Digital mapping and spatial modeling of some soil physical and mechanical properties in a semi-arid region of Iran.

The soil's physical and mechanical (SPM) properties have significant impacts on soil processes, such as water flow, nutrient movement, aeration, microbial activity, erosion, and root growth. To digitally map some SPM properties at four global standard depths, three machine learning algorithms (MLA), namely, random forest, Cubist, and k-nearest neighbor, were employed. A total of 200-point observation was designed with the aim of a field survey across the Marvdasht Plain in Fars Province, Iran. After sampling from topsoil (0 to 30 cm) and subsoil depths (30 to 60 cm), the samples were transferred to the laboratory to determine the mean weight diameter (MWD) and geometric mean diameter (GMD) of aggregates in the laboratory. In addition, shear strength (SS) and penetration resistance (PR) were measured directly during the field survey. In parallel, 79 environmental factors were prepared from topographic and remote sensing data. Four soil variables were also included in the modeling process, as they were co-located with SPM properties based on expert opinion. For selecting the most influential covariates, the variance inflation factor (VIF) and Boruta methods were employed. Two covariate dataset scenarios were used to assess the impact of soil and environmental factors on the modeling of SPM properties including SPM and environmental covariates (scenario 1) and SPM, environmental covariates, and soil variables (scenario 2). From all covariates, nine soil and environmental factors were selected for modeling the SPM properties, of which four of them were the soil variables, three were related to remote sensing, and two factors had topographic sources. The results indicated that scenario 2 outperformed in all standard depths. The findings suggested that clay and SOM are key factors in predicting SPM, highlighting the importance of considering soil variables in addition to environmental covariates for enhancing the accuracy of machine learning prediction. The k-nearest neighbor algorithm was found to be highly effective in predicting SPM, while the random forest algorithm yielded the highest R2 value (0.92) for penetration resistance properties at 15-30 depth. Overall, the approach used in this research has the potential to be extended beyond the Marvdasht Plain of Fars Province, Iran, as well as to other regions worldwide with comparable soil-forming factors. Moreover, this study provides a valuable framework for the digital mapping of SPM properties, serving as a guide for future studies seeking to predict SPM properties. Globally, the output of this research has important significance for soil management and conservation efforts and can facilitate the development of sustainable agricultural practices.

Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches.

This study aimed to identify the most influential soil and environmental factors for predicting wheat yield (WY) in a part of irrigated croplands in southwest Iran, using the FAO-Agro-Climate method and machine learning algorithms (MLAs). A total of 60 soil samples and wheat grain (1 m × 1 m) in 1200 ha of Pasargad plain were collected and analyzed in the laboratory. Attainable WY was assessed using the FAO method for the area. Pearson correlation analysis was used to select the best set of soil properties for modeling. Topographic attributes and vegetation indices were used as proxies of landscape components and cover crop to map actual WY in the study area. Two well-known MLAs, random forest (RF) and artificial neural networks (ANNs), were utilized to prepare an actual continuous WY map. The k-fold method was used to determine the uncertainty of WY prediction and quantify the quality of prediction accuracy. Results showed that soil organic carbon (SOC) and total nitrogen (TN) had a positive and significant correlation with WY. The SOC, TN, normalized different vegetation index (NDVI), and channel network base level (CHN) were recognized as the most important predictors and justifying more than 50% of actual WY. The ANNs outperformed the RF algorithm with an R 2 of 0.75, RMSE of 400 (kg ha-1), and RPD of 2.79, according to statistical indices. The uncertainty analysis showed that the maximum uncertainty of the prediction map [400 (kg ha-1)] was very low compared to the mean value [4937 (kg ha-1)] of WY map. Calculation yield gap using the FAO-agro-climatic model showed that the average yield gap of the region was about 50% of actual yield. The findings of this study demonstrated that integrating simulated attainable crop growth using crop model and a set of soil and environmental covariates with the ANNs algorithm can effectively predict WY gaps in large areas with acceptable and reasonable accuracy. The study emphasizes that the implementation of efficient management practices has the potential to enhance agricultural production in the study area and similar regions. These results represent a significant advancement of sustainable agriculture and provide valuable insights for ensuring global food security.

Is there a relationship between lower-extremity injuries and foot postures in professional football players? A prospective cohort study.

BACKGROUND: Lower extremity injuries are an ongoing concern for professional football players. This study aims to evaluate the relationship between foot posture and lower extremity injuries in professional football players. METHOD: In this prospective cohort study, 420 male players of the Iran Premium football league were evaluated during the 2015-2016 season. The players were assessed for their foot types based on optical and static foot scans and foot posture index (FPI). The trained club physicians recorded all injuries during the season. RESULT: The analyzed data of 244 players showed the highest rate of lower extremity injury in hamstrings, ankle, and groin, respectively. These injuries led to 46% of time loss. The probability of hamstrings injuries was higher among pronated players based on static, optic, FPI, and visual examination, 2.1, 1.8, 1.8, and 2.3, respectively. Medial Collateral Ligament (MCL) injuries were associated with subtalar joint abnormality defined by visual observation. An increased relative risk of leading-to-absence injuries among the flat foot group was significant based on optic scanning, FPI, and visual observation. CONCLUSION: Abnormal foot postures in professional football players may increase the risk of hamstring and MCL injuries and time loss due to lower extremity injuries.

Photoelectron spectra of early 3d-transition metal dioxide molecular anions from GW calculations.

Photoelectron spectra of early 3d-transition metal dioxide anions, ScO2 -, TiO2 -, VO2 -, CrO2 -, and MnO2 -, are calculated using semilocal and hybrid density functional theory (DFT) and many-body perturbation theory within the GW approximation using one-shot perturbative and eigenvalue self-consistent formalisms. Different levels of theory are compared with each other and with available photoelectron spectra. We show that one-shot GW with a PBE0 starting point (G0W0@PBE0) consistently provides very good agreement for all experimentally measured binding energies (within 0.1 eV-0.2 eV or less). We attribute this to the success of PBE0 in mitigating self-interaction error and providing good quasiparticle wave functions, which renders a first-order perturbative GW correction effective. One-shot GW calculations with a Perdew-Burke-Ernzerhof (PBE) starting point do poorly in predicting electron removal energies by underbinding orbitals with typical errors near 1.5 eV. A higher exact exchange amount of 50% in the DFT starting point of one-shot GW does not provide very good agreement with experiment by overbinding orbitals with typical errors near 0.5 eV. While not as accurate as G0W0@PBE0, the G-only eigenvalue self-consistent GW scheme with W fixed to the PBE level provides a reasonably predictive level of theory (typical errors near 0.3 eV) to describe photoelectron spectra of these 3d-transition metal dioxide anions. Adding eigenvalue self-consistency also in W, on the other hand, worsens the agreement with experiment overall. Our findings on the performance of various GW methods are discussed in the context of our previous studies on other transition metal oxide molecular systems.

Comparison of postoperative analgesic effect of intrathecal clonidine and fentanyl added to bupivacaine in patients undergoing cesarean section: a prospective randomized double-blind study.

Objectives. To compare the analgesic efficacy of intrathecal clonidine and fentanyl added to bupivacaine after cesarean section. Methods. Ninety patients scheduled for cesarean section under spinal anesthesia were randomly allocated to one of the three following groups to receive bupivacaine 10 mg combined with 75 µg clonidine (group C), bupivacaine 10 mg combined with 0.5 mL fentanyl (group F), and bupivacaine 10 mg combined with 0.5 mL distilled water (group P), intrathecally. The time to first analgesic request, analgesic requirement in the first 24 hours after surgery, sensory and motor blockade onset time, duration of sensory and motor blockade, the incidence of hypotension, ephedrine requirements, bradycardia, and hypoxemia were recorded. Results. The duration of anesthesia in clonidine group (275.10 ± 96.09) was longer compared to the placebo (211.73 ± 74.80) and fentanyl (192.33 ± 30.36) groups. This difference between group C versus F (P = 0.006) and P groups (P < 0.001) was significant. Similarly, the mean time to first analgesic request was also longer in group C (519.44 ± 86.25) than in groups F (277.88 ± 94.25) and P (235.43 ± 22.35 min). This difference between group C versus F (P < 0.001) and P groups (P < 0.001) was significant. Conclusion. Intrathecal clonidine 75 µg with bupivacaine prolonged the time to first analgesic request compared to fentanyl; however, the total analgesic consumption within the first 24 h postoperative was similar in fentanyl and clonidine groups following cesarean section. This trial is registered with ACTRN12611000909921 and ClinicalTrials.gov NCT01425658.

Temperature: the "ignored" factor at the NanoBio interface.

Upon incorporation of nanoparticles (NPs) into the body, they are exposed to biological fluids, and their interaction with the dissolved biomolecules leads to the formation of the so-called protein corona on the surface of the NPs. The composition of the corona plays a crucial role in the biological fate of the NPs. While the effects of various physicochemical parameters on the composition of the corona have been explored in depth, the role of temperature upon its formation has received much less attention. In this work, we have probed the effect of temperature on the protein composition on the surface of a set of NPs with various surface chemistries and electric charges. Our results indicate that the degree of protein coverage and the composition of the adsorbed proteins on the NPs' surface depend on the temperature at which the protein corona is formed. Also, the uptake of NPs is affected by the temperature. Temperature is, thus, an important parameter that needs to be carefully controlled in quantitative studies of bionano interactions.

Physiological temperature has a crucial role in amyloid ß in the absence and presence of hydrophobic and hydrophilic nanoparticles.

Amyloid beta fibrillation can lead to major disorder of neurons processes and is associated with several neuronal diseases (e.g., Alzheimer's disease). We report here an importance of slight temperature changes, in the physiological range (35-42 °C), on the amyloid fibrillation process in the presence and absence of hydrophilic (silica) and hydrophobic (polystyrene) nanoparticles (NPs). The results highlight the fact that slight increases in temperature can induce inhibitory and acceleratory effects of hydrophobic and hydrophilic NPs on the fibrillation process, respectively. Using further in vivo considerations, the outcomes of this study can be used for considerable modifications on the current diagnosis and treatment approaches in amyloid-involved diseases.

The protein corona mediates the impact of nanomaterials and slows amyloid beta fibrillation.

Put your coat on: It is well recognized that the surfaces of nanomaterials in biological media are covered by various biomolecules (e.g., proteins). A) The protein corona creates a shell over different nanomaterials, regardless of their physicochemical properties (e.g., composition and shape), resulting in reduced levels of amyloid beta fibril formation. B) Pristine nanomaterials might have acceleratory effects on the fibrillation of amyloid beta.