Miracle-dz/UniFAR-FTUs huggingface dataset:
IHC Profiler: An Open Source Plugin for the Quantitative Evaluation and Automated Scoring of Immunohistochemistry Images of Human Tissue Samples

In anatomic pathology, immunohistochemistry (IHC) serves as a diagnostic and prognostic method for identification of disease markers in tissue samples that directly influences classification and grading the disease, influencing patient management. However, till today over most of the world, pathological analysis of tissue samples remained a time-consuming and subjective procedure, wherein the intensity of antibody staining is manually judged and thus scoring decision is directly influenced by visual bias. This instigated us to design a simple method of automated digital IHC image analysis algorithm for an unbiased, quantitative assessment of antibody staining intensity in tissue sections. As a first step, we adopted the spectral deconvolution method of DAB/hematoxylin color spectra by using optimized optical density vectors of the color deconvolution plugin for proper separation of the DAB color spectra. Then the DAB stained image is displayed in a new window wherein it undergoes pixel-by-pixel analysis, and displays the full profile along with its scoring decision. Based on the mathematical formula conceptualized, the algorithm is thoroughly tested by analyzing scores assigned to thousands (n = 1703) of DAB stained IHC images including sample images taken from human protein atlas web resource. The IHC Profiler plugin developed is compatible with the open resource digital image analysis software, ImageJ, which creates a pixel-by-pixel analysis profile of a digital IHC image and further assigns a score in a four tier system. A comparison study between manual pathological analysis and IHC Profiler resolved in a match of 88.6% (P<0.0001, CI = 95%). This new tool developed for clinical histopathological sample analysis can be adopted globally for scoring most protein targets where the marker protein expression is of cytoplasmic and/or nuclear type. We foresee that this method will minimize the problem of inter-observer variations across labs and further help in worldwide patient stratification potentially benefitting various multinational clinical trial initiatives.
Metabolite Profiling for Leaf Senescence in Barley Reveals Decreases in Amino Acids and Glycolysis Intermediates

Leaf senescence is a long developmental phase important for plant performance and nutrient management. Cell constituents are recycled in old leaves to provide nutrients that are redistributed to the sink organs. Up to now, metabolomic changes during leaf senescence have been mainly studied in Arabidopsis (Arabidopsis thaliana L.). The metabolite profiling conducted in barley (Hordeum vulgare L.) during primary leaf senescence under two nitrate regimes and in flag leaf shows that amino acids, hexose, sucrose and glycolysis intermediates decrease during senescence, while minor carbohydrates accumulate. Tricarboxylic acid (TCA) compounds changed with senescence only in primary leaves. The senescence-related metabolite changes in the flag leaf were globally similar to those observed in primary leaves. The effect of senescence on the metabolite changes of barley leaves was similar to that previously described in Arabidopsis except for sugars and glycolysis compounds. This suggests a different role of sugars in the control of leaf senescence in Arabidopsis and in barley.
Usefulness of the Forrest Classification to Predict Artificial Ulcer Rebleeding during Second-Look Endoscopy after Endoscopic Submucosal Dissection

Background/Aims: Delayed post-endoscopic submucosal dissection (ESD) bleeding (DPEB) is difficult to predict and there is controversy regarding the usefulness of prophylactic hemostasis during second-look endoscopy. This study evaluated the risk factors related to DPEB, the relationship between clinical outcomes and the Forrest classification, and the results of prophylactic hemostasis during second-look endoscopy. Methods: Second-look endoscopy was performed on the day after ESD to check for recent hemorrhage or potential bleeding and the presence of artificial ulcers in all patients. Results: DPEB occurred in 42 of 581 patients (7.2%). Multivariate analysis determined that a specimen size ≥40 mm (odds ratio [OR], 3.03; p=0.003), and a high-risk Forrest classification (Forrest Ib+IIa+IIb; OR, 6.88; p<0.001) were risk factors for DPEB. DPEB was significantly more likely in patients classified with Forrest Ib (OR, 24.35; p<0.001), IIa (OR, 12.91; p<0.001), or IIb (OR, 8.31; p<0.001) ulcers compared with Forrest III ulcers. There was no statistically significant difference between the prophylactic hemostasis and non-hemostasis groups (Forrest Ib, p=0.938; IIa, p=0.438; IIb, p=0.397; IIc, p=0.773) during second-look endoscopy. Conclusions: The Forrest classification of artificial gastric ulcers during second-look endoscopy seems to be a useful tool for predicting delayed bleeding. However, routine prophylactic hemostasis during second-look endoscopy seemed to not be useful for preventing DPEB.
On signal–noise decomposition of time‐series using the continuous wavelet transform: application to sunspot index

We show that the continuous wavelet transform can provide a unique decomposition of a time-series into ‘signal-like’ and ‘noise-like’ components. From the overall wavelet spectrum, two mutually independent skeleton spectra can be extracted, allowing the separate detection and monitoring in even non-stationary time-series of the evolution of (i) both stable but also transient, evolving periodicities, such as the output of low-dimensional dynamical systems, and (ii) scale-invariant structures, such as discontinuities, self-similar structures or noise. The idea of the method is to keep from the overall wavelet expansion of the time-series only the wavelet components of locally maximal amplitude at any given time or scale, thus obtaining the instantly maximal and scale maximal wavelet skeleton spectrum, respectively. The scale maximal spectrum was previously proposed for studying possible multifractal scaling properties of time-series. The instantly maximal spectrum proposed here exhibits clearer spectral peaks and reduced noise, as compared to the overall wavelet spectrum. An indicative application to the monthly-averaged sunspot index reveals, apart from the well-known 11-yr periodicity, three of its harmonics, the 2-yr periodicity (quasi-biennial oscillation, QBO) and several more (some of which have been detected previously in various solar, Earth–solar connection and climate indices), here proposed as just harmonics of the QBO, in all supporting the double-cycle solar magnetic dynamo model. The scale maximal spectrum reveals the presence of 1/f fluctuations with time-scales up to 1 yr in the sunspot number, indicating that the solar magnetic configurations involved in the transient solar activity phenomena with those characteristic time-scales are in a self-organized critical state, as previously proposed for the solar flare occurrence.
Range-Sensor-Based Navigation in Three-Dimensional Polyhedral Environments

This paper is concerned with the problem of sensor-based navigation in three dimensions. The robot, which is modeled as a “bug” or a “point robot,” has no a priori knowledge of the environment. It must rather use its sensors to perceive the environment and plan a collision-free path to various targets. The robot is further required to navigate in the most reactive way possible, retaining the smallest amount of information required for global convergence to the target. We assume a three-dimensional polyhedral environment and present two basic results for sensor-based navigation in this environment. First we establish sufficient conditions for range-sensor-based exploration of the entire surface of a general polyhedron and present a strategy for performing this task. Then we characterize the locally shortest path from the current robot location to the target and present a method for estimating this path in time that is linear with the problem size. Based on these results, we present a range-sensor-based navigation algorithm for a bug robot in a general three-dimensional polyhedral environment. The algorithm, called 3 DBug, strives to process the sensory data in the most reactive way possible, without sacrificing its global convergence guarantee. The algorithm uses two modes of motion, called motion-toward-the-target and obstacle- surface-traversal. During the first mode of motion, the robot follows the locally shortest path to the target in a purely reactive fashion. During the second mode of motion, the robot attempts to reach exit points along an obstacle surface, while simultaneously expanding its knowledge of the obstacle based on range data. We provide analysis of the algorithm, showing that if the target is reachable, the robot always finds obstacle exit points from which it reaches the target. Otherwise, the robot eventually possesses full knowledge of the obstacle blocking its path to the target and determines that the target is unreachable. We have also implemented and verified the algorithm on three-dimensional simulated environments. The simulation results show that 3 DBug generates paths that resemble the globally shortest path in simple scenarios and reasonably short paths in concave roomlike environments.
Evolving Neural Controllers Using GA for Warcraft 3-Real Time Strategy Game

This paper presents the research results found for the utilization of a Genetic Algorithm (GA) technique in evolving a set of Artificial Neural Networks (ANNs) weights which functions as controller in deciding what type of unit that should be spawned for winning against the opponent in a RTS game called War craft 3 (custom map). The elitism concept is applied during the optimization processes in order to avoid losing good solutions. The experimentation results show clearly a group of mixed randomized opponent can be defeated by the generated AI army. Hence, it is proof that GA is capable to act as a tuning technique in generating the required controllers in RTS game. Furthermore, the neural controllers generated are able to decide the best group of army used in defeating the opponent.
Physics problem solver towards mental simulation for real world oriented intelligence

General intelligent robots in daily life environment must observe and model external world, understand users' instructions and intentions. In such situations, robots always face to ambiguity of information. Conventional approaches to solve the ambiguity were using common sense database/ontology, or asking users; however developers'/users' cost is too huge. In this paper, we propose an approach in which physics simulation as mental simulation solves the ambiguity instead of descriptive common sense. The target should be focused on physics problems in entrance examination, because physics problems in examination shares important element in general intelligent robots. we state that the required functions of mental simulation for general intelligent robots in real-world can be reduced to functions for physics problem solver. We also propose a framework of software configuration of the physics problem solver.
Hierarchical and parallel processing of modulation spectrum for ASR applications

The modulation spectrum is an efficient representation for describing dynamic information in signals. In this work we investigate how to exploit different elements of the modulation spectrum for extraction of information in automatic recognition of speech (ASR). Parallel and hierarchical (sequential) approaches are investigated. Parallel processing combines outputs of independent classifiers applied to different modulation frequency channels. Hierarchical processing uses different modulation frequency channels sequentially. Experiments are run on a LVCSR task for meetings transcription and results are reported on the RT05 evaluation data. Processing modulation frequencies channels with different classifiers provides a consistent reduction in WER (2% absolute w.r.t. PLP baseline). Hierarchical processing outperforms parallel processing. The largest WER reduction is obtained through sequential processing moving from high to low modulation frequencies. This model is consistent with several perceptual and physiological studies on auditory processing.
Fast structural query with application to chinese treebank sentence retrieval

In natural language processing a huge amount of structured data is constantly used for the extraction and presentation of grammatical structures in sentences. For example the Chinese Treebank corpus developed at the Institute of Information Science Academia Sinica Taiwan is a semantically annotated corpus that has been used to help parse and study Chinese sentences. In this setting users usually use structured tree patterns instead of keywords to query the corpus. In this paper we present an online prototype system that provides exploratory search ability. The system implements two flexible and efficient structural query methods and employs a user-friendly web-based interface. Although the system adopts the XML format to present the corpora and search results it does not use conventional XML query languages. As searching the Chinese Treebank corpora is structural in nature and often deals with structural similarities conventional XML query languages such as XPath and XQuery are inflexible and inefficient. We propose and implement a query algorithm called Parent-Child Relationship Filter <i>(PCRF)</i> which provides flexible and efficient structural search. <i>PCRF</i> is sufficiently flexible to provide several similarity-matching options such as wildcard unordered sibling sub-trees ancestor-descendant matching and their combinations. In addition <i>PCRF</i> supports stream-based matching to help users query their XML documents online. We also present three accelerating rules that achieve a 1.5- to 8-fold performance improvement in query time. Our experiment results show that our method archive a 10- to 1000-fold performance improvement compared to the usual text-based XPath query method.
Studies Upon Asiatic Cholera

Everyone who has written upon the subject of cholera has pointed out that however familiar we may be with respect to the characters of the pathogenic organism discovered by Koch in I883, with regard to its pathogenicity the single fact actually known is that the presence of this vibrio in the intestinal mucosa causes the disease. But why do the vibrios undergo modifications in the body? Why do they lose their virulence? Why do they disappear? Why does the patient recover? These questions have remained un-answered. There is the same obscurity with regard to the epidemiology of the disease, and there is no explanation for the sometimes bizarre progress of epidemics. Some towns and villages remain free of the disease despite the fact that infected persons are brought in, while in a neighboring town or village the arrival of a single cholera case is sufficient to start a violent epidemic. Similarly, no one has yet explained why an epidemic may terminate permanently, and often very suddenly, after having caused hundreds of thousands of deaths. Much has been published upon the subject of cholera, but although purely laboratory studies are numerous, very rare have been those studies made within the devastated regions at the bedside of the patient. Of what value can pure laboratory studies be in such a subject? None at all. When the bacteriologist has wished to study the characteristics of the cholera vibrio he has turned to old laboratory cultures, vibrios which have been maintained for years outside of their natural environment. He may be able to add a new culture medium to the innumerable ones which have already been described, and this may indeed be useful although there is no organism more readily cultivable. And what shall we say of the results obtained by those who have undertaken to study the pathogenesis of cholera by animal inoculation, by attempting to infect with old strains of the vibrio, guinea pigs and rabbits, animals which are completely refractory? A recent and voluminous work of San-
Enhancement of IEEE 802.15.4 MAC layer to combat correlated channel errors

In indoor environment, the IEEE 802.15. 4 low-rate, low-power packet transmissions experience statistical correlation between channel errors due to interference and multipath fading. The 802.15. 4 MAC layer implements an ARQ (Automatic Repeat-Request) mechanism for error recovery. In this work, we investigate the impact of correlated errors on the ARQ mechanism. The MAC layer back-off strategy employs a uniform random distribution to choose back-off values; which often reduces retransmission resolution time when packets confront collisions or transmission errors. We therefore propose the application of non-uniform (skewed) distribution which improves the system performance. Furthermore, an adaptive back off strategy, fastEI-slowED is developed for IEEE 802.15. 4 MAC protocol to combat channel errors in time varying conditions. A detailed comparative analysis is presented employing accurate models of wireless channel and MAC protocol. Numerical results indicate that proposed scheme performs better, especially in worst channel conditions, when long duration error bursts are more frequent.
Feature extraction algorithm for fill level and cap inspection in bottling machine

Automated Visual Inspection Systems (AVIS) have a strong ability to improve bottle manufacturing quality control by means of inspecting products automatically instead of through manual inspections. AVIS automatically tends to make a suitable decision in process and results classification according to the images of the products via image processing and Artificial Intelligence techniques. Since bottling is one of the most common packaging styles in the food and medical industries, in this paper we will concentrate on the visual inspection of bottles. Checking the quality of the cap closure and over-filling/under-filling checks for the level of the liquid in the bottle have been investigated to reach an optimized bottle product. Therefore, in this research general hardware and modules for these systems are investigated. Besides, new techniques of bottle inspection are reviewed along with presenting previous work of other researchers. Subsequently we will propose a feature extraction algorithm to inspect cap closure and level of the liquid in the bottle together, in the same system. According to the new proposed method, our system classifies three situations for cap condition and three situations for the condition of the level of the liquid. As a result the system has investigated 9 situations. The algorithm of the system will accept its system when the liquid level is in the correct position and the cap is in the normal condition. Other situations will be rejected. The proper algorithm which is proposed here using bottle visual inspection techniques leads our system to reach an optimized liquid level with a high quality of the cap closure.
Current Status and Advances in Quantitative Proteomic Mass Spectrometry

The accurate quantitation of proteins and peptides in complex biological systems is one of the most challenging areas of proteomics. Mass spectrometry-based approaches have forged significant in-roads allowing accurate and sensitive quantitation and the ability to multiplex vastly complex samples through the application of robust bioinformatic tools. These relative and absolute quantitative measures using label-free, tags, or stable isotope labelling have their own strengths and limitations. The continuous development of these methods is vital for increasing reproducibility in the rapidly expanding application of quantitative proteomics in biomarker discovery and validation. This paper provides a critical overview of the primary mass spectrometry-based quantitative approaches and the current status of quantitative proteomics in biomedical research.
Minimum-entropy phase adjustment for ISAR

A new technique is developed for phase adjustment in ISAR imaging. The adjustment phase is found by iteratively solving an equation, which is derived by minimising the entropy of the image. This technique can be used to estimate adjustment phases of any form. Moreover, the optimisation method used in this technique is computationally more efficient than trial-and-error methods.
Application of computer vision and color image segmentation for yield prediction precision

Precision agriculture is finding its roots in India. PA always deals with the accuracy and timely information about agriculture products. With the rapid development of computer hardware and software technology, the application of image processing technology in the agricultural research are playing key role [1]. Also, with the advantages of superior speed and accuracy, computer vision has attracted it as an alternative to human inspection [2]. In this paper, we have described a novel application of computer vision and color image segmentation for automating the precise yield prediction process of gerbera flower yield from the polyhouse images. The purpose of the present study is to design a decision support system that could generate flower yield information and serve as base for management & planning of flower marketing. Current study has applied the color image segmentation technique using threshold, to extract the flowers from the scene. Color is considered a fundamental physical property of agriculture products and foods in information analysis [3]. Using HSV color space and histogram analysis, flower color definition is done. Then by the image segmentation process, flowers were separated from the background & detected in the images. Image set with 75 images were tested with this technique.
Different Aneuploidies Arise From the Same Bridge-Induced Chromosomal Translocation Event in Saccharomyces cerevisiae

Chromosome translocations are gross chromosomal rearrangements that have often been associated with cancer development in mammalian cells. The feasibility of drastically reshaping the genome with a single translocation event also gives this molecular event a powerful capacity to drive evolution. Despite these implications and their role in genome instability, very little is known about the molecular mechanisms that promote and accompany these events. Here, at the molecular level, we describe 10 morphologically and physiologically different translocants ensuing from the induction of the same bridge-induced translocation (BIT) event in the budding yeast Saccharomyces cerevisiae. We have demonstrated that, despite their common origin from the integration of the same linear DNA construct, all 10 translocation mutant strains have different phenotypes and the ability to sporulate and regulate gene expression and morphology. We also provide insights into how heterogeneous phenotypic variations originate from the same initial genomic event. Here we show eight different ways in which yeast cells have dealt with a single initial event inducing translocation. Our results are in agreement with the formation of complex rearrangements and abnormal karyotypes described in many leukemia patients, thus confirming the modellistic value of the yeast BIT system for mammalian cells.
X-ray Image Segmentation by Attribute Relational Graph Matching

This paper deals with the automatic threat detection for accompanied baggage based on multi- energy X-ray imagery, we propose a structural segmentation method based on ARG matching. The proposed segmentation algorithms are a series of graph-matching algorithms based on models under a kind of similarity measure, fuzzy similarity distance. The results show a good average integrity of objects segmented from experimental images.