Software defined radio frequency sensing framework for intelligent monitoring of sleep apnea syndrome.

Healthy sleep is vital to all functions in the body. It improves physical and mental health, strengthens resistance against diseases, and develops strong immunity against metabolism and chronic diseases. However, a sleep disorder can cause the inability to sleep well. Sleep apnea syndrome is a critical breathing disorder that occurs during sleeping when breathing stops suddenly and starts when awake, causing sleep disturbance. If it is not treated timely, it can produce loud snoring and drowsiness or causes more acute health problems such as high blood pressure or heart attack. The accepted standard for diagnosing sleep apnea syndrome is full-night polysomnography. However, its limitations include a high cost and inconvenience. This article aims to develop an intelligent monitoring framework for detecting breathing events based on Software Defined Radio Frequency (SDRF) sensing and verify its feasibility for diagnosing sleep apnea syndrome. We extract the wireless channel state information (WCSI) for breathing motion using channel frequency response (CFR) recorded in time at every instant at the receiver. The proposed approach simplifies the receiver structure with the added functionality of communication and sensing together. Initially, simulations are conducted to test the feasibility of the SDRF sensing design for the simulated wireless channel. Then, a real-time experimental setup is developed in a lab environment to address the challenges of the wireless channel. We conducted 100 experiments to collect the dataset of 25 subjects for four breathing patterns. SDRF sensing system accurately detected breathing events during sleep without subject contact. The developed intelligent framework uses machine learning classifiers to classify sleep apnea syndrome and other breathing patterns with an acceptable accuracy of 95.9%. The developed framework aims to build a non-invasive sensing system to diagnose patients conveniently suffering from sleep apnea syndrome. Furthermore, this framework can easily be further extended for E-health applications.

Synergistic impacts of ferromanganese oxide biochar and optimized water management on reducing Cd accumulation in rice.

Ferromanganese oxide biochar composite (FMBC) is an efficient remediation material for cadmium -contaminated soils. However, the effect of FMBC under varied water managements on the remediation of Cd-polluted soil is unclear. In this study, we conducted both incubation and field experiments to investigate the combined effects of corn-stover-derived biochar modified with ferromanganese on the immobilization and uptake of Cd by rice under continuous aerobic (A), aerobic-flooded (AF), and flooded-aerobic (FA) water management regimes. The results showed that loading iron-manganese significantly increased the maximum sorption capacity (Qm) of Cd on FMBC (50.46 mg g-1) due to increased surface area, as compared to the pristine biochar (BC, 31.36 mg g-1). The results revealed that soil Eh and pH were significantly affected by FMBC and it's synergistic application with different water regimes, thus causing significant differences in the concentrations of DTPA-extractable Cd under different treatments. The lowest DTPA-extractable Cd content (0.28-0.46 mg-1) was observed in the treatment with FMBC (2.5 %) combined FA water amendment, which reduced the content of available Cd in soil by 2.63-28.4 %. Moreover, the treatments with FMBC-FA resulted the proportion of residual Cd increased by 22.2 % compared to the control. Variations in the content and fraction of Cd had a significant influence on its accumulation in the rice grains. The FMBC-FA treatments reduced the Cd concentration in roots, shoots and grains by 37.97 %, 33.98 %, and 53.66 %, respectively, when compared with the control. Predominantly because of the reduction in Cd biological toxicity and the improved soil nutrient content, the combined application increased the biomass and yield of rice to some extent. Taken together, the combination of the Fe-Mn modified biochar and flooded-aerobic water management may potentially be applied in Cd-polluted soil to mitigate the impacts of Cd on rice production.

Enhancing System Performance through Objective Feature Scoring of Multiple Persons' Breathing Using Non-Contact RF Approach.

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system's performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system's performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively.

Non-Contact Smart Sensing of Physical Activities during Quarantine Period Using SDR Technology.

The global pandemic of the coronavirus disease (COVID-19) is dramatically changing the lives of humans and results in limitation of activities, especially physical activities, which lead to various health issues such as cardiovascular, diabetes, and gout. Physical activities are often viewed as a double-edged sword. On the one hand, it offers enormous health benefits; on the other hand, it can cause irreparable damage to health. Falls during physical activities are a significant cause of fatal and non-fatal injuries. Therefore, continuous monitoring of physical activities is crucial during the quarantine period to detect falls. Even though wearable sensors can detect and recognize human physical activities, in a pandemic crisis, it is not a realistic approach. Smart sensing with the support of smartphones and other wireless devices in a non-contact manner is a promising solution for continuously monitoring physical activities and assisting patients suffering from serious health issues. In this research, a non-contact smart sensing through the walls (TTW) platform is developed to monitor human physical activities during the quarantine period using software-defined radio (SDR) technology. The developed platform is intelligent, flexible, portable, and has multi-functional capabilities. The received orthogonal frequency division multiplexing (OFDM) signals with fine-grained 64-subcarriers wireless channel state information (WCSI) are exploited for classifying different activities by applying machine learning algorithms. The fall activity is classified separately from standing, walking, running, and bending with an accuracy of 99.7% by using a fine tree algorithm. This preliminary smart sensing opens new research directions to detect COVID-19 symptoms and monitor non-communicable and communicable diseases.

Controlled Silicidation of Silicon Nanowires Using Flash Lamp Annealing.

Among other new device concepts, nickel silicide (NiSix)-based Schottky barrier nanowire transistors are projected to supplement down-scaling of the complementary metal-oxide semiconductor (CMOS) technology as its physical limits are reached. Control over the NiSix phase and its intrusions into the nanowire is essential for superior performance and down-scaling of these devices. Several works have shown control over the phase, but control over the intrusion lengths has remained a challenge. To overcome this, we report a novel millisecond-range flash lamp annealing (FLA)-based silicidation process. Nanowires are fabricated on silicon-on-insulator substrates using a top-down approach. Subsequently, Ni silicidation experiments are carried out using FLA. It is demonstrated that this silicidation process gives unprecedented control over the silicide intrusions. Scanning electron microscopy and high-resolution transmission electron microscopy are performed for structural characterization of the silicide. FLA temperatures are estimated with the help of simulations.

The mechanical property and microscopic deformation mechanism of nanoparticle-contained graphene foam materials under uniaxial compression.

Nanoparticle-contained graphene foams have found more and more practical applications in recent years, which desperately requires a deep understanding on basic mechanics of this hybrid material. In this paper, the microscopic deformation mechanism and mechanical properties of such a hybrid material under uniaxial compression, that are inevitably encountered in applications and further affect its functions, are systematically studied by the coarse-grained molecular dynamics simulation method. Two major factors of the size and volume fraction of nanoparticles are considered. It is found that the constitutive relation of nanoparticle filled graphene foam materials consists of three parts: the elastic deformation stage, deformation with inner re-organization and the final compaction stage, which is much similar to the experimental measurement of pristine graphene foam materials. Interestingly, both the initial and intermediate modulus of such a hybrid material is significantly affected by the size and volume fraction of nanoparticles, due to their influences on the microstructural evolution. The experimentally observed 'spacer effect' of such a hybrid material is well re-produced and further found to be particle-size sensitive. With the increase of nanoparticle size, the micro deformation mechanism will change from nanoparticles trapped in the graphene sheet, slipping on the graphene sheet, to aggregation outside the graphene sheet. Beyond a critical relative particle size 0.26, the graphene-sheet-dominated deformation mode changes to be a nanoparticle-dominated one. The final microstructure after compression of the hybrid system converges to two stable configurations of the 'sandwiched' and 'randomly-stacked' one. The results should be helpful not only to understand the micro mechanism of such a hybrid material in different applications, but also to the design of advanced composites and devices based on porous materials mixed with particles.

Microscopic deformation mechanism and main influencing factors of carbon nanotube coated graphene foams under uniaxial compression.

Many experiments have shown that carbon nanotube-coated (CNT-coated) graphene foam (CCGF) has specific mechanical properties, which further expand the application of graphene foam materials in many advanced fields. To reveal the microscopic deformation mechanism of CCGF under uniaxial compression and the main factors affecting their mechanical properties, numerical experiments based on the coarse-grained molecular dynamics method are systematically carried out in this paper. It is found that the relative stiffness of CNTs and graphene flakes seriously affects the microscopic deformation mechanism and strain distribution in CCGFs. The bar reinforcing mechanism will dominate the microstructural deformation in CCGFs composed of relatively soft graphene flakes, while the microstructural deformation in those composed of stiff graphene flakes will be dominated by the mechanical locking mechanism. The effects of CNT fraction, distribution of CNTs on graphene flakes, the thickness of graphene flakes, and the adhesion strength between CNTs and graphene flakes on the initial and intermediate moduli of foam materials are further studied in detail. The results of this paper should be helpful for a deep understanding of the mechanical properties of CCGF materials and the optimization design of microstructures in advanced graphene-based composites.

Contactless Small-Scale Movement Monitoring System Using Software Defined Radio for Early Diagnosis of COVID-19.

The exponential growth of the novel coronavirus disease (N-COVID-19) has affected millions of people already and it is obvious that this crisis is global. This situation has enforced scientific researchers to gather their efforts to contain the virus. In this pandemic situation, health monitoring and human movements are getting significant consideration in the field of healthcare and as a result, it has emerged as a key area of interest in recent times. This requires a contactless sensing platform for detection of COVID-19 symptoms along with containment of virus spread by limiting and monitoring human movements. In this paper, a platform is proposed for the detection of COVID-19 symptoms like irregular breathing and coughing in addition to monitoring human movements using Software Defined Radio (SDR) technology. This platform uses Channel Frequency Response (CFR) to record the minute changes in Orthogonal Frequency Division Multiplexing (OFDM) subcarriers due to any human motion over the wireless channel. In this initial research, the capabilities of the platform are analyzed by detecting hand movement, coughing, and breathing. This platform faithfully captures normal, slow, and fast breathing at a rate of 20, 10, and 28 breaths per minute respectively using different methods such as zero-cross detection, peak detection, and Fourier transformation. The results show that all three methods successfully record breathing rate. The proposed platform is portable, flexible, and has multifunctional capabilities. This platform can be exploited for other human body movements and health abnormalities by further classification using artificial intelligence.

Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset.

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20-30% of COVID patients require hospitalization, while almost 5-12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne-Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic.

RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices.

Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations.

Rotation of nanoflake driven by strain gradient fields in locally-indented graphene.

Rotation of nano-components is necessary in nanoscale mechanical systems (NMS) to enable various functions of nanomachines, however, the actuation and modulation of nanoscale rotation have been poorly investigated up to now. In this paper, we conduct molecular dynamics simulations to study the in-plane rotation of a graphene nanoflake hinged to a graphene substrate by easily accessible nanoindentation techniques. The flake can be driven to rotate by strain gradient fields (SGFs) induced by indenting the substrate locally. The effect of flake size, indenting velocity and position on flake rotation are studied systematically. It is found that there exists a critical range of flake size which is comparable to that of SGFs. The direction of flake rotation, i.e. clockwise or counterclockwise, can be tuned effectively by indenting the substrate asymmetrically with respect to the flake. Besides, the rotation can be speeded up by simply indenting more quickly. Furthermore, the flake can be trapped in a desired region on the substrate by adopting double SGFs. The continuous rotation of the flake can be realized by intermittently indenting the substrate near the flake. These results may be useful for designing the rotation of components in NMSs and nanoscale manipulation.

Phenolic Profile and Thermal Stability of Monovarietal Extra Virgin Olive Oils Based on Synchronous Fluorescence Spectroscopy.

The olive oil production in Pakistan has recently been started with the cultivation of exotic cultivars that are successfully adapted at Barani Agriculture Research center (BARI), Chakwal, Pakistan in Potohar valley. Therefore, characterization of extra virgin olive oil (EVOO) from this agro-climatic region is mandatory in establishing its biochemical profile and thermal stability. Seventeen monovarietal EVOOs extracted from these cultivars were analysed using synchronous fluorescence spectroscopy (SFS) and subjected to heating at 115, 150 and 170 °C for 15 min to identify their thermal stability. SFS emission spectra differentiated EVOOs on the basis of phenolic compounds that are denatured at high temperature, further chlorophyll contents also decreased with increasing temperature. The strong emission at ca. 351 nm, suggested to be vanillic acid, 391-471 nm for blue green region (BGR) assigned to other phenolic compounds and two peaks at 672 and 723 nm for chlorophyll became the bases for grouping through Hierarchical clustering. Most of the EVOOs were stable at 150 °C but showed denatured spectra at 170 °C, the only EVOO extracted from Spanish cultivar Arbequina was found to have moderate fluorescence emission from both vanillic acid and BGR that are more likely to impart oxidative stability even after heating at 170 °C, also confirmed by lowest values of specific extinction co-efficient (K232 and K270). Moreover, variation in phenolic contents of Arbequina EVOO was observed with different harvesting stages and the early harvested olives produced more thermally stable oil as compared to late harvested olives. Arbequina oil grown in Pakistan can be better suited for cooking at high temperatures, moreover can be blended with other monovarietal EVOOs to enhance the nutritional benefits and thermal stability.

Correlation of MLH1 polymorphisms, survival statistics, in silico assessment and gene downregulation with clinical outcomes among breast cancer cases.

This study aimed to investigate the role of MLH1 polymorphisms, respective protein structure prediction, survival analysis, related clinicopathological details and MLH1 expression in breast cancer (BC). Genotyping of selected SNPs in BC patients (493) and age matched controls (387) were performed by Tetra-ARMS PCR. Gene expression among breast tumors (127) and adjacent control tissues were analysed using reverse transcriptase PCR (RT-PCR) and immunohistochemistry. Statistical analysis was performed by SPSS and MedCalc. Conditional logistic regression analysis was applied to compute the odds ratio and confidence interval. Phyre2 and I-TASSER were used to generate MLH1 protein structures and verified by a variety of computational tools. Genotyping illustrated that MLH1 polymorphisms (rs63749795 and rs63749820) were significantly associated (P ≤ 0.05) with risk of developing BC. Down regulation of MLH1 gene expression/loss of the MLH1 protein (OR 12; CI 2.8-53.1) was observed in BC cases, illustrating its potential role in disease development. Moreover, loss of the MLH1 protein was found to be associated with higher grade cancer (P = 0.02) and lymph node positivity (P = 0.03), highlighting its essential role, as a component of the mismatch repair (MMR) machinery. Bioinformatics analysis confirmed that nonsense mutations produce a truncated MLH1 protein, causing a reduction in MMR efficiency. No association between MLH1 polymorphisms and overall and progression free survival statistics was observed among BC cases, possibly due to short follow-up study. Results at DNA, RNA and protein levels, along with in silico analysis, highlights the potential role of MLH1 in DNA repair mechanisms, within BC. Therefore, it was concluded that MLH1 may contribute towards BC development and progression.

Identification of high cadmium-accumulating oilseed sunflower (Helianthus annuus) cultivars for phytoremediation of an Oxisol and an Inceptisol.

Forty oilseed sunflower cultivars were screened in two soil types for phytoremediation of Cd coupled with maximum biomass yield and oil production. Several cultivars exhibited a significant difference in biomass and yield with enhanced uptake in shoots and low accumulation in roots from two Cd-contaminated soil types, an Oxisol and an Iceptisol. The Transfer Factor of Cd was >1 in several cultivars in both soil types, where as a significant difference in phytoextraction of Cd was observed in the Oxisol (acidic soil), greater than in the Inceptisol (alkaline soil). The results revealed that of the 40 cultivars, S9178, Huanong 667in the Oxisol and cvs. DW 667, HN 667, Huanong 667 and 668F1 in the Inceptisol showed a high biomass, better yield and enhanced accumulation of Cd in the shoots but a lesser accumulation in oil. The screened cultivar S 9178 produced the greatest amount of oil (55.6%) with 77% oleic acid, which makes it suitable for human consumption. Cultivar Huanong 667 was found to be the highest accumulating cultivar in both soil types. It is therefore suggested that some sunflower cultivars do exhibit phytoremediation potential together with agro-production potential.

Comparative assessment of Brassica pekinensis L. genotypes for phytoavoidation of nitrate, cadmium and lead in multi-pollutant field.

Information is needed for comparative assessment and agronomic practices for phytoavoidation in multi-pollutant field. A field study was conducted to explore 97 Brassica pekinensis L. genotypes with permissible limit of contaminants growing in a severely Cd, moderately nitrate and slightly Pb multi-polluted field. Thirteen genotypes, i.e. KGZY, CXQW, CAIB, JINL, JQIN, JFEN, WMQF, XLSH, TAIK, BJXS, JUKA, XYJQ and GQBW, were identified with permissible limit for nitrate, Cd and Pb based on their resistance to heavy metal and nitrate accumulation in leaves when grown in co-contaminated soils. Furthermore, the correlation between essential and toxic elements concentrations in plant of B. pekinensis were inconsistent. Generally speaking, application of increasing Ca, K and S fertilizers in appropriate forms and dosages tended to increase the yield and quality of B. pekinensis cultivated in multi-pollutant field.

Monitoring Movements of Ataxia Patient by Using UWB Technology.

Internet of multimedia things (IoMT) driving innovative product development in health care applications. IoMT requires delay-sensitive and higher bandwidth devices. Ultra-wideband (UWB) technology is a promising solution to improve communication between devices, tracking and monitoring of patients. In the future, this technology has the capability to expand the IoMT world with new capabilities and more devices can be integrated. At the present time, some people face different types of physiological problems because of the damage in different areas of the central nervous system. Thus, they lose their balance coordination. One of these types of coordination problems is named Ataxia, in which patients are unable to control their body movements. This kind of coordination disorder needs a proper supervision system for the caretaker. Previous Ataxia assessment methods are cumbersome and cannot handle regular monitoring and tracking of patients. One of the most challenging tasks is to detect different walking abnormalities of Ataxia patients. In our paper, we present a technique for monitoring and tracking of a patient with the help of UWB technology. This method expands the real-time location systems (RTLS) in the indoor environment by placing wearable receiving tags on the body of Ataxia patients. The location and four different walking movement data are collected by UWB transceiver for the classification and prediction in the two-dimensional path. For accurate classification, we use a support vector machine (SVM) algorithm to clarify the movement variations. Our proposed examined result successfully achieved and the accuracy is above 95%.

Assessment of sunflower germplasm for phytoremediation of lead-polluted soil and production of seed oil and seed meal for human and animal consumption.

Phytoremediation is a valuable technology for mitigating soil contamination in agricultural lands, but phytoremediation without economic revenue is unfeasible for land owners and farmers. The use of crops with high biomass and bioenergy for phytoremediation is a unique strategy to derive supplementary benefits along with remediation activities. Sunflower (Helianthus annuus L.) is a high-biomass crop that can be used for the phytoremediation of polluted lands with additional advantages (biomass and oil). In this study, 40 germplasms of sunflower were screened in field conditions for phytoremediation with the possibility for oil and meal production. The study was carried out to the physiological maturity stage. All studied germplasms mopped up substantial concentrations of Pb, with maximum amounts in shoot > root > seed respectively. The phytoextraction efficiency of the germplasm was assessed in terms of the Transfer factor (TF), Metal removal efficiency (MRE) and Metal extraction ratio (MER). Among all assessed criteria, GP.8585 was found to be most appropriate for restoring moderately Pb-contaminated soil accompanied with providing high biomass and high yield production. The Pb content in the oil of GP.8585 was below the Food safety standard of China, with 59.5% oleic acid and 32.1% linoleic acid. Moreover, amino acid analysis in meal illustrated significant differences among essential and non-essential amino acids. Glutamic acid was found in the highest percentage (22.4%), whereas cysteine in the lowest percentage (1.3%). Therefore, its efficient phytoextraction ability and good quality edible oil and meal production makes GP.8585 the most convenient sunflower germplasm for phytoremediation of moderately Pb-contaminated soil, with fringe benefits to farmers and landowners.

Noninvasive Suspicious Liquid Detection Using Wireless Signals.

Conventional liquid detection instruments are very expensive and not conducive to large-scale deployment. In this work, we propose a method for detecting and identifying suspicious liquids based on the dielectric constant by utilizing the radio signals at a 5G frequency band. There are three major experiments: first, we use wireless channel information (WCI) to distinguish between suspicious and nonsuspicious liquids; then we identify the type of suspicious liquids; and finally, we distinguish the different concentrations of alcohol. The K-Nearest Neighbor (KNN) algorithm is used to classify the amplitude information extracted from the WCI matrix to detect and identify liquids, which is suitable for multimodal problems and easy to implement without training. The experimental result analysis showed that our method could detect more than 98% of the suspicious liquids, identify more than 97% of the suspicious liquid types, and distinguish up to 94% of the different concentrations of alcohol.

Preincubation and vermicomposting of divergent biosolids exhibit vice versa multielements stoichiometry and earthworm physiology.

Sewage sludge and kitchen refuse are ubiquitously mounting wastes with high organic load, which if reprocessed they could salvage the environment. Reckoned with this certitude, an incubating study was initiated on sequential preincubation of sewage sludge with kitchen waste in 100:0, 70:30, 50:50, and 30:70 ratios for 16 days ensued by vermicomposting of 30 days using Eisenia fetida. Concentration of heavy metals (Cd, Cr, Cu, Mn, Pb, and Zn) in the biosolid mixtures increased during preincubation but reduced progressively through vermicomposting due to bioaccumulation of these metals in the earthworm tissues. Earthworm growth parameters data reflected that sewage sludge and kitchen waste mixture with 70:30 ratio increased the number of cocoons (10.6%), biomass (8.2%), growth rate (8.3%), reproduction rate (12.2%), and decreased their mean mortality rate (80.1%) as compared to that in sole sewage sludge (control). Results of chemical analysis and SEM/EDS imaging, showed that alkalinity, organic carbon, C/N ratio, organic matter and concentration of trace elements (Cd, Cr, Cu, Mn, Pb, and Zn) reduced while macronutrients (N, P, K, Ca and Mg) increased in the final vermicompost as compared to that in initial mixtures. The FT-IR analysis also revealed that various biochemical functional groups underwent biodegradation during combined preincubation-vermicomposting. Bioaccumulation factor (BAF) of all trace elements in the earthworm tissues was higher with 70:30 ratio of substrates, with the trend of Cd > Zn > Cu > Mn > Pb > Cr. Hence, this study concludes that combined preincubation-vermicomposting is the most efficient and ecofriendly technique for biodegradation, stabilization, and conversion of sewage sludge and kitchen waste into organic fertilizer. The nutrient rich vermicompost can be safely used as horticultural substrate and soil conditioner for efficient management of degraded soils. Finally, combined preincubation-vermicomposting is a sustainable system of recycling the sewage sludge along with kitchen waste.

Survival analysis of breast cancer patients with different treatments: a multicentric clinicopathological study.

OBJECTIVE: To explore and better understand clinic pathological details of breast cancer patients and analyse their survival rate among different treatment groups. METHODS: The prospective cohort, multi-centric study was conducted from September, 2014, to February, 2018, at five hospitals in Rawalpindi and Islamabad, Pakistan, and comprised histo-pathologically confirmed breast cancer cases. Patient characteristics and medical history were collected using a detailed questionnaire. All the subjects were followed up, and information regarding their current health and treatment status was collected. Data was analysed using SPSS 24. RESULTS: There were 347 subjects with a mean age of 44.3±12.2 years and body mass index of 27.9±4.0 kg/m2. Younger age, increased body mass index, consanguinity and family history were major contributing factors in breast cancer development (p<0.05). Overall, 267(77%) had invasive ductal carcinoma and Grade II tumour 234(67%) was more frequent. A total of 221(64%) cases had positive lymph nodes and 97(28%) had metastasis to different body organs. Overall survival analysis showed statistically significant role (p<0.0001) of all treatment options. CONCLUSIONS: Combination of different treatments can provide more promising health outcomes in breast cancer cases.