Machine learning based stellar classification with highly sparse photometry data.

Background: Identifying stars belonging to different classes is vital in order to build up statistical samples of different phases and pathways of stellar evolution. In the era of surveys covering billions of stars, an automated method of identifying these classes becomes necessary. Methods: Many classes of stars are identified based on their emitted spectra. In this paper, we use a combination of the multi-class multi-label Machine Learning (ML) method XGBoost and the PySSED spectral-energy-distribution fitting algorithm to classify stars into nine different classes, based on their photometric data. The classifier is trained on subsets of the SIMBAD database. Particular challenges are the very high sparsity (large fraction of missing values) of the underlying data as well as the high class imbalance. We discuss the different variables available, such as photometric measurements on the one hand, and indirect predictors such as Galactic position on the other hand. Results: We show the difference in performance when excluding certain variables, and discuss in which contexts which of the variables should be used. Finally, we show that increasing the number of samples of a particular type of star significantly increases the performance of the model for that particular type, while having little to no impact on other types. The accuracy of the main classifier is ∼0.7 with a macro F1 score of 0.61. Conclusions: While the current accuracy of the classifier is not high enough to be reliably used in stellar classification, this work is an initial proof of feasibility for using ML to classify stars based on photometry.

Astronomy is at the forefront of the 'Big Data' regime, with telescopes collecting increasingly large volumes of data. The tools astronomers use to analyse and draw conclusions from these data need to be able to keep up, with machine learning providing many of the solutions. Being able to classify different astronomical objects by type helps to disentangle the astrophysics making them unique, offering new insights into how the Universe works. Here, we present how machine learning can be used to classify different kinds of stars, in order to augment large databases of the sky. This will allow astronomers to more easily extract the data they need to perform their scientific analyses.

The floating duck syndrome: biased social learning leads to effort-reward imbalances.

An increasingly common phenomenon in modern work and school settings is individuals taking on too many tasks and spending effort without commensurate rewards. Such an imbalance of efforts and rewards leads to myriad negative consequences, such as burnout, anxiety and disease. Here, we develop a model to explain how such effort-reward imbalances can come about as a result of biased social learning dynamics. Our model is based on a phenomenon that on some US college campuses is called 'the floating duck syndrome'. This phrase refers to the social pressure on individuals to advertise their successes but hide the struggles and the effort put in to achieve them. We show that a bias against revealing the true effort results in social learning dynamics that lead others to underestimate the difficulty of the world. This in turn leads individuals to both invest too much total effort and spread this effort over too many activities, reducing the success rate from each activity and creating effort-reward imbalances. We also consider potential ways to counteract the floating duck effect: we find that solutions other than addressing the root cause, biased observation of effort, are unlikely to work.

Low Levels of Cortisol are Associated with HIV infection in Older People: A Hormonal Substudy in the OVER50 Cohort.

INTRODUCTION: People with the human immunodeficiency virus (PWH) who were diagnosed long ago are more prone to age-related conditions and comorbidities than the general population. We hypothesized that older PWH have endocrine abnormalities that may influence the patient's health status. METHODS: Mean hormonal values across the thyrotropic, somatotropic, corticotropic, and gonadal axis, and percentage of subjects with abnormal values, were compared between PWH aged ≥50 years (n=30) and people without HIV (n=30) (Over50 cohort). Clinical factors were also analyzed as independent variables. RESULTS: PWH had a higher prevalence of comorbidities (36.67% PWH and 20.69% controls had ≥3 comorbidities). Male PWH exhibited lower estradiol levels than male controls (29.75±7.68 pg/mL vs. 35.45±10.04 pg/mL; p=0.0041). Abnormal concentrations of testosterone were found in 35% of male PWH compared to 55% of male controls (mostly above reference values). Cortisol levels were significantly lower among PWH (9.97±4.33 µg/dL vs. 13.56±3.39 µg/dL; p=0.002); 16.6% of PWH exhibited abnormally low levels (<5 µg/dL), compared to 0% of controls, and 3 PWH met criteria for a definitive diagnosis of adrenal insufficiency (<3.6 µg/dL). For the somatotropic axis, growth hormone (GH) levels were significantly lower in male PWH than in controls (p=0.0394). No significant differences were found in relation to the thyroid axis. CONCLUSION: Hormones are generally similar between the chronic PWH who are receiving ART treatment and the general control population, except for cortisol in both sexes and testosterone and estradiol in men. Some special attention should be given to cortisol in PWH due to a presumably higher risk of adrenal complications.

Athlete Body Image and Eating Disorders: A Systematic Review of Their Association and Influencing Factors.

Body image and eating disorders pose significant challenges to the overall health of athletes. However, divergent findings exist regarding the potential association between athletes' body image and eating disorders. This systematic review aims to examine the relationship between these two variables and identify the modifiers of the association, such as gender, age, race, and exercise type. A search was conducted in five databases (Web of Science, PubMed, APA PsycINFO, ProQuest, and EBSCO), aiming to identify studies on athletes and involved body image and eating disorders in their conclusions. Ultimately, thirty-one studies were included for systematic evaluation. The results of the studies indicate that the relationship between athletes' body image and eating disorders is complex and inconclusive. In some types of sports, eating disorders can occur even when athletes are satisfied with their body image. Furthermore, female athletes, particularly young female athletes, and athletes involved in sports associated with leanness are more prone to eating disorders and body dissatisfaction. Due to limited resources in this type of research, there is a lack of comprehensive inclusivity across sports disciplines, genders, races, and levels of sports proficiency, which warrants further research.

Partial Transfer Learning Method Based on Inter-Class Feature Transfer for Rolling Bearing Fault Diagnosis.

Rolling bearing fault diagnosis methods based on transfer learning always assume that the sample classes in the target domain are consistent with those in the source domain during the training phase. However, it is difficult to collect all fault classes in the early stage of mechanical application. The more likely situation is that the training data in the target domain only contain a subset of the entire health state, which will lead to the problem of label imbalance compared with the source domain. The outlier classes in the source domain that do not have corresponding target domain samples for feature alignment will interfere with the feature transfer of other classes. To address this specific challenge, this study introduces an innovative inter-class feature transfer fault diagnosis approach. By leveraging label information, the method distinctively computes the distribution discrepancies among shared classes, thereby circumventing the deleterious influence of outlier classes on the transfer procedure. Empirical evaluations on two rolling bearing datasets, encompassing multiple partial transfer tasks, substantiate that the proposed method surpasses other approaches, offering a novel and efficacious solution for the realm of intelligent bearing fault diagnosis.

Longitudinal associations of effort and reward at work with changes in cognitive function: evidence from a national study of U.S. workers.

PURPOSE: This study aimed to examine longitudinal associations of workplace effort and reward with changes in cognitive function among United States workers. METHODS: Data from the national, population-based Midlife in the United States (MIDUS) study with a 9-year follow-up were used. Validated workplace effort and reward scales were measured at baseline, and cognitive outcomes (including composite cognition, episodic memory, and executive functioning) were measured with the Brief Test of Adult Cognition by Telephone (BTACT) at baseline and follow-up. Multivariable linear regression analyses based on generalized estimating equations (GEE) examined the longitudinal associations under study. RESULTS: Among this worker sample of 1,399, after accounting for demographics, socioeconomics, lifestyle behaviors, health conditions, and job control, high reward at baseline was associated with increased composite cognition (regression coefficient: 0.118 [95% CI: 0.049, 0.187]), episodic memory (0.106 [0.024, 0.188]), and executive functioning (0.123 [0.055, 0.191]) during follow-up. The joint exposure of 'high effort and high reward' was also associated with increased composite cognition (0.130 [0.030, 0.231]), episodic memory (0.131 [0.012, 0.250]), and executive functioning (0.117 [0.017, 0.216]), while the combination of 'low effort and high reward' was associated with increased composite cognition (0.106 [0.009, 0.204]) and executive functioning (0.139 [0.042, 0.235]). CONCLUSION: Findings suggest that workplace high reward is related to improved cognitive scores among United States workers. Future research should investigate larger cohorts over longer timespans and expand into disease outcomes such as dementia. If these findings emerge as causal, relevant workplace rewards to promote worker cognitive health should be considered.

[Mechanisms of aging and age-related homeodynamics of bio-elementome.]

This article presents a data science review and our own evaluation on bio-element mediated aging of the human body from the point of view of homeodynamics of bioelementome. The study of bio-element basis of aging is currently one of the actively developing fields in gerontology. During postnatal ontogenesis, the bio-elementome shows no signs of stability. Being extremely dependent on endogenous and exogenous circumstances, the levels of macro- and microelements can either remain within the normal range or undergo significant changes, especially with the body aging. These bio-element developments appear to be very important in terms of a large number of currently known molecular, subcellular, cellular, and tissue mechanisms of aging (oxidative stress, loss of proteostasis, excessive telomere attrition, epigenetic landscape alterations, apoptosis, altered intercellular communication, and many others). Better understanding of metabolic pathways of essential bio-elements (intake in the gastrointestinal tract; absorption, including due to interaction with specific transporting proteins; spread through the circulatory system and the entire body; inclusion in specialized macromolecules and participation in their composition in biochemical processes; excretion from the body), as well as realizing their role in the mechanisms of senile tissue and organ involution, and features of age-related homeodynamics can significantly improve existing knowledge on the biology of aging.

Effect of Paravertebral Muscle Degeneration on Lumbar and Pelvic Sagittal Alignment in Patients With Degenerative Scoliosis.

STUDY DESIGN: Retrospective study. OBJECTIVES: To explore the relationship between lumbar spine muscle mass and lumbar pelvic sagittal parameters in patients with degenerative scoliosis. METHODS: This study included ADS patients who were treated in our hospital from 2019 to 2023. The spinal parameters were evaluated through X-rays, and the relative muscle volume (RMV) and fat infiltration (FI) were measured through three-dimensional reconstruction. Patients were categorized into 3 groups based on SRS-Schwab sagittal balance correction (0, +, ++), and into 3 groups based on GAP score (proportioned, moderately dis-proportioned, severely dis-proportioned). Finally, patients were classified into low-quality and high-quality groups based on the FI of Paraspinal muscles (PSM). RESULTS: The study included a total of 63 patients. Significant statistical differences were observed in the FI and RMV of MF, ES and PS among patients classified by SRS-Schwab PT classification. Additionally, significant statistical differences were found in the RMV of MF and PS among patients classified by SRS-Schwab PI-LL classification and GAP score. Furthermore, a significant correlation was found between the FI and RMV of PSM and lumbopelvic sagittal parameters. The ordinal regression model analysis revealed that FI of ES significantly impacted PT imbalance, while RMV of MF significantly impacted PI-LL imbalance. Moreover, significant differences were noted in PT and PI between the low-quality and high-quality multifidus groups. CONCLUSIONS: As sagittal imbalance worsens, PSM degeneration also intensifies, primarily characterized by an increase in FI and a decrease in RMV. Notably, PT and PI-LL are positively correlated with RMV and negatively correlated with FI.

Infant versus noninfant formulas and cow's milk: Transition based on age or weight?

Infant formulas are meant to be used until 1 year of age, at which point children are transitioned to non-infant formulas or cow's milk, depending on their remaining dietary intake. Noninfant formulas and cow's milk are appropriate for children who have an average weight at that 1-year mark (9-9.5 kg); however, can contribute significant protein and/or electrolytes to children who are underweight for age, particularly if they still rely heavily on formula feeding for their caloric intake. In this short communication, we present several cases of patients who received excessive amounts of nutrients for age following the formula transition at the 1-year mark. We also provide recommendations for clinicians to consider when faced with underweight infants who are meant to be transitioning off infant formulas.

A Single Bout of On-Ice Training Leads to Increased Interlimb Asymmetry in Competitive Youth Hockey Athletes.

Interlimb asymmetry (ILA) refers to an anatomical or physiological imbalance between contralateral limbs, which can influence neuromuscular function. Investigating the influence of neuromuscular fatigue on ILA may be critical for optimizing training programs, injury rehabilitation, and sport-specific performance. The purpose of this study was to determine if a single bout of ice hockey-specific exercise creates or exacerbates lower-limb ILA. Before and after an on-ice training session, 33 youth ice-hockey athletes (14.9 [1.7] y; 11 females) performed 3 repetitions of a maximal vertical countermovement jump (CMJ), an eccentric hamstring contraction, and maximal isometric hip adduction and abduction contractions. Force- and power-related variables were analyzed to determine limb-specific neuromuscular function. The on-ice session reduced maximal isometric hip adduction (left: 7.3% [10.3%]; right: 9.5% [9.6%]) and abduction (left: 4.9% [6.9%]; right: 5.0% [8.1%]) force, but did not impair (P ≥ .10) CMJ performance (jump height, relative peak power, braking duration, and total duration). After the on-ice session, ILA was greater for CMJ propulsive impulse (6.3% [2.9%] vs 5.1% [2.6%]), CMJ braking rate of force development (19.3% [7.6%] vs 15.2% [6.4%]), and peak isometric hip adduction force (6.7% [5.5%] vs 6.1% [4.1%]). In conclusion, hockey-specific exercise leads to increased ILA for multiple force-related metrics, which may be a compensatory mechanism to maintain bilateral task performance when fatigued.

DECNet: Left Atrial Pulmonary Vein Class Imbalance Classification Network.

In clinical practice, the anatomical classification of pulmonary veins plays a crucial role in the preoperative assessment of atrial fibrillation radiofrequency ablation surgery. Accurate classification of pulmonary vein anatomy assists physicians in selecting appropriate mapping electrodes and avoids causing pulmonary arterial hypertension. Due to the diverse and subtly different anatomical classifications of pulmonary veins, as well as the imbalance in data distribution, deep learning models often exhibit poor expression capability in extracting deep features, leading to misjudgments and affecting classification accuracy. Therefore, in order to solve the problem of unbalanced classification of left atrial pulmonary veins, this paper proposes a network integrating multi-scale feature-enhanced attention and dual-feature extraction classifiers, called DECNet. The multi-scale feature-enhanced attention utilizes multi-scale information to guide the reinforcement of deep features, generating channel weights and spatial weights to enhance the expression capability of deep features. The dual-feature extraction classifier assigns a fixed number of channels to each category, equally evaluating all categories, thus alleviating the learning bias and overfitting caused by data imbalance. By combining the two, the expression capability of deep features is strengthened, achieving accurate classification of left atrial pulmonary vein morphology and providing support for subsequent clinical treatment. The proposed method is evaluated on datasets provided by the People's Hospital of Liaoning Province and the publicly available DermaMNIST dataset, achieving average accuracies of 78.81% and 83.44%, respectively, demonstrating the effectiveness of the proposed approach.

Sex specific emergence of trisomic Dyrk1a-related skeletal phenotypes in the development of a Down syndrome mouse model.

Skeletal insufficiency affects all individuals with Down syndrome (DS) or Trisomy 21 (Ts21) and may alter bone strength throughout development due to a reduced period of bone formation and early attainment of peak bone mass compared to typically developing individuals. Appendicular skeletal deficits also appear in males before females with DS. In femurs of male Ts65Dn DS model mice, cortical deficits were pronounced throughout development, but trabecular deficits and Dyrk1a overexpression were transitory until postnatal day (P) 30 when there were persistent trabecular and cortical deficits and Dyrk1a was trending overexpression. Correction of DS-related skeletal deficits by a purported DYRK1A inhibitor or through genetic means beginning at P21 was not effective at P30, but germline normalization of Dyrk1a improved male bone structure by P36. Trabecular and cortical deficits in female Ts65Dn mice were evident at P30 but subsided by P36, typifying periodic developmental skeletal normalizations that progressed to more prominent bone deficiencies. Sex-dependent differences in skeletal deficits with a delayed impact of trisomic Dyrk1a are important to find temporally specific treatment periods for bone and other phenotypes associated with Ts21.

Impaired olfactory system in metabolic imbalance-related neuropathology.

Olfactory dysfunction, influenced by factors such as aging and environmental stress, is linked to various neurological disorders. The olfactory bulb's connections to brain areas like the hypothalamus, piriform cortex, entorhinal cortex, and limbic system make olfactory dysfunction a contributor to a range of neuropathological conditions. Recent research has underscored that olfactory deficits are prevalent in individuals with both metabolic syndrome and dementia. These systemic metabolic alterations correlate with olfactory impairments, potentially affecting brain regions associated with the olfactory bulb. In cases of metabolic syndrome, phenomena such as insulin resistance and disrupted glucose metabolism may result in compromised olfactory function, leading to multiple neurological issues. This review synthesizes key findings on the interplay between metabolic-induced olfactory dysfunction and neuropathology. It emphasizes the critical role of olfactory assessment in diagnosing and managing neurological diseases related to metabolic syndrome.

A unified Foot and Mouth Disease dataset for Uganda: evaluating machine learning predictive performance degradation under varying distributions.

In Uganda, the absence of a unified dataset for constructing machine learning models to predict Foot and Mouth Disease outbreaks hinders preparedness. Although machine learning models exhibit excellent predictive performance for Foot and Mouth Disease outbreaks under stationary conditions, they are susceptible to performance degradation in non-stationary environments. Rainfall and temperature are key factors influencing these outbreaks, and their variability due to climate change can significantly impact predictive performance. This study created a unified Foot and Mouth Disease dataset by integrating disparate sources and pre-processing data using mean imputation, duplicate removal, visualization, and merging techniques. To evaluate performance degradation, seven machine learning models were trained and assessed using metrics including accuracy, area under the receiver operating characteristic curve, recall, precision and F1-score. The dataset showed a significant class imbalance with more non-outbreaks than outbreaks, requiring data augmentation methods. Variability in rainfall and temperature impacted predictive performance, causing notable degradation. Random Forest with borderline SMOTE was the top-performing model in a stationary environment, achieving 92% accuracy, 0.97 area under the receiver operating characteristic curve, 0.94 recall, 0.90 precision, and 0.92 F1-score. However, under varying distributions, all models exhibited significant performance degradation, with random forest accuracy dropping to 46%, area under the receiver operating characteristic curve to 0.58, recall to 0.03, precision to 0.24, and F1-score to 0.06. This study underscores the creation of a unified Foot and Mouth Disease dataset for Uganda and reveals significant performance degradation in seven machine learning models under varying distributions. These findings highlight the need for new methods to address the impact of distribution variability on predictive performance.

Mal De Debarquement Syndrome: An Often Unrecognized and Unreported Condition.

Mal de debarquement syndrome (MdDS), also known as "the sickness of disembarkment," is characterized by a persistent bobbing, rocking, or swaying sensation reported by patients long after they have completed travel on a boat or other forms of extended transportation. A detailed patient history, focusing on specific inquiries about recent boat or ship travel, is crucial for a timely diagnosis. The syndrome is unique in that reintroducing similar movements, such as driving, swinging, or returning to the boat, alleviates symptoms temporarily. We describe the case history of a 28-year-old male who experienced a persistent illusion of ground movement for six months following a fishing expedition. The patient reported alleviated symptoms when re-exposed to movements such as driving or swinging. The patient had undergone extensive medical workups and imaging tests under multiple physicians before being diagnosed with MdDS. MdDS is a commonly misdiagnosed, underdiagnosed, unreported, and unrecognized condition. Diagnosing MdDS requires a detailed medical and travel history, accompanying an understanding that the symptoms improve upon re-exposure to the same or similar motion.

From the latin "re-cordis, passing through the heart": autonomic modulation differentiates migraineurs from controls when recounting a significant life event.

OBJECTIVE: The literature on clinical psychophysiology highlights the possibility of using Heart Rate Variability (HRV) as an index of psychophysical balance and resilience to stress. This study investigates the differences in stress reactivity and subsequent recovery between a group of migraineurs and healthy controls. METHODS: Socio-demographic (i.e., sex, age, profession, marital status, and level of education) and psychophysiological (HR and HRV) measures of a group of thirty subjects with migraine (26 migraineurs without aura (86.7%), 2 migraineurs with aura (6.7%), and 2 migraineurs with and without aura (6.7%)) and from thirty healthy control subjects were collected. In particular, HRV was analyzed through frequency-domain parameters, including Low-Frequency (LF; 0.04-0.15 Hz) and High-Frequency (HF; 0.15-0.4 Hz) bands as well as LF/HF ratio during a Psychophysiological Stress Profile (PSP) structured in seven phases: (1) Baseline, (2) Objective stressor 1 (Stroop Test), (3) Rest 1, (4) Objective stressor 2 (Mental Arithmetic Task), (5) Rest 2, (6) Subjective stressor (recount a significant life event), and (7) Rest 3. The LF, HF, and LF/HF ratio values were transformed into a logarithmic scale (i.e., log-LF, log-HF, and log LF/HF ratio). Additionally, LF and HF were converted into normalized units (0-100) (i.e., LF% and HF%) which, in turn, were used to obtain reactivity and recovery to stress through delta values (Δ) calculation. RESULTS: Subjects with migraine reported greater ΔLF% levels of reactivity and recovery to subjective stressor, demonstrating a prevalence of sympathetic activity while recounting a personal life event. At the same time, a lowering of the same values was found in the subjects of the group control. DISCUSSION: Our results underline the importance of conducting a psychophysiological assessment in patients with headaches because reduced stress management skills could influence the clinical manifestations of the disease, considering stress as one of the most common triggers for migraine patients.

Lowered ratio of corticospinal excitation to inhibition predicts greater disability, poorer motor and cognitive function in multiple sclerosis.

Objective: Investigate excitatory-inhibitory (E/I) (im)balance using transcranial magnetic stimulation (TMS) in individuals with Multiple Sclerosis (MS) and determine its validity as a neurophysiological biomarker of disability. Methods: Participants with MS (n = 83) underwent TMS, cognitive, and motor function assessments. TMS-induced motor evoked potential amplitudes (excitability) and cortical silent periods (inhibition) were assessed bilaterally through recruitment curves. The E/I ratio was calculated as the ratio of excitation to inhibition. Results: Participants with greater disability (Expanded Disability Status Scale, EDSS≥3) exhibited lower excitability and increased inhibition compared to those with lower disability (EDSS<3). This resulted in lower E/I ratios in the higher disability group. Individuals with higher disability presented with asymmetrical E/I ratios between brain hemispheres, a pattern not present in the group with lower disability. In regression analyses controlling for demographics, lowered TMS-probed E/I ratio predicted variance in disability (R2 = 0.37, p < 0.001), upper extremity function (R2 = 0.35, p < 0.001), walking speed (R2 = 0.22, p = 0.005), and cognitive performance (R2 = 0.25, p = 0.007). Receiver Operating Characteristic curve analysis confirmed 'excellent' discriminative ability of the E/I ratio in distinguishing high and low disability. Finally, excitation superiorly correlated with the E/I ratio than overall inhibition in both hemispheres (p ≤ 0.01). Conclusion: The E/I ratio is a potential neurophysiological biomarker of disability level in MS, especially when assessed in the hemisphere corresponding to the weaker body side. Interventions aimed at increasing cortical excitation or reducing inhibition may restore E/I balance potentially stalling progression or improving function in MS.

Balancing Acts: Tackling Data Imbalance in Machine Learning for Predicting Myocardial Infarction in Type 2 Diabetes.

Type 2 Diabetes (T2D) is a prevalent lifelong health condition. It is predicted that over 500 million adults will be diagnosed with T2D by 2040. T2D can develop at any age, and if it progresses, it may cause serious comorbidities. One of the most critical T2D-related comorbidities is Myocardial Infarction (MI), known as heart attack. MI is a life-threatening medical emergency, and it is important to predict it and intervene in a timely manner. The use of Machine Learning (ML) for clinical prediction is gaining pace, but the class imbalance in predictive models is a key challenge for establishing a trustworthy deployment of the technology. This may lead to bias and overfitting in the ML models, and it may cause misleading interpretations of the ML outputs. In our study, we showed how systematic use of Class Imbalance Handling (CIH) techniques may improve the performance of the ML models. We used the Connected Bradford dataset, consisting of over one million real-world health records. Three commonly used CIH techniques, Oversampling, Undersampling, and Class Weighting (CW) have been used for Naive Bayes (NB), Neural Network (NN), Random Forest (RF), Support Vector Machine (SVM), and Ensemble models. We report that CW overperforms among the other techniques with the highest Accuracy and F1 values of 0.9948 and 0.9556, respectively. Applying the most appropriate CIH techniques for the ML models using real-world healthcare data provides promising results for helping to reduce the risk of MI in patients with T2D.

Insights into the State of the Art of Urogenital Schistosomiasis with a Focus on Infertility.

Schistosomiasis is a neglected tropical disease that affects developing countries worldwide and is caused by several species of parasites from the Schistosoma genus. Chronic infection is characterized by the formation of granulomas around the parasite eggs, the leading cause of pathology. The hepatosplenic clinical form is one of the most common, but urogenital schistosomiasis is another relevant clinical presentation responsible for infertility in men and women. Inflammatory response, anatomical deformations, and endocrine/biochemical changes are involved in the development of infertility. Schistosome parasites can synthesize catechol estrogen-like molecules and affect the sexual hormone balance in their host. Here, we review many aspects of the pathology of urogenital schistosomiasis, specifically infertility, and point to the biochemical and endocrinal elements that must be investigated in the future.

Nitrogen availability and summer drought, but not N:P imbalance, drive carbon use efficiency of a Mediterranean tree-grass ecosystem.

All ecosystems contain both sources and sinks for atmospheric carbon (C). A change in their balance of net and gross ecosystem carbon uptake, ecosystem-scale carbon use efficiency (CUEECO), is a change in their ability to buffer climate change. However, anthropogenic nitrogen (N) deposition is increasing N availability, potentially shifting terrestrial ecosystem stoichiometry towards phosphorus (P) limitation. Depending on how gross primary production (GPP, plants alone) and ecosystem respiration (RECO, plants and heterotrophs) are limited by N, P or associated changes in other biogeochemical cycles, CUEECO may change. Seasonally, CUEECO also varies as the multiple processes that control GPP and respiration and their limitations shift in time. We worked in a Mediterranean tree-grass ecosystem (locally called 'dehesa') characterized by mild, wet winters and summer droughts. We examined CUEECO from eddy covariance fluxes over 6 years under control, +N and + NP fertilized treatments on three timescales: annual, seasonal (determined by vegetation phenological phases) and 14-day aggregations. Finer aggregation allowed consideration of responses to specific patterns in vegetation activity and meteorological conditions. We predicted that CUEECO should be increased by wetter conditions, and successively by N and NP fertilization. Milder and wetter years with proportionally longer growing seasons increased CUEECO, as did N fertilization, regardless of whether P was added. Using a generalized additive model, whole ecosystem phenological status and water deficit indicators, which both varied with treatment, were the main determinants of 14-day differences in CUEECO. The direction of water effects depended on the timescale considered and occurred alongside treatment-dependent water depletion. Overall, future regional trends of longer dry summers may push these systems towards lower CUEECO.