Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined effect of GSTM1, GSTT1 and GSTP1 polymorphisms on histological subtypes of lung cancer

Abstract Genetic polymorphisms are natural genetic variations in the gene sequence that occur at a frequency of >1% in the population. This genetic variability (polymorphisms) can be a factor in cancer risk. The functional polymorphisms in GST genes play an important role in susceptibility to lung cancer. In our previous study, we reported that the combination of certain genotypes of GSTM1, GSTT1 and CYP1A1 is associated with lung cancer. The study has been extended to investigate the potential role of polymorphism in GSTP1 alone or in combination with the status of GSTM1 and GSTT1 genes in the likelihood of development of lung cancer. A total of 302 subjects (151 cases and 151 controls) were evaluated. Using a case–control design, individuals were genotyped for GSTs using multiplex polymerase chain reaction and restriction fragment length polymorphism techniques. The data obtained were analyzed using multiple logistic regression. The combined ‘at risk’ genotypes of GSTM1 null and GSTT1 null in comparison with ‘wild-type’ genotypes seems to be associated with a greater risk of lung cancer, but the results are not significant (odds ratio (OR) 2.0, 95% confidence interval (CI) 0.68–5.96) and for squamous cell carcinoma (SqCC) it was 1.6-fold (OR 1.6, 95% CI 0.49–5.68). In summary, our case–control study of lung cancer revealed that the effect of these polymorphisms is not very marked for different genotypic combinations of GSTP1, GSTM1 and GSTT1 in the context of developing lung cancer in a north Indian population. However, the increased risk was limited to SqCC, and was not found for other histological subtypes. Further analyses on this topic are needed.
Preliminary validation of a measurement system for emotion recognition

An highly-wearable (wireless, few–channels and dry electrodes) device is proposed for EEG based valence emotion recognition. The component is a part of an instrument for real time engagement assessment in rehabilitation 4.0. The frontal, central, and occipital asymmetry were used as well known features related to emotional valence. The device was metrologically characterized on human subjects emotionally elicited through passive viewing of pictures taken from Oasis data set. As metrological references, a standardized test, the Self Assessment Manikin, was exploited. A 2nd order polynomial kernel-based Support Vector Machine reached 83.2 ± 0.3% accuracy in classifying emotional valence from each 2-s epoch of EEG acquired signals.
Production of Alien Addition Lines in Polyploid Bunching Onion (Allium fistulosum) Carrying 1A Chromosome(s) of Shallot (Allium cepa) and Their Application to Breeding for a New Vitamin C-Rich Vegetable

Eight Allium fistulosum L. -Allium cepa L. Aggregatum group (shallot) monosomic addition lines (2n = 17, FF+1A-FF+8A) have been useful in revealing the effects of single alien chromosomes from A. cepa on the production of L-ascorbic acid in the leaf tissue of A. fistulosum. In this study, the determination of ascorbic acid content revealed that the incorporation of alien chromosome 1A into a diploid background of A. fistulosum increased the internal ascorbic acid content of the leaf blade tissue. We produced a 1A disomic addition in the tetraploid of A. fistulosum (2n = 34) and demonstrated high-frequency transfer of the alien chromosome in crosses with A. fistulosum. Five plants of the 1A disomic additions were regenerated via apical meristem culture of the FF+1A on a Murashige and Skoog medium containing colchicine. These 1A disomic additions showed partial fertility for female and male gametes. Most of the progenies from selfing of the 1A disomic additions and reciprocal crossing with A. fistulosum possessed chromosome 1A. Interestingly, 64% (18 of 28) of the plants obtained from the reciprocal crosses were 1A monosomic additions in a triploid background of A. fistulosum. These monosomic additions were more vigorous and vitamin C-rich than euploid plants of A. fistulosum.
Myofibrillar myopathy caused by novel dominant negative αB‐crystallin mutations

We here report the second and third mutations in αB‐crystallin causing myofibrillar myopathy. Two patients had adult‐onset muscle weakness. Patient 1 had cervical, limb girdle, and respiratory muscle weakness and died of respiratory failure. Patient 2 had proximal and distal leg muscle weakness. Both had myopathic electromyogram with abnormal electrical irritability and muscle biopsy findings of myofibrillar myopathy and mild denervation. Myofibrillar disintegration begins at the Z‐disk and results in abnormal local expression of desmin, αB‐crystallin, dystrophin, neural cell adhesion molecule (NCAM), and CDC2 kinase. Seven to 8% of nuclei display early apoptotic changes. Both patients carry a truncating mutation in the C‐terminal region of αB‐crystallin (464delCT in Patient 1 and Q151X in Patient 2) which is crucial for the solubilization and chaperone functions of the molecule. cDNA analysis shows the same mutations and no alternatively spliced transcripts. Immunoblots of muscle demonstrate increased expression of wild‐type and reduced expression of the mutant protein. Immunoblots under nondenaturing conditions show that the mutant protein forms lower than normal molecular weight multimeric complexes with wild type. We conclude that (1) despite its reduced expression, the mutant protein exerts a dominant negative effect; (2) mutations in αB‐crystallin are an infrequent cause of myofibrillar myopathy; (3) αB‐crystallin–related myopathies display phenotypic heterogeneity. Ann Neurol 2003;54:804–810
Breeding for feed efficiency and adaptation to feed in poultry

The following paper reviews the current data regarding the physiological origins of variations in feed efficiency, and the effects of genetic selection on energy balance parameters. Effects of diet composition on response to genetic selection are considered by focusing on three feed factors: protein concentration, energy concentration and utilisation of specific ingredients. Analyses of effects of genetic selection on responses to diet composition highlight the problems associated with the increased dietary protein concentrations needed by the latest commercial broilers as a result of genetic selection. The advantages and disadvantages in producing lean birds are discussed from the point of view of feed efficiency and adaptation to feed variation. Lastly, the problems of variation in digestibility resulting from broiler selection are discussed.
Optimal Bidding of Price-Maker Retailers With Demand Price Quota Curves Under Price Uncertainty

This paper proposes an optimal bidding method of price-maker retailers in the electricity market with demand price quota curves (DPQC)–based probability distribution function (PDF) estimation of the market price. Different from traditional game-theory methods or agent-based methods, the proposed DPQC-based PDF estimation method unnecessity to have full knowledge of the strategies of each rival or the market operation. In detail, the DPQC method is applied to consider the impacts of the market clearing price from the price-maker retailers themselves, and the PDF model is utilized to consider the market price uncertainty. The technical key point of the proposed method is to amend the PDF along with the PQCs dynamically. Moreover, the DPQC-based PDF estimation with one-segment and multi-segment bidding rules are presented, respectively. The optimization model of the bidding problem is formulated then, and we use the genetic algorithm to solve it. The case study shows that the proposed method can help the price-maker retailers better to consider the price impacts from their bidding behaviors, and enable them to make a higher profit in the electricity market.
Efficient Classification of Motor Imagery using Particle Swarm Optimization-based Neural Network for IoT Applications

The Internet of Things (IoT) has found wide applications in human endeavors, and has recently drawn massive attention in the field of cognitive science by connecting the Brain-Computer Interface (BCI) to the cloud. The integration of EEG-based BCI with internet is rapidly developing due to its non-invasiveness, and the benefits are inexhaustible such as control of assistive robots, telemedicine and telehealth for secured monitoring. However, some challenges exist which would prevent the application of BCIs, including the accurately predicting movement intention from EEG signals, computational complexity while preprocessing EEG tasks, and the problem of overfitting. Several algorithms have been used to address these challenges, but they are still limited to be applied efficiently on the BCI-based IoT. In this paper, in order to tackle the above mentioned issues, we proposed an extended Particle Swarm Optimization (PSO)-based neural network (NN) which is able to provide a seamless interaction between the BCI and IoT devices. In the experiments, we designed a BCI system that captured relevant EEG information by means of the PSO, and then projected the features into a neural network system for training. The experimental results demonstrated the feasibility of the proposed PSO-based NN technique in classifying the motor imagery (MI) tasks with an accuracy of 98.9%. Some possible improvement in future work was also suggested.
Unlocking the Secrets Behind Secondary Metabolism: A Review of Aspergillus flavus from Pathogenicity to Functional Genomics

Aspergillus flavus has received a considerable amount of attention due to its ability to produce aflatoxin, a secondary metabolite that is both immunosuppressive and carcinogenic to animals and humans. Research on aflatoxin over the last 40 years has made it one of the best studied fungal secondary metabolites. In spite of the large volume of research in this area, many unanswered questions remain concerning the genetic regulation of aflatoxin production and the molecular signals that intimately associate the synthesis of aflatoxin with specific environmental and nutritional conditions. It is anticipated that the tools now available in the field of genomics will build upon our existing knowledge and provide answers to some of these questions. Complete genome sequences are now available for a number of fungal species that are closely related to A. flavus. This information can be used along with current genomic analyses in A. flavus to more closely examine the biosynthesis and regulation of secondary metabolism. The intent of this review is to summarize the large body of knowledge that exists from many years of research on A. flavus, with the hope that this information in the light of new genomic studies may bring scientists closer to unraveling the web of regulatory circuits that govern aflatoxin biosynthesis. Specifically, scientific findings in the following areas will be presented: classification and phylogenetic analyses of A. flavus, population biology, ecology and pathogenicity in agricultural environments, classical genetics including linkage group and mutant analyses, gene clusters, regulation of aflatoxin biosynthesis, and genomics.
Blood Pressure Estimation using Arterial Diameter: Exploring Different Machine Learning Methods

Blood pressure (BP) variation have been shown to result in health complications and are continuously related to the risk of stroke and coronary heart disease. Measurement of BP is one of the most useful parameters for early diagnosis. In this paper, we present a unique approach for extensive BP measurement that involves Machine Learning (ML) techniques which utilizes the extracted inherent features that are characteristic of arterial pressure and pulse transit relationships. Concurrent measurement of luminal diameter waveform were obtained from the arterial segments using our developed dualchannel A-mode ultrasound system and a probe with a pair of single-element ultrasound transducers. The data recorded by carrying out an in vivo study of 20 subjects was trained and validated on three ML and one Deep Learning (DL) models. The proposed technique uses a combination of 71 unique features of local arterial dimensions and blood pulse propagation characteristics for continuous cuff-less BP estimation. Out of the proposed techniques Multivariate adaptive regression spline (MARS) exhibited an acceptable accuracy, with a root-mean- square-error of 1.4 mmHg and 4.77 mmHg for Diastolic blood pressure (DBP) and Systolic blood pressure(SBP) respectively. The proposed technique could be used to develop a single PPG based cuff-less BP monitoring system with an accuracy that can be clinically and practically employed.
Proficiency Testing for Software Analysis and Cybersecurity Laboratories*

Proficiency tests are a central activity in demonstrating the competence of accredited laboratories. In the present work, we developed a proposal for the realization of Proficiency Testing for laboratories that work in the areas of software testing and cybersecurity. This is the first example of a Proficiency Testing round focusing on software code analysis activities, an important activity in software compliance assessment programs. The proposed approach uses code coverage metrics to evaluate the ability of laboratories in evaluating the source code of a software. We prove the feasibility of the proposal through the execution of a proficiency test “round” with the participation of six laboratories accredited to conduct software tests in compliance assessment programs in Brazil.
Clean-Breathing: a Novel Sensor Fusion Algorithm Based on ICA to Remove Motion Artifacts from Breathing Signal

Although smart-textile solutions based on piezoresistive technology have emerged as a tool to assess unobtrusively breathing activity, their signals are affected by the artifacts related to the subjects' movements during common activities (e.g. walking or running). In order to remove such artifacts, we implemented a novel algorithm combining the information recorded by four piezoresistive textile sensors, which allowed to measure rib cage movements due to the breathing activity, with the data synchronously recorded by an inertial measurement unit. Specifically, by using an Independent Component Analysis (ICA), our algorithm allowed to blindly reduce movement artifacts from the signals recorded by the piezo sensors, leading to highlight the breathing activity. We tested our algorithm in a pilot study, in which we enrolled one healthy subject during a free-running task. In order to assess our approach, we compared the signal spectrum obtained applying our algorithm with the one computed after a standard band-pass filter at 0.05-3 Hz. To this aim, we compared the average amplitude of the Power Spectral Densities (PSDs), computed after both approaches, along three frequency ranges: i) [0–1] Hz, related to the breathing activity; ii) [1-2] Hz, related with the torso rotation, during a running a task; iii) [2-3] Hz, related with the pace, during a running a task. Although the study was performed on one single subject, the results obtained seem to be promising. Indeed, within the range [0–1] Hz, the average reduction of the Power Spectral Density (PSD) is only about 4%, while it is considerably higher within frequency range related to the walking/running activity. Specifically, considering the ranges [1-2] Hz and [2-3] Hz such a reduction is around 39% and 36% respectively.
Predatory interactions between the invasive amphipod Gammarus tigrinus and the native opossum shrimp Mysis relicta

Abstract The invasive North American amphipod Gammarus tigrinus is successfully established in Lough Neagh, Northern Ireland. Gammarus tigrinus is increasingly recognized as having significant predatory impacts on macroinverebrates, contrary to the accepted functional feeding group status of Gammarus species. The native opossum shrimp Mysis relicta overlaps in habitat use with G. tigrinus. However, its interaction with benthic macroinvertebrates is rarely appreciated. Mutual predatory interactions between G. tigrinus and M. relicta were assessed in a series of laboratory experiments. Gammarus tigrinus actively preyed on adult and juvenile M. relicta at a range of spatial scales. Females and recently molted M. relicta were particularly vulnerable to predation. Mysis relicta did not prey on adult G. tigrinus, but rapidly eliminated juvenile G. tigrinus in microcosms. Changes in dissolved O2 saturation did not alter the predatory interaction between these species. Microhabitat use by M. relicta was altered in the presence of G. tigrinus, and the presence of G. tigrinus facilitated fish predation on M. relicta. A balance of mutual predatory pressure between these invasive and native species may explain their coexistence. Both species are likely to be strongly interactive with other macroinvertebrates in both native and invasive ranges.
Seeing Through the Occluders: Robust Monocular 6-DOF Object Pose Tracking via Model-Guided Video Object Segmentation

To deal with occlusion is one of the most challenging problems for monocular 6-DOF object pose tracking. In this letter, we propose a novel 6-DOF object pose tracking method which is robust to heavy occlusions. When the tracked object is occluded by another object, instead of trying to detect the occluder, we seek to see through it, as if the occluder doesnt exist. To this end, we propose to combine a learning-based video object segmentation module with an optimization-based pose estimation module in a closed loop. Firstly, a model-guided video object segmentation network is utilized to predict the accurate and full mask of the object (including the occluded part). Secondly, a non-linear 6-DOF pose optimization method is performed with the guidance of the predicted full mask. After solving the current object pose, we render the 3D object model to obtain a refined, model-constrained mask of the current frame, which is then fed back to the segmentation network for processing the next frame, closing the whole loop. Experiments show that the proposed method outperforms the state-of-arts by a large margin for dealing with heavy occlusions, and could handle extreme cases which previous methods would fail.
DeepSelfie: Single-Shot Low-Light Enhancement for Selfies

Taking a high-quality selfie photo in a low-light environment is challenging. Because the foreground and background often have different illumination conditions, they suffer heavily from over/under-exposure issues and cannot be treated in the same manner when applying image enhancement algorithms. In this work, we propose DeepSelfie, a learning-based image enhancement framework for low-light selfie photos. We address selfie enhancement as a dual-layer image enhancement problem. The foreground and background are thus separately enhanced and combined together via image fusion. To train the selfie enhancement network, we also introduce a method of synthesizing pairs of noisy and dark raw selfie images and their corresponding well-illuminated images. Through extensive experiments of no-reference image quality assessment as well as human subjective evaluation, we show that DeepSelfie provides better results in comparison to several state-of-the-art methods. The code and datasets can be found at https://sites.google. com/view/deepselfie.
Cross‐Reactions between Pseudomonas aeruginosa and Thirty‐Six Other Bacterial Species

Cross‐reactions between Pseudomonas aeruginosa and 36 other bacterial species were studied by various quantitative immunoelectrophoretic techniques. A complex Pseudomonas aeruginosa antigen and a corresponding rabbit antiserum were used as a reference system. Ten of the 55 Pseudomonas aeruginosa antigens were cross‐reactive with antigens from as many as 33 other bacterial species, gram‐negative as well as gram‐positive. As judged by absorption of antibodies the degree of cross‐reactivity of each of the 10 Pseudomonas aeruginosa antigens with antigens from other species was found to be 25–100%, depending on the species and the antigen in question. The closest antigenic relationship to Pseudomonas aeruginosa was found in 5 other Pseudomonas species, but members of the Enterobacteriaceae also cross‐reacted fairly extensively with Pseudomonas aeruginosa. One of the cross‐reactive antigens reacted with a normally occurring antibody in sera from rabbits.
Biological Consequences of Ecosystem Fragmentation: A Review

Abstract. Abstract Research on fragmented ecosystems has focused mostly on the biogeograpbic consequences of the creation of habitat “islands” of different sizes and has provided little of practical value to managers. However, ecosystem fragmentation causes large changes in the physical environment as well as biogeograpbic changes. Fragmentation generally results in a landscape that consists of remnant areas of native vegetation surrounded by a matrix of agricultural or other developed land. As a result fluxes of radiation, momentum (La, wind), water, and nutrients across the landscape are altered significantly. These in turn can have important influences on biota within remnant areas, especially at or near the edge between the remnant and the surrounding matrix. The isolation of remnant areas by clearing also has important consequences for the biota. These consequences vary with the time since isolation distance from other remnants, and degree of connectivity with other remnants. The influences of physical and biogeographic changes are modified by the size, shape, and position in the landscape of individual remnant, with larger remnants being less adversely affected by the fragmentation process. The Dynamics of remnant areas are predominantly driven by factors arising in the surrounding landscape. Management of, and research on, fragmented ecosystems should be directed at understanding and controlling these external influences as much as at the biota of the remnants themselves. There is a strong need to develop an integrated approach to landscape management that places conservation reserves in the context of the overall landscape
Models of Food-Web Evolution

While it is often possible to model the nature of the dynamics with which constituents of a fixed network interact with each other, it is frequently much more difficult to specify the range and nature of the topologies and interaction strengths of possible networks. Here we argue, in the context of food webs, that it is necessary to go beyond simply studying dynamics on a network with a fixed number of constituents and fixed interaction strengths, and to couple this dynamics to processes connected with the construction of the network. This could include a dynamics for the introduction and deletion of constituents and for the changing of interaction strengths depending on the states of the local constituents. In the case of food webs, where the constituents are species and the interactions are predator-prey relationships, this involves going beyond conventional population dynamics to include mechanisms which allow for the introduction and extinction of species and changing of feeding preferences. We review a model of food web evolution which we have been involved in developing over the last few years which encapsulates