Genomic Dynamics and Functional Insights under Salt Stress in Gossypium hirsutum L.

The changing climate is intensifying salt stress globally. Salt stress is a menace to cotton crop quality and yield. The seedling, germination, and emergence phases are more prone to the effects of salt stress than other stages. Higher levels of salt can lead to delayed flowering, a reduced number of fruiting positions, shedding of fruits, decreased boll weight, and yellowing of fiber, all of which have an adverse effect on the yield and quality of the seed cotton. However, sensitivity toward salt stress is dependent on the salt type, cotton growth phase, and genotype. As the threat of salt stress continues to grow, it is crucial to gain a comprehensive understanding of the mechanisms underlying salt tolerance in plants and to identify potential avenues for enhancing the salt tolerance of cotton. The emergence of marker-assisted selection, in conjunction with next-generation sequencing technologies, has streamlined cotton breeding efforts. This review begins by providing an overview of the causes of salt stress in cotton, as well as the underlying theory of salt tolerance. Subsequently, it summarizes the breeding methods that utilize marker-assisted selection, genomic selection, and techniques for identifying elite salt-tolerant markers in wild species or mutated materials. Finally, novel cotton breeding possibilities based on the approaches stated above are presented and debated.

In Vivo Analysis of a Biodegradable Magnesium Alloy Implant in an Animal Model Using Near-Infrared Spectroscopy.

Biodegradable magnesium-based implants offer mechanical properties similar to natural bone, making them advantageous over nonbiodegradable metallic implants. However, monitoring the interaction between magnesium and tissue over time without interference is difficult. A noninvasive method, optical near-infrared spectroscopy, can be used to monitor tissue's functional and structural properties. In this paper, we collected optical data from an in vitro cell culture medium and in vivo studies using a specialized optical probe. Spectroscopic data were acquired over two weeks to study the combined effect of biodegradable Mg-based implant disks on the cell culture medium in vivo. Principal component analysis (PCA) was used for data analysis. In the in vivo study, we evaluated the feasibility of using the near-infrared (NIR) spectra to understand physiological events in response to magnesium alloy implantation at specific time points (Day 0, 3, 7, and 14) after surgery. Our results show that the optical probe can detect variations in vivo from biological tissues of rats with biodegradable magnesium alloy "WE43" implants, and the analysis identified a trend in the optical data over two weeks. The primary challenge of in vivo data analysis is the complexity of the implant interaction near the interface with the biological medium.

Near-Infrared Spectroscopy for the In Vivo Monitoring of Biodegradable Implants in Rats.

Magnesium (Mg) alloys possess unique properties that make them ideal for use as biodegradable implants in clinical applications. However, reports on the in vivo assessment of these alloys are insufficient. Thus, monitoring the degradation of Mg and its alloys in vivo is challenging due to the dynamic process of implant degradation and tissue regeneration. Most current works focus on structural remodeling, but functional assessment is crucial in providing information about physiological changes in tissues, which can be used as an early indicator of healing. Here, we report continuous wave near-infrared spectroscopy (CW NIRS), a non-invasive technique that is potentially helpful in assessing the implant-tissue dynamic interface in a rodent model. The purpose of this study was to investigate the effects on hemoglobin changes and tissue oxygen saturation (StO2) after the implantation of Mg-alloy (WE43) and titanium (Ti) implants in rats' femurs using a multiwavelength optical probe. Additionally, the effect of changes in the skin on these parameters was evaluated. Lastly, combining NIRS with photoacoustic (PA) imaging provides a more reliable assessment of tissue parameters, which is further correlated with principal component analysis.

In Vitro Monitoring of Magnesium-Based Implants Degradation by Surface Analysis and Optical Spectroscopy.

Magnesium (Mg)-based degradable alloys have attracted substantial attention for tissue engineering applications due to their biodegradability and potential for avoiding secondary removal surgeries. However, insufficient data in the existing literature regarding Mg's corrosion and gas formation after implantation have delayed its wide clinical application. Since the surface properties of degradable materials constantly change after contact with body fluid, monitoring the behaviour of Mg in phantoms or buffer solutions could provide some information about its physicochemical surface changes over time. Through surface analysis and spectroscopic analysis, we aimed to investigate the structural and functional properties of degradable disks. Since bubble formation may lead to inflammation and change pH, monitoring components related to acidosis near the cells is essential. To study the bubble formation in cell culture media, we used a newly developed Mg alloy (based on Mg, zinc, and calcium), pure Mg, and commercially available grade 2 Titanium (Ti) disks in Dulbecco's Modified Eagle Medium (DMEM) solution to observe their behaviour over ten days of immersion. Using surface analysis and the information from near-infrared spectroscopy (NIRS), we concluded on the conditions associated with the medical risks of Mg alloy disintegration. NIRS is used to investigate the degradation behaviour of Mg-based disks in the cell culture media, which is correlated with the surface analysis where possible.

Climate change impact uncertainty assessment and adaptations for sustainable maize production using multi-crop and climate models.

Future climate scenarios are predicting considerable threats to sustainable maize production in arid and semi-arid regions. These adverse impacts can be minimized by adopting modern agricultural tools to assess and develop successful adaptation practices. A multi-model approach (climate and crop) was used to assess the impacts and uncertainties of climate change on maize crop. An extensive field study was conducted to explore the temporal thermal variations on maize hybrids grown at farmer's fields for ten sowing dates during two consecutive growing years. Data about phenology, morphology, biomass development, and yield were recorded by adopting standard procedures and protocols. The CSM-CERES, APSIM, and CSM-IXIM-Maize models were calibrated and evaluated. Five GCMs among 29 were selected based on classification into different groups and uncertainty to predict climatic changes in the future. The results predicted that there would be a rise in temperature (1.57-3.29 °C) during the maize growing season in five General Circulation Models (GCMs) by using RCP 8.5 scenarios for the mid-century (2040-2069) as compared with the baseline (1980-2015). The CERES-Maize and APSIM-Maize model showed lower root mean square error values (2.78 and 5.41), higher d-index (0.85 and 0.87) along reliable R2 (0.89 and 0.89), respectively for days to anthesis and maturity, while the CSM-IXIM-Maize model performed well for growth parameters (leaf area index, total dry matter) and yield with reasonably good statistical indices. The CSM-IXIM-Maize model performed well for all hybrids during both years whereas climate models, NorESM1-M and IPSL-CM5A-MR, showed less uncertain results for climate change impacts. Maize models along GCMs predicted a reduction in yield (8-55%) than baseline. Maize crop may face a high yield decline that could be overcome by modifying the sowing dates and fertilizer (fertigation) and heat and drought-tolerant hybrids.

An Experimental Study on Properties of Pre-Coated Aggregates Grouting Asphalt Concrete for Bridge Deck Pavement.

Epoxy asphalt concrete, mortar asphalt concrete and Gussasphalt concrete are commonly used types of deck pavement materials in bridge deck pavement engineering. However, achieving the high-temperature stability and anti-fatigue performance of the deck pavement materials is still challenging. In order to reduce the rutting and cracking risks of the asphalt mixture, this paper proposed pre-coated aggregates grouting asphalt concrete (PGAC) for bridge deck pavement. Laboratory tests were conducted to determine the optimum grouting materials and to evaluate the mechanical performances of the PGAC material. Test results showed that the mechanical properties for PGAC with grouting material of high-viscosity-modified asphalt binder blending with mineral filler were superior to that of GMA-10 used for the Hong Kong-Zhuhai-Macau Bridge deck pavement. Microstructural analysis showed that the PGAC had a more stable skeleton structure compared to other typical aggregate mixtures. This study highlights the performances of the proposed PGAC and sheds light on the deck pavement material improvement of both high-temperature stability and anti-fatigue performance that could be achieved.

Study on the Road Performance of Foamed Warm-Mixed Reclaimed Semi-Flexible Asphalt Pavement Material.

Warm-mixed reclaimed asphalt pavement (RAP) technology has been widely studied worldwide as a recycled environmental method to reuse waste materials. However, the aggregate skeleton structure of the warm-mixed reclaimed asphalt mixture is not stable because of the existence of the recycled materials. Warm-mixed recycled semi-flexible pavement material can solve the defects of the above materials. In this study, five different types of open-graded asphalt mixtures containing different contents of RAP were designed, and relevant laboratory tests were conducted to assess the road performance of the warm-mixed recycled semi-flexible pavement material. The test results indicated that the road performance of warm-mixed reclaimed semi-flexible pavement materials has good resistance to rut deformation ability. Furthermore, the materials also had good water stability and fatigue performance. The grey correlation analysis shows that the asphalt binder content has the most significant correlation with the high-temperature stability, and the correlation between RAP content and the fatigue performance was the greatest. Furthermore, the curing age has the most remarkable with the low-temperature crack resistance of the warm-mixed reclaimed semi-flexible material.

Effects of proprioception training programme on balance among patients with diabetic neuropathy: A quasi-experimental trial.

OBJECTIVE: To determine the effects of proprioception training in improving balance in patients with diabetic neuropathy. METHODS: The quasi-experimental study was conducted at Safi Hospital, Faisalabad, Pakistan, from August to December 2019, and comprised diabetic neuropathy patients of both genders aged 60-83 years. They were divided into two groups of cases and controls, with the former getting. proprioception training twice a week for eight weeks along with diabetes awareness lectures once a week, while the former group only received awareness lectures. Static and dynamic balance were assessed using one leg standing test with eyes open and closed, Berg balance scale and timed-up and go test. Data was collected at baseline and post-intervention. Data was analysed using SPSS 23. RESULTS: Of the 38 patients, there were 19(50%) in the exercise group with a mean age of 64±7.7 years; 10(52.6%) males and 9(47.3%) females. The control group had 19(50%) patients with a mean age of 63±8.2 years; 12(63.1%) males and 7(36.8%) females. The one leg standing score with eyes open improved significantly (p<0.05), but the difference was non-significant with eyes closed (p=0.073). Berg balance scale and timed-up and go scores revealed significant improvement in the exercise group (p<0.05). CONCLUSIONS: Proprioception training exercises were found to be effective in improving balance among patients with diabetic neuropathy.

An overview of assessment tools for determination of biological Magnesium implant degradation.

Medical implants made of biodegradable materials are advantageous for short-term applications as fracture fixation and mechanical support during bone healing. After completing the healing process, the implant biodegrades without any long-term side effects nor any need for surgical removal. In particular, Magnesium (Mg) implants, while degrading, can cause physiological changes in the tissues surrounding the implant. The evaluation of structural remodeling is relevant, however, the functional assessment is crucial to provide information about physiological changes in tissues, which can be applied as an early marker during the healing process. Hence, non-invasive monitoring of structural and functional changes in the surrounding tissue during the healing process is essential, and the need for new assessing methods is emerging. This paper provides an assessment of Mg based implants, and an extensive review of the literature is presented with the focus on the imaging techniques for investigation of the Mg implants' biodegradation. The potential of a hybrid analysis, including Near-Infrared Spectroscopy (NIRS) and photoacoustic imaging (PAI) technology, is further discussed. A hybrid solution may play a significant role in monitoring implants and have several advantages for monitoring tissue oxygenation in addition to tissue's acidity, which is directly connected to the Mg implants degradation process. Such a hybrid assessment system can be a simple, ambulant, and less costly technology with the potential for clinically monitoring of Mg implants at site.

Dissection of Drought Tolerance in Upland Cotton Through Morpho-Physiological and Biochemical Traits at Seedling Stage.

Cotton is an important fiber and cash crop. Extreme water scarceness affects the growth, quality, and productivity of cotton. Water shortage has threatened the future scenario for cotton growers, so it is imperative to devise a solution to this problem. In this research, we have tried to machinate a solution for it. 23 genotypes have been screened out against drought tolerance at the seedling stage by evaluating the morphological, physiological, and biochemical traits in a triplicate completely randomized design plot experiment with two water regimes [50 and 100% field capacity]. Genotypic differences for all the morphological and physiological traits revealed highly significant differences except transpiration rate (TR). Moreover, the interaction between genotype and water regime (G × W) was highly significant for root length (RL, 5.163), shoot length (SL, 11.751), excised leaf water loss (ELWL, 0.041), and stomatal conductance (SC, 7.406). A positively strong correlation was found in TR with relative water content (RWC; 0.510) and SC (0.584) and RWC with photosynthesis (0.452) under drought conditions. A negative correlation was found in SC with SL (-0.428) and photosynthesis (-0.446). Traits like RL, SL, SC, photosynthesis, proline, catalase, and malondialdehyde were visible indicators, which can differentiate drought-tolerant genotypes from the susceptible ones. A wide range of diversity was found in all the morpho-physiological traits with the cumulative variance of four principal components (PCs) 83.09% and three PCs 73.41% under normal and water-stressed conditions, respectively, as per the principal component analysis. Hence, selection criteria can be established on the aforementioned traits for the development of drought-tolerant cultivars. Moreover, it was found that out of 23 experimental varieties, NIAB-135, NIAB-512, and CIM-554 could be used to devise breeding strategies for improving drought tolerance in cotton.

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review.

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain-computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go.

Associations of Most Prevalent Risk Factors with Lung Cancer and Their Impact on Survival Length.

Lung cancer is one of the most common malignancies in the world. Its incidence and mortality rates are on the rise in Pakistan. However, epidemiological studies to identify common lung cancer determinants in the Pakistani population have been limited. In this study, data of 440 cases and 323 controls were collected from different hospitals in Peshawar and Islamabad, along with information about socio-demographic factors including age, sex and smoking. Univariate and multi-factorial analyses of socio-demographic factors in association with each other were also performed. Overall survival analysis highlighted that, out of 440 patients in the lung cancer dataset, 204 people were uncensored with a median survival time of 13 months (95% CI=12-18). There were 41 femaleand 399 male patients. Differences were observed between length of survival in the males and females (χ12 = 6.1; p-value = 0.01). Gender was observed to be significantly related to survival (p-value< 0.01), with better survival in females (hazard ratio=2). Cox regression was extended to adjust for the covariate age (z = 2.5; p-value = 0.02). Survival analysis was also performed on the basis of smoking groups (current smokers, former smokers and never smoked individuals) and smoking duration (smoking duration>10 years, <10 years and never smoked). Smoking duration was significantly associated with survival (p-value < 0.01), with better survival in never smokers in comparison to both smoking for greater or less than 10 years. Strong associations were observed for smoking group with duration greater than 10 years, OR=6.1(3.9-9.5) on univariate and multi-factorial analysis OR=11.3(CI=6.8-19.3).

Central pain processing in osteoarthritis: implications for treatment.

Osteoarthritis (OA) is a major cause of pain and is characterized by loss of articular cartilage integrity, synovitis and remodeling of subchondral bone. However, OA pain mechanisms remain incompletely understood. Pain severity does not always correlate with the extent of joint damage. Furthermore, many people with OA continue to experience pain despite optimal use of standard therapies that target the joints, including joint-replacement surgery. There is compelling evidence that altered central pain processing plays an important role in maintaining pain and increasing pain severity in some people with OA. A key challenge is to identify this subgroup of patients with abnormal central pain processing in order to improve their clinical outcomes by developing and targeting specific analgesic treatments.

Risk mapping of dengue in Selangor and Kuala Lumpur, Malaysia.

Dengue fever is a recurring public health problem afflicting thousands of Malaysians annually. In this paper, the risk map for dengue fever in the peninsular Malaysian states of Selangor and Kuala Lumpur was modelled based on co-kriging and geographical information systems. Using population density and rainfall as the model's only input factors, the area with the highest risk for dengue infection was given as Gombak and Petaling, two districts located on opposite sides of Kuala Lumpur city that was also included in the risk assessment. Comparison of the modelled risk map with the dengue case dataset of 2010, obtained from the Ministry of Health of Malaysia, confirmed that the highest number of cases had been found in an area centred on Kuala Lumpur as predicted our risk profiling.