can be differentiated in vitro and then transplanted to replace dead cells (for a review see Bull and Martin, 2011; Dahlmann-Noor et al., 2010; Locker et al., 2010). Another promising strategy is to promote the regeneration in vivo of the tissues (or organs) affected by the disease. Although the field of regenerative medicine is relatively young, the continuous findings on the mechanisms regulating regeneration in model organisms is improving our knowledge on the regenerative phenomena, and in some cases this information can be used to induce or increase regeneration in particular contexts (reviewed in Stoick-Cooper et al., 2007). In the first part of this review, we will introduce some of the most common retina degenerative diseases that affect people worldwide, namely retinitis pigmentosa (RP) and age-related macular degeneration (AMD), providing the reader with a general description of these retinopathies. In the second part, we will discuss some of the animal models used
Identification by a hybrid 3D/2D gait recognition algorithm

Recently, the research community has given much interest in gait as a biometric. However, one of the key challenges that affects gait recognition performance is its susceptibility to view variation. Much work has been done to deal with this problem. The implicit assumptions made by most of these studies are that the view variation in one gait cycle is small and that people walk only along straight trajectories. These are often wrong. Our strategy for view independence is to enrol people using their 3D volumetric data since a synthetic image can be generated and used to match a probe image. A set of experiments was conducted to illustrate the potential of matching 3D volumetric data against gait images from single cameras inside the Biometric Tunnel at Southampton University using the Gait Energy Image as gait features. The results show an average Correct Classification Rate (CCR) of 97% for matching against affine cameras and 42% for matching against perspective cameras with large changes in appearance. We modified and expanded the Tunnel systems to improve the quality of the 3D reconstruction and to provide asynchronous gait images from two independent cameras. Two gait datasets have been collected; one with 17 people walking along a straight line and a second with 50 people walking along straight and curved trajectories. The first dataset was analysed with an algorithm in which 3D volumes were aligned according to the starting position of the 2D gait cycle in 3D space and the sagittal plane of the walking people. When gait features were extracted from each frame using Generic Fourier Descriptors and compared using Dynamic Time Warping, a CCR of up to 98.8% was achieved. A full performance analysis was performed and camera calibration accuracy was shown to be the most import factor. The shortcomings of this algorithm were that it is not completely view-independent and it is affected by changes in walking directions. A second algorithm was developed to overcome the previous limitations. In this, the alignment was based on three key frames at mid-stance phase. The motion in the first and second parts of the gait cycle was assumed to be linear. The second dataset was used for evaluating the algorithm and a CCR of 99% was achieved. However, when the probe consisted of people walking on a curved trajectory, the CCR dropped to 82%. But when the gallery was also taken from curved walking, the CCR returned to 99%. The algorithm was also evaluated using data from the Kyushu University 4D Gait Database where normal walking achieved 98% and curved walking achieved 68%. Inspection of the data indicated that the assumption made previously that straight ahead walking and curved walking are similar, is invalid. Finally, an investigation into more appropriate features was also carried out but this only gave a slight improvement.
Klotho inhibits EGF-induced cell migration in Caki-1 cells through inactivation of EGFR and p38 MAPK signaling pathways

Klotho is a single-pass transmembrane protein with documented anti-cancer properties. Recent reports have implicated Klotho as an inhibitor of transforming growth factor β1 induced cell migration in renal fibrosis. Overexpression of epidermal growth factor receptor (EGFR) is known to promote tumor initiation and progression in clear-cell renal cell carcinoma (cRCC). We tested our hypothesis that Klotho inhibits EGF-mediated cell migration in cRCC by interfering with the EGFR signaling complex and mitogen-activated protein kinase (MAPK) pathways. We performed cell adhesion, migration, and biochemical studies in vitro using Caki-1 cell line. In addition, we validated the cell culture studies with expression analysis of six de-identified FFPE tissues from primary and metastatic cRCC patients. Our studies show that Klotho inhibited EGF-induced Caki-1 de-adhesion and decreased spreading on collagen type 1. Klotho also inhibited EGF-induced α2β1 integrin-dependent cell migration on collagen type 1. To test the involvement of MAPK pathways in EGF-induced Caki-1 cell motility, the cells were pretreated with either SB203580, a specific p38 MAPK inhibitor, or Klotho. SB203580 blocked the EGF-induced Caki-1 cell migration. Klotho had a comparable inhibitory effect. Our FFPE clinical specimens revealed decreased Klotho mRNA expression compared to a control, non-cancer kidney tissue. The decrease in Klotho mRNA levels correlated with increased c-Src expression, while E-Cadherin was relatively reduced in metastatic FFPE specimens where Klotho was least expressed. Taken together, these results suggest that secreted Klotho inhibits EGF-induced pro-migratory cell morphological changes and migration in Caki-1 cells. Our data additionally suggest that decreased Klotho expression may be involved in cRCC metastasis.
Believe It or Not : Modeling Adversary Belief Formation in Stackelberg Security Games with Varying Information

There has been significant amount of research in Stackelberg Security Games (SSG), and a common assumption in that literature is that the adversary perfectly observes the defender’s mixed strategy. However, in real-world settings the adversary can only observe a sequence of defender pure strategies sampled from the actual mixed strategy. Therefore, a key challenge is the modeling of adversary’s belief formation based on such limited observations. The SSG literature lacks a comparative analysis of these models and a principled study of their strengths and weaknesses. In this paper, we study the following shortcomings of previous work and introduce new models that address these shortcomings. First, we address the lack of empirical evaluation or head-to-head comparison of existing models by conducting the first-of-its-kind systematic comparison of existing and new proposed models on belief data collected from human subjects on Amazon Mechanical Turk. Second, we show that assuming a homogeneous population of adversaries, a common assumption in the literature, is unrealistic based on our experiments, which highlight four heterogeneous groups of adversaries with distinct belief update mechanisms. We present new models that address this shortcoming by clustering and learning these disparate behaviors from data when available. Third, we quantify the value of having historical data on the accuracy of belief prediction.
Energy Efficient Tri-State CNFET Ternary Logic Gates

Traditional silicon binary circuits continue to face challenges such as high leakage power dissipation and large area of interconnections. Multiple-Valued Logic (MVL) and nano-devices are two feasible solutions to overcome these problems. In this paper, we present a novel method to design ternary logic circuits based on Carbon Nanotube Field Effect Transistors (CNFETs). The proposed designs use the unique properties of CNFETs, e.g., adjusting the Carbon Nanotube (CNT) diameters to have the desired threshold voltage and have the same mobility of P-FET and N-FET transistors. Each of our designed logic circuits implements a logic function and its complementary via a control signal. Also, these circuits have a high impedance state which saves power while the circuits are not in use. We show a more detailed application of our approach by designing a two-digit adder-subtractor circuit. We simulate the proposed ternary circuits using HSPICE via standard 32nm CNFET technology. The simulation results indicate the correct operation of the designs under different process, voltage and temperature (PVT) variations. Moreover, we designed a two-digit adder/subtractor and a power efficient ternary logic ALU based on the proposed gates. Simulation results show that the two-digit adder/subtractor using our proposed gates has 12X and 5X lower power consumption and PDP (power delay product) respectively, compared to previous designs.