Miracle-dz/UniFAR-FTUs huggingface dataset:
The military spouse experience of living alongside their serving/veteran partner with a mental health issue: A systematic review and narrative synthesis

Introduction Military healthcare studies have reported a wide range of mental health issues amongst military personnel. Globally, mental health issues are one of the main causes of ill health. Military personnel have a greater prevalence of mental health issues than that of the general population. The impact of mental health issues can be wide and far reaching for family and carers. This systematic narrative review explores the military spouse experience of living alongside their serving or veteran partner with a mental health issue. Methods The systematic review performed was based on the PRISMA guide for searching, screening, selecting papers for data extraction and evaluation. Studies were identified from CINHAL, ASSIA, Proquest Psychology, Proquest Nursing & Allied Health source, Proquest Dissertations & Theses, ETHOS, PsychArticles, Hospital collection, Medline, Science Direct Freedom Collection and hand searching of citations and reference lists. Results Twenty-seven studies were included in the narrative synthesis. Five overarching themes from the experiences of military spouses’ living alongside their serving/veteran partners mental health issue were identified: caregiver burden, intimate relationships, psychological/psychosocial effects on the spouse, mental health service provision and spouse’s knowledge and management of symptoms. Conclusions The systematic review and narrative synthesis identified that the majority of studies focused on spouses of veterans, very few were specific to serving military personnel, but similarities were noted. Findings suggest that care burden and a negative impact on the intimate relationship is evident, therefore highlight a need to support and protect the military spouse and their serving partner. Likewise, there is a need for greater knowledge, access and inclusion of the military spouse, in the care and treatment provision of their serving partner’s mental health issue.
Predictive effect of investor sentiment on current and future returns in emerging equity markets

This study uses Non-linear Predictive Regression Analysis to analyze the effect of investor sentiment on the returns of the selected developing equity markets, including Brazil, South Africa, Indonesia, India, China, Russia, and Pakistan. The Principal Component Analysis is applied to construct an Investor Sentiment Index. In most selected countries, investor sentiment substantially affects contemporaneous market returns, and this effect remains persistent in the short term. However, it becomes less prominent over time. It suggests that stakeholders should give importance to the investors’ sentiments while making investment decisions.
Attitude Estimation Algorithm of Agricultural Small-UAV Based on Sensors Fusion and Calibration

The real-time attitude information of agricultural small unmanned aerial vehicle(UAV) is a key factor to the decision and operation for variable rate program in precision agriculture. A real-time attitude estimation system of agricultural small UAV is designed here which consists of a microprocessor STM32, micro-electro mechanial system(MEMS) inertial sensors, and wireless transceiver module n RF24L01 a. Detailed description and derivation of sensor calibration method and the multi-sensor fusion algorithm of attitude estimation based on quaternion derivation and the gradient descent algorithm are presented in the paper. Experimental results show that the sensor data acquisition and filtering consumes about 6.2 ms, and the algorithm consumes about 0.96 ms with the step size β = 0.8 in the 72 MHz clock frequency and soft IIC(Inter-integrated circuit). The update frequency of attitude data up to 100 Hz can meet real-time requirements. Statistics shows that the static mean absolute errors of pitch and roll are below 1.5and the mean absolute errors of yaw are below 2.9. The mean absolute error of pitch, roll and yaw will be increased by 1～ 2under the condition of micro-vibration of low frequency. It is indicated that the attitude estimation fusion algorithm and sensors calibration method are practical and effective which could provide accurate attitude data for precision flight control and variable operations implementation of agricultural small UAV.
Arbuscular mycorrhizal fungi improve growth and tolerance of Platycladus orientalis under lead stress

Abstract Platycladus orientalis is a significant woody plant for phytoremediation in heavy metals contaminated soils. The growth and tolerance of host plants under the lead (Pb) stress were enhanced by arbuscular mycorrhizal fungi (AMF). To evaluate the adjustment by AMF on growth and activity of antioxidant system of P. orientalis under Pb stress. The two-factor pot experiment was conducted with three AM fungal treatments (noninoculated, Rhizophagus irregularis, and Funneliformis mosseae) and four Pb levels (0, 500, 1000, and 2000 mg kg−1). AMF increased dry weight, phosphorus uptake, root vitality, and total chlorophyll content of P. orientalis in spite of Pb stress. Compared with nonmycorrhizal treatments, mycorrhizal P. orientalis had lower H2O2 and malondialdehyde (MDA) contents under Pb stress. AMF increased Pb uptake in roots and decreased the Pb translating to the shoots yet under Pb stress. Total glutathione and ascorbate in roots of P. orientalis were decreased by AMF inoculation. Mycorrhizal P. orientalis had higher superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and glutathione S-transferase (GST) activities in shoots and roots than nonmycorrhizal counterparts. Mycorrhizal P. orientalis under Pb stress showed higher expression of PoGST1 and PoGST2 in roots than that in CK treatments. Future studies will explore the function of induced tolerance genes by AMF of P. orientalis under Pb stress. NOVELTY STATEMENT Arbuscular mycorrhizal fungi (AMF) decreased the reduced toxicity of lead to Platycladus orientalis under lead stress, including improving growth, root activity, photosynthesis, and antioxidant system activity, while reducing its oxidative damage. At the same time, lead inhibited the symbiosis between AMF and Platycladus orientalis. HIGHLIGHTS Inoculation of AMF improved the growth of P. orientalis and decreased the Pb accumulation and damage to P. orientalis under Pb stress. AMF improved the antioxidant system and relevant genes expression of P. orientalis.
Dominant follicle growth patterns and associated endocrine dynamics in anovulatory and ovulatory waves in women

Growth patterns and associated endocrine profiles were compared between dominant anovulatory (ADF) and ovulatory follicles (OvF) developing from different waves within and between menstrual cycles in women. Follicular mapping profiles of 49 healthy women of reproductive age and blood samples were obtained every 1–3 days during one interovulatory interval. Sixty-three dominant follicles were classified into wave 1 (W1ADF; n = 8) and wave 2 (W2ADF; n = 6) anovulatory follicles and wave 2 (W2OvF; n = 33) and wave 3 (W3OvF; n = 16) ovulatory follicles. Comparisons were made between W1ADF and W2ADF, W2ADF and W2OvF, and W2OvF and W3OvF. The waves were numbered 1, 2, or 3 based on when the waves emerged relative to the preceding ovulation. W1ADF emerged closer to the preceding ovulation, and W2ADF emerged in the late luteal or early follicular phase. The interval from emergence to maximum diameter was shorter for W2ADF than W1ADF and for W3OvF than W2OvF. Selection of W3OvF occurred at a smaller diameter compared to W2OvF. W1ADF regressed at a faster rate than W2ADF. Also, W1ADF were associated with lower mean follicle stimulating hormone (FSH) and higher mean estradiol than W2ADF. In contrast, W3OvF were associated with higher FSH and luteinizing hormone (LH) compared to W2OvF. However, W2OvF were associated with higher progesterone than W3OvF. This study contributes to the understanding of the physiologic mechanisms underlying selection of the dominant follicle, ovulation, and pathophysiology of anovulation in women, as well as optimization of ovarian stimulation protocols for assisted reproduction. Lay summary Ovarian follicles (the fluid-filled sacs that contain the eggs) develop within 2 or 3 waves during the human menstrual cycle. Typically, only one follicle in each cycle releases an egg during ovulation. The rest of the follicles grow then regress. In this study, we characterized differences in the growth, regression, and associated hormone production between follicles that ovulated and those that did not, developing from different waves within and between menstrual cycles. Differences regarding follicle growth patterns and hormone concentrations were found when comparing different waves and whether a follicle ovulated. Results from this study improve our understanding of the processes underlying follicular growth and ovulation, which in turn may assist in improving fertility and birth control treatments.
The numerical analysis of functional integral and integro-differential equations of Volterra type

The qualitative and quantitative analysis of numerical methods for delay differential equations is now quite well understood, as reflected in the recent monograph by Bellen and Zennaro (2003). This is in remarkable contrast to the situation in the numerical analysis of functional equations, in which delays occur in connection with memory terms described by Volterra integral operators. The complexity of the convergence and asymptotic stability analyses has its roots in new ‘dimensions’ not present in DDEs: the problems have distributed delays; kernels in the Volterra operators may be weakly singular; a second discretization step (approximation of the memory term by feasible quadrature processes) will in general be necessary before solution approximations can be computed. The purpose of this review is to introduce the reader to functional integral and integro-differential equations of Volterra type and their discretization, focusing on collocation techniques; to describe the ‘state of the art’ in the numerical analysis of such problems; and to show that – especially for many ‘classical’ equations whose analysis dates back more than 100 years – we still have a long way to go before we reach a level of insight into their discretized versions to compare with that achieved for DDEs.
3D T1 relaxation time measurements in an equine model of subtle post‐traumatic osteoarthritis using MB‐SWIFT

The aim of this study is to assess whether articular cartilage changes in an equine model of post‐traumatic osteoarthritis (PTOA), induced by surgical creation of standard (blunt) grooves, and very subtle sharp grooves, could be detected with ex vivo T1 relaxation time mapping utilizing three‐dimensional (3D) readout sequence with zero echo time. Grooves were made on the articular surfaces of the middle carpal and radiocarpal joints of nine mature Shetland ponies and osteochondral samples were harvested at 39 weeks after being euthanized under respective ethical permissions. T1 relaxation times of the samples (n = 8 + 8 for experimental and n = 12 for contralateral controls) were measured with a variable flip angle 3D multiband‐sweep imaging with Fourier transform sequence. Equilibrium and instantaneous Young's moduli and proteoglycan (PG) content from OD of Safranin‐O‐stained histological sections were measured and utilized as reference parameters for the T1 relaxation times. T1 relaxation time was significantly (p < 0.05) increased in both groove areas, particularly in the blunt grooves, compared with control samples, with the largest changes observed in the superficial half of the cartilage. T1 relaxation times correlated weakly (Rs ≈ 0.33) with equilibrium modulus and PG content (Rs ≈ 0.21). T1 relaxation time in the superficial articular cartilage is sensitive to changes induced by the blunt grooves but not to the much subtler sharp grooves, at the 39‐week timepoint post‐injury. These findings support that T1 relaxation time has potential in detection of mild PTOA, albeit the most subtle changes could not be detected.
Using Big Data to Uncover Association Between Sildenafil Use and Reduced Risk of Alzheimer's Disease

Alzheimer's disease is a chronic neurodegenerative disease with multiple pathogenesis pathways. Sildenafil, one of the phosphodiesterase-5 inhibitors, was proven to have effective benefits in transgenic Alzheimer's disease mice. The purpose of the study was to investigate the relationship between sildenafil use and the risk of Alzheimer's disease based on the IBM® MarketScan® Database covering over 30 million employees and family members per year. Sildenafil and non-sildenafil-matched cohorts were generated using propensity-score matching with the greedy nearest-neighbor algorithm. The propensity score stratified univariate analysis and the Cox regression model showed that sildenafil use was significantly associated with a 60% risk reduction of developing Alzheimer's disease (HR=0.40; 95%CI:0.38-0.44; P<.0001) compared to the cohort of individuals who did not take sildenafil. Sex-stratified analyses revealed that sildenafil was related to a lower risk of Alzheimer's disease in subgroups of both males and females. Our findings demonstrated a significant association between sildenafil use and a lower risk of Alzheimer's disease.
Application of Process Mining for Modelling Small Cell Lung Cancer Prognosis

Process mining is a relatively new method that connects data science and process modelling. In the past years a series of applications with health care production data have been presented in process discovery, conformance check and system enhancement. In this paper we apply process mining on clinical oncological data with the purpose of studying survival outcomes and chemotherapy treatment decision in a real-world cohort of small cell lung cancer patients treated at Karolinska University Hospital (Stockholm, Sweden). The results highlighted the potential role of process mining in oncology to study prognosis and survival outcomes with longitudinal models directly extracted from clinical data derived from healthcare.
Predictive markers for pathological complete response (pCR) after neo-adjuvant chemotherapy in HER2-positive breast carcinoma.

OBJECTIVES Patients with HER2-positive invasive breast cancer that is node-positive and/or larger than 3 cm are generally treated with neoadjuvant chemotherapy (NAC). We aimed to identify predictive markers for pathological complete response (pCR) after NAC in HER2-positive breast carcinoma Methods. Hematoxylin/eosin-stained slides of 43 HER2-positive breast carcinoma biopsies were histopathologically reviewed. Immunohistochemistry (IHC) was performed on pre-NAC biopsies, comprising HER2, estrogen receptor (ER), progesterone receptor (PR), Ki-67, epidermal growth factor receptor (EGFR), mucin-4 (MUC4), p53 and p63. Dual-probe HER2 in situ hybridization (ISH) was performed to study the mean HER2 and CEP17 copy numbers. ISH and IHC data were retrospectively collected for a validation cohort, comprising 33 patients. RESULTS Younger age at diagnosis, 3+ HER2 IHC scores, high mean HER2 copy numbers and high mean HER2/CEP17 ratios were significantly associated with an increased chance of achieving a pCR, and the latter two associations were confirmed in the validation cohort. No other immunohistochemical or histopathological markers were associated with pCR. CONCLUSIONS This retrospective study of two community-based NAC-treated HER2-positive breast cancer patient cohorts identified high mean HER2 copy numbers as a strong predictor for pCR. Further studies on larger cohorts are required to determine a precise cut-point for this predictive marker.
Association of Cortical and Subcortical Microstructure With Clinical Progression and Fluid Biomarkers in Patients With Parkinson Disease

Background and Objectives Mean diffusivity (MD) of diffusion MRI (dMRI) has been used to measure cortical and subcortical microstructural properties. This study investigated relationships of cortical and subcortical MD, clinical progression, and fluid biomarkers in Parkinson disease (PD). Methods This longitudinal study using data from the Parkinson's Progression Markers Initiative was collected from April 2011 to July 2022. Clinical symptoms were assessed with Movement Disorder Society–sponsored revision of the Unified Parkinson's Disease Rating Scale (UPDRS) and Montreal Cognitive Assessment (MoCA) scores. Clinical assessments were followed up to 5 years. Linear mixed-effects (LME) models were performed to examine associations of MD and the annual rate of changes in clinical scores. Partial correlation analysis was conducted to examine the associations of MD and fluid biomarker levels. Results A total of 174 patients with PD (age 61.9 ± 9.7 years, 63% male) with baseline dMRI and at least 2 years of clinical follow-up were included. Results of LME models revealed a significant association between MD values, predominantly in subcortical regions, temporal lobe, occipital lobe, and frontal lobe, and annual rate of changes in clinical scores (UPDRS-Part-I, standardized β > 2.35; UPDRS-Part-II, standardized β > 2.34; postural instability and gait disorder score, standardized β > 2.47; MoCA, standardized β < −2.42; all p < 0.05, false discovery rate [FDR] corrected). In addition, MD was associated with the levels of neurofilament light chain in serum (r > 0.22) and α-synuclein (right putamen r = 0.31), β-amyloid 1–42 (left hippocampus r = −0.30), phosphorylated tau at 181 threonine position (r > 0.26), and total tau (r > 0.23) in CSF at baseline (all p < 0.05, FDR corrected). Furthermore, the β coefficients derived from MD and annual rate of changes in the clinical score recapitulated the spatial distribution of dopamine (DAT, D1, and D2), glutamate (mGluR5 and NMDA), serotonin (5-HT1a and 5-HT2a), cannabinoid (CB1), and γ-amino butyric acid A receptor neurotransmitter receptors/transporters (p < 0.05, FDR corrected) derived from PET scans in the brain of healthy volunteers. Discussion In this cohort study, cortical and subcortical MD values at baseline were associated with clinical progression and baseline fluid biomarkers, suggesting that microstructural properties could be useful for stratification of patients with fast clinical progression.
Dead-Space Ventilation Indices and Mortality in Acute Respiratory Distress Syndrome: A Systematic Review and Meta-Analysis*

OBJECTIVES: Acute respiratory distress syndrome (ARDS) is associated with high ventilation-perfusion heterogeneity and dead-space ventilation. However, whether the degree of dead-space ventilation is associated with outcomes is uncertain. In this systematic review and meta-analysis, we evaluated the ability of dead-space ventilation measures to predict mortality in patients with ARDS. DATA SOURCES: MEDLINE, CENTRAL, and Google Scholar from inception to November 2022. STUDY SELECTION: Studies including adults with ARDS reporting a dead-space ventilation index and mortality. DATA EXTRACTION: Two reviewers independently identified eligible studies and extracted data. We calculated pooled effect estimates using a random effects model for both adjusted and unadjusted results. The quality and strength of evidence were assessed using the Quality in Prognostic Studies and Grading of Recommendations, Assessment, Development, and Evaluation, respectively. DATA SYNTHESIS: We included 28 studies in our review, 21 of which were included in our meta-analysis. All studies had a low risk of bias. A high pulmonary dead-space fraction was associated with increased mortality (odds ratio [OR], 3.52; 95% CI, 2.22–5.58; p < 0.001; I2 = 84%). After adjusting for other confounding variables, every 0.05 increase in pulmonary-dead space fraction was associated with an increased odds of death (OR, 1.23; 95% CI, 1.13–1.34; p < 0.001; I2 = 57%). A high ventilatory ratio was also associated with increased mortality (OR, 1.55; 95% CI, 1.33–1.80; p < 0.001; I2 = 48%). This association was independent of common confounding variables (OR, 1.33; 95% CI, 1.12–1.58; p = 0.001; I2 = 66%). CONCLUSIONS: Dead-space ventilation indices were independently associated with mortality in adults with ARDS. These indices could be incorporated into clinical trials and used to identify patients who could benefit from early institution of adjunctive therapies. The cut-offs identified in this study should be prospectively validated.
Optical design of a wide-angle lens for long-wave infrared earth sensors

This article describes the optical design of a wide-angle lens suitable for long-wave infrared (LWIR) earth sensor whose operating wavelength range is 10 to 16 microns, and full angle of view is up to 135 degrees, having an uncooled staring focal plane array. The optical system only consists of three germanium lenses with a "negative-positive-positive" reversed telephoto telecentric structure in the image space. This type of wide-angle lens can balance the correctness of off-axis aberrations against the evenness of illumination at its image plane very well. The designed lens has the advantages of simplicity, compactness and long back working distance. Its imaging quality is nearly diffraction limited, so that its modulated transfer function (MTF) value is higher than 0.6 at the 20 lines/mm spatial frequency in the image plane, and the relative error of the linearity between its image height and field angle is lower than 15%. Its manufacture and alignment tolerance are also analyzed with the CODE V optical design software.
Evaluating and Explaining the Variability of Honey Bee Field Studies across Europe Using BEEHAVE

To assess the effect of plant protection products on pollinator colonies, the higher tier of environmental risk assessment (ERA), for managed honey bee colonies and other pollinators, is in need of a mechanistic effect model. Such models are seen as a promising solution to the shortcomings, which empirical risk assessment can only overcome to a certain degree. A recent assessment of 40 models conducted by the European Food Safety Authority (EFSA) revealed that BEEHAVE is currently the only publicly available mechanistic honey bee model that has the potential to be accepted for ERA purposes. A concern in the use of this model is a lack of model validation against empirical data, spanning field studies conducted in different regions of Europe and covering the variability in colony and environmental conditions. We filled this gap with a BEEHAVE validation study against 66 control colonies of field studies conducted across Germany, Hungary, and the United Kingdom. Our study implements realistic initial colony size and landscape structure to consider foraging options. Overall, the temporal pattern of colony strength is predicted well. Some discrepancies between experimental data and prediction outcomes are explained by assumptions made for model parameterization. Complementary to the recent EFSA study using BEEHAVE, our validation covers a large variability in colony conditions and environmental impacts representing the Northern and Central European Regulatory Zones. Thus we believe that BEEHAVE can be used to serve the development of specific protection goals as well as the development of simulation scenarios for the European Regulatory Zone. Subsequently, the model can be applied as a standard tool for higher tier ERA of managed honey bees using the mechanistic ecotoxicological module for BEEHAVE, BEEHAVEecotox. Environ Toxicol Chem 2023;42:1839–1850. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
T2‐Fluid‐attenuated inversion recovery (FLAIR) pseudoprogression in patients with anaplastic oligodendrogliomas treated with procarbazine, lomustine and vincristine (PCV) chemotherapy alone

Pseudoprogression in gliomas has been extensively described after radiotherapy with or without chemotherapy, but not after chemotherapy alone. Here we describe the occurrence of pseudoprogression in patients with anaplastic oligodendrogliomas treated with postoperative procarbazine, lomustine and vincristine (PCV) chemotherapy alone.