Development of a dual point humidity sensor using POF based on twisted fiber structure.

The humidity has often been measured through a single point sensor. Where, the humidity could be varied at different locations as well as depending on environmental conditions. The present paper developed the dual point humidity measuring sensor by using a polymer optical fiber (POF) based on a single illuminating fiber. The sensor's basic structure is to twist two fibers and bend them at a certain radius. However, the dual point sensor is developed through the cascading of twisted micro bend (TMB-1 and TMB-2). The twisting of fibers couples the light from one fiber to another fiber through the side coupling method. An increase in the humidity level leads to a change in the reflective index, which helps to get variation in coupled light intensity. To measure the humidity, the dual point sensors are placed into the control humidity chamber at two random positions. The power reading variation is significantly linear when the humidity level increases from 30 to 80%. The sensor has a fast response of about 1 s and a recovery time of about 4 s. Furthermore, the chemical coating is applied to improve the sensor's sensitivity. Between 30 and 80% range of humidity, the both sensors of dual point TMB-1 and TMB-2 have appropriate sensitivity and detection limits, which is about 680.8 nW/% and 763.9 nW/% and 1.37% and 1.98%, respectively. To measure the humidity at variable positions, the present dual points humidity sensor is well-stable, easy, and straightforward, which uses a less expensive method.

Insights into the involvement of male Hyalomma anatolicum ticks in transmitting Anaplasma marginale, lumpy skin disease virus and Theileria annulata.

Ticks can transmit viruses, bacteria, and parasites to humans, livestock, and pet animals causing tick-borne diseases (TBDs) mechanically or biologically in the world. Lumpy skin disease virus, Anaplasma marginale, and Theileria annulata inflict severe infections in cattle, resulting in significant economic losses worldwide. The study investigated the potential transmissions of LSDV, A. marginale, and T. annulata through male Hyalomma anatolicum ticks in cattle calves. Two 6-month-old Holstein crossbred calves designated as A and B were used. On day 1, 15 uninfected female ticks (IIa) and infected batch of 40 male ticks (I) were attached on calf A for 11 days. Filial transmission of the infections was observed in female ticks (IIb) collected from calf A, where 8 female ticks had been co-fed with infected male ticks. The blood sample of calf B was found positive through PCR for the infections. The larvae and egg pools obtained from the infected ticks were also tested positive in PCR. The study confirmed the presence of these mixed pathogens and potential intra-stadial and transovarial transmissions of A. marginale, T. annulata, and LSDV in male and female ticks of H. anatolicum and experimental calves to establish the feasibility of infections through an in vivo approach.

Local surface warming assessment in response to vegetation shifts over arid lands of Central Asia (2001-2020).

The Northern Eurasia Earth Science Partnership Initiative (NEESPI) was established to address the large-scale environmental change across this region. Regardless of the increasingly insightful literature addressing vegetation change across Central Asia, the biogeophysical warming effects of vegetation shifts still need to be clarified. To contribute, the utility of robust satellite observation is explored to evaluate the surface warming effects of vegetation shifts across Central Asia, which is among NEEPSI's hotspots. We estimated an average increase of +1.9 °C in daytime local surface temperature and + 1.5 °C in the nighttime due to vegetation shift (2001-2020). Meanwhile, the mean local latent heat increased by 4.65Wm-2, following the mild reduction of emitted longwave radiation (-0.8Wm-2). We found that vegetation shifts led to local surface warming with a bright surface, noting that the average air surface temperature was revealed to have increased significantly (2001-2020). This signal was driven mainly by agricultural expansion in western Kazakhstan stretching to Tajikistan and Xinjiang, then deforestation confined in Tajikistan, southeast Kazakhstan, and the northwestern edge of Xinjiang, and finally, grassland encroachment occurred massively in the west to central Kazakhstan. These findings address the latest information on Central Asia's vegetation shifts that may be substantial in landscape change mitigation plans.

Plant root mechanisms and their effects on carbon and nutrient accumulation in desert ecosystems under changes in land use and climate.

Deserts represent key carbon reservoirs, yet as these systems are threatened this has implications for biodiversity and climate change. This review focuses on how these changes affect desert ecosystems, particularly plant root systems and their impact on carbon and mineral nutrient stocks. Desert plants have diverse root architectures shaped by water acquisition strategies, affecting plant biomass and overall carbon and nutrient stocks. Climate change can disrupt desert plant communities, with droughts impacting both shallow and deep-rooted plants as groundwater levels fluctuate. Vegetation management practices, like grazing, significantly influence plant communities, soil composition, root microorganisms, biomass, and nutrient stocks. Shallow-rooted plants are particularly susceptible to climate change and human interference. To safeguard desert ecosystems, understanding root architecture and deep soil layers is crucial. Implementing strategic management practices such as reducing grazing pressure, maintaining moderate harvesting levels, and adopting moderate fertilization can help preserve plant-soil systems. Employing socio-ecological approaches for community restoration enhances carbon and nutrient retention, limits desert expansion, and reduces CO2 emissions. This review underscores the importance of investigating belowground plant processes and their role in shaping desert landscapes, emphasizing the urgent need for a comprehensive understanding of desert ecosystems.

Effects of graded levels of dietary protein supplementation on milk yield, body weight gain, blood biochemical parameters, and gut microbiota in lactating ewes.

Diet-associated characteristics such as dietary protein levels can modulate the composition and diversity of the gut microbiota, leading to effects on the productive performance and overall health of animals. The objective of this study was to see how changes in dietary protein levels affect milk yield, body weight gain, blood biochemical parameters, and gut microbiota in lactating ewes. In a completely randomized design, eighteen ewes were randomly assigned to three groups (n = 6 ewes/group), and each group was assigned to one of three dietary treatments with different protein contents. The ewes' groups were fed on 8.38% (S-I), 10.42% (S-m), and 13.93% (S-h) dietary protein levels on a dry basis. The body weight gain and milk yield were greater (p < 0.05) in ewes fed the S-h dietary treatment than in those fed the S-m and S-1 diets, respectively. However, milk protein contents were similar (p > 0.05) across the treatments. The blood glucose, total protein, cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein, lactate, creatinine, and C-reactive protein contents of lactating ewes were not influenced (p > 0.05) by different dietary protein levels. The alanine transaminase, aminotransferase, and lactate dehydrogenase activities were also not changed (p > 0.05) across the groups. However, blood urea nitrogen and albumin contents of lactating ewes were changed (p < 0.05) with increasing levels of dietary protein, and these metabolite concentrations were higher (p < 0.05) for S-h than the rest of the treatments. In the different treatment groups, Firmicutes and Bacteroidetes were found to be the most dominant phyla. However, the abundance of Lachnospiraceae species decreased as dietary protein levels increased. Within the Bacteroidetes phylum, Rikenellaceae were more abundant, followed by Prevotellaceae, in ewes fed the S-m diet compared to those fed the other diets. Based on the results, feeding at an optimal protein level improved milk yield and body weight gain through modifying the digestive tract's beneficial bacterial communities. The results of blood metabolites suggested that feeding higher-protein diets has no negative impact on health.

Dietary protein levels modulate the gut microbiome composition through fecal samples derived from lactating ewes.

In ruminants, the digestion and utilization of dietary proteins are closely linked to the bacterial populations that are present in the gastrointestinal tract. In the present study, 16S rDNA sequencing, together with a metagenomic strategy was used to characterize the fecal bacteria of ewes in the early lactation stage after feeding with three levels of dietary proteins 8.58%, 10.34%, and 13.93%, in three different groups (H_1), (H_m) and (H_h), respectively. A total of 376,278,516 clean data-points were obtained by metagenomic sequencing. Firmicutes and Bacteroidetes were the dominant phyla, regardless of the dietary protein levels. In the H_h group, the phyla Proteobacteria, Caldiserica, and Candidatus_Cryosericota were less abundant than those in the H_I group. In contrast, Lentisphaerae, Chlamydiae, and Planctomycetes were significantly more abundant in the H_h group. Some genera, such as Prevotella, Roseburia, and Firmicutes_unclassified, were less abundant in the H_h group than those in the H_I group. In contrast, Ruminococcus, Ruminococcaceae_noname, Anaerotruncus, Thermotalae, Lentisphaerae_noname, and Paraprevotella were enriched in the H_h group. The acquired microbial genes were mainly clustered into biological processes; molecular functions; cytosol; cellular components; cytoplasm; structural constituents of ribosomes; plasma membranes; translation; and catalytic activities. 205987 genes were significantly enriched in the H_h group. In contrast, 108129 genes were more abundant in the H_I group. Our findings reveal that dynamic changes in fecal bacteria and their genes are strongly influenced by the levels of dietary proteins. We discovered that differentially expressed genes mainly regulate metabolic activity and KEGG demonstrated the primary involvement of these genes in the metabolism of carbohydrates, amino acids, nucleotides, and vitamins. Additionally, genes responsible for metabolism were more abundant in the H_h group. Investigating fecal bacterial characteristics may help researchers develop a dietary formula for lactating ewes to optimize the growth and health of ewes and lambs.

Parasitological, molecular, and epidemiological investigation of Trypanosoma evansi infection among dromedary camels in Balochistan province.

Surra is a zoonotic disease caused by Trypanosoma evansi (T. evansi), which affects a wide variety of animals worldwide. The disease has a severe impact on the productivity, health, and working capacity of camels and causes mortality and extensive economic losses if not diagnosed early. This is the first comprehensive report on the prevalence of T. evansi infection in dromedaries in Balochistan province. In the present study, 393 blood samples (indigenous, n = 240; imported, n=153) were collected from one-humped camels (Camelus dromedarius) and were tested by molecular methods to estimate the prevalence of T. evansi in three districts (Pishin, Nushki, and Lasbella) of Balochistan province. The overall prevalence of T. evansi among examined camel samples was 28.24% (95% confidence interval (CI): 24.02-32.89%). The risk of T. evansi infection in adult camels (> 10 years) is higher than that in young ones (odd-ration (OR) = 2.7; 95% CI: 1.3357-5.3164%). Moreover, male camels were six times more likely to get an infection than female camels. The detection of T. evansi infection in camels sampled in summer and spring was 3.12- and 5.10-fold higher, respectively, than in camels sampled in winter. In conclusion, our findings showed a high rate of T. evansi infection in camels from the three districts. Our study emphasizes the need for a strict surveillance program and risk assessment studies as prerequisites for control measures.

Strigolactones can be a potential tool to fight environmental stresses in arid lands.

BACKGROUND: Environmental stresses pose a significant threat to plant growth and ecosystem productivity, particularly in arid lands that are more susceptible to climate change. Strigolactones (SLs), carotenoid-derived plant hormones, have emerged as a potential tool for mitigating environmental stresses. METHODS: This review aimed to gather information on SLs' role in enhancing plant tolerance to ecological stresses and their possible use in improving the resistance mechanisms of arid land plant species to intense aridity in the face of climate change. RESULTS: Roots exude SLs under different environmental stresses, including macronutrient deficiency, especially phosphorus (P), which facilitates a symbiotic association with arbuscular mycorrhiza fungi (AMF). SLs, in association with AMF, improve root system architecture, nutrient acquisition, water uptake, stomatal conductance, antioxidant mechanisms, morphological traits, and overall stress tolerance in plants. Transcriptomic analysis revealed that SL-mediated acclimatization to abiotic stresses involves multiple hormonal pathways, including abscisic acid (ABA), cytokinins (CK), gibberellic acid (GA), and auxin. However, most of the experiments have been conducted on crops, and little attention has been paid to the dominant vegetation in arid lands that plays a crucial role in reducing soil erosion, desertification, and land degradation. All the environmental gradients (nutrient starvation, drought, salinity, and temperature) that trigger SL biosynthesis/exudation prevail in arid regions. The above-mentioned functions of SLs can potentially be used to improve vegetation restoration and sustainable agriculture. CONCLUSIONS: Present review concluded that knowledge on SL-mediated tolerance in plants is developed, but still in-depth research is needed on downstream signaling components in plants, SL molecular mechanisms and physiological interactions, efficient methods of synthetic SLs production, and their effective application in field conditions. This review also invites researchers to explore the possible application of SLs in improving the survival rate of indigenous vegetation in arid lands, which can potentially help combat land degradation problems.

Developing a Low-Cost IoT-Based Remote Cardiovascular Patient Monitoring System in Cameroon.

(1) Background: Cameroonians are exposed to poor health services, more so citizens with cardiovascular-related diseases. The global high cost of acquiring healthcare-related technologies has prompted the government and individuals to promote the need for local research and the development of the health system. (2) Objectives: The main goal of this study is to design and develop a low-cost cardiovascular patient monitoring system (RPM) with wireless capabilities that could be used in any region of Cameroon, accessible, and very inexpensive, that are able to capture important factors, well reflecting the patient's condition and provide alerting mechanisms. (3) Method: Using the lean UX process from the Gothelf and Seiden framework, the implemented IoT-based application measures the patients' systolic, diastolic, and heart rates using various sensors, that are automated to record directly to the application database for analysis. The validity of the heuristic evaluation was examined in an ethnographic study of paramedics using a prototype of the system in their work environment. (4) Results: We obtained a system that was pre-tested on demo patients and later deployed and tested on seven real human test subjects. The users' task performances partially verified the heuristic evaluation results. (5) Conclusions: The data acquired by the sensors have a high level of accuracy and effectively help specialists to properly monitor their patients at a low cost. The proposed system maintains a user-friendliness as no expertise is required for its effective utilization.

Metaverse in Healthcare Integrated with Explainable AI and Blockchain: Enabling Immersiveness, Ensuring Trust, and Providing Patient Data Security.

Digitization and automation have always had an immense impact on healthcare. It embraces every new and advanced technology. Recently the world has witnessed the prominence of the metaverse which is an emerging technology in digital space. The metaverse has huge potential to provide a plethora of health services seamlessly to patients and medical professionals with an immersive experience. This paper proposes the amalgamation of artificial intelligence and blockchain in the metaverse to provide better, faster, and more secure healthcare facilities in digital space with a realistic experience. Our proposed architecture can be summarized as follows. It consists of three environments, namely the doctor's environment, the patient's environment, and the metaverse environment. The doctors and patients interact in a metaverse environment assisted by blockchain technology which ensures the safety, security, and privacy of data. The metaverse environment is the main part of our proposed architecture. The doctors, patients, and nurses enter this environment by registering on the blockchain and they are represented by avatars in the metaverse environment. All the consultation activities between the doctor and the patient will be recorded and the data, i.e., images, speech, text, videos, clinical data, etc., will be gathered, transferred, and stored on the blockchain. These data are used for disease prediction and diagnosis by explainable artificial intelligence (XAI) models. The GradCAM and LIME approaches of XAI provide logical reasoning for the prediction of diseases and ensure trust, explainability, interpretability, and transparency regarding the diagnosis and prediction of diseases. Blockchain technology provides data security for patients while enabling transparency, traceability, and immutability regarding their data. These features of blockchain ensure trust among the patients regarding their data. Consequently, this proposed architecture ensures transparency and trust regarding both the diagnosis of diseases and the data security of the patient. We also explored the building block technologies of the metaverse. Furthermore, we also investigated the advantages and challenges of a metaverse in healthcare.

Fertility results after exocervical insemination using goat semen cryopreserved with extenders based on egg yolk, skim milk, or soybean lecithin.

To evaluate the effects of four extenders on the post-thaw quality and fertility of goat semen, six Yunshang Black bucks' semen was collected, pooled, diluted with Andromed® (Andr®), Optidyl® (Opt®), P3644 Sigma l-phosphatidylcholine (l-α SL), and skim milk-based (Milk) extenders, and then cryopreserved. The sperm motilities, abnormalities, membrane and acrosome integrity, mitochondrial activity, apoptosis, and reactive oxygen species (ROS) production were evaluated after thawing. After exocervical insemination with the thawed semen, the pregnancy, lambing, and twinning rates were recorded and compared. The results showed that sperm motilities, membrane integrity, acrosome integrity, mitochondrial activity, and viable spermatozoa were significantly higher in the Andr® and Opt® groups than those in the l-α SL and Milk groups (p < .05). Furthermore, there was no difference between Andr® and Opt® (p > .05). The sperm abnormality was lower in semen frozen with the Andr® or Opt® extenders, as compared to the l-α SL or Milk extender (p < .05). Regarding, the viable cells with low ROS production, the optimal results were obtained in the semen frozen with Andr® and Opt® extenders. Following exocervical insemination, the pregnancy and lambing rates in the Milk group were significantly lower than those in the other groups (p < .05). No difference was found in the pregnancy and lambing rates between Andr®, Opt®, and l-α SL (p > .05). Furthermore, the twinning rates were similar between these four groups (p > .05). In conclusion, egg yolk or skim milk can be substituted by soybean lecithin during cryopreservation of goat semen.

Effect of In Ovo Trace Element Supplementation on Immune-Related Cells of the Small Intestine of Post-hatched Broiler Chicken.

Pathological conditions and harmful drugs cause many gastrointestinal diseases in broiler chicken. The current study was conducted to investigate the effect of trace elements zinc (Zn) and selenium (Se) supplementation on histomorphology, immunological role, and functional activity of goblet cells (GCs) of the small intestine. The Alcian blue-periodic acid-Schiff (AB-PAS) was performed to assess the histomorphological changes in GCs, which revealed the regular dispersion with high electron density of GCs throughout the mucosal surface in the supplemented group. However, irregular dispersion with low electron density of GCs was present in the control group. The immunological functional role of GCs within the small intestine was examined by mucicarmine staining, immunohistochemistry, and immunofluorescence. The results showed a high mucin glycol protein secretion in the supplemented group, whereas limited mucin glycol protein secretion in the control group. Furthermore, the biological significance showed a high and low immunoreactivity of Muc2 and Muc13 in the supplemented and control groups, respectively. Immunofluorescence was used to confirm the immunosignaling of Muc2. Results revealed high immunosignaling of Muc2 at the apical part of the small intestine in the supplementation group, while low immunosignaling of Muc2 in the control group. Results suggest that trace element supplementation had significant effect on morphology and immunological role of GCs, which might be essential for immune function and health status of broiler chicken.

Effects of climate change on vegetation and snow cover area in Gilgit Baltistan using MODIS data.

The Hindukush-Karakoram-Himalaya (HKH) mountain ranges are the sources of Asia's most important river systems, which provide fresh water to 1.4 billion inhabitants in the region. Environmental and socioeconomic conditions are affected in many ways by climate change. Globally, climate change has received widespread attention, especially regarding seasonal and annual temperatures. Snow cover is vulnerable to climate warming, particularly temperature variations. By employing Moderate Resolution Imaging Spectroradiometer (MODIS) datasets and observed data, this study investigated the seasonal and interannual variability using snow cover, vegetation and land surface temperature (LST), and their spatial and temporal trend on different elevations from 2001 to 2020 in these variables in Gilgit Baltistan (GB), northern Pakistan. The study region was categorized into five elevation zones extending from < 2000 to > 7000 masl. Non-parametric Mann-Kendall trend tests and Sen's slope estimates indicate snow cover increases throughout the winter, but decreases significantly between June and July. In contrast, GB has an overall increasing annual LST trend. Pearson correlation coefficient (PCC) reveals a significant positive relationship between vegetation and LST (PCC = 0.73) and a significant negative relationship between LST and snow cover (PCC = - 0.74), and vegetation and snow cover (PCC = - 0.78). Observed temperature data and MODIS LST have a coefficient of determination greater than 0.59. Snow cover decreases at 3000-2000 masl elevations while increases at higher 5000 masl elevations.The vegetation in low and mid-elevation < 4000 masl zones decreases significantly annually. The temperature shows a sharply increasing trend at lower 2000-3000 masl elevations in the autumn, indicating the shifting of the winter seasons at this elevation zone. These findings better explain the spatiotemporal variations in snow cover, vegetation, and LST at various elevation zones and the interactions between these parameters at various elevations across the HKH region.

Effects of Dietary Protein Levels on Sheep Gut Metabolite Profiles during the Lactating Stage.

Diet-associated characteristics such as dietary protein levels can modulate the gut's primary or secondary metabolites, leading to effects on the productive performance and overall health of animals. Whereas fecal metabolite changes are closely associated with gut metabolome, this study aimed to see changes in the rumen metabolite profile of lactating ewes fed different dietary protein levels. For this, eighteen lactating ewes (approximately 2 years old, averaging 38.52 ± 1.57 kg in their initial body weight) were divided into three groups (n = 6 ewes/group) by following the complete randomized design, and each group was assigned to one of three low-protein (D_I), medium-protein (D_m), and high-protein (D_h) diets containing 8.58%, 10.34%, and 13.93% crude protein contents on a dry basis, respectively. The fecal samples were subjected to untargeted metabolomics using ultra-performance liquid chromatography (UPLC). The metabolomes of the sheep fed to the high-protein-diet group were distinguished as per principal-component analysis from the medium- and low-protein diets. Fecal metabolite concentrations as well as their patterns were changed by feeding different dietary protein levels. The discriminating metabolites between groups of nursing sheep fed different protein levels were identified using partial least-squares discriminant analysis. The pathway enrichment revealed that dietary protein levels mainly influenced the metabolism-associated pathways (n = 63 and 39 in positive as well as negative ionic modes, respectively) followed by protein (n = 15 and 8 in positive as well as negative ionic modes, respectively) and amino-acid (n = 14 and 7 in positive as well as negative ionic modes, respectively) synthesis. Multivariate and univariate analyses showed comparative changes in the fecal concentrations of metabolites in both positive and negative ionic modes. Major changes were observed in protein metabolism, organic-acid biosynthesis, and fatty-acid oxidation. Pairwise analysis and PCA reveal a higher degree of aggregation within the D-h group than all other pairs. In both the PCA and PLS-DA plots, the comparative separation among the D_h/D_m, D_h/D_I, and D_m/D_I groups was superior in positive as well as negative ionic modes, which indicated that sheep fed higher protein levels had alterations in the levels of the metabolites. These metabolic findings provide insights into potentiated biomarker changes in the metabolism influenced by dietary protein levels. The target identification may further increase our knowledge of sheep gut metabolome, particularly regarding how dietary protein levels influence the molecular mechanisms of nutritional metabolism, growth performance, and milk synthesis of sheep.

Detection of COVID-19 in X-ray Images Using Densely Connected Squeeze Convolutional Neural Network (DCSCNN): Focusing on Interpretability and Explainability of the Black Box Model.

The novel coronavirus (COVID-19), which emerged as a pandemic, has engulfed so many lives and affected millions of people across the world since December 2019. Although this disease is under control nowadays, yet it is still affecting people in many countries. The traditional way of diagnosis is time taking, less efficient, and has a low rate of detection of this disease. Therefore, there is a need for an automatic system that expedites the diagnosis process while retaining its performance and accuracy. Artificial intelligence (AI) technologies such as machine learning (ML) and deep learning (DL) potentially provide powerful solutions to address this problem. In this study, a state-of-the-art CNN model densely connected squeeze convolutional neural network (DCSCNN) has been developed for the classification of X-ray images of COVID-19, pneumonia, normal, and lung opacity patients. Data were collected from different sources. We applied different preprocessing techniques to enhance the quality of images so that our model could learn accurately and give optimal performance. Moreover, the attention regions and decisions of the AI model were visualized using the Grad-CAM and LIME methods. The DCSCNN combines the strength of the Dense and Squeeze networks. In our experiment, seven kinds of classification have been performed, in which six are binary classifications (COVID vs. normal, COVID vs. lung opacity, lung opacity vs. normal, COVID vs. pneumonia, pneumonia vs. lung opacity, pneumonia vs. normal) and one is multiclass classification (COVID vs. pneumonia vs. lung opacity vs. normal). The main contributions of this paper are as follows. First, the development of the DCSNN model which is capable of performing binary classification as well as multiclass classification with excellent classification accuracy. Second, to ensure trust, transparency, and explainability of the model, we applied two popular Explainable AI techniques (XAI). i.e., Grad-CAM and LIME. These techniques helped to address the black-box nature of the model while improving the trust, transparency, and explainability of the model. Our proposed DCSCNN model achieved an accuracy of 98.8% for the classification of COVID-19 vs normal, followed by COVID-19 vs. lung opacity: 98.2%, lung opacity vs. normal: 97.2%, COVID-19 vs. pneumonia: 96.4%, pneumonia vs. lung opacity: 95.8%, pneumonia vs. normal: 97.4%, and lastly for multiclass classification of all the four classes i.e., COVID vs. pneumonia vs. lung opacity vs. normal: 94.7%, respectively. The DCSCNN model provides excellent classification performance consequently, helping doctors to diagnose diseases quickly and efficiently.

Tracheo-Cutaneous Fistula: A Rare Presentation Due To A Forgotten Rubber Band In Neck Of A Young Girl.

The presence of foreign bodies in a chronic wound is a well-known phenomenon. The common locations are soft tissues of the wrist and neck, gastro-intestinal foreign objects, foreign objects in the ear and in the Uro-genital tract. aero digestive tract and chest. Retained foreign bodies in soft tissues become reason for formation of sinus which can be superimposed by infection. Most of foreign bodies in soft tissues remain un-detected, becoming the reason for delayed diagnosis. Some of the foreign bodies are inert and stay in the body tissues for prolonged time without causing any deleterious effect but few of them erode into surrounding tissues and cause prolonged illness. Tracheo-cutaneous fistula secondary to a foreign body in a child is rarely reported. In this case, the patient had retained foreign body in her neck for almost eight years which remained un diagnosed while being treated for various types of skin disease.

Identification of Differentially Expressed Genes Associated with the Prognosis and Diagnosis of Hepatocellular Carcinoma by Integrated Bioinformatics Analysis.

Objective: The goal of this study was to understand the possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis. Methods: GEO contains datasets of gene expression, miRNA, and methylation patterns of diseased and healthy/control patients. The GSE62232 dataset was selected by employing the server Gene Expression Omnibus. A total of 91 samples were collected, including 81 HCC and 10 healthy samples as control. GSE62232 was analysed through GEO2R, and Functional Enrichment Analysis was performed to extract rational information from a set of DEGs. The Protein-Protein Relationship Networking search method has been used for extracting the interacting genes. MCC method was used to calculate the top 10 genes according to their importance. Hub genes in the network were analysed using GEPIA to estimate the effect of their differential expression on cancer progression. Results: We identified the top 10 hub genes through CytoHubba plugin. These included BUB1, BUB1B, CCNB1, CCNA2, CCNB2, CDC20, CDK1 and MAD2L1, NCAPG, and NDC80. NCAPG and NDC80 reported for the first time in this study while the remaining from a recently reported literature. The pathogenesis of HCC may be directly linked with the aforementioned genes. In this analysis, we found critical genes for HCC that showed recommendations for future prognostic and predictive biomarkers studies that could promote selective molecular therapy for HCC.

Enhancing drought stress tolerance in Camelina (Camelina sativa L.) through exogenous application of potassium.

The current study was performed under controlled conditions to study the effects of exogenous potassium application on carotenoid contents and drought tolerance in Camelina. Water deficit levels such as 100% FC (control) and 40% FC (drought stress) were imposed after germination of Camelina plants grown to maturity, and different treatments of exogenous K+ were applied at the vegetative stage. We have reported 17 traits of plant growth, physiology, antioxidant enzyme activity, focusing on carotenoids in Camelina to explore their potential yield and yield components. For this purpose, we used multivariate analysis techniques (descriptive statistics, correlation matrix, analysis of variance [ANOVA] and principal components analysis [PCA] to determine the best relation between potassium and studied traits). The results showed a large number of variations in the studied trait under control and water deficit condition. Plant fresh weight (g) was negatively correlated with shoot length and SOD insignificantly correlated with plant fresh weight (g) under water deficit conditions. Potassium loading predicted that foliar application (3 mM K2 SO4 ), foliar application (6 mM KNO3 ), foliar application (12 mM KNO3 ) and foliar application (12 mM K2 SO4 ) are the important doses that contribute the most to enhance the growth, physiological and biochemical activities and carotenoids to improve the Camelina yield under water deficit condition. These doses should be considered in the future to improve the Camelina yield under semi-arid conditions with increased genetic diversity (varietal selection).

Pneumonia Detection in Chest X-Ray Images Using Enhanced Restricted Boltzmann Machine.

The process of pneumonia detection has been the focus of researchers as it has proved itself to be one of the most dangerous and life-threatening disorders. In recent years, many machine learning and deep learning algorithms have been applied in an attempt to automate this process but none of them has been successful significantly to achieve the highest possible accuracy. In a similar attempt, we propose an enhanced approach of a deep learning model called restricted Boltzmann machine (RBM) which is named enhanced RBM (ERBM). One of the major drawbacks associated with the standard format of RBM is its random weight initialization which leads to improper feature learning of the model during the training phase, resulting in poor performance of the machine. This problem has been tried to eliminate in this work by finding the differences between the means of a specific feature vector and the means of all features given as inputs to the machine. By performing this process, the reconstruction of the actual features is increased which ultimately reduces the error generated during the training phase of the model. The developed model has been applied to three different datasets of pneumonia diseases and the results have been compared with other state of the art techniques using different performance evaluation parameters. The proposed model gave highest accuracy of 98.56% followed by standard RBM, SVM, KNN, and decision tree which gave accuracies of 97.53%, 92.62%, 91.64%, and 88.77%, respectively, for dataset named dataset 2. Similarly, for the dataset 1, the highest accuracy of 96.66 has been observed for the eRBM followed by srRBM, KNN, decision tree, and SVM which gave accuracies of 90.22%, 89.34%, 87.65%, and 86.55%, respectively. In the same way, the accuracies observed for the dataset 3 by eRBM, standard RBM, KNN, decision tree, and SVM are 92.45%, 90.98%, 87.54%, 85.49%, and 84.54%, respectively. Similar observations can also be seen for other performance parameters showing the efficiency of the proposed model. As revealed in the results obtained, a significant improvement has been observed in the working of the RBM by introducing a new method of weight initialization during the training phase. The results show that the improved model outperforms other models in terms of different performance evaluation parameters, namely, accuracy, sensitivity, specificity, F1-score, and ROC curve.

Alhagi sparsifolia: An ideal phreatophyte for combating desertification and land degradation.

The upcoming launch of the post-2020 biodiversity framework entails elucidating the future priorities and strategies for conserving biodiversity on a regional and global scale. Desertification has caused significant environmental and socio-economic problems in many arid and semiarid areas of the world. Phreatophytes increase nutrient levels and water reserves in the ground below them and are essential resources for other plants and animals. Factors that can enhance or reduce the island effect of phreatophytes are worthy of research since they can contribute to the survival and growth of this group of plants. Furthermore, these plants substantially impact the whole environment and boast necessary evolutionary adaptations for sustaining islands of life in the arid landscape to prevent complete desertification. Alhagi sparsifolia is a semi-shrub legume and a typical desert phreatophyte. The main objective of this review is to disseminate research-based knowledge and improve awareness of this important plant species and provide a framework that could effectively improve regional socio-ecological environments, particularly in controlling desertification, thereby enhancing human welfare. First, we synthesized knowledge of the physiological acclimation of this species, above all in terms of water and nutrient use, the impact of salinity, and its response to different light environments and temperatures. Then, we analyzed the socio-ecological services that A. sparsifolia offers, highlighted the anthropogenic threats to its habitats, and strategies for its sustainable utilization and management. We conclude that more knowledge of how A. sparsifolia interacts with groundwater will contribute significantly to elucidating the possible effects of climate change and human activities on the sustainable use of groundwater. Protected areas must be set up, and key knowledge shared to combat desertification and improve people's welfare as a means of guaranteeing sustainable development. An integrative socio-ecological approach must be adapted to improve understanding of this valuable phreatophyte's importance and sustainable use and fulfill Sustainable Development Goals (SDGs).