Bu-Ali Sina University

The fair allocation of river pollution assimilation capacity among stakeholders (polluters) is one of the most critical issues in river water quality management. In this study, the bankruptcy rules approach was employed to address this challenge. To this end, the QUAL2Kw water quality simulation model was linked with the particle swarm optimization algorithm, incorporating the four bankruptcy rules [constrained equal awards (CEA), constrained equal loss, proportional (P), and Talmud] as constraints. The objective function of the study is to maintain water quality, measured by biochemical oxygen demand, within standard limits along the river by optimally allocating pollutant loads. The applicability of this method was evaluated in the Abbas-Abad mountainous river, located in a semiarid basin in Iran. The results indicated that, using this modeling approach and based on the bankruptcy rules investigated, river pollution was maintained within standard limits. However, from the perspective of fairness among stakeholders, the P rule appears to be the most equitable, as each point-source polluter discharges a proportionate share of their pollution load into the river. On the other hand, from the perspective of maximizing pollutant discharge while still maintaining water quality standards or, in other words, enhancing the river’s self-purification capacity, the CEA rule is the most effective. This is because it allows for the highest total pollutant discharge, resulting in lower overall treatment costs for polluters and greater reliance on the river’s natural self-purification.

Leishmania parasites are transmitted through the bite of infected female sand flies. The sand fly inoculum contains both the parasite and the salivary proteins, which can modulate the immune system's function. Some of these salivary proteins have the potential to be used as a vaccine candidate. Since there have been fewer studies investigating the salivary proteins of Phlebotomus (Ph.) sergenti, this prompted us to select among the three protein members of Ph. sergenti apyrase family (PsSP40, PsSP41, and PsSP42) and measure its effectiveness as vaccine candidate against Leishmania (L.) tropica. To select among the three family members as the candidate for immunization, different parameters including the physicochemical characters, three-dimensional structure, virtual immune stimulatory potential, and human leukocyte antigen (HLA) class II-binding epitope content were considered. To investigate the effect of immunization with the selected antigen through immunoinformatics analysis (PsSP42) against L. tropica infection, we immunized BALB/c mice with two distinct recombinant plasmids (conventional VR1020 and new-generation NTC9385R) two times at 3-week intervals followed by immediate electroporation. Eight weeks post-challenge, the parasite load in draining lymph nodes was measured by quantitative real-time polymerase chain reaction (PCR). The interferon (IFN)-γ and interleukin (IL)-4 cytokines before (against recombinant Leishmania tarentolae expressing PsSP42) and after (against parasite frozen/thawed antigens) L. tropica infection (2 × 107 parasite per footpad plus Ph. sergenti salivary gland homogenate (SGH)) were measured by enzyme-linked immunosorbent assay (ELISA). On the basis of immunoinformatics analysis of three apyrase salivary proteins from Ph. sergenti, PsSP42 demonstrated superior HLA class II-binding peptides compared with the other two proteins (PsSP40 and PsSP41) and was selected for immunization studies. Our findings indicated that NTC-PsSP42 and not VR1020-PsSP42 plasmid immunization relatively reduced the parasite load in the draining lymph nodes. This was assigned to a significant higher IFN-γ to IL-4 ratio induced by NTC-PsSP42 immunization in comparison with pertinent controls. In our study, although the expected protective response was not achieved by any of the recombinant plasmids, the NTC-PsSP42 platform induced a weak Th1-polarized immune response, which partially influenced the parasite load. Since the new generation of plasmids are worth evaluating owing to the lack of antibiotic resistance genes on the backbone, we recommend further assessment of NTC-PsSP42 potential adjuvnated with immunostimulatory sequences such as as CpG motifs or even in heterologous prime-boost regimens.

Abstract As a prevalent knee joint disorder, Patellofemoral Pain Syndrome (PFPS) is identified by musculoskeletal issues, often involving symptoms such as pain experienced around or posterior to the patella. Hip joint kinematics may play an essential role in PFPS. Although this condition has been studied from various perspectives, there is no definitive standard clinical method for diagnosis and functional classification. This study aimed to identify the most significant hip joint kinematic features for classifying PFPS patients during running using machine learning (ML) methods. Seven females with unilateral PFPS were paired with controls considering factors such as age, weight, height, and duration of physical activity. In total, 560 running cycles were captured utilizing a motion analysis system based on an inertial measurement unit (IMU). Hip joint kinematic variables, including three-dimensional angles, velocity, and acceleration, were measured. Nineteen features were used as inputs for ML algorithms. Four models—SVM, KNN, ANN, and RF—achieved 99% accuracy in classifying healthy and PFPS patients. Maximum hip adduction emerged as the most significant kinematic feature, and the SVM model performed best for PFPS classification. In conclusion, this study demonstrates that combining IMU sensors with machine learning techniques provides an accurate approach for diagnosing PFPS during running in non-laboratory and clinical environments. Moreover, frontal plane hip joint kinematics appears to be a critical factor in identifying this condition.

Abstract We develop a Heisenberg-picture kinematical framework in which (i) time is treated as a quantum observable, admitting both a relational POVM construction for semibounded spectra and a fully self-adjoint realization on an enlarged (conjugate-energy) Hilbert space enabled by a gravitational conjugation symmetry $$\mathcal {C}_g$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>C</mml:mi> <mml:mi>g</mml:mi> </mml:msub> </mml:math> , and (ii) the generators of spacetime translations need not commute in curved backgrounds. The central postulate, $$[\,\hat{x}_\mu ,\hat{P}_\nu ]=\textrm{i}\hbar \,\hat{g}_{\mu \nu }(\hat{x})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mo>[</mml:mo> <mml:mspace/> <mml:msub> <mml:mover> <mml:mi>x</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>μ</mml:mi> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mover> <mml:mi>P</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>ν</mml:mi> </mml:msub> <mml:mo>]</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:mtext>i</mml:mtext> <mml:mi>ħ</mml:mi> <mml:mspace/> <mml:msub> <mml:mover> <mml:mi>g</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mi>ν</mml:mi> </mml:mrow> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mover> <mml:mi>x</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> , makes the spacetime metric a metric operator defined by the symmetrized commutator. Jacobi identities close the algebra and imply an operator form of metric compatibility; in a worked FRW example we obtain $$[\,\hat{P}_0,\hat{P}_i]=2\textrm{i}\hbar \,N^2(t)\,H(t)\,\hat{P}_i$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mo>[</mml:mo> <mml:mspace/> <mml:msub> <mml:mover> <mml:mi>P</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mn>0</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mover> <mml:mi>P</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>i</mml:mi> </mml:msub> <mml:mo>]</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:mn>2</mml:mn> <mml:mtext>i</mml:mtext> <mml:mi>ħ</mml:mi> <mml:mspace/> <mml:msup> <mml:mi>N</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>t</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mspace/> <mml:mi>H</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>t</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mspace/> <mml:msub> <mml:mover> <mml:mi>P</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>i</mml:mi> </mml:msub> </mml:mrow> </mml:math> , which reduces to $$2\textrm{i}\hbar \,H\,\hat{P}_i$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mtext>i</mml:mtext> <mml:mi>ħ</mml:mi> <mml:mspace/> <mml:mi>H</mml:mi> <mml:mspace/> <mml:msub> <mml:mover> <mml:mi>P</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>i</mml:mi> </mml:msub> </mml:mrow> </mml:math> in cosmic-time gauge $$N=1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> , exhibiting Hubble–controlled non-commuting “translations.” A key structural ingredient is the symmetry $$\mathcal {C}_g$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>C</mml:mi> <mml:mi>g</mml:mi> </mml:msub> </mml:math> : an antiunitary map that flips all translation generators, $$\hat{P}_\mu \!\rightarrow \!-\Theta \hat{P}_\mu \Theta ^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mover> <mml:mi>P</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>μ</mml:mi> </mml:msub> <mml:mspace/> <mml:mo>→</mml:mo> <mml:mspace/> <mml:mo>-</mml:mo> <mml:mi>Θ</mml:mi> <mml:msub> <