Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-leg cycling increases limb-specific blood flow without concurrent increases in normalised power output when compared with double-leg cycling in healthy middle-aged adults

Abstract This study examined the acute performance, cardiovascular and local muscular responses to perceived exertion-based high-intensity interval exercise using either double- or single-leg cycling. Fifteen healthy middle-aged adults completed, on separate occasions, ten 30-s double-leg intervals interspersed with 60 s passive recovery and twenty (ten with each leg) 30-s single-leg intervals interspersed with 60 s passive recovery. Impedance cardiography, blood pressure, muscle oxygenation and total haemoglobin content (near-infrared spectroscopy), oxygen consumption and power output were measured throughout each session. Normalised to the lean mass used during each trial, single-leg cycling resulted in lower power output (single-leg: 8.92 ± 1.74 W kg−1 and double-leg: 10.41 ± 3.22 W kg−1; p < 0.05) but greater oxygen consumption (single-leg: 103 ± 11 mL kg−1 min−1 and double-leg: 84 ± 21 mL kg−1 min−1; p < 0.01) and cardiac output (single-leg: 1407 ± 334 mL kg−1 min−1 and double-leg: 850 ± 222 mL kg−1 min−1; p < 0.01), compared with double-leg cycling. Mean arterial pressure (double-leg: 108 ± 11 mmHg and single-leg: 102 ± 10 mmHg), change in total haemoglobin content (double-leg: 8.76 ± 10.65 µM cm s−1 and single-leg: 13.42 ± 4.10 µM cm s−1) and change in tissue oxygenation index (double-leg: −4.51 ± 3.56% and single-leg: −3.97 ± 3.91%) were not different between double-leg and single-leg cycling. When compared to double-leg cycling, single-leg cycling elicited a higher cardiac output relative to the lean mass, but this did not result in greater power output. The dissociation between blood availability and power output is consistent with an ageing model characterised by a decrease in local oxygen delivery and distribution capability.
Betweenness Ordering Problem : An Efficient Non-Uniform Sampling Technique for Large Graphs

Centrality measures, erstwhile popular amongst the sociologists and psychologists, has seen wide and increasing applications across several disciplines of late. In conjunction with the big data problems there came the need to analyze big networks and in this connection, centrality measures became of great interest to the community of mathematicians, computer scientists and physicists. While it is an important question to ask how one can rank vertices based on their importance in a network, there hasn’t been a commonly accepted definition, mainly due to the subjectivity of the term “importance”. Amongst a plethora of application specific definitions available in the literature to rank the vertices, closeness centrality, betweenness centrality and eigenvector centrality (page-rank) have been the most important and widely applied ones. In the current paper, we formulate a method to determine the betweenness ordering of two vertices without exactly computing their betweenness indices which is a daunting task for networks of large size. We apply our approach to find the betweenness ordering of two vertices in several synthetic and real world graphs. We compare our method with the available techniques in the literature and show that our method fares several times better than the currently known techniques. We further show that the accuracy of our algorithm gets better with the increase in size and density of the network.
Wide-band predistortion linearization for externally modulated long-haul analog fiber-optic links

We analyze the performance of a custom wide-band predistortion linearizer designed to operate in conjunction with an externally modulated analog photonic link. In addition, we propose an extended spurious-free dynamic-range (SFDR) specification for analog photonic links employing linearization techniques. The benefits of predistortion linearization for analog fiber links limited by optical amplifier noise are demonstrated and a 6-12-GHz 20-km analog link is shown to achieve a SFDR of 108.4 dB/spl middot/Hz/sup 2/3/, a 6-dB increase relative to the uncompensated link.
A Human Antibody to the CD4 Binding Site of gp120 Capable of Highly Potent but Sporadic Cross Clade Neutralization of Primary HIV-1

Primary isolates of HIV-1 resist neutralization by most antibodies to the CD4 binding site (CD4bs) on gp120 due to occlusion of this site on the trimeric spike. We describe 1F7, a human CD4bs monoclonal antibody that was found to be exceptionally potent against the HIV-1 primary isolate JR-FL. However, 1F7 failed to neutralize a patient-matched primary isolate, JR-CSF even though the two isolates differ by <10% in gp120 at the protein level. In an HIV-1 cross clade panel (n = 157), 1F7 exhibited moderate breadth, but occasionally achieved considerable potency. In binding experiments using monomeric gp120s of select resistant isolates and domain-swap chimeras between JR-FL and JR-CSF, recognition by 1F7 was limited by sequence polymorphisms involving at least the C2 region of Env. Putative N-linked glycosylation site (PNGS) mutations, notably at position 197, allowed 1F7 to neutralize JR-CSF potently without improving binding to the cognate, monomeric gp120. In contrast, flow cytometry experiments using the same PNGS mutants revealed that 1F7 binding is enhanced on cognate trimeric Env. BN-PAGE mobility shift experiments revealed that 1F7 is sensitive to the diagnostic mutation D368R in the CD4 binding loop of gp120. Our data on 1F7 reinforce how exquisitely targeted CD4bs antibodies must be to achieve cross neutralization of two closely related primary isolates. High-resolution analyses of trimeric Env that show the orientation of glycans and polymorphic elements of the CD4bs that affect binding to antibodies like 1F7 are desirable to understand how to promote immunogenicity of more conserved elements of the CD4bs.
Comparison of conventional, amplification and bio-assay detection methods for a chronic wasting disease inoculum pool

Longitudinal studies of chronic wasting disease (CWD) in the native host have provided considerable understanding of how this prion disease continues to efficiently spread among cervid species. These studies entail great cost in animal, time and financial support. A variety of methods have emerged including transgenic mouse bioassay, western blot, enzyme-linked immunoassay (ELISA), immunohistochemistry (IHC), serial protein misfolding cyclic amplification (sPMCA) and real time quaking-induced conversion (RT-QuIC), that deepen our understanding of this and other protein misfolding disorders. To further characterize an inoculum source used for ongoing CWD studies and to determine how the readouts from each of these assays compare, we assayed a CWD-positive brain pool homogenate (CBP6) and a mouse dilutional bioassay of this homogenate using the above detection methods. We demonstrate that: (i) amplification assays enhanced detection of amyloid seeding activity in the CWD+ cervid brain pool to levels beyond mouse LD50, (ii) conventional detection methods (IHC and western blot) performed well in identifying the presence of PrPSc in terminal brain tissue yet lack sufficient detection sensitivity to identify all CWD-infected mice, and (iii) the incorporation of amplification assays enhanced detection of CWD-infected mice near the LD50. This cross-platform analysis provides a basis to calibrate the relative sensitivities of CWD detection assays.
Protecting Numerical Confidential Data using Data Shuffling: A Demonstration of Effectiveness of Approach and Flexibility of Delivery

Protecting numerical confidential data in data released by government agencies and other organizations poses a considerable challenge. There have been many different procedures that have been developed for this purpose. Among these procedures, Data shuffling offers the best compromise between preventing disclosure of information while preserving the usefulness of the data. Specifically Data shuffling offers the following advantages: (1) Individual data values are not modified but are shuffled between records thereby maintaining the distribution of individual variables exactly, (2) All linear and monotonic non-linear relationships between all variables are maintained providing a high level of analytical value, and (3) Providing the highest possible level of security (lowest level of both identity and value disclosure risk). In this demonstration, we provide an extensive analysis of the analytical value and disclosure risk characteristics of Data shuffling. We also demonstrate three modes of delivery based on the specific needs of the organization: (1) Excel based solution for small applications; (2) Web based solution for larger applications where the organizations wish to perform Data shuffling by themselves, and (3) Third party solution for organizations and agencies for large complex data sets. We believe that this demonstration will be useful for any organization that intends to analyze, share, or disseminate numerical confidential data without risk of disclosure.
Journal of Medical Ethics and History of Medicine a Conceptual Model of Physician-patient Relationships: a Qualitative Study

In any clinical encounter, an effective physician-patient relationship is necessary for achieving the desired outcome. This outcome is successful treatment, and therefore, the relationship should be a healing one. In addition, in the Islamic view, the physician is a manifestation of God's healing attribute, which is usually undermined in everyday therapeutic communications. Yet there are few empirical data about this experience and how it occurs in the clinical context. This study was conducted to develop a model of physician-patient relationship, with the healing process at its core. Our goal was to explain the nature and characteristics of this encounter. In Islamic teachings, healing is defined as " cure " when possible and if not, reducing pain and suffering and ultimately finding a meaning in the illness experience. This study was a qualitative inquiry. Data were collected through 17 open-ended, semi-structured interviews with physicians who had an effective relationship with their patients. The participants' experiences and their perception regarding the relationship were subjected to grounded theory content analysis. For establishing the trustworthiness of the data collection and analysis we used triangulation, peer review, and member checking. The findings showed that the components of the patient-physician healing relationship could be categorized in the four key processes of valuing the patient as a person, effective management of power imbalance, commitment, and the physician's competence and character. This leads to forming the three necessary relational elements of trust, peace and hope, and being acknowledged. Their importance has been better demonstrated in a relationship which incorporates the spiritual aspects of patient care and also physician's satisfaction. The physician-patient relationship has a central role in patient outcome. This relationship has an understandable structure and its components may have an effective impact on promoting the patient's experience of the health system. Introduction The physician-patient relationship is a clinical encounter and its effective outcome has a direct impact on the quality of care (1) and achievement of a successful treatment. Hence, a healing relation must be formed between a physician and his/her patient. However, today, the physician-patient relationship undermined and the medical professional is considered as the technician and the patient is considered as the machine (1). Nevertheless, the establishment of an effective communication with the patient requires an understanding of the fact that the patient is not just a set of symptoms of a damaged organ, but a human with dignity and his or her own concerns …
Learning Semantics-Preserving Attention and Contextual Interaction for Group Activity Recognition

In this paper, we investigate the problem of group activity recognition by learning semantics-preserving attention and contextual interaction among different people. Conventional methods usually aggregate the features extracted from individual persons by pooling operations, which lack physical meaning and cannot fully explore the contextual information for group activity recognition. To address this, we develop a Semantics-Preserving Teacher-Student (SPTS) networks architecture. Our SPTS networks first learn a Teacher Network in the semantic domain that classifies the word of group activity based on the words of individual actions. Then, we design a Student Network in the appearance domain that recognizes the group activity according to the input video. We enforce the Student Network to mimic the Teacher Network in the learning procedure. In this way, we allocate semantics-preserving attention to different people, which is more effective to seek the key people and discard the misleading people, while no extra labeled data are required. Moreover, a group of people inherently lie in a graph-based structure, where the people and their relationship can be regarded as the nodes and edges of a graph, respectively. Based on this, we build two graph convolutional modules on both the Teacher Network and the Student Network to reason the dependency among different people. Furthermore, we extend our approach on action segmentation task based on its intermediate features. The experimental results on four datasets for group activity analysis clearly show the superior performance of our method in comparison with the state-of-the-art.
Synthesis of Metallic Nanocrystals: From Noble Metals to Base Metals

Metallic nanocrystals exhibit superior properties to their bulk counterparts because of the reduced sizes, diverse morphologies, and controllable exposed crystal facets. Therefore, the fabrication of metal nanocrystals and the adjustment of their properties for different applications have attracted wide attention. One of the typical examples is the fabrication of nanocrystals encased with high-index facets, and research on their magnified catalytic activities and selections. Great accomplishment has been achieved within the field of noble metals such as Pd, Pt, Ag, and Au. However, it remains challenging in the fabrication of base metal nanocrystals such as Ni, Cu, and Co with various structures, shapes, and sizes. In this paper, the synthesis of metal nanocrystals is reviewed. An introduction is briefly given to the metal nanocrystals and the importance of synthesis, and then commonly used synthesis methods for metallic nanocrystals are summarized, followed by specific examples of metal nanocrystals including noble metals, alloys, and base metals. The synthesis of base metal nanocrystals is far from satisfactory compared to the tremendous success achieved in noble metals. Afterwards, we present a discussion on specific synthesis methods suitable for base metals, including seed-mediated growth, ligand control, oriented attachment, chemical etching, and Oswald ripening, based on the comprehensive consideration of thermodynamics, kinetics, and physical restrictions. At the end, conclusions are drawn through the prospect of the future development direction.
Conceptual Design of a Micro Gripper with Electrostatic Micro Stepper-Motor Actuation

Micro grippers are essential tools for manipulation of objects in micron size. An electrostatic micro stepper-motor is used for actuating a proposed gripper mechanism and performance of this gripper is compared with the previous ones. The characteristic of the proposed mechanism is analyzed by simulation and it is shown that the designed gripper has the capability of doing manipulation in micron dimension with an acceptable performance. (Farshad Shadbakhsh, Mohammadali Shahriari, Abolghasem Zabihollah. Conceptual Design of a Micro Gripperwith Electrostatic Micro Stepper-Motor Actuation. Life Sci J 2013;10(8s): 290_293) (ISSN:1097-8135). http://www.lifesciencesite. com. 45
New biometric approach based on motor imagery EEG signals

A research on biometry based on motor imagery EEG signals was described. In this study, I select EEG signals related to motor imagery, and a model was built. Estimated model parameters as feature vector were extracted, and then to classified by an artificial neural network. Two different classify cases, including authentication and identification, were investigated. Four types of motor imagery EEG signals and three subjects were compared. Experiment results show that EEG carrying individual-specific information can be successfully exploited for purpose of person authentication and identification.
Sequential Innovation, R&D Intensity, and Patent Policy∗

This paper studies optimal patent policy in a dynamic setting with sequential innovation. Firms innovate by undertaking “research” activities to generate new ideas and by undertaking “developement” activities to commercialize these ideas. To provide firms with incentive to invest in R&D, the social planner grants a monopoly right (patent) to an innovator to exclude its opponent from producing the goods based on its innovation for a certain period of time. If the idea arrival process is exogenous, the optimal policy is stationary and a new idea is patentable if its quality is better than a constant cutoff. If idea arrival is endogenously determined by firms’“research” investment, the optimal policy is in general nonstationary and characterized by two cutoffs, and the policy switches from one cutoff to the other cutoff only once.
Influence of Habitat and Intrinsic Characteristics on Survival of Neonatal Pronghorn

Increased understanding of the influence of habitat (e.g., composition, patch size) and intrinsic (e.g., age, birth mass) factors on survival of neonatal pronghorn (Antilocapra americana) is a prerequisite to successful management programs, particularly as they relate to population dynamics and the role of population models in adaptive species management. Nevertheless, few studies have presented empirical data quantifying the influence of habitat variables on survival of neonatal pronghorn. During 2002–2005, we captured and radiocollared 116 neonates across two sites in western South Dakota. We documented 31 deaths during our study, of which coyote (Canis latrans) predation (n = 15) was the leading cause of mortality. We used known fate analysis in Program MARK to investigate the influence of intrinsic and habitat variables on neonatal survival. We generated a priori models that we grouped into habitat and intrinsic effects. The highest-ranking model indicated that neonate mortality was best explained by site, percent grassland, and open water habitat; 90-day survival (0.80; 90% CI = 0.71–0.88) declined 23% when grassland and water increased from 80.1 to 92.3% and 0.36 to 0.40%, respectively, across 50% natal home ranges. Further, our results indicated that grassland patch size and shrub density were important predictors of neonate survival; neonate survival declined 17% when shrub density declined from 5.0 to 2.5 patches per 100 ha. Excluding the site covariates, intrinsic factors (i.e., sex, age, birth mass, year, parturition date) were not important predictors of survival of neonatal pronghorns. Further, neonatal survival may depend on available land cover and interspersion of habitats. We have demonstrated that maintaining minimum and maximum thresholds for habitat factors (e.g., percentages of grassland and open water patches, density of shrub patches) throughout natal home ranges will in turn, ensure relatively high (>0.50) neonatal survival rates, especially as they relate to coyote predation. Thus, landscape level variables (particularly percentages of open water, grassland habitats, and shrub density) should be incorporated into the development or implementation of pronghorn management plans across sagebrush steppe communities of the western Dakotas, and potentially elsewhere within the geographic range of pronghorn.
Maximal flexibility in dynamic functional connectivity with critical dynamics revealed by fMRI data analysis and brain network modelling

Objective. The exploration of time-varying functional connectivity (FC) through human neuroimaging techniques provides important new insights on the spatio-temporal organization of functional communication in the brain’s networks and its alterations in diseased brains. However, little is known about the underlying dynamic mechanism with which such a dynamic FC is flexibly organized under the constraint of structural connections. In this work, we explore the relationship between critical dynamics and FC flexibility based on both functional magnetic resonance imaging data and computer models. Approach. First, we proposed the connectivity number entropy (CNE), which was an entropy measure for the flexibility of FC. Through an analysis of resting-state fMRI (rs-fMRI) data from 95 healthy participants, we explored the correlation between CNE and long-range temporal correlations (LRTCs), which can represent the critical dynamics. Then, we employed a whole-brain computer model based on diffusion tensor imaging (DTI) to further demonstrate this relationship. Main results. We found that the most flexible FC is present when the brain is operating close to the critical point of a phase transition. Additionally, around this point, our model can yield the best prediction for the regional distribution of CNE because structural information is reflected the most by the CNE through critical dynamics. Significance. Our results not only reveal the underlying dynamic mechanism for the organization of time-dependent FC but also provide a possible pathway to model the flexible functional organization in the human brain and may have potential application in the analysis of altered dynamic FC in diseased brains.
The chemical and visual bases of the pollination of the Neotropical sexually deceptive orchid Telipogon peruvianus.

Deception of floral visitors in pollination systems is widely distributed among flowering plants. In deceptive systems, the flower (or part of it) or inflorescence mimics either a specific or an unspecific model to attract pollinators. A previous study showed that Telipogon peruvianus flowers developed sexual deception for pollination. However, it was unknown which stimuli were playing a role in pollination. Therefore, we aim to throw some light onto these questions using colour and chemical analysis and biotests. Interestingly, using spectral reflectance, we show here that the flowers present high contrast similar to that produced by a female tachinid fly sitting on a daisy inflorescence, which is used as food resource. We also tested the role of chemical signals in pollinator attraction by collecting floral and female extracts for chemical and electrophysiological analyses, and carried out behavioural tests. For biotests, various treatments, including synthetic mixtures of the electrophysiologically active compounds found in common in females and flowers, have demonstrated that T. peruvianus flowers mimic the sexual pheromone of their pollinator's females. Thus, we give evidence that T. peruvianus flowers mimic a model composed of two organisms. Our study contributes to the understanding of the evolution of deceptive pollination.
An Intrusion Detection Game with Limited Observations

We present a 2 -player zero-sum stochastic (Markov) security game which models the interaction between malicious attackers to a system and the IDS who allocates system resources for detection and response. We capture the operation of a sensor network observing and reporting the attack information to the IDS as a ﬁnite Markov chain. Thus, we extend the game theoretic framework in [1] to a stochastic and dynamic one. We analyze the outcomes and evolution of an example game numerically for various game parameters. Furthermore, we study limited information cases where players optimize their strategies ofﬂine or online depending on the type of information available, using methods based on Markov decision process and Q-learning