Investigations for the Development of a Physiological Profile in Women's Soccer

The present PhD thesis is compilation of various investigations for the development of a physiological profile in women’s soccer. After an extensive literature review, several literature gaps in this area were identified including: physiological demands of a women’s soccer match including simultaneous measurements with portable metabolic equipment and GPS technology; physical performance analysis of women’s soccer competitive matches of different competition levels using GPS technology; and fitness testing and fitness profiles of female soccer players of different competitive levels by means of laboratory and field tests. The investigations that are part of this thesis targeted these gaps and provided for the first time novel and objective findings in these subjects. The popularity of women’s soccer as well as the number of female soccer players worldwide has increased exponentially in the last 30 years. Furthermore, there are now multiple women’s soccer international competitions and professional leagues around the globe and they will continue to increase in the next few years. Therefore, there is currently high demand for scientific research specific to the women’s game in these topics which may aid coaches, physical trainers, and other practitioners to develop more effective fitness assessments and training programs for their female players in order to improve their fitness status and overall match performance according to their competitive level and positional role.:DEDICATION iii ACKNOWLEDGMENTS iv BIBLIOGRAPHICAL INFORMATION vi ABSTRACT vii LIST OF PUBLICATIONS viii TABLE OF CONTENTS ix LIST OF TABLES xi LIST OF FIGURES xii LIST OF ABBREVIATIONS xiii 1 INTRODUCTION 1 1.1. WOMEN’S SOCCER BACKGROUND 1 1.2. PHYSICAL AND PHYSIOLOGICAL CHARACTERISTICS OF FEMALE SOCCER PLAYERS 3 1.3. PHYSICAL AND PHYSIOLOGICAL DEMANDS OF WOMEN’S SOCCER 4 2 DISSERTATION AIMS 6 3 STATE OF THE ART 8 3.1. PORTABLE METABOLIC, HEART RATE AND BLOOD LACTATE MEASUREMENTS 8 3.2. GLOBAL POSITIONING SYSTEM (GPS) MEASUREMENTS 9 3.3. TEST PROTOCOLS 10 3.3.1. ANTHROPOMETRY ASSESSMENT 10 3.3.2. AEROBIC CAPACITY TESTS 11 3.3.3. SPEED, ANAEROBIC ABILITY AND EXPLOSIVE POWER TESTS 15 4 OWN AREAS OF RESEARCH 18 4.1. PHYSIOLOGICAL DEMANDS OF A WOMEN’S FOOTBALL MATCH (ENGLISH SUMMARY FROM “PHYSIOLOGISCHE BEANSPRUCHUNG EINES FRAUENFUẞBALLSPIELS)” – (P-I) 18 4.2. GPS PERFORMANCE ANALYSIS OF WOMEN’S SOCCER COMPETITIVE MATCHES OF THE SECOND AND FOURTH GERMAN LEAGUES – (P-IV) 21 4.3. VALIDITY OF THE YO-YO INTERMITTENT RECOVERY TEST LEVEL 1 FOR DIRECT MEASUREMENT OR INDIRECT ESTIMATION OF MAXIMAL OXYGEN UPTAKE AMONG FEMALE SOCCER PLAYERS – (P-II) 25 4.4. FITNESS PROFILES OF GERMAN FEMALE SOCCER PLAYERS (UNPUBLISHED RESULTS) 26 5 CONCLUSIONS AND OUTLOOK 31 5.1. MAIN FINDINGS 31 5.2. PRACTICAL APPLICATIONS 33 5.3. STRENGTHS AND LIMITATIONS 33 5.4. FUTURE DIRECTIONS 34 6 REFERENCES 36 EIGENSTÄNDIGKEITSERKLÄRUNG 39 AUTHOR’S RESUME 40 ORIGINAL PUBLICATIONS 42 PUBLICATION 1 (P-I) 43 PUBLICATION 2 (P-II) 50 PUBLICATION 3 (P-III) 58 PUBLICATION 4 (P-IV) 7

LoRa Modulation for Split Learning

In this paper we introduce a task-oriented communication design for split learning (SL) over a communication channel. Our approach involves the Expressive Neural Network (ENN), a novel neural network featuring adaptive activation functions (AAF) based on the Discrete Cosine Transform (DCT). This architecture does not only provide better learning capabilities, but also facilitates data transmission using the Long Range (LoRa) modulation. The frequency nature of LoRa is adequate for the communication side of the problem, while allowing to construct the AAFs at the receiver. Additionally, we propose orthogonal chirp division multiplexing (OCDM) for multiple access and a modified modulation aimed at preserving communication bandwidth. Our experimental results demonstrate the effectiveness of this scheme, achieving high accuracy in challenging scenarios, including low signal to noise Ratio (SNR) and absence of channel state information (CSI) for both additive white Gaussian noise (AWGN) and Rayleigh fading channels.Comment: Accepted in 2023 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2023

LoRa-based Over-the-Air Computing for Sat-IoT

Satellite Internet of Things (Sat-IoT) is a novel framework in which satellites integrate sensing, communication and computing capabilities to carry out task-oriented communications. In this paper we propose to use the Long Range (LoRa) modulation for the purpose of estimation in a Sat-IoT scenario. Then we realize that the collisions generated by LoRa can be harnessed in an Over-the-Air Computing (AirComp) framework. Specifically, we propose to use LoRa for Type-based Multiple Access (TBMA), a semantic-aware scheme in which communication resources are assigned to different parameters, not users. Our experimental results show that LoRa-TBMA is suitable as a massive access scheme, provides large gains in terms of mean squared error (MSE) and saves scarce satellite communication resources (i.e., power, latency and bandwidth) with respect to orthogonal multiple access schemes. We also analyze the satellite scenarios that could take advantage of the LoRa-TBMA scheme. In summary, that angular modulations, which are very useful in satellite communications, can also benefit from AirComp.Comment: Paper accepted in 2023 European Signal Processing Conference (EUSIPCO

DCT-based Air Interface Design for Function Computation

With the integration of communication and computing, it is expected that part of the computing is transferred to the transmitter side. In this paper we address the general problem of Frequency Modulation (FM) for function approximation through a communication channel. We exploit the benefits of the Discrete Cosine Transform (DCT) to approximate the function and design the waveform. In front of other approximation schemes, the DCT uses basis of controlled dynamic, which is a desirable property for a practical implementation. Furthermore, the proposed modulation allows to recover both the measurement and the function in a single transmission. Our experiments show that this scheme outperforms the double side-band (DSB) modulation in terms of mean squared error (MSE). This can also be implemented with an agnostic receiver, in which the function is unknown to the receiver. Finally, the proposed modulation is compatible with some of the existing transmission technologies for sensor networks.Comment: Paper accepted in IEEE Open Journal of Signal Processing (2023

Frequency Modulation Aggregation for Federated Learning

Federated edge learning (FEEL) is a framework for training models in a distributed fashion using edge devices and a server that coordinates the learning process. In FEEL, edge devices periodically transmit model parameters to the server, which aggregates them to generate a global model. To reduce the burden of transmitting high-dimensional data by many edge devices, a broadband analog transmission scheme has been proposed. The devices transmit the parameters concurrently using a linear analog modulation, which are aggregated by the superposition nature of the wireless medium. However, linear analog modulations incur in an excessive power consumption for edge devices and are not suitable for current digital wireless systems. To overcome this issue, in this paper we propose a digital frequency broadband aggregation. The scheme integrates a Multiple Frequency Shift Keying (MFSK) at the transmitters and a type-based multiple access (TBMA) at the receiver. Using concurrent transmission, the server can recover the type (i.e., a histogram) of the transmitted parameters and compute any aggregation function to generate a shared global model. We provide a extensive analysis of the communication scheme in an AWGN channel and compare it with linear analog modulations. Our experimental results show that the proposed scheme achieves the same performance, although it requires 14 dB less in peak-to-average power ratio (PAPR) than linear analog modulations.Comment: Paper submitted to 2023 IEEE Global Communications Conferenc

Adaptive function approximation based on the Discrete Cosine Transform (DCT)

This paper studies the cosine as basis function for the approximation of univariate and continuous functions without memory. This work studies a supervised learning to obtain the approximation coefficients, instead of using the Discrete Cosine Transform (DCT). Due to the finite dynamics and orthogonality of the cosine basis functions, simple gradient algorithms, such as the Normalized Least Mean Squares (NLMS), can benefit from it and present a controlled and predictable convergence time and error misadjustment. Due to its simplicity, the proposed technique ranks as the best in terms of learning quality versus complexity, and it is presented as an attractive technique to be used in more complex supervised learning systems. Simulations illustrate the performance of the approach. This paper celebrates the 50th anniversary of the publication of the DCT by Nasir Ahmed in 1973.Comment: Accepted paper in 26th International Conference on Circuits, Systems, Communications and Computers (CSCC

Taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism

Objective: To evaluate the taxonomic composition of the gut microbiome in gout patients with and without tophi formation, and predict bacterial functions that might have an impact on urate metabolism. Methods: Hypervariable V3–V4 regions of the bacterial 16S rRNA gene from fecal samples of gout patients with and without tophi (n=33 and n=25, respectively) were sequenced and compared to fecal samples from 53 healthy controls. We explored predictive functional profles using bioinformatics in order to identify diferences in taxonomy and metabolic pathways. Results: We identifed a microbiome characterized by the lowest richness and a higher abundance of Phascolarctobacterium, Bacteroides, Akkermansia, and Ruminococcus_gnavus_group genera in patients with gout without tophi when compared to controls. The Proteobacteria phylum and the Escherichia-Shigella genus were more abundant in patients with tophaceous gout than in controls. Fold change analysis detected nine genera enriched in healthy controls compared to gout groups (Bifdobacterium, Butyricicoccus, Oscillobacter, Ruminococcaceae_UCG_010, Lachnospiraceae_ND2007_group, Haemophilus, Ruminococcus_1, Clostridium_sensu_stricto_1, and Ruminococcaceae_ UGC_013). We found that the core microbiota of both gout groups shared Bacteroides caccae, Bacteroides stercoris ATCC 43183, and Bacteroides coprocola DSM 17136. These bacteria might perform functions linked to one-carbon metabo‑ lism, nucleotide binding, amino acid biosynthesis, and purine biosynthesis. Finally, we observed diferences in key bacterial enzymes involved in urate synthesis, degradation, and elimination. Conclusion: Our fndings revealed that taxonomic variations in the gut microbiome of gout patients with and with‑ out tophi might have a functional impact on urate metabolism. Keywords: Gout, Gut microbiota, Uric acid metabolis

Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems

Satellite communications (SatComs) systems are facing a massive increase in traffic demand. However, this increase is not uniform across the service area due to the uneven distribution of users and changes in traffic demand diurnal. This problem is addressed by using flexible payload architectures, which allow payload resources to be flexibly allocated to meet the traffic demand of each beam. While optimization-based radio resource management (RRM) has shown significant performance gains, its intense computational complexity limits its practical implementation in real systems. In this paper, we discuss the architecture, implementation and applications of Machine Learning (ML) for resource management in multibeam GEO satellite systems. We mainly focus on two systems, one with power, bandwidth, and/or beamwidth flexibility, and the second with time flexibility, i.e., beam hopping. We analyze and compare different ML techniques that have been proposed for these architectures, emphasizing the use of Supervised Learning (SL) and Reinforcement Learning (RL). To this end, we define whether training should be conducted online or offline based on the characteristics and requirements of each proposed ML technique and discuss the most appropriate system architecture and the advantages and disadvantages of each approach

ATM/ATR kinases link the synaptonemal complex and DNA double-strand break repair pathway choice

DNA double-strand breaks (DSBs) are deleterious lesions, which must be repaired precisely to maintain genomic stability. During meiosis, programmed DSBs are repaired via homologous recombination (HR) while repair using the nonhomologous end joining (NHEJ) pathway is inhibited, thereby ensuring crossover formation and accurate chromosome segregation.1,2 How DSB repair pathway choice is implemented during meiosis is unknown. In C. elegans, meiotic DSB repair takes place in the context of the fully formed, highly dynamic zipper-like structure present between homologous chromosomes called the synaptonemal complex (SC).3,4,5,6,7,8,9 The SC consists of a pair of lateral elements bridged by a central region composed of the SYP proteins in C. elegans. How the structural components of the SC are regulated to maintain the architectural integrity of the assembled SC around DSB repair sites remained unclear. Here, we show that SYP-4, a central region component of the SC, is phosphorylated at Serine 447 in a manner dependent on DSBs and the ATM/ATR DNA damage response kinases. We show that this SYP-4 phosphorylation is critical for preserving the SC structure following exogenous (γ-IR-induced) DSB formation and for promoting normal DSB repair progression and crossover patterning following SPO-11-dependent and exogenous DSBs. We propose a model in which ATM/ATR-dependent phosphorylation of SYP-4 at the S447 site plays important roles both in maintaining the architectural integrity of the SC following DSB formation and in warding off repair via the NHEJ repair pathway, thereby preventing aneuploidy.This work was supported by CIHR grant 119468 to M.Z., a MRC core-funded grant to E.M.-P., and National Institutes of Health grant R01GM072551 to M.P.C.Peer reviewe

Nuclear expression of Rac1 in cervical premalignant lesions and cervical cancer cells

<p>Abstract</p> <p>Background</p> <p>Abnormal expression of Rho-GTPases has been reported in several human cancers. However, the expression of these proteins in cervical cancer has been poorly investigated. In this study we analyzed the expression of the GTPases Rac1, RhoA, Cdc42, and the Rho-GEFs, Tiam1 and beta-Pix, in cervical pre-malignant lesions and cervical cancer cell lines.</p> <p>Methods</p> <p>Protein expression was analyzed by immunochemistry on 102 cervical paraffin-embedded biopsies: 20 without Squamous Intraepithelial Lesions (SIL), 51 Low- grade SIL, and 31 High-grade SIL; and in cervical cancer cell lines C33A and SiHa, and non-tumorigenic HaCat cells. Nuclear localization of Rac1 in HaCat, C33A and SiHa cells was assessed by cellular fractionation and Western blotting, in the presence or not of a chemical Rac1 inhibitor (NSC23766).</p> <p>Results</p> <p>Immunoreacivity for Rac1, RhoA, Tiam1 and beta-Pix was stronger in L-SIL and H-SIL, compared to samples without SIL, and it was significantly associated with the histological diagnosis. Nuclear expression of Rac1 was observed in 52.9% L-SIL and 48.4% H-SIL, but not in samples without SIL. Rac1 was found in the nucleus of C33A and SiHa cells but not in HaCat cells. Chemical inhibition of Rac1 resulted in reduced cell proliferation in HaCat, C33A and SiHa cells.</p> <p>Conclusion</p> <p>Rac1 is expressed in the nucleus of epithelial cells in SILs and cervical cancer cell lines, and chemical inhibition of Rac1 reduces cellular proliferation. Further studies are needed to better understand the role of Rho-GTPases in cervical cancer progression.</p